Emmanuelle Kesse-Guyot

Julia Baudry

Karen E Assmann

Pilar Galan

Serge Hercberg

Denis Lairon

Prospective association between consumption frequency of organic food and body weight change, risk of overweight or obesity: Results from the NutriNet-Santé Study

Keywords: organic food, obesity, prospective cohort study, weight gain body mass index, OS: organic score, mPNNS-GS, PCA: Principal component analysis Q; Quartile

A lower body mass index (BMI) has been reported among consumers of organic foods but this relationship has never been examined in a prospective design. Our aim was to prospectively investigate the association between the frequency of organic foods and weight change. We analyzed data from 62,224 participants of the NutriNet-Santé cohort (78% women, mean age=45y) with information on consumption frequency of organic foods, dietary intake, and repeated anthropometric data. For 16 products, participants reported their consumption frequency of labeled organic foods (never, occasionally, most of the time). An organic score (OS) with a maximum of 32 points was computed. The associations of the OS (modeled as quartiles (Q)) with change in BMI during follow-up (on average 3•1 y), and with the risk of overweight and obesity were estimated ANCOVA and multivariable logistic regression. A lower BMI increase was observed across quartiles of the OS (mean difference (95%CI) Q4 versus Q1= -0•16 (-0•32, -0•01). An increase in OS was associated with a lower risk of overweight and obesity (among non-overweight and non-obese participants at inclusion): odds ratios (95%CI) Q4 versus Q1 were 0•77 (0•68, 0•86) and 0•69 (0•58, 0•82), respectively. Concerning obesity risk, the association was stronger among participants with higher adherence to nutritional guidelines. This study supports a strong protective role of consumption frequency of organic foods concerning the risk of overweight and obesity that depends on overall dietary quality. Upon confirmation, these results may contribute to finetune nutritional guidelines by accounting for farming practices in food production.

Introduction

The global obesity epidemic, involving multifaceted origins, is a major public health issue [START_REF] Who | Obesity: preventing and managing the global epidemic[END_REF] .

Overweight and obesity contribute to the global burden of chronic diseases [START_REF] Who | Global Health Risks[END_REF] -especially type 2 diabetes and ischemic cardiovascular diseases [START_REF]The challenge of obesity in the WHO European Region[END_REF] , along with cancers [START_REF] Latino-Martel | Alcoholic beverages, obesity, physical activity and other nutritional factors, and cancer risk: A review of the evidence[END_REF][START_REF]Food, Nutrition, Physical activity, and the Prevention of Cancer: a global perspective[END_REF] .

Among the factors involved in the etiology of obesity, excess caloric consumption, sedentary lifestyle and genetic susceptibility are well-recognized, but there is a growing concern for the identification of novel factors involved such as gut microbiota [START_REF] Hansen | The gut microbiome in cardio-metabolic health[END_REF] or environmental chemicals [START_REF] Lee | Chlorinated persistent organic pollutants, obesity, and type 2 diabetes[END_REF][START_REF] Snedeker | Do interactions between gut ecology and environmental chemicals contribute to obesity and diabetes?[END_REF][START_REF] Thayer | Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review[END_REF][START_REF] Lubrano | Obesity and metabolic comorbidities: environmental diseases?[END_REF] .

Among the different dietary factors, organic food is of major interest since it presents multiple features that could potentially protect from weight gain and obesity. Notably, compared to conventional food, organic food has been suggested to present better nutritional values concerning fatty acids profiles and specific micronutrients [START_REF] Baranski | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Srednicka-Tober | Composition differences between organic and conventional meat: a systematic literature review and meta-analysis[END_REF][START_REF] Srednicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, alpha-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF] . Nonetheless, the possible implications at an individual level in terms of daily nutrient intake are unknown due to the lack of food composition tables accounting for farming practices. A small number of clinical studies have been conducted that compared specific nutritional biomarkers according to the type of diet (organic or conventional). However, due to short study durations, these studies were not well-equipped to provide reliable results, and findings were inconsistent [START_REF] Smith-Spangler | Are organic foods safer or healthier than conventional alternatives?: a systematic review[END_REF] .

Organic foods are also characterized by a markedly low level or an absence of pesticide residues, as repeatedly reported in food residue analyses [START_REF] Baranski | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Smith-Spangler | Are organic foods safer or healthier than conventional alternatives?: a systematic review[END_REF][START_REF] Lairon | Nutritional quality and safety of organic food: a review[END_REF] and in experimental studies showing that adopting an organic diet leads to a drastic reduction in pesticide residues and urine metabolites in children and adults [START_REF] Curl | Organophosphorus pesticide exposure of urban and suburban preschool children with organic and conventional diets[END_REF][START_REF] Lu | Organic diets significantly lower children's dietary exposure to organophosphorus pesticides[END_REF][START_REF] Bradman | Effect of Organic Diet Intervention on Pesticide Exposures in Young Children Living in Low-Income Urban and Agricultural Communities[END_REF][START_REF] Oates | Reduction in urinary organophosphate pesticide metabolites in adults after a week-long organic diet[END_REF][START_REF] Curl | Estimating pesticide exposure from dietary intake and organic food choices: the Multi-Ethnic Study of Atherosclerosis (MESA)[END_REF] . For most pesticide families (organochlorines (now banned in the EU but still persistent), organophosphates, and pyrethrynoïds), a large number of molecules have been recognized as endocrine disruptors [START_REF] Mnif | Effect of endocrine disruptor pesticides: a review[END_REF] , leading to possible metabolic disorders [START_REF] Casals-Casas | Endocrine disruptors: from endocrine to metabolic disruption[END_REF] . Indeed, a higher exposure to some of these compounds has been associated with a higher risk for obesity or type 2 diabetes in humans [START_REF] Thayer | Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review[END_REF] . In this context, consumption of organic foods might contribute to the management of weight gain and of obesity risk. In a recent cross-sectional analysis based on the NutriNet-Santé cohort, we showed that participants identified as regular consumers of organic food, compared to non-consumers, presented reduced odds of being overweight or obese (-36% and -62% in men and -42% and -48% in women, respectively) [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF] . Comparable findings were reported in the German National Nutrition Survey II (NVS II), a nationwide food consumption study conducted among 13,074 adults: German buyers of organic food exhibited healthier lifestyles compared with non-buyers, and presented lower body weight [START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF] .

To the best of our knowledge, no epidemiologic study has yet investigated the prospective relation between consumption frequency of organic foods and the risk of overweight and obesity. The main objective of the present study was thus to investigate the longitudinal association between the frequency consumption of organic foods and change in body mass index, the risk of overweight, and the risk of obesity in a very large adult cohort. Given our previous finding that organic food consumers show a higher level of adherence to nutritional recommendations [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF] -especially a higher consumption of fruit and vegetables and a lower consumption of animal products -a secondary objective was to explore a potential modifying effect of the nutritional quality of the diet on the investigated associations.

Material and methods

Study population

The NutriNet-Santé is a web-based prospective observational cohort that was launched in France in May 2009. The objectives, design and methodology have been described elsewhere [START_REF] Hercberg | The Nutrinet-Sante Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status[END_REF] . The study was conducted according to the guidelines laid down in the Declaration of Helsinki and was approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the "Commission Nationale de l'Informatique et des Libertés" (CNIL no. 908450 and no. 909216). All subjects signed an electronic informed consent.

Data collection and computation

Volunteers filled in self-administrated questionnaires using a dedicated website at baseline and during follow-up on an approximately monthly basis. The baseline questionnaires, inquired sociodemographic data and lifestyles, health status, physical activity, anthropometrics and diet. These questionnaires were first pilot-tested and then compared to traditional assessment methods or objectively validated [START_REF] Lassale | Validity of web-based self-reported weight and height: results of the Nutrinet-Sante study[END_REF][START_REF] Lassale | Validation of a Web-based, self-administered, non-consecutive-day dietary record tool against urinary biomarkers[END_REF][START_REF] Lassale | Correlations between Fruit, Vegetables, Fish, Vitamins, and Fatty Acids Estimated by Web-Based Nonconsecutive Dietary Records and Respective Biomarkers of Nutritional Status[END_REF] .

Consumption frequency of organic products data

Two months after enrollment, volunteers were asked to provide information on their consumption frequency of 16 labeled organic products (fruit, vegetables, soya, dairy products, meat and fish, eggs, grains and legumes, bread and cereals, flour, vegetable oils and condiments, ready-to-eat meals, coffee/tea/herbal tea, wine, biscuits/chocolate/sugar/marmalade, other foods, dietary supplements). Initially, the collection of these data was related to research questions focused on reasons for nonconsumptions. Consumption frequencies were presented in 8 modalities: (1) most of the time;

2) occasionally; 3) never ("too expensive"); 4) never ("product not available"); 5) never ("I'm not interested in organic products"); 6) never ("I avoid such products"); 7) never ("for no specific reason"); and 8) "I don't know"). For each product, we allocated two and one points to the "most of the time" and "occasionally" modalities, respectively (and 0 otherwise) -since the objective of the present study was to focus on the level of frequency, and not on reasons for non-consumption. The 16 dietary components were summed up to provide an organic score (ranging from 0 to 32).

Anthropometric data

At enrollment and yearly after, participants were asked to report weight and height assessed during a medical or occupational health examination by a physician, or from selfmeasurement guided by standardized procedures (on flat surface, lightly dressed, and without shoes). Self-reported anthropometric data have been shown to present an elevated concordance with clinical assessment [START_REF] Lassale | Validity of web-based self-reported weight and height: results of the Nutrinet-Sante study[END_REF] .

Body mass index (kg/m2) was calculated as weight divided by the square of height. Subjects were classified as underweight or normal weight (BMI<25), overweight (including obesity; BMI≥25), or obese (BMI≥30) according to the World Health Organization (WHO) reference values [START_REF] Who | Obesity: preventing and managing the global epidemic[END_REF] .

Dietary data and physical activity

At baseline, quantitative dietary intakes were assessed using three 24-hour records (24HR), randomly allocated over a two-week period, including two week-days and one weekend day, using a validated method [START_REF] Lassale | Validation of a Web-based, self-administered, non-consecutive-day dietary record tool against urinary biomarkers[END_REF][START_REF] Lassale | Correlations between Fruit, Vegetables, Fish, Vitamins, and Fatty Acids Estimated by Web-Based Nonconsecutive Dietary Records and Respective Biomarkers of Nutritional Status[END_REF] . Participants reported all foods and beverages consumed at each eating occasion. Portion sizes were estimated with the help of photographs, derived from a previously validated picture booklet [START_REF] Moullec | Validation du manuel photos utilisé pour l'enquête alimentaire de l'étude SU.VI.MAX[END_REF] or directly entered as grams, volumes or purchased units. Since alcohol is only episodically consumed by most individuals, alcohol intake was calculated using either the 24HR or a frequency questionnaire for those identified as abstainers in the three 24HR days. Moreover, since fish and seafood are infrequently consumed by many individuals, the weekly consumption of this food group was assessed by a specific frequency question. . Individual daily mean food consumption was calculated from the three 24HR, weighted for the type of day (week or weekend day). Nutrient intakes were calculated using the NutriNet-Santé composition table [START_REF]Table de composition des aliments -Etude NutriNet-Santé[END_REF] . Under-reporters were identified and excluded using the validated method developed by Black [START_REF] Black | Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations[END_REF] . To assess nutritional diet quality, a modified version of the validated PNNS-GS (without physical activity) was computed. This modified score, the mPNNS-GS, reflects adherence to the official French nutritional recommendations [START_REF] Estaquio | Adherence to the French Programme National Nutrition Sante Guideline Score is associated with better nutrient intake and nutritional status[END_REF] . The score includes 12 components: eight refer to foodserving recommendations (fruit and vegetables; starchy foods; whole grain products; dairy products; meat, eggs and fish; fish and seafood; vegetable fat; water vs soda) and four refer to moderation in consumption (added fat; salt; sweets; alcohol). Moreover, points are deducted for overconsumption of salt, added sugars, or when energy intake exceeds the estimated energy needs by more than 5%.

In order to also account for a posteriori dietary patterns, we performed a principal component analysis (PCA), on 31 aggregated food groups. Dietary patterns obtained by PCA are independent linear combinations of the 31 food group consumptions, maximizing the explained variance. Two dietary patterns were retained based on Cattel's Scree plots and the interpretability of the factors. Supplementary table S1 presents all factor loading coefficients (corresponding to the correlations between the different food groups and the two dietary patterns) > 0•3. For each participant, the individual pattern score was calculated by summing the intake of the 31 food groups, weighted by their factor loading.

Covariates

At baseline, self-administered questionnaires were used to collect data including age, gender, formal education (≤ high school diploma, high school, post-secondary graduate), occupation (managerial staff, intermediate profession, employee/manual worker, retired, unemployed, never employed/homemaker and self-employed), marital status (cohabiting or single), income, number of children and smoking status (never, former and current). Income per household unit was calculated by dividing the household's total monthly income by the number of consumption units (CU), using the following coefficients: 1 CU for the first adult in the household, 0.5 CU for all other household members aged 14 or older and 0.3 CU for children under 14 (33) . The following categories of monthly income were used: <1,200, 1,200-1,800, 1,800-2,700 and >2,700 euros per household unit.

Physical activity was assessed by the International Physical Activity Questionnaire (IPAQ) [START_REF] Hagstromer | The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity[END_REF] . Metabolic equivalents (MET) measured in minutes per week were computed. The recommended IPAQ categories of physical activity were used: low (<30 min of brisk walking/day), moderate (30 -<60 min/day of brisk walking /day or equivalent) and high (≥60 min of brisk walking /day or equivalent).

The inquired baseline health data included use of medication and self-reported history of diseases (cancer, cardiovascular diseases, hypertension, dyslipidemia and diabetes).

Statistical analysis

For the present study, we used data from volunteers who were included before June 2014 and initially aged 18-74 y. Among them, we selected those 1) who completed the organic questionnaire, 2) with baseline anthropometric data and at least one measurement during follow-up, 3) with available data to compute PNNS-GS and 4) who were not identified as energy underreporters (Figure 1). Follow-up anthropometric data were collected until June

2015.

The participants included (N=62,224) into our analyses were compared to those excluded, The second model was further adjusted for year and month of inclusion, follow-up duration, occupation, marital status, education, monthly income per household unit, baseline use of dietary supplements, the mPNNS-GS, PCA-extracted dietary patterns scores, energy intake, physical activity and smoking status. The final model was further adjusted for history of diseases (cancer, cardiovascular diseases, diabetes, hypertension and dyslipidemia).

In a second set of analyses, we estimated odds ratios (OR) and 95% confidence intervals (CI)

for becoming overweight or obese after exclusion of overweight and obese subjects at baseline, respectively (leading to new study samples of n= 43,301 and n=56,806). Three multivariate logistic regression models were computed with similar covariables to those used in the covariance analysis.

A set of supplementary analyses was performed for the obesity risk outcome. First, stratified analyses were conducted according to physical activity level/day brisk walking versus ≥30 min/day brisk walking or equivalent), use of dietary supplements (yes versus no), tobacco status (never and former smokers versus current smokers), education level, and level of adherence to nutritional guidelines (using tertiles of the mPNNS-GS). Second, we used an alternative method of accounting for potential confounder bias: adjustment for a 'propensity score' that contains information on potential confounders in a combined manner [START_REF] Ali | Best (but oft-forgotten) practices: propensity score methods in clinical nutrition research[END_REF] . To obtain the propensity score, a multinomial logistic regression model was used to estimate the predicted probability of organic food consumption (using quartiles of the 16-point organic score) as a function of a wide range of factors (sociodemographic, health characteristics, food group consumptions). Finally, we used inverse probability weighting to correct the estimates for potential selection bias [START_REF] Seaman | Review of inverse probability weighting for dealing with missing data[END_REF] . All tests of statistical significance were two-sided and the type I error was set at 5%. Statistical analyses were performed using SAS® software (version 9.3, SAS Institute Inc, Cary, NC, USA).

Results

Comparison of included and excluded participants

Compared with excluded subjects, included subjects (N=62,224) were older, had a higher education level and income, were less often smokers and less physically active. They also presented a lower BMI and a higher nutritional quality of the diet (all P-values<0•05, data not tabulated).

Baseline Characteristics of the sample

Baseline characteristics across quartiles of organic score are shown in Table 1. Higher levels of the organic score were related to higher proportions of women, participants who were cohabiting, former smokers, physically active participants, individuals reaching a postsecondary educational level, and participants with a high income or occupational level. A positive association was also observed with age, follow-up duration and the mPNNS-GS (reflecting the nutritional quality of the diet), while there was a negative association with energy intake, alcohol consumption and BMI.

Components of the organic score across quartiles of the organic score are shown in Table 2.

In the first quartile (Q1), participants mostly reported no consumption of any organic products. In the 4 th quartile (Q4), participants reported more frequently consuming organic products, especially products of the following food groups: eggs, starchy food, vegetables, vegetable oil, fruits and flour.

Then mean follow-up time in our study sample was 3•12 (SD=1•37) years. Results on the prospective association between the organic score and change in BMI over time are presented in Table 3. In the second model, higher organic score levels were related to a substantially lower increase in BMI over time (mean difference Q4 versus Q1= -0•15; confidence interval= --0•31, -0•01, P for trend =0•05). After further adjustment for history of chronic diseases (third model), an even stronger association was observed (mean difference Q4 versus Q1= -0•16; confidence interval= -0•32, -0•01, P for trend =0•04).

Results on the prospective association between the organic score and the risk of overweight and obesity are presented in Table 4. In the fully-adjusted model, accounting for sociodemographic data, lifestyles and history of chronic diseases, a linear decrease in the risk of overweight was observed across quartiles of the organic score, with risk reduction of 23% in Q4 compared to Q1. Findings concerning the risk of obesity were similar, with a risk reduction of 31% in Q4 compared to Q1.

Stratified analyses are presented in Figure 2 and in Supplemental Table S2. We observed that the association between the organic score and the risk of obesity was stronger among participants with a higher nutritional quality of the diet. Overall, in the different stratified analyses, the association between the organic score and the risk of obesity was observed in each sub-group, except for dietary supplement users and participants with an intermediate education level. Accounting for selection bias via inverse probability weighting did not substantially modify our findings (data not shown). Models with additional adjustment for a propensity score are presented in Supplemental Table S3. Here, the investigated associations were attenuated but remained statistically significant.

Discussion

The present results show, for the first time, a strong negative association between the consumption frequency of organic foods and BMI change over time, as well as a marked reduction of the risk of overweight and obesity.

In stratified analyses, significant associations were observed in almost all investigated subgroups, except for dietary supplement users and participants with an intermediate level of education. Importantly, both participants with a low level of physical activity and participants with a low education level presented a significantly lower risk of obesity with increasing organic food consumption. It is noteworthy that the nutritional quality of the diet, estimated by using an a priori dietary index reflecting adherence to the French nutritional guidelines [START_REF] Estaquio | Adherence to the French Programme National Nutrition Sante Guideline Score is associated with better nutrient intake and nutritional status[END_REF] , appears to be a key effect modifier. Indeed, the strongest associations were observed among participants presenting a high nutritional quality of the diet.

Consumption frequency of organic foods and the risk of obesity or overweight

We prospectively observed a markedly lower risk of obesity among subjects with a high consumption frequency of organic foods. Previous investigations of data from the NutriNet-Santé cohort have revealed that regular consumers of organic foods presented a healthier diet and healthier lifestyle characteristics (related to physical activity and tobacco use) as compared to irregular consumers or non-consumers. Moreover, regular consumers of organic foods in the NutriNet-Santé study presented specificities with respect to chronic disease history [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF][START_REF] Baudry | Health and dietary traits of organic food consumers: results from the NutriNet-Sante study[END_REF] . After accounting for these potential confounders, the findings of the present study were partially attenuated but the association remained strong and highly significant, with a reduction in the risk of obesity of 37% after a 3•1 y follow-up.

A similar association was observed for overweight, although the strength of the association was smaller.

Comparison of our results with the findings of other studies

No previous longitudinal study has investigated the association between organic food consumption frequency and weight change or the risk of overweight and obesity, but a potential beneficial link between body weight and organic food consumption or purchase has been documented in several cross-sectional studies [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF][START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF][START_REF] Torjusen | Characteristics associated with organic food consumption during pregnancy; data from a large cohort of pregnant women in Norway[END_REF] . For instance, a previous investigation of NutriNet-Santé data [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF] has shown that regular organic food consumers showed a markedly lower probability of overweight (excluding obesity) and obesity compared to non-consumers (-36% and -62% in men and -42% and -48% in women, respectively). In addition, in the German National Nutrition Survey II [START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF] , as compared to non-buyers of organic food, buyers of organic food presented lower proportions of overweight (35•5% versus 39•2%) and obesity (17•9% versus 22•5%). Our findings are also concordant with a small Italian clinical study (including 100 healthy males and 50 males suffering from chronic kidney disease, CDK) that reported a statistically significant reduction in weight among CDK patients after introducing an organic diet for a 2-week period (85•17±13•97 kg at baseline versus 79•52±10•41 kg after the 2-week intervention, p<0•05) [START_REF] Lorenzo | The effects of Italian Mediterranean organic diet (IMOD) on health status[END_REF] .

Overall, the currently available cross-sectional or longitudinal surveys consistently reported an association between higher organic food consumption and a lower BMI. The association with a lower increase in BMI over time that we observed in the present study is of particular interest as it supports a possible role of the organic-based diet in weight management among all subjects, beyond the risk of overweight or obesity among initially normal weight (or underweight) individuals.

Modulating effect of the nutritional quality of the diet

There is growing evidence supporting the observation that consumers of organic food present a nutritionally healthier diet as well as other beneficial lifestyles such as lower alcohol consumption, no smoking and a higher physical activity level [START_REF] Kesse-Guyot | Profiles of organic food consumers in a large sample of French adults: results from the Nutrinet-Sante cohort study[END_REF][START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF][START_REF] Oates | Characteristics and consumption patterns of Australian organic consumers[END_REF][START_REF] Petersen | Sociodemographic characteristics and food habits of organic consumers--a study from the Danish National Birth Cohort[END_REF] . Thus, we hypothesized that the link between consumption of organic foods and body weight might be modulated by the overall nutritional quality of the diet. Indeed, we found that the strongest reduction in obesity risk (related to a higher consumption frequency of organic foods) was observed among participants with a healthier diet. Conversely, among participants with a less healthy diet (susceptible to promote weight gain), the association between consumption frequency of organic food and obesity risk was of smaller magnitude. It is well known that unhealthy eating habits play a key role in the etiology of obesity [START_REF]The challenge of obesity in the WHO European Region[END_REF] . Therefore, the mode of production of the foods consumed may be of secondary importance in these subjects. It is also possible that their dietary patterns, which are low in fruit and vegetables, lead to less contaminated diets.

Several hypotheses can be proposed to explain the differences in the risk of obesity between organic food consumers and non-consumers who present a healthy diet. Because we adjusted and then stratified for the global nutritional quality of the diet (using the mPNNS-GS score) in our models, it is not probable that our results were biased by the fact that organic food consumers present healthier diets. Other possible explanations include differences in organic food and conventional food with respect to various nutritional compounds.

First, the results of a number of studies argue for a higher concentration of polyunsaturated fatty acids (especially n-3 fatty acids) in organic dairy and meat products; and of antioxidants (especially vitamin C and phenolic compounds) in organic plant foods [START_REF] Baranski | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Srednicka-Tober | Composition differences between organic and conventional meat: a systematic literature review and meta-analysis[END_REF][START_REF] Srednicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, alpha-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF] . While observed differences in nutrient content can vary by about 10-68% at the food level (not accounted for in our study), it is possible that the overall variations of nutrient intake in an organic diet is sufficient to affect weight management [START_REF] Lorente-Cebrian | Role of omega-3 fatty acids in obesity, metabolic syndrome, and cardiovascular diseases: a review of the evidence[END_REF][START_REF] Meydani | Dietary polyphenols and obesity[END_REF] . However, this remains to be further evaluated.

Another hypothesis is related to the fact that individuals with a higher adherence to the French nutritional guidelines tend to consume more plant-based foods. It is well known that plant foods are frequently contaminated by various pesticide residues (about 45% of the tested samples in Europe) [START_REF]The 2013 European Union Report on Pesticide Residues in Food[END_REF] because they are heavily sprayed with pesticides during conventional agricultural production and storage. This hypothesis is in line with findings of human surveys that have related obesity and type 2 diabetes to pesticide exposure [START_REF] Lee | Chlorinated persistent organic pollutants, obesity, and type 2 diabetes[END_REF][START_REF] Thayer | Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review[END_REF][START_REF] Lubrano | Obesity and metabolic comorbidities: environmental diseases?[END_REF][START_REF] Lee | Low dose organochlorine pesticides and polychlorinated biphenyls predict obesity, dyslipidemia, and insulin resistance among people free of diabetes[END_REF][START_REF] Heindel | Endocrine disruptors and obesity[END_REF] . Thus, unlike the consumption of pesticide-free or only slightly contaminated plant products [START_REF] Baranski | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Smith-Spangler | Are organic foods safer or healthier than conventional alternatives?: a systematic review[END_REF][START_REF] Lairon | Nutritional quality and safety of organic food: a review[END_REF] , a high consumption of conventionally grown plant foods may be related to adverse health effects related to higher pesticide exposure.

Indeed, replacing conventional food by organic food has repeatedly been shown to drastically reduce the level of organophosphate residues in human urines [START_REF] Curl | Organophosphorus pesticide exposure of urban and suburban preschool children with organic and conventional diets[END_REF][START_REF] Lu | Organic diets significantly lower children's dietary exposure to organophosphorus pesticides[END_REF][START_REF] Bradman | Effect of Organic Diet Intervention on Pesticide Exposures in Young Children Living in Low-Income Urban and Agricultural Communities[END_REF][START_REF] Oates | Reduction in urinary organophosphate pesticide metabolites in adults after a week-long organic diet[END_REF][START_REF] Curl | Estimating pesticide exposure from dietary intake and organic food choices: the Multi-Ethnic Study of Atherosclerosis (MESA)[END_REF] . A specific example of a potential adverse health effect of contaminated fruit and vegetables is that high consumers of conventional or contaminated fruits and vegetables presented a particularly low semen quality [START_REF] Chiu | Fruit and vegetable intake and their pesticide residues in relation to semen quality among men from a fertility clinic[END_REF][START_REF] Juhler | Human semen quality in relation to dietary pesticide exposure and organic diet[END_REF] . This reinforces the concept that different dietary profiles (with various degrees of intake of contaminated food) lead to different levels of exposure to "obesogen" chemicals [START_REF] Thayer | Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review[END_REF] , but this hypothesis needs to be investigated in future biomonitoring-based studies that compare organic and conventional diets with various dietary profiles.

Potential mechanistic pathways explaining the association between organic food consumption and body weight

Our findings may be interpreted in light of the reduced exposure to pesticides among organic food consumers. Pesticides (prohibited in organic farming when they are synthetic) often present endocrine-disrupting properties that cause developmental and reproductive abnormalities via the modification of signaling processes [START_REF] Heindel | Endocrine disruptors and obesity[END_REF] . Besides, new scientific research argues for a role of pesticides in metabolic disruption, [START_REF] Casals-Casas | Endocrine disruptors: from endocrine to metabolic disruption[END_REF] leading to obesity and type 2 diabetes [START_REF] Thayer | Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review[END_REF] . Mechanistic pathways depend on the type of pesticides. Previous studies have shown an alteration of glucose and lipid metabolism by organochlorines [START_REF] Androutsopoulos | A mechanistic overview of health associated effects of low levels of organochlorine and organophosphorous pesticides[END_REF] . Moreover, organochlorines have been shown to affect the control of adipogenesis through an alteration of glucose transport and glycolysis, an alteration of mitochondrial activity, and of fatty acid oxidation [START_REF] Androutsopoulos | A mechanistic overview of health associated effects of low levels of organochlorine and organophosphorous pesticides[END_REF] . Organophosphates have been shown to alter carbohydrate and lipid metabolism by disrupting glucose homeostasis [START_REF] Androutsopoulos | A mechanistic overview of health associated effects of low levels of organochlorine and organophosphorous pesticides[END_REF] . In addition, some pesticides have been shown to affect the regulation of eating behavior and the differentiation of adipocytes [START_REF] Mostafalou | Pesticides and human chronic diseases: evidences, mechanisms, and perspectives[END_REF] .

Further studies are necessary to better evaluate metabolic disruption and the "obesogen" capacity of endocrine-disrupting chemicals contained in conventional foods.

Strengths and limitations

The main limitation of our study is that it is based on self-reported weight and height data.

However, these data have been shown to have a good concordance with data from clinical assessments in a validation study [START_REF] Lassale | Validity of web-based self-reported weight and height: results of the Nutrinet-Sante study[END_REF] . This validation study showed high intraclass correlation coefficients, ranging from 0.94 for height to 0.99 for weight, and the concordance for BMI classification was 93% (sensibility=88% and specificity=99%). Second, the generalizability of our findings is limited since participants were volunteers involved in a long-term cohort focused on nutrition and health. The individuals included in our study sample are thus likely to be particularly health conscious. A final limitation pertains to the difficulty to disentangle the role of overall dietary patterns from the role of organic food consumption, despite of the extensive adjustment and stratification made. Since the design of our study is observational, residual confounding cannot be ruled out. In particular, it is likely that unmeasured or only indirectly measured factors, including genetic factors, ethnicity, environmental (e.g. food or built environment) or psychological factors (e.g. occupational stress) may modify the association between organic food consumption and obesity.

Our study also presents important strengths. First, the rich and accurately collected data permitted to account for a broad range of potential confounders, including lifestyles and health outcomes. Moreover, the very large sample size of our study enabled us to conduct statistically powerful stratified analyses. Another important strength is the prospective design of our analysis that implies a high level of evidence. Finally, the availability of accurate dietary data allowed us to adjust for the nutritional quality of the diet, using a validated dietary index.

Conclusion

This study, based on data collected in a very large prospective cohort, is the first to support a prospective relation between consumption frequency of organic foods and body weight change, as well as a strong negative association with the risk of overweight and obesity. The overall nutritional quality of the diet may exert a modulating effect in these relationships, with a stronger effect observed among those presenting a healthy plant-based diet. Further studies, especially studies based on quantitative organic consumption data taking into account a diversity of dietary profiles (plant based, western etc.), are needed to confirm these results. If confirmed, these findings are of major interest from a public health point of view, as they reinforce the need to fine-tune nutritional guidelines by accounting for the mode of food production.

  using Mann-Whitney-U tests and Chi square tests. Baseline characteristics were presented by quartiles of the organic score. Values represent means (±SD) or percentages, and P-values were calculated using linear contrast tests (for continuous variables) or Chi square trend tests (for categorical variables). The association of the organic score with BMI change was assessed by covariance analysis (ANCOVA), modeling change in BMI as a percentage of the baseline value. Mean differences (95% confidence intervals) were presented across quartiles of the organic score. Three different models were performed. The first model was adjusted for baseline age and gender.

Fruits 0 •Figure 1 .Figure 2 .

 012 Figure 1. Participants of the NutriNet-Santé selected for the present analyses, 2009-2015

Table 2 Components of the organic score, across organic score quartiles, Nutrinet-Santé study, N=62,224 *

 2

	Q1	Q2	Q3	Q4	P †

Table 1 Baseline characteristics of the sample across organic score quartiles, Nutrinet-Santé study, 2009-2014, N=62,224 *

 1

	528
		Q1	Q2	Q3	Q4	P	†
	N	15,245	16,249	15,807	14,923

Acknowledgment

The authors Nathalie Arnault, Stephen Besseau, Laurent Bourhis, Julien Allègre, Than Duong Van, Younes Esseddik, Cédric Agaesse, Claudia Chahine, Paul Flanzy, Mac Rakotondrazafy and Fabien Szabo for their technical contribution to the NutriNet-Santé study.

Financial support

The NutriNet-Santé study is supported by the French Ministry of Health (DGS), the French Institute for Health Surveillance (InVS), the National Institute for Prevention and Health Education (INPES), the Foundation for Medical Research (FRM), the National Institute for Health and Medical Research (INSERM), the National Institute for Agricultural Research (INRA), the National Conservatory of Arts and Crafts (CNAM), and the University of Paris 13.

Conflict of Interest

None of the authors declares any conflict of interest.

Authorship

EKG: conducted the literature review and drafted the manuscript; EKG: performed analyses; EKG, JB, KEA, PG, SH and DL: were involved in the interpretation of results and critically reviewed the manuscript; and SH, PG and EKG: were responsible for the development of the design and the protocol of the study. All authors read and approved the final manuscript.

Obesity (1,337/55,469)

* Overweight (including obesity) and obesity analyses were performed among participants who were not overweight or obese at inclusion, respectively. Values are odds ratios (95% confident intervals), using 1 st quartile Q1 as the reference.


Julia Baudry
email: j.baudry@eren.smbh.univ-paris13.fr

Helene Lelong

Solia Adriouch

Chantal Julia

Benjamin Allès

Serge Hercberg

Mathilde Touvier

Denis Lairon

Pilar Galan

Emmanuelle Kesse-Guyot

Hélène Lelong

Association between organic food consumption and Metabolic Syndrome: cross-sectional results from the NutriNet-Santé study

Keywords: metabolic syndrome, metabolic traits, organic food consumption, dietary pattern BMI, Body Mass Index, CI, Confidence Intervals, CNIL, National Commission

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Metabolic Syndrome (MetS), a collection of metabolic abnormalities, is a clinical condition used to identify individuals at increased risk of cardiovascular mortality early in the disease process [START_REF] Galassi | Metabolic Syndrome and Risk of Cardiovascular Disease: A Meta-Analysis[END_REF][START_REF] Gami | Metabolic Syndrome and Risk of Incident Cardiovascular Events and Death[END_REF][START_REF] Wang | The metabolic syndrome predicts cardiovascular mortality: a 13-year follow-up study in elderly non-diabetic Finns[END_REF][START_REF] Benetos | All-Cause and Cardiovascular Mortality Using the Different Definitions of Metabolic Syndrome[END_REF]. Features of MetS combine dyslipidaemia, elevated blood pressure and glycaemia, and abdominal obesity [START_REF] Alberti | Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity[END_REF]. The prevalence of MetS reached 25% of the population in Europe [START_REF] Grundy | Metabolic Syndrome Pandemic[END_REF], in line with the rising rate of obesity and type-2 diabetes. In France, where the MetS appeared to be lower than in most industrialised countries, MetS prevalence would be comprised between 14.1% and 21.1% depending on definitions used, according to the French National Nutrition and Health Survey (ENNS) carried out in 2006-2007 [START_REF] Vernay | Metabolic syndrome and socioeconomic status in France: The French Nutrition and Health Survey (ENNS, 2006-2007)[END_REF].

Although the aetiology of MetS is complex, some major environmental risk factors are wellknown, and include diet [START_REF] Nicklas | Diet Quality Is Inversely Related to Cardiovascular Risk Factors in Adults[END_REF][START_REF] Lassale | Association between Adherence to Nutritional Guidelines, the Metabolic Syndrome and Adiposity Markers in a French Adult General Population[END_REF][START_REF] Kesse-Guyot | Adherence to Mediterranean diet reduces the risk of metabolic syndrome: A 6-year prospective study[END_REF][START_REF] Sabaté | A perspective on vegetarian dietary patterns and risk of metabolic syndrome[END_REF][START_REF] Veissi | Mediterranean diet and metabolic syndrome prevalence in type 2 diabetes patients in Ahvaz, southwest of Iran[END_REF], physical activity [START_REF] He | Association between leisure time physical activity and metabolic syndrome: a meta-analysis of prospective cohort studies[END_REF] or tobacco smoking [START_REF] Weitzman | Tobacco Smoke Exposure Is Associated With the Metabolic Syndrome in Adolescents[END_REF]. While the role of dietary patterns (e.g. vegetarian or Mediterranean diets [START_REF] Kesse-Guyot | Adherence to Mediterranean diet reduces the risk of metabolic syndrome: A 6-year prospective study[END_REF][START_REF] Sabaté | A perspective on vegetarian dietary patterns and risk of metabolic syndrome[END_REF][START_REF] Veissi | Mediterranean diet and metabolic syndrome prevalence in type 2 diabetes patients in Ahvaz, southwest of Iran[END_REF][START_REF] Turner-Mcgrievy | Key Elements of Plant-Based Diets Associated with Reduced Risk of Metabolic Syndrome[END_REF]) on the risk of onset of MetS has been thoroughly investigated, no studies have taken into consideration the mode of food production (i.e. organic or conventional farming practices) in the association between diet and metabolic status.

Consumers are increasingly concerned about what they eat and about the potential harmful effects of pesticide residues on their health [START_REF] Aertsens | Personal determinants of organic food consumption: a review[END_REF][START_REF] Dickson-Spillmann | Attitudes toward chemicals are associated with preference for natural food[END_REF][START_REF] Michaelidou | The role of health consciousness, food safety concern and ethical identity on attitudes and intentions towards organic food[END_REF], and as a result, more and more are turning to organic food [START_REF]Baromètre de consommation et de perception des produits biologiques en France, 13ème édition[END_REF], since the use of synthetic pesticides and chemical fertilisers is prohibited by organic farming regulations [20]. The latest European Food safety Authority (EFSA) report on pesticide residues in food [START_REF]The 2013 European Union report on pesticide residues in food: The 2013 European Union report on pesticide residues[END_REF] indicates a lower dietary exposure to pesticides via organic foods than via conventional crop-based foods. In addition, a cross-over Australian study showed a drastic (about 90%) reduction in urinary organophosphate exposure in adults after a change toward a 80% organic food-based diet [START_REF] Oates | Reduction in urinary organophosphate pesticide metabolites in adults after a week-long organic diet[END_REF], in line with several studies previously conducted among children [START_REF] Curl | Organophosphorus Pesticide Exposure of Urban and Suburban Preschool Children with Organic and Conventional Diets[END_REF][START_REF] Bradman | Effect of Organic Diet Intervention on Pesticide Exposures in Young Children Living in Low-Income Urban and Agricultural Communities[END_REF][START_REF] Lu | Organic Diets Significantly Lower Children's Dietary Exposure to Organophosphorus Pesticides[END_REF]. Existing systematic reviews and meta-analyses comparing organically and conventionally produced foods also pointed differences in nutritional composition amongst which higher content in polyphenol compounds [START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Brantsaeter | Organic Food in the Diet: Exposure and Health Implications[END_REF][START_REF] Mie | Are Organic Foods Safer or Healthier Than Conventional Alternatives?A Systematic Review[END_REF][29], antioxidants [START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Brandt | Agroecosystem Management and Nutritional Quality of Plant Foods: The Case of Organic Fruits and Vegetables[END_REF][START_REF] Palupi | Comparison of nutritional quality between conventional and organic dairy products: a meta-analysis[END_REF][START_REF] Średnicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, α-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF][START_REF] Lairon | Nutritional quality and safety of organic food. A review[END_REF] and vitamins [START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Brantsaeter | Organic Food in the Diet: Exposure and Health Implications[END_REF][START_REF] Mie | Are Organic Foods Safer or Healthier Than Conventional Alternatives?A Systematic Review[END_REF][START_REF] Brandt | Agroecosystem Management and Nutritional Quality of Plant Foods: The Case of Organic Fruits and Vegetables[END_REF] in organic crops as well as the beneficial fatty acid profiles in organic dairy products [START_REF] Brantsaeter | Organic Food in the Diet: Exposure and Health Implications[END_REF][START_REF] Palupi | Comparison of nutritional quality between conventional and organic dairy products: a meta-analysis[END_REF][START_REF] Średnicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, α-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF]. They also found lower concentrations of nitrate [START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF][START_REF] Brantsaeter | Organic Food in the Diet: Exposure and Health Implications[END_REF][START_REF] Brandt | Agroecosystem Management and Nutritional Quality of Plant Foods: The Case of Organic Fruits and Vegetables[END_REF][START_REF] Lairon | Nutritional quality and safety of organic food. A review[END_REF] and lower levels of iodine and selenium in organic milk [START_REF] Średnicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, α-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF]. Significantly lower concentrations of cadmium in organic food have also been reported [START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF] while no differences have been observed for other toxic metals [START_REF] Mie | Are Organic Foods Safer or Healthier Than Conventional Alternatives?A Systematic Review[END_REF]. Although these findings overall argue for a more favourable nutritional composition of organic products compared to conventional alternatives, the clinical relevance of these differences, however, remains uncertain.

The study of the impact of organic food consumption on metabolic status is of high interest as several epidemiological studies have suggested a link between pesticide exposure and metabolic disorders [START_REF] Evangelou | Exposure to pesticides and diabetes: A systematic review and meta-analysis[END_REF][START_REF] Nicolopoulou-Stamati | Chemical Pesticides and Human Health: The Urgent Need for a New Concept in Agriculture[END_REF][START_REF] Kim | Exposure to pesticides and the associated human health effects[END_REF][START_REF] Mostafalou | Pesticides: an update of human exposure and toxicity[END_REF]. In addition to well-established contributors to cardio-metabolic diseases (such as lack of physical activity and energy overconsumption), newly identified potential risk factors include endocrine disrupting pesticides such as organochlorides, organophosphates, carbamates and pyrethroids [START_REF]Pesticides : Effets sur la santé, une expertise collective de l'Inserm[END_REF][START_REF] Mnif | Effect of Endocrine Disruptor Pesticides: A Review[END_REF]. Besides, various nutritional factors -for which differences have been observed between organic and conventional products -have been linked to a decreased risk for some cardiovascular outcomes (such as omega-3 polyunsaturated fatty acids (PUFA) [START_REF] Mozaffarian | Omega-3 Fatty Acids and Cardiovascular Disease[END_REF][START_REF] Pan | -Linolenic acid and risk of cardiovascular disease: a systematic review and meta-analysis[END_REF], polyphenols [START_REF] Del Rio | Dietary (Poly)phenolics in Human Health: Structures, Bioavailability, and Evidence of Protective Effects Against Chronic Diseases[END_REF], several metal pollutants and more specifically cadmium [START_REF] Solenkova | Metal pollutants and cardiovascular disease: Mechanisms and consequences of exposure[END_REF] and more controversially nitrate and nitrite [START_REF] Hord | Regulation of Dietary Nitrate and Nitrite: Balancing Essential Physiological Roles with Potential Health Risks[END_REF][START_REF] Ahluwalia | Dietary Nitrate and the Epidemiology of Cardiovascular Disease: Report From a National Heart, Lung, and Blood Institute Workshop[END_REF]).

Nonetheless, to our knowledge, the link between a diet largely based on organic foods (namely with a potential low pesticide exposure and potentially a more favourable nutritional profile) and metabolic status has never been explored. Studies evaluating the associations between obesity and organic food consumption are scarce ; only three studies (two of which were conducted in the NutriNet-Santé cohort) have shown that regular organic food consumption was negatively related to obesity [START_REF] Kesse-Guyot | Profiles of Organic Food Consumers in a Large Sample of French Adults: Results from the Nutrinet-Santé Cohort Study[END_REF][START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF][START_REF] Kesse-Guyot | Prospective association between consumption frequency of organic food and body weight change, risk of overweight or obesity: Results from the NutriNet-Santé Study[END_REF].

Hence, the objective of the present study was to examine the specific relationship between organic food consumption (overall and by food groups) and the presence of MetS using a crosssectional design, among a large sample of adults residing in France from the NutriNet-Santé study. Through this exploratory analysis, we aimed to provide some evidence in the field of organic food consumption and metabolic disorders to determine whether this particular domain merits further investigation.

Methods

Study population and design

This study is based on the NutriNet-Santé study data, an ongoing observational web-based cohort study launched in May 2009. Its overall aim is to investigate the relationships between nutrition and health as well as the determinants of dietary behaviours and nutritional status. The NutriNet-Santé study's rationale, design, and methodology have been described elsewhere [START_REF] Hercberg | The Nutrinet-Santé Study: a webbased prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status[END_REF]. In brief, upon enrolment in the NutriNet-Santé study and each year thereafter, participants are asked to

Data collection

Dietary data

Food and organic food consumption were assessed through a validated self-administered semiquantitative food frequency questionnaire [START_REF] Kesse-Guyot | Relative validity and reproducibility of a food frequency questionnaire designed for French adults[END_REF], to which additional questions about frequencies of consumption of organic foods were added (Org-FFQ). The Org-FFQ has been described in details elsewhere [START_REF] Baudry | Contribution of Organic Food to the Diet in a Large Sample of French Adults (the NutriNet-Santé Cohort Study)[END_REF]. Briefly, the Org-FFQ included 264 food and beverage items, coupled for the most part with specified serving sizes. The questionnaire also included photographs for some specific categories that are not usually consumed in predetermined portion. For each food item, participants were asked to provide their consumption frequency over the past year (through a drop-down list including yearly, monthly, weekly or daily units) as well as the quantities consumed. In addition, the frequency of organic food consumption was assessed using the following statement: 'How often was the product of organic origin?'. Answers modalities were assessed by a 5 frequency-categories scale with modalities from 'never' to 'always' (never, rarely, half of time, often and always).

Modalities of frequencies of organic food consumption were translated into quantitative data by attributing a weight of 0, 0.25, 0.5, 0.75 and 1 to the respective following categories: 'never', 'rarely', 'half the time', 'often' and 'always'. Thus, we calculated the proportion of organic food in the whole diet by dividing the total organic food consumption (in grams per day) out of the total consumption without water (in grams per day). We also calculated the proportion of organic food in 16 main food groups among consumers. Nutrient intakes were assessed using a generic food composition database (independent of the food production -organic or conventional), specifically developed for the items of the Org-FFQ, which was based on the NutriNet-Santé original food composition table that includes more than 3000 items [START_REF]NutriNet-Santé coordination[END_REF].

In addition, we computed the mPNNS-GS (modified Programme National Nutrition Santé Guideline Score), a diet quality score based on adherence to the French dietary guidelines excluding the physical activity component [START_REF] Baudry | Dietary intakes and diet quality according to levels of organic food consumption by French adults: cross-sectional findings from the NutriNet-Santé Cohort Study[END_REF].

Information on anteriority of organic food consumption (referring to the number of years for which participants have been consuming organic products) was collected via a questionnaire pertaining to attitudes towards organic food administered in July 2014.

Covariates

At baseline and during follow-up phases, self-administered questionnaires were used to collect information on sociodemographic factors and lifestyle, and medication use, including sex, age, education (highest degree achieved), marital status, number of children, smoking habits, income, place of residence, physical activity (as assessed by the International Physical Activity Questionnaire (IPAQ) [START_REF] Hagströmer | The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity[END_REF][START_REF] Craig | International physical activity questionnaire: 12-country reliability and validity[END_REF]), and antidiabetic, antihypertensive and lipid-lowering medications. Monthly income per household unit was calculated using INSEE (French National Institute of Statistics and Economic Studies) calculation [START_REF]Definitions and methods[END_REF].

Clinical data assessment

During the clinical examination, anthropometrics and blood pressure were measured by trained personnel using standardised protocols. Systolic and diastolic blood pressures (SBP and DBP respectively) were measured 3 times at 1-minute intervals in a seated position after lying down for 5 minutes using an automatic validated device (HEM-7015IT; OMRON, Rosny-sous-Bois, France). Mean values were calculated for the analyses. The clinical examination also comprised measures of weight, height, and waist circumference. Weight was measured once using an electronic scale (BC-418MA; TANITA, Tokyo, Japan) with participants only wearing underwear and barefoot. Height was measured once with a wall-mounted measuring rod. Body mass index (BMI in kg/m 2 ) was calculated. Waist circumference was measured as the circumference midway between the lower ribs and iliac crests.

Biological data assessment

During the visit, blood samples were collected after at least a 6h-fast period and centralised and analysed at a single laboratory (IRSA, Tours, France). Total serum cholesterol (cholesterol oxidase C8000, Abbott), HDL-cholesterol (High Density Protein -cholesterol) (direct accelerator C8000, Abbott), serum triglycerides (glycerol kinase C8000, Abbott) and fasting blood glucose were measured (hexokinase on C 8000 automat, Abbott, Suresnes, France). LDL-cholesterol (Low Density Protein -cholesterol) was calculated using the Friedwald formula [START_REF] Planella | Calculation of LDL-cholesterol by using apolipoprotein B for classification of nonchylomicronemic dyslipemia[END_REF].

Definition of MetS

Individuals defined as presenting MetS were those having any three of the following five criteria according to the 2009 interim consensus statement [START_REF] Alberti | Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity[END_REF]:

-abdominal obesity (waist circumference ≥94 cm for men and ≥80 cm for women), -elevated blood pressure (SBP/DBP ≥130/85 mmHg or antihypertensive medication), -elevated triglyceridemia (≥150 mg/dL or fibrate medication), -low HDL-cholesterolemia (<40 mg/dL for men or <50 mg/dL for women), -elevated glycaemia (fasting glycaemia >100 mg/dL or antidiabetic medication).

Statistical analysis

Among respondents to the Org-FFQ (n=33,384), we selected those who attended the medical visit (n=9,373), with valid anthropometric and biological data (n=8,354), with available covariates, which led to a final sample of 8,174 individuals (2,602 men and 5,572 women).

Participant characteristics (sociodemographic and dietary traits) were compared across tertiles of proportion of organic food in the diet using ANCOVA with linear contrast (continuous variables) and Cochran-Mantel-Haenszel trend test (categorical variables). Values are reported as means with standard deviations (SD) or percent as appropriate. Based on the source population (respondents to the Org-FFQ), excluded and included participants were compared using Mann-Whitney U tests and Chi²-tests.

A total of 16.7% of subjects met the criteria for MetS (23.8% in men and 13.4% in women) in the study sample. Since the occurrence of the dependent variable exceeded 10%, odds ratios from standard logistic regressions could not be considered as appropriate proxies for relative risks [START_REF] Zhang | What's the Relative Risk?: A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes[END_REF].

We therefore used an alternative method recommended by Zou et al. [START_REF] Zou | A Modified Poisson Regression Approach to Prospective Studies with Binary Data[END_REF] based on Poisson regression models with robust errors to evaluate the association between tertiles of proportion of organic food in the diet, as well as tertiles of proportion of organic food in 16 food groups, and having MetS (binary dependent variable). The lower tertile of proportion of organic food in the diet was considered as reference category. We reported Prevalence Ratios (PR) and 95% Confidence Interval (95% CI) as well as P-values from linear contrast.

A first model was crude (model 1). A second model (main model, model 2) was adjusted for age, sex, educational level, monthly income, physical activity, smoking habits, season of blood sampling, time lag between organic food questionnaire completion and clinical visit, occupational status, location, energy intake and overall dietary quality (reflected by the mPNNS-GS as a continuous variable). A supplementary model (model 3) was further controlled for BMI to determine the association of organic food consumption with MetS beyond adiposity. An additional model (model 4) was also performed by adding to the main model the anteriority of organic food consumption (as a categorical variable: non-organic food consumption, organic food consumption for less than 5 years, organic food consumption for more than 5 years). The models pertaining to the association between the proportion of organic food in several food groups and MetS were additionally adjusted for the corresponding food group consumption. Furthermore, in an attempt to distinguish the role of organic food from that of potential correlated lifestyles, we investigated the association between the proportion of organic food in the diet (as a continuous variable) and having MetS stratified by various behavioural factors: overall nutritional quality of the diet (using tertiles of mPNNS-GS), smoking habits and physical activity.

ANCOVA models were also performed to evaluate the relationship between the proportion of organic food in the diet and various metabolic traits (fasting blood glucose, waist circumference, SBP, DBP, serum triglycerides and HDL-cholesterol) among sub-samples who were not taking any related medications for the specific outcome. To improve normality, anthropometric variables and biomarkers were logarithmically transformed before analysis and adjusted geometric means (95%CI) across tertiles of proportion of organic food in the diet are provided.

Same confounding covariates as those previously described for MetS in model 2 were used.

All tests of significance were two-sided, and the type I error was set at 5%. All analyses were performed using SAS software (version 9.4, SAS Institute, Inc.).

Results

Participants included in our study were older (58.16 ± 12.34 y vs. 53.20 ± 14.07 y), more often men (32% vs. 25%) and had more frequently a monthly income higher than 2700 € per household unit (32% vs. 39%) than respondents to the Org-FFQ excluded from the analysis (data not tabulated).

Characteristics of the participants

Main characteristics of the study sample across tertiles of proportion of organic food in the diet are presented in Table 1. Low organic food consumers (tertile 1) consumed no or very little organic food while, in tertile 3, organic food made up, on average, 0.62 (0.18) of the diet.

Compared to low organic food consumers, high organic food consumers were less often men, younger and more often highly educated. No statistically significant difference was observed concerning monthly income per household unit across tertiles of proportion of organic food in the diet. High organic food consumers also lived less frequently in a population-dense urban unit (>200 000 inhabitants) than low organic food consumers. They were also more often never smokers and more frequently presented a high physical activity. Higher organic food consumption was also associated with a higher overall nutritional quality of the diet, lower alcohol consumption and a lower body mass index.

Association between the proportion of organic food in the diet and MetS

Table 2 shows the association between tertiles of proportion of organic food in the diet and MetS. In the main model (model 2), higher organic food consumption was associated with a lower probability of having MetS (PR tertile3 vs. tertile 1 =0.69, 95%CI=0.61-0.78; p for linear contrast <0.0001). The association was attenuated but persisted even after controlling for BMI (PR tertile3 vs. tertile1 =0.86, 95%CI=0.76-0.98; p for linear contrast =0.02) (model 3) or for the anteriority of organic food consumption (PR tertile3 vs. tertile1 =0.69, 95%CI=0.59-0.80; p for linear contrast <0.0001) (model 4).

Association between the proportion of organic food according to food groups and MetS

Table 3 shows a marked negative association between an increased consumption of products from organic origin for plant food groups (including fruit and vegetables, starchy foods, wholegrain products and oil; all p for linear contrast ≤0.0005) as well as sweetened foods and nonalcoholic beverages (p for linear contrast <0.0001) and MetS. Similar results were observed, but to a lesser extent, for eggs, dairy products and fast food. In contrast, no significant association was found in the cases of seafood and meat, poultry and processed meat (p>0.05).

Association between the proportion of organic food in the diet and metabolic components

In the fully adjusted model, among subgroups of individuals who were not taking any related medication for the specific outcome, higher organic food consumption was significantly associated with lower fasting blood glucose, waist circumference, SBP and DBP, and triglycerides (all p for linear contrast <0.05), but not with HDL-cholesterol (Table 4).

Association between the proportion of organic food in the diet and MetS according to overall dietary quality, smoking status and physical activity

Stratified analyses are illustrated in Figure 1. After stratifying by tertiles of the mPNNS-GS (reflecting overall dietary quality), higher organic food consumption (modelled as a continuous variable) was negatively associated with MetS, regardless the level of dietary quality, when adjusting for sociodemographic and lifestyle variables. Similar results were obtained when stratifying by other lifestyle factors including smoking status and physical activity level, except among current smokers.

Discussion

In this cross-sectional study, we observed that a higher organic food consumption (i.e. a proportion of organic food in the diet >38%, corresponding to the 3 rd tertile) was associated with a lower probability of having MetS as well as lower levels of glycaemia, blood pressure, triglycerides, and waist circumference after adjustment for the major known confounding factors.

No significant association was observed with the HDL-cholesterol. The negative association was observed, in particular, when the proportion of plant food from organic sources increased, and persisted among different subgroups of the study sample.

To the best of our knowledge, no studies had previously investigated the specific association between organic food consumption and metabolic disorders, and MetS in particular. Some data is however available regarding anthropometric traits of organic food consumers. A study conducted in Germany showed that buyers of organic food exhibited lower body weight compared to non-buyers [START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF]. However, the primary aim of that study was descriptive and did not take into account potential confounding factors. Hence, lower body weights among frequent buyers of organic foods could be attributable to healthier lifestyles rather than to differences in consumption of organic and conventional foods. Previous cross-sectional findings from the NutriNet-Santé have also highlighted a negative association between regular organic food consumption and BMI in fully adjusted models [START_REF] Kesse-Guyot | Profiles of Organic Food Consumers in a Large Sample of French Adults: Results from the Nutrinet-Santé Cohort Study[END_REF]. More recently, in a prospective study carried out in the same cohort [START_REF] Kesse-Guyot | Prospective association between consumption frequency of organic food and body weight change, risk of overweight or obesity: Results from the NutriNet-Santé Study[END_REF], we showed, after controlling for major confounders, that an increase of self-reported organic food frequency was associated with a significantly lower risk of overweight and obesity.

In the present work, associations between high organic food consumption and the prevalence of MetS remained significant even after carefully adjusting for a wide range of confounders (including overall dietary quality, physical activity, sex, and BMI).

A potential hypothesis explaining, at least in part, our findings may be the reduced dietary exposure of high organic food consumers to synthetic pesticides. Indeed, as European Union legislation allows only a very limited number of natural pesticides to be used in organic food production, the occurrence of synthetic pesticide residues in organic crops is much lower than in conventional crops [START_REF]The 2013 European Union report on pesticide residues in food: The 2013 European Union report on pesticide residues[END_REF][START_REF] Barański | Higher antioxidant and lower cadmium concentrations and lower incidence of pesticide residues in organically grown crops: a systematic literature review and meta-analyses[END_REF]. In the last report of the EFSA, 15.5% of organic foods contained pesticide residues (0.8% above the Maximum Residue Level) vs. 44.4% of conventional products (2.7% above the Maximum Residue Level) [START_REF]The 2013 European Union report on pesticide residues in food: The 2013 European Union report on pesticide residues[END_REF]. It can thus be hypothesised that individuals who consume a high level of organic food in their diet would reduce their overall dietary exposure to pesticides, as highlighted in studies conducted in adults [START_REF] Oates | Reduction in urinary organophosphate pesticide metabolites in adults after a week-long organic diet[END_REF][START_REF] Curl | Estimating Pesticide Exposure from Dietary Intake and Organic Food Choices: The Multi-Ethnic Study of Atherosclerosis (MESA)[END_REF] or in children [START_REF] Curl | Organophosphorus Pesticide Exposure of Urban and Suburban Preschool Children with Organic and Conventional Diets[END_REF][START_REF] Bradman | Effect of Organic Diet Intervention on Pesticide Exposures in Young Children Living in Low-Income Urban and Agricultural Communities[END_REF].

Several epidemiological studies have suggested a link between pesticide exposure, obesity and metabolic disorders. Our findings may be interpreted in light of some studies which showed, for instance, a positive association between an increased risk for abnormal glucose regulation and exposure to pyrethroids [START_REF] Wang | Abnormal glucose regulation in pyrethroid pesticide factory workers[END_REF]. The findings from the Agricultural Health cohort study, conducted in 30,000 farmers in the US, suggested that long-term occupational exposure, in particular organochlorine and organophosphate insecticide exposure, could be associated with increased risk of developing diabetes [START_REF] Montgomery | Incident diabetes and pesticide exposure among licensed pesticide applicators: Agricultural Health Study, 1993-2003[END_REF]. However, epidemiological studies about the impact of exposure to organophosphorus compounds (widely used in agriculture) in the general population, on metabolic status are lacking.

Among potential mechanisms, it has been reported that exposure to endocrine disruptors such as various currently used pesticides, i.e. organophosphates and pyrethroids would impact glucose homeostasis and fat metabolism [START_REF]Pesticides : Effets sur la santé, une expertise collective de l'Inserm[END_REF][START_REF] Mostafalou | Pesticides and human chronic diseases: evidences, mechanisms, and perspectives[END_REF]. Organophosphorus compounds would disrupt carbohydrate homeostasis, resulting in elevated serum glucose levels [START_REF] Androutsopoulos | A mechanistic overview of health associated effects of low levels of organochlorine and organophosphorous pesticides[END_REF]. Among disrupting effects of the organophosphates, mechanistic pathways may involve oxidative damage and inflammatory cytokines, possibly leading to compensatory responses accompanied with reduced insulin signalling in insulin sensitive organs [START_REF] Mostafalou | Pesticides and human chronic diseases: evidences, mechanisms, and perspectives[END_REF].

This seems in agreement with our findings concerning biomarkers related to glucose and fat metabolism, namely glycaemia and triglyceride levels. As regards blood pressure, to our knowledge, no studies have investigated the specific association between exposure to widely used pesticides and blood pressure. Thus experimental and epidemiological data focusing on effects of organophosphate, pyrethroid or carbamate exposure and their synergistic association to hypertension risks are necessary. We did not find a significant higher level of HDL-cholesterol in individuals with higher organic food consumption after controlling for main confounding factors, in line with the few available data regarding dietary pesticide exposure and this specific biomarker.

Moreover, in addition to the potential effects of dietary pesticide exposure, mainly through conventional plant-food consumption, on cardiometabolic disorders, our results can also be interpreted in light of findings of a study carried out in rats which found overall better health and physiological parameters among animals fed with an organic diet, of which higher concentrations of polyphenols, plasma glucose, leptin or insulin-like growth factor 1 [START_REF] Średnicka-Tober | Effect of Crop Protection and Fertilization Regimes Used in Organic and Conventional Production Systems on Feed Composition and Physiological Parameters in Rats[END_REF].

In addition, our analyses interestingly showed a negative association between higher consumption of plant-based foods from organic origin and MetS while no association was detected in the case of meat, poultry and processed meat.

These results can be considered in the light of the EFSA report [START_REF]The 2013 European Union report on pesticide residues in food: The 2013 European Union report on pesticide residues[END_REF] which showed that about half plant food samples were contaminated by pesticide residues and that Maximum Residue Levels were frequently exceeded for some plant-based products (e.g. strawberries, lettuce, apples or oats) while this was not the case for animal-based foods like pork meat or cow milk. Thus, conventional foods from plant origin would be the major contributors to current dietary pesticide exposure, unlike animal-based foods.

Interestingly, we found significant negative associations between MetS prevalence and increasing consumption of dairy products and eggs. These findings can be interpreted in light of studies reporting more favourable fatty acid compositions in organic dairy products [START_REF] Palupi | Comparison of nutritional quality between conventional and organic dairy products: a meta-analysis[END_REF][START_REF] Średnicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, α-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF]. Indeed, recent meta-analyses have indicated that organic milk [START_REF] Średnicka-Tober | Higher PUFA and n-3 PUFA, conjugated linoleic acid, α-tocopherol and iron, but lower iodine and selenium concentrations in organic milk: a systematic literature review and meta-and redundancy analyses[END_REF], and potentially organic meat [START_REF] Średnicka-Tober | Composition differences between organic and conventional meat: a systematic literature review and meta-analysis[END_REF], contained a higher amount of omega-3 fatty acids, which are main components of a beneficial diet for the cardiovascular system, compared to conventional products. Switch from conventional to organic dairy and meat products could therefore lead to higher amounts of omega-3 fatty acids and in turn positively affect cardiometabolic status. However, it is difficult to conclude to the nutritional significance of these differences on health, as these increases remain probably modest and their effects unknown.

It should be borne in mind that high organic food consumers are high consumers of fruit, vegetables and whole-grain products [START_REF] Eisinger-Watzl | Customers Purchasing Organic Food -Do They Live Healthier? Results of the German National Nutrition Survey II[END_REF][START_REF] Baudry | Dietary intakes and diet quality according to levels of organic food consumption by French adults: cross-sectional findings from the NutriNet-Santé Cohort Study[END_REF]. Plant-based diets have also been associated with reduced risk of having MetS [START_REF] Kesse-Guyot | Adherence to Mediterranean diet reduces the risk of metabolic syndrome: A 6-year prospective study[END_REF][START_REF] Turner-Mcgrievy | Key Elements of Plant-Based Diets Associated with Reduced Risk of Metabolic Syndrome[END_REF]. In order to take into account the healthier dietary patterns of high organic food consumers, as well as other potentially healthy lifestyle factors that play a role in the development of MetS, we conducted stratified analyses, allowing to partially overcome the 'nutritional' role of dietary patterns. Except in smokers, a higher proportion of organic food in the diet was negatively related to MetS, whatever the subgroups considered. In particular, the association was observed across all subgroups. Among smokers, no association was observed between organic food consumption and MetS. One explanation might be that the tobacco risk factor prevails, and in turn, in this 'high risk subgroup', the consumption of organic food could play a minor role. Another explanation might be the relatively small size of that specific subgroup. The non-significant result may therefore be related to limited statistical power.

Limitations and strengths

A major limitation of this study includes its cross-sectional design, restricting causal inference.

Although we adjusted for a large number of cofounding factors including variables related to healthy lifestyles, we cannot omit the residual confounding due to the specific profiles of high organic food consumers [START_REF] Kim | Exposure to pesticides and the associated human health effects[END_REF][START_REF] Mostafalou | Pesticides: an update of human exposure and toxicity[END_REF]. In addition, the NutriNet-Santé participants are more interested in nutrition and health topics than the general population. Specifically, the participants of the NutriNet-Santé study exhibit specific sociodemographic profiles [START_REF] Andreeva | Comparison of the sociodemographic characteristics of the large NutriNet-Santé e-cohort with French Census data: the issue of volunteer bias revisited[END_REF] and healthier dietary habits [START_REF] Andreeva | Comparison of Dietary Intakes Between a Large Online Cohort Study (Etude NutriNet-Santé) and a Nationally Representative Cross-Sectional Study (Etude Nationale Nutrition Santé) in France: Addressing the Issue of Generalizability in E-Epidemiology[END_REF] than the general population. Besides, organic food consumption was assessed using a selfadministered food frequency questionnaire which is prone to measurement error. This has also probably led to an overestimation of the consumption in particular for organic food [START_REF] Baudry | Contribution of Organic Food to the Diet in a Large Sample of French Adults (the NutriNet-Santé Cohort Study)[END_REF]. Finally, we did not have data regarding genetic factors that can play a role in the onset of MetS. Caution is therefore needed when generalising the results.

Important strengths should also be acknowledged. We used accurate biological measures (performed in a single laboratory) and clinical data (assessed by trained technicians using standardised procedures). The use of the Org-FFQ permitted to obtain detailed data on food consumption, including information on the usual proportion coming from organic sources of several food groups, within the overall diet. The use of a wide range of covariables, including sociodemographic and lifestyle variables, enabled us to precisely characterise the individuals of our study. Furthermore, the large size of the sample enabled us to conduct stratified analyses with a sufficient statistical power, and to have a wide diversity of profiles.

Conclusion

In conclusion, our results provide the first insights into the undocumented research field of metabolic health and organic food consumption. Our findings show that the highest the consumption of organic food, the lowest the probability of having MetS. The associations that were observed merit future investigation, and bolster the argument for the conduction of welldesigned randomised controlled trials. Besides, further prospective research based on accurate data with regard to the nature of foods consumed is also needed to confirm these findings and assess the long-term effects of organic food consumption on metabolic disorders. In a context of tremendous growth of organic food consumption and where cardiovascular diseases remain the first cause of mortality worldwide, these findings could be of major interest to drive the design of future public health policies.

  provide information on sociodemographic and various lifestyle factors, health status, physical activity, anthropometric factors, and diet. The NutriNet-Santé study is conducted in accordance with the Declaration of Helsinki, and all procedures were approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm number 0000388FWA00005831) and the National Commission on Informatics and Liberty (CNIL numbers 908450 and 909216). Electronic informed consent was obtained from each participant (EudraCT no.2013-000929-31). Additionally, in 2011-2014, the NutriNet-Santé participants were invited, on a voluntary basis, to attend a visit for biological sampling and clinical examination in one of the local centres throughout France. Electronic and paper written informed consents were obtained from all participants attending the visit. All procedures were approved by the 'Consultation Committee for the Protection of Participants in Biomedical Research' (C09-42 on May 5th 2010) and the CNIL (n° 1460707).

c

  As a percentage of alcohol-free energy intake.

Figure 1 .

 1 Figure 1. Association between the proportion of organic food in the diet (as a continuous variable) and MetS across subgroups, n=8,174, NutriNet-Santé study Abbreviations: CI, Confidence Interval; mPNNS-GS, modified Programme Nationale Nutrition Santé Guideline Score; PR, Prevalence Ratios. a Model adjusted for age, sex, educational level, monthly income, season of blood sampling, time lag between organic food questionnaire completion and clinical visit, occupational status, location, energy intake and overall dietary quality (estimated by the mPNNS-GS as a continuous variable) and all other variables presented in theFigure.

Figure 1 .

 1 Figure 1. Association between the proportion of organic food in the diet (as a continuous variable) and MetS across subgroups, n=8,174, NutriNet-Santé study a

Table 1 . Main characteristics of the sample across tertiles of proportion of organic food in the diet, n=8,174, NutriNet-Santé study a

 1 Values are means (SD) or percent, as appropriate. Linear contrast test from ANCOVA (continuous variables) or Mantel-Haenszel Chi² trend-test (categorical variables).

	Characteristics	Tertile 1 (n=2,724)	Tertile 2 (n=2,725)	Tertile 3 (n=2,725)	P trend b
	Proportion of organic food in the whole diet (ratio)	0.04 (0.04)	0.24 (0.07)	0.62 (0.18)	<0.0001
	Men (%)	38.99	30.53	25.98	<0.0001
	Age (y)	59.03 (12.84)	57.96 (12.41)	57.50 (11.71)	<0.0001
	Educational level (%)				<0.0001
	Primary	25.48	23.05	20.99
	High school diploma	14.72	14.09	13.69
	University level or equivalent	59.80	62.86	65.32
	Occupational status (%)				<0.0001
	Retired	56.06	51.01	47.05
	Employees	9.14	9.69	9.69
	Intermediate profession	10.61	12.48	12.4
	Managerial staff	15.16	19.60	20.37
	Never employed	7.60	6.24	8.55
	Self employed	1.43	0.99	1.94
	Monthly income per household unit (%)				0.49
	Refuse to declare	4.70	4.18	5.21
	900-1200 €	8.26	6.72	7.78
	1200-1800 €	20.15	19.63	20.15
	1800-2700 €	27.90	28.77	29.43
	>2700 €	38.99	40.70	37.43
	Location (%)				<0.0001
	Rural community	16.45	18.79	21.98
	Urban unit with a population smaller than 20 000 inhabitants	13.99	14.13	16.11
	Urban unit with a population between 20 000 and 200 000 inhabitants	15.16	16.59	16.00
	Urban unit with a population higher than 200 000 inhabitants	54.41	50.50	45.91
	Smoking habits (%)				0.03
	Never smoker	46.26	47.63	50.61
	Former smoker	44.35	43.89	43.08
	Current smoker	9.40	8.48	6.31
	Physical activity level (%)				<0.0001
	Low	20.30	17.03	14.72
	Moderate	40.42	42.06	39.60
	High	39.28	40.92	45.69
	Time lag between dietary data collection and visit (months)	23.90 (11.38)	22.99 (11.39)	22.17 (11.36)	<0.0001
	mPNNS-GS (/13.5)	8.35 (1.79)	8.65 (1.71)	8.86 (1.78)	<0.0001
	Alcohol consumption (g/d)	127.77 (173.76)	121.85 (159.03)	98.07 (121.78)	<0.0001

a b

Acknowledgements

We thank all the people who helped carry out the NutriNet-Santé study and all dedicated and conscientious volunteers. We especially thank Younes Esseddik, Paul Flanzy, Nathalie Arnault, Fabien Szabo, Laurent Bourhis and Cédric Agaesse.

Activity Questionnaire; LDL, Low Density Protein; MetS, Metabolic Syndrome; mPNNS-GS, modified Programme National Nutrition Guideline Score; Org-FFQ, organic Food Frequency Questionnaire; Persistent Organic Pollutants, POP; PUFA, Polyunsaturated Fatty Acids; PR, Prevalence Ratios; SBP, Systolic Blood Pressure

Funding

The BioNutriNet project was supported by the French National Research Agency (Agence Nationale de la Recherche) in the context of the 2013 Programme de Recherche Systèmes Alimentaires Durables (ANR-13-ALID-0001). The NutriNet-Santé cohort study is funded by the Contribution statement SH, PG, DL and EKG conceived and designed research; JB performed the statistical analysis and wrote the article; JB, HL, SA, CJ, BA, SH, MT, DL, PG and EKG were involved in revising the work critically for important intellectual content; JB had primary responsibility for final content.

All authors read and approved the final manuscript.

Conflicts of interest

None of the authors declare any conflicts of interest. a Values are prevalence ratios, PR (95%CI) for the relation between the proportion of organic food as a categorical variable (tertiles) and Metabolic Syndrome modelled as a binary variable.

Model 1 is unadjusted. Model 2 is adjusted for age, sex, educational level, monthly income, physical activity, smoking habits, season of blood sampling, time lag between organic food questionnaire completion and clinical visit, occupational status, location, energy intake and overall dietary quality (estimated by the mPNNS-GS as a continuous variable). Model 3 is Model 2 further adjusted for BMI. Model 4 is Model 2 further adjusted for the anteriority of organic food consumption. b As the questionnaire from which the question was extracted was optional, sample size was 6,911. c Groups were defined using the median value as the number of non-consumers of the food group was particularly high. a Values are adjusted geometric means (95%CI) for the relation between the proportion of organic food as a categorical variable (tertiles) and metabolic traits. P-value referred to log-transformed variables.

Model 1 is unadjusted. Model 2 is performed among sub-samples who are not taking any related medications for the specific outcome and is adjusted for age, sex, educational level, monthly income, physical activity, smoking habits, season of blood sampling, time lag between organic food questionnaire completion and clinical visit, occupational status, location, energy intake and overall dietary quality (estimated by the mPNNS-GS as a continuous variable).