Physical activity, cardiometabolic diseases, and cancer risk: a prospective analysis

Physical activity, cardiometabolic diseases, and cancer risk: a prospective analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Physical activity, cardiometabolic diseases, and cancer risk: a prospective analysis Heinz Freisling, Alem Gebremariam, Emma Fontvieille, Quan Gan, and 30 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7824019/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Purpose Whether physical activity is associated with the risk of cancer among adults with a cardiometabolic disease is not well understood. This study investigated associations between physical activity and cancer risk in adults with and without a history of cardiometabolic diseases (cardiovascular disease and/or type 2 diabetes). Methods We conducted a meta-analysis of individual participant data of 564,622 men and women, aged 35–70 years at recruitment, across six European countries from the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank. We excluded participants from these cohorts who had cancer, cardiovascular disease, or type 2 diabetes at baseline. Data on physical activity were assessed at baseline with self-reported validated questionnaires and modelled as metabolic equivalent task hours per week (MET-h/week). We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between physical activity and the risk of physical activity-related cancers, with a multiplicative interaction between physical activity and time-varying cardiometabolic disease status. Results After a median of 11 years of follow-up, 28,345 participants developed a first primary physical activity-related cancer (EPIC and UK Biobank combined). In the meta-analysis of both cohorts, a 1 standard deviation (SD) increment of physical activity was associated with a lower risk of physical activity-related cancer, with HRs of 0.96 (95% CI: 0.95, 0.97) and 0.94 (95% CI: 0.90, 0.99) in adults without and in those with a cardiometabolic disease, respectively (p-interaction > 0.3). Conclusions The findings of this study among European adults suggest that higher physical activity is equally beneficial for cancer prevention in adults with and without underlying cardiometabolic diseases. Health sciences/Diseases/Cancer/Cancer prevention Health sciences/Diseases/Cancer/Cancer epidemiology Physical activity type 2 diabetes cardiovascular diseases cancer relative excess risk due to interaction multimorbidity Figures Figure 1 Figure 2 Introduction Higher levels of physical activity (PA) are inversely associated with the risk of total cancer and several types of cancer in the general population ( 1 , 2 ). Site-specific cancers with evidence for inverse associations with PA include breast, oesophageal adenocarcinoma, endometrial, colon, renal, gastric cardia, urinary bladder, gallbladder, liver, lung, small intestine, multiple myeloma, myeloid leukemia, rectal, and head and neck cancer ( 1 , 3 – 6 ). The 2020 physical activity guidelines from the World Health Organization (WHO) provide recommendations for age groups, including children aged 5 to 17 years, adults aged 18 to 64 years, and older adults aged 65 years and above. For the first time, these guidelines also include recommendations for adults with chronic conditions such as cancer, hypertension, type 2 diabetes (T2D), and human immunodeficiency virus (HIV) ( 7 ). These recommendations are based on extrapolation from the general population, with limited direct evidence on the role of PA in cancer prevention for individuals with T2D. Furthermore, there is a lack of recommendations for individuals with cardiovascular diseases (CVD). The co-occurrence of cardiometabolic diseases (CMDs), such as T2D and CVD, and cancer in individuals is becoming increasingly common ( 8 ), which may be partly due to shared risk factors, such as low levels of PA ( 9 ). Evidence also indicates an association between CMDs, and cancer risk ( 10 ). T2D is a recognized risk factor for certain types of cancer, such as liver, pancreatic, and endometrial cancer ( 11 ). Three prior prospective cohort studies have examined whether a history of T2D modifies the association between PA and the risk of colon cancer ( 12 ), hepatocellular carcinoma ( 13 ), and endometrial cancer ( 14 ). The findings of these studies suggest that higher PA levels may lower the risk of these cancer types, regardless of T2D status. However, the joint associations of PA and T2D on cancer risk were not investigated. Additionally, the studies relied on self-reported diabetes status, had small sample sizes, and focused on a limited range of cancer types. Although there are common risk factors shared between CVD and cancer, the extent to which CVD increases cancer risk is not yet well-understood ( 15 ). Only one study assessed the association between PA and cancer risk among patients with CVD, but did not compare with those without CVD or evaluate interaction effects ( 16 ). To address these research gaps, we evaluated multiplicative and additive interactions between PA and CMD status in association with the risk of PA-related cancer (a composite of 15 cancer sites associated with low PA levels) and of total cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study and UK Biobank (UKB). Methods Study settings and participants The EPIC study has been described previously (17, 18). Briefly, the EPIC cohort comprises half a million adults aged mostly 35 to 70 years at recruitment (1992-2000) from 23 research centres across 10 European countries. Participants completed health and lifestyle questionnaires, had anthropometric measurements, and were followed until the last date of centre- and event-specific ascertainment of CVD, T2D, cancer, death, loss to follow-up, or end of the study (December 2008), whichever came first (17). We excluded participants from France, Greece, Norway, and Sweden due to insufficient information regarding their CVD and T2D status. We further excluded individuals with cancer, CVD, or T2D at baseline, and those with missing data for PA. Additional exclusion criteria are detailed in Supplementary Figure 1. After these exclusions, a total of 230,026 EPIC participants remained for analysis (Figure S1). UK Biobank is a prospective cohort study of about half a million adults aged 40 to 69 years at recruitment (2006-2010) from 22 centres in England, Scotland, and Wales. Data on socio-demographic characteristics, lifestyle factors, diet, and anthropometric measurements were collected at recruitment (18). Participants were followed until they were diagnosed with cancer, died, lost to follow-up, or last ascertainment of cancer or death (between February 2020 and January 2021, depending on the centre). We excluded participants with prevalent cancer, CVD, or T2D at recruitment, and those with missing data for PA, resulting in a sample size of 334,596 UKB participants (Figure S2). Ethics approval and consent to participate All participants from UK Biobank and EPIC have provided written informed consent to participate in the respective cohorts. UK Biobank has ethical approval from the Northwest Multi-Centre Research Ethics Committee. The EPIC study was approved by the Cancer Ethical Review Committee of IARC and by local ethical committees at the participating centres. The current study was approved by the IARC’s Ethics Committee (No. 23–40). Physical activity assessment In both cohorts, PA was calculated in metabolic equivalent task hours per week (MET-h/week) based on self-reported PA questionnaires. In EPIC, PA was assessed at recruitment using an interview-based questionnaire administered as part of the baseline health and lifestyle questionnaire (17). In UKB, PA was assessed at recruitment through a self-completed touch-screen questionnaire based on the short International Physical Activity Questionnaire (IPAQ) (18). The validity of the PA assessment tools used in EPIC and UKB studies was evaluated to confirm their ability to rank individuals by PA levels with a weighted kappa of 0.60 in EPIC (19) and 0.67 in UKB (20). The EPIC study defines PA as the total MET-h/week from household activities (like cleaning, cooking, gardening, and stair climbing) and recreational activities (such as walking, cycling, swimming, and jogging). The questionnaire assessed the number of hours spent on household and recreational activities in a typical week over the past year but did not provide a quantitative measure for the duration of occupational activity. Thus, total weekly PA was calculated by summing the mean hours spent on the household and recreational activities in summer and winter, then multiplying by their MET values: 3.0 for walking, 6.0 for cycling, 4.0 for gardening, 6.0 for sports, 4.5 for DIY work, 3.0 for housework, and 8.0 for stair climbing (21). UK Biobank assessed participants' engagement in walking, moderate, and vigorous physical activities lasting over 10 minutes each week. This included activities such as housework, recreation, and work. Total weekly PA is measured in MET-h/week, calculated by summing walking (3.3 METs), moderate activities (4.0 METs), and vigorous activities (8.0 METs) (22). Cancer ascertainment The primary outcome of interest was the incidence of PA-related cancers combined, defined as pre- and postmenopausal breast, adenocarcinoma of the oesophagus, endometrial, colon, kidney, gastric cardia, urinary bladder, gallbladder, liver, lung, small intestine, multiple myeloma, myeloid leukemia, rectal, and head and neck cancer (1, 3-6). Overall cancer, defined as all primary cancers combined, excluding non-melanoma skin cancer, was our secondary outcome. Non-melanoma skin cancer cases were censored at the diagnosis date but not classified as cancer cases (1, 3-6). Cancers were coded according to ICD-10 and information on tumour morphology and histology using ICD-O-3 (Table S1), identified through health insurance records, cancer pathology registries, and active follow-up in EPIC, and for UKB, data on cancer diagnosis was obtained from the National Health Service (NHS) Digital and Public Health England for participants from England and Wales. For participants residing in Scotland, it was obtained from the NHS Central Register (NHSCR). Cardiometabolic disease ascertainment The ascertainment of incident CVD and T2D has been described previously (23). Cardiometabolic disease included CVD and T2D, coded using ICD-10. Cardiovascular disease refers to a composite of ischemic heart diseases (I20-I25), and cerebrovascular disease (I60-I69), while T2D was defined as E11. Incident CVD was identified and validated through the EPIC-Heart study using questionnaires, medical records, and death certificates. Incident T2D cases were identified and validated in the EPIC-Interact study via self-reports, care register linkages, medication use, and hospital admissions. In UK Biobank, both CVD and T2D cases were identified using hospital admission records (23). Covariates Highest level of education or qualifications was categorized as none, primary, technical, secondary, or higher education in the EPIC study. In UK Biobank, the categories were none, other professional qualifications, National Vocational Qualifications (NVQ) or Higher National Diplomas (HND) or Higher National Certificates (HNC) or equivalent, Certificate of Secondary Education (CSEs) or equivalent, O levels/General Certificate of Secondary Education (GCSEs) or equivalent, A levels/AS levels or equivalent, and college or university degrees. Additional covariates included smoking status, classified as never, former, or current smoking, and alcohol intake measured in grams per day in the EPIC study, while in UKB, it was categorized as never, former, or current alcohol drinker. Also, we considered PA at work (EPIC)/employment status (UKB) as a covariate. Physical activity at work was considered in EPIC with categories including sedentary occupation, standing occupation, manual work, heavy manual work, and non-worker. Employment status in UKB included paid employment or self-employed, retired, looking after home and/or family, unable to work due to sickness or disability, unemployed, doing unpaid or voluntary work, and full or part-time student. Diet quality included the modified relative Mediterranean diet score in EPIC as low, medium, and high (24), and a healthy diet score in UKB as a scale ranging from 0 (unhealthier) to 6 (healthier) (25). Other covariates included height, body mass index (BMI), and among women, use of hormones for menopause, and menopausal status. In UKB, medication use, average total household income, overall self-rated health, and the Townsend deprivation index were considered. Statistical Analysis After filtering for the exclusion criteria (Figures S1 and S2), the overall missing rate of the covariates was below 2% (Table S2). Covariates with missing values were imputed using multiple imputation with chained equations (MICE) (26). Each of the covariates was imputed conditionally on the others using appropriate models (e.g., polytomous regression for unordered categorical variables) with 5 imputations and 10 iterations for each using the mice package in R. To address right-skewness of MET-h/week in EPIC, we capped the outliers at 1.5 times the interquartile range above the third quartile (233.9 MET-h/week corresponding to the 99th percentile). In UKB, participants with incomplete responses or missing information regarding the number of days or duration were excluded, along with those who reported more than 960 minutes of PA per day. Additionally, for each of the behaviours, the total time spent walking, total moderate intensity activity, and total vigorous intensity activity were capped at 180 minutes/day (22). For descriptive analysis, we categorized MET-h/week into four groups based on sex-specific quartiles: low, medium, high, and very high. We used Cox proportional hazards regression to estimate cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between PA and cancer outcomes. Associations were modelled per one standard deviation (SD) increment in PA, standardized within each cohort before meta-analysis to account for differences in scale. The SD was 51.4 MET-h/week in EPIC and 44.3 MET-h/week in UK Biobank. The entry time for cancer follow-up was age at recruitment, and the exit time was age at first primary cancer diagnosis, end of follow-up, loss to follow-up, or death, whichever occurred first. Deaths from any cause were modelled as a censored observation. Follow-up for CVD and T2D also started at age at recruitment and the exit time was the same as for cancer. To address potential detection bias, i.e., the elevated likelihood of cancer diagnosis shortly after a CMD diagnosis (27, 28), we implemented a 12-month lag period after a CMD diagnosis in all analyses. Three models were evaluated: Model 1 was stratified by age (5-year categories), sex, and recruitment centre, and adjusted for education, BMI, height, smoking status, alcohol consumption, diet quality, occupational work in EPIC, and employment status in UKB, and in women for menopausal status and use of hormones after menopause. Model 2 was further adjusted for CVD and T2D status (modelled as binary time-varying variables), and time since diagnosis of CVD and T2D. Model 3, our main model, further included a multiplicative interaction between PA (continuous, time-invariable) and each of the two CMDs (time-varying categorical). Likelihood ratio tests were used to compare models with the interaction term to nested models without interaction. We checked the proportional hazard assumption using Schoenfeld residuals, which was met. The linearity of the association between primary exposure, PA, and cancer risk was assessed using natural splines with three knots at the 25 th , 50 th , and 75 th percentiles of PA (Figures S3, S4, S5, and S6). Next, we assessed separate and joint associations of PA and CMDs with PA-related cancer and overall cancer, and quantified additive interaction through relative excess risk due to interaction (RERI) with the interactionR package (29). PA was dichotomized as active if PA was above the median (>81 MET-h/week in EPIC and >30 MET-h/week in UK Biobank) and inactive if it was below or equal to the median. We built four exclusive categories: (1) active, without CMD (reference); (2) active, with CMD; (3) inactive, without CMD; and (4) inactive, with CMD (joint effect). We calculated additive interaction using the RERI for each of the CMDs for each outcome, risk of PA-related cancers, and risk of all cancers as RERI HR = HR 11 – HR 10 – HR 01 + 1, with HR 11 the risk of being exposed to both factors (e.g., low PA and T2D), HR 10 exposed to one of the factors (low PA), and HR 01 to the other one (e.g., T2D). Estimates of 95% CIs were based on the delta method (30). Each of the two cohorts' models were fitted separately, and the results were meta-analysed using a random-effects model. We used I 2 to describe the percentage of overall variability attributable to between-study heterogeneity. Sensitivity analyses were carried out to assess the robustness of results. First, in both cohorts, we evaluated the associations across population subgroups defined by smoking status (never-smokers versus ever smokers), occupation status (unemployed, versus employed: sedentary occupation, standing occupation, manual worker, and heavy manual worker), BMI categorized as less than 25 kg/m² and 25 kg/m² or greater, and sex (male versus female). In UK Biobank, we further examined the associations by medication use (individuals on prescribed blood pressure, cholesterol-lowering drugs, or insulin versus those not taking any of these medications), household income (having incomes of = 31,000 GBP), and self-rated health status (dichotomized as healthy if rated as excellent or good and unhealthy if they rated as fair or bad). Lastly, we evaluated the associations based on the Townsend deprivation index, which was categorized based on the median score into low deprivation (score ≤ -2.2686) and high deprivation (score > -2.2686). We also conducted a complete case analysis after excluding those with missing data on any of the covariates. All statistical tests were two-sided, and significance was considered at a p-value of less than 0.05. Data were processed and analyzed using R version 4.3.1 (31). Results A total of 564,622 participants were included in the analysis, with 230,026 from the EPIC study and 334,596 from UKB. Tables 1 and 2 show the participants’ characteristics by levels of PA in EPIC and UKB, respectively. In EPIC, higher levels of PA were more common among women, never smokers, individuals with higher diet quality, those who were unemployed, and those with lower educational level ( Table 1 ). In UKB, higher levels of PA were more common among individuals who were retired and postmenopausal women ( Table 2 ). During a median follow-up period of 10.8 years (IQR: 9.4 to 12.4) in EPIC, 18,139 first primary cancers (2,433,935 person-years of follow-up) were recorded ( Table S3 ). Of these, 11,536 (2,453,298.92 person-years of follow-up) were PA-related cancers. The age-standardized transition rates from baseline to cancer and after a cardiometabolic disease to cancer are shown in Figure S7 . In UKB, with a median follow-up of 11.3 years (IQR: 8.7 to 12.2), there were 32,218 recorded first primary cancers (3,792,008 person-years of follow-up). Of these, 16,809 (3,883,086 person-years of follow-up) were PA-related cancers. The age-standardized transition rates from baseline to cancer and after a cardiometabolic disease to cancer are shown in Figure S8 . Physical activity and cancer risk by cardiometabolic disease status Multivariable-adjusted model 1, where we ignored CMD status, showed an inverse association between higher levels of PA and the risk of PA-related cancers in both cohorts. Per 1 SD increment in MET-h/week of PA, HRs were 0.95 (95% CI: 0.94 to 0.98) and 0.96 (95% CI: 0.94 to 0.97) in EPIC and UK Biobank, respectively ( Table S4 ). In model 2, these inverse associations were similar after further adjusting for T2D, CVD, and their durations ( Table S4 ). In our model 3 (interaction model), we estimated associations by CMD status. In the meta-analysis of both cohorts, a 1 SD increment in MET-h/week of PA was associated with lower risk of PA-related cancers among those without CMDs with a summary HR of 0.96 (95% CI: 0.95, 0.97), and among those with a CMD with a summary HR of 0.94 (95% CI: 0.90, 0.99) ( Figure 1 ). Findings for T2D or CVD were similar in magnitude. There was little evidence for a multiplicative interaction between PA and CMD status (all p-interaction > 0.3) ( Table S4 ). The findings were similar for the risk of all cancers combined ( Figure S9 and Table S4 ). In sensitivity analyses, in both cohorts, associations between PA and PA-related cancers remained consistent among individuals with and without CMD across population subgroups (all p-interaction > 0.1) ( Table S5 ). The findings were generally similar when all cancers combined was the outcome ( Table S6 ). Furthermore, results were also consistent in complete case analyses ( Table S7 and Figure S10 ). Joint association of physical (in)activity and cardiometabolic diseases with cancer In both cohorts, compared to active adults (PA above median) without CMDs, there was a graded increase in risk of PA-related cancers for inactive adults (PA below median) without CMDs (HR=1.10; 95% CI: 1.07, 1.13), active adults with CMDs (HR=1.23; 95% CI: 1.10, 1.37), and inactive adults with CMDs (HR=1.31; 95% CI: 1.18, 1.46) ( Figure 2 ). Similar graded increases in risk of PA-related cancers were observed for combinations of PA and T2D or CVD. However, there was little evidence for additive interactions as quantified by RERI values around the null ( Figure 2) . When assessing the additive interaction of PA and CMDs on the risk of all cancers, results were consistent but lower in magnitude. Similarly, there was little evidence for additive interactions as assessed with the RERI ( Figure S11) . Discussion In this meta-analysis of two of the largest European prospective cohorts with together over 500,000 adult participants, we found that higher levels of PA were associated with a lower risk of PA-related cancers and overall cancer in adults living with a CMD (T2D and/or CVD). The lower risk of cancer was comparable to that in adults without a history of a CMD. In contrast, both low PA (below study-specific median) and a history of CMD were separately and jointly associated with a gradually higher risk of PA-related cancers and overall cancer as compared to active adults without a history of CMD. Taken together, these results suggest that being physically active is beneficial for cancer prevention in adults living with or without T2D and/or CVD. Numerous studies have shown that PA is consistently associated with a reduced risk of various types of cancers in adults of the general population (1, 2, 4, 32-34). For example, a meta-analysis of nine prospective cohort studies reported that individuals who had PA levels of 7.5-15 MET-h/week had a 6% to 27% lower risk of cancers such as breast, kidney, and liver compared to individuals with no leisure-time PA (4). A prospective study in UKB reported a 13% (95% CI 9% to 17%) lower risk of PA-related cancers per 1 SD increment (8.3 milligravity units) of accelerometer-based total PA (34). The findings of our study concur with this previous evidence. Compared to existing evidence, our study adds new insights on the role of PA in cancer prevention among adults living with a CMD. Few studies have investigated PA-related cancer risk among adults with a CMD (12-14, 16), and have not consistently investigated multiplicative and additive interaction between PA and CMD status. In UKB, a similarly lower risk for PA-related cancers was observed among adults with and without diabetes in relation to higher PA (34). A meta-analysis of both retrospective and prospective studies found an inverse association between higher PA and renal cancer with little evidence for effect modification by T2D status (subgroup p-value = 0.18) (35). The results of our study are largely consistent with these previous studies with one exception. In contrast to the results of the Swedish Mammography Cohort (14), we did not observe an additive interaction between PA and T2D status. Differences in study design are the most likely explanation. For example, in our study, we used validated T2D events and accounted for diabetes duration. To our knowledge, only one previous study has stratified PA-associated cancer risk by CVD status (34). In that study, higher levels of total PA were similarly associated with a lower risk of PA-related cancer among adults with and without a history of CVD (34). However, separate and joint associations were not formally tested, and only baseline CVD status without accounting for CVD duration was investigated (34). Our study, therefore, adds important evidence that can inform clinical practice and public health recommendations. People living with CVD may be reluctant to engage in regular PA because of uncertain health implications (36, 37). These findings support broader recommendations that PA should be promoted across populations, including those living with a CMD. Our study has notable strengths. The analysis is based on multinational data across six European countries from two large prospective cohort studies, which also allowed us to model the temporal relationship between PA, T2D and/or CVD, and cancer risk. We also assessed both multiplicative and additive interactions between PA and CMD on cancer risk, providing a more comprehensive understanding of the role of PA in cancer development. Our findings, however, have the following limitations. The PA exposure variable was assessed only at baseline, so changes over time were not evaluated. Also, as it relied on a self-reported questionnaire, reporting bias may be inevitable. However, this bias is likely minimal, as we used standardized rankings instead of the actual responses, and previous research supports the questionnaire’s validity for ranking PA levels (19, 20). EPIC assessed domain-specific activities (e.g., walking, gardening, housework) over the past year using task-specific MET values, while UKB captured walking, moderate, and vigorous PA over the past week using intensity-based METs and a 10-minute bout criterion. EPIC’s approach is more detailed but may be more prone to recall bias and tends to smooth out seasonal and short-term variation. Notably, the median MET-h/week was higher in EPIC than in UKB, reflecting differences in assessment scope and time frame. Despite these methodological differences, hazard ratios were similar across cohorts, suggesting that both instruments effectively rank individuals by physical activity level. Furthermore, the low participation rate of only 5% in the UK Biobank cohort (38, 39) suggests that the study participants may not accurately represent the overall UK population. Additionally, the absence of detailed medication use information in the EPIC cohort could have impacted our estimates. However, we accounted for this in the UKB analysis and estimates remained unchanged. In this study among European adults, higher levels of PA were associated with lower cancer risk, which was similar among adults with and without a history of T2D and/or CVD. Physical inactivity and a history of a CMD were both separately and jointly associated with a higher risk of cancer compared to being physically active and having no history of a CMD. Our findings add to the growing body of evidence supporting PA as a key cancer prevention strategy encompassing population subgroups with cardiometabolic diseases. Abbreviations CI confidence interval CMD cardiometabolic diseases CVD cardiovascular disease EPIC, European Prospective Investigation into Cancer and Nutrition HR hazard ratio MET-h/week, metabolic equivalent task hours per week SD, standard deviation T2D type 2 diabetes UKB UK Biobank. Declarations Funding: World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant program and the French National Cancer Institute (l’Institut National du Cancer, INCA_16824). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. Acknowledgments We acknowledge the use of data from the EPIC-Aarhus cohort, PI Christina C. Dahm; EPIC-Varese cohort, PI Sabina Sieri; EPIC-Ragusa cohort, PI Rosario Tumino; EPIC-Bilthoven cohort, PI Monique Verschuren; EPIC-Asturias cohort, PI J. Ramón Quirós; EPIC-Murcia cohort, PI Maria Dolores Chirlaque Lopez and Huerta JM; and EPIC-Norfolk cohort, PI Nick Wareham. UK Biobank is an open-access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/. This research has been conducted using the UK Biobank Resource under Application Number 55870 and we express our gratitude to the participants and those involved in building the resource. Disclaimer Where authors are identified as personnel of the International Agency for Research on Cancer/ World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/ World Health Organization. Authors contribution HF conceived and designed the work. AG, HZ, EF, and PF were responsible for data analysis and interpretation, wrote the original draft, and carried out subsequent editing. ALRH, JLMA, KS, RTF, MBS, CS, SP, MF, NCOM, EM, JB, MF, MJS, MG, OMC, STT, AKH, DA, ER contributed to data acquisition. All authors contributed to writing, editing, and final approval of the manuscript. HZ is accountable for all aspects of the work Conflict of interest The authors declare that they have no competing interests. Additional information Supplemental file 1 Availability of data and materials The UK Biobank data were available from the UK Biobank and can be accessed by researchers on the application ( https://www.ukbiobank.ac.uk/use-our-data/apply-for-access/). EPIC data are available for investigators who seek to answer important questions on health and disease in the context of research projects that are consistent with the legal and ethical standard practices of IARC/WHO and the EPIC Centres. For information on how to apply to gain access to EPIC data and/or biospecimens, please follow the instructions http://epic.iarc.fr/access/. The datasets used and/or analyzed during the current study were accessed in 2021 and are available from the corresponding author upon reasonable request ( [email protected] ). References Moore SC, Lee I-M, Weiderpass E, Campbell PT, Sampson JN, Kitahara CM, et al. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. 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Tables Table 1 : Baseline characteristics of the study population in EPIC by level of physical activity Physical activity level Characteristics Low Medium High Very high Overall N (%) 45187 (19.6) 52794 (22.9) 61042 (26.5) 71003 (31.0) 230026 (100) Physical activity, MET-h/week Median (IQR) 28.5 (18.8, 38.3) 56.6 (47.8, 70.2) 90.2 (76.2, 105.8) 146.5 (126.6, 171.0) 81.0 (49.0, 123.0) Sex, n (%) Male 20430 (45.2) 21512 (40.7) 21834 (35.8) 22784 (32.1) 86560 (37.6) Female 24757 (54.8) 31282 (59.3) 39208 (64.2) 48219 (67.9) 143466 (62.4) Physical activity at work, n (%) Non-worker 8247 (18.3) 11407 (21.6) 18467 (30.3) 37133 (52.3) 75254 (32.7) Sedentary occupation 19110 (42.3) 21226 (40.2) 19372 (31.7) 12772 (18.0) 72480 (31.5) Standing occupation 9806 (21.7) 11740 (22.2) 14285 (23.4) 10938 (15.4) 46769 (20.3) Manual work 6033 (13.4) 6306 (11.9) 6352 (10.4) 6596 (9.3) 25287 (11.0) Heavy manual work 1477 (3.3) 1459 (2.8) 1639 (2.7) 2222 (3.1) 6797 (3.0) Unknown 514 (1.1) 656 (1.2) 927 (1.5) 1342 (1.9) 3439 (1.5) Age at recruitment, years Median (IQR) 52.8 (47.0, 58.0) 52.7 (46.4, 58.2) 52.5 (45.7, 58.6) 52.5 (45.2, 58.9) 52.6 (46.0, 58.4) Height, cm Median (IQR) 167 (161, 173) 167(161, 174) 164 (159, 173) 164 (158, 172) 166 (160, 173) Body mass index, kg/m 2 Median (IQR) 25.8 (23.3, 28.6) 25.3 (23.0, 28.0) 25.4 (23.1, 28.1) 25.7 (23.3, 28.5) 25.5 (23.2, 28.3) Alcohol consumption, g/d Median (IQR) 10 (2, 24) 9 (2, 22) 7 (1, 20) 5 (0.4, 17) 8 (1, 20) Education, n (%) None 1884 (4.2) 1432 (2.7) 3172 (5.2) 5710 (8) 12198 (5.3) Primary school completed 13725 (30.4) 14069 (26.6) 17800 (29.2) 25331 (35.7) 70925 (30.8) Technical school 12551 (27.8) 15637 (29.6) 16844 (27.6) 17840 (25.1) 62872 (27.3) Secondary school 6983 (15.5) 7626 (14.4) 8606 (14.1) 9100 (12.8) 32315 (14.0) Longer education (incl. University) 9478 (21.0) 13117 (24.8) 13434 (22.0) 11397 (16.1) 47426 (20.6) Missing 566 (1.3) 913 (1.7) 1186 (1.9) 1625 (2.3) 4290 (1.9) Smoking status, n (%) Never 17459 (38.6) 22368 (42.4) 28129 (46.1) 35383 (49.8) 103339 (44.9) Previous 13048 (28.9) 16166 (30.6) 17931 (29.4) 19211 (27.1) 66356 (28.8) Current 14494 (32.1) 14091 (26.7) 14787 (24.2) 16263 (22.9) 59635 (25.9) Missing 186 (0.4) 169 (0.3) 195 (0.3) 146 (0.2) 696 (0.3) Med. diet, n (%) Low 11838 (26.2) 14418 (27.3) 15145 (24.8) 16409 (23.1 57810 (25.1) Medium 19985 (44.2) 24573 (46.5) 29027 (47.6) 32193 (45.3) 105778 (46.0) High 13247 (29.3) 13703 (26.0) 16769 (27.5) 22307 (31.4) 66026 (28.7) Missing 117 (0.3) 100 (0.2) 101 (0.2) 94 (0.1) 412 (0.2) Menopausal status a , n (%) Pre-menopause 7098 (28.7) 10684 (34.1) 14177 (36.2) 17897 (36.4) 49856 (34.8) Post-menopause 12807 (51.7) 14922 (47.7) 18224 (46.5) 21853 (44.4) 67806 (47.3) Perimenopause 3853 (15.6) 4569 (14.6) 5435 (13.9) 6524 (13.3) 20381 (14.2) Surgical menopause 999 (4.0) 1107 (3.5) 1372 (3.5) 1945 (3.9) 5423 (3.8) Use of hormones for menopause , n (%) No 19460 (78.2) 25246 (80.7) 32815 (83.4) 41654 (86.7) 119175 (83.0) Yes 4942 (19.9) 5666 (18.1) 5934 (15.1) 5834 (12.1) 22376 (15.6) Missing 355 (1.4) 370 (1.2) 459 (1.2) 731 (1.5) 1915 (1.3) Country of recruitment, n (%) Italy 10061 (22.3) 8033 (15.2) 10093 (16.5) 14361 (20.2) 42548 (18.5) Spain 6566 (14.5) 5531 (10.5) 8966 (14.7) 14183 (20.0) 35246 (15.3) United Kingdom 4879 (10.8) 7281 (13.8) 8548 (14.0) 9226 (13.0) 29934 (13.0) The Netherlands 2403 (5.3) 5267 (10.0) 8517 (14.0) 12537 (17.7) 28724 (12.5) Germany 5372 (11.9) 10494 (19.9) 13151 (21.5) 13115 (18.5) 42132 (18.3) Denmark 15906 (35.2) 16188 (30.7) 11767 (19.3) 7581 (10.7) 51442 (22.4) Values are medians (interquartile range) unless otherwise stated. a Only in women; Med. diet: Mediterranean diet; MET-h/week, metabolic equivalent task hours per week; EPIC: European Prospective Investigation into Cancer and Nutrition Table 2 : Baseline characteristics of the study population in UK Biobank by levels of physical activity Physical activity level Characteristics Low Medium High Very high Overall N (%) 82526 (24.6) 83883 (25.1) 84006 (25.1) 84247 (25.2) 334596 Physical activity, MET-h/week Median (IQR) 7.3 (3.7, 10.5) 21.3 (17.3, 25.3) 41.7 (35.5, 49.7) 92.4 (73.3, 125.3) 30.2 (13.9, 59.7) Sex, n (%) Female 43466 (52.7) 44041 (52.5) 43894 (52.3) 43934 (52.2) 175335 (52.4) Male 39060 (47.3) 39842 (47.5) 40112 (47.7) 40247 (47.8) 159261 (47.6) Employment, n (%) Unemployed 1425 (1.7) 1360 (1.6) 1392 (1.7) 1306 (1.6) 5483 (1.6) Retired 19912 (24.1) 24353 (29.0) 27357 (32.6) 28131 (33.4) 99753 (29.8) Looking after home and/or family 1672 (2.0) 2152 (2.6) 2471 (2.9) 2771 (3.3) 9066 (2.7) Unable to work because of sickness or disability 3368 (4.1) 1549 (1.8) 1347 (1.6) 1150 (1.4) 7414 (2.2) Full or part-time student 239 (0.3) 256 (0.3) 281 (0.3) 219 (0.3) 995 (0.3) Doing unpaid or voluntary work 342 (0.4) 398 (0.5) 433 (0.5) 430 (0.5) 1603 (0.5) In paid employment or self-employed 55005 (66.7) 53218 (63.4) 50116 (59.7) 49489 (58.8) 207828 (62.1) Missing 563 (0.7) 597 (0.7) 609 (0.7) 685 (0.8) 2454 (0.7) Age at assessment, years Median (IQR) 56.2 (49.3, 62.1) 56.8 (49.3, 62.7) 57.2 (49.2, 63.1) 58.0 (49.9, 63.6) 57 (49.4, 62.8) Height, cm Median (IQR) 169 (162, 176) 169 (162, 176) 169 (162, 176) 168 (162, 175) 169 (162, 1.76) Body mass index, kg/m 2 Median (IQR) 27.3 (24.6, 30.7) 26.5 (24.0, 29.5) 26.1 (23.7, 29.0) 26.1 (23.7, 29.0) 26.5 (24.0, 29.5) Townsend deprivation, score Median (IQR) -2.3 (-3.7, 0.3) -2.3 (-3.7, 0.2) -2.3 (-3.7, 0.1) -2.1 (-3.6, 0.4) -2.3 (-3.7, 0.3) Qualifications, n (%) CSEs or equivalent 3872 (4.7) 3624 (4.3) 4004 (4.8) 5982 (7.1) 17482 (5.2) O levels/GCSEs or equivalent 17677 (21.4) 16865 (20.1) 17535 (20.9) 18990 (22.6) 71067 (21.2) A levels/AS levels or equivalent 10625 (12.9) 10566 (12.6) 9982 (11.9) 9110 (10.8) 40283 (12.0) NVQ or HND or HNC or equivalent 4689 (5.7) 4673 (5.6) 5179 (6.2) 6721 (8.0) 21262 (6.4) College or University degree 31640 (38.3) 35123 (41.9) 32973 (39.3) 23562 (28.0) 123298 (36.8) Others a 3864 (4.7) 3948 (4.7) 4067 (4.8) 4626 (5.5) 16505 (4.9) None of the above 9678 (11.7) 8685 (10.4) 9819 (11.7) 14500 (17.2) 42682 (12.8) Missing 481 (0.6) 399 (0.5) 447 (0.5) 690 (0.8) 2017 (0.6) Smoking status, n (%) Never 46163 (55.9) 48042 (57.3) 47463 (56.5) 45937 (54.6) 187605 (56.1) Previous 26785 (32.5) 28025 (33.4) 28950 (34.5) 29160 (34.6) 112920 (33.7) Current 9369 (11.4) 7629 (9.1) 7412 (8.8) 8885 (10.6) 33295 (10.0) Missing 209 (0.3) 187 (0.2) 181 (0.2) 199 (0.2) 776 (0.2) Alcohol status, n (%) Never 3534 (4.3) 2927 (3.5) 2687 (3.2) 3265 (3.9) 12413 (3.7) Previous 2809 (3.4) 2302 (2.7) 2356 (2.8) 2877 (3.4) 10344 (3.1) Current 76121 (92.2) 78615 (93.7) 78921 (93.9) 77978 (92.6) 311635 (93.1) Missing 62 (0.1) 39 (0.0) 42 (0.0) 61 (0.1) 204 (0.1) Healthy diet Score a 6, healthier 1848 (2.2) 2217 (2.6) 2444 (2.9) 2697 (3.2) 9206 (2.8) 5 14009 (17.0) 16672 (19.9) 17603 (21.0) 18180 (21.6) 66464 (19.9) 4 32956 (39.9) 35954 (42.9) 36577 (43.5) 35813 (42.5) 141300 (42.2) 3 23519 (28.5) 22004 (26.2) 21114 (25.1) 20490 (24.3) 87127 (26.0) 2 8136 (9.9) 5849 (7.0) 5282 (6.3) 5679 (6.7) 24946 (7.5) 1 1811 (2.2) 1068 (1.3) 873 (1.0) 1170 (1.4) 4922 (1.5) 0, unhealthier 247 (0.3) 119 (0.1) 113 (0.1) 152 (0.2) 631 (0.2) Menopause c , n (%) Yes 24596 (56.6) 25629 (59.0) 25806 (59.4) 27131 (62.4) 103162 (58.8) No 12324 (28.4) 12450 (28.6) 12339 (27.4) 10748 (24.7) 47861 (27.3) Had a hysterectomy 4661 (10.7) 4265 (9.8) 4142 (9.5) 4608 (10.6) 17676 (10.1) Missing 1885 (4.3) 1697 (3.9) 1607 (3.7) 1447 (3.3) 6636 (3.8) Ever use of HRT c , n (%) Yes 15058 (34.6) 15278 (34.7) 15434 (35.2) 16698 (38.0) 62468 (35.6) No 28247 (65.0) 28644 (65.0) 28364 (64.6) 27143 (61.8) 112398 (64.1) Missing 161 (0.4) 119 (0.3) 96 (0.2) 93 (0.2) 469 (0.3) MET-h/week, metabolic equivalent task hours per week; HRT: hormonal replacement therapy; IQR: interquartile range; AS: Advanced Subsidiary; GCSEs: General Certificate of Secondary Education; CSEs: Certificate of Secondary Education; NVQs: National Vocational Qualifications; HNDs: Higher National Diplomas; and HNCs: Higher National Certificates. a Refers to other professions such as nursing, teaching. b Scores are arranged from the healthier (6) to unhealthier (0)—Healthy diet score was calculated based on consumption of commonly eaten food groups following recommendations. c Only in women. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7824019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550850598,"identity":"cef17b06-c868-42fb-be4c-f9e950571986","order_by":0,"name":"Heinz 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Saieva","email":"","orcid":"","institution":"Institute for cancer research, prevention and clinical network (ISPRO) Florence, Italy","correspondingAuthor":false,"prefix":"","firstName":"Calogero","middleName":"","lastName":"Saieva","suffix":""},{"id":550850618,"identity":"65f5c151-f9e9-4e2c-9846-c83535d85f7c","order_by":20,"name":"Salvatore Panico","email":"","orcid":"","institution":"Dipartimento di Medicina e Chirurgia, Federico II University","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Panico","suffix":""},{"id":550850619,"identity":"0b9b3c65-c151-4c2d-9a0a-4ace198b185e","order_by":21,"name":"Matteo Franco","email":"","orcid":"","institution":"Department of Clinical and Biological Sciences, University of Turin, Orbassano, Turin, Italy","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Franco","suffix":""},{"id":550850620,"identity":"7a05e389-1b8e-4d03-9b44-015df8e264de","order_by":22,"name":"N. Charlotte Onland-Moret","email":"","orcid":"https://orcid.org/0000-0002-2360-913X","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"Charlotte","lastName":"Onland-Moret","suffix":""},{"id":550850621,"identity":"ff03036c-163f-4299-b29a-90bd8fd55310","order_by":23,"name":"Evelyn Monninkhof","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"prefix":"","firstName":"Evelyn","middleName":"","lastName":"Monninkhof","suffix":""},{"id":550850622,"identity":"8b08eb37-d2c9-404f-b3d0-d85d23ef3ec6","order_by":24,"name":"Jolanda Boer","email":"","orcid":"https://orcid.org/0000-0002-9714-4304","institution":"Department Life Course and Health, Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment","correspondingAuthor":false,"prefix":"","firstName":"Jolanda","middleName":"","lastName":"Boer","suffix":""},{"id":550850623,"identity":"53aa6abf-2a02-4de1-aefb-ec25fcabfc0a","order_by":25,"name":"Marta Farràs","email":"","orcid":"","institution":"Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Barcelona, Spain","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Farràs","suffix":""},{"id":550850624,"identity":"d93bd109-6d57-471a-b962-a67444e0b343","order_by":26,"name":"Maria-José Sanchez","email":"","orcid":"","institution":"Andalusian School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Maria-José","middleName":"","lastName":"Sanchez","suffix":""},{"id":550850625,"identity":"b49223a2-e93b-46d0-a54c-def41d4aecab","order_by":27,"name":"Marcela Guevara","email":"","orcid":"","institution":"Instituto de Salud Pública y Laboral de 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21:23:58","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197022,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7824019/v1/5c068de10b95d6a3ce77a770.html"},{"id":97389335,"identity":"f5b7728b-0e37-475b-99fe-3f4610f98031","added_by":"auto","created_at":"2025-12-03 21:23:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between physical activity (per 1 SD increment in MET-h/week) and the risk of physical activity-related cancers by cardiometabolic disease status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne SD of MET-h/week ~51.4 MET-h/week in EPIC and 44.3 MET-h/week in UK Biobank.\u003c/p\u003e\n\u003cp\u003eThe models were stratified by age (5-year categories), sex, and recruitment centre, and adjusted for education, BMI, height, smoking status, alcohol consumption, diet quality, occupational work/employment, menopausal status (in women), menopausal hormone use (in women), CVD and T2D (modelled as binary time-varying variables) and time since diagnosis of CVD and T2D, and interaction terms between PA and each condition (T2D, CVD, CMD) in each model.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI: confidence interval; CMD: cardiometabolic diseases; CVD: cardiovascular disease; EPIC, European Prospective Investigation into Cancer and Nutrition; HR: hazard ratio; MET-h/week, metabolic equivalent task hours per week; SD, standard deviation; T2D: type 2 diabetes; UKB: UK Biobank.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7824019/v1/fdb454ec2884b399bb298eb5.png"},{"id":97389337,"identity":"ab66db6d-b9a9-4d67-bca4-bd01486f2d49","added_by":"auto","created_at":"2025-12-03 21:23:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJoint association of physical activity and cardiometabolic diseases with the risk of physical activity-related cancers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReference category (not shown): Active without cardiometabolic disease.\u003c/p\u003e\n\u003cp\u003eInactive was defined as having less than the median MET-h/week of physical activity.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI: confidence interval; CMD: cardiometabolic diseases; CVD: cardiovascular disease; EPIC: European Prospective Investigation into Cancer and Nutrition; HR: hazard ratio; MET-h/week: metabolic equivalent task hours per week; SD: standard deviation; T2D: type 2 diabetes; UKB: UK Biobank.\u003c/p\u003e\n\u003cp\u003eRelative excess risk due to interactions (RERI) for those inactive with T2D, CVD, and CMD was -0.02 (95% CI: -0.18, 0.14), -0.08 (95% CI: -0.23, 0.07), and -0.02 (95% CI: -0.14, 0.09), respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7824019/v1/5db6cc885898dcd695b343f6.png"},{"id":98420901,"identity":"87930f14-2188-4e95-93dc-5bc9de3ddefa","added_by":"auto","created_at":"2025-12-17 16:18:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2016538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7824019/v1/5cf25ebc-b3f0-42d0-a72d-d4279d5bbb7c.pdf"},{"id":97664886,"identity":"dbd3041b-b62b-4138-8fb3-b51bbd92a953","added_by":"auto","created_at":"2025-12-08 09:15:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":919105,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SMGebremariametalComMed.docx","url":"https://assets-eu.researchsquare.com/files/rs-7824019/v1/2d32c4a7379811c31cdf0af2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Physical activity, cardiometabolic diseases, and cancer risk: a prospective analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigher levels of physical activity (PA) are inversely associated with the risk of total cancer and several types of cancer in the general population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Site-specific cancers with evidence for inverse associations with PA include breast, oesophageal adenocarcinoma, endometrial, colon, renal, gastric cardia, urinary bladder, gallbladder, liver, lung, small intestine, multiple myeloma, myeloid leukemia, rectal, and head and neck cancer (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e The 2020 physical activity guidelines from the World Health Organization (WHO) provide recommendations for age groups, including children aged 5 to 17 years, adults aged 18 to 64 years, and older adults aged 65 years and above. For the first time, these guidelines also include recommendations for adults with chronic conditions such as cancer, hypertension, type 2 diabetes (T2D), and human immunodeficiency virus (HIV) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These recommendations are based on extrapolation from the general population, with limited direct evidence on the role of PA in cancer prevention for individuals with T2D. Furthermore, there is a lack of recommendations for individuals with cardiovascular diseases (CVD).\u003c/p\u003e\u003cp\u003eThe co-occurrence of cardiometabolic diseases (CMDs), such as T2D and CVD, and cancer in individuals is becoming increasingly common (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), which may be partly due to shared risk factors, such as low levels of PA (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Evidence also indicates an association between CMDs, and cancer risk (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). T2D is a recognized risk factor for certain types of cancer, such as liver, pancreatic, and endometrial cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Three prior prospective cohort studies have examined whether a history of T2D modifies the association between PA and the risk of colon cancer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), hepatocellular carcinoma (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and endometrial cancer (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The findings of these studies suggest that higher PA levels may lower the risk of these cancer types, regardless of T2D status. However, the joint associations of PA and T2D on cancer risk were not investigated. Additionally, the studies relied on self-reported diabetes status, had small sample sizes, and focused on a limited range of cancer types. Although there are common risk factors shared between CVD and cancer, the extent to which CVD increases cancer risk is not yet well-understood (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Only one study assessed the association between PA and cancer risk among patients with CVD, but did not compare with those without CVD or evaluate interaction effects (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address these research gaps, we evaluated multiplicative and additive interactions between PA and CMD status in association with the risk of PA-related cancer (a composite of 15 cancer sites associated with low PA levels) and of total cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study and UK Biobank (UKB).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy settings and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EPIC study has been described previously (17, 18). Briefly, the EPIC cohort comprises half a million adults aged mostly 35 to 70 years at recruitment (1992-2000) from 23 research centres across 10 European countries. Participants completed health and lifestyle questionnaires, had anthropometric measurements, and were followed until the last date of centre- and event-specific ascertainment of CVD, T2D, cancer, death, loss to follow-up, or end of the study (December 2008), whichever came first (17). We excluded participants from France, Greece, Norway, and Sweden due to insufficient information regarding their CVD and T2D status. We further excluded individuals with cancer, CVD, or T2D at baseline, and those with missing data for PA. Additional exclusion criteria are detailed in Supplementary Figure 1. After these exclusions, a total of 230,026 EPIC participants remained for analysis (Figure S1).\u003c/p\u003e\n\u003cp\u003eUK Biobank is a prospective cohort study of about half a million adults aged 40 to 69 years at recruitment (2006-2010) from 22 centres in England, Scotland, and Wales. Data on socio-demographic characteristics, lifestyle factors, diet, and anthropometric measurements were collected at recruitment (18). Participants were followed until they were diagnosed with cancer, died, lost to follow-up, or last ascertainment of cancer or death (between February 2020 and January 2021, depending on the centre). We excluded participants with prevalent cancer, CVD, or T2D at recruitment, and those with missing data for PA, resulting in a sample size of 334,596 UKB participants (Figure S2). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants from UK Biobank and EPIC have provided written informed consent to participate in the respective cohorts. UK Biobank has ethical approval from the Northwest Multi-Centre Research Ethics Committee. The EPIC study was approved by the Cancer Ethical Review Committee of IARC and by local ethical committees at the participating centres. The current study was approved by the IARC’s Ethics Committee (No. 23–40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical activity assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both cohorts, PA was calculated in metabolic equivalent task hours per week (MET-h/week) based on self-reported PA questionnaires. In EPIC, PA was assessed at recruitment using an interview-based questionnaire administered as part of the baseline health and lifestyle questionnaire (17). In UKB, PA was assessed at recruitment through a self-completed touch-screen questionnaire based on the short International Physical Activity Questionnaire (IPAQ) (18). The validity of the PA assessment tools used in EPIC and UKB studies was evaluated to confirm their ability to rank individuals by PA levels with a weighted kappa of 0.60 in EPIC (19) and 0.67 in UKB (20). The EPIC study defines PA as the total MET-h/week from household activities (like cleaning, cooking, gardening, and stair climbing) and recreational activities (such as walking, cycling, swimming, and jogging). The questionnaire assessed the number of hours spent on household and recreational activities in a typical week over the past year but did not provide a quantitative measure for the duration of occupational activity. Thus, total weekly PA was calculated by summing the mean hours spent on the household and recreational activities in summer and winter, then multiplying by their MET values: 3.0 for walking, 6.0 for cycling, 4.0 for gardening, 6.0 for sports, 4.5 for DIY work, 3.0 for housework, and 8.0 for stair climbing (21). UK Biobank assessed participants' engagement in walking, moderate, and vigorous physical activities lasting over 10 minutes each week. This included activities such as housework, recreation, and work. Total weekly PA is measured in MET-h/week, calculated by summing walking (3.3 METs), moderate activities (4.0 METs), and vigorous activities (8.0 METs) (22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer ascertainment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome of interest was the incidence of PA-related cancers combined, defined as pre- and postmenopausal breast, adenocarcinoma of the oesophagus, endometrial, colon, kidney, gastric cardia, urinary bladder, gallbladder, liver, lung, small intestine, multiple myeloma, myeloid leukemia, rectal, and head and neck cancer (1, 3-6). Overall cancer, defined as all primary cancers combined, excluding non-melanoma skin cancer, was our secondary outcome. Non-melanoma skin cancer cases were censored at the diagnosis date but not classified as cancer cases (1, 3-6). Cancers were coded according to ICD-10 and information on tumour morphology and histology using ICD-O-3 (Table S1), identified through health insurance records, cancer pathology registries, and active follow-up in EPIC, and for UKB, data on cancer diagnosis was obtained from the National Health Service (NHS) Digital and Public Health England for participants from England and Wales. For participants residing in Scotland, it was obtained from the NHS Central Register (NHSCR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiometabolic disease ascertainment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ascertainment of incident CVD and T2D has been described previously (23). Cardiometabolic disease included CVD and T2D, coded using ICD-10. Cardiovascular disease refers to a composite of ischemic heart diseases (I20-I25), and cerebrovascular disease (I60-I69), while T2D was defined as E11. Incident CVD was identified and validated through the EPIC-Heart study using questionnaires, medical records, and death certificates. Incident T2D cases were identified and validated in the EPIC-Interact study via self-reports, care register linkages, medication use, and hospital admissions. In UK Biobank, both CVD and T2D cases were identified using hospital admission records (23). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHighest level of education or qualifications was categorized as none, primary, technical, secondary, or higher education in the EPIC study. In UK Biobank, the categories were none, other professional qualifications, National Vocational Qualifications (NVQ) or Higher National Diplomas (HND) or Higher National Certificates (HNC) or equivalent, Certificate of Secondary Education (CSEs) or equivalent, O levels/General Certificate of Secondary Education (GCSEs) or equivalent, A levels/AS levels or equivalent, and college or university degrees.\u003c/p\u003e\n\u003cp\u003eAdditional covariates included smoking status, classified as never, former, or current smoking, and alcohol intake measured in grams per day in the EPIC study, while in UKB, it was categorized as never, former, or current alcohol drinker. Also, we considered PA at work (EPIC)/employment status (UKB) as a covariate. Physical activity at work was considered in EPIC with categories including sedentary occupation, standing occupation, manual work, heavy manual work, and non-worker. Employment status in UKB included paid employment or self-employed, retired, looking after home and/or family, unable to work due to sickness or disability, unemployed, doing unpaid or voluntary work, and full or part-time student.\u003c/p\u003e\n\u003cp\u003eDiet quality included the modified relative Mediterranean diet score in EPIC as low, medium, and high (24), and a healthy diet score in UKB as a scale ranging from 0 (unhealthier) to 6 (healthier) (25). Other covariates included height, body mass index (BMI), and among women, use of hormones for menopause, and menopausal status. In UKB, medication use, average total household income, overall self-rated health, and the Townsend deprivation index were considered. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical\u003c/strong\u003e\u003cstrong\u003e Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter filtering for the exclusion criteria (Figures S1 and S2), the overall missing rate of the covariates was below 2% (Table S2). Covariates with missing values were imputed using multiple imputation with chained equations (MICE) (26). Each of the covariates was imputed conditionally on the others using appropriate models (e.g., polytomous regression for unordered categorical variables) with 5 imputations and 10 iterations for each using the mice package in R. \u003c/p\u003e\n\u003cp\u003eTo address right-skewness of MET-h/week in EPIC, we capped the outliers at 1.5 times the interquartile range above the third quartile (233.9 MET-h/week corresponding to the 99th percentile). In UKB, participants with incomplete responses or missing information regarding the number of days or duration were excluded, along with those who reported more than 960 minutes of PA per day. Additionally, for each of the behaviours, the total time spent walking, total moderate intensity activity, and total vigorous intensity activity were capped at 180 minutes/day (22). \u003c/p\u003e\n\u003cp\u003eFor descriptive analysis, we categorized MET-h/week into four groups based on sex-specific quartiles: low, medium, high, and very high. We used Cox proportional hazards regression to estimate cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between PA and cancer outcomes. Associations were modelled per one standard deviation (SD) increment in PA, standardized within each cohort before meta-analysis to account for differences in scale. The SD was 51.4 MET-h/week in EPIC and 44.3 MET-h/week in UK Biobank. \u003c/p\u003e\n\u003cp\u003eThe entry time for cancer follow-up was age at recruitment, and the exit time was age at first primary cancer diagnosis, end of follow-up, loss to follow-up, or death, whichever occurred first. Deaths from any cause were modelled as a censored observation. Follow-up for CVD and T2D also started at age at recruitment and the exit time was the same as for cancer. To address potential detection bias, i.e., the elevated likelihood of cancer diagnosis shortly after a CMD diagnosis (27, 28), we implemented a 12-month lag period after a CMD diagnosis in all analyses. \u003c/p\u003e\n\u003cp\u003eThree models were evaluated: Model 1 was stratified by age (5-year categories), sex, and recruitment centre, and adjusted for education, BMI, height, smoking status, alcohol consumption, diet quality, occupational work in EPIC, and employment status in UKB, and in women for menopausal status and use of hormones after menopause. Model 2 was further adjusted for CVD and T2D status (modelled as binary time-varying variables), and time since diagnosis of CVD and T2D. Model 3, our main model, further included a multiplicative interaction between PA (continuous, time-invariable) and each of the two CMDs (time-varying categorical). \u003c/p\u003e\n\u003cp\u003eLikelihood ratio tests were used to compare models with the interaction term to nested models without interaction. We checked the proportional hazard assumption using Schoenfeld residuals, which was met. The linearity of the association between primary exposure, PA, and cancer risk was assessed using natural splines with three knots at the 25\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e, and 75\u003csup\u003eth\u003c/sup\u003e percentiles of PA (Figures S3, S4, S5, and S6). \u003c/p\u003e\n\u003cp\u003eNext, we assessed separate and joint associations of PA and CMDs with PA-related cancer and overall cancer, and quantified additive interaction through relative excess risk due to interaction (RERI) with the interactionR package (29). PA was dichotomized as active if PA was above the median (\u0026gt;81 MET-h/week in EPIC and \u0026gt;30 MET-h/week in UK Biobank) and inactive if it was below or equal to the median. We built four exclusive categories: (1) active, without CMD (reference); (2) active, with CMD; (3) inactive, without CMD; and (4) inactive, with CMD (joint effect). We calculated additive interaction using the RERI for each of the CMDs for each outcome, risk of PA-related cancers, and risk of all cancers as RERI\u003csub\u003eHR\u003c/sub\u003e = HR\u003csub\u003e11\u003c/sub\u003e – HR\u003csub\u003e10\u003c/sub\u003e – HR\u003csub\u003e01\u003c/sub\u003e + 1, with HR\u003csub\u003e11\u003c/sub\u003e the risk of being exposed to both factors (e.g., low PA and T2D), HR\u003csub\u003e10\u003c/sub\u003e exposed to one of the factors (low PA), and HR\u003csub\u003e01\u003c/sub\u003e to the other one (e.g., T2D). Estimates of 95% CIs were based on the delta method (30).\u003c/p\u003e\n\u003cp\u003eEach of the two cohorts' models were fitted separately, and the results were meta-analysed using a random-effects model. We used I\u003csup\u003e2\u003c/sup\u003e to describe the percentage of overall variability attributable to between-study heterogeneity.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were carried out to assess the robustness of results. First, in both cohorts, we evaluated the associations across population subgroups defined by smoking status (never-smokers versus ever smokers), occupation status (unemployed, versus employed: sedentary occupation, standing occupation, manual worker, and heavy manual worker), BMI categorized as less than 25 kg/m² and 25 kg/m² or greater, and sex (male versus female). In UK Biobank, we further examined the associations by medication use (individuals on prescribed blood pressure, cholesterol-lowering drugs, or insulin versus those not taking any of these medications), household income (having incomes of \u0026lt;= 30,999 GBP versus \u0026gt;= 31,000 GBP), and self-rated health status (dichotomized as healthy if rated as excellent or good and unhealthy if they rated as fair or bad). Lastly, we evaluated the associations based on the Townsend deprivation index, which was categorized based on the median score into low deprivation (score ≤ -2.2686) and high deprivation (score \u0026gt; -2.2686).\u003c/p\u003e\n\u003cp\u003eWe also conducted a complete case analysis after excluding those with missing data on any of the covariates. \u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, and significance was considered at a p-value of less than 0.05. Data were processed and analyzed using R version 4.3.1 (31). \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 564,622\u0026nbsp;participants\u0026nbsp;were included in the analysis, with 230,026 from the EPIC study and 334,596 from UKB. Tables 1 and\u0026nbsp;2 show the participants’ characteristics by levels of PA in EPIC and UKB, respectively. In EPIC, higher levels of PA were more common among women, never smokers, individuals with higher diet quality, those who were unemployed, and those with lower educational level (\u003cstrong\u003eTable 1\u003c/strong\u003e). In UKB, higher levels of PA were more common among individuals who were retired and postmenopausal women (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up period of 10.8 years (IQR: 9.4 to 12.4) in EPIC, 18,139 first primary cancers (2,433,935 person-years of follow-up) were\u0026nbsp;recorded (\u003cstrong\u003eTable S3\u003c/strong\u003e). Of these, 11,536 (2,453,298.92 person-years of follow-up) were PA-related cancers. The age-standardized transition rates from baseline to cancer and after a cardiometabolic disease to cancer are shown in \u003cstrong\u003eFigure S7\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn UKB, with a median follow-up of 11.3 years (IQR: 8.7 to 12.2), there were 32,218 recorded first primary cancers (3,792,008 person-years of follow-up). Of these, 16,809 (3,883,086 person-years of follow-up) were PA-related cancers. The age-standardized transition rates from baseline to cancer and after a cardiometabolic disease to cancer are shown in \u003cstrong\u003eFigure S8\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical activity and cancer risk by cardiometabolic disease status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable-adjusted model 1, where we ignored CMD status, showed an inverse association between higher levels of PA and the risk of PA-related cancers in both cohorts. Per 1 SD increment in MET-h/week of PA, HRs were 0.95 (95% CI: 0.94 to 0.98) and 0.96 (95% CI: 0.94 to 0.97) in EPIC and UK Biobank, respectively (\u003cstrong\u003eTable S4\u003c/strong\u003e). In model 2, these inverse associations were similar after further adjusting for T2D, CVD, and their durations (\u003cstrong\u003eTable S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn our model 3 (interaction model), we estimated associations by CMD status. In the meta-analysis of both cohorts, a 1 SD increment in MET-h/week of PA was associated with lower risk of PA-related cancers among those without CMDs with a summary HR of 0.96 (95% CI: 0.95, 0.97), and among those with a CMD with a summary HR of 0.94 (95% CI: 0.90, 0.99) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Findings for T2D or CVD were similar in magnitude. There was little evidence for a multiplicative interaction between PA and CMD status (all p-interaction \u0026gt; 0.3) (\u003cstrong\u003eTable S4\u003c/strong\u003e). The findings were similar for the risk of all cancers combined (\u003cstrong\u003eFigure S9\u003c/strong\u003e and \u003cstrong\u003eTable S4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn sensitivity analyses, in both cohorts, associations between PA and PA-related cancers remained consistent among individuals with and without CMD across population subgroups (all p-interaction \u0026gt; 0.1) (\u003cstrong\u003eTable S5\u003c/strong\u003e). The findings were generally similar when all cancers combined was the outcome (\u003cstrong\u003eTable S6\u003c/strong\u003e).\u0026nbsp;Furthermore, results were also consistent in complete case analyses (\u003cstrong\u003eTable S7\u003c/strong\u003e and \u003cstrong\u003eFigure S10\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoint association of physical (in)activity and cardiometabolic diseases with cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both cohorts, compared to active adults (PA above median) without CMDs, there was a graded increase in risk of PA-related cancers for inactive adults (PA below median) without CMDs (HR=1.10; 95% CI: 1.07, 1.13), active adults with CMDs (HR=1.23; 95% CI: 1.10, 1.37), and inactive adults with CMDs (HR=1.31; 95% CI: 1.18, 1.46) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Similar graded increases in risk of PA-related cancers were observed for combinations of PA and T2D or CVD. However, there was little evidence for additive interactions as quantified by RERI values around the null (\u003cstrong\u003eFigure 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eWhen assessing the additive interaction of PA and CMDs on the risk of all cancers, results were consistent but lower in magnitude. Similarly, there was little evidence for additive interactions as assessed with the RERI (\u003cstrong\u003eFigure S11)\u003c/strong\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this meta-analysis of two of the largest European prospective cohorts with together over 500,000 adult participants, we found that higher levels of PA were associated with a lower risk of PA-related cancers and overall cancer in adults living with a CMD (T2D and/or CVD). The lower risk of cancer was comparable to that in adults without a history of a CMD. In contrast, both low PA (below study-specific median) and a history of CMD were separately and jointly associated with a gradually higher risk of PA-related cancers and overall cancer as compared to active adults without a history of CMD. Taken together, these results suggest that being physically active is beneficial for cancer prevention in adults living with or without T2D and/or CVD.\u003c/p\u003e\n\u003cp\u003eNumerous studies have shown that PA is consistently associated with a reduced risk of various types of cancers in adults of the general population (1, 2, 4, 32-34). For example, a meta-analysis of nine prospective cohort studies reported that individuals who had PA levels of 7.5-15 MET-h/week had a 6% to 27% lower risk of cancers such as breast, kidney, and liver compared to individuals with no leisure-time PA (4). A prospective study in UKB reported a 13% (95% CI 9% to 17%) lower risk of PA-related cancers per 1 SD increment (8.3 milligravity units) of accelerometer-based total PA (34). The findings of our study concur with this previous evidence. Compared to existing evidence, our study adds new insights on the role of PA in cancer prevention among adults living with a CMD. \u003c/p\u003e\n\u003cp\u003eFew studies have investigated PA-related cancer risk among adults with a CMD (12-14, 16), and have not consistently investigated multiplicative and additive interaction between PA and CMD status. In UKB, a similarly lower risk for PA-related cancers was observed among adults with and without diabetes in relation to higher PA (34). A meta-analysis of both retrospective and prospective studies found an inverse association between higher PA and renal cancer with little evidence for effect modification by T2D status (subgroup p-value = 0.18) (35).\u003c/p\u003e\n\u003cp\u003eThe results of our study are largely consistent with these previous studies with one exception. In contrast to the results of the Swedish Mammography Cohort (14), we did not observe an additive interaction between PA and T2D status. Differences in study design are the most likely explanation. For example, in our study, we used validated T2D events and accounted for diabetes duration.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, only one previous study has stratified PA-associated cancer risk by CVD status (34). In that study, higher levels of total PA were similarly associated with a lower risk of PA-related cancer among adults with and without a history of CVD (34). However, separate and joint associations were not formally tested, and only baseline CVD status without accounting for CVD duration was investigated (34). Our study, therefore, adds important evidence that can inform clinical practice and public health recommendations. People living with CVD may be reluctant to engage in regular PA because of uncertain health implications (36, 37). These findings support broader recommendations that PA should be promoted across populations, including those living with a CMD. \u003c/p\u003e\n\u003cp\u003eOur study has notable strengths. The analysis is based on multinational data across six European countries from two large prospective cohort studies, which also allowed us to model the temporal relationship between PA, T2D and/or CVD, and cancer risk. We also assessed both multiplicative and additive interactions between PA and CMD on cancer risk, providing a more comprehensive understanding of the role of PA in cancer development. \u003cbr\u003eOur findings, however, have the following limitations. The PA exposure variable was assessed only at baseline, so changes over time were not evaluated. Also, as it relied on a self-reported questionnaire, reporting bias may be inevitable. However, this bias is likely minimal, as we used standardized rankings instead of the actual responses, and previous research supports the questionnaire’s validity for ranking PA levels (19, 20). EPIC assessed domain-specific activities (e.g., walking, gardening, housework) over the past year using task-specific MET values, while UKB captured walking, moderate, and vigorous PA over the past week using intensity-based METs and a 10-minute bout criterion. EPIC’s approach is more detailed but may be more prone to recall bias and tends to smooth out seasonal and short-term variation. Notably, the median MET-h/week was higher in EPIC than in UKB, reflecting differences in assessment scope and time frame. Despite these methodological differences, hazard ratios were similar across cohorts, suggesting that both instruments effectively rank individuals by physical activity level. Furthermore, the low participation rate of only 5% in the UK Biobank cohort (38, 39) suggests that the study participants may not accurately represent the overall UK population. Additionally, the absence of detailed medication use information in the EPIC cohort could have impacted our estimates. However, we accounted for this in the UKB analysis and estimates remained unchanged. \u003c/p\u003e\n\u003cp\u003eIn this study among European adults, higher levels of PA were associated with lower cancer risk, which was similar among adults with and without a history of T2D and/or CVD. Physical inactivity and a history of a CMD were both separately and jointly associated with a higher risk of cancer compared to being physically active and having no history of a CMD. Our findings add to the growing body of evidence supporting PA as a key cancer prevention strategy encompassing population subgroups with cardiometabolic diseases. \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiometabolic diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEPIC, European Prospective Investigation into Cancer and Nutrition\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMET-h/week, metabolic equivalent task hours per week\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD, standard deviation\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT2D\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etype 2 diabetes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUKB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUK Biobank.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eWorld Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant program and the French National Cancer Institute (l\u0026rsquo;Institut National du Cancer, INCA_16824). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the use of data from the EPIC-Aarhus cohort, PI Christina C. Dahm; EPIC-Varese cohort, PI Sabina Sieri; EPIC-Ragusa cohort, PI Rosario Tumino; EPIC-Bilthoven cohort, PI Monique Verschuren; EPIC-Asturias cohort, PI J. Ram\u0026oacute;n Quir\u0026oacute;s; EPIC-Murcia cohort, PI Maria Dolores Chirlaque Lopez and Huerta JM; and EPIC-Norfolk cohort, PI Nick Wareham.\u003c/p\u003e\n\u003cp\u003eUK Biobank is an open-access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/. This research has been conducted using the UK Biobank Resource under Application Number 55870 and we express our gratitude to the participants and those involved in building the resource.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere authors are identified as personnel of the International Agency for Research on Cancer/ World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/ World Health Organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors \u003c/strong\u003e\u003cstrong\u003econtribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHF conceived and designed the work. AG, HZ, EF, and PF were responsible for data analysis and interpretation, wrote the original draft, and carried out subsequent editing. ALRH, JLMA, KS, RTF, MBS, CS, SP, MF, NCOM, EM, JB, MF, MJS, MG, OMC, STT, AKH, DA, ER contributed to data acquisition. All authors contributed to writing, editing, and final approval of the manuscript. HZ is accountable for all aspects of the work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of \u003c/strong\u003e\u003cstrong\u003einterest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional \u003c/strong\u003e\u003cstrong\u003einformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental file 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u003c/strong\u003e\u003cstrong\u003e of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank data were available from the UK Biobank and can be accessed by researchers on the application\u003cstrong\u003e (\u003c/strong\u003ehttps://www.ukbiobank.ac.uk/use-our-data/apply-for-access/). \u003c/p\u003e\n\u003cp\u003eEPIC data are available for investigators who seek to answer important questions on health and disease in the context of research projects that are consistent with the legal and ethical standard practices of IARC/WHO and the EPIC Centres. For information on how to apply to gain access to EPIC data and/or biospecimens, please follow the instructions http://epic.iarc.fr/access/. \u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study were accessed in 2021 and are available from the corresponding author upon reasonable request ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoore SC, Lee I-M, Weiderpass E, Campbell PT, Sampson JN, Kitahara CM, et al. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA Internal Medicine. 2016;176(6):816-25.\u003c/li\u003e\n\u003cli\u003eGarcia L, Pearce M, Abbas A, Mok A, Strain T, Ali S, et al. Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose-response meta-analysis of large prospective studies. British Journal of Sports Medicine. 2023;57(15):979-89.\u003c/li\u003e\n\u003cli\u003eFriedenreich CM, Ryder‐Burbidge C, McNeil J. Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms. Molecular Oncology. 2021;15(3):790-800.\u003c/li\u003e\n\u003cli\u003eMatthews CE, Moore SC, Arem H, Cook MB, Trabert B, Hakansson N, et al. Amount and Intensity of Leisure-Time Physical Activity and Lower Cancer Risk. Journal of Clinical Oncology. 2020;38(7):686-97.\u003c/li\u003e\n\u003cli\u003eContinuous update Project Expert Report 2018. Physical activity and the risk of cancer [Internet]. 2018. Available from: dietandcancerreport.org.\u003c/li\u003e\n\u003cli\u003eMcTiernan A, Friedenreich CM, Katzmarzyk PT, Powell KE, Macko R, Buchner D, et al. Physical Activity in Cancer Prevention and Survival: A Systematic Review. Medicine and Science in Sports and Exercise. 2019;51(6):1252-61.\u003c/li\u003e\n\u003cli\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine. 2020;54(24):1451-62.\u003c/li\u003e\n\u003cli\u003eFowler H, Belot A, Ellis L, Maringe C, Luque-Fernandez MA, Njagi EN, et al. Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers. BMC Cancer. 2020;20:1-15.\u003c/li\u003e\n\u003cli\u003eKyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ. 2016;354.\u003c/li\u003e\n\u003cli\u003eKoene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Circulation. 2016;133(11):1104-14.\u003c/li\u003e\n\u003cli\u003eLing S, Brown K, Miksza JK, Howells L, Morrison A, Issa E, et al. Association of Type 2 Diabetes With Cancer: A Meta-analysis With Bias Analysis for Unmeasured Confounding in 151 Cohorts Comprising 32 Million People. Diabetes Care. 2020;43(9):2313-22.\u003c/li\u003e\n\u003cli\u003eSchmid D, Behrens G, Matthews CE, Leitzmann MF. Physical Activity and Risk of Colon Cancer in Diabetic and Nondiabetic US Adults. Mayo Clinic Proceedings. 2016;91(12):1693-705.\u003c/li\u003e\n\u003cli\u003eLuo X, Yang W, Ma Y, Simon TG, Meyerhardt JA, Chan AT, et al. Physical activity and risk of hepatocellular carcinoma among US men and women. Cancer Prevention Research. 2020;13(8):707-14.\u003c/li\u003e\n\u003cli\u003eFriberg E, Mantzoros CS, Wolk A. Diabetes and risk of endometrial cancer: a population-based prospective cohort study. Cancer Epidemiology Biomarkers \u0026amp; Prevention. 2007;16(2):276-80.\u003c/li\u003e\n\u003cli\u003eVenturini E, Iannuzzo G, D\u0026rsquo;andrea A, Pacileo M, Tarantini L, Canale M, et al. Oncology and cardiac rehabilitation: an underrated relationship. Journal of Clinical Medicine. 2020;9(6):1810.\u003c/li\u003e\n\u003cli\u003eMakram OM, Kunhiraman HH, Harris RA, Hedrick CC, Nasir K, Weintraub NL, et al. Examining the interplay between cardiovascular disease and cancer incidence: Data from NHANES III and continuous. American Heart Journal Plus: Cardiology Research and Practice. 2024;40:100380.\u003c/li\u003e\n\u003cli\u003eRiboli E, Hunt K, Slimani N, Ferrari P, Norat T, Fahey M, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutrition. 2002;5(6b):1113-24.\u003c/li\u003e\n\u003cli\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine. 2015;12(3):e1001779.\u003c/li\u003e\n\u003cli\u003eWareham NJ, Jakes RW, Rennie KL, Schuit J, Mitchell J, Hennings S, et al. Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public Health Nutrition. 2003;6(4):407-13.\u003c/li\u003e\n\u003cli\u003ePearce M, Strain T, Kim Y, Sharp SJ, Westgate K, Wijndaele K, et al. Estimating physical activity from self-reported behaviours in large-scale population studies using network harmonisation: findings from UK Biobank and associations with disease outcomes. International Journal of Behavioral Nutrition and Physical Activity. 2020;17(1):40.\u003c/li\u003e\n\u003cli\u003eCust AE, Smith BJ, Chau J, van der Ploeg HP, Friedenreich CM, Armstrong BK, et al. Validity and repeatability of the EPIC physical activity questionnaire: a validation study using accelerometers as an objective measure. The International Journal of Behavioral Nutrition and Physical Activity. 2008;5:33.\u003c/li\u003e\n\u003cli\u003eCommittee IR. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. http://www ipaq ki se/scoring pdf. 2005.\u003c/li\u003e\n\u003cli\u003eFontvieille E, Viallon V, Recalde M, Cordova R, Jansana A, Peruchet-Noray L, et al. Body mass index and cancer risk among adults with and without cardiometabolic diseases: evidence from the EPIC and UK Biobank prospective cohort studies. BMC Medicine. 2023;21(1):418.\u003c/li\u003e\n\u003cli\u003eRomaguera D, Norat T, Vergnaud A-C, Mouw T, May AM, Agudo A, et al. Mediterranean dietary patterns and prospective weight change in participants of the EPIC-PANACEA project. The American journal of clinical nutrition. 2010;92(4):912-21.\u003c/li\u003e\n\u003cli\u003eHepsomali P, Groeger JA. Diet and general cognitive ability in the UK Biobank dataset. Scientific Reports. 2021;11(1):11786.\u003c/li\u003e\n\u003cli\u003eVan Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. Journal of statistical software. 2011;45:1-67.\u003c/li\u003e\n\u003cli\u003eDankner R, Boffetta P, Balicer RD, Boker LK, Sadeh M, Berlin A, et al. Time-dependent risk of cancer after a diabetes diagnosis in a cohort of 2.3 million adults. American Journal of Epidemiology. 2016;183(12):1098-106.\u003c/li\u003e\n\u003cli\u003eJohnson J, Bowker S, Richardson K, Marra C. Time-varying incidence of cancer after the onset of type 2 diabetes: evidence of potential detection bias. Diabetologia. 2011;54:2263-71.\u003c/li\u003e\n\u003cli\u003eAlli BY. InteractionR: an R package for full reporting of effect modification and interaction. Software Impacts. 2021;10:100147.\u003c/li\u003e\n\u003cli\u003eHosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology. 1992:452-6.\u003c/li\u003e\n\u003cli\u003eTeam RC. RA language and environment for statistical computing, R Foundation for Statistical Computing. Computing. 2023.\u003c/li\u003e\n\u003cli\u003eLai Y-J, Wang C-C, Lin Y-K, Chen M-J, Chou Y-S, Chen C-C, et al. Association between leisure-time physical activity and incident cancer risk: a nationwide population-based cohort study. Sports Medicine-Open. 2024;10(1):1-10.\u003c/li\u003e\n\u003cli\u003eShi Y, Li T, Wang Y, Zhou L, Qin Q, Yin J, et al. Household physical activity and cancer risk: a systematic review and dose-response meta-analysis of epidemiological studies. Scientific Reports. 2015;5(1):14901.\u003c/li\u003e\n\u003cli\u003eShreves AH, Small SR, Walmsley R, Chan S, Saint-Maurice PF, Moore SC, et al. Amount and intensity of daily total physical activity, step count, and risk of incident cancer in the UK Biobank. British Journal of Sports Medicine. 2025;59(12):839-47.\u003c/li\u003e\n\u003cli\u003eBehrens G, Leitzmann M. The association between physical activity and renal cancer: systematic review and meta-analysis. British Journal of Cancer. 2013;108(4):798-811.\u003c/li\u003e\n\u003cli\u003eJoussain C, Joubert J, Laroche D, D\u0026rsquo;antono B, Juneau M, Gr\u0026eacute;meaux V. Barriers to physical activity in coronary artery disease patients: development and validation of a new scale. Annals of Physical and Rehabilitation Medicine. 2017;60(5):289-98.\u003c/li\u003e\n\u003cli\u003eAlsaleh E, Baniyasin F. Prevalence of physical activity levels and perceived benefits of and barriers to physical activity among Jordanian patients with coronary heart disease: A cross-sectional study. Frontiers in public health. 2023;10:1041428.\u003c/li\u003e\n\u003cli\u003eFry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. American Journal of Epidemiology. 2017;186(9):1026-34.\u003c/li\u003e\n\u003cli\u003eAllen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: Current status and what it means for epidemiology. Health Policy and Technology. 2012;1(3):123-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"114%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Baseline characteristics of the study population in EPIC by level of physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedium\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery high\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e45187 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e52794 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e61042 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e71003 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e230026 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity, MET-h/week\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28.5 (18.8, 38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e56.6 (47.8, 70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e90.2 (76.2, 105.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e146.5 (126.6, 171.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e81.0 (49.0, 123.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e20430 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21512 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21834 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22784 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e86560 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24757 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31282 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39208 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48219 (67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e143466 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity at work, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNon-worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8247 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11407 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e18467 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e37133 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e75254 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSedentary occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19110 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21226 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19372 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e12772 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e72480 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStanding occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9806 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11740 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14285 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10938 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46769 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eManual work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6033 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6306 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6352 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6596 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25287 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHeavy manual work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1477 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1459 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1639 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2222 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6797 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e514 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e656 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e927 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1342 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3439 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at recruitment, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e52.8 (47.0, 58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e52.7 (46.4, 58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e52.5 (45.7, 58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e52.5 (45.2, 58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e52.6 (46.0, 58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight, cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e167 (161, 173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e167(161, 174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e164 (159, 173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e164 (158, 172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e166 (160, 173)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25.8 (23.3, 28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25.3 (23.0, 28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25.4 (23.1, 28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25.7 (23.3, 28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25.5 (23.2, 28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption, g/d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10 (2, 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9 (2, 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7 (1, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (0.4, 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8 (1, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1884 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1432 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3172 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5710 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12198 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePrimary school completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13725 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14069 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17800 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25331 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e70925 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTechnical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12551 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15637 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16844 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e17840 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e62872 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6983 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7626 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8606 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9100 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32315 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eLonger education (incl. University)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9478 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13117 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13434 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11397 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e47426 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e566 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e913 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1186 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1625 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4290 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17459 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22368 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28129 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e35383 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e103339 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13048 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16166 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17931 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e19211 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e66356 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14494 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14091 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14787 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16263 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e59635 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e186 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e195 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e146 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e696 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMed. diet, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11838 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14418 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15145 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16409 (23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57810 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19985 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24573 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29027 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e32193 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e105778 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13247 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13703 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16769 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22307 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e66026 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e117 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e100 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e101 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e94 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e412 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status \u003csup\u003ea\u003c/sup\u003e, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePre-menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7098 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10684 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14177 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e17897 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e49856 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePost-menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12807 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14922 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e18224 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e21853 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e67806 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePerimenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3853 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4569 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5435 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6524 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e20381 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSurgical menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e999 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1107 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1372 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1945 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5423 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of hormones for menopause\u003c/strong\u003e\u003cstrong\u003e, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19460 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25246 (80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32815 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e41654 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e119175 (83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4942 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5666 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5934 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5834 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22376 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e355 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e370 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e459 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e731 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1915 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of recruitment, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10061 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8033 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10093 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14361 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42548 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6566 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5531 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8966 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14183 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e35246 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4879 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7281 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8548 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9226 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29934 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eThe Netherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2403 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5267 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8517 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e12537 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28724 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5372 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10494 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13151 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13115 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42132 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15906 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16188 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11767 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7581 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e51442 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eValues are medians (interquartile range) unless otherwise stated.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eOnly in women; Med. diet: Mediterranean diet; MET-h/week, metabolic equivalent task hours per week; EPIC: European Prospective Investigation into Cancer and Nutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"115%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e: Baseline characteristics of the study population in UK Biobank by levels of physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery high\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e82526 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e83883 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e84006 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e84247 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e334596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity, MET-h/week\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7.3 (3.7, 10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21.3 (17.3, 25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e41.7 (35.5, 49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e92.4 (73.3, 125.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e30.2 (13.9, 59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e43466 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e44041 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e43894 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e43934 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e175335 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39060 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39842 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e40112 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e40247 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e159261 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1425 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1360 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1392 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1306 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5483 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19912 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24353 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e27357 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28131 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e99753 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eLooking after home and/or family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1672 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2152 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2471 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2771 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9066 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eUnable to work because of sickness or disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3368 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1549 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1347 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1150 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7414 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFull or part-time student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e239 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e256 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e281 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e219 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e995 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDoing unpaid or voluntary work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e342 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e398 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e433 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e430 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1603 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eIn paid employment or self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55005 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e53218 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e50116 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e49489 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e207828 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e563 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e597 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e609 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e685 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2454 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at assessment, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e56.2 (49.3, 62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e56.8 (49.3, 62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57.2 (49.2, 63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e58.0 (49.9, 63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57 (49.4, 62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight, cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169 (162, 176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169 (162, 176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169 (162, 176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e168 (162, 175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169 (162, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e27.3 (24.6, 30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26.5 (24.0, 29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26.1 (23.7, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e26.1 (23.7, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26.5 (24.0, 29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTownsend deprivation, score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-2.3 (-3.7, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-2.3 (-3.7, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-2.3 (-3.7, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-2.1 (-3.6, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-2.3 (-3.7, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQualifications, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCSEs or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3872 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3624 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4004 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5982 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17482 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eO levels/GCSEs or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17677 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16865 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17535 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18990 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e71067 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eA levels/AS levels or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10625 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10566 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9982 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9110 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e40283 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNVQ or HND or HNC or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4689 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4673 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5179 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6721 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21262 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCollege or University degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31640 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e35123 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32973 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e23562 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e123298 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3864 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3948 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4067 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4626 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16505 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNone of the above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9678 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8685 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9819 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14500 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42682 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e481 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e399 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e447 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e690 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2017 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46163 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e48042 (57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e47463 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e45937 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e187605 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26785 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28025 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28950 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29160 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e112920 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9369 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7629 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7412 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8885 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e33295 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e209 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e187 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e181 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e199 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e776 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3534 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2927 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2687 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3265 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12413 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2809 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2302 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2356 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2877 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10344 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e76121 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e78615 (93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e78921 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e77978 (92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e311635 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e62 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e61 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e204 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy diet Score\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e6, healthier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1848 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2217 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2444 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2697 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9206 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e14009 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16672 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17603 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18180 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e66464 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32956 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e35954 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e36577 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e35813 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e141300 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e23519 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22004 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21114 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20490 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e87127 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8136 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5849 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5282 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5679 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24946 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1811 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1068 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e873 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1170 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4922 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0, unhealthier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e247 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e119 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e113 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e152 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e631 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopause \u003csup\u003ec\u003c/sup\u003e, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24596 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25629 (59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25806 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e27131 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e103162 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12324 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12450 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12339 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10748 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e47861 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHad a hysterectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4661 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4265 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4142 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4608 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17676 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1885 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1697 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1607 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1447 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6636 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver use of HRT \u003csup\u003ec\u003c/sup\u003e, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15058 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15278 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15434 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16698 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e62468 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28247 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28644 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e28364 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e27143 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e112398 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e161 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e119 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e96 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e93 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e469 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMET-h/week, metabolic equivalent task hours per week; HRT: hormonal replacement therapy; IQR: interquartile range; AS: Advanced Subsidiary; GCSEs: General Certificate of Secondary Education; CSEs: Certificate of Secondary Education; NVQs: National Vocational Qualifications;\u0026nbsp;HNDs: Higher National Diplomas; and\u0026nbsp;HNCs: Higher National Certificates.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eRefers to other professions such as nursing, teaching.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eScores are arranged from the healthier (6) to unhealthier (0)\u0026mdash;Healthy diet score was calculated based on consumption of commonly eaten food groups following recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eOnly in women.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Physical activity, type 2 diabetes, cardiovascular diseases, cancer, relative excess risk due to interaction, multimorbidity","lastPublishedDoi":"10.21203/rs.3.rs-7824019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7824019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eWhether physical activity is associated with the risk of cancer among adults with a cardiometabolic disease is not well understood. This study investigated associations between physical activity and cancer risk in adults with and without a history of cardiometabolic diseases (cardiovascular disease and/or type 2 diabetes).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e We conducted a meta-analysis of individual participant data of 564,622 men and women, aged 35\u0026ndash;70 years at recruitment, across six European countries from the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank. We excluded participants from these cohorts who had cancer, cardiovascular disease, or type 2 diabetes at baseline. Data on physical activity were assessed at baseline with self-reported validated questionnaires and modelled as metabolic equivalent task hours per week (MET-h/week). We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between physical activity and the risk of physical activity-related cancers, with a multiplicative interaction between physical activity and time-varying cardiometabolic disease status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter a median of 11 years of follow-up, 28,345 participants developed a first primary physical activity-related cancer (EPIC and UK Biobank combined). In the meta-analysis of both cohorts, a 1 standard deviation (SD) increment of physical activity was associated with a lower risk of physical activity-related cancer, with HRs of 0.96 (95% CI: 0.95, 0.97) and 0.94 (95% CI: 0.90, 0.99) in adults without and in those with a cardiometabolic disease, respectively (p-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.3).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe findings of this study among European adults suggest that higher physical activity is equally beneficial for cancer prevention in adults with and without underlying cardiometabolic diseases.\u003c/p\u003e","manuscriptTitle":"Physical activity, cardiometabolic diseases, and cancer risk: a prospective analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 21:23:53","doi":"10.21203/rs.3.rs-7824019/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b49b1a7-32e9-4856-89ac-0d1cf7a9888e","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58613544,"name":"Health sciences/Diseases/Cancer/Cancer prevention"},{"id":58613545,"name":"Health sciences/Diseases/Cancer/Cancer epidemiology"}],"tags":[],"updatedAt":"2026-05-12T16:04:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 21:23:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7824019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7824019","identity":"rs-7824019","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}