Claude Universite

Bernard -Lyon

M François-Noël

Gilly M Le Professeur

Hamda Ben

Hadid M Le Professeur

Philippe Lalle

M Le Professeur

Germain Gillet

M Alain Helleu

Composantes Sante

J Etienne

La Mme

C Professeure

Burillon

D Bourgeois

Vinciguerra

Y Matillon

Mme La Professeure

A-M Schott

David C Muller

Anouar Fanidi

Jian-Min Yuan

Victoria L Stevens

Stephanie J Weinstein

Demetrius Albanes

Ross Prentice

Cynthia A Thomsen

Mary Pettinger

Qiuyin Cai

William J Blot

Jie Wu

Alan A Arslan

Anne Zeleniuch-Jacquotte

Marjorie L Mccullough

Loic Le Marchand

Lynne R Wilkens

Christopher A Haiman

Xuehong Zhang

Jiali Han

Meir J Stampfer

Stephanie A Smith-Warner

Edward Giovannucci

Graham G Giles

Allison M Hodge

Gianluca Severi

Mikael Johansson

Kjell Grankvist

Arnulf Langhammer

Steinar Krokstad

Marit Naess

Renwei Wang

Yu-Tang Gao

Lesley M Butler

Woon-Puay Koh

Xiao-Ou Shu

Yong-Bing Xiang

Honglan Li

Wei Zheng

Qing Lan

Kala Visvanathan

Per Judith Hoffman Bolton

Magne Ueland

Øivind Midttun

Arve Ulvik

Neil Caporaso

Mark Purdue

Regina Zeigler

Neal D Freedman

Julie E Buring

I-Min Lee

Howard D Sesso

J Michael Gaziano

Jonas Manjer

Ulrika Ericson

Caroline Relton

Paul Brennan

Mattias Johansson

Vitamin B6 Methionine Circulating Folate

Mattias Johansson

Julie K Bassett

Emil Stein

Ruth C Vollset

Domenico Travis

Amalia Palli

Sabina Mattiello

Antonia Sieri

Pagona Trichopoulou

Dimitrios Lagiou

Börje Trichopoulos

Göran Ljungberg

Elisabete Hallmans

Guri Weiderpass

Carlos A Skeie

Miren González

Petra H Dorronsoro

H B Peeters

Martine M Bueno-De-Mesquita

Marie-Christine Ros

Ruault Boutron

Guy Fagherazzi

Françoise Clavel

María-José Sánchez

Aurelio Barricarte Gurrea

Carmen J Navarro

Ramon Quiros

Kim Overvad

Anne Tjønneland

Krassimira Aleksandrova

Paolo Vineis

Marc J Gunter

Elio Riboli

Per Heiner Boeing

Keywords: métabolisme monocarboné, cancer du poumon, cancer de la tête et du cou, carcinome à cellules rénales, EPIC, LC3 One-carbon metabolism, lung cancer, head and neck cancer, renal cell carcinoma, EPIC, LC3, prospective study

en français Les vitamines B et les facteurs liés au métabolisme monocarboné (C1) aident à maintenir la synthèse de l'ADN, régulent l'expression des gènes, et peuvent affecter le risque de cancer. L'objectif général de cette thèse est d'étudier l'importance des biomarqueurs du C1 dans l'étiologie de trois cancers distincts qui diffèrent dans leur force d'association avec le tabagisme. Les articles inclus dans cette thèse ont été conduits au sein de deux études prospectives : l'étude prospective européenne sur la nutrition et le cancer (EPIC) et le consortium de cohortes du cancer du poumon (LC3). Dans l'article 1, nous avons étudié la relation entre les biomarqueurs du C1 et l'incidence ainsi que le pronostic du cancer de la sphère oto-rhino-laryngée (ORL) et de l'oesophage au sein de l'étude EPIC. Nous avons observé que les sujets ayant des concentrations élevées d'homocystéine avaient un risque accru de développer un cancer de la sphère ORL. Dans l'article 2, nous avons examiné la relation entre les biomarqueurs du C1 et l'incidence ainsi que le pronostic du carcinome à cellules rénales (CCR) au sein de l'étude EPIC. Nous avons constaté que les participants ayant des concentrations de vitamine B6 élevées avaient une diminution du risque de CCR avec un effet dose-réponse ainsi qu'une amélioration de la survie post-diagnostic. Dans l'article 3, nous avons étudié si les biomarqueurs du C1 sont associés au risque de cancer du poumon au sein de l'étude LC3. Dans l'ensemble, nous avons mis en exergue une faible association inverse, sans tendance claire, entre les concentrations de vitamine B6 et de folate et le risque du cancer du poumon. La principale conclusion de nos études est que les concentrations élevées de vitamine B6 sont associées à un risque plus faible de développer un CCR, et également à un meilleur pronostic chez les patients atteints de cette pathologie. Davantage d'études sont nécessaires afin d'évaluer si la vitamine B6 exerce une influence causale sur l'étiologie et la mortalité du CCR, ou si d'autres facteurs métaboliques sont impliqués.

Résumé substantiel en français

Les cancers liés au tabagisme sont définis comme les cancers causés par des expositions au tabac. À ce jour, plus de 3000 constituants ont été isolés à partir du tabac. Les avancées technologiques ont permis l'identification d'environ 70 substances cancérigènes dans la fumée du tabac, ainsi qu'une substance puissamment addictive, la nicotine. La fumée des produits du tabac, et en particulier des cigarettes est le facteur de risque le plus important pour le cancer. Il a été montré que cette exposition est clairement associée à un tiers de tous les cancers à l'échelle mondiale incluant les cancers du poumon (70-80% de tous les cas), de la tête et du cou (60-70%), du foie (50%) et du rein (30%). Ces connaissances ont été traduites par l'évitement du tabac et la mise en place de programmes de sevrage, ce qui a conduit à des réductions d'incidence de cancers liés au tabagisme (notamment le cancer du poumon) chez les hommes dans la plupart des pays européens. Même chez les sujets qui ont cessé de fumer, le risque à vie de cancer lié au tabagisme reste élevé. De plus, dans certaines populations où les campagnes anti-tabac ont été couronnées de succès, comme aux États-Unis, près de 50% des cas de cancer du poumon surviennent chez les non et anciens fumeurs. Il parait essentiel d'identifier des facteurs de risque afin d'en dégager des informations supplémentaires inter et intra groupes de fumeurs. Les conclusions mises en évidence revêtiraient un intérêt majeur pour la santé publique. L'alimentation et la nutrition sont supposées jouer un rôle fondamental dans le développement des cancers les plus courants. Il a été estimé qu'une mauvaise alimentation, en particulier une faible consommation de fruits et légumes, contribuerait à environ un tiers des cancers évitables. Les fruits et légumes sont des sources de vitamines B et d'autres micronutriments pour le métabolisme monocarboné (C1). Des carences et/ou déséquilibres de micronutriments dans la voie du C1 peuvent affecter le risque de cancer par le biais de multiples mécanismes tels que les méthylations anormales d'ADN ou des altérations dans les processus de réparation et de synthèse de l'ADN. Malgré un certain nombre d'études épidémiologiques suggérant un rôle possible du folate (vitamine B9) dans la prévention du cancer, en particulier le cancer colorectal, les preuves suggérant un lien de causalité entre le C1 et la carcinogenèse sont limitées. De fortes associations inverses entre des concentrations pré-diagnostiques élevées de biomarqueurs du C1 (notamment la vitamine B6, le folate, et la méthionine) et le risque du cancer du poumon ont été récemment signalées dans une grande étude cas-témoins nichée au sein de l'étude EPIC. L'étude a notamment révélé des associations fortes et cohérentes parmi les non et anciens fumeurs, suggérant que les résultats n'étaient probablement pas dus au facteur de confusion qu'est le tabagisme.

L'objectif général de cette thèse est d'étudier l'importance des biomarqueurs du C1 dans l'étiologie de trois cancer distincts (sphère ORL, rein et poumon) qui diffèrent dans leur force d'association avec le tabagisme.

Dans l'article 1, nous avons étudié la relation entre les concentrations circulantes des facteurs du C1 et l'incidence ainsi que le pronostic du cancer de la sphère ORL et de l'oesophage. Un échantillon de 516 cas et 516 témoins appariés individuellement sur le pays, le sexe, la date de naissance, le pays et le prélèvement de sang a été recruté à partir de l'étude EPIC. Les concentrations plasmatiques de vitamines B2, B6, B9 (folate), B12, d'homocystéine et de méthionine ont été mesurées à partir d'échantillons prédiagnostiques sanguins. Nous avons remarqué que les sujets ayant des concentrations élevées d'homocystéine avaient un risque accru de développer un cancer de la tête et du cou. L'association positive de l'homocystéine était présente à la fois chez les non-fumeurs et les non-consommateurs d'alcool. Les concentrations circulantes des autres biomarqueurs ne présentaient aucun lien évident avec le risque ou la survie. Dans l'article 2, nous voulions évaluer l'ampleur avec laquelle l'homocystéine ou d'autres biomarqueurs inclus dans la voie du C1 pouvait être liée à l'incidence et au pronostic du carcinome à cellules rénales (CCR). Une raison supplémentaire d'étudier l'association entre les biomarqueurs du C1 et les CCR est que ce type de cancer présente une force d'association avec le tabagisme moindre comparé au cancer de la tête et du cou. Les concentrations plasmatiques de vitamines B2, B6, B9, B12, d'homocystéine et de méthionine ont été mesurées à partir d'échantillons pré-diagnostiques sanguins auprès de tous les participants de cette étude. Dans un premier temps nous avons recruté 556 paires cas-témoins au sein de la cohorte EPIC. Globalement, nous avons observé que les sujets d'EPIC ayant des concentrations de vitamine B6 élevées avaient moins de risque de CCR, de manière dose-réponse, résultant à une diminution du risque approximative de 60% lorsque les quartiles supérieurs et inférieurs de la population étaient comparés. Nous avons également remarqué une claire amélioration de la survie auprès des cas atteints de CCR ayant des concentrations de vitamine B6 plus élevées. Afin de s'assurer que la relation entre la vitamine B6 et le carcinome à cellules rénales n'était pas restreinte à l'étude EPIC, une étude de réplication a été menée dans une cohorte distincte d'Australie, la Melbourne Collaborative Cohort Study (MCCS) incluant 144 paires cas-témoins. Nous avons obtenu des résultats significativement similaires à ceux observés dans la cohorte EPIC concernant les risques de CCR ainsi que la mortalité par cancer.

Dans l'article 3, nous voulions valider certaines conclusions antérieurement citées et observées au sein de la cohorte EPIC, impliquant de fortes associations inverses entre le folate, la vitamine B6 et la méthionine et le risque de cancer du poumon. Nous avons donc étudié l'association de ces trois biomarqueurs avec le risque de cancer du poumon au sein d'un vaste consortium, le consortium de cohortes du cancer du poumon (LC3). Ce dernier rassemble plusieurs populations d'Amérique, d'Europe, d'Asie et d'Australie. Au total, vingt études cas-témoins nichées ont contribué à cette étude avec des échantillons sanguins prédiagnostiques de vitamines B6, B9 et de méthionine mesurées auprès des 5 364 paires de cas-témoins. Les critères d'appariement étaient la cohorte d'appartenance, le sexe, la date du prélèvement de sang, la date de naissance et le statut tabagique. Dans l'ensemble, nous avons observé de faibles associations inverses, sans tendance claire, entre les concentrations de vitamine B6, de folate et le risque de cancer du poumon, associations principalement présentes chez les fumeurs anciens et quotidiens. Réciproquement, les fumeurs anciens et quotidiens ayant un déficit en concentrations circulantes de vitamine B6 et de folate comparés à ceux ayant des concentrations normales ont montré une faible augmentation du risque du cancer du poumon.

En résumé, nos études suggèrent que les concentrations circulantes d'homocystéine affectent le risque de développer un cancer de la tête et du cou. Ces résultats nouveaux doivent être répliqués dans d'autres études prospectives de grandes tailles ainsi que dans différentes populations éthniques.

Les résultats de l'étude LC3 ne soutiennent pas l'association inverse entre les concentrations circulantes élevées de vitamine B6 et la diminution du risque de cancer du poumon, comme précédemment indiqué dans l'étude EPIC du poumon. Cependant, nous avons remarqué de fortes associations entre le déficit en vitamine B6 et le risque du cancer du poumon suivant les trois ans après le diagnostic chez des fumeurs quotidiens. Cette observation soulève l'hypothèse que la vitamine B6 circulante pourrait être un potentiel prédicteur de risque chez les fumeurs quotidiens ayant été diagnostiqués proches de l'échantillon sanguin.

La principale conclusion de nos recherches est que les participants de l'étude EPIC ayant des concentrations circulantes de vitamine B6 élevées ont un moindre risque de développer un carcinome à cellules rénales tout en ayant un meilleur pronostic une fois cette maladie diagnostiquée. Cependant, des études complémentaires sont nécessaires pour évaluer si la vitamine B6 exerce une influence causale sur l'étiologie et la mortalité du CCR, ou si d'autres facteurs métaboliques sont impliqués.

LIST OF TABLES

LIST OF FIGURES

BACKGROUND AND INTRODUCTION

Overview and aim

Smoking-related cancers are defined as cancers caused by tobacco exposures. Over 3000 constituents have been isolated from tobacco and advancing technologies have permitted the identification around 70 carcinogens in tobacco smoke to date, as well as a powerfully addictive substance, nicotine [START_REF]Tobacco smoke and involuntary smoking/ IARC Working Group on the Evaluation of Carcinogenic Risks to Humans[END_REF] . Approximately 1.1 billion people worldwide smoke with important sex-differences. In many countries, smoking rates are increasing in young women, with potentially grave consequences for cancer [START_REF] Jha | Avoidable global cancer deaths and total deaths from smoking[END_REF] . Smoking of tobacco products, and in particular cigarettes is the most important risk factor for cancer and has been shown to be

clearly associated with about one in three of all cancers globally [START_REF] Doll | Mortality from cancer in relation to smoking: 50 years observations on British doctors[END_REF] , including lung (70-80% of all cases) [START_REF]World Cancer Report[END_REF] , head and neck (60-70%,) [START_REF] Limeback | Comprehensive Preventive Dentistry[END_REF] , liver (50%) [START_REF] Trichopoulos | Hepatocellular carcinoma risk factors and disease burden in a European cohort: a nested case-control study[END_REF] and kidney (30%) [START_REF] Hunt | Renal cell carcinoma in relation to cigarette smoking: meta-analysis of 24 studies. International journal of cancer[END_REF] . This knowledge has been translated into tobacco avoidance and cessation programs, which has led to reductions in smoking-related cancer (most notably lung cancer) incidence among men in most European countries [START_REF] Peto | Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies[END_REF] . While it has been observed that in populations where the large proportion of the population are no longer current smokers, the majority of smokingrelated cancer cases continue to occur among former smokers. In addition, survival from smoking-related cancers is poor, with 5-year mortality in the region of approximately 85%

for lung cancer and 50% for other cancers sites such as head and neck cancer [START_REF] Howlader | SEER Cancer Statistics Review, 1975-2012[END_REF] . Identifying modifiable additional risk factors within and across smoking groups remains of great clinical and public health interest.

Diet and nutrition are thought to play a fundamental role in the development of most common cancers [START_REF] Donaldson | Nutrition and cancer: a review of the evidence for an anti-cancer diet[END_REF] . It has been estimated that poor diet, in particular low intake of fruit and vegetables contribute to about one-third of preventable cancers [START_REF] Key | Diet, nutrition and the prevention of cancer[END_REF] . Fruits and vegetables are sources of B-vitamins and other metabolites of the one-carbon metabolism (OCM) pathway.

Deficiencies and/or imbalances of the micronutrients in the OCM pathway may impact cancer risk through multiple mechanisms such as aberrant DNA methylation patterns or DNA damage through an oxidative imbalance. Despite a number of epidemiological studies suggesting a possible role of folate (vitamin B9) in cancer prevention, particularly for colorectal cancer, evidence supporting a causal relationship between OCM and carcinogenesis is lacking. Strong inverse associations of elevated pre-diagnostic circulating concentrations of OCM biomarkers (in particular vitamin B6, folate, and also methionine)

with lung cancer risk have been recently reported in a large nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) [START_REF] Riboli | European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection[END_REF] . The study found strong and consistent associations for never-and former smokers, suggesting that the result was unlikely to be due to confounding by smoking.

The overarching aim of this thesis is to investigate the importance of OCM biomarkers in the etiology of three distinct cancer sites (head and neck, kidney and lung) that differed in their strength of association with smoking. The head and neck cancer (HNC) and the renal cell carcinoma (RCC) studies investigated in EPIC cohort whether pre-diagnostic circulating markers of the one-carbon pathway including vitamin B2, vitamin B6, folate, vitamin B12, homocysteine and methionine are related to incidence and survival of cancer of the head and neck and squamous cell eosphagus (Chapter III), and RCC (Chapter IV), in a similar or different way as for lung cancer. The Lung Cancer Cohort Consortium (LC3) study (Chapter V) evaluated whether pre-diagnostic circulating concentrations of vitamin B6, folate and methionine are associated with lung cancer risk in a worldwide consortium of prospective cohort studies, in order to assess the generalisability of the recently published findings [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] .

Smoking related-cancers -epidemiologic evidence

Lung cancer

Incidence and mortality

Lung cancer has been reported as the most common cancer globally, with approximately 1,800,000 new cases diagnosed annually [START_REF] Jemal | Global cancer statistics[END_REF] . In 2012, lung cancer accounted for an estimated 240,000 new cases in the United States (US), representing 15% of lung cancers worldwide [START_REF] Jemal | Global cancer statistics[END_REF] .

However, incidence rates of lung cancer display remarkable variation across the world (see Figure 1) [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] . Among men, the highest estimated age-standardised incidence rates (ASR) are observed in Central and Eastern Europe (53.5 per 100,000), Eastern Asia (50.4 per 100,000) and North America (48.5 per 100,000) and the lowest ASR are located in sub-Saharan Africa (4.8 per 100,000) [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] . Similar to men, the highest incidence rates among women are also observed in North America (33.8 per 100,000) and Eastern Asia (19.2 per 100,000) while the lowest ASR are also located in sub-Saharan Africa (2.5 per 100,000). When comparing incidence rates by sex, rates are generally lower in women than in men, mainly reflecting different historical exposure to tobacco smoking where women started smoking in large numbers several decades later than men [START_REF] Harris | Cigarette smoking among successive birth cohorts of men and women in the United States during 1900-80[END_REF] . However, recent trends show that lung cancer among women is increasing considerably in many countries including European countries [START_REF] Bray | Lung cancer mortality trends in 36 European countries: secular trends and birth cohort patterns by sex and region 1970-2007. International journal of cancer[END_REF] .

Lung cancer has an enormous impact on worldwide mortality, estimated to be responsible for nearly 1.6 million deaths worldwide in 2012 [START_REF] Jha | Avoidable global cancer deaths and total deaths from smoking[END_REF] . Geographical patterns of mortality are quite similar to those of incidence for both sexes (see Figure 1) due to the rapid course of the disease after diagnosis, then reflecting the poor 5-year survival. Lung cancer mortality among men is decreasing in most Western countries, including many European countries, where the tobacco epidemic diminished after the middle of the last century [START_REF] Bray | Lung cancer mortality trends in 36 European countries: secular trends and birth cohort patterns by sex and region 1970-2007. International journal of cancer[END_REF][START_REF] Jemal | Annual report to the nation on the status of cancer, 1975-2005, featuring trends in lung cancer, tobacco use, and tobacco control[END_REF][START_REF] Peto | Mortality From Smoking in Developed Countries 1950-2000[END_REF] . By contrast, it has risen sharply in many low-and middle-income countries who were at an earlier stage in the smoking epidemic, such as China and Indonesia 2 .

Lung cancer five-year relative survival rate for all stages combined is about 15% in the United States and Europe 9 20 21 . Survival is strongly influenced by histology and stage at diagnosis. The main histological types of lung cancer encompass small cell lung carcinoma (SCLC), squamous cell carcinoma (SCC), adenocarcinoma and large cell carcinoma (the latter three types being commonly referred as non-small cell lung carcinoma, or NSCLC). SCLC, representing less than 20% of the overall lung cancer burden, is usually classified as limited or extensive and is generally responsive to therapy [START_REF] Abidin | Targeted therapies in small cell lung cancer: a review[END_REF] . In contrast, NSCLC representing over 80% of lung cancers are categorized using the TNM Classification of Malignant Tumours into various stages [START_REF] Molina | Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship[END_REF] . Recent data from US (see Figure 2) shows that overall 5-year survival of lung cancer is over 20%. When stratifying by stage (see Figure 3), it has been observed that cases diagnosed at early stages have a 5-year survival ranging from 30%-60% (stage I and II).

In contrast, cases diagnosed at a later stage (i.e. IV) have very poor prognosis with 5-year survival of below 5%.

Risk factors of lung cancer

Epidemiological evidence of the association between tobacco smoking and cancer began to emerge in the 1920's [START_REF] Levin | Cancer and tobacco smoking; a preliminary report[END_REF] and by the 1950's the causal relationship with lung cancer was established by Doll and Hill 25 26 . They reported that smokers who smoked 25 or more cigarettes per day had 50 times increased risk of lung cancer compared to non-smokers. This work was followed by several epidemiological studies, and by the mid-1960s, the causal association between lung cancer and smoking was recognized by public health and regulatory authorities. In 1986, the International Agency for Research on Cancer (IARC) monograph also reported the causality of tobacco smoking with lung cancer [START_REF]Tobacco smoking[END_REF] . The excess risk of lung cancer among smokers relative to non-smokers depends on several aspects including intensity of smoking, duration of smoking, type of smoking product and age at start 28 29 .

While tobacco exposure is well known to be strongly associated with lung cancer in ever smokers, there is still limited evidence of the causes of lung cancer in never. Secondhand smoke has been established as a major risk factor among never smokers [START_REF] Thun | Lung cancer occurrence in never-smokers: an analysis of 13 cohorts and 22 cancer registry studies[END_REF] . Also, other factors including indoor pollutants (coal, wood fire indoors) 31 32 and outdoor air pollution [START_REF] Samet | Lung cancer in never smokers: clinical epidemiology and environmental risk factors. Clinical cancer research : an official journal of the[END_REF] may affect lung cancer development. Additional chemical substances have been linked to lung cancer, such as radon. According to the US Environmental Protection Agency (EPA), this radioactive gas causes about 20,000 cases of lung cancer each year 34 35 . There is also convincing evidence from several ecological, case-control and cohort studies showing that arsenic in drinking water has a causal relationship with lung cancer 36 37 . Other substances found to increase lung cancer risk include asbestos and silica [START_REF]Food Nutrition and the Prevention of Cancer: a global perspective[END_REF] .

Recent conclusion from the IARC monograph stated that there was possible but not established evidence for carcinogenicity of alcohol consumption with lung cancer [START_REF]Alcohol consumption and ethyl carbamate/ IARC Working Group on the Evaluation of Carcinogenic Risks to Humans[END_REF] .

However, it is difficult to disentangle the role of alcohol in lung cancer development given the strong correlation between alcohol drinking and tobacco smoking. Heavy drinkers tend to be heavy smokers suggesting that tobacco may be a confounding variable in the relation between alcohol and lung cancer. A recent meta-analysis among never smokers diagnosed with lung cancer suggested that alcohol does not play an independent role in lung cancer [START_REF] Bagnardi | Light alcohol drinking and cancer: a meta-analysis[END_REF] .

High consumption of fruit and vegetables might protect against lung cancer. While consistent inverse associations between dietary intake of fruit and lung cancer risk have been reported in both case-control and cohort studies [START_REF]Food Nutrition and the Prevention of Cancer: a global perspective[END_REF] , the role of vegetables is less clear.

A review of several case-control studies have suggested a protective effect of high vegetable intake in lung cancer, but several prospective studies did not support this hypothesis [START_REF] Wang | The effect of fruit and vegetable intake on the development of lung cancer: a meta-analysis of 32 publications and 20 414 cases[END_REF] . Possible reasons of this discrepancy might result from bias including misclassification of exposure, retrospective dietary assessment and variability in food consumption between absence and presence of the disease. Also, residual confounding by cigarette smoking might explain these results, as some of the studies investigating the relation of vegetables with lung cancer did not adjust for smoking.

Several micronutrients have been investigated in relation to lung cancer and one of the most controversial is beta-carotene. Risk estimates from a polled analysis of case-control and cohort studies reported lower lung cancer incidence for participants having high betacarotene intake [START_REF]Food Nutrition and the Prevention of Cancer: a global perspective[END_REF] . Thus, investigators hypothesised that beta-carotene may be related to lower lung cancer risk, and initiated randomized controlled trials (RCTs) were initiated in high lung cancer risk populations (middle-aged men that were smokers and/or exposed to asbestos). However, the Alpha-Tocopherol, Beta-Carotene, Prevention Cancer (ATBC) trial reported that supplementation of beta-carotene increased lung cancer incidence [START_REF]Food Nutrition and the Prevention of Cancer: a global perspective[END_REF] . An evaluation work from IARC concluded that high doses of beta-carotene supplements might not prevent lung cancer [START_REF]IARC Handbooks of Cancer Prevention[END_REF] . The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) and several health authorities do not recommend that smokers take supplemental doses of beta-carotene. Several other micronutrient including vitamin A and E, lycopene, zinc and alpha-carotene have been investigated in relation to lung cancer risk, but to date evidence is not conclusive of any protective effect for lung cancer 37 42 .

Head and neck cancer

Incidence and mortality

Head and neck cancer (HNC) encompass a group of tumours, at various sites around oral cavity, larynx, oropharynx and hypopharynx 43 44 . Over 90% of head and neck cancers are squamous cell carcinoma (HNSCC), arising from the epithelial cells that line the mucosal surfaces of the head and neck [START_REF] Schottenfeld | Cancer Epidemiology and Prevention[END_REF] (see Figure 4). HNC is the sixth most common cancer type worldwide and affects nearly 650,000 individuals per year, and causes over 350,000 deaths 46 47 . The geographical inequalities in the burden of HNC are noticeable, with over 50 % of all cases occurring in developing countries [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] .

OCM and smoking-related cancers -Background

30

According to world estimations (see Figure 5), incidence rates of HNC are highest in Melanesia and South-Central Asia, in both sexes. Globally, men are affected significantly more by HNC than women with a ratio ranging from 2:1 to 4:1 [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] . However, incidence by sex varies with anatomic location and has been changing

as the number of female smokers has increased over the last decades. The male-female ratio is currently about 8:1 for laryngeal cancer and approximately 2:1 for nasopharyngeal cancer [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] .

Despite significant advances in multidisciplinary treatments of HNC including surgery, chemotherapy and radiation, the current 5-year survival rate from HNSCC of 50% has improved very little over the last 30 years 48 49 . Moreover, the prognosis is generally better for women compared to men and for neoplasms located in oral cavity and larynx than for those arising in the hypopharynx (see Figure 6).

Risk factors of head and neck cancer

Most HNC cases are attributable to tobacco smoking, alcohol drinking, and the established interaction between these two factors [START_REF] Olshan | Pathogenesis, and Prevention of the Head and Neck Cancer[END_REF] . A positive association between tobacco smoking and HNC has been reported consistently in many studies 1 27 50-52 . Although the association with tobacco smoking is not as strong as with lung cancer, it remains very high with excess risk estimates among current smokers approaching 4 to 6-fold that among non-smokers 1 .

Smoking is likely to be differentially associated with risk of subtypes of the HNC 38 39 51 52 .

Both a recent meta-analysis of 28 case-control studies [START_REF] Zhang | Different levels in alcohol and tobacco consumption in head and neck cancer patients from 1957 to 2013[END_REF] and a large cohort study from Netherlands (NLCS) [START_REF] Maasland | Alcohol consumption, cigarette smoking and the risk of subtypes of head-neck cancer: results from the Netherlands Cohort Study[END_REF] reported that smoking was more strongly associated with laryngeal and oropharyngeal cancers than those from the oral cavity.

Similarly, substantial epidemiologic evidence from the last three decades demonstrates that alcohol consumption increases risk of the HNC. Recently, the Monograph Working Group of IARC concluded that there "was sufficient evidence" for the carcinogenicity of alcohol in animals and classified alcoholic beverages as carcinogenic to humans [START_REF] Baan | Carcinogenicity of alcoholic beverages[END_REF] . In particular, the group confirmed, and newly established, the causal link between alcohol consumption and several cancers including the cancer of the oral cavity, pharynx, larynx and oesophagus [START_REF]Alcohol consumption and ethyl carbamate/ IARC Working Group on the Evaluation of Carcinogenic Risks to Humans[END_REF] .

Furthermore, several epidemiological studies showed a significant, positive, multiplicative interaction between tobacco smoking and alcohol consumption with HNC risk. As an example, the NLSC cohort reported that current smokers compared to never had higher risk 49, 95% CI 3.11-6.48) and that participants consuming ≥30 grams of alcohol per day compared with abstinence had also increased risk of HNC overall (RR = 2.74, 95% CI 1. 85-4.06). When comparing participants smoking ≥ 20 cigarettes and drinking ≥30 g alcohol per day compared with never smokers abstaining from alcohol the RR was 8.28 (95% confidence interval [95% CI] 3. 98-17.22 (interaction between categories of alcohol consumption and cigarette smoking was 0.03) [START_REF] Maasland | Alcohol consumption, cigarette smoking and the risk of subtypes of head-neck cancer: results from the Netherlands Cohort Study[END_REF] . Moreover, a positive association of tobacco smoking with HNC risk has been observed among non-drinkers and similarly alcohol consumption has been shown to increase risk of HNC among nonsmokers [START_REF] Hashibe | Alcohol drinking in never users of tobacco, cigarette smoking in never drinkers, and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium[END_REF] . Other studies reported similar results which aim to support the independent role of tobacco smoking and alcohol intake in HNC development.

Viral infection is a recognised risk factor for cancer of the head and neck. The association between Epstein-Barr virus infection and the development of head and neck cancer subtypes in particular nasopharyngeal cancer was first recognised in 1966 [START_REF] Old | Precipitating antibody in human serum to an antigen present in cultured burkitt's lymphoma cells[END_REF] . More recently, human papilloma virus (HPV) has attracted attention [START_REF] Mehanna | Oropharyngeal carcinoma related to human papillomavirus[END_REF] , with recent observational studies finding it to be a strong risk factor for the development of head and neck cancer. In particular, there is substantial evidence for a role of HPV16 in the oropharyngeal cancer etiology 38 59 .

Diet is estimated to contribute to about one-third of preventable cancers 37 60 . One of the most convincing pieces of epidemiological evidence for the role of dietary factors in cancer risk is the inverse relationship between the consumption of fruit and vegetables and HNC.

Indeed, comprehensive evidence, both from case-control and cohort studies have shown that people with a low intake of fruit and vegetables have higher cancer incidence of the head and neck compared with those having a high fruit and vegetable intake 37 61 62 .

Importantly, the last expert report from the World Cancer Research Fund (WCRF) stated that fruits, non-starchy vegetables, and foods containing carotenoids are probable decreased risk for HNC [START_REF]Food Nutrition and the Prevention of Cancer: a global perspective[END_REF] . However, the evidence of the role of other food groups and the risk of HNC is inconsistent. Some epidemiological studies suggested that consumption of meat was associated with increased risk of HNC 37 63 64 , whereas other have reported no association 65 66 . Despite the potential role of vegetable and fruit consumption in the etiology of HNC, residual confounding by smoking and alcohol is still a real concern.

While it can be difficult to disentangle the exact role of micronutrients individually with HNC, experimental and observational studies suggested that vitamin C 37 67 , vitamin E 37 68 , and beta-carotene 37 69 might reduce risk of HNC. Additionally, mainly retrospective studies have investigated folate and HNC risk, most reporting that higher dietary folate is inversely associated with HNC risk [START_REF] Galeone | Folate intake and the risk of oral cavity and pharyngeal cancer: a pooled analysis within the International Head and Neck Cancer Epidemiology Consortium. International journal of cancer[END_REF] . However, given the potential for reverse causality and recall bias to influence the results in retrospective case-control studies, it is difficult to interpret these results causally.

Esopheageal cancer

Incidence and mortality

Esopheageal cancer is the eighth most common cancer worldwide, with an estimated 450,000 new cases in 2012, and the sixth most common cause of cancer death with an estimated 400,000 deaths [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] . Cancer of the esophagus includes mostly two histologic types, esophagus squamous cell carcinoma (ESCC) and esophagus adenocarcinoma (EAC), although ESCC is the predominant histologic type of esophageal cancer worldwide [START_REF] Arnold | Global incidence of oesophageal cancer by histological subtype in 2012[END_REF] . Throughout this dissertation we will focus on ESCC. In 2012, ESCC accounted for an estimated 398,000 new incident cases worldwide with a global ASR of 5.2 per 100,000 (7.7 in men and 2.8 in women, see Figure 7). The highest incidence rates are observed in Eastern/ South-eastern Asia (13.6 in men and 4.3 in women), sub-Saharan Africa (6.4 in men and 4.0 in women), and Central Asia (5.9 in men and 3.6 in women). The lowest incidence rates occurred in North America (1.7 in men and 0.7 in women), and Southern Europe (2.4 in men and 0.4 in women) [START_REF] Arnold | Global incidence of oesophageal cancer by histological subtype in 2012[END_REF] . Furthermore, it has been reported that about 80% of ESCC occurred within what is named the "esophageal cancer belt," an area extends from northeast China to the Middle East [START_REF] Siegel | Cancer statistics, 2014. CA: a cancer journal for clinicians[END_REF] .

Globally, incidence rates of ESSC have been steadily declining in Western countries due to long-term reductions in alcohol intake and tobacco use in contrast to some Asian countries such as Taiwan where ESCC has been increasing, likely due to increase of tobacco and alcohol consumption [START_REF] Castro | Patterns and trends in esophageal cancer mortality and incidence in Europe (1980-2011) and predictions to 2015[END_REF] . It has been also reported that ESCC incidence vary across ethnicities with two to three times higher estimates in blacks than in whites, in particular in North America [START_REF] Cummings | Descriptive epidemiology of esophageal carcinoma in the Ohio Cancer Registry[END_REF] . There is generally no difference reported in survival between the two histological types, EAC and ESCC [START_REF] De Angelis | Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE--5-a population-based study[END_REF] . Geographical patterns of mortality are quite similar to those of incidence for both sexes due to the rapid course of the disease after diagnosis, then reflecting very poor survival, with 5-year survival rates of 19% (2003-2009) in the USA 72 and 12% (2000-2007) in Europe [START_REF] De Angelis | Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE--5-a population-based study[END_REF] .

Risk factors of esophagus squamous cell carcinoma.

The positive association between tobacco smoking and ESCC is well established. Cancer of the esophagus consistently occurs less frequently among participants who do not smoke, with supporting data from both control and cohort studies [START_REF] Freedman | A prospective study of tobacco, alcohol, and the risk of esophageal and gastric cancer subtypes[END_REF][START_REF] Mao | Epidemiologic risk factors for esophageal cancer development[END_REF][START_REF] Morita | Alcohol drinking, cigarette smoking, and the development of squamous cell carcinoma of the esophagus: epidemiology, clinical findings, and prevention[END_REF][START_REF] Steevens | Alcohol consumption, cigarette smoking and risk of subtypes of oesophageal and gastric cancer: a prospective cohort study[END_REF][START_REF] Zhang | Epidemiology of esophageal cancer[END_REF] Furthermore, the excess risk of ESCC among smokers relative to non-smokers depends on several aspects including smoking intensity and duration of smoking [START_REF]Esophageal cancer: epidemiology, pathogenesis and prevention. Nature clinical practice[END_REF] . A number of studies also observed that smokeless tobacco products and betel liquid (with or without tobacco) increased risk of ESCC, in particular in Asia [START_REF] Lee | Independent and combined effects of alcohol intake, tobacco smoking and betel quid chewing on the risk of esophageal cancer in Taiwan. International journal of cancer[END_REF][START_REF] Phukan | Betel nut and tobacco chewing; potential risk factors of cancer of oesophagus in Assam, India[END_REF][START_REF] Wu | Interaction between cigarette, alcohol and betel nut use on esophageal cancer risk in Taiwan[END_REF] .

The association between alcohol consumption and risk of ESCC has been described in many studies 76-80 85 and showed consistent positive association between alcohol consumption and ESSC risk. As an example, a recent meta-analysis of 40 case-control and 13 cohort studies showed that that after adjusting for age, sex, and tobacco smoking the RR for the association between light alcohol drinking (12.5 g/d) and risk of ESCC was 1.38 (95% CI 1.14-1.67). The RRs were 2.62 (95% CI 2.07-3.31) among moderate drinkers (>12.5-<50 g/d) and 5.54 (95% CI 3.92-7.28) for high alcohol intake (50 g/d) [START_REF] Islami | Alcohol drinking and esophageal squamous cell carcinoma with focus on light-drinkers and never-smokers: a systematic review and metaanalysis. International journal of cancer[END_REF] .

Tobacco smoking and alcohol intake act also synergistically (effect arising from the two factors) in a multiplicative manner on ESCC. As an example, a recent meta-analysis reported the effects of alcohol consumption and tobacco use on ESCC, both alone and in combination [START_REF] Prabhu | The synergistic effects of alcohol and tobacco consumption on the risk of esophageal squamous cell carcinoma: a meta-analysis[END_REF] . They found that never smokers with alcohol use had increased risk of ESCC (odds ratio [OR] being 1.21, 95% CI 0.81-1.81), although this association was statistically nonsignificant, and that never drinkers with tobacco use had also increase risk of ESCC (OR 1.36, 95% CI 1.14-1.61). This association was stronger when they looked both among alcohol and tobacco users with OR being 3.28 (95 % CI 2.11-5.08).

There have been numerous studies assessing the relation between diet and nutrition with ESSC etiology. Many case-controls and cohort studies showed that high consumption of fruit and vegetables were inversely associated with ESCC risk 37 88-90 . As an example, a recent prospective cohort study of 38,790 Japanese men aged 45-74 years showed that participants with highest consumption of total fruit and vegetables compared to the lowest had around 50% decreased risk of ESCC (HR 0.52, 95% CI 0.30-0.88) [START_REF] Yamaji | Fruit and vegetable consumption and squamous cell carcinoma of the esophagus in Japan: the JPHC study. International journal of cancer[END_REF] .

A number of epidemiological studies have investigated the relation between micronutrients including folate, beta-carotene, vitamin C and vitamin E, and ESCC risk 37 92-94 , and some have proved an inverse association 88 95 . Folate intake is one of the micronutrient that has been assessed in several studies and showed consistent results. In 2006, Larson C et al. reported from a meta-analysis that the summary relative risk for the highest versus the lowest category of folate intake was 0.66 (95% CI 0.53-0.83) for ESCC [START_REF] Larsson | Folate intake, MTHFR polymorphisms, and risk of esophageal, gastric, and pancreatic cancer: a meta-analysis[END_REF] . Similarly, a recent large prospective study including 492 293 participants found that those with low levels of folate intake compared to high levels had around 90% increased risk of ESCC, the RR being 1.91 (95% CI 1.17-3.10) [START_REF] Xiao | Intakes of folate, methionine, vitamin B6, and vitamin B12 with risk of esophageal and gastric cancer in a large cohort study[END_REF] .

Over the past two decades, the HPV in ESCC has received increasing attention and several studies using various methods have confirmed the presence of HPV in ESCC [START_REF] Antonsson | High-risk human papillomavirus in esophageal squamous cell carcinoma. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF][START_REF] Cao | HPV infection in esophageal squamous cell carcinoma and its relationship to the prognosis of patients in northern China[END_REF][START_REF] Chang | Evaluation of HPV, CMV, HSV and EBV in esophageal squamous cell carcinomas from a high-incidence area of China[END_REF][100] . A recent case-control study from China showed that the presence of HPV in the esophagus was associated with increased risk of ESCC (OR 6.4, 95% CI 4.4-9.2) 101 .

Body mass index, a surrogate indicator of obesity, has been inversely associated with the risk of ESCC in some investigations 37 102 , although issues remain regarding confounding by smoking as alternative explanations. However, a recent population-based case-control study comprising 287 ESCC cases and 1,544 controls reported that after adjusting for smoking the risk of ESCC was reduced by 35% (range, 23%-44%) per 5-unit increase in recent BMI 103 .

Kidney cancer

Incidence and mortality

As for cancer of the head and neck and lung, incidence rates of kidney cancer vary greatly across the world (see Figure 8). In 2012, the incidence rate of kidney cancer was the highest in Europe, with Czech Republic rising the top ASR worldwide per 100,000 for both sexes (24.1 in men and 10.5 in women) [START_REF] Ferlay | Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11[END_REF] . The lowest rates are observed in Asia (3.8 in men and 1.9 in women) and in Africa (1.4 in men and 1.1 in women).

In addition, stratifications by ethnic group as shown in United States indicate that African Americans and European Americans have on average two-fold higher ASR than Asian Americans 104 . Figure 7 demonstrates also that men have approximately 2-fold higher kidney cancer incidence than women.

Risk factors of kidney cancer

Kidney cancer is less affected by smoking exposure than lung and head and neck. However, this exposure is considered as a causal risk factor for kidney cancer 104 106 . Tobacco smoking has been consistently reported to be a risk factor for this type of cancer, with increased risks compared to never smokers in the order of 50% 1 7 . Several epidemiological studies showed a clear dose-response pattern resulting in two to three-fold risk differences between heavy smokers and people who have never smoked 1 7 107 .

Obesity has been frequently studied in relation to cancer risk and different reports showed that this exposure increases the risk of kidney cancer 37 108 . In prospective studies conducted worldwide, overweight and obese individuals have an elevated risk of RCC in a doseresponse manner [109][110][111] . Furthermore, hypertension has been consistently associated with higher risk of kidney cancer 104 112 . Several observational studies have reported an association with a history of long-term hypertension, and cohort studies of blood pressure measurements taken at baseline have generally shown a positive dose-response 5-years survival relationship [113][114][115][116] . Despite the high correlation between obesity and hypertension, it has been suggested that these two risk factors alone may play an independent role in kidney cancer 104 117 . Furthermore, it has been pointed out that diabetes mellitus may increase kidney cancer risk, but results from case-controls as well cohort studies are not consistent 104 118 .

Despite alcohol intake being strongly positively related to cancer of the head and neck as previously shown, evidence from both case-control and cohort studies suggest that alcoholic drinks might protect against kidney cancer 119 120 . A recent meta-analysis of fifteen case-control studies observed an inverse association between alcohol consumption and RCC in both the overall alcohol intake group (OR 0.67, 95% CI 0.62-0.73) and subgroups analyses including sex, study design and geographical region 123 . A similar trend was observed in a meta-analysis of cohort studies involving over 750,000 subjects indicating that moderate alcohol consumption was associated with a lower risk (pooled multivariable RR 0.72, 95% CI 0.60-0.86) of RCC overall and this inverse association was also seen among both women and men 124 .

The evidence supporting dietary influences on kidney cancer etiology is less established than those observed for HNC. However, a recent pooled analysis of 13 prospective studies reported significant inverse associations between combined intake of fruit and vegetables in relation to renal cell carcinoma, with a stronger association for fruit 125 . Despite a potential role of fruit and vegetable intake in RCC etiology, it is difficult to disentangle which micronutrient might be involved in those associations. Different epidemiological studies investigating the role of beta-carotene, lycopene and folate in relation to kidney carcinogenesis are weak, but seems to point out an inverse association between both carotene [125][126][127] and vitamin E and kidney cancer 128 129 .

One-carbon metabolism: a candidate pathway

As mentioned previously, dietary intake of fruit and vegetables has been associated with a potential decrease in risk among the three specific smoking-related cancer sites investigated in this dissertation. Fruit and vegetables are important dietary sources of B-vitamins and additional nutrients that are involved in the one-carbon pathway. Because these nutrients are important in maintaining DNA integrity and gene expression, they might have a potential role in preventing carcinogenesis, and present potential for cancer risk modification through changes in diet.

Overview of the one-carbon metabolism (OCM)

OCM encompasses a series of cyclical biochemical reactions that involve the transfer of one carbon unit (see Figure 10). OCM pathways can be viewed several modular units that comprise in particular two interconnected cycles: the folate cycle and the methionine cycle.

Folate cycle

Folate plays a central role in OCM as a source of methyl groups. The term folate represents all naturally occurring forms of vitamin B9 in foods such as meat, fruits, vegetables, grain products, legumes, bread and seeds 130 , whereas folic acid is the synthetic form found in dietary supplements and fortified food.

In cells, folate either from dietary intake or folic acid, is reduced by a series of enzymatic reactions, leading to the generation of dihydrofolate and then tetrahydrofolate (THF) which is the most important carrier of OCM groups. THF is later converted to 5,10-methylene-THF (me-THF) by serine hydroxymethyl transferase (SHMT). me-THF is then either reduced to 5methyltetrahydrofolate (mTHF) by methylenetetrahydrofolate reductase (MTHFR) or converted to 10-formyltetrahydrofolate (F-THF) through a sequence of steps. mTHF is demethylated to complete the folate cycle. After the demethylation of mTHF, the carbon is donated into the methionine cycle through the methylation of homocysteine to methionine by methionine synthase (MS) and its cofactor vitamin B12 (B12).

Methionine cycle

Methionine is an essential, amino acid and a precursor of S-adenosylmethionine (SAM), the primary methyl group donor for most biological methylations such as DNA, RNA, proteins, phospholipids, histones and neurotransmitters 131 132 . As shown in Figure 10, the methionine cycle begins with homocysteine that accepts the carbon from the folate pool through mTHF to generate methionine. Methionine, through methionine adenyltransferase (MAT), is converted to S-adenosylmethionine (SAM), which is demethylated to form Sadenosylhomocysteine (SAH). SAH is finally converted back to homocysteine, completing the methionine cycle.

Vitamin B6 influence

Vitamin B6 is abundant in food and its major sources come from whole grain, nuts, vegetables, bananas, milk and some types of meat 130 134 . In OCM, vitamin B6 is a co-factor of several enzymes (see Figure 10), including serine hydroxylmethyltransferase (SHMT), cystathionine β-synthase (CBS) and cystathionine γ-lyase (CGL).

OCM and carcinogenesis

Many epidemiological studies have investigated OCM nutrients in relation to cancer. While some studies reported significant associations, the evidence to date is not clear. The biological mechanisms behind the association between OCM and carcinogenesis is still evolving. In this dissertation, we introduce two primary mechanisms by which one-carbon metabolism has been proposed to influence cancer development: 1) damage in DNA repair and DNA synthesis and 2) influence on gene expression via aberrant methylation pattern.

DNA repair and DNA synthesis

Folate is necessary for the de novo biosynthesis of purines and pyrimidines nucleotides, adequate of which is essential for genome stability 135 .

Purine nucleotides are necessary for DNA synthesis. In the process of the de novo biosynthesis of purines, the methyl group is provided by F-THF (see Figure 10) and then incorporated into the purine ring producing the following nucleotides adenine and guanine.

OCM factors and head and neck cancer

While little evidence have emerged on food groups linked to specific cancer sites, cancer of the head and neck consistently occurs less frequently among subject with high consumption of vegetables and fruit, with supporting data from both case-control [START_REF] Chuang | Diet and the risk of head and neck cancer: a pooled analysis in the INHANCE consortium[END_REF] and prospective studies 61 197 . To date, very few epidemiological studies -mainly case-control studiesassessed the association between B-vitamins and HNC. Results of these studies are summarized in Table 2. Overall, some case-control studies reported no or little relationship between intake of folate and vitamin B6 with HNC risk 92 198 199 where as other studies indicated a significant inverse association 69 200-202 . Furthermore, a recent pooled analysis of individual data from ten case-control studies participating in the International Head and Neck Cancer Epidemiology (INHANCE) Consortium and including over 5,000 cases and 13,000 controls, reported that high levels of folate intake compared to those with low levels were found to be inversely associated with overall oropharyngeal cancer risk [START_REF] Galeone | Folate intake and the risk of oral cavity and pharyngeal cancer: a pooled analysis within the International Head and Neck Cancer Epidemiology Consortium. International journal of cancer[END_REF] . The other OCM nutrients measured by dietary intake, including vitamin B2, B12 and methionine, did not reveal any clear association with HNC risk.

Only a few retrospective case-control studies have reported on circulating OCM biomarkers and HNC risk, most indicating lower folate concentrations and higher homocysteine concentrations among cases than among controls [203][204][205][206] . However, recall bias and reverse causality is particularly a concern because the nutrient (from dietary questionnaire or blood measurement) is assessed after diagnosis for cancer cases and thus, it is difficult to interpret those studies in the context of cancer etiology.

OCM factors and esophageal squamous cell carcinoma

Evidence on the association between the selected OCM micronutrients in this dissertation and cancer risk of the squamous cell esophagus has been limited and is summarized in Table 3. Globally, most of epidemiological studies were case-control studies and assessed the association between folate intake and ESCC risk. Three independent retrospective casecontrol studies 93 207 208 did not find any clear association between folate intake and risk of ESCC. Surprisingly, a meta-analysis comprising four case-control studies showed a significant inverse association between folate intake and ESCC risk with a summary relative risk when comparing the highest versus the lowest category of folate intake being 0.66 (95% CI 0.53-0.83) [START_REF] Larsson | Folate intake, MTHFR polymorphisms, and risk of esophageal, gastric, and pancreatic cancer: a meta-analysis[END_REF] . Similarly, a recent large prospective study including 492 293 participants found that those with low levels of folate intake compared to high levels had around 90% increased risk of ESCC, the relative risk being 1.91 (95% CI 1.17-3.10) [START_REF] Xiao | Intakes of folate, methionine, vitamin B6, and vitamin B12 with risk of esophageal and gastric cancer in a large cohort study[END_REF] . We did not find any study assessing the relation of circulating concentrations of B-vitamins and related metabolites of the OCM with ESCC.

OCM factors and kidney cancer

Although the relation between fruit and vegetable intake with kidney cancer is intriguing and consistently observed in both retrospective and prospective case-control studies, the associations between micronutrients from dietary intake related to the OCM with kidney cancer are few and inconsistent. To date, only four studies (two retrospective case-control studies and two prospective cohorts) have investigated intake of OCM nutrients, including vitamin B2, B6, B12, folate and methionine, in relation to risk of kidney cancer (see Table 3).

As a whole, these studies found little evidence that higher intakes of nutrients related to OCM lower risks of RCC and concluded that those OCM nutrients may have little influence on kidney carcinogenesis [209][210][211] .

Circulating concentrations of B-vitamins have been assessed in relation to multiple cancers, but only one study with a prospective design has been published regarding RCC to date 212 .

The ATBC study investigated the association between serum OCM biomarkers (folate, vitamin B2, B6, B12, cysteine and homocysteine) and risk of RCC. This nested case-control study of 224 case-control pairs did not find any clear association between those biomarkers and RCC risk. A role of OCM in renal cell development is biologically plausible, but evidence regarding such a role is limited.

Study designs in analytical epidemiology

Descriptive epidemiology concentrates on describing events (events or outcomes mostly refer to diseases) in population by characteristics related to time, place and individual 213 .

From those observations, hypotheses are generated and tested through analytical approach. The principal objective of analytic epidemiology is to determine causes and effects, or the "why?" and the "how?" , through investigation of exposure and disease at the individual level 214 . A definition of this approach was further developed by Rothman et al. 215 , pointing that analytical epidemiology aims to evaluate whether particular exposure such as a diet or a biomarker, is associated or not to an event such as cancer, and then whether this association is independent. However, even if the association between the exposure and the event is statistically significant, causation is not necessarily present due to possible artefact, confounding, chance, or bias 216 . Nowadays, experimental and observational studies (mainly cohort and case-control studies) are the principal designs used in analytical epidemiology.

Experimental studies

The specific characteristic of experimental studies is that the investigator controls the allocation of exposures to study subjects. Experimental studies, in particular clinical trials are superior to observational studies in the evidence hierarchy 217 . The reference of experimental studies is RCTs, where participants are randomly allocated to the groups (e.g. exposure or placebo). One advantage of such design is that potential unknown confounders are maintained symmetrically within the groups, and the comparability of the groups is maintained throughout the investigation. Thus, properly executed, RCT is the superior methodology to demonstrate causality and clinical efficacy of preventive and therapeutic procedures in the clinical setting.

Until now, the main focus of experimental studies of B-vitamins and cancer prevention has been on folate and colorectal cancer. Three RCTs of folate supplementation investigated whether it may prevent colorectal adenomas among high-risk population, but failed to identify a protective effect [218][219][220] . Although RCTs may restrict confounding, they have several limitations including; (1) limited size, limited number of cancers that occur in the follow-up period; (2) the exposure such as supplementation is randomized over a relatively short period (usually shorter than few years); and (3) they are unrelated to lifelong nutrient levels prior to the study.

While the RCT may be considered as the "gold standard" of scientific studies, this design is not always suitable in epidemiological research, mainly because of ethical issues. It would for instance not be ethical to study the impact of a potential dangerous substance e.g. arsenic on kidney cancer by allocating arsenic to the exposed group and a placebo to the control group. Furthermore, RCTs overall are very costly and it may not be feasible to conduct them over many years.

In contrast to RCTs where investigators record outcome in relation to exposure that has been allocated in some random fashion, in observational studies, researchers limit themselves to observing the outcome in people who are already segregated into different exposure groups. The two main nonexperimental, observational study designs are cohort and case-control studies.

Cohort studies

According to Breslow and Day 221 , cohort studies are longitudinal studies and start by identifying a group of individuals about whom certain exposure information is collected; the group is then followed forward in time to ascertain the occurrence of the disease(s) of interest, so that for each individual prior exposure information can be related to subsequent disease experience. Later, Rothman and Greenland 222 termed cohort as group (s) of subjects followed over time who share a common experience or status. For example, this could be a generation defined by the same period of birth, a group carrying a genetic trait (i.e. Down syndrome), a specific population who undergone special exposure (i.e. the Chernobyl cohort), or subjects gathered on a geographical basis (the Framingham cohort).

In public health, one the main objectives of cohort studies is to determine circumstances of disease occurrence and progression. First cohort investigations enrolled to assess the relation between risk factors and a disease of interest emerged during the 1950s in United-States 223 and in Grand Britain 25 224 . These studies were first devoted to evaluate the associations already strongly suggested by case-control studies or case-cohort studies. As an example, the British Doctors' Study from Doll and Hill was designed to assess the role of tobacco smoking on cancer mortality 224 . Usually, the cohort study design is referred to exposed/unexposed studies because at least two groups of subjects (one exposed to a factor and the other not), without having initially the event, are followed and compared to the incidence of this event. The principle of this comparison is not far from experimental studies, although the exposition is not allocated in some random fashion by the investigators.

Once enrolled, all cohort members are classified according to their exposure status at the baseline (initial) examination, and these statuses can be updated during further examinations at different times throughout follow-up of participants.

Cohort participants can arise from different population sources. Cohort can originate entirely, or almost entirely, from a population defined geographically and temporally. For example, Hoffamns et al. carried out a cohort study" among the entire 1932 male birth cohort to investigate the impact of body mass index at the age of 18 on survival during the subsequent 32-year time interval 225 . However, it is more usual that the cohort is arising from a fraction or a sample of the population defined geographically and temporally. One well-known example concerns a cohort study that was initiated in the late 1940s, the Framingham Study, where participants were recruited in Framingham, Massachusetts. 6,507 adult subjects were selected to participate from a total list of 18,000 residents aged 35 to 59 years old in order to investigate risk factors of coronary heart disease 223 .

In order to correctly estimate the incidence of the disease(s) and its determinants, it is crucial to obtain accurate quality information regarding the disease occurrence. To ensure this quality information, document such as clinical records or death certificate of the participant are compared to previous documents recorded in interviews. In addition to validating the diagnosis of the disease, this comparison method permits to improve information regarding diagnosis characteristics, such as specific histological types in a cancer. A significant challenge in cohort studies is the validation of the cause of death, where either the physician is directly interviewed or information are collected from clinical records. Nevertheless, it is usually unfeasible to collect data on event for all members of the cohort due to some reasons including lost to follow-up (prior to the date of event) or death (from unrelated causes).

The design of a cohort study can vary and be prospective or retrospective. In a prospective cohort study, none of the participants have developed any of the event(s) of interest at study enrolment whereas in a retrospective cohort study (or historical study), exposure(s) and event (s) occurred before participants are enrolled in the study, and this specific cohort design can be defined for example from historic records.

In epidemiological methods, we can also differentiate closed cohorts from open cohorts.

The main difference between these two types of cohorts is how membership is fixed. In a closed cohort, memberships are fixed and no participant can be added to the cohort once this latter started. Even if the size of the closed cohort diminishes due to death or loss to follow-up, no additional participant is further included. For example, people who were living in Hiroshima and Nagasaki when the atomic bombs were dropped in 1945 are part of a cohort whose membership started at the date of this nuclear disaster 226 , and no additional membership was included through time. These subjects remain in the cohort until they die.

In contrast, open cohorts who dominate cancer epidemiology and form the conceptual basis for most case-control studies, are dynamic with members leaving or being added to the cohort. Rothman et al. has been given typical examples of open cohorts, in particular state cancer registries 215 . Every time a resident will be diagnosed with a cancer, he or she will be added to the cohort. Also, if the resident immigrates in another country or die, he or she can also leave the cohort.

In cohort studies, time is a key element and the concept of "person-time" is fundamental for design and analysis. Person-time corresponds to the number of study subjects and the time they are followed, or more explicitly the sum of all times contributed in a study by subjects at risk of a disease. Let's assume for example that we have some participants exposed to arsenic water and were followed since they were exposed until the date of diagnosis, loss to follow-up, death or end of the study (censured date). Among those, 5 were followed 5 years and 10 were followed 2 years. This gives us a total person-time equal to 45 person-years (5*5 + 10*2 = 45).

In cohort design, person-time is necessary to estimate disease risk between exposed and unexposed participants. The estimation can be relative that is about the degree by which the exposure increases risk (relative risk), or absolute that is about how much difference in risk is there between the exposed and unexposed participants (attributable risk). The relative risk provides an idea of what is the strength of the association between the exposure and the disease which can guide for any potential causal relationship. Attributable risk indicates the burden of disease that an exposure displays in a population, which emphasize its public health impact 213 .

Many books and articles have reported on advantages and disadvantages of cohort studies 215 222 227 . In general, the main advantages are: exposures collected pre-diagnostically permit to avoid recall bias or reverse causality; they have the power to examine rare exposure and multiple events; incidence rates of disease in exposed and unexposed individuals can be determined. However, the cohort study design is not exempt of limitations such as (1) data on exposure and/or specimens for exposure analysis must be collected on the entire cohort (in prospective designs) at baseline which leads to cost issues and time consuming, (2) presence of bias from loss to follow-up (selection bias) between case and controls might induce the presence of an indirect non-causal association, and from outcome information being influenced by knowledge of exposures (information bias), (3) need to follow large number of subjects for long time to get enough statistical power , and ( 4) are limited for studies with rare events or long induction period.

Case-control studies

Case-control studies are defined as those studies which compare the frequency of patients having the disease (cases) with subjects free of disease (controls) with regards to prior exposure(s) or characteristic(s) 213 . Case-control studies often constitute one of the first approaches to study the etiology of a disease or condition. The major objective of a casecontrol study is to strengthen hypothesized causal relationships by providing accurate and effective estimates.

Many epidemiological works reported on principal case-control methodologies 213 215 222 227 and include specification of the study hypothesis, definition and selection of cases and controls, collection of information, analysis and interpretation.

As with any other type of design, the study hypothesis as to be clearly defined earlier in order to avoid design weaknesses and problems when interpreting the results 215 .

A case-control study begins with the identification of cases which has to be ideally representative of all cases observed in a determined and identified population. Cases are selected on the basis of the disease and not the exposure. A case must be precisely and explicitly defined 227 . Objective evidence confirming the diagnosis of cases is usually advisable, even though the study must separate with some cases. For example, in cancer epidemiology, cancer cases are often defined thanks to the International Classification of Diseases (ICD) codes which confirm diagnosis of new incident cancer cases and limit ambiguity. Selection bias were raised by Rothman et al. when selecting all prevalent cases because those cases may not be representative of all cases 215 . For instance, the subpopulation of prevalent cases with a short duration will be excluded from the entire group of prevalent cases because of either death or recovery and hence this might bias the relation between the exposure and the outcome. If documentation of incident cases is limited and non-precise this could lead to dilute the case group with some non-cases and the probability to discover real risk differences between cases and controls are diminished.

After cases identification, an important work has to be conducted for the selection of controls. Some epidemiologists noted that ideally controls should be directly sampled from the source population in order to have a well-representative selection. However, there are some debates pointing out that comparability of controls with cases might be more efficient. Rothman et al. discussed these two directions and argued that priority need to be done on the comparability of controls with the cases and that representativeness of the population is less important 215 . However, increasing attention on many levels is observed when considering population dimension. Conducting a population-based case-control study need generalisability, and to rich this, controls should be randomly selected from the disease free members of the same population from which all incident cases were drawn 227 . We could imagine for example all the newly diagnosed lung cancer cases in the catchment area of Lyon, assuming that they come from a regional cancer registry. Ideally, controls would suitably be drawn from the population of the same area within the same groups of age and sex than the cases. Sometimes, controls can also be drawn from hospitals although they are considered less suitable than population controls as they can lead to bias arising from the factors which lead individual to use health care services. In addition to population and hospital, controls can be selected from other sources such as neighbours, friends, or relatives, although control selection should always be motivated by the assumption 227 .

An important aspect of case-control studies consists in determining the start and duration of exposure for cases and for controls. Several methodologies are used when recording data.

Data on the relevant exposures can be obtained by telephone interview, by questionnaire, by face to face interview, by examining medical, occupational or other records, or by taking biological samples. Whatever method is chosen, it is crucial to guaranty that validated tools are used by trained investigators in the collection of data, and that such investigators should be "blind" (where possible) to the hypothesis under study and to the case/control status of the study participant. Even though this may be complex to realize in practice, strict procedures should be followed in order to limit bias from investigators (influence the cases and controls to remember exposures) and to maximized data collection.

Since the number of cases and controls is determined by the group conducting the research in traditional case-control studies, incidence rate ratio cannot be measured. However, if well-conducted, a case-control study can provide close estimates (odds ratio) of cohort studies (relative risk). The odds ratio is determined by the ratio of the odds of an exposure in the case group to the odds of an exposure in the control group 215 . Confidence interval for each odds ratio must be calculated in order to provide information on the precision of the estimates.

In addition, several statistical analysis methods are used in case-control studies in order to control for confounding or to evaluate interactions 222 . Measurement error and missing data are not rare in retrospective case-control studies and this can affect results. Sensitivity analysis which aims to quantitatively explore uncertainty in interpreting results has been proposed as one way of addressing these biases 215 228 .

Several methodological design books in epidemiology have been discussed limitations of case-controls studies 213 215 222 227 229 . When interpreting results, one should take into account potential biases including information bias in the data collection, selection bias in the selection of cases and controls, and oblivion of confounding factors 216 . For example, in traditional case-control studies, cases will usually report the exposure information after learning of their diagnosis. Then, diagnosis may produce differential abilities in repotting, for example, by improving memory which might increase sensitivity among cases, or by inducing more false memory and this reduce sensitivity. These characteristics are examples or recall bias. However, recall bias can be limited by keeping the study participants unaware of the hypotheses under study.

If cases and controls are not representative of the source population from where cases arisen, this could induce distortion in the results (selection bias). Despite approaches in selecting population-based controls which limit this type of bias, clear and precise monitoring need to be performed on response rates and participation bias 227 .

Another concern in case-control studies it that diagnosis related behavioural or premalignant changes may affect the exposure, such as biomarkers close to diagnosis, i.e. as would be expected by reverse causality 230 .

One of the major issue when conducting analytical epidemiology and in particular casecontrol study is question related to validity and precision. First, several methods can help in improving results precision or diminishing random errors by obtaining large sample size population (using pre-study power analysis) or by improving study design (for example matching control group with cases with respect to certain characteristics other than the exposure under study such as age or sex). In addition, one could think to validate results internally by conducting several sensitivity analysis (accuracy of measurement), and also to validate them externally by conducting the same study outside that population (generalisability of findings) 231 .

In addition, confounding is a considerable important issue in nonexperimental research. On the simplest level, confounding is a confusion of effects of the exposure of interest 231 . The distortion introduced by a confounding factor can lead to over-or underestimation of an association, or consequently more, can change the direction of the association. It is therefore essential to control for confounding throughout each investigation notably in observational study designs compared to intervention studies.

Although traditional case-control designs are more likely to have bias and incorrect inference compared to cohort studies, several epidemiologists highlighted important advantages of case-control designs including that they are cost-effective, easy to conduct, relatively quick, very suitable for rare events (e.g. cancer), and there is no participant that can be loss to follow-up 215 227 230 . If well-designed and well-conducted, case-control studies permit to evaluate multiples hypotheses and interactions which can make this design efficient.

Nested case-control studies

We explained previously that in traditional case-control studies, controls should be selected to be representative of the study population source from which the cases were drawn. If they are not, selection bias will be introduced. A variant of case-control studies, named "nested case-control studies", permit readily to accomplish this selection. As the name implies, a nested case-control study is a case-control study nested within a cohort, in which the cases and the controls arise from a clearly defined population, the cohort. However, in order to be able to adapt such design, it is important to enumerate times of entry and exit known for all members in order to list each risk set. These risk sets are necessary to ensure a rough representativeness when using sampling methods. In general, the selection method mostly used in nested case-control study is the incidence density sampling 229 . This method aims to sampled controls from the risk set at the time each case is diagnosed and that this risk set is completely independent from the other risk sets. In order to improve comparability between cases and controls, they might be matched on various demographic characteristics such as sex, age, or year of recruitment. Importantly, a control being sampled from the population at time t could become afterwards a case a time t+n, and also the same participant could be sampled more than once as a control. Figure 11 shows a simple situation of two risk sets. For the first case (risk set n°1) 4 controls are eligible for the matching and for the second case (risk set n°2) only 2 controls remained eligible. Similar density sampling methods were applied throughout the different nested case-controls studies used in this dissertation. When using this approach for case-control matching, the association estimate (odds ratio) from the nested case-control design is an unbiased estimate of constant incidence rate ratio from the cohort 232 . As an example, Hak E et al. and colleagues compared relative and absolute estimates of associations in an influenza vaccine study using both cohort and nested case-control designs 233 . After selection of different ratios (1:1 to 1:4) of cases and matched controls, it has been observed that the nested case-control design provided valid and precise estimates of associations compared to the full-cohort analysis.

Because the nested design conserves the validity of the prospective study, bias due to the difference in recall and bias of the disease modifying biological characteristics as mentioned for traditional case-control designs are avoided. Also, nested case-control studies compared to cohort design are more efficient in a way that they can give similar statistical and significant estimation with a smaller sample size than required for analysis in cohorts. By selecting a sample of control(s) for each case, the number of participants for whom exposure information needs to be collected, such as a biomarker, is reduced, which might decrease cost and logistics compared to obtain the exposure of interest in the entire cohort 234 . However, important data including baseline characteristics must be collected at enrolment for all participants in the cohort making the costs of nested design more likely higher than a traditional case-control study. In line with the advantages of the nested design, in particular time and cost, we conducted several nested case-controls studies in order to contribute in understanding the role of OCM biomarkers in relation to smoking-related cancers.

OCM and smoking-related cancers -Material and Methods

66

CHAPTER II:

MATERIAL AND METHODS

Data sources

The European Prospective Investigation into nutrition and Cancer (EPIC)

Overview of cohort

In 1992, the International Agency for Research on Cancer (IARC) initiated a comprehensive collaboration across Europe, the EPIC cohort, a multi-centre prospective cohort study in Western Europe 12 235 . The main objective of the study was to investigate the etiology of human cancer of various sites in relation to nutritional and other lifestyle factors, further motivated by difficulties in measuring dietary exposures and the need for very large prospective studies in order to establish associations between these factors and disease.

Originally, seven EPIC countries were included in the project including France, Germany, Greece, Italy, The Netherlands, Spain and the United Kingdom. These core countries were subsequently followed by three Nordic countries, Denmark, Sweden and Norway comprising in total 10 countries and 23 individual centres across Europe (see Figure 12).

Study population and data collection

Individuals who agreed to participate signed an informed agreement and received two questionnaires on dietary and lifestyle variables, respectively. As a whole, subjects completed questionnaires at home and were then invited to a study centres for additional examination including anthropometric measurements and blood samples donation. From all participants recruited in the EPIC cohort, 385,747 provided blood samples, which were collected to a standardised protocol. After the blood draw, fractions of blood (plasma, serum, erythrocytes and buffy coat for DNA) were aliquoted into several 0.5 mL straws. One set containing half of the straws was stored locally and the remaining straws were transported to IARC biobank (Lyon) for storage in liquid nitrogen (-196°C). However, with exception of the Danish and Umeå (Sweden) samples that were stored in plastic tubes locally. The Danish samples were stored in liquid nitrogen tanks in the biobank of The Danish Cancer Society in Copenhagen, and the samples from the Umeå study centre were stored in -80°C freezers in Umeå.

Different methods of dietary assessment were adopted in the EPIC cohort. First, extensive self-administrated quantitative dietary questionnaires, containing more than 250 food items and estimating individual average portions systematically, were used in several countries including northern Italy, The Netherlands, Germany and Greece (where dietary questionnaires were interviewer-administered). Questionnaires, similar in content to the self-administered quantitative dietary questionnaires but structured by meals, were used in Spain, France and Ragusa (south Italy). To increase compliance, the centres in Spain and Ragusa performed a face-to-face dietary interview using a computerised dietary program, whereas the dietary questionnaire was self-reported in France.

Then, semi-quantitative food-frequency questionnaires (with the same standard portion(s) assigned to all subjects) were used in Denmark, Norway, Naples in Italy and Umea˚ in Sweden. Finally, combined dietary methods were used in the UK and Malmö (Sweden). The two British centres used both a semi-quantitative food-frequency questionnaire and a 7-day record, whereas a method combining a short non-quantitative food-frequency questionnaire with a 14-day record on hot meals (lunches and dinners) was developed in Malmö.

The ascertainment of cases within EPIC reflects the predominant pattern of diagnosis in the general population because the majority of cohort's subjects were invited from the general adult population residing in a giving town or geographical area. Incident cancer cases were identified at regular intervals through population-based cancer registries (Denmark, Italy except Naples, the Netherlands, Norway, Spain, Sweden and United Kingdom) or by active follow-up (France, Germany, Greece and Naples), which involved a combination of methods, including review of health insurance records, cancer and pathology registries, as well as direct contact with participants and their next-of-kin.

Mortality data, including vital status, cause of death, and date of death, were obtained from mortality registries at the regional or national level. Subjects were followed up from study entry until cancer diagnosis (except non-melanoma skin cancer), death, emigration, or the end of the follow-up period for the relevant study centre. End of follow-up was defined as the latest date of complete follow-up for both cancer incidence and vital status and varied between study centres from December 2004 to June 2010. Vital status at follow-up is over 98% complete.

The Lung Cancer Cohort Consortium (LC3)

Overview of cohorts

The LC3 was initially created in 2011. This initiative followed an investigation within the EPIC study where it was reported strong associations between circulating concentrations of vitamin B6, folate and methionine and lung cancer risk [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] . The overarching objectives of this large consortium are two-fold; (1) to evaluate potential etiological risk factors of lung cancer over and above smoking history, and (2) to develop extensive risk prediction models using biomarkers in multiple cohorts. The LC3 was initiated in the collaboration of several cohorts participating in the National Cancer Institute (NCI). All cohorts with at least 150 lung cancer cases having prospectively collected questionnaire data and either plasma or serum samples were invited to participate to the consortium. Many cohorts fulfilled those criteria and accepted to participate, and to date, the LC3 includes 20 prospective cohorts with a combined study population of over 2,000,000 participants from North America, Europe, Asia and Australia, and are listed in the table below. Further design information and follow-up procedures of each cohort are provided in Appendix 1.

Biochemical analyses

All serum/plasma samples from the studies were shipped and centralized to Bergen under appropriate conditions, using established protocols (e.g sent on dry ice). Microbiological methods were used to determine concentrations of folate (Lactobacillus casei) 241 and vitamin B12 (Lactobacillus leichmannii) 242 .

The detection limit, and within-day and between-day coefficients of variations (Cvs) using these methods are low for all of the biomarkers 239 240 . For HNC and RCC studies, CVs within and between batches were, respectively, 6 and 11% for vitamin B2, 3% and 6% for vitamin B6, 4% and 5% for folate and vitamin B12, 1% and 3% for methionine, and 1%and 2% for homocysteine. The CVs within and between batches of biomarkers studied in LC3 study were, respectively, 3 and 7% for vitamin B6, 4 and 5% for folate and 1 and 3% for methionine.

All samples were analysed with similar methods in batches of 86 and quality control included six calibration samples, two control samples, and one blank sample in each batch.

For each study, all serum or plasma samples were kept at -80°C and all cancer cases and their individual matched controls were analysed together within the same batches in random order. The laboratory staff was blinded to the case-control status of the blood samples. The raw data from the separate platforms and analysis sets were merged, handled and controlled by specifically designed software. Outliers were identified by the ratio of concentrations as measured by the different platforms. Unlikely combinations of values were flagged by installed macros. This increases accuracy and precision, minimizes the possibility of assay interference and ensures adequate sample handling and logistics. The system also generated summary statistics and printouts of selected samples. These routines for data handling ensured efficiency and reduced the chance of logistic problems and human errors.

In the LC3 study, about 1,000 repeat blood samples from subjects were additionally measured in order to estimate the within person variation in each biomarker. These repeat samples came from two participating cohorts which are the CSP (390 repeat samples) and NSHDS (600 repeat samples) studies. The sample size of repeat samples have been designed to select approximately one third of repeat samples collected within a short time of each other (< 1 year), as well as one third collected around 3 to 5 years of each other, and one third within 10 years or more of each other.

Statistical methods

Standard statistical methods, such as linear and logistic regression, were used throughout the different studies of this thesis in order to investigate various relationships. In order to investigate if OCM biomarkers were related to cancer survival probability of the head and neck and kidney, estimation of hazard ratios (HRs) for all-cause mortality for cases were calculated using Cox proportional hazards models (Chapter III and IV, respectively).

Time since diagnosis was used as the timescale, and all models were adjusted for age at diagnosis, sex, and country and additional variables when it was relevant.

We adjusted overall analysis by cotinine (in quartiles) within each study, by smoking status (in HNC and RCC studies), and further smoking variables when available, including duration of smoking and average cigarettes smoked per day. Further potential confounders, including alcohol consumption, BMI, educational attainment, hypertension and dietary intake were included in our analyses where necessary.

For estimated interclass correlations of repeat measurements in the LC3 study, we used spearman correlation methods 215 .

All statistical analyses were conducted using SAS 9.

Selection of cases and controls

This study was nested in the EPIC cohort and further study design information has been described previously in Chapter II (see Material and Methods, Study population and data collection).

In this study, 1,273 subjects diagnosed with incident head and neck or oesophagus cancers where identified from nine EPIC sub-cohorts contributing to this nested case-control study.

These cancer cases were defined on the basis of the International Classification of Diseases for Oncology, Second Edition (ICD-O-2), and included: oral cavity (ICD C02.0-C02.9, C04.0-C04.9, C03.0-C03.9, C05.0-C06.9, C14.0-C14.9), oropharynx (C01.9, C02.4, C09.0-C10.9), hypopharynx (C13.0-C13.9), larynx (C32.0-C32.9), and oesophagus (C15.0-C15.9). Criteria of exclusion included cases who (1) did not donate a blood sample (n=152), ( 2) did not have enough plasma available for biochemical analysis (n=20), ( 3) had a history of another cancer (n=158, except non melanoma skin cancer), ( 4) were not histologically confirmed, were prevalent at the time of blood donation, or did not have questionnaire information available (n=22), (5)

Biochemical analyses

All biomarkers were sent on dry ice to the Bevital A/S laboratory, in Bergen, Norway.

Because the studies of this thesis share common biochemical methods, details of the biochemical analyses have been provided previously in Chapter II (see Material and Methods, Biochemical analyses).

Statistical analyses

Throughout this study, we used both conditional and unconditional logistic regression in order to investigate circulating OCM biomarkers in relation to cancer risk of the HNC and ESCC. Hazard ratios for all-cause mortality for HNC and ESCC cases were calculated using Cox proportional hazard regression models. These calculations were performed using SAS

Objective

In the previous chapter, we found that participants having higher circulating concentrations of homocysteine had elevated risk of HNC. We wanted then to evaluate the extent to which homocysteine or other biomarkers included in the OCM pathway may be related to the incidence and survival of RCC. An additional reason for studying the association between OCM biomarkers and RCC was that this specific cancer site had less strength of association with smoking compared to HNC.

Selection of cases and controls

As for the HNC study, the RCC case-control study nested within the EPIC cohort used similar selection methods and further study design information has been described previously in

Chapter II (see Material and Methods, Study population and data collection).

In this study, we initially identified 905 cases from 10 participating countries within EPIC that were diagnosed with RCC as C64.9 according to the International Classification of Diseases for Oncology, Second Edition (ICD-O-2). We excluded prevalent cases and cases with a prior history of another cancer (n = 85, except non melanoma skin cancer), cases who did not donate a blood sample (n = 153), were not histologically confirmed (n = 27), did not have questionnaire information available (n = 6), and cases from the Malmö centre that did not participate in this study (n = 64), leaving 570 eligible RCC cases.

As in HNC study, one control was randomly chosen for each case in RCC study, from appropriate risk sets consisting of all cohort members alive and free of cancer (except nonmelanoma skin cancer) at the time (and hence age) of diagnosis of the index case. The matching criteria is the same of the HNC studies being country, sex, date of blood collection (±1 month, relaxed to ±5 months for sets without available controls), and date of birth (±1 year, relaxed to ±5 years for sets without available control participants).

As previously explained for the HNC study, we also included a control group 2 (n=553)

unmatched to the RCC incident cases but matched to cases of the HNC study (Chapter III).

Similar matching criteria for the control group 2 compared to the RCC matched controls were also used. This control group 2 participated to unconditional and stratified analyses to assess the consistency of any association and increase the statistical power.

Replication study

The first analyses from the EPIC sub-sample in the RCC study showed strong inverse association between circulating vitamin B6 and risk of renal cell carcinoma. In order to determine if this association was restricted to the EPIC study population, a replication study was conducted in a separate cohort, the Melbourne Collaborative Cohort Study (MCCS). The MCCS is a prospective cohort study of 41,514 women and men aged 40 to 79 years at the time of recruitment which took place between 1990 and 1994 in Melbourne, Australia.

Following blood draw, plasma fractions were stored in liquid nitrogen tanks at -196°C.

Incident cancer diagnoses were identified via routine linkage to the Victorian Cancer Registry (VCR). There is a statutory requirement that all cancers diagnosed in the state of Victoria (the state in which Melbourne is situated), excluding non-melanoma skin cancer be reported to the VCR. Vital status and cause of death information were obtained via linkage to the National Death Index of Australia.

The nested case-control study from MCCS counted a total of 144 case-control matched pairs. We ensured that incident cases and controls were selected using the same protocol as the EPIC cohort. As a note, data from the MCCS study were used as an independent replication analysis and then were not pooled with the EPIC data.

Biochemical analyses

Biochemical procedures used in this study are identical to the HNC study (Chapter II), and details of the biochemical analyses have been provided previously in Chapter II (see

Material and Methods, Biochemical analyses).

Statistical analyses

In this study, we used similar statistical methods to the HNC study including conditional and unconditional logistic regression to investigate the association of OCM biomarkers with RCC risk. Hazard ratios for all-cause mortality for RCC cases were calculated using Cox proportional hazard regression models. Model based estimates of the survival function by biomarker quartiles were calculated using flexible parametric survival models. These calculations were performed using SAS 9.

CHAPTER V:

B-VITAMINS AND LUNG CANCER IN THE LC3

Objective

In addition to investigate whether OCM biomarkers were associated with other smokingrelated cancers than lung cancer, we wanted to externally validate and support some previous findings from the EPIC lung cancer study [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] . We then further investigated the association between circulating concentrations of vitamin B6, folate and methionine with lung cancer risk in the LC3 study which gather multiple populations from America, Europe, Asia and Australia.

Selection of cases and controls

In total, twenty nested case-control studies contributed in this study, gathering over 5,500 case-control pairs. Further study design information of each participated cohort is described in Appendix 1. Lung cancer cases were defined on the basis of the International Classification of Disease for Oncology, Second Edition (ICD-O-2), and included invasive cancers coded as C34.0-C34-9. After excluding cases who were not correctly matched on smoking status (n=124 cases), who had insufficient plasma samples (n=42), or had a revised date of diagnosis prior to blood draw (n=13), 5,364 lung cancer cases and their matched controls remained eligible.

For each case, one control was randomly chosen from risk-sets consisting of all cohort members alive and free of cancer (except non-melanoma skin cancer) at the time of diagnosis of the index case. Matching criteria were cohort, sex, date of blood collection (± 1 month, relaxed to ± 3 months for sets without available controls), and date of birth (± 1 year, relaxed to ± 3 years). To adequately control for confounding by tobacco exposure controls were also matched for 3 categories of smoking status including never smoker, short/long term quitters among former smokers (< 10 years/ 10+ years since quitting), and light/heavy smokers among current smokers (< 15 years/ 15+ cigarettes per day).

Data harmonization

The LC3 consortium was led by our group, the Genetic Epidemiology Group, at the International Agency for Research on Cancer. Once the consortium started, I was strongly involved in the coordination of biosample shipments. We created a data dictionary (see

Biochemical analyses

All biosamples from the LC3 study were also sent on dry ice to the Bevital A/S laboratory, in Bergen, Norway, which permit us to have a good comparability with the two previous studies (Chapter III and IV). Additional biochemical analysis information has been provided

previously in Chapter II (see Material and Methods, Biochemical analyses).

Statistical analyses

In the LC3 study we used conditional logistic regression in order to investigate circulating OCM biomarkers in relation to lung cancer risk. In addition, hierarchical linear models were used to describe within-and between-cohort differences in (log) concentrations of each biomarker by demographic and host factors. These calculations were performed using SAS

Introduction

The most important and effective means for lung cancer control is by reducing the number of people who smoke tobacco products. However, even among subjects who have quit smoking, the lifetime risk of lung cancer remains high, [249][250][251] and in some regions where antitobacco campaigns have been successful, such as the US, a large proportion of lung cancer cases now occurs among former and never smokers. Given these considerations, exploring additional means of primary prevention of lung cancer is important for subgroups of high risk-in particular former smokers-who seek additional means to further reduce their risk.

One-carbon metabolism (OCM) encompasses a series of biochemical reactions involving Bvitamins that are essential to ensure balanced DNA synthesis and methylation, 133 180 and changes in specific OCM factors have been implicated in cancer development. 132 180 195 If imbalances in B-vitamins such as folate (B9) and vitamin B6 are proved to be causally implicated in lung cancer etiology, they would provide an appealing target for chemoprevention as they are modifiable by changes in diet or supplementation. 145 252 Only two prospective studies have been published on lung cancer and circulating biomarkers of OCM, the EPIC study [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] and the Alpha-Tocopherol, Beta-Carotene, Prevention

Cancer (ATBC) study of smoking men, 185 both of which reported an inverse association between circulating vitamin B6 and lung cancer risk. In particular, the EPIC nested casecontrol study of 899 cases and 1,770 controls reported that the inverse association of vitamin B6 with lung cancer risk was strong and consistent regardless of tobacco smoking history, and also reported strong inverse associations of methionine and folate with risk. [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] In order to provide conclusive evidence on the importance of circulating B-vitamins and lung cancer risk, we initiated the Lung Cancer Cohort Consortium (LC3) with a combined cohort population of over 2,000,000 participants from North America, Europe, Asian, and Australia, to retrieve blood samples and conduct biochemical analysis on over 5,000 case-controls pairs.

Material and Methods

Study population

We invited all prospective cohort studies with cryopreserved baseline plasma and serum samples that in 2009 were members in the US National Cancer Institute (NCI) Cohort

Consortium to participate in the study. Twenty cohorts fulfilled those criteria and accepted to participate, resulting in a combined cohort population of over 2,000,000 participants from North America, Europe, Asia and Australia. Brief details on design of the cohorts and their follow-up procedures are provided in the Supplementary methods.

Selection of cases and controls

Lung cancer cases were defined on the basis of the International Classification of Diseases for Oncology, Second Edition (ICD-O-2), and included all invasive cancers coded as C34.0-C34-9. We selected a total 5,545 lung cancer cases, and in order optimize the statistical power in smoking stratified analyses, never and former smoking cases were oversampled.

For each case, one control was matched by cohort, sex, date of blood collection (± 1 month, relaxed to ± 3 months for sets without available controls), and date of birth (± 1 year, relaxed to ± 3 years), as well as smoking status in 5 categories; never smokers, short and long term quitters among former smokers (<10 years, 10 years since quitting), and light and heavy smokers among current smokers (< 15 years, 15 cigarettes per day). After various exclusions (see Supplementary methods), 5,364 lung cancer case-control pairs remained eligible for the risk analysis.

Biochemical analyses

Concentrations of vitamin B6, methionine and cotinine were determined by mass spectrometry based methods (LC-MS/MS, GC-MS/MS), 239 240 and microbiological methods were used to determine concentrations of folate (Lactobacillus casei). 241 Further details are provided in Supplementary methods.

Statistical analyses

Hierarchical linear models were used to describe the variation in average biomarker concentrations between the cohorts, and the extent to which these could be explained by differences in baseline characteristics. Relative risks of lung cancer were estimated by calculating odds ratios (OR) and 95% confidence intervals (CI) using conditional logistic regression (conditioning on matched case-control sets) with the first quartile as the referent. Additional covariates were included to account for confounding by risk factors, including indicators of educational attainment (in six categories) and tobacco exposure (in addition to matching to smoking status by design: cotinine concentrations [quartiles defined in current smokers]). Including additional covariates of body mass index (BMI) and alcohol intake did not appreciably alter the results and were not included in the final models. As a sensitivity analysis, we fitted models that were additionally adjusted for smoking duration or pack-years of smoking among ever smokers. All risk analyses were conducted overall, and stratified by smoking status and region (US, EU/AU, Asia).

As an indication of the overall statistical strength of association between each biomarker and risk, we calculated a p-value for trend by including the base-2 logarithm (log2) of the biomarker concentration as a continuous variable in a separate conditional logistic regression model. The same approach was used in stratified risk analyses according to other pre-defined demographic characteristics and risk factors. OR estimates per log2 unit (log 2 OR) may be interpreted as the relative risk associated with a doubling in the concentration of a circulating biomarker.

All statistical analyses were conducted using SAS 9. Additional details on the statistical methods are provided in the Supplementary methods.

Results

Baseline Characteristics

Baseline characteristics of the study subjects separated by geographic areas are shown in Overall, 46% of the participants were women, though the sex distribution varied by region, the US cohorts contributing more women and the cohorts from Europe and Asia contributed more men.

Variation in circulating biomarkers

There was substantial between-cohort variation in concentrations of all biomarkers (Table 1), in particular for folate and vitamin B6 concentrations that were substantially higher in US cohorts than for EU/AU or Asian cohorts. This could only partly be explained by differences in age, sex, smoking status, or BMI (Figure 1). For circulating vitamin B6 and methionine, the between-cohort variability was similar before and after covariate adjustment (Figure 1).

Conversely, for circulating folate, adjustment for age, sex, smoking status, and BMI reduced the between-cohort variability, the sd being 0.70 (90% CI 0.54-0.97) without adjustment and 0.44 (90% CI 0.32-0.65) with adjustment.

Risk analysis overall and stratified by region

Participants with higher circulating vitamin B6 and folate had an approximate 20% lower risk of lung cancer overall (Table 2 and3), the odds ratio when comparing the top and bottom quartiles [OR 4vs1 ] being 0.81 for vitamin B6 (95% CI 0.72-0.92) and 0.80 for folate (95% CI 0.70-0.92). These OR estimates were slightly attenuated after adjustment for circulating cotinine and education (OR 4vs1 0.88, 95% CI 0.78-1.00 for vitamin B6 and OR 4vs1 0.86, 95% CI 0.74-0.99 for folate). We did not observe any clear association between circulating methionine and risk of lung cancer, the overall OR 4vs1 being 0.95 (95%, 0.85-1.07, Table 4).

Among participants with available information on smoking duration and pack-years of smoking (90% of ever smokers), further adjustment by continuous number of years of smoking or pack-years of smoking did not notably affect the estimates (Supplementary

Risk analysis stratified by smoking status and sex

In analyses stratified by smoking status, we observed an inverse association between vitamin B6 and overall risk for former (OR 4vs1 0.72, 95% CI 0.57-0.91) and current smokers (OR 4vs1 0.79, 95% CI 0.65-0.95) (Table 2). Folate was also inversely associated with risk for former (OR 4vs1 0.66, 95% CI 0.51-0.85), but not for current smokers (OR 4vs1 0.97, 95% CI 0.77-1.21) (Table 3). When analyzing ever smokers overall (i.e. former and current smokers combined), we observed an inverse association for circulating vitamin B6 (OR 4vs1 0.79, 95% CI 0.88-0.91) and folate (OR 4vs1 0.85, 95% CI 0.72-0.99). Of note, whilst no clear association was seen between folate and risk for never smokers (OR 4vs1 0.86, 95% CI 0.63-1.17), we observed a positive association between vitamin B6 and risk for never smokers, the OR 4vs1 being 1.51 (95% CI 1.14-2.01) (Table 2).

Further analyses stratified by sex showed that any inverse association between folate or vitamin B6 and lung cancer risk was restricted to ever smokers and men. First, the association of vitamin B6 in men (OR 4vs1 0.74, 95% CI 0.62-0.89) was consistently observed in cohorts from each region (Table 2), with similar observations for folate (overall OR 4vs1 0.75; 95% CI 0.61-0.93) (Table 3). Conversely, no association was seen among for women overall, nor for women separated by region (Table 23).

Stratified risk analysis by demographic and diagnostic parameters

To assess potential of effect modification, we fitted models that included interaction terms between circulating folate or vitamin B6 and various covariates (Supplementary Figures 12345678).

Gender modified the associations between circulating concentrations of vitamin B6 and folate and lung cancer risk (P heterogeneity ≤0.05, Supplementary Tables 1 and4). When assessing blood samples taken up to 10 years prior to diagnosis, we observed that the overall inverse association between vitamin B6 and lung cancer risk was driven by cases diagnosed closer to blood draw (P heterogeneity = 0.001, Supplementary Figure 1), as well as by former (OR log2 0.81; 95% CI 0.70-0.90, Supplementary Figure 3) and current smokers (OR log2 0.79; 95% CI 0.69-0.91, Supplementary Figure 4). Stratified analyses by histology showed a stronger inverse association between vitamin B6 and folate and risk of lung squamous cell cancer (OR log2 0.86; 95% CI 0.77-0.95 for vitamin B6 and OR log2 0.88; 95% CI 0.78-0.98 for folate) than for other histological types (Supplementary Figures 1 and5). Stratified analysis by period of blood collection before and after 1996 when folate fortification of food items took effect in US did not reveal any discernable difference in lung cancer risk for circulating folate (Supplementary Figure 12). The associations between biomarker concentrations and lung cancer risk for individual cohorts are presented in Supplementary Figures 91011. We also estimated the potential risk increase associated with being clinically deficient in vitamin B6 (Supplementary Figure 13). Similarly, for folate we observed an increased risk for those classified as deficient (OR=1.27, 95% CI 1.07-1.49, Supplementary Figure 14). Further stratified analyses are presented in Supplementary Figures 13 and14.

Discussion

Our aim was to evaluate the associations between lung cancer risk and circulating concentrations of folate, vitamin B6, and methionine, within a consortium of 20 prospective cohorts from the US, Europe, Asia and Australia. We found that individuals with low concentrations of vitamin B6 and folate have a small risk increase of lung cancer, particularly among men, current and former smokers, and participants living outside the United States.

Previous studies and the Lung Cancer Cohort Consortium (LC3)

To date, this is the largest cohort consortium initiative for which circulating biomarkers where assessed in relation to a single cancer outcome with coordinated biochemical analysis conducted in one centralized laboratory. The background to this study was the initial findings from the EPIC cohort involving 899 lung cancer cases and 1,770 controls. [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] The EPIC study reported a strong inverse association between vitamin B6 and lung cancer regardless of smoking history, and additionally, found similar inverse associations with risk for folate and methionine. The results of the EPIC study were noteworthy as they suggested

Anouar Fanidi, 2016 123 a potentially important role of the one-carbon metabolism pathway in lung cancer etiology, in addition to tobacco exposure. Along with additional evidence from the ATBC cohort, 185 this motivated the organization of the Lung Cancer Cohort Consortium (LC3) in order to conclusively evaluate the extent to which these findings translate into other populations from Asia, the United States, Europe and Australia.

Interpreting the results

The current study discerned relatively weak inverse associations between folate, vitamin B6

and lung cancer risk overall, associations that differed substantially between groups of different tobacco exposure history. For former and current smokers, we observed slightly lower risk among for those study participants with higher concentrations of folate and vitamin B6 compared to those having lower concentrations. This inverse association for lung cancer risk with vitamin B6 or folate can also be interpreted as an increased risk among participants in the lowest category of concentrations compared to those in the remaining three higher categories (Tables 2 and3). In contrast, methionine was not clearly associated with risk overall or in any important subgroup.

Previous studies have shown that being a current smoker is clearly associated with lower circulating concentrations of folate and vitamin B6, [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] and our analysis confirms this relation (data not shown). This highlights the importance of carefully accounting for tobacco exposure in risk analyses of B-vitamins and lung cancer. We used circulating cotinine, an objective and accurate measure of current tobacco exposure, 253 and the weak inverse associations of vitamin B6 and folate with lung cancer risk remained after adjusting for cotinine. Further accounting for smoking duration and intensity did not substantively alter the OR estimates. In contrast to current smokers, former smokers tend to have similar circulating B-vitamin concentrations as never smokers. [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] This suggests that the potential confounding effect of tobacco exposure on the associations between circulating vitamin B6/folate and lung cancer risk is of less concern for former smokers than for current smokers. Despite these considerations, and given the important impact of both past and current tobacco exposure on lung cancer risk, our results do not allow ruling out confounding by tobacco smoking as a possible explanation for the observed associations.

Another potential explanation for the observed association of vitamin B6 with risk is reverse causation. In analysis stratified on smoking status, we observed a gradually stronger inverse association between vitamin B6 and risk of lung cancer in both former and current smokers when measured in blood drawn closer to diagnosis (Supplementary Figure 1, 3 and 4), results that would be consistent with pre-clinical metabolic changes due to the underlying disease progression. In contrast, no such clear relation by time from blood draw to diagnosis was observed for folate.

On the whole, we interpret these results as being consistent with a small benefit in terms of lung cancer risk for smokers who avoid low circulating concentrations of vitamin B6 and folate, but cannot rule out the possibility that residual confounding by tobacco exposure and/or reverse causation due to the disease progression, underlie their associations with risk.

The most notable difference between our results and those from the previous EPIC study was for never smokers, where vitamin B6 was positively associated with risk among for women in the current study, the opposite of what was observed in the previous EPIC study.

The reason for this stark discrepancy is unclear and not explained by differences in study characteristics.

Study strengths and limitations

Strengths of this study included the large study sample of 5,364 case-control pairs, the prospective study design and the use of pre-diagnostic plasma or serum, the centralized biochemical analysis with a robust quality control protocol, and the wide participation of cohorts from different geographical regions. We benefitted from over-sampling never and former smokers, as well as matching by history of tobacco exposure, thus allowing for wellpowered stratified analysis. However, this oversampling also meant that the individual cohorts provided different proportions of subjects by smoking status. For instance, the WHI only contributed never smoking women and the ATBC study only contributed current smoking men. Another particular feature of this study was that circulating vitamin B6 and folate varied substantially across cohorts and continents. Indeed, US participants had about 65% higher B6 and almost three-fold the median folate concentrations of European and

Anouar Fanidi, 2016 125

Asian study participants (see Table 1). A likely explanation of this is that up to 50% of U.S.

citizens aged 50 years or more regularly consume dietary supplements such as multi-vitamin pills, 254 and because of the folate fortification in the US. 255 Differences in baseline characteristics only partially accounted for these differences, and this means that we could not efficiently compare participants at extremes of the distribution of folate concentrations because the controls were matched to cases within the same cohort, and we were reluctant to break that matching.

Study implications and conclusions

This study highlights the importance of re-evaluating promising associations of putative risk factors indicated in initial studies across multiple study populations in a coordinated fashion. The LC3 clearly demonstrates that it is feasible to gather cohorts from around the world and provide robust information on disease risk association before resorting to intervention studies that are costly and risk harming the study participants.

Whilst our results were consistent with a modest decrease in lung cancer risk among former and current smokers who avoid low circulating concentrations of vitamin B6 and folate, we could not rule out the possibility that confounding due to tobacco or reverse causation may explain these associations. Given that any potential beneficial effect of vitamin B6 or folateif real-is likely to be small, our findings do not support the conduct of additional prevention studies with the view of using B-vitamins for primary prevention of lung cancer. access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. PB and MJ are the overall coordinators of the LC3 consortium that was implemented in collaboration with the main investigators in the collaborating cohorts.

We thank the study participants and LC3 consortium members for their contributions in making this study possible.

Figure 1. Estimated between-cohort variation in mean concentration of vitamin B6, folate, and methionine. Before (left) and after (right) adjustment for age, sex, smoking status, and BMI. Histograms show the posterior distribution of the overall mean and standard deviation (SD) of the cohort-specific intercepts.

The "caterpillar plots" show the estimated cohort-specific mean concentrations, along with 50% and 90% credible intervals (thick and thin bars, respectively). (11.83-22.77) 632/600 1.00 (reference)

1.00 (reference)

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The one-carbon metabolism and smoking-related cancers

Recall on objectives

Over the past two decades, the role of one-carbon metabolism (OCM) in cancer has received considerable attention. Circulating B-vitamins and related analytes have been assessed prospectively for multiple cancer sites including colorectal [256][257][258][259] , prostate 237 238 , and breast cancer [260][261][262][263] , although the evidence is inconsistent. To date, data examining associations of OCM deficiency/ imbalance and smoking related cancers are sparse, and my thesis aims to fill this knowledge gap. In order to achieve this, circulating OCM biomarkers were examined in relation to three specific cancer sites that differed in their strength of association with smoking.

Summary of the results

One-carbon metabolism and head and neck cancer etiology and outcome (Chapter III)

To the best of our knowledge, this was the first prospective study assessing the association between circulating OCM biomarkers and risk of the head and neck cancer (HNC). Only a few retrospective case-control studies had been published and reported lower circulating concentrations of folate and higher concentrations of homocysteine in cases than in controls [203][204][205][206] . In this study, we observed that subjects in the top quartile of circulating concentrations of homocysteine had a two-fold increase in risk of HNC compared with those in the bottom quartile. The positive association of homocysteine was present both among never-smokers and non-alcohol consumers.

Furthermore, we observed that participants with high circulating concentrations of folate had overall lower risk of HNC. Because folate and homocysteine are inversely correlated in the OCM pathway 135 , we cannot exclude the possibility that the folate association with risk is underlying the positive association of homocysteine with HNC risk. Mutually adjusted conditional analysis (data not shown) indicated that these risk associations were not independent, homocysteine accounting for the association of folate, but not vice-versa.

However, the inverse folate association with HNC risk was not as consistent as that of homocysteine, and when performing different sensitivity analyses, the positive homocysteine association with risk was not materially affected whereas the folate association was attenuated and no longer significant. This finding might not support for a decrease of HNC risk in participants having elevated dietary intake of folate, as seen in previous epidemiological studies 69 200-202 .

Overall, the results from Chapter II indicate that circulating homocysteine affects the risk of developing squamous cell carcinoma of the head and neck.

One-carbon metabolism and renal cell carcinoma etiology and outcome (Chapter IV)

Evidence on the role of vitamin B6 in RCC is limited, , and to date, only one prospective study assessing circulating concentrations of vitamin B6 and RCC risk has been published 212 , and did not report any clear association.

In contrast, our study showed that subjects from the EPIC cohort with elevated vitamin B6 concentrations had lower risk of RCC, in a dose-response fashion, resulting in two to threefold risk differences between the top and the bottom quartiles of the population. We also observed a clear improved survival among cases with higher vitamin B6 concentrations.

Subsequent replication of the EPIC results using the independent MCCS cohort from Melbourne, Australia, yielded nearly identical associations for both RCC risk as well as for cancer mortality.

Because the RCC study from the EPIC cohort and the previous study from the ATBC study are different in term of study design (population-based observational study vs trial) and populations sources (10 European countries vs Finish men smokers) and also because these two studies used different biological measurement methods to measure circulating vitamin B6 (liquid chromatography/tandem mass spectrometry vs the tyrosine decarboxylase assay),

we cannot exclude that these study differences might explain this discordance in the findings.

One of the main questions that arose following the findings from the RCC study was whether the association of circulating vitamin B6 with survival was also present among Together, these studies strongly suggest that circulating concentrations of vitamin B6 are inversely related to both RCC risk and mortality.

One-carbon metabolism and lung cancer etiology in the LC3 (Chapter V)

This large consortium of lung cancers, the LC3 study, was based on previous results from EPIC cohort where a strong dose-response association between circulating vitamin B6 and lung cancer risk was reported including among never smokers [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] .

In the current study, the LC3 study, we observed a weak inverse association, with no clear trend, between circulating concentrations of folate and B6 and lung cancer risk, and this association was mainly present among ever smoking study participants. Similarly, men and former smokers who were deficient in circulating vitamin B6 and folate showed a marginal increased risk of lung cancer compared to those with normal levels. The observed results from LC3 study did not confirm the reported results from the previous EPIC lung study.

However, it is not straightforward to compare our results with those of the EPIC lung study because the EPIC cohort was conducted mainly in Western European countries whereas the LC3 study is more diverse, and included four continents, several ethnic backgrounds and sex-specific cohorts. It would seem clear that the discordant results between the studies cannot be readily explained by differences in baseline characteristics such as sex or smoking status. It is possible that unobserved differences between the cohorts might partially account for the discrepant results.

One particular feature of the LC3 study is that circulating vitamin B6 and folate varied substantially across cohorts and continents; American participants displayed almost threefold higher median folate concentrations and 1.5-fold higher median vitamin B6 concentrations compare to both the European and Asian participants. This drastic variation complicated comparisons, as well as pooling of data across continents and cohorts. In addition, the sizable sample size of 5,364 case-control pairs quickly diminished when stratifying by continent and smoking status resulting in reduced statistical power in some important subgroups such as never smoking men.

Furthermore, when looking for deficiency, we noted a relatively small proportion among US participants being deficient in circulating folate (2% being below 7 nmol/L) and vitamin B6 (10% being below 20 nmol/L) compare to European/Australian and Asian participants.

Speculatively, the lack of variability from deficiency to excess of these biomarkers might be

Observations across the studies

Tobacco exposure

Because the control of confounding by smoking depends on accurate smoking history, and because some misclassification of the true smoking history is unavoidable, investigations of smoking related-cancers overall are difficult to conduct. Moreover, the proportion of cancers explained by smoking, i.e. the attributable fraction, differs among cancer histology and cancer sites, as for the three cancers examined in this thesis. While tobacco smoking dramatically increases the risk of lung cancer, the risk increase is moderate for HNC and weaker for RCC. As such, residual confounding by smoking is difficult to exclude, in particular for lung cancer.

In HNC study, the association between homocysteine and HNC risk was observed among both never and current smokers, suggesting that residual confounding due to tobacco smoking might not explain the association with risk.

In RCC study, the test of interaction between smoking exposures and vitamin B6 revealed that the inverse association between circulating concentrations of vitamin B6 and RCC risk was prominent among current compared to never-and former smokers, although a nonsignificant inverse association among the two latter strata was observed. However, current smokers have about 30 to 50% higher risk of RCC compared to never smokers 1 7 , and given that the association of circulating vitamin B6 indicated 2 to 3-fold increased risk among current smokers compared to never smokers, it would seem unlikely that confounding by smoking can explain the observed results.

In LC3 study, associations were mainly observed among ever smokers. We attempted to control for confounding by tobacco exposure by matching by detailed smoking status. In addition, circulating cotinine, which is an indicator of current smoking intensity and is known to explain lung cancer risk beyond that captured by questionnaire information 253 , was also included to control tobacco smoking among current smokers in each study of this dissertation. Even though we took all possible measure to account for residual confounding by tobacco exposure, we cannot completely rule out the possibility that it may still partially account for the observed associations of lung cancer.

While we observed an inverse association between circulating vitamin B6 and lung cancer risk among ever smokers, the vitamin B6 association surprisingly tend to be positively associated to lung cancer among never smokers. We do not know exactly the reason for this discrepant results but this could be due to the difference of comparability between never smoking lung cancer cases and controls. While ever smoking cases and controls shared a similar strong risk factor that lead to lung cancer development, we might miss comparability of exposures between never smoking lung cancer cases and controls. Lung cancer among never smokers is low [START_REF] Thun | Lung cancer occurrence in never-smokers: an analysis of 13 cohorts and 22 cancer registry studies[END_REF] and to date, the descriptive epidemiology of never smokers is limited, although several risk factors have been suggested including second hand smoke 30 33 , occupational exposure 32 264 , asbestos exposure 265 , and genetic factors 266 . It is possible that never smoker participants diagnosed with lung cancer in LC3 study were related to these previous or unknown risk factors, which may account for these surprising findings among non-smokers. However, we did not have such information in the LC3 study, and therefore this complicates the comparability between never smoking lung cancers cases and controls, even though they were matched by relevant confounding including age, sex and cohort.

Assembling further studies on never smoking lung cancers will ensure a better assessment of their risk factors.

Other potential confounders

A potential explanation of the results observed throughout the studies of this thesis is that underlying preclinical disease may affect concentrations of OCM biomarkers. However, the associations observed in HNC and RCC studies were stable over the 10-year of follow-up after blood collection. In the same two studies, our estimates did not change appreciably after excluding cases with their matched controls who were diagnosed within 1 or 2 years after blood draw, which would seem to exclude any possible reverse causation bias.

Furthermore, the observed associations throughout the three studies of this thesis are unlikely to be explained by the other OCM biomarkers because our estimates were not affected after controlling for them, either one-at-time or simultaneously.

As mentioned in the beginning of this thesis, major sources of one-carbon nutrients and related vitamins are many and include fruits and green leafy vegetables (folate) as well as fortified cereals and whole grains (vitamin B6). Thus, one could ask whether food intake affects circulating OCM biomarkers or cancer development. As example from the RCC study, we observed no association between vitamin B6 estimated from food frequency questionnaire (FFQ) and risk of RCC, in contrast to the strong association from circulating concentrations. There are suggestive explanations that could explain this discrepancy. First, is that blood measurement reflects more accurately vitamin B6 intake than estimates from self-reported questionnaires. Although measurement correction error models have been developed 267 , misclassification of dietary intake is still a major concern in dietary assessment. Moreover, in RCC study, the correlation between vitamin B6 intake from FFQ and plasma vitamin B6 was very modest (0.17), an observation very similar to multiple previous studies 268 269 . Also, adjusting for fruit, vegetables, nuts and seed, that are rich in vitamin B6, did not attenuate our estimates suggesting that food rich in vitamin B6 are unlikely to explain our results.

A second explanation is that circulating vitamin B6 varies significantly between cases and controls not due to differences in food intake but from diverse biological processes including absorption or catabolism of the circulating micronutrients. This could induce lower concentrations among cases participants even if their food consumption was similar to controls. Accordingly, results of this would mean that modification through diet might not be a good candidate for reducing cancer incidence. If it turns out that relations with vitamin B6 are causal, it would be very important to identify which of these two interpretations is true.

Anouar Fanidi, 2016 141

When we were able to capture the relation of food intake rich on B-vitamins in the HNC and RCC studies (Chapter III and IV), data provided by the LC3 cohorts was incomplete and did not permit us to control for such exposure. Also, we had partial information on supplementation used (dietary or vitamins) that is very common among US population 270 . It is plausible that in the LC3 study, US participants with the highest circulating of B-vitamins tend to be healthier with important use supplement minerals and vitamins. Numerous studies have shown that supplement use is associated with a healthier lifestyle. For example, it was reported that supplement users compared to non-users have higher education and incomes and are more likely to be physically active. In contrast, they are less likely to be tobacco or alcohol consumers compared to non-users 271 . Because ( 1) the design of LC3 study is prospective, ( 2) cases and controls were carefully matched on sex, age, cohort and smoking status, and ( 3) we adjusted by well-known risk factors of lung cancer, confounding effect may be reduced and any bias would be non-differential. However, we could not assure adjustment and cannot exclude the possibility that this missing information on dietary intake and vitamin supplementation affected our estimates in the LC3 study.

Main strengths and limitations

This entire dissertation is based on prospective studies, which are known to be the best design in term of observational epidemiology. Indeed, in retrospective case-control studies reversed causality and confounding by treatment are particularly a concern because the exposure is assessed at or after diagnosis of cancer cases which lead to many biases. In HNC, RCC and LC3 studies, cases were enrolled prior to their cancer diagnosis with information on different variables including baseline characteristics variables as well as biomarker measurements. Thus, this minimizes the chance that any differences between cases and controls are caused by existing tumors and are therefore better designed than retrospective case-control studies to investigate cancer etiology.

However, prospective studies are large enterprises and demand consequent efforts in order to obtain large sample size populations. One approach to deal with, it is to conduct a casecontrol study nested within a cohort which has similar precision of prospective cohorts but costs less. Our group had the great opportunity to conduct two nested case-control studies (HNC and RCC studies) from one of the largest single prospective cohorts worldwide, the EPIC cohort, reaching about 520,000 participants of whom almost 360,000 donated a blood sample. Furthermore, different collaborations and efforts permitted our group to form the biggest cohort consortium of lung cancers to date which gathered twenty nested casecontrol studies and coordinate over 12,000 prospective blood analyses of biomarkers.

Despite EPIC and LC3 studies have strengths in terms of sample size, methods for recording participants information were not always similar. For example, the EPIC cohort includes 10

European countries, and in some of them, incident cancer case identification and follow-up were based on cancer registers where as in other countries used a combination of methods including health insurance records, cancer and pathology registries, as well as direct contact participants and their next-of-kin. This issue was also observed in the LC3 study (See Supplementary Methods of LC3 study). It is also important to note that some of the subpopulations from EPIC and LC3 tend to be healthier than the overall population, thus limiting generalizability. For instance, the NHS or the PHS studies are composed exclusively of nurses and physicians respectively, or the UK center in the EPIC cohort is mainly composed of vegetarians.

Furthermore, as indicated in the LC3 study, large differences in folate and vitamin B6 concentrations were observed across the cohorts and continents. This observation underlines the large lifestyle differences inherent within this consortium. While this observation could permit to assess a larger range of exposure values, it might also raise the issue if unknown life-style related factor interacts with the exposure of interest. In other words, an association found in one sub-population (e.g. SWHS study which is composed mainly of Chinese never smoking women) might not be identified in another (e.g. the ATBC study composed of Finish men smokers) because of different exposure to unknown interacting factors.

As indicated in HNC and RCC studies, OCM biomarkers were measured from a single blood sample drawn in adulthood and therefore may be imperfect estimates of underlying historical exposure. Intra-individual variation may still be important through time and thus having only one precise measure of recent exposure could lead to misclassification of true underlying historical exposure. However, because cases and controls were matched on blood draw they should not have differential misclassification bias. Furthermore, in the LC3 study we looked explicitly on the variability, the intra-individual variation of biomarkers across two samples, and repeat testing indicated an intra-class correlation (ICC) greater than 0.6 for both circulating vitamin B6 and folate, which suggests a reliable collinearity.

Importantly, biosample measurements were centralized and took place in a single laboratory for all studies, including the LC3 study, thus minimizing systematic interlaboratory variation. Coefficients of variations within and between batches were low for all OCM biomarkers measured across our studies.

Future directions

Findings from the three studies in this dissertation expanded our scientific knowledge on the potential role of OCM biomarkers in smoking-related cancer etiology and outcome.

However, it is difficult to disentangle the role of each micronutrient independently of the other in relation to the outcome because the one-carbon pathway is a complex mechanism in which many vitamins and related metabolites interact together. In line with other studies [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] , we observed for example that circulating folate increases circulation of both vitamin B2 and B6, or lower the folate levels increase the concentrations of homocysteine. It won't be surprising that these biomarkers might be correlated with some other OCM analytes. In order to better understand the interactions between OCM nutrients, one could look at additional cornerstones of the OCM pathway such as glycine or serine which were recently found necessary and sufficient for cell transformation and malignancy 133 . In line with this hypothesis, I recently generated some preliminary analyses using the same datasets and observed interesting results indicating that circulating concentrations of both serine and glycine were significantly and inversely associated to HNC risk.

This thesis is composed of three studies with different cancer sites and suggests different associations. First, the HNC study indicates potential role of homocysteine and folate with cancer of the head and neck, although the results are preliminary and hence it is difficult to make any conclusion. These novel findings need to be replicated in other sufficientlypowered prospective studies as well as in different ethnic populations.

Second, the RCC study strongly suggests that circulating concentrations of vitamin B6 are inversely related to both RCC risk and mortality. In addition to the RCC study, an independent new study from our group (Muller DC et al., personal communication) strongly supports the survival benefit of vitamin B6 after diagnosis of RCC. Even though our findings are strong and consistent they need to be replicated in other future large-scale prospective studies as well as in some other ethnic groups than Western. One could envisage in a near future to gather studies across the globe as it was done within lung cancer cohort consortium. This will contribute to our understanding of the role of circulating vitamin B6 in cancer risk and survival, and its exact role in the pathogenesis of this cancer. However, even Then, identifying further genetic variants that influence circulating vitamin B6 through large collaborative studies and investigate their relation to RCC risk will be also a major topic in the near future.

Third, although findings from LC3 study did not support the strong inverse association between higher circulating vitamin B6 and decreased risk lung cancer, as previously reported in the EPIC lung study [START_REF] Johansson | Serum B vitamin levels and risk of lung cancer[END_REF] , circulating vitamin B6 may contribute in risk prediction of lung cancer. One of the final analysis in the LC3 study indicated that circulating vitamin B6

OCM and smoking-related cancers -Discussion 146 displays a compatible association across all the cohort consortium with that previously seen in two European cohorts (ATBC and EPIC) 13 185 in current smokers diagnosed close to blood draw, with a 2-fold risk increase for lung cancer happening within 3 years for participants who we defined as vitamin B6 deficient. This observation might support circulating vitamin B6 as a potential useful lung cancer risk predictor close to blood draw.

The rational for using biomarker is to measure more adequately the biologically relevant exposure. Most biomarkers from both prospective and retrospective studies rely on a single serum or plasma sample, which only provides an estimate of biomarker exposure over the previous days. As it was evoked previously, one approach to assess temporal changes and to better estimate long-life exposure is to study samples from each subject obtained at several time points. However, given financial and logistic issues this won't be possible for all participants of studies, in particular for a cohort. A compromise is to repeat sample measurements for a representative and large enough fraction of the original cohort, as we did in the LC3 study (Chapter IV). Alternatively, one could think on a single blood sample which gives a good estimation of a medium-long-term nutrient status. For example, blood cell folate concentrations respond slowly to changes in folate intake because the erythrocytes, which have a 120-day lifespan, accumulate folate only during erythropoiesis 277 . Biomarkers represent to date an important tool in cancer epidemiology and, advances technologies are rapid and will facilitate the application of the markers to large sample size studies.

In conclusion, one-carbon metabolism is a complicated pathway, and there are a number of gaps in our understanding its relation to cancer, in particular smoking-related cancers.

Findings from this thesis research contributed evidence to fill these gaps. This dissertation is a first "pillar" in my long research aim to better understand the OCM implication in the etiology of smoking-related cancers.

Brief description of the participating cohorts in the Lung Cancer Cohort Consortium

US cohorts

The Women's Health Initiative (WHI)

WHI is a long-term health study of 161,808 post-menopausal women aged 50 to 79 years at 40 clinical centers throughout the U.S. WHI comprises a Clinical Trial (CT) component (68,132 women), and an

Observational Study (OS) component (93,676 women), and has included several extension studies.

Some detailed descriptions of WHI have been previously presented 278 279 .The CT evaluated two forms of postmenopausal hormone therapy, a low-fat dietary pattern intervention, and calcium and vitamin D supplementation in a randomized, controlled fashion, in a partial factorial design. The hormone therapy component findings led to major reductions in the use of hormone therapy worldwide, and are thought to have led to noteworthy reductions in breast cancer incidence.

In the present study lung cancer cases occurring during the follow-up of WHI cohorts since enrolment (1993-1998) among non-smoking women, were matched 1-1 to corresponding non-smoking lung cancer free controls, for serum and DNA analyte comparisons.

The Southern Community Cohort Study (SCCS)

The Southern Community Cohort Study (SCCS) 280 is a prospective cohort of African and non-African Americans which during 2002-2009 enrolled approximately 86,000 residents aged 40-79 years across 12 southern states. Recruitment occurred mainly at community health centers, institutions providing basic health services primarily to the medically uninsured, so that the cohort includes many adults of lower income and educational status. Each study participant completed a detailed baseline questionnaire, and nearly 90% provided a biologic specimen (approximately 45% a blood sample and 45% buccal cells). Follow-up of the cohort is conducted by linkage to national mortality registers and to state cancer registries. Included in this study are 240 incident African American lung cancer cases and 240 individually matched African American cohort members without lung cancer at the index date selected by incidence density sampling.

Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO)

The PLCO study, a randomized trial aimed at evaluating the efficacy of screening in reducing cancer mortality, recruited approximately 155,000 men and women age 55 to 74 years from 1992 to 2001 281 .

follow-up of the cohort conducted with questionnaires mailed approximately every two to four years and record linkages with state tumor registries in New York, New Jersey, and Florida, as well as record linkage with the National Death Index (NDI). Medical records were obtained to verify reported cancer outcomes. A total of 171 incident lung cancer cases and 171 individually matched controls were included for this analysis.

The American Cancer Society Cancer Prevention Study-II (CPS-II) Nutrition Cohort

The ACS Study-II is a prospective study of cancer incidence and mortality among 86,404 men and 97,786 women. The CPS-II Nutrition Cohort, which is described in detail elsewhere 282 Incident lung cancer cases were identified through self-report on a follow-up questionnaire, linkage with state cancer registries, or death certificates. Self-reported cancers were verified through medical records. 200 incident lung cancer cases and 200 matched controls from the CPS-II Nutrition Cohort were provided for this analysis.

The Campaign Against Cancer and Stroke (CLUE I) and the Campaign Against Cancer and Heart

Disease (CLUE II).

The CLUE studies include two large cohorts of volunteers from Washington County, Maryland that were enrolled in 1974 and 1989, respectively. CLUE I was conducted in Washington County, Maryland, in the fall of 1974. Brief health histories and blood pressures were taken and 15 ml of blood was drawn from 26,147 volunteers (23,951 were residents of Washington County) at the time of enrollment. Linkage of the records from this program to those of a private census in the summer of 1975 indicated that almost a third of the adult population of the county had participated. CLUE II was an outgrowth of CLUE I conducted from May through October in 1989. As in CLUE I, a brief health history was obtained and 20 ml of blood was drawn. A blood sample was collected from 32,894 volunteers at the time of enrollment (25,076 were residents of Washington County). Participants were also given a food frequency

Physicians' Health Study (PHS)

The PHS I began in 1982 as a randomized trial of aspirin and beta-carotene for the primary prevention of heart disease and cancer among 22,071 male, Caucasian physicians initially aged 40 to 84 years 285 , followed by the PHS II trial beginning in 1997 to evaluate beta-carotene, vitamin C, vitamin E, and a daily multivitamin on the prevention of cancer, CVD, and other endpoints. The PHS II included 14,641 men, with 7,641 participants from the PHS I plus 7,000 new physicians, for a total of 29,071 PHS participants 286 . A wide range of demographic, clinical, and lifestyle factors were assessed via baseline questionnaires, along with baseline bloods. PHS participants reported major clinical endpoints, including cancer, yearly in a mailed questionnaire and postcards every six months. Self-reported, incident lung cancer cases were confirmed through medical record review by the PHS Endpoints Committee in included in the present analyses.

The Nurses' Health Study (NHS)

The The follow-up rate has been greater than 90%. The institutional review board at the Brigham and Women's Hospital approved the study. As approved by the committee, return of the questionnaires was considered to imply informed consent. Cases of lung cancer were self-reported by the participants or identified on their death certificates and were subsequently confirmed by medical records.

Incident lung cancer cases under 80 years of age up to 31 st December 2010 were identified through record linkage. A total of 361 lung cancer cases and 361 controls matched for date of birth, gender, smoking status, and ethnicity were included for this analysis.

The Malmö Diet and Cancer Study (MDCS)

The Malmö Diet and Cancer Study (MDCS) is a population-based prospective cohort study that between 1991 and 1996 recruited men and women aged 44 to 74 years of age living in Malmö, Sweden 297 . The main goal of the MDCS is to study the impact of diet on cancer incidence and mortality. It consists of a baseline examination including dietary assessment, a self-administered questionnaire, anthropometric measurements and collection of blood samples. A total of 201 incident lung cancer cases and 201 individually matched controls were available for this analysis.

The Northern Sweden Health and Disease Study Cohort (NSHDS)

The Northern Sweden Health and Disease Study (NSHDS) encompasses several prospective cohorts, the current study involving study participants from the Västerbotten Intervention Project (VIP), a subcohort within NSHDS 298 . VIP is an ongoing prospective cohort and intervention study intended for health promotion of the general population of the Västerbotten County in northern Sweden. VIP was initiated in 1985 and all residents in the Västerbotten County were invited to participate by attending a health check-up at 40, 50 and 60 years of age. Participants were asked to complete a self-administered questionnaire including various demographic factors such as education, smoking habits, physical activity and diet. In addition, height and weight were measured and participants were asked to donate a fasting blood sample for future research. Incident lung cancer cases were identified through linkage with the regional cancer registry. A total of 245 incident lung cancer cases and 245 individually matched controls were included for this analysis.

The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC)

The ATBC Study was a randomized, double-blind, placebo-controlled, primary cancer prevention trial testing daily supplementation with α-tocopherol (50 mg/day) or β-carotene (20 mg/day), or both 299 .

Between (1995-97) and HUNT 3 (2006-08) (http://www.ntnu.edu/hunt). Comprehensive data on life style, health status, symptoms, diseases and anthropometrics have been collected through questionnaires, interviews and clinical examinations, and in HUNT2 and HUNT3 biological material as blood and urine additionally were collected and stored. For this study, data have been linked to the Norwegian Cancer Registry and 238 incident lung cancer cases and 238 individually matched controls have been included.

2) Asian cohorts

The Shanghai Men's Health Study (SMHS) and the Shanghai Women's Health Study (SWHS)

The SMHS and SWHS are population-based cohort studies conducted in eight communities of urban Shanghai. Their designs and methods have been described elsewhere 300 301 .

Briefly, the SWHS recruited 74,941 women during 1997-2000 (response rate: 93%) and the SMHS recruited 61,480 men during 2002-2006 (response rate: 74%). Similar methods and questionnaires were used in both studies. At baseline in-person interviews, information on sociodemographic, diet, lifestyle, occupation and medical history was obtained; height, body weight, and waist circumference were measured. Blood samples were collected from 75% of the study participants in both studies, processed within 6 hours, and stored at -70 until analysis.

The SMHS and SWHS have been followed up by annual record linkage with the population-based Shanghai Cancer Registry and Shanghai Vital Statistics Registry and in-person surveys every 2-3 years.

Exposure information, including dietary intake, was updated in the in-person follow-up surveys. All possible matches from the linkages are checked manually and verified by home visits. Medical charts were obtained from the initial diagnostic hospitals to verify cancer diagnosis. Death certificate data from the Shanghai Vital Statistics Unit was used to identify the primary cause of death.

The studies were approved by the Institutional Review boards of the Shanghai Cancer Institute and Vanderbilt University. Informed consent was obtained from all participants.

The Singapore Chinese Health Study (SCHS)

The design of the SCHS study has been described 193 302 . Briefly, the cohort was drawn from permanent residents or citizens of Singapore who resided in government-built housing estates (86% of the Singapore population reside in such facilities). The eligible age range for cohort enrolment was 45-74 years. We restricted study subjects to the two major dialect groups of Chinese in Singapore: the Beginning in April 1994, a random 3% sample of cohort participants were asked to provide blood or buccal cell (if request for blood sample was denied), and spot urine samples. Eligibility for this biospecimen subcohort was extended to all surviving cohort participants starting in January 2000. By

April 2005, all surviving cohort subjects had been contacted for biospecimen donation. Approximately 60% of eligible cohort participants donated biospecimens.

The cohort has been passively followed for death and cancer occurrence through regular record linkage with the population-based Singapore Cancer Registry and the Singapore Registry of Births and Deaths.

Migration out of Singapore, especially among housing estate residents, is negligible. As of latest update, only 55 individuals from this cohort were known to be lost to follow-up due to migration and other reason.

A nested case-control study of incident lung cancer cases within the Singapore Chinese Health Study was used to examine the association between serum levels of vitamin B 6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. Briefly, 422 lung cancer cases were identified among cohort participants with available prediagnostic plasma samples as of 12/31/2011. For each case, one control subject was randomly selected from all eligible cohort members who were alive and free of cancer on the date of cancer diagnosis of the index case. The control subject was individually matched to the index case by gender, dialect group (Hokkien, Cantonese), age at enrolment (±3 years), date of baseline interview (±2 year), date of biospecimen collection (±6 months), and smoking status (current, former, and never smokers). For current smokers, cases and controls were further matched by number of cigarettes per day (<15, ≥15 cigarettes/day). For former smokers, cases and controls were further matched by years since quitting smoking (<10, ≥10 years). One plasma aliquot per subject was retrieved from the biorepository and all plasma samples were sent to the laboratory (B-vital) for measurements.

The Shanghai Cohort Study (SCS)

The SCS study is a residential cohort of 18,244 men in Shanghai, China, assembled during 1986-89 when subjects were between the ages of 45 and 64 years. Approximately 80% of eligible men participated in the study. At the time of recruitment, each cohort subject was interviewed in-person by a trained nurse interviewer using a structured questionnaire that included background information, history of tobacco and alcohol use, current diet, and medical history 303 304 .

At the completion of the interview, the nurse collected a 10 ml blood and a single void urine specimen from the study participant. Blood and urine samples were kept in insulated boxes with ice (0-2 o C). The serum was separated from blood specimen within 3-4 hours after collection. Two sets of serum (2 ml and 1 ml, respectively) and two sets of urine samples (10 ml each) per subject have been stored at -

80 o C.
The cohort has been followed for the occurrence of cancer and death through routine ascertainment of new cases from the population-based Shanghai Cancer Registry and Shanghai Vital Statistics Units. To maximize the cancer findings and minimize the loss of follow-up, we have recontacted each surviving cohort member annually. Retired nurses visit the last known address of each living cohort member and record details of the interim health history of the cohort member. As of December 31, 2014, cumulatively 612 (3.4%) original subjects were lost to follow-up (i.e., persons we have no record of death and we have been unable to locate through our annual follow-up recontacts), and 574 (3.1%) refused to our continued follow-up interview (their cancer and vital status has been continually updated through record linkage analyses) after 26 years of follow-up since the beginning of the study.

A nested case-control study of incident lung cancer cases within the Shanghai Cohort Study was used to examine the association between serum levels of vitamin B 6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. Briefly, 516 lung cancer cases were identified among cohort participants with available serum samples as of 12/31/2006. For each case, we randomly selected one control subject from all cohort members who were free of cancer and alive at the time of cancer diagnosis of the index case. Controls were matched to the index case by age at enrolment (±2 years), date of biospecimen collection (±1 month) and neighbourhood of residence at recruitment, and smoking status (current, former and never smokers) as established previously for other studies. For former smokers, cases and controls were further matched by years since quitting smoking (<10 vs ≥10 years). One serum vial per subject was retrieved from biorepository and all serum samples were sent to the laboratory (B-vital) for measurements.

Biochemical analyses

Serum or plasma samples from all study participants were sent on dry ice to the Bevital A/S laboratory (http://www.bevital.no) in Bergen, Norway, were they were kept below -80°C until analysis.

Concentrations of vitamin B6, methionine and cotinine were determined by mass spectrometry based methods (LC-MS/MS, GC-MS/MS), 239 240 , and microbiological methods were used to determine concentrations of folate (Lactobacillus casei). 241 Samples were analyzed in batches of 86 samples, and quality control procedures included 6 calibration samples, 2 control samples, and 1 blank sample in each batch. The coefficients of variation (CVs) within and between batches were, respectively, 3% and 7% for vitamin B6, 4% and 5% for folate, and 1% and 3% for methionine. All lung cancer cases and their individually matched controls were analyzed together within the same batches in random order. The laboratory staff was blinded to the case-control status of the blood samples.

Statistical analyses

We fitted hierarchical linear models to the biomarker concentrations to evaluate between-cohort variability in mean concentrations, and the extent to which this variability could be accounted for by individual level covariates such as age, sex, body mass index, and smoking status. To account for the nested nature of the data (with participants nested within cohorts) and to allow model parameters to vary across cohorts, we used a hierarchical linear regression model to regress standardized, log transformed biomarker concentrations on these covariates. In particular, let denote the outcome variable (i.e., log transformed concentrations), and denote a matrix of covariates. We model the outcome of participant from cohort as Each of the components of the cohort-specific vector of regression coefficients are modeled with independent normal distributions, as are the cohort-specific intercepts :

The overall intercept and mean regression coefficients were given diffuse prior distributions, and the hierarchical standard deviation parameters and were given weakly informative hyperprior distributions. The residual standard deviation was also given a prior distribution. We conducted sensitivity analyses in which we specified different scales for the Half-Cauchy hyperpriors, and found that the posterior distributions of and were insensitive to the specified scale of the hyperpriors.

Risk analysis involved calculating quartiles of circulating concentrations for each biomarker based on the distribution among controls, both overall and by region (United States [US], Europe and Australia combined [EU/AU], and Asia). Relative risks of lung cancer were estimated by calculating odds ratios (OR) and 95% confidence intervals (CI) using conditional logistic regression (conditioning on matched case-control sets) with the first quartile as the referent. Additional covariates were included to account for confounding by risk factors, including indicators of educational attainment (in six categories) and tobacco exposure (in addition to matching to smoking status in 5 categories: cotinine concentrations [defined using quartiles of the distribution among participants reporting to be current smokers]).

Including additional covariates of body mass index (BMI) and alcohol intake did not appreciably alter the results and were not included in the final models. As a sensitivity analysis, we fitted models that were additionally adjusted for smoking duration or pack-years of smoking among ever smokers, 90% of which had such information available. All risk analyses were conducted overall and by stratifying for never, former and current smokers, as well as by region (US, EU/AU, Asia).

As an indication of the overall statistical strength of association between each biomarker and risk, we calculated a p-value for trend by including the base-2 logarithm (log2) of the biomarker concentration as a continuous variable in a separate conditional logistic regression model. The same approach was used in stratified risk analyses according to other pre-defined demographic characteristics and risk factors. OR estimates per log2 unit (log 2 OR) may be interpreted as the relative risk associated with a doubling in the concentration of a circulating biomarker. We also performed risk analyses for log 2 OR on each biomarker separately for each individual cohort, as well for US cohorts by the period of blood sample collection (before and after 1996 when folate fortification of food items took effect) to evaluate potential impact of folate fortification. X² tests were used to assess heterogeneity in log 2 OR estimates in stratified analyses.

Finally, in order to evaluate the specific hypothesis of whether deficiency in vitamin B6 or folate is particularly important in lung cancer etiology, we conducted analysis by categorizing subjects into deficient, or normal category based on clinically defined cut-off values. For vitamin B6, <20 nmol/L was considered deficient whereas ≥20nmol/L was considered normal 305 . For folate deficiency, subjects were classified in three categories; <7 nmol/L as deficient, 7-12.9 nmol/L as moderately deficient, and ≥ 13 nmol/L as normal 244 306 .

All statistical analyses were conducted using SAS 9.2 (Cary, North Carolina) 245 , R version 3.1.3 247 or Stan version 2.9.0 248 .

If not all variables were collected at time of blood draw, it is important to record the timing of data collection. We have included variables with the same root name and "_YR" to indicate year of collection. As given below they refer to an entire group of similar variables. If you need to specify different time points for individual variables, you can create your own "_YR" variables using the same root as the variable you are recording. Please see below for definitions of time points:

any -Data can be collected at any time. blood draw -Data were collected at time of blood draw. closest to blood draw -Preference is for data collected at blood draw. If not available, then choose data from a time point as close as possible to blood draw. Data could be collected after blood draw as long as the collection was prior to case diagnosis. most recent -The most recent data collection where these data are available. index date or most recent prior to index date -Date of diagnosis for the case, date of case diagnosis for the matched control. Only requested for specific variables, e.g. smoking variables.

MISSING VALUE CODES

. = missing/unknown value [the question was asked, but data are missing/unknown for subject(s)] .U = unavailable [the question was not asked] .N = not applicable [the question does not apply to the subject] NOTE: The . and .U codes may be used in any variable. The .N code may be used when provided in the list of valid values. 0.07 a Ratios of plasma levels were calculated as 2 raised to the power of the beta estimates from linear regression models with log2-plasma levels as response. The linear regression models were adjusted for case-control status, age, sex, country and as indicated adjusted for cotinine (in quartiles) and alcohol at recruitment. The base 2 logarithm of each nutrient or food group was included as covariate in the linear regression models. The level ratio can thus be interpreted as the average relative change in serum levels that would be expected for a doubling in dietary intake. b Among current smokers c coded 0/1/2/3/4/5/6/ ~ alcohol[g/day]=0 / 0. Numbers include all cancer cases of the head and neck and esophagus, and individually matched control for whom laboratory measurements were available for complete case sets. Uninformative case-sets were excluded. c Assessed by analysing HNC cases and their individually matched controls by conditional logistic regression, conditioning on individual case set. d Further adjusted for educational attainment (in 5 groups), smoking status (never/former/current/missing), cotinine (quartiles defined among current smokers) and alcohol intake at recruitment(g/day) e Assessed by analysing HNC cases and all controls combined by unconditional logistic regression, adjusting for country, sex, age at recruitment (in 5 year groups), educational attainment (in 5 groups), smoking status (never/former/current/missing), cotinine (quartiles defined among current smokers) and alcohol intake at recruitment (g/day). f Assessed by analysing ESCC cases and their individually matched controls by conditional logistic regression, conditioning on individual case set. g Assessed by analysing ESCC cases and all controls combined by unconditional logistic regression, adjusting for country, sex, age at recruitment (in 5 year groups), educational attainment (in 5 groups), smoking status (never/former/current/missing), cotinine (quartiles defined among current smokers) and alcohol intake at recruitment (g/day). h Quartile cut-off points were determined based on the plasma level distribution of each biomarker among the 516 individually matched controls of the head and neck and esophagus . i P for trend assessed by the base 2 logarithm of the serum levels 1 (10.63-22.89) 598 607 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (22.90-27.40) 623 582 1.12 (0.94 -1.32) 1.12 (0.95 -1.33) 1.12 (0.95 -1.33) 3 (27.40-31.

Basic cohort information

  Techniques des Activités Physiques et Sportives Observatoire des Sciences de l'Univers de Lyon Polytech Lyon Ecole Supérieure de Chimie Physique Electronique Institut Universitaire de Technologie de Lyon 1 Ecole Supérieure du Professorat et de l'Education Institut de Science Financière et d'Assurances Directeur : M. F. DE MARCHI Directeur : M. le Professeur F. FLEURY Directeur : Mme Caroline FELIX Directeur : M. Hassan HAMMOURI Directeur : M. le Professeur S. AKKOUCHE Directeur : M. le Professeur Georges TOMANOV Directeur : M. le Professeur H.

Figure 1 . 25 Figure 2 . 27 Figure 3 . 5 - 27 Figure 4 .Figure 5 .Figure 7 . 34 Figure 8 . 37 Figure 9 . 38 Figure 10 .Figure 11 . 64 Figure 12 .

 12522735274573483793810116412 Figure 1. Age-standardized incidence and mortality rates (world) per 100,000 for lung cancer in 2012. Source: Globocan 2012 15 . ............................................................................ 25 Figure 2. Observed 5-years survival based in patients diagnosed with NSCLC in 2004-2009 on the SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah, and Atlanta), adapted from the SEER database 9 . .................................. 27 Figure 3. 5-year survival by stage for people diagnosed with lung cancer in 2004-2009 on the SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah, and Atlanta), adapted from the SEER database 8 . ............................................... 27 Figure 4. Schematic of various head and neck malignancies. Source from Terese Winslow LLC. ......................................................................................................................... 29 Figure 5. Age-standardized incidence and mortality rates (world) per 100,000 for head and neck cancer (lip, oral cavity, larynx, nasopharynx and other pharynx) in 2012. Source: Globocan 2012 15 . ..................................................................................................... 30 Figure 6. 5-year relative survival of HNC subsites in US, adapted from the SEER database 49 . ................................................................................................................................ 31 Figure 7. Age-standardised incidence rates per 100, 000 of oesophageal cancers by histological subtype, region and sex. AC, adenocarcinoma; SCC, squamous cell carcinoma. Source from Arnold M et al 71 . .................................................................. 34 Figure 8. Age-standardized incidence and mortality rates (world) per 100,000 for kidney cancer in 2012. Source: Globocan 2012 15 . .................................................................. 37 Figure 9. Survival estimates in 4,063 patients with renal tumours according to histologic subtype and TNM stage. (A) TNM stage I to II. (B) TNM stage III to IV. Red dashed lines correspond to five-year survival. Source from Patard JJ et al. 105 . ................................. 38 Figure 10. Overview of the OCM pathway. The OCM consists of the folate cycle (green) and the methionine cycle (gray). Cofactors are in purple and enzymes are in orange. SHMT: serine hydroxylmethyltransferase, TS: thymidylate synthase, MTHFR: methylenetetrahydrofolate reductase, MAT: methionine adenosyltransferase, CBS: cystathionine-synthase, CGL: cystathionine y-lyase, MS: methionine synthase, THF: tetrahydrofolate, me-THF: 5,10-methylenetetrahydrofolate, mTHF: 5methyltetrahydrofolate, F-THF:10-formyltetrahydrofolate, dTMP: deoxythymidine monophosphate, dUMP: deoxyuridine monophosphate, SAH: S-adenosylhomocysteine, SAM: S-adenosylmethionine, B2: riboflavin, B6: pyridoxal 5'-phosphate, B12: cobalamin, adapted from Locasale et al., 2013 133 . ....................................................................... 41 Figure 11. Representation of risk sets in nested case-control designs. ............................... 64 Figure 12. EPIC study collaborating centres. ..................................................................... 67

Figure 1 .

 1 Figure 1. Age-standardized incidence and mortality rates (world) per 100,000 for lung cancer in 2012. Source: Globocan 2012 15 .

Figure 2 .

 2 Figure 2. Observed 5-years survival based in patients diagnosed with NSCLC in 2004-2009 on the SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah, and Atlanta), adapted from the SEER database 9 .

Figure 3 . 5 -

 35 Figure 3. 5-year survival by stage for people diagnosed with lung cancer in 2004-2009 on the SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah, and Atlanta), adapted from the SEER database

Figure 4 .

 4 Figure 4. Schematic of various head and neck malignancies. Source from Terese Winslow LLC.

Figure 5 .

 5 Figure 5. Age-standardized incidence and mortality rates (world) per 100,000 for head and neck cancer (lip, oral cavity, larynx, nasopharynx and other pharynx) in 2012. Source: Globocan 2012 15 .

Figure 6 .

 6 Figure 6. 5-year relative survival of HNC subsites in US, adapted from the SEER database 49 .

  of HNC overall (relative risk [RR] 4.

Figure 7 .

 7 Figure 7. Age-standardised incidence rates per 100, 000 of oesophageal cancers by histological subtype, region and sex. AC, adenocarcinoma; SCC, squamous cell carcinoma. Source from Arnold M et al 71 .

Figure 8 . 15 .

 815 Figure 8. Age-standardized incidence and mortality rates (world) per 100,000 for kidney cancer in 2012. Source: Globocan 2012 15 .

Figure 9 .

 9 Figure 9. Survival estimates in 4,063 patients with renal tumours according to histologic subtype and TNM stage. (A) TNM stage I to II. (B) TNM stage III to IV. Red dashed lines correspond to five-year survival. Source from Patard JJ et al.105 .

Figure 11 .

 11 Figure 11. Representation of risk sets in nested case-control designs.

  Risk set 2: subject n°5 (second case) can be matched with subject n°2, and 4Risk set 1: subject n°1 (first case) can be matched with subject n°2,3,4 and 5

Figure 12 .

 12 Figure 12. EPIC study collaborating centres.

Appendix 2 )

 2 which was sent to all participated cohorts including epidemiological and clinical OCM and renal cell carcinoma in EPIC 110 variables. With support from Dr Brennan and Dr Johansson, I mostly coordinated and led the data management of the LC3 study database by completing the study database including biochemical, epidemiological and clinical data for all participating cohorts, and harmonizing the baseline data. The LC3 database includes 44 biomarkers and over 200 variables for over 12,000 participants.

  , was initiated in 1992 as a subgroup of CPS-II, a prospective study of cancer mortality involving approximately 1.2 million Americans begun in 1982. Participants in the CPS-II Nutrition Cohort were recruited from CPS-II members who resided in 21 states and were between the ages of 50 and 74 years. At enrollment in 1992/1993, participants completed a self-administered questionnaire that included demographic, medical, dietary, and lifestyle information. Follow-up questionnaires were sent to all living Nutrition Cohort members in 1997, and every two years after this to update exposure information and to ascertain newly diagnosed cancers. Between June 1998 and June 2001, blood samples were collected from a subset of CPS-II Nutrition Cohort participants(21,965 women and 17,411 men).

  Nurses' Health Study (NHS) 287 288 was established in 1976, when 121,700 married female registered nurses aged 30 to 55 years residing in 11 States in the U.S. completed and returned a self-administered questionnaire. Questionnaires have been mailed to participants in both cohorts every 2 years since baseline to collect updated information on demographics, lifestyle factors, medical history, and disease outcomes. A semi quantitative food frequency questionnaire (FFQ) was administered to obtain information on usual dietary intake over the previous year. The reproducibility and validity of the FFQs have been established[289][290][291][292][293] . The FFQ was first administered in 1980 in the NHS, and were repeated almost every 4 years thereafter. For each food item, the questionnaire specified a common serving size and queried respondents on average intake during the previous year; responses in 9 categories ranged from almost never to 6 or more per day. Most nutritional variables measured by these FFQs have //regepi.bwh.harvard.edu/health/).

  1985 and 1988, the study enrolled and randomized 29,133 50-69 year old male cigarette smokers from southwestern Finland. Study supplementation continued for 5-8 years (median 6.1 years) until death or trial closure(April 30, 1993). At baseline, participants completed questionnaires regarding general risk factors, medical history, smoking habits, and dietary intake. Height, weight, heart rate, and blood pressure were measured by trained nurses and fasting serum samples were collected and stored at -70 o C. Lung cancer cases diagnosed during follow-up through 2009 were identified through linkage with the Finnish Cancer Registry, and 200 were individually matched to 200 controls for this analysis.The Nord-Trøndelag Health Study (HUNT)The HUNT study is a longitudinal population based study having invited all persons aged 20-100 years living in the county of Nord-Trøndelag, Norway to three data collections, HUNT1(1984-86), HUNT2

  Hokkiens and the Cantonese, who originated from Fujian and Guangdong provinces in SouthernChina, respectively. Between April 1993 and December 1998, 63,257 subjects (approximately 85% of eligible subjects) were enrolled into the cohort study. At recruitment, each study subject was interviewed in person by a trained interviewer using a structured questionnaire that emphasized current diet assessed via a validated, 165-item food frequency questionnaire. The questionnaire also requested information on demographics, lifetime use of tobacco, incense use, current physical activity, usual sleep duration, reproductive history (women only), occupational exposure, medical history, and family history of cancer.

  by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles) and education (in 7 categories). b) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of years of smoking (continuous). c) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of pack-years of smoking (continuous). d) Quartile cut-off points were determined based on the circulating level distribution of LC3 matched controls. e) P for trend assessed by the base 2 logarithm of the circulating levels.

Table 1 .

 1 Associations between OCM factors (vitamin B2, B6, B12, folate, methionine and homocysteine) and lung cancer risk .

.........................................................................

Table 2 .

 2 Associations between OCM factors (vitamin B2, B6, B12, folate, methionine and homocysteine) and the head and neck cancer risk .

....................................................

Table 3 .

 3 Associations between OCM biomarkers factors (vitamin B2, B6, folate and methionine) and esophageal squamous cell carcinoma risk .

.......................................

Table 4 .

 4 Associations between OCM factors (vitamin B2, B6, B12, folate, methionine and homocysteine) and kidney cancer risk .

......................................................................

Table 5 .

 5 Characteristics of participating cohorts and lung cancer cases with serum/plasma available .

.................................................................................................................

  ,606 participants that compared with non-smokers, current smokers were at increased risk for ESCC with a hazard ratio (HR) equal to9.27 (95% CI 4.04-21.29) [START_REF] Freedman | A prospective study of tobacco, alcohol, and the risk of esophageal and gastric cancer subtypes[END_REF] .

. A recent prospective cohort study from Netherlands 79 reported that current smokers had higher risk of ESSC compared with never smokers, the RR being 2.63 (95% CI 1.

47-4.69)

. However, the risk of ESCC among current smokers can be much stronger. Fredman et al. showed in a large prospective US cohort of 476

Nutrients of the candidate pathway and smoking-related cancer

	While limited availability of folate in regular healthy cells could induce genetic instability, it dinucleotides are methylated in mammals. CpG sites are generally dispersed in the genome OCM and methylation quintiles (OR 0.51; 95% CI 0.23-0.93). Ten years after the ATBC study on lung cancer, a
	has been suggested that folate may promote tumor progression after the neoplasm has non-uniformly, with the exception of short regions that are grouped in clusters and called Many nutrients involved in the OCM including vitamin B6 (SHMT activity), vitamin B2 nested case-control study of 899 cases and 1,770 controls from the EPIC cohort reported
	been developed 142 . This controversial issue may be explained by the function of folate in 'CpG islands' 152 . These CpG islands rich in CpG are observed mostly in the promoter region (MTHFR stability) and vitamin B12 (MS function) 170 are important to maintain the one Overview strong inverse associations between circulating concentrations of vitamin B6 and
	nucleotide synthesis where tissues, including tumours, proliferate rapidly, and thus have an of many genes and are in general unmethylated, whereas CpG sites dispersed non-uniformly carbon flux, and deficiencies or imbalances of these biomarkers may lead to aberrant DNA Among OCM biomarkers, folate is by far the most studied analyte in relation to cancer. Over methionine independently to smoking status with lung cancer risk, resulting in 2.5-fold risk
	increased requirement for nucleotides. Then many cancers up-regulate folate receptors, are usually methylated under regular conditions. When methylation of promoter CpG methylation and subsequently carcinogenesis. the past decades, most human studies assessing the role of folate (measurement from differences between the top and bottom 25% of the population 13 . Moreover, this study
	and anti-folate drugs, such as 5-fluorouracil or methotrexate, are efficacious in treatment of islands occurs, transcription is inhibited and gene expression silenced 152 . The OCM, and thus DNA methylation, operate under the control of multiple influences. dietary intake or blood) with carcinogenesis focused on colorectal cancer (CRC). The reported significant inverse association between circulating concentrations of folate and
	cancer by blocking the DNA replication of cancer cells 142 143 . Moderate elevations in plasma homocysteine concentration have been reported to be accumulative of epidemiologic and clinical evidence have reported about 20% to 40% lung cancer risk. Overall, it remains unclear if factors of OCM pathway are important in lung
	in dysregulation of DNA synthesis and DNA methylation, which leads to cancer development. It has been reported that vitamin B6 deficient subjects are thought to be DNA methylation and cancer in this process may lead to diverse diseases, including cancer. In fact, cells in cancer display several in vitro studies demonstrated a clear association between folate deficiency and DNA circulating levels of folate 178-180 . Adequate DNA methylation is crucial for cell development and function, thus any anomalies concentration has been associated with global DNA hypomethylation 171 . For example, compared to those being in the lowest intake, although the evidence is less consistent for In addition, vitamin B6 imbalance may disturb different reactions in the OCM resulting also associated with increased concentration of SAH, but not SAM, and increased SAH decreased risk of CRC or adenoma in subjects with the highest dietary intake of folate cancer development.
	more susceptible to DNA and chromosome breaks by a similar mechanism as described above. In vitamin B6 deficiency, availability of me-THF is decreased, reducing the methylation of dUMP and thereby increasing misincorporation of uracil, causing genomic instability. In vitro studies showed growth inhibition by vitamin B6 on different type of stability where it has been shown that deficiency of folate decreases genomic DNA a different methylome compared to normal cells. Interestingly, a decrease in genomic methylation 132 . However, studies in humans have been inconclusive. Some suggest that methylation at dispersed CpG sites and abnormal hypermethylation in gene promoter CpG islands are often observed in human cancers. The hypermethylation phenomena may contribute to silencing of tumor suppressors, whilst hypomethylation may activate OCM analytes and lung cancer altered folate status can affect global DNA methylation in humans 172 173 whereas others Among OCM biomarkers, folate is also by far the most studied analyte in relation to lung reports no significant changes in DNA methylation in relation to folate concentrations 174 175 . cancer as previously shown for CRC. However, data on the effect of folate on the
	cancers 144 145 , including hepatoma cells of rat 146 . Most human studies on the association of oncogenes 154 . In addition, it has been suggested that in vitamin B6 deficiency, DNA methylation is subsequent incidence of lung cancer are limited and inconclusive. Table 1 summarizes the
	when me-THF methylates deoxyuridylate monophosphate (dUMP) to form deoxythymidylate monophosphate (dTMP), a reaction essential for DNA replication and repair 136 . It has been shown that mutations can occur either during the process of DNA repair, or during semiconservative DNA replication that leads to duplication of the cell genome 137 . In vitamin B6 with carcinogenesis focused on colorectal cancer and have suggested that deficiency of vitamin B6 may increase risk 147 . However, since vitamin B6 is not solely dependent to the OCM pathway, this nutrient may also suppress carcinogenesis by reducing cell proliferation, angiogenesis, oxidative stress, inflammation, and nitric oxide synthesis 148-150 . Regional increases in DNA methylation concentrations have been reported from many decreased because of less availability of SAM, the methyl group donor. Some studies have literature on the selected OCM nutrients in relation to lung cancer risk. Inverse association shown an association between B6 and hypermethylation 176 177 , but to date no studies have of dietary folate intake with lung cancer risk have been reported in few case-control studies. Many genes have been found to be hypermethylated in cancer including p16, investigated the relation of hypomethylation with B6 deficiency. studies 181 182 . In contrast, a recent meta-analysis of nine cohort studies found a very small or BRACA1 and RASSF1A 155 . Hypermethylation was reported in many types of human cancers no association with lung cancer risk 183 , and only one of the included studies alone reported including breast 156 157 , prostate 158 , leukemia 159 160 and lung 161 . a significant inverse association 184 . Dietary intake of other micronutrients related to the
	normal cells, a limited presence of folate reduces thymidylate from an imbalanced synthesis Global loss of DNA methylation of the genome, termed hypomethylation, has been One-carbon pathway is a complex mechanism involving many factors. With an aim to OCM pathway has been studied in relation to lung cancer development, including vitamin
	of dTMP (thymine) from dUMP (uracil) which leads in an increase in uracil content of DNA. suggested to initiate carcinogenesis. Hypomethylation is observed among several further understand its relation in cancer, we conducted studies with a specific focus on B2, B6, B12 and methionine, but no significant associations were found in those studies (see
	This results in the incorporation of uracil into human DNA and which produces different DNA methylation some of the OCM cornerstones such as the B-vitamins involved in the folate cycle as well as malignancies 162 163 including solid tumours such as prostate tumours 164 , metastatic Table 1).
	types of genetic instability during DNA replication such as DNA strands breaks and DNA methylation is an important epigenetic modification in mammals that confers an hepatocellular cancers 165 , cervical cancers 163 , and also in haematological malignancies the methionine and homocysteine engaged in methionine cycle. For clarity, OCM nutrients,
	chromosomal damage 138 . Thus, depletion of folate could not only block DNA repair capacity but also increase the need of DNA repair. In animal studies, this DNA and chromosomal adequate regulation of gene expression, embryonic development and chromosome factors or biomarkers refers in this dissertation to the combination of vitamin B2, vitamin Investigations on the relation between circulating concentrations of OCM factors with lung including leukaemia 166 . stability 135 151 152 . Epigenetics is defined as "the study of heritable changes in gene B6, folate, vitamin B12, homocysteine and methionine. cancer risk have been assessed very sparsely in a prospective setting. The ATBC study was
	damage has been shown to activate proto-oncogenes and inactivate tumour suppressor expression that occur without a change in DNA sequence" 153 , and unlike genetics, epigenetic DNA methylation levels may be tissue-specific because the variation among different tissues the first to evaluate whether pre-diagnostic circulating of B-vitamins (B6, B9 and B12) and
	genes 139 , which are involved in cancer development. Knock et al. showed that low folate changes are dynamic and reversible. In humans, DNA methylation is a covalent chemical is substantial 131 167 . Some studies reported that DNA methylation levels in carcinogenic homocysteine were associated with lung cancer risk. This nested case-control study of 300
	exposure increased intestinal tumorigenesis in mice through increased dUMP/dTMP ratio modification, resulting in the addition of a methyl (-CH3) group at the carbon 5 position of tissues did not differ from tissues characterised as benign or normal tissues surrounding lung cancer cases and matched controls among Finnish male smokers did not find any clear
	and DNA damage 140 . It has been also observed that a moderate dietary deficiency of folate the cytosine ring. This chemical modification can occur at millions of locations on the DNA carcinogenic tissues 168 169 . association between circulating concentrations of folate, vitamin B12 and homocysteine and
	increased incidence of colonic dysplasia and carcinoma in rats 141 . sequence and occurs most of the time at CpG sites 151 . Between 70 to 80% of CpG lung cancer risk 185 . However, subjects in the top quintiles of circulating concentrations of
	vitamin B6 had a two-fold decrease risk of lung cancer compared with those in the bottom

Insufficient methyl groups from OCM factors imbalances may induce impaired DNA synthesis and repair. Synthesis of pyrimidines occurs

Table 1 .

 1 Associations

			between OCM factors (vitamin B2, B6, B12, folate, methionine and
	homocysteine) and lung cancer risk
	Author	Year	Exposure	Studies	Subjects	Results a
	Dietary assessment
	Yu-Fei	2014 dietary folate	meta-	9 cohort	High vs low dietary folate intake: RR
	Zhang et		intake	analysis	studies	= 0.92(0.84-1.01), p for trend= 0.08
	al. 183					Per 100ug/day increment in folate lung cancer RR= 0.99(0.97-1.01), p
						for trend0.31
	Yumie	2013 dietary folate	prospective	359 lung	highest vs. lowest quartile of dietary
	Takata et al. 186		intake	cohort study	cancer cases	folate intake: HR=0.99 (0.70 -1.40), p for trend= 0.92
					(men)
	Dai WM et	2013 dietary folate	meta-	4,390	Overall inverse assocition of dietary
	al. 181		intake	analysis	lung	foalte intake: OR=0.74 (0.65-0.84), p
					cancer	for trend <0.001
					cases and
					6,138
					controls
	Yumie	2012 dietary intake of	prospective	359 lung	highest vs. lowest quartile of dietary
	Takata et		B2, B6, folate,	cohort study	cancer	B2 intake: HR=0.62 (0.43-0.89), p
	al. 187		B12 and		cases	for trend =0.03
			methionine		(women)	highest vs. lowest quartile of dietary
						B6 intake: HR=1.21 (0.84-1.74), p
						for trend =0.44
						highest vs. lowest quartile of dietary
						folate intake: HR=0.96 (0.70 -1.32),
						p for trend = 0.79
						highest vs. lowest quartile of dietary B12 intake: HR=0.76 (0.55-1.05), p
						for trend =0.19
						highest vs. lowest quartile of dietary
						methionine intake: HR=0.87 (0.58-
						1.29), p for trend =0.26

Table 2 .

 2 Associations between OCM factors (vitamin B2, B6, B12, folate, methionine and homocysteine) and the head and neck cancer risk

	Author	Year	Exposure	Studies	Subjects	Results a
	Dietary assessment
	Carlotta	2014 dietary intake of	case-	5,127 cancers of	highest vs. lowest quintile of dietary
	Galeone et al. 70	folate	control	oral cavity and pharynx and	folate intake: OR=0.65 (0.43-0.99), p for trend =0.04
					13,249 controls
	Keitaro	2012 dietary intake of	case-	409 HNC cases	highest vs. lowest quartile of dietary
	Matsuo et al. 201	folate	control		folate intake: OR=0.53 (0.36-0.77), p for trend <0.001
	D. Aune et	2011 dietary folate	case-	798 UADT*	highest vs. lowest quartile of dietary
	al. 92	intake	control	cancer cases	folate intake: OR=0.41 (0.26 -0.65), p
					and 2,032	for trend <0.0001
					controls	highest vs. lowest quartile of dietary
					283 cancer	folate intake: OR=0.49 (0.24 -0.98), p
					cases of oral	for trend =0.04
					cavity and	highest vs. lowest quartile of dietary
					pharynx	folate intake: OR=0.54 (0.27 -1.10), p
					281 larynx	for trend =0.07
					cancers
	C.	2003 dietary intake of	case-	749 cancer	highest vs. lowest tertile of dietary
	Pelucchi	folate and	control	cases of the oral	folate intake: OR=0.53 (0.40-0.69), p
	et al. 202	methionine		cavity and	for trend <0.001
					pharynx and	highest vs. lowest tertile of dietary
					1,772 controls	methionine intake: OR=1.14 (0.87-
						1.50), p for trend =0.33

Table 3 .

 3 Associations

				between OCM biomarkers factors (vitamin B2, B6, folate and
	methionine) and esophageal squamous cell carcinoma risk
	Author Year	Exposure	Studies	Subjects	Results a
	Linda	1998 dietary intake	case-	114 ESCC cases	highest vs. lowest quartile of dietary
	M.			of B2 and	control	and 681 controls	B2 intake OR=0.90, p for trend =0.42
	Brown		folate		(whites)	highest vs. lowest quartile of dietary
	et al.	207				folate intake OR=0.70, p for trend
						=0.63
						219 ESCC cases	highest vs. lowest quartile of dietary
						and 557 controls	B2 intake OR=1.20, p for trend =0.67
						(blacks)	highest vs. lowest quartile of dietary
						folate intake OR=1.10, p for trend
						=0.85
	Susan T.	2001 dietary folate,	case-	206 ESCC cases	75th vs. 25th of dietary B2 intake
	Mayne		intake	control	and 687 controls	OR=1.26 (0.84-1.89)
	et al.	208				75th vs. 25th of dietary B6 intake
						OR=0.45 (0.30-0.69)
						75th vs. 25th of dietary folate intake
						OR =0.58 (0.39-0.86)
						75th vs. 25th of dietary B12 intake
						OR=1.51 (1.15-2.00)
	C.		2005 dietary intake	case-	351 ESCC cases	highest vs. lowest tertile of dietary B6
	Galeone		of B6, folate	control	and 875 controls	intake OR=0.88 (0.60-1.31), p for trend
	et al.	93		and		=0.55
				methionine		highest vs. lowest tertile of dietary
						folate intake OR=0.68 (0.46-1.00), p
						for trend =0.05
						highest vs. lowest tertile of dietary
						methionine intake OR=1.11 (0.76-
						1.62), p for trend =0.58
	Susanna	2006 dietary intake	meta-	929 ESCC cases	highest vs. lowest category of dietary
	C.			of folate	analysis (4	folate intake OR=0.66 (0.53-0.83)
	Larsson			case-
	et al.	94			control
					studies)

Table 4 .

 4 Associations between OCM factors (vitamin B2, B6, B12, folate, methionine and homocysteine) and kidney cancer risk

	Author	Year	Exposure	Studies	Subjects	Results a
	Dietary assessment
	Kaye E.	2012 dietary folate	case-control	323 RCC*	highest vs. lowest quartile of
	Brock et al. 210	intake		cases and 1,827	dietary folate intake: OR=0.80 (0.50 -1.20), p for trend= 0.12
					matched
					controls
	Christina	2006 dietary intake	case-control	767 RCC*	highest vs. lowest quintile of
	Bosseti et	of B2, B6 and		cases and	dietary B2 intake: OR=0.88
	al. 209	folate		1,534	(0.66-1.19)
					matched	highest vs. lowest quintile of
					controls	dietary B6 intake: OR=0.85
						(0.64-1.13)
						highest vs. lowest quintile of
						dietary folate intake: OR=0.85
						(0.64 -1.12)
	Eunyoung Cho et	2013 Total intake of B6, folate,	pooled analysis (2 prospective	436 RCC* cases	highest vs. lowest quintile of total B6 intake: RR=0.88 (0.65-
	al. 211	B12 and	studies)
		methionine
						trend=0.10
	Circulating concentrations
	Todd M. Gibson et	2010 Serum of vitamin,B2,	nested case-control	224 RCC* cases and	highest vs. lowest quartile of serum B2: OR=0.76 (0.42-1.40),
	al. 212	B6, folate,		224	p for trend =0.68
		B12, and		matched	highest vs. lowest quartile of
		homocysteine		controls	serum B6: OR=1.33 (0.73-2.42),
						p for trend =0.43
						highest vs. lowest quartile of
						serum folate: OR=0.67 (0.37-1.20), p for trend =0.19
						highest vs. lowest quartile of
						serum B12: OR=0.87 (0.46-1.63), p for trend =0.98
						highest vs. lowest quartile of
						serum homocysteine: OR=0.92 (0.51-1.66), p for
						trend = 0.60
	RCC*= renal cell carcinoma
	a Multivariate-adjusted OR or RR and 95% confidence intervals (CIs) in parentheses

1.19)

, p for trend=0.55 highest vs. lowest quintile of total folate intake: RR=0.

84 (0.60 -1.18)

, p for trend = 0.52 highest vs. lowest quintile of total B12 intake: RR=1.24 (0.90-1.70), p for trend=0.61 highest vs. lowest quintile of total methionine: intake RR=1.

29 (0.93-1.78)

, p for

Table 5 .

 5 Characteristics of participating cohorts and lung cancer cases with serum/plasma available

	Nb.	Full Cohort name	Abbr.	Representative (co-applicant)	Location	Enrolment	Cohort size
	Cohorts recruited in US
	1	Women´s Health Initiative	WHI	Ross Prentice	Fred Hutchinson CRC	1994-1998	161,808
	2	Southern Community Cohort Study	SCCS	Qiuyin Cai Shiman Qu	Vanderbilt Medical Center University	2002-2009	86,000
		Prostate, Lung, Colorectal
	3	and Ovarian Cancer	PLCO	Neil Caporaso	DCP/DCEG, NCI, US	1993-2001	155,000
		Screening Trial
				Alan Arslan
	4	NY Univ. Women Health Study	NYUWHS	Anne Zeleniuch-	NYU School of Medicine	1985-1991	14,274
				Jacquotte
				Victoria
	5	ACS Cancer Prevention Study-II	CPS-II	Stavens Marji	American Cancer Society	1992	184,190
				McCullough
					John Hopkins
	6	Clue studies1974 CLUE (CLUE I) & 1989 (CLUE II)	CLUE	Kala Visvanathan	Bloomberg School of Public	1974 &1989	50,000
					Health
				Loic le	Cancer Research
	7	Multiethnic Cohort Study	MEC	Marchand	Center,	2001-2007	215,000
				Lynne Wilkens	University of Hawaii
					Brigham and
	8	Women's Health Study	WHS	Shuming Zhang	Women's Hospital / Harvard	1992-1995	39,876
					Uni
					Brigham and
	9	Nurses' Health Study	NHS I	Jiali Han Xuehong Zhang	Women's Hospital / Harvard	1989	32,826
					Uni
	10	Health Professionnals Follow-up Study	HPFS	Jiali Han	Harvard University	1993	18,159
	11	Physicians health study I & II	PHS I+II	Jing Ma Howard Sesso	Brigham and Women's Hospital	1982-1983 1997-2000	29,071
	Cohorts recruited in Europe/ Australia
	12	Melbourne Colloborative Cohort Study	MCCS	Gianluca Severi Graham Gilles	Cancer Centre / Victoria Epidemiology	1990-1994	41,514
	14	Malmö Diet and Cancer	MDCS	Jonas Manjer	Lund University,	1991-1996	28,098

Study population and data collection

In total, the consortium included 11,399 incident lung cancer cases with available prediagnostic blood samples. Of these, we selected a subsample of 5,545 cases, comprising all incident cases among never smoking participants (n=1336), and a random selection of the cases diagnosed among former and current smoking participants. A standardised data base dictionary was sent to each cohort and we requested a standardized set of covariates for each study subject, including variables on (1) anthropometric and demographic variables such as weight, height, ethnicity, smoking status, educational attainment; (2) dietary intake variables such as vegetables, fruit, vitamins supplements; (3) clinical variables such as tumour stage, histology, date of diagnosis, and (4) follow-up information such as date of death and cause of death. All plasma and serum samples were sent and centralized to the Bevital AS laboratory in Bergen, Norway, and once on site they were kept at -80°C. A minimum of 500 μL of blood fraction (plasma or serum) from all participating subjects in each individual cohort was requested.

  Sample volumerequirements have been minimized by using a combination of microbiological assays and mass-spectrometry based methods, and sample handling was carried out by robotic workstations ensuring high sample throughput. Measurements performed at Bevital have been used in several high profile, recently published epidemiological studies13 236-238 . All

	biochemical analyses of circulating OCM biomarkers were performed at the the Bevital A/S
	laboratory (http://www.bevital.no) in Bergen, Norway. Plasma from all study participants
	were analyzed on four analytical platforms (platforms B, C, D, and F). Concentration
	measurements of plasma vitamin B2 (riboflavin), vitamin B6 (measured as pyridoxal 5'-
	phosphate [PLP], its active form), vitamin B9 (folate), vitamin B12 (cobalamin), total
	homocysteine, and methionine were provided in HNC (Chapter III) and RCC (Chapter IV)

studies. LC3 study (Chapter V) comprised plasma/serum measurement concentrations of vitamin B6, folate and methionine. All case and control participants were successfully analysed, for at least one of the biomarker. Circulating cotinine was also measured as an indicator of recent smoking behaviour in each study. Circulating vitamin B2, vitamin B6, homocysteine, methionine, and cotinine were determined by mass spectrometry-based methods (liquid chromatography coupled to tandem mass spectrometry; gas chromatography coupled to tandem mass spectrometry -LC-MS/MS, GC-MS/MS) 239 240 .

  Because of the substantial variation of circulating biomarkers across continent in LC3 study (Chapter V), quartiles were additionally based on the distribution among all controls of each continent (United States [US], Europe and Australia combined [EU/AU], and Asia). OR and 95% CI for participants in the second, third, and fourth quartiles were calculated relative to the first quartile using conditional logistic regression, conditioning on individual case sets. Unconditional logistic regression were also used mostly in HNC and RCC studies for stratified analysis or in order to increase the statistical power (since we had about half controls who were not matched to the cases, see Selection of cases and controls in Chapter III and IV) and further evaluate the consistency of any association observed in conditional logistic regression.

	Throughout the papers included in this thesis, we conducted initial analyses involving
	extensive assessments of demographic variables such as age, smoking status, body mass
	index (BMI) and factor related to socio economical status as well as for dietary intake of fruit
	and vegetables. Also, the relation of lifestyle and dietary factors with biomarker
	concentrations were assessed using linear models, adjusted for age, sex, country and other
	covariates if necessary. Subsequent statistical analysis included estimation of RR by

The strength of associations was estimated by OR. P-values were assessed as indicators of associations, and p-values of less than 0.05 were considered statistically significant. calculating OR, using both conditional and unconditional logistic regression. In the three studies of this thesis, risk analysis involved calculating quartiles of circulating concentration for each biomarker of interest based on their distribution among all matched control participants.

In order to evaluate whether deficiency of the OCM biomarkers of interest in LC3 study are harmful for lung cancer etiology, we conducted analyses by using clinical cut-points for deficiency of vitamin B6 and folate. Because methionine deficiency is very poorly documented, we could not conduct such analysis. We have considered vitamin B6 deficiency as circulating vitamin B6 concentration less than 20nmol/L, compared with normal range, defined as ≥ 20nmol/L 243 . Folate was classified as deficient (<7 nmol/L), moderate deficiency (7 to 13 nmol/L), or normal (≥ 13 nmol/L)

244

. For these additional analyses in the LC3 study, we also used conditional logistic regression to estimate RRs by calculating ORs and 95% CI of lung cancer risk for deficiency relative to normal concentrations.

To assess potential effect modification throughout the studies, we performed stratified analyses by different variables including place of recruitment (country or continent), sex, smoking status, age at diagnosis, time from blood draw to diagnosis.

  cases from Denmark (n=288) because of flooding of the Danish biobank, and the For each case, one control was randomly chosen from appropriate risk sets consisting of all cohort members alive and free of cancer (except non-melanoma skin cancer) at the time (and hence age) of diagnosis of the index case. Matching criteria were country, sex, date of

	OCM and head and neck cancer in EPIC
	blood collection (±1 month, relaxed to ±5 months for sets without available controls), and
	date of birth (±1 year, relaxed to ±5 years for sets without available control participants). An
	additional control group (control group 2, n=479) was included in the HNC study. This
	control group 2 was matched to cases of the study on RCC (from Chapter IV) using similar
	matching criteria. Control group 2 participated essentially to unconditional and stratified
	analyses in order to assess the consistency of any association and increase the statistical
	power.
	Malmo centre in Sweden (n=101) who did not participate in this study. We thought also to
	exclude adenocarcinoma of the head and neck (n=16) because the etiology of
	adenocarcinoma and SCC are likely to differ and the vast majority of the HNC are SCC. After
	all participant exclusions, 516 cancer cases of the head and neck and oesophagus remained
	eligible in this study.
	78

  Circulating Folate, Vitamin B6 and Methionine in relation to Lung Cancer Risk in the Lung Cancer Cohort Consortium (LC3)

	Abbreviations:
	CI	confidence interval
	EPIC European Prospective Investigation into Cancer and Nutrition
	OCM one-carbon metabolism
	OR	odds ratio
	LC3	Lung Cancer Cohort Consortium
	Article category: Research article -Epidemiology
	Number of words -article: 2,998
	Number of Tables: 4
	Number of Figures: 1
	Number of Supplementary Tables: 4
	Number of Supplementary Figures: 14
	Number of References: 24

9.2, R version 3.1.3 and Stan version 2.6.0. Supplementary statistical details are provided in Chapter II (see Material and Methods, Statistical methods).

Table 1 .

 1 The final study population included 5,364 lung cancer cases and 5,364 individually matched controls. Median age at cohort enrolment was 63 years and the median time between blood draw and lung cancer diagnosis was 6.3 years. Because we oversampled

never and former smokers, approximately half of the study population were current smokers at recruitment (2,519 case-control pairs, 47%), whereas the other half were either former (1,518 case-control pairs, 28%) or never smokers (1,327 case-control pairs, 25%).

Table 1 -

 1 4). 4vs1 was 0.84 for the Asian cohorts (95% CI 0.68-1.03) and 0.77 for the EU/AU cohorts (95% CI 0.60-0.99), and 0.95 for the US cohorts (95% CI 0.78-1.15).

	In risk analyses stratified by region (Table 2-4), the overall inverse associations of vitamin B6
	and folate were mainly driven by Asian and EU/AU participants. The adjusted OR 4vs1 for
	vitamin B6 was 0.82 in the Asian cohorts (95% CI 0.65-1.02) and 0.78 for the EU/AU cohorts

(95% CI 0.62-1.00), and 0.95 for the US cohorts (95% CI 0.80-1.13). Similarly, for folate, the adjusted OR

  and folate, and observed a 23% increase in risk of lung cancer for those deficient in vitamin B6 (OR = 1.23, 95% CI 1.09-1.38) that was more apparent for being diagnosed with squamous cell cancer (OR=1.54, 95% CI 1.18-2.02), and also those for developing lung cancer within the first 3 years following blood collection (OR=1.83, 95% CI 1.42-2.36)

Table 1 .

 1 Baseline and clinical characteristics of study participants overall and per continent

	Overall	No.(%) of participants in group	Matched controls Cases	(n=5364) (n=5364)	2908 (54.2%) 2908 (54.2%)	2456 (45.8%) 2456 (45.8%)		1327 (24.7%) 1327 (24.7%)	1518 (28.3%) 1518 (28.3%)	2519 (47%) 2519 (47%)		1696 (31.6%) 1797 (33.5%)	784 (14.6%) 759 (14.1%)	914 (17%) 891 (16.6%)	718 (13.4%) 680 (12.7%)	496 (9.2%) 524 (9.8%)	710 (13.2%) 646 (12%)	46 (0.9%) 67 (1.2%)		1512 (28.2%) 1488 (27.7%)	3485 (65%) 3485 (65%)	367 (6.8%) 391 (7.3%)		153 (2.9%) 201 (3.7%)	2647 (49.3%) 2812 (52.4%)	1818 (33.9%) 1678 (31.3%)	683 (12.7%) 609 (11.4%)	63 (1.2%) 64 (1.2%)		60 (44-72) 60 (44-72)		37.1 (13.9-197) 35.2 (12.6-205)	17.9 (6.3-90.3) 17.0 (6.0-91.8)	26.6 (18.6-40.3) 26.3 (18.4-40.4)	69.8 (53.6-82.0)
	Asian cohorts	No.(%) of participants in group	Matched controls Cases	(n=1775) (n=1775)	1229 (69.2%) 1229 (69.2%)	546 (30.8%) 546 (30.8%)		602 (33.9%) 602 (33.9%)	176 (9.9%) 176 (9.9%)	997 (56.2%) 997 (56.2%)		883 (49.7%) 898 (50.6%)	230 (13%) 243 (13.7%)	279 (15.7%) 289 (16.3%)	196 (11%) 171 (9.6%)	113 (6.4%) 104 (5.9%)	65 (3.7%) 62 (3.5%)	9 (0.5%) 8 (0.5%)		974 (54.9%) 946 (53.3%)	801 (45.1%) 829 (46.7%)	0 (0%) 0 (0%)		113 (6.4%) 157 (8.8%)	1192 (67.2%) 1203 (67.8%)	424 (23.9%) 369 (20.8%)	46 (2.6%) 46 (2.6%)	0 (0%) 0 (0%)		60 (46-72) 60 (46-72)		31.2 (12.4-119) 29.0 (11.1-114)	15.1 (6.7-40.7) 13.9 (6.2-40.5)	27.4 (18.2-41.9) 27.3 (18.0-42.4)	69 (52-80)
	US cohorts EU/AU cohorts	No.(%) of participants in group No.(%) of participants in group	Cases Matched controls Cases Matched controls	(n=2400) (n=2400) (n=1189) (n=1189)	991 (41.3%) 991 (41.3%) 688 (57.9%) 688 (57.9%)	1409 (58.7%) 1409 (58.7%) 501 (42.1%) 501 (42.1%)		569 (23.7%) 569 (23.7%) 156 (13.1%) 156 (13.1%)	1007 (42%) 1007 (42%) 335 (28.2%) 335 (28.2%)	824 (34.3%) 824 (34.3%) 698 (58.7%) 698 (58.7%)		237 (9.9%) 215 (9%) 661 (55.6%) 596 (50.2%)	357 (14.9%) 374 (15.6%) 159 (13.4%) 180 (15.2%)	422 (17.6%) 435 (18.1%) 180 (15.2%) 200 (16.8%)	402 (16.8%) 393 (16.4%) 107 (9%) 129 (10.9%)	357 (14.9%) 319 (13.3%) 63 (5.3%) 64 (5.4%)	574 (23.9%) 637 (26.5%) 10 (0.8%) 8 (0.7%)	51 (2.1%) 27 (1.1%) 8 (0.7%) 10 (0.8%)		250 (10.4%) 252 (10.5%) 292 (24.6%) 286 (24.1%)	2013 (83.9%) 2041 (85%) 643 (54.1%) 643 (54.1%)	137 (5.7%) 107 (4.5%) 254 (21.4%) 260 (21.9%)		30 (1.3%) 31 (1.3%) 14 (1.2%) 9 (0.8%)	1088 (45.3%) 1020 (42.5%) 521 (43.8%) 435 (36.6%)	841 (35%) 858 (35.8%) 468 (39.4%) 536 (45.1%)	378 (15.8%) 430 (17.9%) 185 (15.6%) 207 (17.4%)	63 (2.6%) 61 (2.5%) 1 (0.1%) 2 (0.2%)	Continuous variables, median (5th-95th percentile)	60 (42-74) 60 (42-74) 60 (45-70) 60 (45-70)		47.6 (15.2-266) 49.9 (16.4-271) 28.9 (12.6-127) 30.9 (13.3-102)	32.9 (8.3-114.2) 33.7 (8.1-114.9) 10.2 (4.4-30.1) 10.8 (4.9-32.9)	25.8 (18.4-39.6) 26.2 (18.8-39.7) 26.1 (19.1-37.8) 26.3 (18.7-37.8)	Clinical characteristics, case participants only	70 (55-83) 69 (54-79)
			Discrete variables	Sex	Men	Women	Smoking status	Never	Former	Current	Education	less than high school	completed high school	vocational school	some college	college graduate	graduate studies	Unknown	Alcohol use (any type)	Never	Ever	Unknown	Body Mass index a	<18.5	18.5-25	25-30	≥30	Unknown		Age at recruitment (years)	Blood concentrations for biomakers	Vitamin B6 (Pyridoxal 5'-phosphate), nmol/L	Vitamin B9 (folate), nmol/L	Methionine, μmol/L	Age at diagnosis, median (range), years

a Body mass index is calculated as weight in kilograms divided by height in meters squared

Table 2 .

 2 Odds ratios of lung cancer for circulating concentrations of vitamin B6

	Odds Ratio (95% Confidence Interval)
	Cases compared	to matched	controls,	adjusted for	cotinine and	education
		Cases compared	to matched	controls,	unadjusted a

Table 3 .

 3 Odds ratios of lung cancer for circulating concentrations of folate

	1.00 (reference)

b) Assessed by analysing lung cancer cases and their individually matched controls by conditional logistic regression, conditioning on individual case set, and adjusting for circulating cotinine (in quartiles) and education (in 7 categories) . c) Assessed by analysing lung cancer cases and their individually matched controls by conditional logistic regression, conditioning on individual case set, and adjusting for education (in 7 categories) . d) Quartile cut-off points were determined based on the blood concentration distribution of each biomarker for 2400 individually matched controls (US cohorts; n=11). e) Quartile cut-off points were determined based on the blood concentration distribution of each biomarker for 1189 individually matched controls (European/Australian cohorts; n=5). f) Quartile cut-off points were determined based on the blood concentration distribution of each biomarker for 1775 individually matched controls (Asian cohorts; n=4). g) Quartile cut-off points were determined based on the blood concentration distribution of each biomarker for 5364 individually matched controls (pooled cohorts; n=20). h) P for trend assessed by the base 2 logarithm of the circulating concentrations.

Table 4 .

 4 Odds ratios of lung cancer for circulating concentrations of methionine

	Odds Ratio (95% Confidence Interval)
	Cases compared	to matched	controls,	adjusted for	cotinine and	education
		Cases compared	to matched	controls,	unadjusted a

  OCM and smoking-related cancers -Discussion 138 a potential explanation of the null association observed among US participants when evaluating the relation of deficiency with lung cancer risk, and therefore cannot rule out the possibility that low circulating of folate and vitamin B6 may lead to stronger increased risk of lung cancer compared to what we found in the LC3 study.

	Results from the LC3 study suggest that ever smoking study participants with elevated
	circulating vitamin B6 and folate had a slight decreased risk in lung cancer, and they should
	avoid deficiency both in circulating folate and vitamin B6, but cannot rule out that tobacco
	exposure may explain this result.

  Conclusive examples of this method include the role of cholesterol for coronary heart disease 275 or folate concentrations in mothers and neural tube defects in offspring 276 .

	Anouar Fanidi, 2016
	pathways implicated in cancer development are dependent on vitamin B6, examples of
	which include the tryptophan metabolism pathway, which is involved in immune function
	and inflammatory processes 272-274 , as well as cell proliferation, angiogenesis, and oxidative
	stress 149 , the latter being particularly relevant in smoking-related cancers. For example,
	inflammation can be the underlying cause of RCC etiology and outcome whereas circulating
	vitamin B6 is only an indicator of inflammation and thus, have no direct and independent
	role in carcinogenesis and survival.
	RCTs could help to elucidate these interpretations. However, it is unlikely that intervention
	trials of vitamin B6 with RCC would be advisable in a very near future because (1) we do not
	have enough evidence to date to potentially induce an harm to the patient, (2) recent
	pooled analysis of two randomized trials reported no benefits for all cancer combined
	among participants randomized to receive vitamin B6 196 , and (3) a large number of study
	participants would be required to have a relevant statistical power due to the complexity to
	evaluate impact of vitamin B6 separately controlling for the other nutrient status or/and
	genetic variation.
	145

if our observations regarding circulating vitamin B6 are shown to be causal, it might not be straightforward to confirm that OCM pathway is implicated in this relation. Indeed, multiple One promising alternative which could help in elucidating the role of vitamin B6 in RCC without going through an intervention study is the Mendelian Randomization (MR) approach. This method aims to identify gene variants associated independently with exposure (i.e. with B6 vitamin concentration), and test whether there is an association between any such gene variants and disease status. Given that allocation of gene variants at meiosis is largely at random, and that such variants should not be associated with lifestyle factors, an independent association between a gene variant and disease status should only occur if it is a true causal association and would therefore provide strong evidence of causality.

variables adjusted for cotinine (in quartiles) and alcohol at recruitment, among all controls

	g/day g/day ethanol g/day	g/day	g/day g/day ethanol g/day	hours g/day ethanol g/day g/day minutes g/day ethanol g/day	g/day g/day	cm g/day g/day ethanol g/day	hrs/week g/day	hrs/week g/day g/day g/day	Unit g/day g/day g/day	hrs/week mg/day g/day μg/day kg	NA hrs/week μg/day g/day g/day μg/day	g/day	mg/day	g/day	mg/day g/day	mg/day	mg/day g/day	kg μg/day	μg/day g/day	mg/day	mg/day	mg/day	mg/day	kcal/day mg/day μg/day	mg/day	mg/day	g/day	g/day	g/day	g/day
	Free text including the variable names and 0 = no actual categories used for the matching factors. 1 = yes If those variables are not already included in the 1 = yes 0 = no 1 = yes variable list, please add the matching variables continuous numeric and their corresponding formats to the data set YYYY If collected as never/ever: 0 = never 1 = ever If collected as current (no/yes): 2 = no current integer if available; if collected as categorical, 0 = no HHMM using 24-hour time report the range as free text .N = subject was never a smoker continuous numeric if available; if collected as Continuous numeric .N = subject was never a smoker integer if available; if collected as categorical, report the range as free text .N = subject was never a smoker 1 = black 2 = blonde 3 = light brown 4 = medium or dark brown 5 = red continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric .N = current use status unknown continuous numeric .N = current use status unknown continuous numeric MM/DD/YYYY (include slashes); if exact day and/or month are not known, then replace day and/or month with "99". For example, "MM/99/YYYY" or "99/99/YYYY". .N = subject is a control Vitamin B2 (nutrient) Vitamin B6 (nutrient) Folate (nutrient) Vitamin B12 (nutrient) Vegetables Leafy vegetables Fruiting vegetables Root vegetables Dairy products	free text YYYYMMDD 1 = yes 0 = no 1 = ever 1 = male2 = female 0 = no 0 = never If collected as current (no/yes): 2 = no current integer if available; if collected as categorical, .N = not a current user cohortnumber_serial ID number (EX: 16_0001) report the range as free text free cohort number integer if available; if collected as categorical, If collected as never/ever: 0 = never 1 = ever report the range as free text .N = subject was never a smoker .N = subject was never a smoker integer if available; if collected as categorical, report the range as free text .N = subject was never a smoker integer if available; if collected as categorical, report the range as free text .N = subject was never a smoker or did not quit 1 = white 1 = yes 3 = current continuous numeric if available; if collected as continuous numeric 1 = light 2 = moderate 3 = vigorous .N = METs data are provided YYYY YYYY continuous numeric continuous numeric YYYY continuous numeric 1 = yes YYYY .N = subject was never a smoker or did not quit 0 = no (e.g. "VA", "MD", "CA", "ITALY", "FINLAND") report the range as free text .N = subject was never a smoker cohortnumber_serial ID number (EX: 16_0001) full name of country if not in USA integer if available; if collected as categorical, categorical, report the range as two-letter US state abbreviation if in USA; continuous numeric if available; if collected as YYYY YYYY report the range as free text .N = subject was never a smoker .N = subject was never a smoker Free text Continuous numeric integer if available; if collected as categorical, report the range as free text free text Continuous numeric continuous numeric .N = subject was never a smoker integer if available; if collected as categorical, continuous numeric 1 = yes free text 0 = no .N = subject was never a smoker Values categorical, report the range as free text Continuous numeric 0 = never 1 = ever 3 = vigorous .N = METs data are provided 1 = light 2 = moderate 3 = vigorous .N = METs data are provided continuous numeric continuous numeric 1 = self-reported 2 = measured YYYY continuous numeric 1 = self-reported 2 = measured continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric 2 = black 0 = no 3 = asian 1 = yes 4 = other If collected as never/former/current: 4 = never 5 = former categorical, report the range as free text .N = subject was never a smoker Continuous numeric YYYY free text (e.g. "Pacific Islander", hispanic, free text african, amercican) 6 = current .N = subject was never a smoker integer if available; if collected as categorical, 0= no defined by individual studies 1 =yes YYYYMMDD 0= no integer 1 =yes report the range as free text .N = subject was never a smoker integer if available; if collected as categorical, 0 = never 1 = ever integer if available; if collected as categorical, report the range as free text .N = subject was never a smoker continuous numeric if available; if collected as categorical, report the range as free text .N = subject was never a smoker YYYYMMDD report the range as free text 1 = brown or black 2 = blue 3 = grey 4 = green 5 = other continuous numeric free text continuous numeric continuous numeric continuous numeric integer .N = subject was never a smoker or did not quit continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric Format and/or List of Valid Continuous numeric YYYY continuous numeric if available; if collected as report the range as free text .N = subject was never a smoker or did not quit 1 = light 2 = moderate continuous numeric .N = subject was never a smoker 1 = yes 6 = graduate studies 0 = no 5 = college graduate integer if available; if collected as categorical, 4 = some college report the range as free text report the range as free text .N = subject was never a smoker integer if available; if collected as categorical, continuous numeric continuous numeric 1 = self-reported 2 = measured YYYY continuous numeric 3 = vocational school variables in the data set. (e.g. "VA", "MD", "CA", "CHINA", "FINLAND") 2 = completed high school the formatting for valid values for those full name of country if not in USA 1 = less than high school variables, a description of those variables, and two-letter US state abbreviation if in USA; 5 = former 6 = current Continuous numeric included in the variable list, please include those 4 = never .N = subject was never a smoker integer if available; if collected as categorical, report the range as free text If the matching factors for your study are not continuous numeric If collected as never/former/current: categorical, report the range as free text 3 = current .N = subject was never a smoker integer if available; if collected as categorical, .N = subject was never a smoker or did not quit YYYY 0 = never 1 = ever YYYY continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric continuous numeric YYYY If collected as never/ever: 0 = never 1 = ever If collected as current use (no/yes): 2 = no current use 3 = current use .N = current use status unknown continuous numeric .N = current use status unknown continuous numeric .N = current use status unknown YYYY 0 = no 1 = yes MM/DD/YYYY (include slashes); if exact day and/or month are not known, then replace day and/or month with "99". For example, "MM/99/YYYY" or "99/99/YYYY". .N = subject is not deceased free text, provided in whatever format is available .N = subject is not deceased free text (e.g. "ICD9") .N = subject is not deceased free text YYYY MM/DD/YYYY (include slashes); if exact day and/or month are not known, then replace day and/or month with "99". For example, "MM/99/YYYY" or "99/99/YYYY". .N = subject is not deceased continuous numeric 0 = no 1 = yes ICD-9 or ICD-10 codes, or whatever format is available. .N = subject is a control Free text WHO ICD-O morphology codes, or whatever format is available. .N = subject is a control free text (e.g. "WHO ICD-O") .N = subject is a control whatever format is available .N = subject is a control free text (e.g. "AJCC fourth edition") .N = subject is a control TNM Stage, or whatever format is available. .N = subject is a control free text (e.g. "AJCC fourth edition") .N = subject is a control If collected as never/ever: 0 = never 1 = ever If collected as current use (no/yes): 2 = no current use 3 = current use If collected as never/former/current: 4 = never 5 = former 6 = current .N = subject is male continuous numeric 995 1.22 (1.12 -1.32) 2x10 -7 995 1.01 (0.94 -1.09) 0.62 995 1.07 (1.00 -1.16) 995 1.11 (1.00 -1.22) 0.04 995 1.10 (1.00 -1.20) 0.04 995 1.10 (1.00 -1.20) 995 1.1 (1.01 -1.21) 0.01 995 1.13 (1.04 -1.23) 0.004 995 1.26 (1.17 -1.37) .N = subject is male, or current use status is "no" or unknown 0 = never 1 = ever .N = subject is male 0 = never 1 = ever 995 1.06 (1.00 -1.12) 0.01 995 0.98 (0.94 -1.03) 0.57 995 1.00 (0.95 -1.05) 995 1.01 (0.97 -1.06) 0.87 995 1.06 (1.02 -1.10) 0.006 995 1.10 (1.06 -1.15) 959 1.02 (0.99 -1.04) 0.08 959 1.03 (1.00 -1.05) 0.02 959 1.05 (1.02 -1.07) 992 1.03 (1.00 -1.06) 0.22 992 1.05 (1.02 -1.08) 0.003 992 1.05 (1.02 -1.08) 950 0.98 (0.96 -1.01) 0.30 950 1.02 (1.00 -1.05) 0.18 950 1.02 (1.00 -1.05) .N = subject is male 992 1.08 (1.05 -1.12) 5x10 -7 992 1.00 (0.97 -1.03) 0.57 992 0.99 (0.96 -1.02)	0.06 0.04 2x10 -8 0.85 3x10 -5 2x10 -5 4x10 -4 0.42 0.54
	Meat products				995	1.00 (0.97 -1.03)	0.75	995		0.98 (0.95 -1.01)		0.14		995	0.93 (0.91 -0.96)	1x10 -5
							Serum vitamin B12					Serum homocysteine					Serum methionine
	BASELINE_EXCL_CRITERIA all subjects per cohort). For example, indicate whether participants were excluded from the any DOB Date of birth any MEDS_DIAB data set at baseline if they had a history of any Current use of diabetes medication closest to blood draw SMOKE_STATUS Smoking status (all types of tobacco) closest to blood draw smokers, or former smokers) SMOKE_DUR_CIGAR cancer, history of lung cancer only RACE Race any MEDS_HBURN Current use of heartburn medication closest to blood draw Smoking duration (number of years smoked cigars regularly) closest to blood draw SMOKE_AGE_QUIT_CIGAR_MR Age at which the subject stopped cigar smoking index date or most recent prior to index date SMOKE_PIPE_MR Pipe smoking status index date or most recent prior to index date available; if leisure/occupation is not specified) determined by date of blood draw, common for GENDER Description of baseline exclusion criteria (as Gender any MEDS_HIGHBP Current use of high blood pressure medication closest to blood draw SMOKE_INT_CIGAR (note: we would like these data for individuals who indicated they were ever smokers, current closest to blood draw prior to index date Smoking intensity (number of cigars per day) CASESET Caseset ID for the matched case-control pair any MEDS_NSAIDS_DUR COHORT_ID Cohort internal ID variable any Duration of current use of aspirin/NSAIDS (years) closest to blood draw SMOKING (AT TIME OF BLOOD DRAW) SMOKE_CIGAR Cigar smoking status closest to blood draw prior to index date SMOKE _PACKYRS_CIGAR_MR specific covariate database MEDS_NSAIDS correspond to the ID variable of the cohort Current use of aspirin/NSAIDS closest to blood draw RESIDENCE_YR Year of data collection (RESIDENCE data) closest to blood draw SMOKE_AGE_QUIT_CIGARETTE LC3_ID is 16_0001), the LC3_ID should LC3_ID "cohortnumber_serialIDnumber" (EX for ATBC: any MEDICATION USE (AT TIME OF BLOOD DRAW) RESIDENCE Geographic region where subject resides closest to blood draw Age at which the subject stopped cigarette smoking closest to blood draw SMOKE_DUR_CIGAR_MR Smoking duration (number of years smoked using sample ID Format FAMHX_YR Participant ID (unique participant identifier) Year of data collection (FAMILY HISTORY data) index date or most recent prior to index date INCOME_YR Year of data collection (INCOME data) closest to blood draw SMOKE_AGE_START_CIGARETTE Age at which the subject started cigarette (if FAMHX_LUNG=Yes) COHORT_ACCR Cohort accronym NA FAMHX_LUNG_NUMBER Number of first degree relative with lung cancer GENERAL INFORMATION relatives) BASELINE INFORMATION FAMHX_LUNG Family history of lung cancer (first degree index date or most recent prior to index date index date or most recent prior to index date INCOME_VARIABLE information you have with a description of the items and the units. We will then set up common variables. If several variables are available, please include all of them. closest to blood draw SMOKE _PACKYRS_CIGARETTE Cigarette smoking pack years closest to blood draw smoking closest to blood draw prior to index date Time Variable Name Variable Description Data Collection FAMHX_ANYCA_NUMBER Number of first degree relative with other cancer, most recent (if FAMHX_ANYCA_MR=Yes) index date or most recent prior to index date EDUCATION_YR Year of data collection (EDUCATION data) closest to blood draw Information on income to be included when available (Each type allowed), please send the smokers, current smokers, or former smokers) VARIABLE LIST FAMHX_ANYCA_MR Family history of any cancer (first degree relatives), most recent index date or most recent prior to index date SMOKE_INT_CIGARETTE day CPD) (note: we would like these data for individuals who indicated they were ever closest to blood draw SMOKE_DUR_CIGARETTE Smoking duration (number of years smoked cigarettes regularly) closest to blood draw smoking FAMILY HISTORY (MOST RECENT PRIOR TO INDEX DATE) EDUCATION Highest Education level closest to blood draw Smoking intensity (number of cigarettes per SMOKE_AGE_START_CIGARETTE_MR Age at which the subject started cigarette index date or most recent prior to index date SMOKE_AGE_QUIT_CIGARETTE_MR Age at which the subject stopped cigarette smoking index date or most recent prior to index date SMOKE_CIGAR_MR Cigar smoking status index date or most recent SMOKE_INT_CIGAR_MR Smoking intensity (number of cigars per day) (note: we would like these data for individuals who indicated they were ever smokers, current smokers, or former smokers) index date or most recent prior to index date cigars regularly) index date or most recent prior to index date Cigar smoking pack years index date or most recent SMOKE_AGE_START_CIGAR_MR Age at which the subject started cigar smoking index date or most recent PHYSICAL ACTIVITY (AT TIME OF BLOOD DRAW) PHYSACT_METS_LEISURE Current leisure time activity, MET hrs/week closest to blood draw PHYSACT_HRS_LEISURE Current leisure time activity, avg. hrs/week closest to blood draw PHYSACT_INT_LEISURE Intensity of current leisure time activity (if METs not available) closest to blood draw PHYSACT_METS_OCCUP Current occupational activity, MET hrs/week closest to blood draw PHYSACT_HRS_OCCUP Current occupational activity, avg. hrs/week closest to blood draw PHYSACT_INT_OCCUP Intensity of current occupational activity (if METs not available) closest to blood draw PHYSACT_METS_TOTAL Current total activity, MET hrs/week (if leisure/occupation is not specified) closest to blood draw PHYSACT_HRS_TOTAL Current total activity, avg. hrs/week (if leisure/occupation is not specified) closest to blood draw PHYSACT_INT_TOTAL Intensity of current total activity (if METs not closest to blood draw PHYSACT_YR Year of data collection (PHYSACT data) closest to blood draw SUN EXPOSURE/SUSCEPTIBILITY HEIGHT prior to index date SMOKE_YR_MR BIRTHPLACE Birthplace any SMOKE _PACKYRS_CIGARETTE_MR Cigarette smoking pack years index date or most recent prior to index date Year of data collection (SMOKE_MR data) index date or most recent SUNBURN_YR prior to index date study protocol DRAWTOCENT MATCH_DESCRIPTION factors. This would be particularly important if the matching protocol differed from the LC3 draw (hours) Description of variables used as matching FASTTIME Amount of time spent fasting prior to blood blood draw Amount of time between blood draw and centrifuge/processing (minutes) blood draw HX_HIGHBP least 8 hours) FASTING Subject was fasting prior to blood draw (for at blood draw HX_DIAB DRAWTIME Time of blood draw blood draw History of adult onset diabetes closest to blood draw History of high blood pressure closest to blood draw HX_YR Year of data collection (HX data) closest to blood draw LIFESTYLE INFORMATION GENERAL INFORMATION SMOKE_STATUS_CIGARETTE Cigarette smoking status closest to blood draw SMOKE_INT_CIGARETTE_MR Smoking intensity (number of cigarettes per day CPD) (note: we would like these data for individuals who indicated they were ever smokers, current smokers, or former smokers) index date or most recent prior to index date SMOKE_DUR_CIGARETTE_MR Smoking duration (number of years smoked cigarettes regularly) index date or most recent prior to index date SMOKE _PACKYRS_PIPE_MR Pipe smoking pack years index date or most recent prior to index date SMOKE_AGE_START_PIPE_MR Age at which the subject started pipe smoking index date or most recent prior to index date SMOKE_AGE_QUIT_PIPE_MR Age at which the subject stopped pipe smoking index date or most recent HAIRCOLOR Hair color any SUNBURN Blistering sun burn closest to blood draw Year of data collection (SUNBURN) closest to blood draw ANTHROPOMETRIC MEASURES Height (cm) any HEIGHT_SOURCE Source of height data any HEIGHT_YR Year of data collection (Height data) closest to blood draw WEIGHT Weight (kg) closest to blood draw WEIGHT_SOURCE Source of weight data closest to blood draw WEIGHT_YR Year of data collection (WEIGHT data) closest to blood draw WHR Waist-to-hip ratio (WHR) closest to blood draw WHR_SOURCE Source of WHR data closest to blood draw WHR_YR Year of data collection (WHR data) closest to blood draw WEIGHT_YA Weight (kg) in young adulthood any WEIGHT_YA_AGE Age for which weight in young adulthood was assessed (eg, 18, 20) any WEIGHT_YA_YR Year of data collection (WEIGHT_YA data) any DIETARY INFORMATION FOOD AND NUTRIENT INTAKE (AT TIME OF BLOOD DRAW) DIET_KCAL Current diet: total caloric/energy intake including alcoholic beverages, kcal/day Description of whether individuals were MEDS_YR excluded as cases and potential controls if they RACE_ADDITION FOLLOW-UP_EXCL_CRITERIA were diagnosed with a different cancer after any Description of other race any Year of data collection (MEDS data) closest to blood draw HISTORY OF DISEASE (AT TIME OF BLOOD DRAW) SMOKE _PACKYRS_CIGAR Cigar smoking pack years closest to blood draw SMOKE_INT_PIPE_MR Smoking intensity (number of pipes per day CPD) (note: we would like these data for individuals who indicated they were ever baseline and during follow-up prior to diagnosis STUDYCTR of the cancer of interest Study center any HX_ANYCANCER History of any cancer (except nonmelanoma skin cancer) DRAWDATE Date of blood draw blood draw closest to blood draw SMOKE_AGE_START_CIGAR Age at which the subject started cigar smoking closest to blood draw smokers, current smokers, or former smokers) index date or most recent prior to index date EYECOLOR Eye color any closest to blood draw TYPE_DIETQUEST Type of dietary Questionnaire (EX: FFQ, semi-quantitative dietary questionnaire, 24h-recalls, 7-day records, food propensity questionnaire) closest to blood draw DIET_PROT Current diet: total protein, g/day (average) closest to blood draw DIET_CARB Current diet: carbohydrates, g/day (average) closest to blood draw DIET_FAT Current diet: total fat, g/day (average) closest to blood draw DIET_SFAT Current diet: saturated fat, g/day (average) closest to blood draw DIET_MFAT Current diet: monounsaturated fat, g/day (average) closest to blood draw DIET_PFAT Current diet: polyunsaturated fat g/day (average) closest to blood draw DIET_CHORL Current diet: Cholesterol g/day closest to blood draw DIET_AFAT Current diet: animal fat, g/day (includes fat from all animal sources including meat, dairy products and eggs) closest to blood draw DIET_PLFAT Current diet: fat plant sources, g/day closest to blood draw DIET_STARCH Current diet: starch g/day closest to blood draw DIET_SUGAR Current diet: sugars g/day closest to blood draw DIET_DIETARY_FIBER Current diet: dietary fiber g/day closest to blood draw DIET_VITD Current diet: vitamin D from foods, μg/day closest to blood draw DIET_CA Current diet: calcium from foods, mg/day closest to blood draw DIET_FOL Current diet: folate from foods, μg/day closest to blood draw DIET_TOTAL_FOL Current diet: folate from foods & supplements, μg folate equivalents/day closest to blood draw DIET_METHIONINE Current diet: methionine from foods, mg/day closest to blood draw DIET_TOTAL_METHIONINE Current diet: methionine from foods & supplements, mg/day closest to blood draw DIET_MG Current diet: magnesium from foods, mg/day closest to blood draw DIET_P Current diet: phosphorus from foods, mg/day closest to blood draw DIET_RET Current diet: retinol from foods, μg/day closest to blood draw DIET_BETACAROTENE Current diet: beta-carotene from foods, μg/day closest to blood draw DIET_IRON Current diet: iron from foods, mg/day closest to blood draw DIET_POTASSIUM Current diet: potassium from foods, mg/day closest to blood draw DIET_RIBOFLAVIN Current diet: riboflavin (B2) from foods, mg/day closest to blood draw DIET_THIAMIN Current diet: thiamin (B1) from foods, mg/day closest to blood draw DIET_VITB6 Current diet: vitamin (B6) from foods, mg/day closest to blood draw DIET_VITB12 Current diet: vitamin (B12) from foods, μg/day closest to blood draw DIET_VITC Current diet: vitamin C from foods, mg/day closest to blood draw DIET_VITE Current diet: vitamin E from foods, mg/day closest to blood draw FOOD_DAIRY DRAWAGE Age at blood draw blood draw HX_LUNGCANCER History of Lung cancer closest to blood draw SMOKE_DUR_PIPE_MR Age at which the subject stopped cigar DATE_RECR Date of recruitment any SMOKE_AGE_QUIT_CIGAR smoking closest to blood draw Smoking duration (number of years smoked pipes regularly) index date or most recent prior to index date DIET_PROTAN Current diet: protein from animal sources, g/day closest to blood draw DIET_PROTPL AGE_RECR Age at recruitment any Current diet: protein from plant sources, g/day closest to blood draw Current diet: total dairy (includes milk, cheese, yogurt, butter, margarine, creamers, ice cream, frozen yogurt, chocolate milk, and milkshakes), g/day (average) closest to blood draw FOOD_VEG Current diet: vegetables (EXCLUDING POTATOES), g/day closest to blood draw FOOD_LEAFYVEG Current diet: leafy vegetables (lettuce, spinach, Swiss chard leaf, watercress), g/day closest to blood draw FOOD_CRUCIF Current diet: total cruciferous vegetables (broccoli, cauliflower, kale, cabbage, etc), g/day (average) closest to blood draw FOOD_OLIVES Current diet: olives intake (green olives, black olives, stuffed olive), g/day closest to blood draw FOOD_FRUIT_NUTS closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw closest to blood draw most recent most recent most recent most recent most recent most recent most recent Never smokers 539 1.00 [reference] 539 1.00 [reference] 539 1.00 [reference] index date or most recent prior to index date index date index date index date index date index date index date index date index date index date index date closest to blood draw closest to blood draw closest to blood draw closest to blood draw Former smokers 489 1.06 (1.01 -1.11) 0.02 489 1.02 (0.98 -1.07) 0.33 489 0.98 (0.95 -1.01) Current smokers 456 1.00 (0.95 -1.05) 0.85 456 1.08 (1.04 -1.13) 0.001 456 0.95 (0.92 -0.98) log2-cotinine b 456 0.99 (0.97 -1.00) 0.12 456 1.03 (1.01 -1.05) 0.001 456 0.98 (0.96 -0.99) Alcohol intake at recruitment c 995 1.01 (0.99 -1.03) 0.72 995 1.03 (1.01 -1.05) 0.003 995 1.00 (0.99 -1.02) Following Vitamin B2 (nutrient) 995 1.09 (1.04 -1.14) 2x10 -4 995 0.88 (0.85 -0.92) 3x10 -10 995 1.04 (1.00 -1.07) Vitamin B6 (nutrient) 995 1.05 (0.99 -1.11) 0.06 995 0.88 (0.84 -0.93) 2x10 -7 995 1.05 (1.01 -1.10) individual intake of fruits, nuts and seeds, g/day closest to blood draw FOOD_FRUITS individual intake of fruits, g/day FOOD_CEREALS Current diet: cereal and cereal products intake, g/day FOOD_FORTCER Current diet: fortified cereals (Total, Product 19, Most, etc.), g/day (average) FOOD_MEAT individual intake of meat and meat products (including processed meat), g/day FOOD_REDMEAT individual intake of red meat, g/day FOOD_POULTRY individual intake of poultry, g/day FOOD_PROCMEAT Current diet: total processed meat (lunchmeat, bacon, sausage, etc.), g/day (average) FOOD_FISH_SHELLFISH individual intake of fish and shellfish, g/day FOOD_FISH individual intake of fish, g/day FOOD_OFISH Current diet: fatty/oily fish (dark meat fish such as salmon, tuna, mackerel, etc.), g/day (average) FOOD_SOY Current diet: soy, soy milk, tofu, g/day (average) DIET_YR Year of data collection (DIET data) SUPPLEMENT USE (AT TIME OF BLOOD DRAW) SUPP_ANY Any supplement use ALC_ETOH Current alcohol use (any type), ethanol g/day ALC_BEER_ETOH Current alcohol use (beer), ethanol g/day ALC_WINE_ETOH Current alcohol use (wine), ethanol g/day ALC_SPIRITS_ETOH Current alcohol use (spirits), ethanol g/day ALC_LIQ_ETOH Current alcohol use (liquor), ethanol g/day ALC_YR Year of data collection (ALC data) DEATH AND FOLLOW-UP INFORMATION DEATH Deceased DEATH_DATE Death date DEATH_CAUSE Cause of death DEATH_CODING Coding system used for DEATH_CAUSE DEATH_CODING_SOURCE Source of the death information DEATH_YR Year of data collection (DEATH data) LAST_FUP Date of last follow-up LUNG CANCER OUTCOME INFORMATION DIAGNOSISAGE Age at diagnosis (for cases or of matched case for controls), if applied LUNG_CANCER_CASE LUNG cancer case LUNG_CANCER_ICD LUNG cancer ICD code LUNG_CANCER_ICD_CODING Coding system used for LUNG_CANCER_ICD LUNG_CANCER_DATE Date of diagnosis of lung cancer LUNG_CANCER_HIST LUNG cancer histology LUNG_CANCER_HIST_CODING Coding system used for LUNG_CANCER_HIST LUNG_CANCER_GRADE LUNG cancer grade LUNG_CANCER_GRADE_CODING Coding system used for LUNG_CANCER_GRADE LUNG_CANCER_STAGE LUNG cancer stage LUNG_CANCER_STAGE_CODING Coding system used for LUNG_CANCER_STAGE ADDITIONAL VARIABLES HRT HRT_DUR HRT_ESTALONE HRT_PROGESTIN HORMONE THERAPY USE (AT TIME OF BLOOD DRAW) Hormone therapy use Duration of current hormone therapy use (years) Any use of estrogen alone with estrogen) Folate (nutrient) 0.21 995 0.89 (0.86 -0.93) 4x10 -8 995 1.04 (1.01 -1.08) 995 1.02 (0.97 -1.08) Vitamin B12 (nutrient) 995 1.08 (1.04 -1.11) 2x10 -7 995 0.92 (0.90 -0.95) 1x10 -8 995 1.02 (1.00 -1.04) Vegetables 995 1.02 (0.99 -1.05) 0.13 995 0.96 (0.94 -0.98) 2x10 -4 995 1.01 (1.00 -1.03) Leafy vegetables 959 1.01 (1.00 -1.03) 0.09 959 0.99 (0.98 -1.00) 0.11 959 1.00 (0.99 -1.01) Fruiting vegetables 992 0.99 (0.97 -1.01) 0.23 992 0.98 (0.97 -1.00) 0.02 992 1.00 (0.99 -1.02) Root vegetables 950 1.01 (1.00 -1.03) 0.06 950 0.99 (0.98 -1.00) 0.40 950 1.00 (0.99 -1.01) Dairy products 992 1.04 (1.02 -1.06) 8x10 -5 992 0.97 (0.96 -0.99) 9x10 -4 992 1.01 (0.99 -1.02) Any use of progestin (alone or in combination Meat products 995 1.03 (1.01 -1.05) 0.002 995 0.96 (0.94 -0.97) 1x10 -6 995 1.01 (1.00 -1.02)	0.21 0.002 9x10 -5 0.09 0.02 0.003 0.01 0.04 0.09 0.72 0.4 0.92 0.16

  1-6 / 6.1-12 / 12.1-24 / 24.1-60 / 60.1-96 in men or >60 in women / >90 in men. Adjusted for case-control status, age, sex, country and cotinine (in quartiles) among all controls.Hazard ratios and 95% Confidence Intervals of all-cause mortality among Head and Neck Cancer (HNC) and Esophagus Squamous Cell Carcinoma (ESCC) cases for plasma levels of Vitamins B2, B6, Folate, B12, Homocysteine and Methionine Vital status among cancer cases of the head and neck and esophagus at the last follow-up. c Assessed by analysing HNC cases by Cox-proportional hazards regression, adjusting for country, sex, and age at diagnosis. Assessed by analysing ESCC cases by Cox-proportional hazards regression, adjusting for country, sex, and age at diagnosis. f Assessed by analysing ESCC cases by Cox-proportional hazards regression, adjusting for country, sex, and age at diagnosis, and further by educational attainment (5 groups), smoking status (never/former/current/missing), plasma cotinine (quartiles defined among current smokers) and alcohol intake at recruitment (g/day). g Quartile cut-off points were determined based on the plasma level distribution of each biomarker among 516 individually matched controls of the head and neck and esophagus.

	Quartile (Range) Riboflavin (Vitamin B2), nmol/L g Decesaed b Quartile (Range) Case b Control b	Head and neck cancer a Odds Ratio (95% Confidence Interval) Esophagus Squamous Cell Carcinoma Head and neck cancer a Esophagus Squamous Cell Carcinoma Minimally adjusted c Adjusted for risk factor d Minimally adjusted e Adjusted for risk factor f Matched controls unadjusted c Alive b (n=323/323) c Matched controls adjusted d (n=323/323) d All controls combined adjusted e (n = 323/923) e Matched controls unadjusted f (n=68/68) f All controls Matched controls combined adjusted d adjusted g (n=68/68) d n = (68/923) g
	1 (2.5 -9.4) Riboflavin (Vitamin B2), nmol/L h 75 (27.1%)	85 (31.3%)			1[Reference]	1[Reference]	1[Reference]	1[Reference]
	2 (9.5 -13.1) 1 (2.5 -9.4)	54 (19.5%) 140 (29.2%) 122 (25.5%) 47 (17.3%) 1[Reference] 1.26 (0.78 -2.03) 1[Reference]	1.32 (0.81 -2.16) 1[Reference]	0.76 (0.28 -2.09) 1[Reference] 1[Reference] 0.59 (0.18 -1.97) 1[Reference]
	3 (13.2 -21.3) 2 (9.5 -13.1)	81 (29.2%) 90 (18.8%) 115 (24.0%) 76 (27.9%) 0.66 (0.42 -1.03) 1.07 (0.69 -1.65) 0.83 (0.48 -1.43)	1.13 (0.72 -1.78) 0.81 (0.54 -1.21)	0.83 (0.32 -2.12) 0.51 (0.17 -1.54) 0.31 (0.03 -2.76) 1.07 (0.34 -3.32) 0.51 (0.21 -1.25)
	4 (21.4 -199) 3 (13.2 -21.3)	67 (24.2%) 135 (28.2%) 119 (24.8%) 64 (23.5%) 1.07 (0.69 -1.66) 1.05 (0.62 -1.77) 1.50 (0.88 -2.55)	1.30 (0.74 -2.28) 1.17 (0.79 -1.73)	0.85 (0.33 -2.18) 0.68 (0.25 -1.88) 0.42 (0.05 -3.41) 1.03 (0.33 -3.25) 0.96 (0.44 -2.06)
	P for trend h 4 (21.4 -199) P for trend i Pyridoxal 5'-phosphate (Vitamin B6), nmol/L g 114 (23.8%) 123 (25.7%) 0.67 (0.42 -1.07) 0.21 Pyridoxal 5'-phosphate (Vitamin B6), nmol/L h	0.76 1.20 (0.67 -2.15) 0.22	0.29 1.22 (0.78 -1.89) 0.29	0.92 0.73 (0.26 -2.04) 0.43	1.14 (0.19 -6.73) 0.69	1.17 (0.51 -2.69) 0.55 0.46
	1 (7.2 -25.6) 1 (7.2 -25.6)	80 (28.9%) 150 (31.3%) 122 (25.5%) 85 (31.3%) 1[Reference]	1[Reference] 1[Reference]	1[Reference] 1[Reference]	1[Reference] 1[Reference]	1[Reference] 1[Reference] 1[Reference]
	2 (25.7 -34.5) 2 (25.7 -34.5)	59 (21.3%) 106 (22.1%) 115 (24.0%) 66 (24.3%) 0.72 (0.46 -1.12) 0.80 (0.50 -1.27) 0.85 (0.5 -1.44)	0.81 (0.49 -1.32) 0.97 (0.65 -1.44)	2.02 (0.76 -5.36) 0.86 (0.32 -2.31) 1.51 (0.34 -6.64) 3.65 (1.13 -11.81) 0.98 (0.42 -2.28)
	3 (34.6 -47.6) 3 (34.6 -47.6)	64 (23.1%) 104 (21.7%) 119 (24.8%) 61 (22.4%) 0.68 (0.44 -1.06) 0.89 (0.56 -1.40) 0.99 (0.57 -1.7)	0.91 (0.56 -1.48) 1.10 (0.73 -1.66)	1.78 (0.67 -4.71) 0.92 (0.32 -2.60) 0.93 (0.17 -5.11) 3.26 (1.02 -10.46) 1.28 (0.55 -2.98)
	4 (47.7 -272) 4 (47.7 -272) P for trend h P for trend i	74 (26.7%) 119 (24.8%) 123 (25.7%) 60 (22.1%) 0.75 (0.48 -1.16) 0.96 (0.60 -1.53) 0.98 (0.57 -1.7) 0.87 0.23 0.83	0.96 (0.59 -1.56) 1.01 (0.67 -1.53) 0.87 0.68	1.73 (0.71 -4.19) 1.17 (0.46 -2.94) 1.96 (0.40 -9.48) 2.66 (0.96 -7.39) 2.36 (1.08 -5.18) 0.51 0.26 0.86 0.72 0.06
	Serum folate (vitamin B9), nmol/L g Serum folate (vitamin B9), nmol/L h
	1 (0.3 -9.1) 1 (0.3 -9.1) 2 (9.2 -12.8)	73 (26.4%) 152 (31.7%) 117 (24.4%) (38.2%) 104 1[Reference] 101 (21.1%) 121 (25.3%) 0.50 (0.32 -0.81) 1[Reference] 1[Reference] 0.63 (0.35 -1.13)	1[Reference] 1[Reference] 0.81 (0.54 -1.21)	1[Reference] 1[Reference] 0.89 (0.31 -2.54) 5.09 (0.72 -35.75) 1[Reference] 1[Reference] 1[Reference] 1.05 (0.49 -2.28)
	2 (9.2 -12.8) 3 (12.9 -18.1)	64 (23.1%) 106 (22.1%) 118 (24.6%) 52 (19.1%) 0.58 (0.36 -0.96) 1.37 (0.85 -2.21) 0.73 (0.40 -1.33)	1.40 (0.86 -2.27) 1.09 (0.73 -1.65)	1.16 (0.48 -2.81) 0.61 (0.20 -1.84) 1.92 (0.30 -12.34) 1.83 (0.68 -4.89) 0.94 (0.40 -2.20)
	3 (12.9 -18.1) 4 (18.2 -109)	71 (25.6%) 120 (25.1%) 123 (25.7%) 53 (19.5%) 0.47 (0.28 -0.79) 1.43 (0.90 -2.26) 0.62 (0.33 -1.16)	1.40 (0.87 -2.25) 0.93 (0.61 -1.42)	0.77 (0.30 -2.01) 0.96 (0.30 -3.09) 6.71 (0.63 -71.05) 1.20 (0.41 -3.51) 0.90 (0.40 -2.01)
	4 (18.2 -109) P for trend i P for trend h Cobalamin (Vitamin B12), pmol/L h 69 (24.9%)	63 (23.2%) 2x10 -4	0.98 (0.58 -1.66) 0.009 0.97	1.01 (0.58 -1.77) 0.32 0.98	1.06 (0.42 -2.69) 0.66 0.78	0.27	1.11 (0.44 -2.83) 0.97 0.91
	Cobalamin (Vitamin B12), pmol/L g 1 (75.1 -265) 139 (29.0%) 119 (24.8%)	1[Reference]	1[Reference]	1[Reference]	1[Reference]	1[Reference]	1[Reference]
	1 (75.1 -265) 2 (266 -328)	84 (30.3%) 121 (25.3%) 120 (25.1%) 70 (25.7%) 0.79 (0.51 -1.23) 1[Reference] 1.01 (0.59 -1.72)	1[Reference] 0.83 (0.56 -1.23)	1[Reference] 1.20 (0.39 -3.74) 1.17 (0.23 -5.90) 1[Reference] 1.08 (0.49 -2.36)
	2 (266 -328) 3 (329 -391)	65 (23.5%) 86 (18.0%) 120 (25.1%) 71 (26.1%) 0.56 (0.34 -0.91) 0.87 (0.56 -1.34) 0.59 (0.32 -1.07)	0.90 (0.57 -1.41) 0.64 (0.42 -0.98)	1.24 (0.51 -3.01) 0.80 (0.26 -2.42) 1.41 (0.31 -6.32) 0.91 (0.33 -2.52) 0.92 (0.41 -2.07)
	3 (329 -391) 4 (392 -2737)	49 (17.7%) 133 (27.8%) 120 (25.1%) 53 (19.5%) 0.86 (0.53 -1.39) 0.74 (0.45 -1.21) 1.06 (0.59 -1.90)	0.73 (0.44 -1.21) 0.89 (0.60 -1.31)	0.96 (0.39 -2.39) 1.16 (0.36 -3.75) 1.02 (0.21 -4.94) 0.66 (0.23 -1.86) 1.05 (0.49 -2.27)
	4 (392 -2737) P for trend i		79 (28.5%)	78 (28.7%)	0.89	0.60 (0.38 -0.96) 0.78	0.55 (0.34 -0.90) 0.70	1.74 (0.78 -3.87) 0.88	0.73	1.18 (0.45 -3.08) 0.86
	P for trend h Homocysteine, μmol/L h							0.06	0.06	0.17	0.49
	Homocysteine, μmol/L g 1 (4.9 -8.3) 101 (21.1%)	122 (25.5%)	1[Reference]	1[Reference]	1[Reference]	1[Reference]	1[Reference]	1[Reference]
	1 (4.9 -8.3) 2 (8.4-10.1)	53 (19.1%) 114 (23.8%) 122 (25.5%) 66 (24.3%) 1.40 (0.86 -2.25) 1[Reference] 1.47 (0.83 -2.60)	1[Reference] 1.33 (0.88 -2.03)	1[Reference] 0.9 (0.31 -2.62) 0.62 (0.12 -3.13) 1[Reference] 1.10 (0.48 -2.54)
	2 (8.4-10.1) 3 (10.2 -12.4)	62 (22.4%) 88 (18.4%) 117 (24.4%) 68 (25.0%) 1.15 (0.70 -1.89) 1.00 (0.59 -1.70) 1.06 (0.59 -1.94)	1.01 (0.59 -1.74) 0.91 (0.58 -1.44)	0.93 (0.38 -2.25) 1.23 (0.42 -3.58) 1.05 (0.23 -4.79) 0.62 (0.23 -1.67) 0.84 (0.34 -2.05)
	3 (10.2 -12.4) 4 (12.5 -64.9) 4 (12.5 -64.9) P for trend i P for trend h Methionine, μmol/L h	58 (20.9%) 176 (36.7%) 118 (24.6%) 46 (16.9%) 3.21 (1.84 -5.62) 1.39 (0.82 -2.34) 2.61 (1.32 -5.17) 104 (37.5%) 92 (33.8%) 1.53 (0.94 -2.50) 7x10 -5 0.007 0.44	1.36 (0.80 -2.32) 1.89 (1.22 -2.91) 1.42 (0.85 -2.37) 0.003 0.62	1.27 (0.49 -3.28) 1.72 (0.64 -4.64) 0.99 (0.23 -4.24) 0.98 (0.34 -2.81) 1.85 (0.80 -4.29) 1.19 (0.52 -2.74) 0.06 0.53 0.07 1.03 (0.40 -2.63) 0.39 0.25
	1 (12.6 -21.8) Methionine, μmol/L g 138 (28.8%) 1 (12.6 -21.8) 83 (30.0%) 121 (25.3%) 72 (26.5%) 1[Reference] 2 (21.9 -25.1) 103 (21.5%) 118 (24.6%) 0.74 (0.50 -1.12) 1[Reference] 1[Reference] 0.96 (0.59 -1.56) 2 (21.9 -25.1) 61 (22.0%) 58 (21.3%) 0.84 (0.54 -1.32) 3 (25.2 -29.3) 118 (24.6%) 120 (25.1%) 0.86 (0.55 -1.34) 1.21 (0.7 -2.11) 3 (25.2 -29.3) 67 (24.2%) 74 (27.2%) 0.64 (0.39 -1.03) 4 (29.4 -62.9) 120 (25.1%) 120 (25.1%) 0.91 (0.56 -1.46) 1.4 (0.77 -2.54) P for trend i 0.42 0.36	1[Reference] 1[Reference] 0.86 (0.58 -1.29) 0.85 (0.54 -1.35) 0.92 (0.61 -1.38) 0.66 (0.41 -1.09) 1.06 (0.70 -1.61) 0.94	1[Reference] 1[Reference] 0.87 (0.35 -2.19) 4.18 (0.48 -36.12) 1[Reference] 1[Reference] 1[Reference] 0.69 (0.32 -1.50) 0.80 (0.31 -2.07) 0.57 (0.20 -1.59) 0.78 (0.13 -4.84) 0.87 (0.40 -1.92) 0.75 (0.28 -2.06) 0.93 (0.39 -2.22) 0.60 (0.19 -1.85) 1.50 (0.18 -12.27) 1.08 (0.49 -2.38) 1.12 (0.40 -3.14) 0.18 0.59 0.90
	4 (29.4 -62.9) a Adenocarcinoma excluded P for trend h b	66 (23.8%)	68 (25.0%)		0.69 (0.44 -1.11) 0.01	0.73 (0.45 -1.17) 0.02	1.89 (0.88 -4.04) 0.56	3.22 (1.31 -7.94) 0.15
	d	Assessed by analysing HNC cases by Cox-proportional hazards regression, adjusting for country, sex, and age at diagnosis, and further by educational attainment (5 groups),
	smoking status (never/former/current/missing), plasma cotinine (quartiles defined among current smokers) and alcohol intake at recruitment (g/day).

a Adenocarcinoma excluded b e h P for trend assessed by the base 2 logarithm of the serum levels Appendix 5-Supplementary table

3

for Article 1

Pyridoxal 5'-phosphate) [nmol/L] d Former smokers

  Supplementary figure 12 for Article 3 Forest plot showing stratified ORs of lung cancer for log2 of circulating vitamin B6 and folate by period of recruitment and by smoking status in US cohorts (n=2400 cases and matched controls). ORs were assessed by conditional logistic regression by including the base 2 logarithm of circulating concentrations (ORs indicate relative risks of a doubling in circulating concentrations), and where relevant adjusted for cotinine and education; the black squares and horizontal lines indicate the ORs and 95% Cis; the size of the black squares are proportional to the inverse variance of logistic regression estimates. indicates results of chi-square test assessing the null hypothesis of ORs being identical.

	73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 Cases a Controls a 994 20 157 152 195 123 39 229 79 994 994 994 994 436 341 91 242 200 82 298 457 174 393 567 609 385 117 343 134 182 174 34 By time from blood draw to diagnosis (p-heterogeneity= 0.33) c 1.53 38.8 1.79 2.86 1.15 0.67 7.34 1.08 1.09 1.76 1.22 2.00 2.00 1.88 1.30 1.24 1.55 1.56 1.92 1.54 1.74 1.02 1.59 1.32 1.56 1.67 3.51 1.28 1.61 2.22 2.18 1.12 study group OR b 1-35 months 36-75 months 76-107 months 108-151 months 111 115 104 108 227 251 239 267 2.01 1.73 2.35 1.17 73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 995 20 158 152 195 123 39 229 79 993 993 993 993 436 342 91 242 200 82 298 458 174 393 568 684 386 117 343 134 182 175 34 1.10 6 14 1.31 0.93 1.09 1.15 1.09 1.05 0.98 1.17 0.98 1.21 1.00 1.17 1.10 1.00 1.08 0.98 0.93 1.05 1.17 1.12 1.02 1.15 0.97 1.31 2.04 1.18 0.92 0.70 1.13 1.40 study group Cases a Controls a OR b 1-35 months 36-75 months 76-107 months 108-151 months By time from blood draw to diagnosis (p-heterogeneity= 0.35) 111 115 104 108 227 252 239 267 0.97 1.33 1.13 0.95 73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 995 20 158 152 195 123 39 229 79 995 995 995 995 436 342 91 242 200 82 277 458 174 393 568 609 386 117 343 134 182 175 34 1.11 2.25 1.07 0.66 1.26 1.30 1.56 1.22 0.93 1.33 1.06 0.82 1.06 1.27 1.14 1.43 1.11 1.00 0.50 1.12 1.09 1.27 1.20 1.08 1.10 1.18 2.10 1.11 0.97 1.06 0.89 3.24 study group Cases a Controls a OR b 1-35 months 36-75 months 76-107 months 108-151 months By time from blood draw to diagnosis (p-heterogeneity= 0.46) 111 115 104 108 227 252 239 267 1.09 1.18 0.88 1.29 73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 994 20 158 152 195 123 39 228 79 995 995 995 995 436 341 91 242 200 82 277 457 174 393 567 609 385 117 343 134 182 174 34 0.95 2 -14 0.72 0.42 1.05 1.46 0.70 1.10 1.55 1.02 1.01 0.78 1.05 0.95 1.07 1.36 0.92 0.82 1.10 0.86 0.91 1.09 0.91 0.95 0.91 0.99 1.04 1.15 1.22 0.77 0.72 1.01 study group Cases a Controls a OR b 1-35 months 36-75 months 76-107 months 108-151 months By time from blood draw to diagnosis (p-heterogeneity= 0.60) c 111 115 104 108 227 251 239 267 1.11 0.81 1.03 0.99 73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 995 20 158 152 195 123 39 228 79 995 995 995 995 436 342 91 242 200 82 298 458 174 393 568 609 386 117 343 134 182 175 34 0.97 0.26 1.46 0.87 0.90 1.74 0.36 1.22 0.33 0.93 0.96 0.91 0.96 0.88 0.97 0.59 1.26 0.99 1.79 0.83 1.04 0.83 1.03 1.00 0.82 1.24 1.84 1.00 2.03 0.53 0.48 2.18 0.768-1.22 1.17-1.98 0.00-4 67 0.89-3.59 1.46-5.59 0.63-2.09 0.23-1.92 0.50-110 0.57-2.07 0.34-3.49 1.00-3.09 0.79-1.88 1.32-3.02 1.19-3.37 1.08-3.28 0.80-2.11 0.51-3.00 0.80-3.01 1.03-2.35 0.65-5.63 1.00-2.37 1.13-2.70 0.51-2.04 1.06-2.39 0.92-1.90 1.13-2.15 1.00-2.76 1.23-10.00 0.78-2.10 0.57-4.59 1.03-4.78 1.16-4.10 0.34-3.70 95%CI 1.10-3.70 1.04-2.89 1.20-4.61 0.72-1.91 0.95-1.27 60 16 -5 44 0.90-1.92 0.60-1.44 0.76-1.56 0.80-1.67 0.45-2.61 0.76-1.46 0.50-1.90 0.87-1.58 0.76-1.26 0.96-1.53 0.73-1.36 0.91-1.52 0.86-1.40 0.67-1.48 0.76-1.53 0.76-1.27 0.51-1.70 0.81-1.37 0.94-1.44 0.77-1.62 0.81-1.29 0.96-1.40 0.80-1.17 1.03-1.65 1.23-3.40 0.91-1.52 0.54-1.54 0.46-1.07 0.76-1.68 0.68-2.92 95%CI 0.70-1.33 1.01-1.77 0.81-1.56 0.70-1.30 0.96-1.30 0.11-44.91 0.62-1.85 0.40-1.09 0.89-1.79 0.84-2.02 0.64-3.81 0.88-1.70 0.46-1.88 0.99-1.77 0.83-1.36 0.63-1.08 0.77-1.46 0.95-1.70 0.84-1.54 0.76-2.70 0.74-1.66 0.79-1.26 0.21-1.18 0.82-1.54 0.87-1.36 0.89-1.81 0.93-1.53 0.88-1.33 0.90-1.34 0.93-1.50 1.25-3.53 0.84-1.46 0.52-1.83 0.68-1.67 0.57-1.38 0.88-11.92 95%CI 0.79-1.51 0.87-1.61 0.61-1.29 0.94-1.77 0.81-1.10 0.0-4 23 0.44-1.19 0.26-0.66 0.76-1.47 0.89-2.41 0.21-2.37 0.78-1.55 0.72-3.31 0.75-1.38 0.79-1.30 0.60-1.00 0.77-1.44 0.72-1.26 0.81-1.41 0.83-2.24 0.63-1.36 0.63-1.05 0.58-2.08 0.65-1.15 0.72-1.16 0.76-1.58 0.72-1.14 0.76-1.18 0.75-1.11 0.77-1.27 0.69-1.57 0.85-1.56 0.73-2.04 0.48-1.24 0.47-1.10 0.50-2.03 95%CI 0.79-1.55 0.58-1.12 0.74-1.44 0.73-1.34 0.010-6.82 0.668-3.20 0.511-1.49 0.540-1.50 0.725-4.18 0.078-1.61 0.719-2.07 0.071-1.52 0.571-1.51 0.660-1.39 0.607-1.37 0.582-1.57 0.559-1.40 0.654-1.43 0.294-1.20 0.743-2.15 0.661-1.48 0.644-5.00 0.541-1.27 0.725-1.50 0.481-1.42 0.719-1.47 0.731-1.38 0.602-1.10 0.851-1.80 0.892-3.80 0.618-1.62 0.912-4.50 0.248-1.15 0.272-0.83 0.481-9.84 study group Cases a Controls a OR b 95%CI 0.2 1-35 months 36-75 months 76-107 months 108-151' months By time from blood draw to diagnosis (p-heterogeneity= 0.46) 111 115 104 108 227 251 239 267 0.99 0.85 1.39 0.79 0.614-1.61 0.557-1.29 0.801-2.40 0.467-1.33 73 145 110 67 95 110 45 59 233 36 143 195 68 197 230 295 148 78 107 40 65 96 42 995 20 158 152 195 123 39 229 79 995 995 995 995 436 342 91 242 200 82 298 458 174 393 568 609 386 117 343 134 182 175 34 1.05 2.45 0.58 0.80 1.88 1.49 7.92 0.53 1.25 1.35 1.23 0.86 0.44 1.37 1.75 1.43 1.81 0.78 0.10 1.47 1.06 0.99 0.92 1.16 1.11 0.93 1.17 1.34 1.75 2.39 0.45 1.91 0.731-1.51 0.5 0.5 0.5 0.147-41.4 0.150-2.25 0.269-2.40 0.772-4.60 0.562-3.96 0.363-175 0.248-1.12 0.09-16.36 0.639-2.87 0.653-2.32 0.460-1.60 0.229-0.83 0.665-2.81 0.881-3.48 0.408-4.99 0.678-4.84 0.416-1.46 0.014-0.72 0.761-2.85 0.604-1.87 0.357-2.73 0.487-1.74 0.721-1.86 0.704-1.75 0.491-1.78 0.351-3.92 0.655-2.75 0.511-5.96 0.821-6.98 0.172-1.17 0.17-21.12 study group Cases a Controls a OR b 95%CI 1-35 months 36-75 months 76-107 months 108-151 months By time from blood draw to diagnosis (p-heterogeneity= 0.59) c 111 115 104 108 227 252 239 267 1.01 1.06 1.87 0.84 0.490-2.06 0.494-2.27 0.777-4.50 0.386-1.83 Participating countries France 13 32.2 (11.2-314.8) 13 Italy 88 25.1 (11.9-58.2) 88 Spain 52 29.5 (17.3-53.3) 52 United Kingdom 67 35.5 (11.9-123.7) 67 The Netherlands 46 28.0 (14.2-77.4) 46 Greece 17 23.1 (9.0-64.9) 17 Germany 125 34.0 (15.0-104) 125 Odds ratio (95% confidence interval) 1.0 2.0 P trend : 0.001 5.0 1.0 2.0 Odds ratio (95% confidence interval) P trend : 0.19 1.0 2.0 5.0 P trend : 0.16 Odds ratio (95% confidence interval) 0.5 1.0 2.0 P trend : 0.48 Odds ratio (95% confidence interval) 0.5 1.0 2.0 5.0 P trend : 0.77 Odds ratio (95% confidence interval) 0.20 0.50 2.00 5.00 P trend : 0.78 Odds ratio (95% confidence interval) 31.4 (23.2-128.3) 31.7 (12.4-69.8) 37.4 (19.2-71.4) 38.6 (16.8-203.9) 26.5 (14.2-91.3) 28.5 (13.3-245) 35.6 (13.9-78.7) Sweden 32 30.9 (15.4-148.1) 32 43.2 (19.5-270.1) Denmark 112 30.6 (13-101.1) 112 46.6 (17.6-194.6) Norway 4 27.1 (9.9-39.2) 4 36.4 (19.8-51.6) Smoking status Never smokers 225 31.9 (13.9-104) 244 36.0 (16.2-98.0) Former smokers 160 33.7 (14.2-84.9) 180 37.7 (16.1-95.7) Current smokers 166 26.2 (11.7-65.1) 129 33.8 (13.7-150.1) Educational attainment Primary school 229 27.3 (13.2-66.9) 206 33.8 (13.6-127) Technical/professional school 124 31.7 (11.9-86.8) 136 37.9 (14-122.1) Secondary school 77 30.8 (12.1-66.4) 66 31.6 (16.7-89.2) Higher education 110 35.7 (16.2-117) 134 39.0 (17.0-110) Body mass index <18.5 2 25.9 (7.3-44.5) 2 34.9 (18.1-51.7) 18.5-25 179 31.9 (13-148.1) 221 36.0 (16.4-128.3) 25-30 247 32.8 (13.6-81.3) 241 37.0 (16.4-101.5) 30-35 99 27.5 (11.1-80.6) 69 36.4 (16.6-176.1) ≥35 29 26.0 (13.2-45.3) 23 20.4 (9.5-52.7) Waist to hip ratio 0.56-0.79 92 32.5 (11.9-89) 113 34.4 (16.2-127) 0.80-0.89 129 31.5 (13.7-116.6) 145 33.1 (13.3-121.9) 0.90-0.94 103 32.7 (13.9-66.2) 114 38.4 (16.6-145.8) 0.95-1.30 196 28.4 (12.9-86.8) 148 38.3 (15.7-101.1) Hypertension No 276 29.8 (13.6-91.6) 325 37.8 (14.2-128.3) Yes 192 31.0 (12.9-86.8) 140 33.0 (14.1-89.3) Alcohol intake Never drinkers 37 28.4 (10.3-172.5) 22 33.5 (18.9-72.5) Ever drinkers 509 30.8 (13.6-86.8) 530 36.7 (14.6-121.9) Alcohol intake at recruitment <5 g/day 248 27.9 (11.9-102.6) 221 31.7 (13.5-108.5) 5-20 g/day 156 30.0 (13.0-83.1) 173 37.9 (16.8-121.9) ≥20 g/day 152 34 (14.2-86.8) 162 41.3 (17.5-108.9) Stage of the tumour Localised 161 30.4 (13.9-102.6) Metastatic 45 30.5 (14.7-66.2) Metastatic regional 18 33.2 (12.9-183.9) Metastatic distant 27 27.9 (13.2-81.3) Unknown 305 30.7 (13.0-83.5) Participating countries France 1 (0.4%) 12 (3.9%) Italy 33 (13.1%) 55 (18%) Spain 37 (14.7%) 15 (4.9%) United Kingdom 21 (8.4%) (15.1%) The Netherlands 0 (0%) (15.1%) Greece 12 (4.8%) 5 (1.6%) Germany 95 (37.8%) 30 (9.8%) Sweden 0 (0%) (10.5%) Denmark 49 (19.5%) (20.7%) Norway 3 (1.2%) 1 (0.3%) Smoking status Never smokers 100 (39.8%) 125 (41%) Former smokers 68 (27.1%) (30.2%) Current smokers 81 (32.3%) (27.9%) Unknown 2 (0.8%) 3 (1%) Educational attainment Primary school 116 (46.2%) 113 (37%) Technical/professional school 61 (24.3%) (20.7%) Secondary school 24 (9.6%) (17.4%) Higher education 48 (19.1%) (20.3%) Unknown 2 (0.8%) 14 (4.6%) Body mass index <18.5 1 (0.4%) 1 (0.3%) 18.5-25 68 (27.1%) 111 (36.4%) 25-30 117 (46.6%) 130 (42.6%) 30-35 50 (19.9%) (16.1%) ≥35 15 (6%) 14 (4.6%) Waist to hip ratio 0.56-0.79 32 (12.7%) (19.7%) 0.80-0.89 58 (23.1%) (23.3%) 0.90-0.94 56 (22.3%) (15.4%) 0.95-1.30 102 (40.6%) (30.8%) Unknown 3 (1.2%) (10.8%) Hypertension No 132 (52.6%) 144 (47.2%) Yes 94 (37.5%) (32.1%) Unknown 25 (10%) (20.7%) Alcohol intake Never drinkers 20 (8%) 17 (5.6%) Ever drinkers 230 (91.6%) 279 (91.5%) Unknown 1 (0.4%) 9 (3%) Alcohol intake at recruitment <5 g/day 101 (40.2%) 147 (48.2%) 5-20 g/day 71 (28.3%) (27.9%) ≥20 g/day 79 (31.5%) (23.9%) Age at recruitment < 40 6 (2.4%) 7 (2.3%) 40-45 19 (7.6%) 16 (5.2%) 45-50 20 (8%) 27 (8.9%) 50-55 53 (21.1%) 79 (25.9%) 55-60 55 (21.9%) 66 (21.6%) 65-70 81 (32.3%) 81 (26.6%) 75-80 11 (4.4%) 21 (6.9%) > 80 6 (2.4%) 8 (2.6%) WHI SCCS PLCO NYUWHS CPS CLUE MEC WHS NHS HPFS PHS MCCS MDCS NSHDS ATBC HUNT SCS SCHS SMHS SWHS Overall log2-Vitamin B6 US cohorts EU/AU cohorts Asian cohorts 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 0.96 1.04 0.95 0.94 1.03 1.12 1.05 0.77 1.01 1.08 1.08 0.90 1.11 0.99 0.82 1.04 0.83 0.93 0.94 0.78 1.10 0.93-1.00 0.89-1.21 0.77-1.16 0.83-1.05 0.85-1.26 0.90-1.38 0.86-1.28 0.63-0.93 0.79-1.29 0.93-1.25 0.86-1.36 0.61-1.34 0.94-1.32 0.80-1.23 0.66-1.02 0.84-1.30 0.64-1.07 0.76-1.13 0.83-1.06 0.65-0.94 0.92-1.32 study group Cases Controls OR a 95%CI 0.6 0.8 1.0 1.2 Odds ratio (95 CI%) WHI SCCS PLCO NYUWHS CPS CLUE MEC WHS NHS HPFS PHS MCCS MDCS NSHDS ATBC HUNT SCS SCHS SMHS SWHS Overall log2-Folate US cohorts EU/AU cohorts Asian cohorts 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 0.94 1.17 1.08 0.95 0.86 1.10 0.96 0.84 1.02 1.00 1.12 0.66 0.87 0.90 0.78 1.10 0.98 0.85 0.86 0.81 1.07 0.90-0.97 0.91-1.50 0.84-1.38 0.81-1.12 0.67-1.12 0.86-1.41 0.79-1.16 0.65-1.10 0.83-1.26 0.86-1.16 0.90-1.40 0.43-0.99 0.73-1.04 0.70-1.16 0.61-0.99 0.81-1.49 0.72-1.33 0.72-1.02 0.73-1.02 0.66-0.98 0.89-1.28 study group Cases Controls OR a 95%CI 0.6 0.8 1.0 1.2 Odds ratio (95 CI%) WHI SCCS PLCO NYUWHS CPS CLUE MEC WHS NHS HPFS PHS MCCS MDCS NSHDS ATBC HUNT SCS SCHS SMHS SWHS Overall log2-Methionine US cohorts EU/AU cohorts Asian cohorts 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 5364 241 226 450 171 182 191 174 184 345 155 81 354 198 244 200 193 513 422 421 419 0.96 2.10 0.90 0.84 0.87 0.79 0.50 0.79 1.13 1.25 0.52 1.53 0.92 0.71 1.40 0.99 1.20 1.21 0.93 1.07 0.97 0.85-1.08 0.83-5.30 0.60-1.34 0.56-1.26 0.49-1.56 0.46-1.37 0.26-0.97 0.31-2.01 0.51-2.51 0.67-2.34 0.24-1.14 0.41-5.71 0.53-1.60 0.38-1.34 0.62-3.18 0.42-2.33 0.71-2.03 0.82-1.77 0.63-1.37 0.73-1.55 0.68-1.41 study group Cases Controls OR a 95%CI 0.5 1.0 2.0 Odds ratio (95 CI%) Appendix 31-never smokers former smokers current smokers never smokers former smokers current smokers Recruited before 1996 Log2-Vitamin B6 By smoking status (p-heterogeneity=0.65) b Log2-Folate By smoking status (p-heterogeneity=0.67) b 228 652 528 228 652 528 596 498 719 596 498 719 228 647 528 228 647 528 597 493 700 597 493 700 0.89 0.98 0.95 0.87 0.96 0.96 1.15 0.87 0.86 0.97 0.90 0.94 0.74-1.07 0.89-1.09 0.86-1.06 0.71-1.07 0.86-1.07 0.86-1.07 1.01-1.32 0.77-0.98 0.77-0.97 0.83-1.13 0.77-1.04 0.83-1.07 study group Large Cell Small Cell Squamous Cell Adeno 1-35months 36-72 months 72-120 months >120 months Men Women USA Europe Asia Overall 227 study group cases 4 > 59.10 117 122 1.06 (0.75 -1.51) 1.06 (0.74 -1.51) 1.06 (0.75 -1.52) study group 3 (33.36-59.10) 157 140 1.07 (0.79 -1.44) 1.09 (0.8 -1.47) 1.06 (0.78 -1.44) Case 1.23 1.35 1.05 1.10 1.34 1.23 0.37 1.10 1.54 1.03 1.83 1.07 1.28 0.99 2 (17.46-33.35) 206 229 0.91 (0.71 -1.17) 0.93 (0.73 -1.2) 0.91 (0.71 -1.17) OR a 1 (1.70-17.45) 340 329 1.00 (reference) 1.00 (reference) 1.00 (reference) Controls OR a 95%CI 0.7 0.8 0.9 1.0 1.1 0.8 0.9 1.0 1.1 1.2 1.3 Recruited after 1996 Case Controls OR a 95%CI (p-heterogeneity=0.001 ) b 817 195 145 227 445 8 85 211 200 163 167 200 287 1.36 1.37 1.33 1.12 1.49 1.38 0.39 1.12 1.59 1.30 2.03 1.28 1.35 1.11 1.0 1.5 2.0 2.5 622 p-heterogeneity=0.75 p-heterogeneity=0.21 p-heterogeneity=0.01 Current smokers (n=2519/2519) 2.0 Anouar Fanidi, 2016 Odds ratio (95 CI%) 1.0 1.5 Odds ratio (95 CI%) Current smokers P for trend e . . 0.97 0.95 0.98 Methionine [μmol/L] d 1 (11.83-22.77) 235 231 1.00 (reference) 1.00 (reference) 1.00 (reference) (n=2519/2519) 2 (22.78-26.16) 210 200 1.01 (0.77 -1.33) 1 (0.76 -1.32) 1.01 (0.76 -1.33) 3 (26.17-30.47) 188 177 1.03 (0.76 -1.38) 1.03 (0.76 -1.38) 1.03 (0.76 -1.38) p-heterogeneity=0.11 cases OR a 925 325 687 216 238 109 176 41 312 171 437 113 20 10 116 35 215 67 244 101 173 60 202 69 229 76 321 120 1.12 1.19 1.16 1.28 1.03 1.06 1.08 0.88 1.00 1.15 1.33 1.65 0.56 1.73 1.11 0.95 1.18 1.47 1.08 1.38 1.13 1.27 1.00 1.35 1.23 1.51 1.09 0.94 0.5 p-heterogeneity=0.21 p-heterogeneity=0.66 p-heterogeneity=0.47 4 > 30.47 187 212 0.85 (0.63 -1.14) 0.84 (0.62 -1.12) 0.84 (0.62 -1.12) p-heterogeneity=0.12 cases OR a Cases Controls OR (95%CI) OR (95%CI) P for trend e 0.39 0.32 0.36 OR (95%CI) Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] d 1 (5.08-29.89) 487 455 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (29.90-49.90) 386 387 0.96 (0.79 -1.17) 0.98 (0.8 -1.2) 0.96 (0.78 -1.18) 3 (49.91-90.05) 328 343 0.93 (0.76 -1.14) 0.95 (0.76 -1.17) 0.91 (0.74 -1.13) Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] d 1 (5.08-29.89) 221 192 1.00 (reference) 1.00 (reference) 1.00 (reference) Odds ratio (95 CI%) 161 99 62 84 47 30 3 13 34 62 58 49 24 30 1.33 1.42 1.22 1.35 1.21 1.56 0.33 1.10 1.50 1.17 1.72 1.29 1.33 0.96 0.5 1.0 2.0 p-heterogeneity=0.46 p-heterogeneity=0.37 p-heterogeneity=0.67 p-heterogeneity=0.36 cases Former smokers (n=1518/1518) OR a 4 > 90.05 322 338 0.92 (0.74 -1.14) 0.97 (0.77 -1.21) 0.97 (0.78 -1.22) 2 (29.90-49.90) 175 174 0.89 (0.66 -1.2) 0.91 (0.67 -1.24) 0.87 (0.64 -1.18) Odds ratio (95 CI%) 316 89 207 54 109 35 108 16 144 60 64 13 14 4 26 6 50 20 125 35 74 22 71 24 70 18 101 25 1.15 1.53 1.25 1.60 1.00 1.45 0.98 1.33 1.27 1.69 1.42 1.64 0.91 0.97 1.22 0.85 1.18 2.42 1.09 1.42 1.36 1.86 1.31 2.27 1.30 1.02 0.87 1.25 0.5 1.0 2.0 p-heterogeneity=0.62 p-heterogeneity=0.72 p-heterogeneity=0.76 p-heterogeneity=0.54 Odds ratio (95 CI%) cases P for trend e 0.32 0.61 3 (49.91-90.05) 150 156 0.83 (0.6 -1.13) 0.82 (0.59 -1.14) 0.74 (0.53 -1.03) 0.57 Former smokers Folate (Vitamin B9) [nmol/L] d 1 (1.70-17.45) 450 429 1.00 (reference) 1.00 (reference) 1.00 (reference) 4 > 90.05 129 153 0.7 (0.5 -0.98) 0.74 (0.53 -1.06) 0.75 (0.52 -1.06) P for trend e 0.02 0.04 0.04 (n=1518/1518) OR a 2 (17.46-33.35) 369 388 0.93 (0.76 -1.14) 0.96 (0.78 -1.19) Folate (Vitamin B9) [nmol/L] d 0.95 (0.77 -1.17) All ever 3 (33.36-59.10) 353 350 0.98 (0.78 -1.22) 1.01 (0.8 -1.27) 1 (0.8 -1.26) 1 (1.70-17.45) 185 164 1.00 (reference) 1.00 (reference) 1.00 (reference) smokers 4 > 59.10 351 356 0.98 (0.77 -1.25) 1.03 (0.81 -1.32) 1.03 (0.81 -1.32) P for trend e . . 0.75 0.96 0.97 Methionine [μmol/L] d 1 (11.83-22.77) 385 363 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (22.78-26.16) 375 367 0.94 (0.76 -1.16) 0.95 (0.77 -1.18) 0.95 (0.77 -1.18) 3 (26.17-30.47) 372 366 0.96 (0.77 -1.18) 0.95 (0.76 -1.19) 0.98 (0.79 -1.23) 4 > 30.47 391 427 0.85 (0.69 -1.05) 0.85 (0.69 -1.06) 0.85 (0.69 -1.06) 2 (17.46-33.35) 152 176 0.8 (0.57 -1.11) 0.85 (0.6 -1.18) 0.85 (0.6 -1.19) Smoking men 3 (33.36-59.10) 172 166 0.91 (0.64 -1.32) 0.98 (0.67 -1.42) 0.98 (0.67 -1.43) 4 > 59.10 166 169 0.89 (0.6 -1.32) 0.96 (0.64 -1.45) 0.97 (0.64 -1.47) P for trend e 0.93 0.80 0.87 Methionine [μmol/L] d 1 (11.83-22.77) 161 147 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (22.78-26.16) 161 133 1.06 (0.76 -1.49) 1.05 (0.75 -1.49) 1.03 (0.73 -1.47) (p-heterogeneity=0.80) b Odds ratio (95 CI%) 1.09-1.38 1.16-1.56 0.87-1.27 0.89-1.35 1.09-1.65 1.02-1.49 0.20-0.91 0.78-1.56 1.18-2.02 0.84-1.25 1.42-2.36 0.85-1.36 1.00-1.63 0.80-1.21 1.0 1.5 2.0 72 25 47 24 16 32 0 3 6 41 18 9 23 22 0.63 0.89 0.55 0.69 0.65 0.57 1.50 1.00 0.50 1.36 0.20 1.06 0.47 0.5 1.0 2.0 p-heterogeneity=0.15 p-heterogeneity=0.74 p-heterogeneity=0.59 p-heterogeneity=0.006 95%CI Overall 1.12 1.27 1.20 1.37 1.02 1.16 1.07 0.97 1.10 1.27 1.18 1.52 0.77 1.38 1.09 0.97 1.21 1.77 1.22 1.38 1.24 1.52 1.03 1.31 1.24 1.40 1.03 smokers Folate (Vitamin B9) [nmol/L] d cases Current P for trend 0.52 0.68 0.57 e OR a 4 > 90.05 122 139 0.85 (0.63 -1.15) 0.87 (0.65 -1.18) 0.86 (0.64 -1.17) 1.05 1.01-1.23 1.07-1.49 1.06-1.37 1.11-1.70 0.87-1.18 0.89-1.51 0.89-1.28 0.68-1.40 0.91-1.33 1.00-1.62 1.01-1.37 1.13-2.04 0.43-1.38 0.86-2.23 0.79-1.52 0.57-1.65 0.96-1.54 1.19-2.62 1.03-1.44 1.04-1.83 0.98-1.56 1.05-2.22 0.84-1.26 0.90-1.91 1.01-1.52 0.99-1.97 0.87-1.22 0.80-1.38 2 (29.90-49.90) 225 222 0.98 (0.75 -1.28) 1 (0.77 -1.3) 0.98 (0.75 -1.28) 3 (49.91-90.05) 150 162 0.91 (0.69 -1.21) 0.94 (0.7 -1.24) 0.91 (0.69 -1.21) 95%CI 1 (5.08-29.89) 323 297 1.00 (reference) 1.00 (reference) 1.00 (reference) (n=5364/5364) P for trend e 0.13 0.16 0.28 Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] d Odds ratio (95 CI%) 1.0 1.5 2.0 Overall 4 > 30.47 204 215 0.83 (0.61 -1.13) 0.87 (0.62 -1.21) 0.88 (0.63 -1.24) (n=5364/5364) Methionine [μmol/L] d 1 (11.83-22.77) 150 132 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (22.78-26.16) 165 167 0.87 (0.63 -1.2) 0.92 (0.65 -1.3) 0.89 (0.63 -1.27) 3 (26.17-30.47) 184 189 0.85 (0.62 -1.16) 0.85 (0.6 -1.18) 0.93 (0.66 -1.32) Odds ratio (95 CI%) 4 > 59.10 234 234 0.89 (0.62 -1.28) 0.97 (0.66 -1.43) 1.02 (0.7 -1.5) P for trend 0.51 0.83 0.88 e cases Never smokers (n=1327/1327) 3 (33.36-59.10) 196 210 0.84 (0.59 -1.19) 0.87 (0.6 -1.26) 0.94 (0.65 -1.37) OR a 5.0 P for trend e 0.12 0.09 0.14 3 (26.17-30.47) 162 155 0.96 (0.69 -1.33) 0.94 (0.67 -1.33) 0.99 (0.69 -1.4) Odds ratio (95 CI%) 269 56 69 21 200 35 46 8 63 26 160 22 5 0 4 2 13 6 141 26 44 10 65 6 79 18 81 22 1.10 1.45 1.40 1.96 1.02 1.25 1.29 1.22 1.15 1.13 1.04 2.53 3.00 2.00 4.00 1.37 3.26 1.27 1.26 1.47 3.44 0.89 0.57 1.28 1.52 1.02 1.74 0.5 1.0 2.0 p-heterogeneity=0.87 p-heterogeneity=0.26 p-heterogeneity=0.28 cases OR a P for trend e 0.32 0.52 0.61 Folate (Vitamin B9) [nmol/L] d 1 (1.70-17.45) 110 100 1.00 (reference) 1.00 (reference) 1.00 (reference) 2 (17.46-33.35) 163 159 0.92 (0.63 -1.32) 0.94 (0.64 -1.38) 1.02 (0.7 -1.51) p-heterogeneity=0.54 4 > 90.05 200 199 0.97 (0.71 -1.32) 1.03 (0.73 -1.43) 1.08 (0.77 -1.51) Odds ratio (95 CI%) Never smokers Vitamin B6 (1 (5.08-29.89) 164 158 1.00 (reference) 1.00 (reference) 1.00 (reference) 3 (49.91-90.05) 178 181 0.95 (0.69 -1.29) 0.94 (0.67 -1.3) 0.9 (0.65 -1.26) 4 > 30.47 191 240 0.69 (0.5 -0.94) 0.69 (0.49 -0.95) 0.68 (0.49 -0.95) P for trend e 0.02 0.01 0.03 (n=1327/1327) 2 (29.90-49.90) 161 165 0.94 (0.69 -1.28) 0.95 (0.68 -1.31) 0.95 (0.68 -1.32) Vitamin B6 (	1.4 5.0

a b P heterogeneity

Pyridoxal 5'-phosphate) [nmol/L] d Smoking women

  Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles) and education (in 7 categories). b) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of years of smoking (continuous). c) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of pack-years of smoking (continuous). d) Quartile cut-off points were determined based on the circulating level distribution of US matched controls. e) P for trend assessed by the base 2 logarithm of the circulating levels.

	1 (5.08-29.89) 2 (29.90-49.90) 3 (49.91-90.05) 4 > 90.05 P for trend e Folate (Vitamin B9) [nmol/L] 266 211 178 193 d 1 (1.70-17.45) 265 2 (17.46-33.35) 217 3 (33.36-59.10) 181 4 > 59.10 185 P for trend e Methionine [μmol/L] d 1 (11.83-22.77) 224 2 (22.78-26.16) 214 3 (26.17-30.47) 210 4 > 30.47 200 P for trend e Methionine [μmol/L] d 1 (12.32-23.17) 196 2 (23.18-27.39) 245 3 (27.39-32.22) 245 4 >32.22 311 P for trend e Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] 263 213 187 185 265 212 184 187 216 234 211 187 206 226 267 298 d 1.00 (reference) 1.02 (0.77 -1.33) 1 (0.76 -1.32) 1.12 (0.84 -1.49) 0.52 1.00 (reference) 1.05 (0.8 -1.36) 1.01 (0.76 -1.34) 1.05 (0.77 -1.42) 0.77 1.00 (reference) 0.86 (0.65 -1.13) 0.96 (0.73 -1.28) 1.06 (0.79 -1.41) 0.85 0.09 1.00 (reference) 1.28 (0.96 -1.72) 1.02 (0.76 -1.36) 1.16 (0.88 -1.52) 0.71 1 (4.81-20.49) 473 380 1.00 (reference) 2 (20.50-30.82) 293 292 0.89 (0.7 -1.12) 3 (31.23-46.99) 183 233 0.69 (0.53 -0.9) 4 > 47.00 151 195 0.69 (0.51 -0.92) P for trend e 0.003 Folate (Vitamin B9) [nmol/L] d 1 (0.17-10.59) 400 328 1.00 (reference) 2 (10.60-15.00) 296 292 0.84 (0.66 -1.05) 3 (15.01-21.00) 208 250 0.75 (0.58 -0.96) 4 >21.01 196 230 0.76 (0.59 -0.98) P for trend e 0.02 Methionine [μmol/L] d 1 (12.32-23.17) 225 225 1.00 (reference) 2 (23.18-27.39) 261 250 1.11 (0.85 -1.46) 3 (27.39-32.22) 279 301 0.94 (0.72 -1.24) 4 >32.22 335 324 1.06 (0.82 -1.38) P for trend e 0.97 a) P for trend Smoking men Vitamin B6 (	1.00 (reference) 1.03 (0.78 -1.36) 1.03 (0.78 -1.37) 1.15 (0.86 -1.54) 0.34 1.00 (reference) 1.07 (0.82 -1.39) 1.03 (0.77 -1.37) 1.08 (0.79 -1.48) 0.92 1.00 (reference) 0.88 (0.66 -1.16) 0.97 (0.72 -1.3) 1.07 (0.79 -1.43) 0.86 0.18 1.00 (reference) 1.37 (1.02 -1.86) 1.07 (0.79 -1.45) 1.21 (0.91 -1.6) 0.58 1.00 (reference) 0.89 (0.7 -1.13) 0.72 (0.55 -0.95) 0.75 (0.56 -1.01) 0.02 1.00 (reference) 0.88 (0.69 -1.12) 0.8 (0.62 -1.04) 0.82 (0.63 -1.07) 0.15 1.00 (reference) 1.22 (0.92 -1.61) 1.01 (0.76 -1.33) 1.12 (0.86 -1.46) 0.88	1.00 (reference) 1.03 (0.78 -1.36) 1.05 (0.79 -1.4) 1.16 (0.86 -1.55) 0.35 1.00 (reference) 1.04 (0.8 -1.36) 1.01 (0.76 -1.34) 1.07 (0.79 -1.46) 0.91 1.00 (reference) 0.89 (0.68 -1.18) 1 (0.75 -1.33) 1.07 (0.8 -1.44) 0.81 0.18 1.00 (reference) 1.33 (0.98 -1.8) 1.02 (0.75 -1.38) 1.22 (0.92 -1.61) 0.44 1.00 (reference) 0.96 (0.75 -1.22) 0.74 (0.56 -0.98) 0.77 (0.56 -1.04) 0.04 1.00 (reference) 0.89 (0.7 -1.13) 0.82 (0.63 -1.07) 0.81 (0.62 -1.06) 0.09 1.00 (reference) 1.19 (0.89 -1.58) 0.96 (0.72 -1.27) 1.12 (0.86 -1.47) 0.67

Pyridoxal 5'-phosphate) [nmol/L] d Smoking women

  Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles) and education (in 7 categories). b) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of years of smoking (continuous). c) Assessed by conditional logistic regression, conditioning on individual case set and adjusting for circulating cotinine (in quartiles), education (in 7 categories), and number of pack-years of smoking (continuous). d) Quartile cut-off points were determined based on the circulating level distribution of Asian matched controls. e) P for trend assessed by the base 2 logarithm of the circulating levels.

	1 (4.81-20.49) 2 (20.50-30.82) 3 (31.23-46.99) 4 > 47.00 P for trend e Folate (Vitamin B9) [nmol/L] 17 24 22 10 d 1 (0.17-10.59) 18 2 (10.60-15.00) 19 3 (15.01-21.00) 14 4 >21.01 22 P for trend e Methionine [μmol/L] d 1 (12.32-23.17) 33 2 (23.18-27.39) 21 3 (27.39-32.22) 9 4 >32.22 10 P for trend e Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] 11 16 26 20 20 18 24 11 29 17 11 16 Controls d 1.00 (reference) 0.71 (0.22 -2.26) 0.37 (0.12 -1.17) 0.29 (0.08 -1.04) 0.04 1.00 (reference) 1.3 (0.39 -4.33) 0.65 (0.22 -1.91) 3 (0.9 -9.93) 0.17 1.00 (reference) 1.21 (0.44 -3.34) 0.64 (0.2 -2.05) 0.51 (0.2 -1.33) 0.34 OR (95%CI) 1 (4.37-23.93) 1300 1104 1.00 (reference) 2 (23.94-37.05) 911 962 0.83 (0.73 -0.94) 3 (37.06-62.50) 724 795 0.81 (0.7 -0.93) 4 > 62.51 698 772 0.78 (0.67 -0.91) P for trend e 0.003 Folate (Vitamin B9) [nmol/L] d 1 (0.17-10.92) 1154 1039 1.00 (reference) 2 (10.93-17.89) 948 957 0.91 (0.8 -1.04) 3 (17.90-34.92) 776 855 0.85 (0.74 -0.98) 4 > 34.97 755 782 0.87 (0.73 -1.03) P for trend e 0.03 Methionine [μmol/L] d 1 (10.63-22.89) 861 855 1.00 (reference) 2 (22.90-27.40) 902 849 1.07 (0.93 -1.23) 3 (27.40-31.00) 913 913 0.99 (0.86 -1.14) 4 > 31.01 957 1016 0.95 (0.82 -1.09) P for trend e 0.25 a) Cases All ever smokers Vitamin B6 (	1.00 (reference) 0.82 (0.25 -2.7) 0.47 (0.14 -1.57) 0.32 (0.09 -1.18) 0.07 1.00 (reference) 1.63 (0.46 -5.79) 0.59 (0.19 -1.84) 3.96 (1.03 -15.18) 0.16 1.00 (reference) 1.35 (0.48 -3.79) 0.7 (0.22 -2.24) 0.54 (0.2 -1.44) 0.42 OR (95%CI) 1.00 (reference) 0.85 (0.74 -0.97) 0.83 (0.72 -0.96) 0.83 (0.71 -0.97) 0.03 1.00 (reference) 0.93 (0.81 -1.06) 0.88 (0.76 -1.02) 0.92 (0.77 -1.1) 0.15 1.00 (reference) 1.09 (0.94 -1.25) 1.01 (0.87 -1.17) 0.96 (0.83 -1.11) 0.24	1.00 (reference) 0.78 (0.24 -2.51) 0.4 (0.12 -1.3) 0.31 (0.08 -1.14) 0.05 1.00 (reference) 1.66 (0.46 -5.99) 0.78 (0.25 -2.41) 3.86 (1.05 -14.21) 0.11 1.00 (reference) 1.27 (0.45 -3.56) 0.75 (0.23 -2.42) 0.54 (0.2 -1.44) 0.38 OR (95%CI) 1.00 (reference) 0.87 (0.76 -0.99) 0.82 (0.71 -0.95) 0.81 (0.7 -0.95) 0.02 1.00 (reference) 0.94 (0.82 -1.08) 0.89 (0.77 -1.03) 0.91 (0.76 -1.08) 0.10 1.00 (reference) 1.09 (0.94 -1.25) 1.02 (0.88 -1.18) 0.96 (0.83 -1.11) 0.44

Pyridoxal 5'-phosphate) [nmol/L] d Former smokers

		P for trend e			0.15	0.26	0.20
		Methionine [μmol/L]
		1 (4.37-23.93)	210	163	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (23.94-37.05)	266	279	0.74 (0.57 -0.97)	0.77 (0.58 -1.01)	0.77 (0.58 -1.03)
		3 (37.06-62.50)	270	288	0.72 (0.55 -0.94)	0.76 (0.57 -1.01)	0.7 (0.53 -0.94)
		4 > 62.51	378	394	0.73 (0.56 -0.95)	0.79 (0.6 -1.04)	0.77 (0.58 -1.02)
		P for trend e			0.17	0.41	0.31
		Folate (Vitamin B9) [nmol/L] d
		1 (0.17-10.92)	211	164	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (10.93-17.89)	200	215	0.69 (0.52 -0.93)	0.74 (0.54 -1.01)	0.74 (0.54 -1)
		3 (17.90-34.92)	275	281	0.69 (0.51 -0.92)	0.77 (0.56 -1.05)	0.79 (0.57 -1.08)
		4 > 34.97	438	464	0.63 (0.47 -0.86)	0.73 (0.53 -1.01)	0.72 (0.52 -1)
		P for trend e			0.05	0.26	0.18
		Methionine [μmol/L] d
		1 (10.63-22.89)	263	248	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (22.90-27.40)	279	267	0.98 (0.77 -1.26)	1.01 (0.78 -1.31)	1.01 (0.77 -1.31)
		3 (27.40-31.00)	298	300	0.93 (0.73 -1.18)	0.96 (0.75 -1.24)	0.99 (0.76 -1.28)
		4 > 31.01	284	309	0.86 (0.67 -1.1)	0.86 (0.66 -1.12)	0.87 (0.67 -1.13)
		P for trend e			0.09	0.08	0.15
		Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] d
		1 (4.37-23.93)	1090	941	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (23.94-37.05)	645	683	0.85 (0.74 -0.99)	0.87 (0.75 -1.01)	0.89 (0.76 -1.03)
		3 (37.06-62.50)	454	507	0.83 (0.7 -0.98)	0.85 (0.72 -1.01)	0.85 (0.72 -1.01)
		4 > 62.51	320	378	0.78 (0.64 -0.95)	0.81 (0.67 -0.99)	0.8 (0.65 -0.97)
	Current	P for trend e			0.005	0.03	0.01
	smokers	Folate (Vitamin B9) [nmol/L] d
		1 (0.17-10.92)	943	875	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (10.93-17.89)	748	742	0.96 (0.83 -1.12)	0.97 (0.83 -1.13)	0.98 (0.84 -1.14)
		3 (17.90-34.92)	501	574	0.88 (0.74 -1.04)	0.89 (0.75 -1.05)	0.9 (0.76 -1.06)
		4 > 34.97	317	318	0.97 (0.78 -1.21)	1.01 (0.8 -1.26)	0.98 (0.78 -1.23)

d

Pyridoxal 5'-phosphate) [nmol/L] d Smoking women

		00)	615	613	1.02 (0.86 -1.21)	1.03 (0.86 -1.22)	1.03 (0.87 -1.23)
		4 > 31.01	673	707	0.99 (0.84 -1.17)	1 (0.84 -1.19)	1 (0.84 -1.19)
		P for trend e			0.72	0.72	0.92
		Vitamin B6 (Pyridoxal 5'-phosphate) [nmol/L] d
		1 (4.37-23.93)	952	789	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (23.94-37.05)	593	655	0.76 (0.65 -0.89)	0.79 (0.67 -0.92)	0.82 (0.7 -0.96)
		3 (37.06-62.50)	461	497	0.81 (0.68 -0.96)	0.84 (0.7 -1.01)	0.82 (0.69 -0.99)
		4 > 62.51	344	409	0.68 (0.56 -0.84)	0.72 (0.59 -0.89)	0.7 (0.57 -0.87)
		P for trend e			<.0001	0.001	0.001
		Folate (Vitamin B9) [nmol/L] d
		1 (0.17-10.92)	799	693	1.00 (reference)	1.00 (reference)	1.00 (reference)
	Smoking men	2 (10.93-17.89) 3 (17.90-34.92) 4 > 34.97	677 489 385	687 562 408	0.88 (0.75 -1.03) 0.78 (0.66 -0.94) 0.78 (0.61 -0.98)	0.9 (0.77 -1.06) 0.82 (0.68 -0.99) 0.86 (0.67 -1.1)	0.92 (0.78 -1.08) 0.84 (0.7 -1.01) 0.82 (0.64 -1.05)
		P for trend e			0.005	0.05	0.02
		Methionine [μmol/L] d
		1 (10.63-22.89)	476	464	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (22.90-27.40)	538	490	1.11 (0.92 -1.33)	1.14 (0.94 -1.38)	1.13 (0.93 -1.37)
		3 (27.40-31.00)	618	624	0.97 (0.81 -1.17)	1.02 (0.84 -1.23)	1 (0.83 -1.21)
		4 > 31.01	718	772	0.92 (0.77 -1.1)	0.94 (0.79 -1.13)	0.94 (0.78 -1.13)
		P for trend e			0.16	0.17	0.32
		Vitamin B6 (1 (4.37-23.93)	348	315	1.00 (reference)	1.00 (reference)	1.00 (reference)
		2 (23.94-37.05)	318	307	0.96 (0.77 -1.2)	0.98 (0.78 -1.23)	0.96 (0.76 -1.2)
		3 (37.06-62.50)	263	298	0.81 (0.64 -1.03)	0.83 (0.65 -1.06)	0.82 (0.65 -1.05)
		4 > 62.51	354	363	0.94 (0.74 -1.19)	0.98 (0.77 -1.25)	0.97 (0.77 -1.24)
		P for trend e			0.96	0.71	0.91
		Folate

(Vitamin B9) [nmol/L]

		d
	1 (0.17-10.92)	355	346	1.00 (reference)	1.00 (reference)	1.00 (reference)
	2 (10.93-17.89)	271	270	0.98 (0.77 -1.25)	0.98 (0.76 -1.25)	0.97 (0.76 -1.24)
	3 (17.90-34.92)	287	293	0.99 (0.78 -1.28)	1 (0.77 -1.28)	1 (0.77 -1.28)
	4 > 34.97	370	374	1.01 (0.78 -1.31)	1.03 (0.79 -1.34)	1.03 (0.79 -1.33)
	P for trend e			0.89	0.93	0.90
	Methionine [μmol/L] d
	1 (10.63-22.89)	385	391	1.00 (reference)	1.00 (reference)	1.00 (reference)
	2 (22.90-27.40)	364	359	1.02 (0.83 -1.26)	1.02 (0.82 -1.26)	1.03 (0.83 -1.28)
	3 (27.40-31.00)	295	289	1.03 (0.82 -1.29)	1.01 (0.8 -1.27)	1.05 (0.83 -1.33)
	4 > 31.01	239	244	1.02 (0.8 -1.29)	1.02 (0.8 -1.31)	1.02 (0.8 -1.3)
	P for trend

.

Acknowledgement

Anouar Fanidi 1 *, David Muller 1,2 *, Jian-Min Yuan 3,4 *, Victoria L. Stevens 5 *, Stephanie J. Weinstein 6 *, Demetrius Albanes 6 *, Ross Prentice 7 *, Cynthia A. Thomsen 8 , Mary Pettinger 7 , Qiuyin Cai 9 , William J. Blot 9,10 , Jie Wu 9 , Alan A. Arslan 11 , Anne Zeleniuch-Jacquotte 12 ,, Marjorie L. McCullough 5 , Loic Le Marchand 13 , Lynne R. Wilkens 13 , Christopher A. Haiman 13 , Xuehong Zhang 14 , Jiali Han 15 , Meir J. Stampfer 14,15,16 , Stephanie A. Smith-Warner 15,16 , Edward Giovannucci 14,15,16 , Graham G. Giles 17,18 , Allison M Hodge 17 , Gianluca Severi 17,19,20,21 , Mikael Johansson 22 , Kjell Grankvist 22 , Arnulf Langhammer 23 , Steinar Krokstad 23 , Marit Naess 23 , Renwei Wang 3 , Yu-Tang Gao 24 , Lesley M. Butler 3,4 , Woon-Puay Koh 25 , Xiao-Ou Shu 26 , Yong-Bing Xiang 27 , Honglan Li 27 , Wei Zheng 26 , Qing Lan 27 , Kala Visvanathan 28 , Judith Hoffman Bolton 28 , Per Magne Ueland 29,30 , Øivind Midttun 31 , Arve Ulvik 31 , Neil Caporaso 6 , Mark Purdue 6 , Regina Zeigler 6 , Neal D. Freedman 6 , Julie E. Buring 32,33,34 , I-Min Lee 32,34 , Howard D. Sesso 32,33,34 , J. Michael Gaziano 32,33,35 , Jonas Manjer 36 , Ulrika Ericson 37 , Caroline Relton 38,39 Paul Brennan 1 **, Mattias Johansson 1 ** *Co-first authors **These authors jointly supervised this work Corresponding author: Paul Brennan International Agency for Research on Cancer (IARC/WHO) 150 cours Albert Thomas 69372 Lyon cedex 08, France Phone: +33 (0) 4 72 73 83 91 Fax: +33 (0) 4 72 73 83 42 Email: gep@iarc.fr

Funding

The Lung Cancer Cohort Consortium (LC3) was supported by Grant NIH / NCI (n° 1U01CA155340-01). The funding organization had no role in design and conduct of the Following variables adjusted for cotinine (in quartiles) and alcohol at recruitment, among all controls

Article 2

Circulating biomarkers of one-carbon metabolism in relation to renal cell carcinoma incidence and survival

CHAPTER III:

ONE-CARBON METABOLISM AND HEAD AND NECK CANCER

CHAPTER IV:

ONE-CARBON METABOLISM AND RENAL CELL CARCINOMA

Article 3

Circulating Folate, Vitamin Methods -Within 20 prospective cohorts from the NCI Cohort Consortium, a nested casecontrol study was designed including 5,364 incident lung cancer cases and 5,364 controls that were individually matched to cases by age, sex, cohort, and smoking status. Centralized biochemical analyses were performed to measure circulating concentrations of vitamin B6, folate and methionine, as well as cotinine as an indicator of recent tobacco exposure. The association between these biomarkers and lung cancer risk was evaluated using conditional logistic regression models.

Results -Participants with higher circulating concentrations of vitamin B6 and folate had a modestly decreased risk of lung cancer risk overall, the odds ratio when comparing the top and bottom fourths [OR 4vs1 ] being 0.88 (95% confidence interval [95% CI], 0.78-1.00) and 0.86 (95% CI 0.74-0.99), respectively. We found stronger associations among for men (OR 4vs1 0.74; 95% CI 0.62-0.89 for vitamin B6, OR 4vs1 0.75; 95% CI 0.61-0.93 for folate) and ever smokers (OR 4vs1 0.79; 95% CI 0.68-0.91 for vitamin B6, OR 4vs1 0.85; 95% CI 0.72-0.99 for folate). We further noted that the association of folate was restricted to Europe/Australia and Asia, whilst no clear association was observed for the United-States. Circulating concentrations of methionine were not associated with lung cancer risk overall or in important subgroups.

Conclusion -

Although confounding by tobacco exposure or reverse causation cannot be ruled out, these study results are consistent with a small decrease in lung cancer risk in ever smokers who avoid low concentrations of circulating folate and vitamin B6.

CHAPTER VI:

DISCUSSION

APPENDICES Appendix 1-Supplementary methods of LC3 cohorts

Study population

We invited all prospective cohort studies that in 2009 were members in the US National Cancer Institute (NCI) Cohort Consortium to participate in the study. Additional inclusion criteria included the occurrence of at least 200 incident lung cancer cases with baseline questionnaire data and either plasma or serum samples cryopreserved at <80°C available. Twenty cohorts fulfilled those criteria and accepted to participate, resulting in a combined cohort population of over 2,000,000 participants from North America, Europe, Asia and Australia.

Selection of cases and controls

Lung cancer cases were defined on the basis of the International Classification of Diseases for Oncology, Second Edition (ICD-O-2), and included all invasive cancers coded as C34.0-C34-9. Altogether, 11,399 incident lung cancer cases with pre-diagnostic blood samples were identified from the participating cohorts. We selected a total 5,545 lung cancer cases, and in order optimize the statistical power in smoking stratified analyses, never and former smoking cases were oversampled. For each case, one control was randomly chosen from risk-sets consisting of all cohort members alive and free of cancer (except non-melanoma skin cancer) at the time of diagnosis of the index case. Matching criteria were cohort, sex, date of blood collection (± 1 month, relaxed to ± 3 months for sets without available controls), and date of birth (± 1 year, relaxed to ± 3 years), as well as smoking status in 5 categories; never smokers, short and long term quitters among former smokers (<10 years, 10 years since quitting), and light and heavy smokers among current smokers (< 15 years, 15 cigarettes per day).

After excluding cases who were not correctly matched on smoking status (n=124 cases), who had insufficient plasma samples (n=42), or had a revised date of diagnosis prior to blood draw (n=13), 5,364 lung cancer case-control pairs remained eligible for the risk analysis.

Screening for lung cancer among participants in the intervention arm included a chest x-ray at baseline followed by either three annual x-rays (for current or former smokers at enrollment) or two annual xrays (for never smokers); participants in the control arm received routine health care. Screening-arm participants provided data on sociodemographic factors, smoking behavior, anthropometric characteristics, medical history, and family history of cancer, as well as blood samples annually for the first 6 years of the study (baseline [T0] and T1 through T5). Lung cancers were ascertained through annual questionnaires mailed to the participants, and positive reports were followed up by abstracting medical records or death certificates. Follow-up in the trial as of July 2009 was 96.7%.

We conducted a nested case-control study within the screening arm of the PLCO trial. As of December 14,274 women between the ages of 34 and 65 were enrolled in the study. Because the original focus of the study was endogenous hormones and breast cancer, women who had taken hormone medications in the 6 months preceding baseline enrolment were not eligible for the study.

At the time of enrolment, data on demographics, anthropometric measures, medical history, reproductive and lifestyle variables were collected through self-administered questionnaires after written informed consent was obtained. Incident lung cancer cases were identified through active questionnaire to complete at home and were asked to return it with a toenail clipping of the large toe for trace metal assays. Comparisons with published figures from the 1990 Census indicted that approximately 30 percent of adult residents had participated.

The Multiethnic Cohort (MEC)

The MEC includes over 215,000 men and women aged 45-75 years at recruitment from five different racial/ethnic groups (African Americans, Japanese Americans, Native

Women's Health Study (WHS)

The WHS was a randomized trial of low-dose aspirin, vitamin E, and beta-carotene in the primary prevention of cardiovascular disease and cancer beginning in 1992 among 39,876 female US health professionals aged ≥45 years 284 . Information on major clinical, lifestyle, and dietary factors was collected via self-reports on baseline questionnaires. Women also provided baseline bloods. During more than two decades of follow-up, WHS participants reporting new cases of cancer on annual followup questionnaires were confirmed by medical record review by the WHS Endpoints Committee.

Reports of cancer were confirmed on the basis of pathology or cytology reports or, rarely, strong clinical and radiologic or laboratory marker evidence when a pathology or cytology review was not conducted. Only confirmed cases of lung cancer were included in the present analyses, which were matched with eligible controls.

Health Professionnals Follow-up Study (HPFS)

The Health Professionals Follow-up Study (HPFS) 294

Follow-up for cancer incidence and mortality data

The ascertainment of cases within EPIC reflects the predominant pattern of diagnosis in the general population. Incident cancer cases were identified at regular intervals through population-based cancer registries (Denmark, Italy except Naples, the Netherlands, Norway, Spain, Sweden and United Kingdom) or by active follow-up (France, Germany, Greece and Naples), which involved a combination of methods, including review of health insurance records, cancer and pathology registries, as well as direct contact with participants and their next-of-kin.

Mortality data, including vital status, cause of death, and date of death, were obtained from mortality registries at the regional or national level. Subjects were followed up from study entry until cancer diagnosis (except non-melanoma skin cancer), death, emigration, or the end of the follow-up period for the relevant study center. End of follow-up was defined as the latest date of complete follow-up for both cancer incidence and vital status and varied between study centers from December 2004 to June 2010. Vital status at follow-up is over 98% complete.

Validation study -The Melbourne Collaborative Cohort Study (MCCS)

The MCCS is a prospective cohort study of 41,514 women and men aged 40 to 79 years at the time of recruitment which took place between 1990 and 1994 in Melbourne, Australia. Following blood draw, plasma fractions were stored in liquid nitrogen tanks at -196°C. Incident cancer diagnoses were identified via routine linkage to the Victorian Cancer Registry (VCR). There is a statutory requirement that all cancers diagnosed in the state of Victoria (the state in which Melbourne is situated), excluding non-melanoma skin cancer be reported to the VCR. Vital status and cause of death information were obtained via linkage to the National Death Index of Australia.

Biochemical analyses

Plasma samples were sent on dry ice to the Bevital A/S laboratory (http://www.bevital.no) in Bergen, Norway. The study included measurements of plasma concentrations of vitamin B2 (riboflavin), vitamin B6 (measured as pyridoxal 5'-phosphate, its active form), folate (vitamin B9), vitamin B12 (cobalamin), total homocysteine, and methionine. We also measured cotinine as an indicator of recent smoking behavior.

Concentrations of B2, B6, homocysteine, methionine, and cotinine were determined by mass spectrometry based methods (liquid chromatography coupled to tandem mass spectrometry; gas chromatography coupled to tandem mass spectrometry), [307][308][309] and microbiological methods were used to determine concentrations of folate (Lactobacillus casei) 310

and B12 (Lactobacillus leichmannii)._ENREF_285

Samples were analyzed in batches of 86 and quality control included 6 calibration samples, 2 control samples, and 1 blank sample in each batch. The coefficients of variations (CVs) within and between batches were, respectively, 6 and 11% for vitamin B2, 3% and 6% for vitamin B6, 4% and 5% for folate and vitamin B12, 1% and 3% for methionine, and 1% and 2% for homocysteine. All plasma samples were kept at -80°C and all RCC cases and their individual matched controls were analyzed together within the same batches in random order, as were samples from control group 2. The laboratory staff was blinded to the case-control status of the blood samples.

Appendix 15-Supplementary table 1 for Article 2

Partial correlation coefficients between circulating concentrations of vitamins B2, B6, folate, B12, homocysteine and methionine, and dietary intake, alcohol intake, circulating cotinine, BMI, and waist-to-hip ratio

a) Assessed by analyzing RCC cases and additional unmatched controls by unconditional logistic regression, adjusting for country, sex, and age at recruitment (in 5 year groups). Danish subjects did not contribute to this analysis. b) Assessed by analyzing RCC cases and additional unmatched controls by unconditional logistic regression after multiple imputation of missing covariate data, adjusting for country, sex, age at recruitment (in 5 year groups), waist-to-hip ratio (quartiles defined among matched controls), hypertension (yes/no), educational attainment (4 categories

), smoking status (never/former/current), plasma cotinine (quartiles defined by the distribution for current smokers), alcohol intake at recruitment (g/day) and alcohol intake (ever/never) . Danish subjects did not contribute to this analysis. c) Sensitivity analysis in which participants with missing covariate data were excluded. Assessed by analyzing RCC cases and their individually matched controls by conditional logistic regression, conditioning on individual case set, and adjusting for waist-to-hip ratio (quartiles defined among matched controls), hypertension (yes/no), educational attainment (4 categories), smoking status (never/former/current), plasma cotinine (quartiles defined by the distribution for current smokers), alcohol intake at recruitment (g/day) and alcohol intake (ever/never). Case-control numbers only include those case-sets where both the case and matched control had complete plasma measurements and covariate information. d) Assessed by analysing RCC cases and all controls combined by unconditional logistic regression after multiple imputation of missing covariate data, adjusting for country, sex, age at recruitment (in 5 year groups), waist-to-hip ratio (quartiles defined among matched controls), hypertension (yes/no), educational attainment (4 categories), smoking status (never/former/current), plasma cotinine (quartiles defined by the distribution for current smokers), alcohol intake at recruitment (g/day) and alcohol intake (ever/never). e) Quartile cut-off points were determined based on the plasma level distribution of each biomarker for 556 individually matched controls. f) P for trend assessed by the base 2 logarithm of plasma concentrations.

Appendix 17-Supplementary table 3 for Article 2

Hazard ratios of all-cause mortality for RCC cases for quartiles of plasma vitamins B2, B6, folate, B12, and homocysteine and methionine: Sensitivity analysis excluding missing data