Delphine Klein

Mariette Mercier

Edwige Abeilard

Marc Puyraveau

Arlette Danzon

Véronique Dalstein

Astrid Pozet

Anne-Valérie Guizard

Michel Henry-Amar

Michel Velten
email: michel.velten@unistra.fr

Long-term quality of life after breast cancer: a French registry-based controlled study

Keywords: breast cancer, long-term survivors, population-based study, quality of life, time since diagnosis

Population-based studies on quality of life (QOL) of long-term breast cancer survivors are quite recent and insufficient attention has been paid to the effect of time since diagnosis. We compared long-term QOL of population-based breast cancer survivors 5, 10 and 15 years after diagnosis with that of healthy controls. Breast cancer survivors were randomly selected from three population-based cancer registries (Bas-Rhin, Calvados and Doubs, France) along with healthy controls, stratified for age and place of residence, randomly selected from electoral rolls. Participants completed five self-administered questionnaires: the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), Short Form-36 (SF-36), Spielberger State-Trait Anxiety Inventory (STAI), Multidimensional Fatigue Inventory (MFI) and a life conditions questionnaire. A four-level categorical variable and analysis of variance were used to compare QOL scores of breast cancer survivors by period (5, 10 or 15 years) of diagnosis with those of controls, adjusted for sociodemographic data and comorbidities. Six hundred and fifty-two cases and 1,188 controls participated in the study. For many QOL scales, scores were significantly different between cancer survivors and controls. A clinically significant difference was evidenced for the fatigue scales, the SF36 physical functioning, rolephysical and role-emotional scales, with more favorable results for controls. Differences decreased with time and 15-year cancer survivors were generally not different from controls. Scores were particularly influenced by age and mean household income. More efforts should be made, specifically during the first five to ten years after diagnosis, to help women with breast cancer to overcome their impairment in QOL.

Introduction

As for most industrialized countries, breast cancer is an important public health issue in France. It is currently the first cancer affecting women and its incidence has doubled during the last twenty-five years (from 22,000 new cases in 1980 to 50,000 cases in 2005) [1]. Young women pay an increasing tribute to this disease: sixty percent of women developing breast cancer are less than 65 years old [START_REF] Curado | Cancer incidence in five continents[END_REF]. Improvements in cancer treatments, early diagnosis and screening have led to an increased number of long-term breast cancer survivors. Whereas measurement and analysis of quality of life (QOL) in breast cancer patients included in clinical trials has become customary, long-term population-based comparative evaluation of QOL is quite recent [START_REF] Robb | Impact of breast cancer survivorship on quality of life in older women[END_REF][START_REF] Sweeney | Functional limitations in elderly female cancer survivors[END_REF][START_REF] Ganz | Life after breast cancer: understanding women"s health-related quality of life and sexual functioning[END_REF][START_REF] Ganz | Quality of life in long-term, disease-free survivors of breast cancer: a follow-up study[END_REF][START_REF] Baker | Health-related quality of life of cancer and noncancer patients in Medicare managed care[END_REF][START_REF] Michael | The persistent impact of breast carcinoma on functional health status: prospective evidence from the Nurses" Health Study[END_REF][START_REF] Peuckmann | Heath-related quality of life in long-term breast cancer survivors: nationwide survey in Denmark[END_REF]. In population-based studies where long-term effects of cancer on QOL were analyzed, the effect of time since diagnosis was almost never accounted for. Moreover, few studies have been performed on a large scale with a sufficient number of participantsnone of them in France.

Therefore, we decided to perform a multicenter population-based study on patients randomly selected from the French regional cancer registries of Bas-Rhin, Calvados and Doubs to compare QOL of breast cancer survivors 5, 10 and 15 years after diagnosis with QOL of healthy controls.

Materials and methods

Study design

We conducted a cross-sectional descriptive survey of long-term breast cancer survivors (named "cases" hereafter). These cases were randomly selected from files of the three population-based cancer registries of Bas-Rhin (North-Eastern France), Calvados (North-Western France) and Doubs (Eastern France). These registries cover a total population of 2.2 million inhabitants [START_REF] Curado | Cancer incidence in five continents[END_REF], representing 3.6% of the French population. In these three areas, the distribution of women by age group was comparable with the age distribution of women in France.

Cases were eligible if they had only developed breast cancer, and were able to provide a signed informed consent. On the basis of the information provided by the referent physician and by the respondents in the questionnaires, cases who received cancer therapy during the last five yearshormonotherapy exceptedwere not eligible to participate.

In order to study the effect of time since diagnosis on QOL, cases were selected among patients diagnosed in 1990, 1995 or 2000.

Cancer survivors were compared with controls randomly selected from electoral rolls of the three areas covered by the registries, with stratification on age (± 10 years) and place of residence (urban, 2000 inhabitants vs. rural area, < 2000 inhabitants) according to the INSEE (National Institute for Statistics and Economic Studies) definition at the time of the survey. Controls were checked against the registry database to ensure they had no prior history of cancer with the exception of non invasive skin cancer. Two controls were selected per case.

Instruments

Participants completed a questionnaire that included sociodemographic characteristics and a number of standardized validated instruments translated in French language designed to assess QOL, anxiety and fatigue.

General QOL was assessed using two questionnaires: the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) (version 3) and the Medical Outcomes Study 36-item Short Form Health Survey (SF-36) [START_REF] Aaronson | The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology[END_REF][START_REF] Fayers | EORTC QLQ-C30 Scoring Manual, 3rd edn[END_REF][START_REF] Mchorney | The MOS 36-item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs[END_REF][START_REF] Mchorney | The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions and reliability across diverse patients groups[END_REF][START_REF] Leplège | Preliminary analysis of the psychometric properties of the French version of an international questionnaire measuring the quality of life: the MOS SF-36 (version 1.1)[END_REF][START_REF] Leplège | Le questionnaire MOS SF-36. Manuel de l"utilisateur et guide d"interprétation des scores[END_REF]. The EORTC QLQ-C30 core questionnaire contains a global health scale, five functional scales (physical, role, emotional, cognitive and social), three symptom scales (fatigue, nausea/vomiting and pain), and six single items (dyspnea, insomnia, appetite loss, constipation, diarrhea and financial difficulties). For functional scales, scores computed range from 0 to 100, with higher values representing better function. For scales evaluating symptoms and financial impact, scores range from 0 to 100, with higher values representing a higher level of problems. The SF-36 contains eight multi-item scales: physical functioning, role-physical, role-emotional, bodily pain, social functioning, mental health, vitality and general health perceptions. Each scale is scored from 0 to 100, with higher scores representing a more favorable level of health. In addition, two summary scales -Physical Component Summary (PCS) and Mental Component Summary (MCS)computed from the former eight scales may be studied.

Anxiety was assessed using the French version of the Spielberger State-Trait Anxiety Inventory (STAI) [16]. It contains two 20-item forms that measure state anxiety (the level of present anxiety) and trait anxiety (the general level of anxiety experienced). The global score for each form is calculated as the sum of the twelve items, with higher scores indicating more severe anxiety. Only the "state anxiety" scale was analyzed for the present study.

The French version of the Multidimensional Fatigue Inventory (MFI-20) [START_REF] Smets | The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue[END_REF][START_REF] Gentile | Validation of the French "multidimensional fatigue inventory" (MFI-20)[END_REF][START_REF] Smets | Application of the multidimensional fatigue inventory (MFI-20) in cancer patients receiving radiotherapy[END_REF] was used to evaluate the participants" fatigue. This 20-item questionnaire is designed to measure multiple aspects of fatigue. It covers five dimensions: general fatigue, physical fatigue, reduced activity, reduced motivation and mental fatigue. Each dimension includes four items and ranges from 4 to 20 with higher scores indicating a higher degree of fatigue.

A linear transformation was used to standardize raw scores giving all QOL scores ranging from 0 to 100.

In addition to the above mentioned questionnaires, we collected information about family, social, professional status and comorbidity using a questionnaire for living conditions used in previous surveys [START_REF] Joly | Late psychosocial sequelae in Hodgkin"s disease survivors: a French population-based case-control study[END_REF][START_REF] Joly | Long-term quality of life in premenopausal women with node-negative localized breast cancer treated with or without adjuvant chemotherapy[END_REF][START_REF] Joly | Quality of life in long-term survivors of testicular cancer: a population-based casecontrol study[END_REF]. This questionnaire included items on education level, marital status, children, leisure occupations, life insurance problems, employment, use of medical services and familial and social relationships. Prior to the survey, the five questionnaires were tested on 30 subjects (15 cases and 15 controls -10 by registry area) not subsequently enrolled onto the population study.

For cases, information on clinical variables (date of diagnosis, tumor extension, surgery, radiotherapy, chemotherapy, hormone therapy, recurrence) was retrieved from medical records.

Procedure

The project was submitted to and approved by the Ethics Committee of the University Hospital of Besançon (Doubs, France) as well as by the National French Data Protection Authority (CNIL). In 2005, selected subjects were mailed a packet including (1) a letter presenting the aim of the study signed by the physician from the medical department where patients had been treated for cancer, and, for controls, by the co-investigator in charge of the study in the registry area, (2) the survey instruments, (3) an informed consent form, and (4) a postage-paid return envelope. A reminder was mailed after one month when necessary.

Statistical analysis

Descriptive analysis was performed using the Chi-square test or the Fisher"s exact test for categorical variables and the Kruskal-Wallis non parametric test for the QOL scores.

In order to identify sociodemographic variables significantly linked to QOL scores, we performed a multivariate analysis of variance in controls only, separately for each QOL instrument, considering the controls as representative of the general population.

Then, we performed an analysis of variance on both cases and controls in order to compare QOL scores between cases and controls, adjusting for sociodemographic variables found significantly linked to scores in the former step. Considering the study design, the stratification variables: registry area, place of residence (urban vs. rural) and age class (four categories) were systematically introduced as explanatory variables. A four-level categorical variable was used to classify controls and cases according to time (5, 10 and 15 years) since diagnosis.

Following this analysis, adjusted mean scores were computed. Two-sided tests were used in reporting the results.

To account for the number of tests performed, the significance level was set at 0.01 for all tests.

Regarding clinical significance of QOL scores, we relied on the values generally in use, following the work by Osoba et al.: a difference of 5 to 10 units (on a scale ranging from 0 to 100) was considered as small, from 10 to 20 it was considered as moderate, and a difference greater than 20 was considered as large [START_REF] Osoba | Interpreting the significance of changes in healthrelated quality-of-life scores[END_REF].

Missing data for component items of QOL scores were treated according to published recommendations [START_REF] Fayers | EORTC QLQ-C30 Scoring Manual, 3rd edn[END_REF][START_REF] Leplège | Le questionnaire MOS SF-36. Manuel de l"utilisateur et guide d"interprétation des scores[END_REF]16]. Statistical Analysis Software (version 9.1; SAS Institute Inc., Cary, NC) was used to analyze data [24].

Power considerations

Considering general results of QOL studies concerning the variability of scores, the present study was designed to be able to detect a difference of at least 10 points on a scale ranging from 0 to 100 when the standard deviation of the difference was equal to 60 (in a matched setting with two controls per case). With a first-type error of 0.01 and a power of ninety percent, this led to 536 cases being recruited. Consequently approximately 1,700 cases and 4,000 controls had to be selected. A thirty percent participation-rate was expected.

Results

Participation-rate and participants' characteristics

We selected 1,706 eligible breast cancer survivors. Of these, 652 completed the questionnaires, 289 for the 5 year period, 211 for the 10 year period and 152 for the 15 year period. The participation-rate was 33.7% in Bas-Rhin, 44.2% in Doubs and 49.8% in Calvados. Among the 3,935 controls contacted, 1,188 completed the questionnaires. The participation rate was 23.4% in Bas-Rhin, 30% in Doubs and 33.8% in Calvados (Fig. 1).

Main alleged reasons for non participation were: survey too long to complete, no time to answer or the subject was too old. Indeed, nonparticipants were older than participants (69.9 vs. 64.1 years; P < 0.0001 for cases; 67.5 vs. 63.3 years; P < 0.0001 for controls). There were no differences between groups regarding place of residence.

In the breast cancer survivors sample, nonparticipants had less chemotherapy (22.6% vs. 30.1%; P = 0.0024) and were diagnosed more frequently during the first period than participants (33.3% vs. 23.7%; P = 0.0008). There were no significant differences between groups with respect to tumor stage, surgery, radiotherapy or hormone therapy (this analysis was performed only on data from Doubs and Calvados).

Baseline sociodemographic characteristics of cases and controls are indicated in Table 1. There were no statistically significant differences between the two groups except for age, place of residence and monthly income. For age, a slight difference appeared in the age categories above 65, with cases being more represented between 65 and 74 than controls (30.6% vs. 24.9%) . There were also more controls living in urban areas than cases (78.4% vs. 73.0%). This distribution of the subjects was observed whatever the registry area or time since diagnosis, except for cases at 15 years who were more represented in the age category 65-74 than cases at 5 years (40.8% vs. 25.2%).

Clinical characteristics of cases are presented in Table 2. The mean time between the diagnosis and the completion of the questionnaires was 5.6 years for cases at 5 years (standard deviation, 0.96), 10.3 for cases at 10 years (standard deviation, 0.60) and 15.6 for cases at 15 years (standard deviation, 0.95). As was expected, cancer survivors at 5 years were more frequently treated by chemotherapy than cancer survivors at 15 years (54.6% vs. 39.2%). The same was true for hormone therapy. On the other hand, there were no significant differences between the three groups for surgery and radiotherapy. Particularly, the proportion of mastectomy was only moderately decreased for the more recent cases compared with the oldest cases.

Controls and QOL

According to the results of the multivariate analysis of variance, there were no statistically significant differences according to registry area or place of residence. Conversely, age class, marital status, education level, employment status, household monthly income, comorbidities and hospitalization during the last twelve months significantly influenced most of the scales. As a result, all these variables were included in the subsequent analysis of variance.

Comparison of QOL between cancer survivors and controls

EORTC QLQ-C30 questionnaire

Cancer survivors and controls showed significantly different adjusted mean scores for nearly all functional and symptom scales. Adjusted mean scores were only similar for emotional functioning, pain, constipation and diarrhea. We found a clinically significant difference, qualified as moderate, for the fatigue scale between cancer survivors at 5 years and controls (mean difference = 10.0). Interestingly, we observed an improvement with time in most scales except for cognitive functioning and insomnia scales (Table 3). Fifteen years after the diagnosis, cancer survivors were no longer different from controls. Mean household monthly income influenced most of the scores, irrespective of the time since diagnosis. For instance, for social functioning and role functioning, scores raised with increasing income. Nevertheless improvement of scores with time since diagnosis was still observed for all categories of income (Fig. 2A).

SF-36 questionnaire

All of the QOL scale scores were significantly different between cancer survivors and controls, with the exception of mental health (P = 0.015). The most clinically significant results are shown in Table 4. Cancer survivors at 5 years showed a clinically significant difference with controls for physical functioning (mean difference = -10.3), role-physical scale (mean difference = -12.1) and role-emotional scale (mean difference = -12.7). These differences decreased with time and 15 years after the diagnosis, cases were not longer different from controls. Physical functioning scores were higher in younger subjects, as was expected, but scores still increased with time since diagnosis for each age category. The same was true for role-emotional scores (Fig. 2B).

MFI-20 and Spielberger questionnaires

Cancer survivors had higher adjusted mean scores, indicating more fatigue, in all dimensions of the MFI-20 scale compared with the controls. The highest score was observed for general fatigue (49.8). Although all the differences between cases and controls were statistically significant, clinically significant differences were only found at 5 years for the general fatigue scale (mean difference = 11.3) and the physical fatigue scale (mean difference = 12.0). The adjusted mean scores decreased across time, leading to clinically non significant differences 15 years after the diagnosis, except for the mental fatigue scale (mean difference = 5.9). Reduced activity scores increased with age, as was expected, but decreased with time in every age category. Furthermore, physical fatigue scores decreased with increasing income, but the effect of time since diagnosis was not noticeable for the lowest income category (Fig. 3).

For the STAI scale, cancer survivors had higher adjusted mean scores (34.4 for cases at 5 years, 34.7 for cases at 10 years, 33.2 for cases at 15 years), indicating more anxiety, compared with controls (28.5) (P < 0.0001).

Discussion

This study demonstrates that long-term QOL, in general, and more specifically components of QOL such as fatigue, physical functioning and role limitations due to physical problems or emotional problems, are impaired by the occurrence of breast cancer in women. However, the gap between breast cancer survivors and populationbased controls seems to narrow as the time since onset of the disease increases. Indeed, cancer survivors at five years reported significantly poorer QOL scores compared to controls. However, differences decreased with time: cancer survivors at fifteen years showed higher QOL scores than patients with shorter time since diagnosis and were no longer different from controls.

In comparison with other population-based QOL studies, controls of the present study showed similar scores.

For instance, the crude SF-36 mean scores of controls, by age category, were comparable to reference values of the general French population [START_REF] Leplège | Le questionnaire MOS SF-36. Manuel de l"utilisateur et guide d"interprétation des scores[END_REF]. As no reference values for the QLQ-C30 scores were available in the French general population, we compared these scores with results from a German population-based study [START_REF] Schwarz | Reference data for the quality of life questionnaire EORTC QLQ-C30 in the general German population[END_REF]. The crude QLQ-C30 mean scores were similar to the reference values of the general German population except for cognitive functioning, fatigue, pain, dyspnea, insomnia and constipation: for these scales, the French women in our study had higher scores. Finally, the crude MFI-20 mean scores were similar to the reference values of the Danish general population, except for mental fatigue and reduced motivation for which the healthy controls showed higher scores [START_REF] Watt | Fatigue in the Danish general population. Influence of sociodemographic factors and disease[END_REF]. Consequently, we considered that our sample of controls was suited for comparison with the cases.

Results for cases were difficult to compare with information from other population-based studies. Indeed, most of detailed published results for QOL scores are adjusted on many variables, including age and several other factors such as education level or marital status. We could not find published adjusted results for the same covariables as ours. However, when compared with results of a United States cross-sectional study, crude SF-36 scores were lower in our study, even though mean time since diagnosis was only 3 years in the mentioned study [START_REF] Ganz | Life after breast cancer: understanding women"s health-related quality of life and sexual functioning[END_REF].

The findings of better physical functioning in the 15 year group in comparison with the 5 year group could in part be explained by a response shift. Survivors remaining disease-free for up to 15 years since initial diagnosis could have adopted a more pragmatic attitude regarding their everyday life experience, resulting in an improvement of perceived QOL. This result is in accordance with data of a study comparing QOL during disease progression with QOL for disease-free survivors up to 15 years after initial diagnosis and with QOL of a normative population [START_REF] Thong | The impact of disease progression on perceived health status and quality of life of long-term cancer survivors[END_REF].

The study design did not allow to disentangle effects of time since diagnosis from period effects, as time since diagnosis was different between the three groups. Such effects would tend to reduce the estimated difference in QOL between five-, ten-and fifteen-year survivors. Indeed, progress in breast cancer therapy, with a greater proportion of recent cases undergoing breast-conservative therapy or treated with improved chemotherapy, results in reduced side-effects and more favorable outcomes. However, in our study, there were only slight differences in the proportion of lumpectomies between five-and fifteen-year survivors (66.7% vs 62.2%).

Complementary analyses are in progress to explore in greater details the impact of disease severity and of treatment characteristics on QOL five-, ten-and fifteen-years after diagnosis.

Consequently, we may conclude that, in the absence of such effects, even greater differences would have been observed between five-year survivors and fifteen-year survivors.

The environmental and socioeconomic setting being different between the three areas, regional differences in QOL from one registry area to another were expected. Results demonstrated that no such differences existed, at least for the three French geographical areas considered. Place of residence (urban vs. rural) did not influence QOL either.

On the other hand, sociodemographic characteristics such as age, education level and household monthly income were associated with QOL, both for cases and controls. These findings support results of earlier research [START_REF] Cui | The long-term impact of medical and socio-demographic factors on the quality of life of breast cancer survivors among Chinese women[END_REF].

In addition to the use of validated standardized QOL questionnaires, the strength of the present study relies on the number of cases and controls included, on the population-based selection of cases and controls and on the three time-periods after diagnosis considered. A limitation of the study is its cross-sectional design, which did not allow to take into account individual changes in QOL over time.

Participation-rate was similar to rates reported for other population-based studies with mailed questionnaires [START_REF] Hager | Response rates for mail surveys of nonprofit organizations: a review and empirical test[END_REF]. As usually observed, participation for cases was slightly higher than for controls. Participants were slightly younger than nonparticipants for cases as well for controls. In the breast cancer survivors sample, there were no major differences in treatment-related variables between nonparticipants and participants. Thus, selection bias was limited. The variation in response rates across the three registries may be explained by the fact that North-Western inhabitants were more often approached to participate in mail surveys than Eastern inhabitants.

In conclusion, on the basis of these results, more efforts should be made, specifically during the first five to ten years after diagnosis, to help women with breast cancer to overcome the impairment in QOL evidenced on many scales.

Fig. 1 Fig. 2 Fig. 3

 123 Fig. 1 Study design

  scales (higher scores indicate a higher level of problems) C30 = European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 SD = standard deviation a Scores range from 0 to 100 b Adjusted for registry area, age, place of residence (urban/rural), marital status, education level, employment status, mean household monthly income, comorbidities and hospitalization during the last twelve months (Analysis of variance) c Computed as Adjusted mean(Cases) -Adjusted mean(Controls)

Table 1

 1 Sociodemographic characteristics at baseline

	No. (%)

* Chi-square test

Table 2

 2 Clinical characteristics of breast cancer survivors a

	No. (%)

Table 3

 3 EORTC QLQ-C30 scores by group a

	Crude	Adjusted b

Table 4

 4 SF-36 scores by group -Higher scores indicate a better health status a

	Crude	Adjusted b

Acknowledgments

This research is supported by grants from the French hospital research programme (Programme Hospitalier de Recherche Clinique), the "Fondation de France", the "Ligue Contre le Cancer du Doubs" and the "Institut National du Cancer". The authors wish to thank all the physicians from the three cancer registries areas who kindly agreed to ask cases they had treated to participate in the study.


Rino Bellocco

Chongqi Jia

Weimin Ye

Ylva Trolle Lagerros

Effects of physical activity, body mass index, waist-to-hip ratio and waist circumference on total mortality risk in the Swedish National March Cohort

Keywords: Body Mass Index, Cohort Studies, Epidemiology, Exercise, Mortality, Waist-Hip Ratio

Effects of physical activity, body mass index, waist-to-hip ratio and waist circumference on total mortality risk in the Swedish National March Cohort

Rino Bellocco, Chongqi Jia, Weimin Ye, Ylva Trolle Lagerros

Introduction

Both physical inactivity and obesity have been recognized as important risk factors for major chronic diseases and premature mortality in most developed and developing countries [START_REF] Katzmarzyk | Physical inactivity, excess adiposity and premature mortality[END_REF]. The prevalence of physical inactivity and obesity is on a rapid increase and as a consequence has resulted in major public health problems worldwide [START_REF]Obesity: preventing and managing the global epidemic. Report of a WHO consultation[END_REF][START_REF] Brundtland | From the World Health Organization. Reducing risks to health, promoting healthy life[END_REF].

Epidemiological studies have shown an inconsistent relationship between body mass index (BMI) and mortality risk, with associations being J-or U-shaped [START_REF] Calle | Body-mass index and mortality in a prospective cohort of U.S. adults[END_REF][START_REF] Yuan | Body weight and mortality: a prospective evaluation in a cohort of middle-aged men in Shanghai, China[END_REF][START_REF] Durazo-Arvizu | Mortality and optimal body mass index in a sample of the US population[END_REF][START_REF] Adams | Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old[END_REF][START_REF] Corrada | Association of body mass index and weight change with all-cause mortality in the elderly[END_REF][START_REF] Gu | Body weight and mortality among men and women in China[END_REF][START_REF] Jee | Body-mass index and mortality in Korean men and women[END_REF][START_REF] Neovius | Combined effects of overweight and smoking in late adolescence on subsequent mortality: nationwide cohort study[END_REF][START_REF] Whitlock | Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies[END_REF], direct linear [START_REF] Stevens | The effect of age on the association between body-mass index and mortality[END_REF][START_REF] Ajani | Body mass index and mortality among US male physicians[END_REF][START_REF] Hu | Adiposity as compared with physical activity in predicting mortality among women[END_REF][START_REF] Freedman | Body mass index and all-cause mortality in a nationwide US cohort[END_REF] or absent [START_REF] Diehr | Body mass index and mortality in nonsmoking older adults: the Cardiovascular Health Study[END_REF][START_REF] Haapanen-Niemi | Body mass index, physical inactivity and low level of physical fitness as determinants of all-cause and cardiovascular disease mortality--16 y follow-up of middle-aged and elderly men and women[END_REF]. This could be due to the inadequate control for smoking status and pre-existing chronic diseases, both of which are associated with a low BMI and an increased risk of death [START_REF] Willett | Guidelines for healthy weight[END_REF]. One possible approach to control this bias is to disregard deaths occurring in the initial period of follow-up and to restrict the analyses to 'never smokers', without pre-existing chronic diseases. Lately, even offspring BMI has been used as an indicator of BMI to avoid the risk of reverse causality when studying the association between BMI and mortality [START_REF] Smith | The association between BMI and mortality using offspring BMI as an indicator of own BMI: large intergenerational mortality study[END_REF].

There are several methods to measure body fatness. The relative ease and low cost of obtaining BMI make it commonly used in epidemiological studies and routine clinical care. Nonetheless, it does not distinguish fat mass from lean body mass [START_REF] Willett | Guidelines for healthy weight[END_REF], nor does it capture the distribution of fat over the body [START_REF] Snijder | What aspects of body fat are particularly hazardous and how do we measure them?[END_REF]. With a high specificity of detecting obesity but a lower sensitivity, centralized obesity is not always captured by a high BMI alone [START_REF] Chakraborty | Sensitivity and specificity of body mass index as a definition of the obesity component of metabolic syndrome[END_REF]. Waist circumference or waist-to-hip ratio (WHR) may offer additional insights. Pischon et al. [START_REF] Pischon | General and abdominal adiposity and risk of death in Europe[END_REF] showed that measurement of both general and centralized adiposity provides a better assessment of the risk of deathespecially among those with a lower BMI. Other studies have shown that centralized adiposity as measured by WHR may be a more accurate predictor of cardiovascular risk than BMI [START_REF] Yusuf | Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study[END_REF], while waist circumference has been shown to independently predict risk of type II diabetes better than BMI and WHR [START_REF] Wang | Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men[END_REF].

A number of prospective studies [START_REF] Andersen | All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work[END_REF][START_REF] Gregg | Relationship of changes in physical activity and mortality among older women[END_REF][START_REF] Myers | Fitness versus physical activity patterns in predicting mortality in men[END_REF][START_REF] Trolle-Lagerros | Physical activity as a determinant of mortality in women[END_REF][START_REF] Khaw | Work and leisure time physical activity assessed using a simple, pragmatic, validated questionnaire and incident cardiovascular disease and all-cause mortality in men and women: The European Prospective Investigation into Cancer in Norfolk prospective population study[END_REF][START_REF] Chakravarty | Reduced disability and mortality among aging runners: a 21-year longitudinal study[END_REF] and reviews [START_REF] Nocon | Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis[END_REF][START_REF] Oguma | Physical activity and all cause mortality in women: a review of the evidence[END_REF] demonstrate that physical activity could substantially decrease mortality. However, whether the health benefits of physical activity interact with well-known mortality risk factors, such as obesity, has not been studied extensively. It has been proposed that physical activity could counteract the risk of obesity. Physical activity has been shown to weaken, but not eliminate the heightened risk associated with excess weight [START_REF] Hu | Adiposity as compared with physical activity in predicting mortality among women[END_REF][START_REF] Koster | Joint associations of adiposity and physical activity with mortality: the National Institutes of Health-AARP Diet and Health Study[END_REF][START_REF] Hu | The effects of physical activity and body mass index on cardiovascular, cancer and all-cause mortality among 47 212 middle-aged Finnish men and women[END_REF]. However, many studies to date measure only one component of total activity, such as leisure time or occupational physical activity, which may not be a true representation of actual total physical activity.

In this prospective study, we used a validated nine-step instrument to assess total physical activity [START_REF] Lagerros | Validity and reliability of self-reported total energy expenditure using a novel instrument[END_REF]. We focused on examining the inter-and intra-relationships between total physical activity, BMI, WHR, waist circumference and all-cause mortality in men and women in the Swedish National March Cohort.

Subjects and Methods

Study cohort

In September 1997 we targeted a general population cohort, the Swedish National March Cohort which has been described in detail previously [START_REF] Lagerros | Measures of physical activity and their correlates: the Swedish National March Cohort[END_REF]. The National March, was a four day national fund raising event arranged by the Swedish Cancer Society and taking place in almost 3,600 Swedish cities and villages. All National March participants were invited to fill out a 32-page questionnaire with detailed questions about energy intake, energy expenditure, anthropometric measures and conceivable confounders. It was assumed that participants would be particularly motivated to provide valid and high quality exposure information. Follow-up was facilitated by the provision of national registration numbers (unique identification numbers assigned to all Swedish residents), enabling accurate tracking of health status through linkage to existing nationwide and continuously updated databases.

Due to the character of this event, the total number of individuals who were actually given a questionnaire could not be assessed. In total 43,880 participants with a mean age of 50.3 years, (7.7 to 94.2 years old), completed the questionnaire, but we excluded subjects due to any type of cancer (n=2,752), cardiovascular disease (n=362, ICD-10 codes: I00-I99), subjects with an incorrect national registration number (n=12), death at time of enrollment (n=8), subjects below the age of 20 years at the end of follow-up (n=7), emigration at time of enrollment (n=6) and clearly inconsistent answers (n=4). Thus, there were 40,729 subjects included in the final analysis, 14,585 men and 26,144 women. The study was approved by the Regional Ethical Review Board at Karolinska Institutet and all subjects provided informed consent.

Baseline measures

Total physical activity was self-reported. In addition, participants were also requested to measure and report their height, weight, waist circumference (at the umbilicus) and hip circumference (maximal trochanters)for the latter two, there was a pictured instruction in the questionnaire.

Physical activity during an average week day was estimated using an instrument with nine physical activity steps which graded physical activity according to intensity. Each step was assigned a value expressed as a multiple of Metabolic Energy Turnover (MET) and exemplified by common activities. MET is defined as the ratio of work metabolic rate to a standard resting metabolic rate of 1 kcal • kg -1 body weight • hour -1 , with 1 MET equivalent to quiet sitting [START_REF] Ainsworth | Compendium of physical activities: classification of energy costs of human physical activities [see comments[END_REF][START_REF] Ainsworth | Compendium of physical activities: an update of activity codes and MET intensities[END_REF]. The MET values assigned to the nine different steps were 0.9, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0 and 8.0. Participants were instructed to report the time spent on each intensity level during an average day and night, hence total physical activity time should add up to 24 hours and allow for an estimate of MET•hours per day (METh/day) and a total physical activity score using the equation of . This nine-step total physical activity instrument has been previously validated giving a Pearson correlation coefficient of 0.73 with three 24-h recalls [START_REF] Lagerros | Validity and reliability of self-reported total energy expenditure using a novel instrument[END_REF]. In a validation study with doubly labeled water as a reference, the mean difference in PAL (physical activity level, i.e. total energy expenditure minus basal metabolic rate) between the reference method and the instrument was small, 0.07 [START_REF] Bexelius | Measures of physical activity using cell phones: validation using criterion methods[END_REF]. The physical activity questionnaire is available online [START_REF] Egger | The emergence of "lifestyle medicine" as a structured approach for management of chronic disease[END_REF].

The distribution of total physical activity was categorized into low, medium and high levels by dividing METh/day into tertiles, with cutoffs of 34.3 and 46.9 METh/day for males and 31.5 and 38.9 METh/day for females, respectively. Waist circumference was categorized according to the International Diabetes Federation consensus defining central obesity in Europids as a waist circumference ≥94 cm and severe obesity to ≥102 in men and ≥80 respectively ≥88 cm in non-pregnant women [START_REF] Alberti | The metabolic syndrome--a new worldwide definition[END_REF]. BMI was calculated as weight in kilograms divided by height in meters squared and categorized as normal weight (< 25 kg/m 2 ), overweight (25-29.9 kg/m 2 ) and obese (≥30 kg/m 2 ). Waist-hip ratio (WHR) was defined as the ratio of circumference of waist to hip; cut-offs were selected according to the clinical criteria for central obesity, 1.0 for men and 0.8 for women.

Among possible confounders we included cigarette smoking (never, past, current); alcohol drinking (all types of alcoholic beverage: never, low: ≤3 times/month, medium:

1-6 time/week, high: ≥1 time/day); use of vitamin and mineral supplements (no, yes) and level of education (7-9, 10-12 or >12 years), use of contraceptive pills (no, yes) and hormone replacement therapy (no, yes).

Follow-up

Follow-up started on October 1, 1997 until May 31, 2007, death or emigration, whichever came first. Mortality data was obtained from the Swedish Death Register using participants' unique national registration number.

Statistical analyses

Pearson's chi-squared tests and one-way anova models were used to compare the differences between frequencies and means among different levels of total physical activity, respectively. A Cox proportional hazards regression model with age as the underlying time variable, was fitted to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of all-cause mortality at different levels of total physical activity, BMI, WHR, waist circumference and their combinations, as compared with the reference category, normal weight with high physical activity. Ties were handled using the Breslow method. The 'proportional hazards assumption' in the Cox regression model was assessed by using the scaled Schoenfeld residuals, based on the martingale and deviance residuals.

We first studied the graphical plots and then carried out formal statistical tests of their independence over time.

Men and women were analyzed separately. The effect of physical activity, BMI, waist hip ratio, and waist circumference was studied using two sets of Cox models: adjusted only by age at enrollment, and adjusted by age at enrollment plus potential confounders. For men we included cigarette smoking, alcohol drinking, use of vitamin and mineral supplements, and level of education. For women, we also added use of contraceptive pills and hormone replacement therapy. Waist circumference was in an additional analysis adjusted for BMI. Linear trends of HRs were tested by using the median value of each category as a single ordinal variable in the Cox regression model. Interactions between total physical activity and BMI, WHR or waist circumference were assessed by including the cross-product interaction term with the main effect terms in the Cox regression model.

In addition, to better study the functional form of the association of total physical activity, BMI, WHR, and waist circumference with death, a flexible regression model was considered, based on restricted cubic splines, with 4 knots, corresponding to the 5 th , 25 th , 75 th , and 95 th percentiles of the variables.

Statistical significance of interaction was tested using the likelihood ratio test by comparing nested models with and without such term. We also used a graphical technique recently proposed. It is called subpopulation treatment-effect pattern plot (STEPP) [START_REF] Bonetti | A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data[END_REF].

Using this technique, we produced a nonparametric estimate of the physical activity effect, simplified to a binary variable (low/high) within strata defined by BMI, WHR and waist circumference groups. This method is useful for identifying interactions between an exposure binary variable and a continuous covariate of interest. Having several subpopulations, with overlapping tails, the overall effect for the entire sample is visible in the middle part of the plot. With increasing distance from the center, subjects with high values of the covariate are removed from the left and subjects with low values of the covariate are removed from the right.

In order to avoid the influence of a potential bias due to early mortality in individuals with low physical activity and low BMI caused by potential sub-clinical illness, the analyses were repeated after excluding the first two years of follow-up, current/previous smokers and subjects who had lost weight and become underweight (BMI <18.5 kg/m 2 ) between the age of 20 years and age at baseline. The proportion of missing BMI, WHR and waist circumference values was 4.8, 26.8 and 26% respectively. For the physical activity score, 1.25% of the participants did not respond on any of the specific levels of activities. The percentage of missing values for the other variables that were used in the multivariate models was relatively low, always lower than 5%.

All statistical analyses were performed with STATA version 11.0 (Stata Corporation, College Station, TX, USA). All reported probabilities (p-values) were two-sided and less than 0.05 was considered statistically significant.

Results

According to Statistics Sweden, the initial cohort members (before exclusions), were less educated; 38.9% had only compulsory schooling, compared to 22.2% in the general Swedish population in 1997. They were also less likely to be smokers, 9.4% compared to 19.2% of Swedes aged 16-84 at that time. These proportions slightly decreased once selection criteria were applied. Baseline characteristics of subjects (divided into three different total physical activity levels) are shown in Table 1. In general, sedentary men were more likely to be smokers. Both men and women with a high level of physical activity were significantly younger and less educated. The prevalence of alcohol drinking was significantly lower in physically inactive women, but there were no significant differences in alcohol intake across different physical activity levels in men. Table 1 here. During 9.7 years of follow-up, there were 1943 deaths, 4.8% of the total sample (1121 males and 822 females). The mortality rates and HRs of all-cause death for total physical activity are illustrated in Table 2. Compared with a low level of physical activity, multivariate-adjusted HRs associated with medium and high physical activity levels were 0.76 (95% CI: 0.68-0.94) and 0.75 (95% CI: 0.63-0.90) in men. This pattern was not seen in women. Table 2 here.

Compared with normal weight, multivariate-adjusted HRs associated with overweight and obesity were 1.12 (95% CI: 0.97-1.30), and 1.62 (95% CI: 1.21-2.17) in men, and 1.02 (95% CI: 0.85-1.22) and 1.35 (95% CI: 1.02-1.78) in women, respectively (Table 3).

Table 3 here.

WHR was not significantly associated with all-cause mortality risk, but tended to increase the risk in both men and women (Table 4). From the smoothed plot it is apparent that the effect levels off for high values of WHR in both sexes (Figure 1). Hip circumference was not associated with all-cause mortality risk either (data not shown).

Compared with a healthy waist circumference, multivariate-adjusted HRs associated with central obesity were 1.30 in men, and 1.35 in women, respectively. The p value for linear trend was statistically significant in both genders (Table 5), but as seen from the graph (Figure 1) careful interpretation must be paid in this case. Furthermore, when adjusting the effect of waist circumference for BMI, the multivariate-adjusted HRs decreased to 1.10, 95% CI: 0.85-1.42 among men and 1.26, 95% CI: 0.96-1.65 among women. Given these confidence intervals, the change was not substantial. Table 4, 5 and figure 1 here.

The statistical analysis of the combined effect of physical inactivity and anthropometric measures on all cause-mortality risk did not reveal a consistent pattern of interaction between the factors being investigated. The joint effects, based on the underlying categorical variables (BMI, WHR and waist circumference) are shown in Figure 2. The negative combined effect of inactivity and rising waist circumference in women was captured by a hazard ratio increasing from 1.59 (95% CI: 1.09-2.34) to 1.71 (95% CI:

1.16-2.51) and 1.97 (95% CI: 1.35-2.89) compared to women with low waist circumference and high physical activity (p for interaction < 0.05). A less clear effect (p for interaction = 0.13) was seen for men, with a mortality hazard ratio of 1.68 (95% CI:

1.25-2.27) when comparing inactive men with high waist circumference to physically active men with low waist circumference. There were no significant interactions between physical inactivity and BMI (p = 0.25 in men and 0.36 in women), or physical inactivity and WHR (p = 0.33 in men and 0.79 in women). But in the absence of interaction the two risk factors are additive; comparing men with a normal BMI and high total physical activity, to sedentary men with a BMI over 30, the latter group had a multivariate-adjusted HR of 1.80 (95% CI: 1.19-2.74).

In Figure 3 we show the results obtained by fitting the STEPP to our data once we stratified by gender. Independently of strictly statistical significance for the interaction, the plots suggest that the protective effect of high physical activity is somewhat weaker for lean subjects. The moderate proportion of missing values for BMI, and the higher proportion of missing values for the waist circumference and WHR were not associated with our main confounders. As a consequence, statistical inference barely changed when imputation methods were applied (results not shown). The assessment of proportional hazard in Cox regression was fulfilled in all fitted models.

Discussion

In our study, obese men with a low level of total physical activity had an 80 percent increased all-cause mortality risk compared with normal weight men with high level of total physical activity. Women with a high waist circumference and a low level of total physical activity had an almost doubled all-cause mortality risk compared with women who had a low waist circumference and high level of total physical activity.

Several prospective studies have evaluated the joint effects of physical activity and obesity with total mortality. In a cohort of middle-aged and elderly men, physically active obese and overweight men had a 60% higher death rate from all causes than lean active men. Furthermore, the high level of physical activity did not compensate the excess mortality associated with obesity or overweight [START_REF] Orsini | Combined effects of obesity and physical activity in predicting mortality among men[END_REF]. The Lipid Research Clinics Study assessed the effects of fitness and fatness on longevity [START_REF] Stevens | Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study[END_REF]. Although there were no significant interactions on all-cause mortality between fitness and fatness in either men or women, there was an increased multivariate-adjusted mortality risk both in men and women who were categorized as fit-fat, unfit-unfat, and unfit-fat compared with those categorized as fit-unfat. In our study, we did not assess fitness, although physical activity is one of the primary determinants of fitness. Even moderate levels of physical activity can improve cardiorespiratory fitness to a level associated with substantial health benefits [START_REF] Stofan | Physical activity patterns associated with cardiorespiratory fitness and reduced mortality: the Aerobics Center Longitudinal Study[END_REF]. BMI, WHR and waist circumference are all measures of adiposity and are positively associated with total mortality [START_REF] Katzmarzyk | Physical inactivity, excess adiposity and premature mortality[END_REF], but such associations may be sex, and/or age dependent. The Buffalo Health Study demonstrated that there was an increased risk of death associated with increased BMI in men, but not in women [START_REF] Dorn | Body mass index and mortality in a general population sample of men and women. The Buffalo Health Study[END_REF]. The Malmö Diet and Cancer Study reported a significant multivariate-adjusted dose-response relationship between WHR (independent of overall body fat) and all-cause mortality in women, but not in men [START_REF] Lahmann | A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer Study[END_REF]. Recently the European Prospective Investigation into Cancer and Nutrition (EPIC) found the highest relative risk of death among the participants in the lowest third of BMI and the highest quintile of waist circumference or WHR, independent of gender [START_REF] Pischon | General and abdominal adiposity and risk of death in Europe[END_REF]. Bender et al. showed that mortality associated with obesity decreased with age in both sexes [START_REF] Bender | Effect of age on excess mortality in obesity[END_REF], and Gulsvik et al. found that a low BMI was an independent risk factor of mortality in elderly in a large population sample followed for 42 years [START_REF] Gulsvik | Increased mortality in the slim elderly: a 42 years follow-up study in a general population[END_REF]. Another prospective study of mortality showed that BMI had U-shaped and negative linear functions in younger and older women, respectively, but WHR had positive linear trends both in younger and older women [START_REF] Lindqvist | Concurrent and separate effects of body mass index and waist-to-hip ratio on 24-year mortality in the Population Study of Women in Gothenburg: evidence of age-dependency[END_REF]. Recently, a Danish study of anthropometric measures found that hip circumference was positively associated with venous thromboembolism in women [START_REF] Severinsen | Anthropometry, body fat, and venous thromboembolism: a Danish follow-up study[END_REF]. We could not see an association between hip circumference and mortality in our study. However, we found an age and multivariate adjusted dose-response risk of total mortality with increasing BMI and waist circumference, which was significant in both men and women. No clear pattern was seen for WHR, for either genderbut WHR may be more difficult to interpret since it could reflect an effect of larger waist as well as a smaller hip circumference [START_REF] Snijder | What aspects of body fat are particularly hazardous and how do we measure them?[END_REF], while waist circumference alone is more strongly correlated with visceral adiposity [START_REF] Onat | Measures of abdominal obesity assessed for visceral adiposity and relation to coronary risk[END_REF]. Some prospective studies [START_REF] Calle | Body-mass index and mortality in a prospective cohort of U.S. adults[END_REF][START_REF] Yuan | Body weight and mortality: a prospective evaluation in a cohort of middle-aged men in Shanghai, China[END_REF][START_REF] Durazo-Arvizu | Mortality and optimal body mass index in a sample of the US population[END_REF][START_REF] Adams | Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old[END_REF][START_REF] Corrada | Association of body mass index and weight change with all-cause mortality in the elderly[END_REF][START_REF] Gu | Body weight and mortality among men and women in China[END_REF][START_REF] Jee | Body-mass index and mortality in Korean men and women[END_REF][START_REF] Meyer | Body mass index and mortality: the influence of physical activity and smoking[END_REF] and a meta-analysis [START_REF] Katzmarzyk | Physical inactivity, excess adiposity and premature mortality[END_REF] have suggested a J-or U-shaped relationship between BMI and total mortality. A review of guidelines for a healthy weight by Willett et al. [START_REF] Willett | Guidelines for healthy weight[END_REF] suggests that reverse causation and confounding factors (particularly smoking) could distort this relationship. As such, in our analyses, we excluded subjects with cardiovascular diseases (ICD10 codes: I00-I99) and any types of cancer at baseline and carried out multivariate adjustment for potential confounders, including smoking status. The sensitivity analyses excluding death occurring during the first 2 years of follow-up, previous and current smokers and subjects who had lost significant weight since the age of 20, did not alter the association, but rather enhanced them among men.

Our study has several strengths. First, this was a prospective homogeneous populationbased study comprising a large number of both men and women. Second, the period of follow-up was sufficiently long, almost 10 years, during which mortality were ascertained without losses to follow-up. Third, we use a validated instrument designed to capture the whole spectrum of habitual physical activity frequently missed in other studies using more simplified methods of inquiry.

Several potential limitations of our study warrant further consideration. All data were obtained by self-report at baseline, which might result in distorted inter-relationships between physical activity, BMI, WHR, waist circumference and total mortality, because obese people commonly overestimate their physical activity [START_REF] Hu | Adiposity as compared with physical activity in predicting mortality among women[END_REF] and underestimate self-measured WHR [START_REF] Roberts | Accuracy of self-measurement of waist and hip circumference in men and women[END_REF]. Overestimation of height and underestimation of weight might also result in misclassification of BMI, although such errors tend to be small [START_REF] Palta | Comparison of self-reported and measured height and weight[END_REF][START_REF] Stewart | Underestimation of relative weight by use of self-reported height and weight[END_REF].

Nevertheless, self-measured and self-reported weight, waist and hip circumference have been found to be reasonably valid. Rimm at al. [START_REF] Rimm | Validity of self-reported waist and hip circumferences in men and women[END_REF] conducted a validation study where 123 men (aged 40-75 years) and 140 women (aged 41-65 years) were asked to self-report weight, waist and hip circumference in a questionnaire. When comparing this to standardized measurements the crude Pearson correlation coefficients were between 0.69-0.97 for men and 0.7-0.97 for women.

The proportion of missing BMI values was rather low, and men and women had the same propensity in reporting their BMI. The distribution of BMI was virtually identical among those who did and did not report waist circumference and waist hip ratio. However, women were more often able to report their waist circumference and waist hip ratio. It has previously been shown that women are more willing to respond to questionnaires overall [START_REF] Hazell | Factors influencing the response to postal questionnaire surveys about respiratory symptoms[END_REF][START_REF] Feveile | A randomized trial of mailed questionnaires versus telephone interviews: response patterns in a survey[END_REF][START_REF] Eaker | Response rate to mailed epidemiologic questionnaires: a population-based randomized trial of variations in design and mailing routines[END_REF], and this might also apply to response pattern of individual questions.

The lack of follow-up of anthropometric measures is further a limitation of this study.

Body fat distribution may have changed during the follow-up, but this is usually a slow process. A longitudinal study showed that the variation of body weight and adipose tissue distribution was surprisingly stable throughout a similar time span as our study [START_REF] Katzmarzyk | Seven-year stability of indicators of obesity and adipose tissue distribution in the Canadian population[END_REF].

There is a chance that changes in physical activity over time could influence our results.

Other unmeasured confounders might also bias the observed associations between total physical activity, BMI, WHR, waist circumference and total mortality. We conclude that both physical inactivity and adiposity increase total mortality risk independently. Although there are no significant interactions between physical inactivity and anthropometric measures, the additive effect of being both obese and physically inactive increase mortality risk further.

  Abbreviations:

Figure 2

 2 Figure 2 here.

Figure 3

 3 here.The analyses were repeatedly performed after excluding the person-years and deaths occurring during the first 2 years of follow-up, previous and current smokers, and individuals who lost significant weight (as defined earlier) before enrolling in the present cohort study. None of the aforementioned relation patterns were appreciably altered among women, however, the multivariate-adjusted HR increased among obese men with a BMI ≥30 to 2.59 (95% CI: 1.73-3.87) or a waist circumference ≥102 cm 1.54 (95% CI:1.16-2.05).

Figure 1 .

 1 Figure 1. Adjusted Hazard Ratios (HR) to study the association between overall

Figure 2 .

 2 Figure 2. Adjusted Hazard Ratios and 95% confidence intervals for sex specific overall

Figure 3 .

 3 Figure 3. Hazard Ratios and 95% confidence intervals to study the effect of physical

Figure 3

 3 Figure 1

Table 1 .

 1 Selected baseline characteristics of participants, categorized by total physical activity levels. The Swedish National March Cohort.

	Variables	Low	Total physical activity Medium High	p-value
	Male
	Number of participants	4862		4862	4861
	Age, years (median, IQR)	54.8, 26.4		54.2, 25.3	53.3, 32.1	<0.001
	Height, meters (median, IQR)	1.78, 0.9		1.78, 0.9	1.78, 0.9	0.057
	Weight, kg (median, IQR)	79.0, 15.0		78.0, 13.0	78.0, 13.0	<0.001
	BMI, kg/m 2 (median, IQR)	28.8, 4.0		24.7, 3.7	24.5, 3.7	<0.001
	Waist circumference, cm (median,	94.0, 12.0		92.0, 11.0	92.0, 13.0	<0.001
	IQR)
	Hip circumference, cm (median,	100.3±9.4		99.2±9.0	98.7±9.5	<0.001
	IQR)
	WHR (median, IQR)	0.94±0.09		0.93±0.09	0.93±0.09	<0.001
	Education (>12 years, %)	29.4		26.8	13.9	<0.001
	Current smoking (%)	8.3		7.0	8.0	<0.001
	Alcohol drinking (%)	89.1		89.7	89.0	0.513
	Use of vitamin and mineral	37.8		39.5	37.7	0.120
	supplements (%)
	Female
	Number of participants	8716		8717	8711
	Age, years (median, IQR)	51.0±16.6		47.7±15.3	46.9±15.8	<0.001
	Height, meters (median, IQR)	1.65±0.06		1.66±0.06	1.66±0.06	0.001
	Weight, kg (median, IQR)	67.4±11.2		66.2±10.6	65.5±10.4	<0.001
	BMI, kg/m 2 (median, IQR)	24.6±3.9		24.1±3.7	23.9±3.6	<0.001
	Waist circumference, cm (median,	82.0±10.8		80.2±10.2	79.2±9.8	<0.001
	IQR)
	Hip circumference, cm (median,	101.0±10.3	99.6±12.1	98.5±12.8	<0.001
	IQR)
	WHR (median, IQR)	0.81±0.09		0.81±0.10	0.81±0.10	0.002
	Education (>12 years, %)	28.0		34.0	27.8	<0.001
	Current smoking (%)	8.70		8.13	9.09	0.020
	Alcohol drinking (%)	84.7		88.0	86.7	<0.001
	Use of vitamin and mineral	52.4		53.4	54.0	0.114
	supplements (%)
	Use of contraceptive pill (%)	61.1		67.5	65.5	<0.001
	Hormone replacement therapy (%)	29.4		25.5	23.9	<0.001
	BMI = Body Mass Index
	WHR = Waist-to-Hip Ratio
	IQR= Interquartile range

Table 2 .

 2 Mortality rates and hazard ratios of all-cause death for total physical activity. The Swedish National March Cohort.

			Total physical activity		p for
		Low	Medium	High	trend
	Male
	Number of deaths	470	333	318
	Person-years	44652	45541	45380
	a Mortality rate	1001.39	773.18	762.28
	b HR (95% CI)	1.00 (reference)	0.82 (0.71-0.94)	0.82 (0.71-0.95) 0.016
	c HR (95% CI)	1.00 (reference)	0.76 (0.68-0.94)	0.75 (0.63-0.90) 0.003
	Female
	Number of deaths	384	217	221
	Person-years	82731	83294	82832
	a Mortality rate	375.80	290.9	309.31
	b HR (95% CI)	1.00 (reference)	0.85 (0.72-1.00)	0.91 (0.77-1.08) 0.366
	c HR (95% CI)	1.00 (reference)	0.91 (0.74-1.11)	0.94 (0.77-1.15) 0.595
				alcohol
	drinking, use of vitamins and minerals, and educational level in men; further adjusted for
	contraceptive pill use and hormone therapy replacement in women.
	HR = Hazard Ratio
	CI = Confidence Interval

a Mortality rates were presented as per 100,000 person-years, standardized to the age distribution of person-years experienced by all study participants using 5-year age categories. b Adjusted for age at enrollment. c Adjusted for age at enrollment, body mass index, cigarette smoking status,

Table 3 .

 3 Mortality rates and hazard ratios of all-cause death for body mass index. The Swedish National March Cohort. Mortality rates were presented as per 100,000 person-years, standardized to the age distribution of person-years experienced by all study participants using 5-year age categories. Adjusted for age at enrollment, physical activity, cigarette smoking status, alcohol drinking, use of vitamins and minerals, and educational level in men; further adjusted for contraceptive pill use and hormone therapy replacement in women. d p for trend was across overweight and obesity compared with normal weight.

	BMI			p for
	BMI <25	BMI 25-29.9	BMI ≥ 30	trend d

a b Adjusted for age at enrollment. c

Table 4 .

 4 Mortality rates and hazard ratios of all-cause death for waist-hip ratio. The Swedish National March Cohort. Adjusted for age at enrollment, physical activity, cigarette smoking status, alcohol drinking, use of vitamins and minerals, and educational level in men; further adjusted for contraceptive pill use and hormone therapy replacement in women. HR = Hazard Ratio CI = Confidence Interval

		WHR
		≤1	>1 p-value
	Male
	Number of deaths	741	95
	Person-years	79600	10017
	a Mortality rate	809.58	817.94
	b HR (95% CI)	1.00 (reference)	1.01 (0.82-1.26)	0.893
	c HR (95% CI)	1.00 (reference)	1.18 (0.94-1.49)	0.159
		WHR
		≤0.8	>0.8 p-value
	Female
	Number of deaths	256	404
	Person-years	93532	98557
	a Mortality rate	289.43	340.72
	b HR (95% CI)	1.00 (reference)	1.13 (0.97-1.33)	0.140
	c HR (95% CI)	1.00 (reference)	1.18 (0.98-1.41)	0.076

a Mortality rates were presented as per 100,000 person-years, standardized to the age distribution of person-years experienced by all study participants using 5-year age categories. b Adjusted for age at enrollment. c

Table 5 .

 5 Mortality rates and hazard ratios of all-cause death for waist circumference. The Swedish National March Cohort. Mortality rates were presented as per 100,000 person-years, standardized to the age distribution of person-years experienced by all study participants using 5-year age categories. Adjusted for age at enrollment, physical activity, cigarette smoking status, alcohol drinking, use of vitamins and minerals, and educational level in men; further adjusted for contraceptive pill use and hormone therapy replacement in women. d p for trend was across overweight and obesity compared with normal weight as measured by waist. HR = Hazard Ratio CI = Confidence Interval

			WAIST
		<94 cm	≥94 to 102 cm	≥102 cm	p-value
					for trend d
	Male
	Number of	400	257	199
	deaths
	Person-years	48840	26448	15935
	a Mortality rate	783.83	755.56	999.94
	b HR (95% CI)	1.00 (reference) 1.00 (0.85-1.17) 1.34 (1.13-1.59)	0.004
	c HR (95% CI)	1.00 (reference) 0.97 (0.80-1.16) 1.30 (1.07-1.58)	0.032
			WAIST
		<80 cm ≥80 to > 88 cm	≥88 cm	p-value
					for trend d
	Female
	Number of	234	225	203
	deaths
	Person-years	97081	54748	41786
	a Mortality rate	279.57	340.13	349.90
	b HR (95% CI)	1.00 (reference) 1.20 (1.00-1.44) 1.23 (1.02-1.49)	0.030
	c HR (95% CI)	1.00 (reference) 1.32 (1.07-1.63) 1.35 (1.08-1.67)	0.009

a b Adjusted for age at enrollment. c

Acknowledgements

This work was supported by the companies ICA AB and Ericsson. We would like to thank Statistics Sweden for scanning the questionnaires. We also would like to express sincere gratitude to the Swedish Cancer Society and volunteers who worked with the National March.