Multi-trait body shape phenotypes and breast cancer risk in postmenopausal women: a causal mediation analysis in the UK Biobank cohort

Multi-trait body shape phenotypes and breast cancer risk in postmenopausal women: a causal mediation analysis in the UK Biobank cohort | 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 Research Article Multi-trait body shape phenotypes and breast cancer risk in postmenopausal women: a causal mediation analysis in the UK Biobank cohort Amina Amadou, Heinz Freisling, Anja M. Sedlmeier, Patricia Bohmann, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3850301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2024 Read the published version in Journal of Epidemiology and Global Health → Version 1 posted 7 You are reading this latest preprint version Abstract Body shape phenotypes combining multiple anthropometric traits have been linked to postmenopausal breast cancer (BC). However, underlying biological pathways remain poorly understood. This study investigated to what extent the associations of body shapes with postmenopausal BC risk is mediated by biochemical markers. The study included 176,686 postmenopausal women from UK Biobank. Four body shape phenotypes were derived from principal component (PC) analysis of height, weight, body mass index, waist and hip circumferences, and waist-to-hip ratio. The four-way decomposition of the total effect was used to estimate mediation and interaction effects simultaneously as well as the mediated proportions. After 10.9 years median follow-up, 6,396 incident postmenopausal BC were diagnosed. There was strong evidence of positive associations between PC1 (general obesity) and PC2 (tall, low WHR), and BC risk. The association of PC1 with BC risk was mediated positively by testosterone and negatively by insulin-like growth factor-1 (IGF-1), with the overall proportion mediated (sum of the mediated interaction and pure indirect effect (PIE)) accounting for 12.2% (95% confidence intervals: -20.5% to -4.0%) and 11.4%(5.1% to 17.8%) of the total effect, respectively. Small proportions of the association between PC2 and BC were mediated by IGF-1 (PIE: 2.8%(0.6% to 4.9%)), and sex hormone-binding globulin (SHBG) (PIE: -6.1%(-10.9% to -1.3%)). Our findings are consistent with differential pathways linking different body shapes with BC risk, with a suggestive mediation through testosterone and IGF-1 in the relationship of generally obese body shape and BC risk, while IGF-1 and SHBG may mediate the tall/lean body shape-BC risk association. breast cancer anthropometry body shape biomarker mediation interaction Figures Figure 1 INTRODUCTION Strong evidence links obesity (defined by high body mass index (BMI) ≥ 25 kg/m 2 ) ; or indicators of body fat distribution, such as waist circumference (WC : men > 102 cm, women > 88 cm), hip circumference (HC), and waist-to-hip ratio (WHR : men > 0.90, women > 0.80)) with the risk of postmenopausal breast cancer (BC) [ 1 – 4 ], the most frequent cancer which represents an important public health problem in women [ 5 – 9 ]. Most studies investigated single anthropometric traits in relation to BC risk, which might not adequately capture the complexity of body morphology, specifically among women who are similar in one trait but differ in others [ 10 ]. To address this issue, Ried et al. were the first to apply a principal component analysis (PCA)-based approach to estimate principal components (PC) representing body shapes derived from BMI, height, weight, WC, HC, and WHR [ 11 ]. The four derived body shape phenotypes explained over 99% of the total variation in these anthropometric traits and were differently associated with several indicators of metabolic health (e.g., hormonal, metabolic, and inflammatory biomarkers) [ 11 ]. This PCA-based approach has been subsequently applied by our team to evaluate the impact of these body shapes on the risk of cancer in the European Prospective Investigation into Cancer and Nutrition cohort (EPIC) [ 10 ]. A generally obese body shape and a tall, lean body shape were both positively associated with postmenopausal breast cancer risk, while the other two body shapes were not associated with such risk [ 10 ]. The underlying biological and metabolic mechanisms linking obesity to BC are multiple and complex. Obesity has been strongly associated with several metabolic alterations, including deregulation of sex hormones, overexpression of pro-inflammatory cytokines, insulin resistance, hyperactivation of insulin-like growth factors (IGFs) pathways, hypercholesterolemia, as well as excessive oxidative stress [ 5 , 12 – 14 ]. Several of these biomarkers (such as serum sex hormone-binding globulin (SHBG), IGF-1, testosterone, C-reactive protein (CRP)) have also been associated with BC risk [ 15 – 17 ]. However, whether these biomarkers mediate the body shape-BC cancer relationship is unknown. Such knowledge could help understand the impact of body shapes on BC risk and possibly identify biological pathways. The main objective of the present study was to investigate to what extent the presumed associations between body shape phenotypes and postmenopausal BC risk are mediated by biomarkers of metabolic health. The candidate biomarkers were selected based on their implication in the development of BC, as well as their associations with obesity. MATERIAL AND METHODS Study population The UK Biobank ( http://www.ukbiobank.ac.uk/ ) is a prospective cohort study that recruited a total of 502,418 men and women, aged between 39 to 71 years at enrolment between 2006 and 2010. Study design and methodology have been described elsewhere [ 9 , 18 ]. At the initial assessment center visit, participants completed a self-administered touchscreen questionnaire that included information on health, demographic, anthropometric, lifestyle, and medical history data, collected in 22 centers across England, Wales, and Scotland. Biological samples including blood, saliva, and urine were also collected at enrolment. The UK Biobank study was approved by the North West Multi-Center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland ( http://www.ukbiobank.ac.uk/ethics/ ). All participants provided written informed consent. For the present study, we only included women, who were postmenopausal at the time of enrolment. Women were categorized as postmenopausal if they reported “yes” to the question “Have you had your menopause (periods stopped at least one year before enrollment)”, if they were older than 55 years [ 19 ] or reported a bilateral oophorectomy. Among these, we excluded women with prevalent cancer, those with missing or implausible anthropometry data, and with missing biomarker data. The study participants flowchart is given in Supplementary Fig. 1. After exclusions, the analysis involved 176,686 postmenopausal women. Ascertainment of breast cancer cases Data on cancer diagnoses were provided by National Health Service (NHS) Digital and Public Health England for participants from England and Wales and by NHS Central Register (NHSCR) for participants residing in Scotland, and BC cases were ascertained through cancer registries [ 20 ]. For the present study, complete follow-up data were available up to 29 February 2020 for England and Wales; and 31 January 2021 for Scotland. All registrations coded as C50 using the 10th Revision of the International Classification of Diseases (ICD-10) were considered as invasive BC cases. Assessment of anthropometric measures Height, weight, WC, and HC were assessed by trained personnel during the baseline assessment center visit [ 21 ]. Body weight (kilograms, kg) was measured using a Tanita BC418MA body composition analyzer. Height was measured using a Seca 240 cm height measure, while HC and WC measurements (cm) were assessed using a Seca 200 cm tape measure. BMI was calculated as body weight (kg) divided by height in meters squared (kg/m 2 ), and WHR was calculated as WC divided by HC. Biomarkers assays The UK Biobank measures a wide range of biochemical markers from biological samples collected at baseline in all participants [ 22 ]. The biomarkers selected for the assay have been chosen because they are established risk factors for several diseases [ 22 ]. The present study examined biomarkers of metabolic health comprising markers of glucose (glucose, glycated hemoglobin, HbA1c, mmol/mol), insulin metabolism (IGF-1, nmol/L), inflammation (CRP, mg/L), sex hormones (testosterone and SHBG, nmol/L), blood lipids (triglycerides, HDL-cholesterol and cholesterol, mmol/L), as well as total protein (g/L). These biomarkers were selected based on their potential links with overweight/obesity, and BC risk [ 1 , 12 , 23 ]. We further explored other biomarkers of metabolic health that were moderately correlated to body shape phenotypes, to identify novel biomarkers that could influence their association with BC risk. These biomarkers included albumin (g/L), glucose (mmol/L), alanine amino-transferase (U/L), apolipoproteins A and B (g/L), cystatin C (mg/L), Gamma glutamyltransferase (U/L), total bilirubin (umol/L), and urate (umol/L). Using a Beckman Coulter, AU580, triglycerides were quantified by Group Purchasing Organisation-Physician Owned Distributor (GPO-POD) analysis, cholesterol by cholesterol oxidase-peroxidase (CHOD-POD) method, HDL-cholesterol by enzyme immune-inhibition analysis, CRP by immunoturbidimetric-high sensitivity analysis, and total protein by Biuret analysis. Serum levels of HbA1c were measured by high-performance liquid chromatography analysis on a Bio-Rad, VARIANT II Turbo, and IGF-1 was quantified by chemiluminescence immunoassay (CLIA) technique (DiaSorin Ltd LIASON XL). Oestradiol and SHBG were measured using the two-step competitive analysis method (Beckman Coulter, Unicel DxI 800), while testosterone was measured with a one-step competitive analysis (Beckman Coulter, Unicel DxI 800). Statistical analysis PCA was applied to the standardized residuals of height, weight, BMI, WC, HC, and WHR. The residuals were predicted from a separate regression of the six anthropometric traits with age, sex, and study center. From the PCA, we retained the first four PCs that explained 99% of the variation and represented orthogonal linear combinations of the six anthropometric traits [ 11 ]. Each component represented a weighted sum of the six transformed anthropometric traits and is independent of the other components. The weights of each trait per PC are referred to as loadings. Cox proportional hazard regression was used to estimate the hazard ratios (HR) and corresponding 95% confidence intervals (CI) of the associations between each body shape PC (continuous and quintiles), and each biomarker (continuous) with BC risk. Continuous models for an increment of one standard deviation (SD) of each PC and biomarker were estimated. Age at entry was age at recruitment, and exit time was considered one of following: age at diagnosis of first incident BC, age of diagnosis of another cancer except non-melanoma skin cancer, age at end of follow-up, age at loss-to-follow-up, or age at time of death, whichever occurred first. The proportional hazards assumptions were tested using scaled Schoenfeld residuals. The shape of the exposure-response curve between each PC and BC risk was estimated using restricted cubic splines [ 24 ], with five knots placed at the 5th, 27.5th, 50th, 72.5th and 95th percentiles, as recommended by Harrell et al. for larger datasets [ 25 ]. Linear regression was performed to assess the associations between each PC and distinct biomarkers of metabolic health. We employed med4way mediation analysis [ 26 ] to investigate whether metabolic biomarkers can act as individual mediators on the pathway between body shapes and postmenopausal BC risk. Med4way uses parametric regression models to estimate the components of the four-way decomposition of the total effect of the exposure (here PC) on the outcome (BC) in the presence of the mediator (each biomarker of metabolic health) with which the exposure may interact. The total effect (TE) is decomposed into four components, i.e. the controlled direct effect (CDE, i.e. the effect of PC on BC neither due to mediation nor to interaction), the reference interaction effect (INTref, i.e. the effect due to interaction only), the mediated interaction effect (INTmed, i.e. due to both mediation and interaction) and the pure indirect effect (PIE, i.e. only due to mediation, but not interaction) [ 26 , 27 ]. The CDE was estimated at a fixed level of the mediator. Two regression models were fitted: a Cox model for the outcome, and a linear regression model for the mediator. The variable for the interaction between the exposure and the mediator was automatically generated and added to the model for the outcome. In addition to the four components of the TE, we further estimated the proportions of the effect due to each component, including the proportion due to the CDE, the proportion due to the PIE, the proportion due to the INTref, the proportion due to the INTmed, as well as the overall proportion mediated (PIE + INTmed). The crude models were stratified by age at recruitment in 5-year categories, and study center. All multivariable models were adjusted for the following potential confounders, identified by a directed acyclic graph ( Supplementary Fig. 2 ): age at recruitment, study center, healthy diet score, alcohol intake, smoking status, use of oral contraceptive use, use of menopausal hormone treatment (MHT), physical activity, qualifications, Townsend deprivation index and sedentary behavior. Healthy diet score was calculated based on consumption of these commonly food groups (fruits, vegetables, fish, processed meats, unprocessed red meats, whole grains, and refined grains) [ 28 ]. Sedentary behavior is the sum of time spent watching TV, time spent using the computer and time spent driving. Covariates, except for physical activity (missing values = 24.3%) and sedentary behavior (missing values = 4.2%), had less than 2% missing data. The multivariable analyses were thus conducted in the complete-case dataset, excluding all women with a missing value (n = 47,319) for any of the adjusted covariates, which resulted in a final sample size of 129,367 participants. In the mediation analysis, additional mutual adjustment for each biomarker was performed, by adjusting each mediator model for all other biomarkers. Sensitivity analyses excluding participants with the first two years of follow-up were conducted in order to control for potential reverse causation. Additionally, a multiple imputation which accounts for the uncertainty of missing data, was performed to handle missing values of the adjusted variables [ 29 ]. Finally, we conducted sensitivity analyses only among postmenopausal women, who answered having their periods stopped at least one year before enrollment, after exclusion of women, who were 55 years or older, or reported a bilateral oophorectomy (n = 108,754). As sensitivity mediation analyses, additional models without mutual biomarkers adjustment were conducted. Body shape phenotypes from PC analysis was done with the package "FactoMineR" using R version 4.2.3, all other statistical analyses were performed using STATA 14. RESULTS Characteristics of the study population After a median follow-up of 10.9 years (interquartile range = 10.1–11.7), 6,396 incident BC cases were diagnosed among the 176,686 postmenopausal women. The characteristics of the study participants by cases/non-cases status are shown in Supplementary Table 1 . The average age at recruitment (± SD) was 61.0 (± 5.3) years for cases and 60.4 (± 5.7) years for non-cases. Compared to non-cases, participants with BC were more likely to have higher anthropometric measures, to be less physically active, to have a higher level of alcohol intake, and to be MHT users. The distribution of other characteristics was generally similar between the cases and non-cases. Concentration levels of CRP, testosterone, and urate were slightly higher among cases compared to non-cases, while SHBG was lower in cases than in non-cases. All other biomarkers' concentration levels were comparable between cases and non-cases ( Supplementary Table 2). Overall, except between HDL-cholesterol and apolipoprotein A (correlation coefficient = 0.9), there were no strong correlations between biomarkers (Fig. 1 ). Body shape phenotypes Loadings and explained variance of the six PCs for women are presented in Supplementary Table 3 . PC1 (65.7% of the total variation) described individuals with general obesity vs. lean body shape. PC2 (19.1% of the total variation) characterized tall individuals with low WHR vs. short individuals with large WHR. PC3 (13.5% of the total variation) characterized tall individuals with high WHR, but low HC vs. short individuals with low WHR and high HC. PC4 (1.6% of the total variation) showed high loadings for BMI and weight, and low loadings for HC and WC. Baseline characteristics of the study participants are further presented by quintiles of PC1 scores (Table 1 ). As compared to women in the lower quintile (Q1), women in the upper quintile (Q5) had higher anthropometric measures, except for height. Women in the two lowest quintiles of PC1 had a healthier diet, a higher educational level, a higher level of alcohol intake, a lower Townsend deprivation index, were more physically active, less sedentary, and more likely to be never smokers compared with those in the upper quintile of PC1. Table 1 Characteristics of participants according to quintiles of scores of principal component 1 (general obesity) in the UK Biobank cohort study Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 N (%) 35,338 (20) 35,337 (20) 35,336 (20) 35,338 (20) 35,337 (20) Follow-up time (years), median (IQR) 10.9 (10.1–11.7) 10.9 (10.1–11.7) 10.9(10.1–11.7) 10.9(10.1–11.7) 10.9 (10.0-11.6) Age at recruitment (years), mean (SD) 60.4 (5.6) 60.4 (5.6) 60.6 (5.6) 60.7 (5.6) 60.0 (5.8) Age at menarche (years), mean (SD) 13.1 (1.6) 13.0 (1.6) 13.0 (1.6) 12.9 (1.6) 12.7 (1.7) Townsend deprivation index, mean (SD) -1.7 (2.8) -1.7 (2.8) -1.6 (2.9) -1.4 (3.0) -0.78 (3.2) Sedentary behavior (hours/day), mean (SD) 3.9 (1.9) 4.2 (1.9) 4.5 (2.0) 4.7 (2.1) 5.1 (2.3) BMI (kg/m²), mean (SD) 21.8 (1.7) 24.4 (1.5) 26.4 (1.6) 29.0 (1.9) 34.9 (4.4) Weight (kg), mean (SD) 56.4 (4.8) 63.8 (4.1) 69.2 (4.3) 76.1 (5.0) 91.9 (11.6) Height (m), mean (SD) 1.61 (0.1) 1.62 (0.1) 1.62 (0.1) 1.62 (0.1) 1.62 (0.1) WHR, mean (SD) 0.76 (0.05) 0.80 (0.1) 0.82 (0.1) 0.85 (0.1) 0.88 (0.1) Waist circumference (cm), mean (SD) 70.6 (4.1) 78.1 (3.2) 84.0 (3.4) 90.8 (4.0) 103.8 (8.7) Hip circumference (cm), mean (SD) 92.9 (4.4) 98.2 (4.0) 102.0 (4.2) 106.8 (4.8) 118.0 (9.5) Physical activity, n (%) Low 3,555 (12.9) 3,949 (14.3) 4,454 (16.5) 5,339 (20.3) 6,996 (27.8) Moderate 11,425 (41.4) 11,909 (43.3) 12,071 (44.7) 11,537 (43.7) 1,081 (42.9) High 12,624 (45.7) 11,658 (42.4) 10,477 (38.8) 9,498 (36.0) 7,377 (29.3) Alcohol drinking, n (%) Daily or almost daily 7,570 (21.5) 6,913 (19.6) 6,397 (18.1) 5,451 (15.5) 3,621 (10.3) 3 to 4 times a week 7,868 (22.3) 8,172 (23.1) 7,508 (21.3) 6,619 (18.8) 4,978 (14.1) 1 to 2 times a week 8,370 (23.7) 8,951 (25.3) 9,159 (26.0) 9,089 (25.8) 8,318 (23.6) 1 to 3 times a month 3,785 (10.7) 3,969 (11.2) 4,275 (12.1) 4,620 (13.1) 5,380 (15.3) Special occasions only 4,380 (12.4) 4,472 (12.7) 4,904 (13.9) 5,873 (16.5) 7,910 (22.4) Never 3,314 (9.4) 2,813 (8.0) 3,031 (8.6) 3,615 (10.2) 5,056 (14.3) Smoking status, n (%) Never 21,925 (62.3) 21,010 (59.7) 20,524 (58.4) 19,967 (56.8) 19,468 (55.4) Previous 10,185 (28.9) 11,375 (32.3) 11,832 (33.6) 12,324 (35.1) 12,736 (36.3) Current 3,112 (8.8) 2,801 (8.0) 2,808 (8.0) 2,848 (8.1) 2,909 (8.3) Qualification, n (%) None of the above 5,921 (17.0) 6,581 (18.9) 7,424 (21.4) 8,377 (24.2) 8,962 (25.9) College or University degree 11,957 (34.4) 10,806 (31.1) 9,539 (27.5) 8,613 (24.9) 7,753 (22.4) A levels/AS levels or equivalent 4,161 (12.0) 3,849 (11.1) 3,842 (11.1) 3,517 (10.1) 3,415 (10.0) O levels/GCSEs or equivalent 7,834 (22.5) 8,357 (24.1) 8,356 (24.1) 8,186 (23.6) 8,072 (23.4) CSEs or equivalent 1,311 (3.8) 1,401 (4.0) 1,465 (4.2) 1,642 (4.7) 1,814 (5.3) NVQ or HND or HNC or equivalent 1,234 (3.5) 1,507 (4.3) 1,637 (4.7) 1,826 (5.3) 2,144 (6.2) Other professional 2,324 (6.7) 2,227 (6.4) 2,380 (6.9) 2,461 (7.1) 2,386 (6.9) Ever use of oral contraceptive, n (%) No 7,554 (21.4) 7,209 (20.5) 7,556 (21.4) 7,938 (22.6) 8,026 (22.8) Yes 27,662 (78.6) 28,028 (79.5) 27,660 (78.6) 27,237 (77.4) 27,151 (77.2) Ever use of menopausal hormone therapy, n (%) No 18,173 (51.6) 17,147 (48.7) 17,061 (48.4) 16,734 (47.6) 17,756 (50.6) Yes 17,057 (48.4) 18,075 (51.3) 18,158 (51.6) 18,439 (52.4) 17,362 (49.4) Healthy diet score, n (%) 0 (unhealthy) 19 (0.1) 14 (0.04) 21 (0.1) 35 (0.1) 49 (0.1) 1 214 (0.6) 201 (0.6) 230 (0.6) 289 (0.8) 448 (1.3) 2 1,219 (3.4) 1,339 (3.8) 1,442 (4.1) 1,618 (4.6) 2,294 (6.5) 3 6,288 (17.8) 7,004 (19.8) 7,534 (21.3) 8,150 (23.1) 9,032 (25.6) 4 16,539 (46.8) 16,964 (48.0) 16,875 (47.8) 16,557 (46.8) 15,633 (44.2) 5 9,462 (26.8) 8,599 (24.3) 8,176 (23.1) 7,712 (21.8) 7,018 (19.8) 6 (healthy) 1,597 (4.5) 1,216 (3.4) 1,058 (3.0) 977 (2.8) 863 (2.4) Screening mammogram, n (%) No 1,890 (5.4) 1,788 (5.1) 1,696 (4.8) 1,799 (5.1) 2,291 (6.5) Yes 33,408 (94.6) 33,495 (94.9) 33,578 (95.2) 33,467 (94.9) 32,965 (93.5) Ethnicity, n (%) White 33,999 (96.5) 33,959 (96.5) 33,773 (95.9) 33,428 (95.0) 33,086 (94.0) Mixed 156 (0.4) 160 (0.4) 145 (0.4) 200 (0.6) 184 (0.5) Asian 396 (1.1) 490 (1.4) 601 (1.7) 625 (1.8) 515 (1.5) Black 163 (0.5) 240 (0.7) 353 (1.0) 566 (1.6) 1,065 (3.0) Chinese 263 (0.8) 107 (0.3) 80 (0.2) 45 (0.1) 14 (0.04) Other ethnic group 260 (0.7) 249 (0.7) 251 (0.7) 329 (0.9) 333 (1.0) Mean (Standard deviation) and Counts (Percentages) are presented for continuous and categorical variables, respectively. IQR: interquartile range, BMI: body mass index, WHR: waist-to-hip ratio, healthy diet score (from unhealthy to healthy) was calculated based on consumption of these commonly food groups (fruits, vegetables, fish, processed meats, unprocessed red meats, whole grains, and refined grains) [ 28 ], Sedentary behaviour = sum of time spent watching television, time spent using computer and time spent driving, Qualification : A: advanced, AS: advanced subsidiary, O: ordinary, GCSE: general certificate of secondary education, CSE: certificate of secondary education, NVQ: national vocational qualification, HND: higher national diploma, HNC: higher national certificate Body shape phenotypes and breast cancer risk Table 2 shows crude and multivariable-adjusted associations between the four body shapes and BC risk. After adjusting for measured confounders, each 1 SD increment in PC1 was associated with a 12% (95% CI: 9–16%) higher relative risk of BC. Similarly, each 1 SD increment in PC2 was associated with an 8% (95% CI: 5–11%) higher relative risk of BC in the fully adjusted model. Linearity of these associations was confirmed in the analyses by quintiles (Table 2 ) and visually using restricted cubic splines ( Supplementary Figs. 3 and 4 ). In contrast, neither PC3 nor PC4 were associated with BC risk (Table 2 , Supplementary Figs. 5 and 6 ). Table 2 Associations between principal components (PC) of body shape and postmenopausal breast cancer risk PC1 (“general obesity”) PC2 (“tall, low WHR”) PC3 (“tall, high WHR”) PC4 (“athletic”) Cases/non cases HR (95% CI) Cases/non cases HR (95% CI) Cases/non cases HR (95% CI) Cases/non cases HR (95% CI) Model 1 Continuous (for an increment of 1 SD) 6,396/170,290 1.13 (1.10–1.16) 6,396/170,290 1.10 (1.07–1.13) 6,396/170,290 1.05 (1.03–1.08) 6,396/170,290 1.03 (1.00-1.05) Quintiles I 1,005/34,333 1 (ref) 1,093/34,245 1 (ref) 1,190/34,147 1 (ref) 1,259/34,079 1 (ref) II 1,253/34,084 1.24 (1.14–1.35) 1,248/34,091 1.14 (1.05–1.24) 1,258/34,081 1.09 (1.00-1.18) 1,252/34,085 1.03 (0.95–1.12) III 1,296/34,040 1.29 (1.19–1.40) 1,285/34,051 1.17 (1.08–1.27) 1,239/34,096 1.06 (0.98–1.15) 1,273/34,064 1.06 (0.98–1.14) IV 1,368/33,970 1.38 (1.27–1.50) 1,318/34,019 1.21 (1.11–1.31) 1,327/34,011 1.12 (1.04–1.21) 1,331/34,006 1.11 (1.03–1.20) V 1,474/33,863 1.54 (1.42–1.67) 1,452/33,884 1.32 (1.22–1.43) 1,382/33,955 1.14 (1.05–1.23) 1,281/34,056 1.05 (0.97–1.14) Model 2 Continuous (for an increment of 1 SD) 4,654/124,709 1.12 (1.09–1.16) 4,654/124,709 1.08 (1.05–1.11) 4,654/124,709 1.03 (1.00-1.06) 4,654/124,709 1.02 (0.99–1.05) Quintiles I 767/26,075 1 (ref) 762/23,595 1 (ref) 844/24,497 1 (ref) 910/24,363 1 (ref) II 955/25,773 1.26 (1.14–1.38) 906/24,500 1.13 (1.03–1.25) 932/24,938 1.09 (1.00-1.20) 948/25,000 1.05 (0.96–1.15) III 958/25,181 1.29 (1.17–1.42) 920/25,085 1.11 (1.01–1.23) 907/25,131 1.04 (0.95–1.15) 919/25,299 1.01 (0.92–1.11) IV 1.006/24,419 1.42 (1.29–1.57) 992/25,558 1.17 (1.07–1.29) 980/25,119 1.10 (1.00-1.21) 961/25,115 1.08 (0.98–1.18) V 968/23,261 1.49 (1.34–1.64) 1.074/25,971 1.24 (1.13–1.37) 991/25,024 1.08 (0.98–1.18) 916/24,932 1.03 (0.94–1.13) Model 1 : Hazard ratios from Cox proportional hazards regression using age as the underlying time metric. Models were stratified only by age at recruitment in 5-year categories, and study center. N = 176,686 Model 2 : Hazard ratios from Cox proportional hazards regression using age as the underlying time metric. Multivariable models were stratified by age at recruitment in 5-year categories, study center, and adjusted for alcohol intake, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior. All four principal components were mutually adjusted, N = 129.363 PC: principal component; SD: standard deviation. WHR: waist-to-hip ratio. Analyses excluding participants with less than two years of follow-up remained comparable to the main findings. Results from multiple imputation yielded similar results but the 95% CIs were slightly narrowed, except for PC3, where the HR increased and became statistically significant. Finally, sensitivity analyses based exclusively on postmenopausal women, who answered having their periods stopped for at least one year at the time of recruitment showed similar results ( Supplementary Table 4 ). Biomarkers and breast cancer risk The multivariable-adjusted HRs for the associations between the biomarkers of interest and the risk of BC are presented in Supplementary Table 5 . Concentration levels of CRP, IGF-1, testosterone, gamma-glutamyltransferase, and urate were positively associated with BC risk, whereas HDL-cholesterol, SHBG, albumin, and apolipoprotein A were inversely related to BC risk. Moreover, triglycerides, alanine aminotransferase, cystatin C, and total bilirubin showed borderline positive associations with BC. Four-way decomposition mediation analysis As the association between PC and BC risk is a necessary condition for mediation analyses, these analyses were restricted to PC1 and PC2. The potential mediators were metabolic biomarkers which were associated with BC risk ( Supplementary Table 5 ). Furthermore, apart from total protein, cystatin C, and gamma-glutamyltransferase, all biomarkers of interest were either positively or inversely associated with PC1 or PC2 (Supplementary Table 6 ). We considered causal effects for a change in PC1 and PC2 from the 25th to the 75th percentile, and each mediator fixed at its median level, after mutual adjustment for all other biomarkers. General obesity body shape (PC1) The results from the four-way decomposition of each potential mediator of the associations between PC1 and postmenopausal BC risk are shown in Supplementary Table 7 (effect estimates) and Table 3 (attributable proportions). Overall, the CDE, the effect due to neither mediation nor interaction, showed strong positive associations between PC1 and BC risk across all 13 investigated biomarkers (i.e., mediators), with CDEs between 88.9% (95% CI: 82.2–95.6%), when holding testosterone levels fixed, to 101.9% (95% CI: 93.4–109.9%), when holding IGF-1 fixed at its median (Table 3 and Supplementary Fig. 7 ). Table 3 Proportions attributable for the four-way decomposition of each mediator of the associations between a general obese body shape (PC 1) and postmenopausal breast cancer risk P_ CDE P_ INTref P_INTmed P_PIE OP_M Biomarkers Proportion P value Proportion P value Proportion P value Proportion P value Proportion P value C-reactive protein (mg/L) 102.4% < 0.001 -4.0% 0.060 -5.2% 0.089 6.7% 0.031 1.5% 0.660 HDL-cholesterol (mmol/L) 97.2% < 0.001 2.2% 0.476 -1.1% 0.467 1.7% 0.592 0.6% 0.878 IGF-1 (nmol/L) 102.0% < 0.001 10.3% 0.014 -8.2% 0.014 -4.1% 0.311 -12.3% 0.004 SHBG (nmol/L) 89.8% < 0.001 5.2% 0.482 -5.4% 0.474 10.4% 0.112 5.0% 0.576 Testosterone (nmol/L) 89.0% < 0.001 -0.4% 0.250 1.6% 0.442 9.9% 0.002 11.4% < 0.001 Triglycerides (mmol/L) 102.1% < 0.001 -1.5% 0.648 -0.3% 0.650 -0.3% 0.707 -0.6% 0.383 Albumin (g/L) 93.1% < 0.001 0.9% 0.610 -1.6% 0.570 7.6% 0.036 6.0% 0.112 Glucose (mmol/L) 101.1% < 0.001 -0.8% 0.479 -1.2% 0.497 1.0% 0.695 -0.3% 0.850 Alanine aminotransferase (U/L) 100.0% < 0.001 0.0% 0.994 -0.2% 0.953 0.2% 0.954 0.0% 0.996 Apolipoprotein A (g/L) 98.2% < 0.001 0.8% 0.514 0.6% 0.499 0.4% 0.844 1.0% 0.663 Gamma-glutamyltransferase (U/L) 101.7% < 0.001 -1.8% 0.586 0.0% 0.746 0.1% 0.528 0.1% 0.612 Total bilirubin (umol/L) 99.5% < 0.001 2.1% 0.721 -0.4% 0.718 -1.2% 0.190 -1.6% 0.166 Urate (umol/L) 102.9% < 0.001 0.9% 0.661 -2.8% 0.662 -1.0% 0.886 -3.8% 0.535 P_CDE = proportion of controlled direct effect, P_INTref = proportion of reference interaction, P_INTmed = proportion of mediated interaction, P_PIE = proportion of pure indirect effect, OP_M = overall proportion mediated. HDL cholesterol: High-density lipoprotein cholesterol. Output of mediation analysis with causal effects estimated for a change in PC from the 25th to the 75th percentile. Controlled direct effects are computed fixing the mediators at their median levels. HDL cholesterol: high-density lipoprotein cholesterol, IGF-1: insulin-like growth factor, SHBG: sex hormone-binding globulin. Models adjusted for age, center and diet, and adjusted for age alcohol frequency, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior, and mutually adjusted for biomarkers. There was a PIE through IGF-1 and testosterone with mediated proportions equal to -4.1% (95% CI: -11.9–3.8%) and 10% (95% CI: 4–16%), respectively (Table 3 ). The overall proportion mediated after accounting for mediation that was activated because of an interaction between PC1 and the two biomarkers was − 12.2% (95% CI: -20.5% to -4.0%) and 11.4% (95% CI: 5.1–17.8%.) for IGF-1 and testosterone, respectively. There was no evidence for mediation by the other investigated biomarkers (Table 3 and Supplementary Fig. 7). Tall/lean body shape (PC 2) There was little variation in the proportion of CDE after fixing the mediators (i.e., 13 biomarkers) at their median values (Table 4 and Supplementary Table 8 ). Minor proportions of the association between PC2 and BC were mediated by IGF-1 (PIE: 2.8%, 95% CI: 0.6–4.9%), and SHBG (PIE: -6.1%, 95% CI: -10.9% to -1.3%). There was no evidence of mediated interaction for IGF-1 (P = 0.242), while there was some indication of mediated interaction for SHBG (proportion mediated interaction: 3%, 95% CI: 0.7–6.0%) (Table 4 and Supplementary Fig. 8) . Table 4 Proportion attributable for the four-way decomposition of each mediator of the associations between principal component 2 of body shape and postmenopausal breast cancer risk P_ CDE P_ Int Ref P_intmed P_PIE OP_M Biomarkers Proportion P value Proportion P value Proportion P value Proportion P value Proportion P value C-reactive protein (mg/L) 98.3% < 0.000 2.4% 0.709 -0.2% 0.707 -0.5% 0.242 -0.7% 0.133 HDL-cholesterol (mmol/L) 99.1% < 0.000 2.1% 0.289 0.5% 0.285 -1.7% 0.174 -1.2% 0.335 IGF1 (nmol/L) 101.6% < 0.000 -3.0% 0.216 -1.3% 0.242 2.8% 0.012 1.4% 0.145 SHBG (nmol/L) 99.5% < 0.000 3.5% 0.077 3.0% 0.045 -6.1% 0.012 -3.1% 0.115 Testosterone (nmol/L) 100.9% < 0.000 -0.2% 0.942 -0.04% 0.856 -0.7% 0.186 -0.8% 0.172 Triglycerides (mmol/L) 103.5% < 0.000 -5.1% 0.227 1.5% 0.251 0.2% 0.889 1.6% 0.323 Albumin (g/L) 97.8% < 0.000 -0.3% 0.668 0.3% 0.738 2.2% 0.115 2.5% 0.059 Glucose (mmol/L) 101.4% < 0.000 -1.4% 0.474 0.1% 0.548 -0.1% 0.664 0.1% 0.831 Alanine Aminotransferase (U/L) 100.9% < 0.000 -0.3% 0.892 0.1% 0.897 -0.7% 0.472 -0.6% 0.527 Apolipoprotein A (g/L) 96.5% < 0.000 4.8% 0.097 -0.9% 0.091 -0.4% 0.668 -1.3% 0.262 Gamma-Glutamyltransferase (U/L) 101.3% < 0.000 -0.9% 0.837 0.1% 0.834 -0.5% 0.333 -0.4% 0.547 Total bilirubin (umol/L) 97.7% < 0.000 0.6% 0.887 0.2% 0.885 1.5% 0.293 1.7% 0.171 Urate (umol/L) 100.5% < 0.000 -0.4% 0.844 0.02% 0.890 -0.1% 0.627 -0.05% 0.731 P_CDE = proportion of controlled direct effect, P_INTref = proportion of reference interaction, P_INTmed = proportion of mediated interaction, P_PIE = proportion of pure indirect effect, OP_M = overall proportion mediated. Output of mediation analysis with causal effects estimated for a change in PC from the 25th to the 75th percentile. Controlled direct effects are computed fixing the mediators at their median levels. HDL cholesterol: high-density lipoprotein cholesterol, IGF-1: insulin-like growth factor, SHBG: sex hormone-binding globulin. Models adjusted for age, center and diet, and adjusted for age alcohol frequency, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior, and mutually adjusted for biomarkers. Sensitivity analyses Overall, sensitivity mediation analyses without mutual biomarkers adjustment showed no substantial differences as compared to mutual biomarkers adjustment models for PC1. The overall proportion of the TE of PC1 on BC explained by the mediation was 9.7% (P < 0.001) for testosterone and − 8.4% (P < 0.001) for IGF-1 ( Supplementary Tables 9 and 10 ). Regarding PC2, there was evidence of mediation, with the overall proportion mediated (PIE + mediated interaction) accounted for − 3% (P = 0.001), -4.7% (P = 0.024), 2% (P = 0.067), -8.7% (P = 0.018), 2.1% (P = 0.040) and − 5.6% (P = 0.011) of the TE, through CRP, HDL-cholesterol, SHBG albumin and urate, respectively ( Supplementary Tables 11 and 12 ). DISCUSSION Among 176,686 postmenopausal women enrolled in UK Biobank, body shape PC1 (general adiposity) and body shape PC2 (tall; low WHR) were both associated with an increased risk of BC. The controlled direct effects (i.e., associations due to neither mediation nor interaction) were large, suggesting that most of the excess BC risk was due to other pathways than investigated here. Nevertheless, there was evidence that pathways through testosterone and IGF-1 explained a small proportion of the body shape PC1-BC risk association. For the association between body shape PC2 and BC risk, a small proportion was mediated by IGF-1 and by SHBG. For SHBG, there was also evidence for mediated interaction, but in opposite direction of the pure indirect effect, meaning that the overall proportion mediated was negligible. In agreement with our study, the study conducted in EPIC reported an increased risk for BC in relation to both body shape PC1 (general adiposity) and body shape PC2 (tall; low WHR) [ 10 ]. These results are also congruent with previous studies investigating the association between obesity and risk of BC, but using single-trait anthropometric indicators [ 1 , 6 , 13 ]. Altered sex hormone metabolism is a main biological mechanism that could link excess adiposity with postmenopausal BC risk through increased aromatase enzyme activity in peripheral adipose tissue known as aromatization [ 30 , 31 ]. Aromatization leads to increased levels of bioavailable sex hormones including testosterone, which may induce breast carcinogenesis [ 30 ]. Several epidemiological studies, including in the UK Biobank, reported an increased BC risk associated with elevated blood levels of testosterone [ 15 , 17 ]. Our study is in accordance with these findings and in addition, our mediation analysis supports the hypothesis that testosterone links general adiposity (i.e., body shape PC1) with postmenopausal BC risk. There are strong evidence that higher IGF-I levels are associated with a greater risk of BC [ 32 , 33 ]. Our findings for postmenopausal BC risk are congruent with this evidence (Supplementary Table 5). However, whether IGF-I links adiposity to BC is debated [ 30 ]. Levels of IGF-I increase only to a BMI of approximately 27 kg/m 2 , thereafter declining with increasing weight, and overweight individuals, who intentionally lose weight, IGF-I levels tend to increase [ 30 ]. Our mediation analysis may shed some light on this uncertainty. While we did not observe a significant pure indirect effect through IGF-1, there was an interaction between PC1 and IGF-1 (i.e., ‘joint effect’) and a negative overall proportion mediated of 12% mainly due to a mediated interaction between PC1 and IGF-1 (Table 3 ). This suggests that there are antagonistic associations between the joint effect of PC1 and IGF-1 on BC risk, and the effect of PC1 on IGF-1. In contrast, we estimated a small but clearcut mediation of 2.8% through IGF-1 for the association between PC2 (tall, low WHR) and BC risk. IGF-1 signaling is well known to induce expression of several oncogenes and high concentrations of IGF-1 are associated with increased risk of BC [ 34 ]. Because several genetic variants related to the IGF signaling pathway are related to height, height might be a crude anthropometric marker of early-life IGF-1 exposure [ 34 ]. Our results confirm that a proportion, albeit small, of the height-BC relationship is mediated through IGF-1. The mediated effect through IGF-1 observed in our study is in line with a recent study by Loh et al. (2022), who found that IGF-1 mediated 22% of the association between BMI and BC risk [ 35 ]. Consistent with our findings, another recent study using a case-cohort design within the Women's Health Initiative Observational Study, observed a mediating effect through fasting insulin of the effect of adiposity (BMI) on estrogen receptor (ER)-positive BC [ 36 ]. We identified a second molecular pathway linking PC2 with BC risk, which was through SHBG. Since the height component of this body shape was positively associated with SHBG, and in turn SHBG was inversely associated with the risk of BC, the mediated proportion was negative (-6%). This is in line with evidence that higher levels of SHBG lead to lower levels of bioavailable testosterone and estrogens, and thus a lower BC risk via this pathway [ 15 , 16 ]. A meta-analysis of 26 prospective studies showed that high SHBG levels were significantly associated with decreased risk of BC in postmenopausal women, the pooled RR for BC comparing the highest vs. lowest categories of SHBG was 0.64 (95% CI: 0.57–0.72) [ 37 ]. In a recent study from the UK Biobank, SHBG was inversely associated with BC risk in postmenopausal women [ 16 ]. The mediating effect of SHBG in the association between adiposity and BC development has not been investigated in other studies yet. However, SHBG has been reported to mediate a small proportion of the relationship between BMI and endometrial cancer risk (7%) [ 38 ], and the alcohol-BC association (13%) [ 39 ]. Our findings suggest differential pathways linking these body shapes with BC risk. Indeed, PC1 may have similar pathways as obesity measured by BMI, while PC2 may have different biological/metabolic pathways involved in carcinogenesis of BC, more comparable to height. There have been many advances in mediation analysis methodology over the years, with various methods of mediation analysis reported in the literature. One of the novelties of this study is that we considered both the mediation and interaction pathways simultaneously using one of the most recent approaches to causal mediation analysis: “the four-way decomposition”. This approach allows to estimate both the pure indirect and interaction effect, as compared to previous studies using conventional approaches for mediation analysis which may have missed mediated interaction effects. The present study is also the first to investigate the potential mediator role of several biomarkers of metabolic health in the association between body shape phenotypes and BC development, as compared to previous studies investigating classical anthropometric parameters (mainly BMI or WHR). Finally, we accounted for confounding by other biomarkers. Limitations of this study included the lack of data on hormone receptor status since the relationship between obesity and BC risk may differ according to estrogen or progesterone receptor. We lacked sufficient individuals with oestradiol measurements to investigate this sex-hormone as potential mediator which has been both related to obesity and BC development. Also, we could not rule out the possibility of unmeasured confounding. Lack of representativeness of the study sample is acknowledged. CONCLUSION In summary, although the direct effects of body shape phenotypes on BC risk are high, our findings are consistent with a possible mediated effect by testosterone and IGF-1 for body shape PC1 and BC development association, while IGF-1 and SHBG may have a role in the association between body shape PC2 and BC development. As the mediated biomarkers may interact with each other, future studies should also consider a decomposition of the total effect in the presence of multiple mediators, particularly by family of biomarkers. Declarations Ethics approval and consent to participate The UK Biobank study was approved by the North West Multi-Center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland (http://www.ukbiobank.ac.uk/ethics/). All participants provided written informed consent. Consent for publication Not applicable Availability of data and materials This work has been conducted using the UK Biobank data. The UK Biobank is an open access resource, researchers can apply to use the UK Biobank dataset by registering and applying at https://www.ukbiobank.ac.uk/enable-your-research/about-our-data. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the French National Cancer Institute (l'Institut National du Cancer, INCA_14108 to HF). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Anja M. Sedlmeier and Emma Fontvieille. Analysis was performed by Amina Amadou and Anja M. Sedlmeier. 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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-3850301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267510793,"identity":"35eeefb5-b5d3-4caa-b39c-6a607c8da731","order_by":0,"name":"Amina Amadou","email":"data:image/png;base64,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","orcid":"","institution":"Centre Léon Bérard","correspondingAuthor":true,"prefix":"","firstName":"Amina","middleName":"","lastName":"Amadou","suffix":""},{"id":267510794,"identity":"30f690ea-f553-4878-9544-3dd347d6c24e","order_by":1,"name":"Heinz Freisling","email":"","orcid":"","institution":"International Agency for Research on Cancer (IARC)","correspondingAuthor":false,"prefix":"","firstName":"Heinz","middleName":"","lastName":"Freisling","suffix":""},{"id":267510795,"identity":"bece1683-3747-42da-8cc5-f4c16fabb75a","order_by":2,"name":"Anja M. Sedlmeier","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"M.","lastName":"Sedlmeier","suffix":""},{"id":267510796,"identity":"d7f6c952-d116-488f-bbfa-78b0ca9268c3","order_by":3,"name":"Patricia Bohmann","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Bohmann","suffix":""},{"id":267510797,"identity":"2f306dfb-ed11-413d-9eaa-557d950ed0d9","order_by":4,"name":"Emma Fontvieille","email":"","orcid":"","institution":"International Agency for Research on Cancer (IARC)","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Fontvieille","suffix":""},{"id":267510798,"identity":"471d1cb0-c865-41f1-8219-86b27ac61e0b","order_by":5,"name":"Andrea Weber","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Weber","suffix":""},{"id":267510799,"identity":"52fa469d-76c3-492f-8123-9480d22e0f94","order_by":6,"name":"Julian Konzok","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Konzok","suffix":""},{"id":267510800,"identity":"2707aa64-8c2d-4882-a73c-e64b42d588e5","order_by":7,"name":"Michael J Stein","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"J","lastName":"Stein","suffix":""},{"id":267510801,"identity":"81eb2930-f6c5-4efc-b22a-ba696e4c050d","order_by":8,"name":"Laia Peruchet-Noray","email":"","orcid":"","institution":"International Agency for Research on Cancer (IARC)","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Peruchet-Noray","suffix":""},{"id":267510802,"identity":"2130c8ae-962d-4cfd-88b0-0e5057441f76","order_by":9,"name":"Anna Jansana","email":"","orcid":"","institution":"International Agency for Research on Cancer (IARC)","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Jansana","suffix":""},{"id":267510803,"identity":"824d986b-7ba3-4019-ab12-a69b0d61ba1d","order_by":10,"name":"Hwayoung Noh","email":"","orcid":"","institution":"Centre Léon Bérard","correspondingAuthor":false,"prefix":"","firstName":"Hwayoung","middleName":"","lastName":"Noh","suffix":""},{"id":267510804,"identity":"73d2230e-d82c-40b9-88c2-30f3817442bf","order_by":11,"name":"Mathilde His","email":"","orcid":"","institution":"Centre Léon Bérard","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"His","suffix":""},{"id":267510805,"identity":"989458a9-40e5-4ca2-814e-13b142c9b2f5","order_by":12,"name":"Quan Gan","email":"","orcid":"","institution":"International Agency for Research on Cancer (IARC)","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Gan","suffix":""},{"id":267510806,"identity":"16bd6fdf-fb45-40ab-9dd9-a1918f4a740f","order_by":13,"name":"Hansjörg Baurecht","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Hansjörg","middleName":"","lastName":"Baurecht","suffix":""},{"id":267510807,"identity":"fce4caa7-ab6e-4f43-a7f8-05f23256aff1","order_by":14,"name":"Béatrice Fervers","email":"","orcid":"","institution":"Centre Léon Bérard","correspondingAuthor":false,"prefix":"","firstName":"Béatrice","middleName":"","lastName":"Fervers","suffix":""}],"badges":[],"createdAt":"2024-01-10 12:44:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3850301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3850301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44197-024-00226-4","type":"published","date":"2024-04-10T18:16:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49736478,"identity":"0257cc60-837f-4f8c-be76-27ff20cde472","added_by":"auto","created_at":"2024-01-17 07:32:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":230430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePearson's correlation matrix between the biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC: coefficients of correlation, HDL cholesterol: high-density lipoprotein cholesterol, IGF-1: insulin-like growth factor, SHBG: sex hormone-binding globulin.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3850301/v1/c6916be3c55409aca4f99e74.png"},{"id":58305841,"identity":"b45ddc5a-21d9-4d6e-bf2d-c971eb20079c","added_by":"auto","created_at":"2024-06-13 18:16:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3850301/v1/9fd19078-39bc-48cf-9488-154d8f59ce3c.pdf"},{"id":49736479,"identity":"d761c269-c837-405e-ad31-0413734aea53","added_by":"auto","created_at":"2024-01-17 07:32:12","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":808096,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3850301/v1/2b9a1fdb1d66910e09a4aa69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-trait body shape phenotypes and breast cancer risk in postmenopausal women: a causal mediation analysis in the UK Biobank cohort","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStrong evidence links obesity (defined by high body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) ; or indicators of body fat distribution, such as waist circumference (WC : men\u0026thinsp;\u0026gt;\u0026thinsp;102 cm, women\u0026thinsp;\u0026gt;\u0026thinsp;88 cm), hip circumference (HC), and waist-to-hip ratio (WHR : men\u0026thinsp;\u0026gt;\u0026thinsp;0.90, women\u0026thinsp;\u0026gt;\u0026thinsp;0.80)) with the risk of postmenopausal breast cancer (BC) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the most frequent cancer which represents an important public health problem in women [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost studies investigated single anthropometric traits in relation to BC risk, which might not adequately capture the complexity of body morphology, specifically among women who are similar in one trait but differ in others [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To address this issue, Ried et al. were the first to apply a principal component analysis (PCA)-based approach to estimate principal components (PC) representing body shapes derived from BMI, height, weight, WC, HC, and WHR [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The four derived body shape phenotypes explained over 99% of the total variation in these anthropometric traits and were differently associated with several indicators of metabolic health (e.g., hormonal, metabolic, and inflammatory biomarkers) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This PCA-based approach has been subsequently applied by our team to evaluate the impact of these body shapes on the risk of cancer in the European Prospective Investigation into Cancer and Nutrition cohort (EPIC) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A generally obese body shape and a tall, lean body shape were both positively associated with postmenopausal breast cancer risk, while the other two body shapes were not associated with such risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe underlying biological and metabolic mechanisms linking obesity to BC are multiple and complex. Obesity has been strongly associated with several metabolic alterations, including deregulation of sex hormones, overexpression of pro-inflammatory cytokines, insulin resistance, hyperactivation of insulin-like growth factors (IGFs) pathways, hypercholesterolemia, as well as excessive oxidative stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several of these biomarkers (such as serum sex hormone-binding globulin (SHBG), IGF-1, testosterone, C-reactive protein (CRP)) have also been associated with BC risk [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, whether these biomarkers mediate the body shape-BC cancer relationship is unknown. Such knowledge could help understand the impact of body shapes on BC risk and possibly identify biological pathways.\u003c/p\u003e \u003cp\u003eThe main objective of the present study was to investigate to what extent the presumed associations between body shape phenotypes and postmenopausal BC risk are mediated by biomarkers of metabolic health. The candidate biomarkers were selected based on their implication in the development of BC, as well as their associations with obesity.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe UK Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a prospective cohort study that recruited a total of 502,418 men and women, aged between 39 to 71 years at enrolment between 2006 and 2010. Study design and methodology have been described elsewhere [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At the initial assessment center visit, participants completed a self-administered touchscreen questionnaire that included information on health, demographic, anthropometric, lifestyle, and medical history data, collected in 22 centers across England, Wales, and Scotland. Biological samples including blood, saliva, and urine were also collected at enrolment. The UK Biobank study was approved by the North West Multi-Center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/ethics/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/ethics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All participants provided written informed consent.\u003c/p\u003e \u003cp\u003eFor the present study, we only included women, who were postmenopausal at the time of enrolment. Women were categorized as postmenopausal if they reported \u0026ldquo;yes\u0026rdquo; to the question \u0026ldquo;Have you had your menopause (periods stopped at least one year before enrollment)\u0026rdquo;, if they were older than 55 years [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or reported a bilateral oophorectomy. Among these, we excluded women with prevalent cancer, those with missing or implausible anthropometry data, and with missing biomarker data. The study participants flowchart is given in \u003cem\u003eSupplementary Fig.\u0026nbsp;1.\u003c/em\u003e After exclusions, the analysis involved 176,686 postmenopausal women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAscertainment of breast cancer cases\u003c/h2\u003e \u003cp\u003eData on cancer diagnoses were provided by National Health Service (NHS) Digital and Public Health England for participants from England and Wales and by NHS Central Register (NHSCR) for participants residing in Scotland, and BC cases were ascertained through cancer registries [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For the present study, complete follow-up data were available up to 29 February 2020 for England and Wales; and 31 January 2021 for Scotland. All registrations coded as C50 using the 10th Revision of the International Classification of Diseases (ICD-10) were considered as invasive BC cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of anthropometric measures\u003c/h2\u003e \u003cp\u003eHeight, weight, WC, and HC were assessed by trained personnel during the baseline assessment center visit [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Body weight (kilograms, kg) was measured using a Tanita BC418MA body composition analyzer. Height was measured using a Seca 240 cm height measure, while HC and WC measurements (cm) were assessed using a Seca 200 cm tape measure. BMI was calculated as body weight (kg) divided by height in meters squared (kg/m\u003csup\u003e2\u003c/sup\u003e), and WHR was calculated as WC divided by HC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBiomarkers assays\u003c/h2\u003e \u003cp\u003eThe UK Biobank measures a wide range of biochemical markers from biological samples collected at baseline in all participants [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The biomarkers selected for the assay have been chosen because they are established risk factors for several diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The present study examined biomarkers of metabolic health comprising markers of glucose (glucose, glycated hemoglobin, HbA1c, mmol/mol), insulin metabolism (IGF-1, nmol/L), inflammation (CRP, mg/L), sex hormones (testosterone and SHBG, nmol/L), blood lipids (triglycerides, HDL-cholesterol and cholesterol, mmol/L), as well as total protein (g/L). These biomarkers were selected based on their potential links with overweight/obesity, and BC risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We further explored other biomarkers of metabolic health that were moderately correlated to body shape phenotypes, to identify novel biomarkers that could influence their association with BC risk. These biomarkers included albumin (g/L), glucose (mmol/L), alanine amino-transferase (U/L), apolipoproteins A and B (g/L), cystatin C (mg/L), Gamma glutamyltransferase (U/L), total bilirubin (umol/L), and urate (umol/L). Using a Beckman Coulter, AU580, triglycerides were quantified by Group Purchasing Organisation-Physician Owned Distributor (GPO-POD) analysis, cholesterol by cholesterol oxidase-peroxidase (CHOD-POD) method, HDL-cholesterol by enzyme immune-inhibition analysis, CRP by immunoturbidimetric-high sensitivity analysis, and total protein by Biuret analysis. Serum levels of HbA1c were measured by high-performance liquid chromatography analysis on a Bio-Rad, VARIANT II Turbo, and IGF-1 was quantified by chemiluminescence immunoassay (CLIA) technique (DiaSorin Ltd LIASON XL). Oestradiol and SHBG were measured using the two-step competitive analysis method (Beckman Coulter, Unicel DxI 800), while testosterone was measured with a one-step competitive analysis (Beckman Coulter, Unicel DxI 800).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eStatistical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003ePCA was applied to the standardized residuals of height, weight, BMI, WC, HC, and WHR. The residuals were predicted from a separate regression of the six anthropometric traits with age, sex, and study center. From the PCA, we retained the first four PCs that explained 99% of the variation and represented orthogonal linear combinations of the six anthropometric traits [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Each component represented a weighted sum of the six transformed anthropometric traits and is independent of the other components. The weights of each trait per PC are referred to as loadings.\u003c/p\u003e \u003cp\u003eCox proportional hazard regression was used to estimate the hazard ratios (HR) and corresponding 95% confidence intervals (CI) of the associations between each body shape PC (continuous and quintiles), and each biomarker (continuous) with BC risk. Continuous models for an increment of one standard deviation (SD) of each PC and biomarker were estimated. Age at entry was age at recruitment, and exit time was considered one of following: age at diagnosis of first incident BC, age of diagnosis of another cancer except non-melanoma skin cancer, age at end of follow-up, age at loss-to-follow-up, or age at time of death, whichever occurred first. The proportional hazards assumptions were tested using scaled Schoenfeld residuals. The shape of the exposure-response curve between each PC and BC risk was estimated using restricted cubic splines [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with five knots placed at the 5th, 27.5th, 50th, 72.5th and 95th percentiles, as recommended by Harrell et al. for larger datasets [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Linear regression was performed to assess the associations between each PC and distinct biomarkers of metabolic health.\u003c/p\u003e \u003cp\u003eWe employed med4way mediation analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] to investigate whether metabolic biomarkers can act as individual mediators on the pathway between body shapes and postmenopausal BC risk. Med4way uses parametric regression models to estimate the components of the four-way decomposition of the total effect of the exposure (here PC) on the outcome (BC) in the presence of the mediator (each biomarker of metabolic health) with which the exposure may interact. The total effect (TE) is decomposed into four components, i.e. the controlled direct effect (CDE, i.e. the effect of PC on BC neither due to mediation nor to interaction), the reference interaction effect (INTref, i.e. the effect due to interaction only), the mediated interaction effect (INTmed, i.e. due to both mediation and interaction) and the pure indirect effect (PIE, i.e. only due to mediation, but not interaction) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The CDE was estimated at a fixed level of the mediator. Two regression models were fitted: a Cox model for the outcome, and a linear regression model for the mediator. The variable for the interaction between the exposure and the mediator was automatically generated and added to the model for the outcome. In addition to the four components of the TE, we further estimated the proportions of the effect due to each component, including the proportion due to the CDE, the proportion due to the PIE, the proportion due to the INTref, the proportion due to the INTmed, as well as the overall proportion mediated (PIE\u0026thinsp;+\u0026thinsp;INTmed).\u003c/p\u003e \u003cp\u003eThe crude models were stratified by age at recruitment in 5-year categories, and study center. All multivariable models were adjusted for the following potential confounders, identified by a directed acyclic graph (\u003cem\u003eSupplementary Fig.\u0026nbsp;2\u003c/em\u003e): age at recruitment, study center, healthy diet score, alcohol intake, smoking status, use of oral contraceptive use, use of menopausal hormone treatment (MHT), physical activity, qualifications, Townsend deprivation index and sedentary behavior. Healthy diet score was calculated based on consumption of these commonly food groups (fruits, vegetables, fish, processed meats, unprocessed red meats, whole grains, and refined grains) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sedentary behavior is the sum of time spent watching TV, time spent using the computer and time spent driving. Covariates, except for physical activity (missing values\u0026thinsp;=\u0026thinsp;24.3%) and sedentary behavior (missing values\u0026thinsp;=\u0026thinsp;4.2%), had less than 2% missing data. The multivariable analyses were thus conducted in the complete-case dataset, excluding all women with a missing value (n\u0026thinsp;=\u0026thinsp;47,319) for any of the adjusted covariates, which resulted in a final sample size of 129,367 participants. In the mediation analysis, additional mutual adjustment for each biomarker was performed, by adjusting each mediator model for all other biomarkers.\u003c/p\u003e \u003cp\u003eSensitivity analyses excluding participants with the first two years of follow-up were conducted in order to control for potential reverse causation. Additionally, a multiple imputation which accounts for the uncertainty of missing data, was performed to handle missing values of the adjusted variables [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Finally, we conducted sensitivity analyses only among postmenopausal women, who answered having their periods stopped at least one year before enrollment, after exclusion of women, who were 55 years or older, or reported a bilateral oophorectomy (n\u0026thinsp;=\u0026thinsp;108,754). As sensitivity mediation analyses, additional models without mutual biomarkers adjustment were conducted.\u003c/p\u003e \u003cp\u003eBody shape phenotypes from PC analysis was done with the package \"FactoMineR\" using R version 4.2.3, all other statistical analyses were performed using STATA 14.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eAfter a median follow-up of 10.9 years (interquartile range\u0026thinsp;=\u0026thinsp;10.1\u0026ndash;11.7), 6,396 incident BC cases were diagnosed among the 176,686 postmenopausal women. The characteristics of the study participants by cases/non-cases status are shown in \u003cem\u003eSupplementary Table\u0026nbsp;1\u003c/em\u003e. The average age at recruitment (\u0026plusmn;\u0026thinsp;SD) was 61.0 (\u0026plusmn;\u0026thinsp;5.3) years for cases and 60.4 (\u0026plusmn;\u0026thinsp;5.7) years for non-cases. Compared to non-cases, participants with BC were more likely to have higher anthropometric measures, to be less physically active, to have a higher level of alcohol intake, and to be MHT users. The distribution of other characteristics was generally similar between the cases and non-cases.\u003c/p\u003e \u003cp\u003eConcentration levels of CRP, testosterone, and urate were slightly higher among cases compared to non-cases, while SHBG was lower in cases than in non-cases. All other biomarkers' concentration levels were comparable between cases and non-cases (\u003cem\u003eSupplementary Table\u0026nbsp;2).\u003c/em\u003e Overall, except between HDL-cholesterol and apolipoprotein A (correlation coefficient\u0026thinsp;=\u0026thinsp;0.9), there were no strong correlations between biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBody shape phenotypes\u003c/h2\u003e \u003cp\u003eLoadings and explained variance of the six PCs for women are presented in \u003cem\u003eSupplementary Table\u0026nbsp;3\u003c/em\u003e. PC1 (65.7% of the total variation) described individuals with general obesity vs. lean body shape. PC2 (19.1% of the total variation) characterized tall individuals with low WHR vs. short individuals with large WHR. PC3 (13.5% of the total variation) characterized tall individuals with high WHR, but low HC vs. short individuals with low WHR and high HC. PC4 (1.6% of the total variation) showed high loadings for BMI and weight, and low loadings for HC and WC.\u003c/p\u003e \u003cp\u003eBaseline characteristics of the study participants are further presented by quintiles of PC1 scores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As compared to women in the lower quintile (Q1), women in the upper quintile (Q5) had higher anthropometric measures, except for height. Women in the two lowest quintiles of PC1 had a healthier diet, a higher educational level, a higher level of alcohol intake, a lower Townsend deprivation index, were more physically active, less sedentary, and more likely to be never smokers compared with those in the upper quintile of PC1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants according to quintiles of scores of principal component 1 (general obesity) in the UK Biobank cohort study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuintile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuintile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuintile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuintile 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuintile 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,338 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,337 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,336 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,338 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,337 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (years), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9 (10.1\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9 (10.1\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9(10.1\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9(10.1\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9 (10.0-11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at recruitment (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.6 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.7 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.0 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.7 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.6 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.4 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.78 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary behavior (hours/day), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.0 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.9 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.4 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.8 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.2 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.1 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.9 (11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.6 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.0 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.8 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.8 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (cm), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.9 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.2 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.0 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.8 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118.0 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,555 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,949 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,454 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,339 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,996 (27.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,425 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,909 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,071 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,537 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,081 (42.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,624 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,658 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,477 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,498 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,377 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily or almost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,570 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,913 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,397 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,451 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,621 (10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 to 4 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,868 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,172 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,508 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,619 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,978 (14.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 2 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,370 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,951 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,159 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,089 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,318 (23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 3 times a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,785 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,969 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,275 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,620 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,380 (15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial occasions only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,380 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,472 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,904 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,873 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,910 (22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,314 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,813 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,031 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,615 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,056 (14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,925 (62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,010 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,524 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,967 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,468 (55.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,185 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,375 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,832 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,324 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,736 (36.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,112 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,801 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,808 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,848 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,909 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,921 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,581 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,424 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,377 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,962 (25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or University degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,957 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,806 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,539 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,613 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,753 (22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA levels/AS levels or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,161 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,849 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,842 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,517 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,415 (10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO levels/GCSEs or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,834 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,357 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,356 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,186 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,072 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSEs or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,311 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,401 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,465 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,642 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,814 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNVQ or HND or HNC or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,234 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,507 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,637 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,826 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,144 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,324 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,227 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,380 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,461 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,386 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver use of oral contraceptive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,554 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,209 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,556 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,938 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,026 (22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,662 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,028 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27,660 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,237 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27,151 (77.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEver use of menopausal hormone therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,173 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,147 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,061 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,734 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17,756 (50.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,057 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,075 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,158 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,439 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17,362 (49.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy diet score, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 (unhealthy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e289 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e448 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,219 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,339 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,442 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,618 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,294 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,288 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,004 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,534 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,150 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,032 (25.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,539 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,964 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,875 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,557 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,633 (44.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,462 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,599 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,176 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,712 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,018 (19.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 (healthy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,597 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,216 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,058 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e977 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e863 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening mammogram, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,890 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,788 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,696 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,799 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,291 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,408 (94.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,495 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,578 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,467 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,965 (93.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,999 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,959 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,773 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,428 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33,086 (94.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e601 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e625 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e515 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e566 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,065 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e333 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMean (Standard deviation) and Counts (Percentages) are presented for continuous and categorical variables, respectively.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eIQR: interquartile range, BMI: body mass index, WHR: waist-to-hip ratio, healthy diet score (from unhealthy to healthy) was calculated based on consumption of these commonly food groups (fruits, vegetables, fish, processed meats, unprocessed red meats, whole grains, and refined grains) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Sedentary behaviour\u0026thinsp;=\u0026thinsp;sum of time spent watching television, time spent using computer and time spent driving, Qualification : A: advanced, AS: advanced subsidiary, O: ordinary, GCSE: general certificate of secondary education, CSE: certificate of secondary education, NVQ: national vocational qualification, HND: higher national diploma, HNC: higher national certificate\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBody shape phenotypes and breast cancer risk\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows crude and multivariable-adjusted associations between the four body shapes and BC risk. After adjusting for measured confounders, each 1 SD increment in PC1 was associated with a 12% (95% CI: 9\u0026ndash;16%) higher relative risk of BC. Similarly, each 1 SD increment in PC2 was associated with an 8% (95% CI: 5\u0026ndash;11%) higher relative risk of BC in the fully adjusted model. Linearity of these associations was confirmed in the analyses by quintiles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and visually using restricted cubic splines (\u003cem\u003eSupplementary Figs.\u0026nbsp;3 and 4\u003c/em\u003e). In contrast, neither PC3 nor PC4 were associated with BC risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eSupplementary Figs.\u0026nbsp;5 and 6\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between principal components (PC) of body shape and postmenopausal breast cancer risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePC1 (\u0026ldquo;general obesity\u0026rdquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePC2 (\u0026ldquo;tall, low WHR\u0026rdquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePC3 (\u0026ldquo;tall, high WHR\u0026rdquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003ePC4 (\u0026ldquo;athletic\u0026rdquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCases/non cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCases/non cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCases/non cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCases/non cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e \u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous (for an increment of 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6,396/170,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.13 (1.10\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6,396/170,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10 (1.07\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,396/170,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6,396/170,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03 (1.00-1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,005/34,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,093/34,245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,190/34,147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,259/34,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,253/34,084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.24 (1.14\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,248/34,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14 (1.05\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,258/34,081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.09 (1.00-1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,252/34,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03 (0.95\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,296/34,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.29 (1.19\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,285/34,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17 (1.08\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,239/34,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.06 (0.98\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,273/34,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.06 (0.98\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,368/33,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.38 (1.27\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,318/34,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (1.11\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,327/34,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.12 (1.04\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,331/34,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.11 (1.03\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,474/33,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.54 (1.42\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,452/33,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32 (1.22\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,382/33,955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.14 (1.05\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,281/34,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05 (0.97\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e \u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous (for an increment of 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4,654/124,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.12 (1.09\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4,654/124,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08 (1.05\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,654/124,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.03 (1.00-1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,654/124,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e767/26,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e762/23,595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e844/24,497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e910/24,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e955/25,773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.26 (1.14\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e906/24,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13 (1.03\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e932/24,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.09 (1.00-1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e948/25,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05 (0.96\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e958/25,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.29 (1.17\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e920/25,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11 (1.01\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e907/25,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04 (0.95\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e919/25,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01 (0.92\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.006/24,419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.42 (1.29\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e992/25,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17 (1.07\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e980/25,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10 (1.00-1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e961/25,115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.08 (0.98\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e968/23,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.49 (1.34\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.074/25,971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24 (1.13\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e991/25,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.08 (0.98\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e916/24,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03 (0.94\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eModel \u003csup\u003e1\u003c/sup\u003e: Hazard ratios from Cox proportional hazards regression using age as the underlying time metric. Models were stratified only by age at recruitment in 5-year categories, and study center. N\u0026thinsp;=\u0026thinsp;176,686\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eModel \u003csup\u003e2\u003c/sup\u003e : Hazard ratios from Cox proportional hazards regression using age as the underlying time metric. Multivariable models were stratified by age at recruitment in 5-year categories, study center, and adjusted for alcohol intake, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior. All four principal components were mutually adjusted, N\u0026thinsp;=\u0026thinsp;129.363\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003ePC: principal component; SD: standard deviation. WHR: waist-to-hip ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalyses excluding participants with less than two years of follow-up remained comparable to the main findings. Results from multiple imputation yielded similar results but the 95% CIs were slightly narrowed, except for PC3, where the HR increased and became statistically significant. Finally, sensitivity analyses based exclusively on postmenopausal women, who answered having their periods stopped for at least one year at the time of recruitment showed similar results (\u003cem\u003eSupplementary Table\u0026nbsp;4\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBiomarkers and breast cancer risk\u003c/h2\u003e \u003cp\u003eThe multivariable-adjusted HRs for the associations between the biomarkers of interest and the risk of BC are presented in \u003cem\u003eSupplementary Table\u0026nbsp;5\u003c/em\u003e. Concentration levels of CRP, IGF-1, testosterone, gamma-glutamyltransferase, and urate were positively associated with BC risk, whereas HDL-cholesterol, SHBG, albumin, and apolipoprotein A were inversely related to BC risk. Moreover, triglycerides, alanine aminotransferase, cystatin C, and total bilirubin showed borderline positive associations with BC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFour-way decomposition mediation analysis\u003c/h2\u003e \u003cp\u003eAs the association between PC and BC risk is a necessary condition for mediation analyses, these analyses were restricted to PC1 and PC2. The potential mediators were metabolic biomarkers which were associated with BC risk (\u003cem\u003eSupplementary Table\u0026nbsp;5\u003c/em\u003e). Furthermore, apart from total protein, cystatin C, and gamma-glutamyltransferase, all biomarkers of interest were either positively or inversely associated with PC1 or PC2 \u003cem\u003e(Supplementary Table\u0026nbsp;6\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eWe considered causal effects for a change in PC1 and PC2 from the 25th to the 75th percentile, and each mediator fixed at its median level, after mutual adjustment for all other biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGeneral obesity body shape (PC1)\u003c/h2\u003e \u003cp\u003eThe results from the four-way decomposition of each potential mediator of the associations between PC1 and postmenopausal BC risk are shown in \u003cem\u003eSupplementary Table\u0026nbsp;7\u003c/em\u003e (effect estimates) and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (attributable proportions). Overall, the CDE, the effect due to neither mediation nor interaction, showed strong positive associations between PC1 and BC risk across all 13 investigated biomarkers (i.e., mediators), with CDEs between 88.9% (95% CI: 82.2\u0026ndash;95.6%), when holding testosterone levels fixed, to 101.9% (95% CI: 93.4\u0026ndash;109.9%), when holding IGF-1 fixed at its median (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eand Supplementary Fig.\u0026nbsp;7\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportions attributable for the four-way decomposition of each mediator of the associations between a general obese body shape (PC 1) and postmenopausal breast cancer risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eP_ CDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eP_ INTref\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eP_INTmed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eP_PIE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOP_M\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1 (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-glutamyltransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrate (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eP_CDE\u0026thinsp;=\u0026thinsp;proportion of controlled direct effect, P_INTref\u0026thinsp;=\u0026thinsp;proportion of reference interaction, P_INTmed\u0026thinsp;=\u0026thinsp;proportion of mediated interaction, P_PIE\u0026thinsp;=\u0026thinsp;proportion of pure indirect effect, OP_M\u0026thinsp;=\u0026thinsp;overall proportion mediated. HDL cholesterol: High-density lipoprotein cholesterol. Output of mediation analysis with causal effects estimated for a change in PC from the 25th to the 75th percentile. Controlled direct effects are computed fixing the mediators at their median levels.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eHDL cholesterol: high-density lipoprotein cholesterol, IGF-1: insulin-like growth factor, SHBG: sex hormone-binding globulin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModels adjusted for age, center and diet, and adjusted for age alcohol frequency, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior, and mutually adjusted for biomarkers.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was a PIE through IGF-1 and testosterone with mediated proportions equal to -4.1% (95% CI: -11.9\u0026ndash;3.8%) and 10% (95% CI: 4\u0026ndash;16%), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall proportion mediated after accounting for mediation that was activated because of an interaction between PC1 and the two biomarkers was \u0026minus;\u0026thinsp;12.2% (95% CI: -20.5% to -4.0%) and 11.4% (95% CI: 5.1\u0026ndash;17.8%.) for IGF-1 and testosterone, respectively. There was no evidence for mediation by the other investigated biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eand Supplementary Fig.\u0026nbsp;7).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTall/lean body shape (PC 2)\u003c/h2\u003e \u003cp\u003eThere was little variation in the proportion of CDE after fixing the mediators (i.e., 13 biomarkers) at their median values (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eand Supplementary Table\u0026nbsp;8\u003c/em\u003e). Minor proportions of the association between PC2 and BC were mediated by IGF-1 (PIE: 2.8%, 95% CI: 0.6\u0026ndash;4.9%), and SHBG (PIE: -6.1%, 95% CI: -10.9% to -1.3%). There was no evidence of mediated interaction for IGF-1 (P\u0026thinsp;=\u0026thinsp;0.242), while there was some indication of mediated interaction for SHBG (proportion mediated interaction: 3%, 95% CI: 0.7\u0026ndash;6.0%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eand Supplementary Fig.\u0026nbsp;8)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportion attributable for the four-way decomposition of each mediator of the associations between principal component 2 of body shape and postmenopausal breast cancer risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eP_ CDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eP_ Int Ref\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eP_intmed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eP_PIE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOP_M\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF1 (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine Aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-Glutamyltransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrate (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eP_CDE\u0026thinsp;=\u0026thinsp;proportion of controlled direct effect, P_INTref\u0026thinsp;=\u0026thinsp;proportion of reference interaction, P_INTmed\u0026thinsp;=\u0026thinsp;proportion of mediated interaction, P_PIE\u0026thinsp;=\u0026thinsp;proportion of pure indirect effect, OP_M\u0026thinsp;=\u0026thinsp;overall proportion mediated. Output of mediation analysis with causal effects estimated for a change in PC from the 25th to the 75th percentile. Controlled direct effects are computed fixing the mediators at their median levels.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eHDL cholesterol: high-density lipoprotein cholesterol, IGF-1: insulin-like growth factor, SHBG: sex hormone-binding globulin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModels adjusted for age, center and diet, and adjusted for age alcohol frequency, smoking status, oral contraceptive, menopausal hormone treatment, physical activity, qualifications, Townsend deprivation index and sedentary behavior, and mutually adjusted for biomarkers.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eOverall, sensitivity mediation analyses without mutual biomarkers adjustment showed no substantial differences as compared to mutual biomarkers adjustment models for PC1. The overall proportion of the TE of PC1 on BC explained by the mediation was 9.7% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for testosterone and \u0026minus;\u0026thinsp;8.4% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for IGF-1 (\u003cem\u003eSupplementary Tables\u0026nbsp;9 and 10\u003c/em\u003e). Regarding PC2, there was evidence of mediation, with the overall proportion mediated (PIE\u0026thinsp;+\u0026thinsp;mediated interaction) accounted for \u0026minus;\u0026thinsp;3% (P\u0026thinsp;=\u0026thinsp;0.001), -4.7% (P\u0026thinsp;=\u0026thinsp;0.024), 2% (P\u0026thinsp;=\u0026thinsp;0.067), -8.7% (P\u0026thinsp;=\u0026thinsp;0.018), 2.1% (P\u0026thinsp;=\u0026thinsp;0.040) and \u0026minus;\u0026thinsp;5.6% (P\u0026thinsp;=\u0026thinsp;0.011) of the TE, through CRP, HDL-cholesterol, SHBG albumin and urate, respectively (\u003cem\u003eSupplementary Tables\u0026nbsp;11 and 12\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAmong 176,686 postmenopausal women enrolled in UK Biobank, body shape PC1 (general adiposity) and body shape PC2 (tall; low WHR) were both associated with an increased risk of BC. The controlled direct effects (i.e., associations due to neither mediation nor interaction) were large, suggesting that most of the excess BC risk was due to other pathways than investigated here. Nevertheless, there was evidence that pathways through testosterone and IGF-1 explained a small proportion of the body shape PC1-BC risk association. For the association between body shape PC2 and BC risk, a small proportion was mediated by IGF-1 and by SHBG. For SHBG, there was also evidence for mediated interaction, but in opposite direction of the pure indirect effect, meaning that the overall proportion mediated was negligible.\u003c/p\u003e \u003cp\u003eIn agreement with our study, the study conducted in EPIC reported an increased risk for BC in relation to both body shape PC1 (general adiposity) and body shape PC2 (tall; low WHR) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These results are also congruent with previous studies investigating the association between obesity and risk of BC, but using single-trait anthropometric indicators [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAltered sex hormone metabolism is a main biological mechanism that could link excess adiposity with postmenopausal BC risk through increased aromatase enzyme activity in peripheral adipose tissue known as aromatization [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Aromatization leads to increased levels of bioavailable sex hormones including testosterone, which may induce breast carcinogenesis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Several epidemiological studies, including in the UK Biobank, reported an increased BC risk associated with elevated blood levels of testosterone [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our study is in accordance with these findings and in addition, our mediation analysis supports the hypothesis that testosterone links general adiposity (i.e., body shape PC1) with postmenopausal BC risk.\u003c/p\u003e \u003cp\u003eThere are strong evidence that higher IGF-I levels are associated with a greater risk of BC [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our findings for postmenopausal BC risk are congruent with this evidence (Supplementary Table\u0026nbsp;5). However, whether IGF-I links adiposity to BC is debated [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Levels of IGF-I increase only to a BMI of approximately 27 kg/m\u003csup\u003e2\u003c/sup\u003e, thereafter declining with increasing weight, and overweight individuals, who intentionally lose weight, IGF-I levels tend to increase [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our mediation analysis may shed some light on this uncertainty. While we did not observe a significant pure indirect effect through IGF-1, there was an interaction between PC1 and IGF-1 (i.e., \u0026lsquo;joint effect\u0026rsquo;) and a negative overall proportion mediated of 12% mainly due to a mediated interaction between PC1 and IGF-1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that there are antagonistic associations between the joint effect of PC1 and IGF-1 on BC risk, and the effect of PC1 on IGF-1.\u003c/p\u003e \u003cp\u003eIn contrast, we estimated a small but clearcut mediation of 2.8% through IGF-1 for the association between PC2 (tall, low WHR) and BC risk. IGF-1 signaling is well known to induce expression of several oncogenes and high concentrations of IGF-1 are associated with increased risk of BC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Because several genetic variants related to the IGF signaling pathway are related to height, height might be a crude anthropometric marker of early-life IGF-1 exposure [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our results confirm that a proportion, albeit small, of the height-BC relationship is mediated through IGF-1. The mediated effect through IGF-1 observed in our study is in line with a recent study by Loh et al. (2022), who found that IGF-1 mediated 22% of the association between BMI and BC risk [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consistent with our findings, another recent study using a case-cohort design within the Women's Health Initiative Observational Study, observed a mediating effect through fasting insulin of the effect of adiposity (BMI) on estrogen receptor (ER)-positive BC [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe identified a second molecular pathway linking PC2 with BC risk, which was through SHBG. Since the height component of this body shape was positively associated with SHBG, and in turn SHBG was inversely associated with the risk of BC, the mediated proportion was negative (-6%). This is in line with evidence that higher levels of SHBG lead to lower levels of bioavailable testosterone and estrogens, and thus a lower BC risk via this pathway [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A meta-analysis of 26 prospective studies showed that high SHBG levels were significantly associated with decreased risk of BC in postmenopausal women, the pooled RR for BC comparing the highest vs. lowest categories of SHBG was 0.64 (95% CI: 0.57\u0026ndash;0.72) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In a recent study from the UK Biobank, SHBG was inversely associated with BC risk in postmenopausal women [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The mediating effect of SHBG in the association between adiposity and BC development has not been investigated in other studies yet. However, SHBG has been reported to mediate a small proportion of the relationship between BMI and endometrial cancer risk (7%) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and the alcohol-BC association (13%) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings suggest differential pathways linking these body shapes with BC risk. Indeed, PC1 may have similar pathways as obesity measured by BMI, while PC2 may have different biological/metabolic pathways involved in carcinogenesis of BC, more comparable to height.\u003c/p\u003e \u003cp\u003eThere have been many advances in mediation analysis methodology over the years, with various methods of mediation analysis reported in the literature. One of the novelties of this study is that we considered both the mediation and interaction pathways simultaneously using one of the most recent approaches to causal mediation analysis: \u0026ldquo;the four-way decomposition\u0026rdquo;. This approach allows to estimate both the pure indirect and interaction effect, as compared to previous studies using conventional approaches for mediation analysis which may have missed mediated interaction effects. The present study is also the first to investigate the potential mediator role of several biomarkers of metabolic health in the association between body shape phenotypes and BC development, as compared to previous studies investigating classical anthropometric parameters (mainly BMI or WHR). Finally, we accounted for confounding by other biomarkers. Limitations of this study included the lack of data on hormone receptor status since the relationship between obesity and BC risk may differ according to estrogen or progesterone receptor. We lacked sufficient individuals with oestradiol measurements to investigate this sex-hormone as potential mediator which has been both related to obesity and BC development. Also, we could not rule out the possibility of unmeasured confounding. Lack of representativeness of the study sample is acknowledged.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, although the direct effects of body shape phenotypes on BC risk are high, our findings are consistent with a possible mediated effect by testosterone and IGF-1 for body shape PC1 and BC development association, while IGF-1 and SHBG may have a role in the association between body shape PC2 and BC development. As the mediated biomarkers may interact with each other, future studies should also consider a decomposition of the total effect in the presence of multiple mediators, particularly by family of biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe UK Biobank study was approved by the North West Multi-Center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland (http://www.ukbiobank.ac.uk/ethics/). All participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThis work has been conducted using the UK Biobank data. The UK Biobank is an open access resource, researchers can apply to use the UK Biobank dataset by registering and applying at https://www.ukbiobank.ac.uk/enable-your-research/about-our-data.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the French National Cancer Institute (l\u0026apos;Institut National du Cancer, INCA_14108 to HF). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by\u0026nbsp;Anja M. Sedlmeier and Emma Fontvieille. Analysis was performed by\u0026nbsp;Amina Amadou and\u0026nbsp;Anja M. Sedlmeier.\u0026nbsp;The first draft of the manuscript was written by\u0026nbsp;Amina Amadou,\u0026nbsp;with support from\u0026nbsp;Heinz Freisling\u0026nbsp;and\u0026nbsp;B\u0026eacute;atrice Fervers. All authors contributed to the\u0026nbsp;interpretation of data, and the drafting or critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eDisclaimer\u003cstrong\u003e:\u0026nbsp;\u003c/strong\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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArgolo DF, Hudis CA, Iyengar NM. The Impact of Obesity on Breast Cancer. 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Int J Cancer 2020;146:759\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijc.32324\u003c/span\u003e\u003cspan address=\"10.1002/ijc.32324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, anthropometry, body shape, biomarker, mediation, interaction","lastPublishedDoi":"10.21203/rs.3.rs-3850301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3850301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBody shape phenotypes combining multiple anthropometric traits have been linked to postmenopausal breast cancer (BC). However, underlying biological pathways remain poorly understood. This study investigated to what extent the associations of body shapes with postmenopausal BC risk is mediated by biochemical markers.\u003c/p\u003e\n\u003cp\u003eThe study included 176,686 postmenopausal women from UK Biobank. Four body shape phenotypes were derived from principal component (PC) analysis of height, weight, body mass index, waist and hip circumferences, and waist-to-hip ratio. The four-way decomposition of the total effect was used to estimate mediation and interaction effects simultaneously as well as the mediated proportions.\u003c/p\u003e\n\u003cp\u003eAfter 10.9 years median follow-up, 6,396 incident postmenopausal BC were diagnosed. There was strong evidence of positive associations between PC1 (general obesity) and PC2 (tall, low WHR), and BC risk. The association of PC1 with BC risk was mediated positively by testosterone and negatively by insulin-like growth factor-1 (IGF-1), with the overall proportion mediated (sum of the mediated interaction and pure indirect effect (PIE)) accounting for 12.2% (95% confidence intervals: -20.5% to -4.0%) and 11.4%(5.1% to 17.8%) of the total effect, respectively. Small proportions of the association between PC2 and BC were mediated by IGF-1 (PIE: 2.8%(0.6% to 4.9%)), and sex hormone-binding globulin (SHBG) (PIE: -6.1%(-10.9% to -1.3%)).\u003c/p\u003e\n\u003cp\u003eOur findings are consistent with differential pathways linking different body shapes with BC risk, with a suggestive mediation through testosterone and IGF-1 in the relationship of generally obese body shape and BC risk, while IGF-1 and SHBG may mediate the tall/lean body shape-BC risk association.\u003c/p\u003e","manuscriptTitle":"Multi-trait body shape phenotypes and breast cancer risk in postmenopausal women: a causal mediation analysis in the UK Biobank cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 07:32:07","doi":"10.21203/rs.3.rs-3850301/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-23T11:46:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-02T21:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cbcaa2a9-236e-475a-af7f-e376c368dd79","date":"2024-01-23T13:56:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-16T09:50:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-16T07:37:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-15T10:42:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Epidemiology and Global Health","date":"2024-01-10T12:43:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f3850ff-66c1-4eba-9b52-16ccaead8ce9","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-13T18:16:54+00:00","versionOfRecord":{"articleIdentity":"rs-3850301","link":"https://doi.org/10.1007/s44197-024-00226-4","journal":{"identity":"journal-of-epidemiology-and-global-health","isVorOnly":false,"title":"Journal of Epidemiology and Global Health"},"publishedOn":"2024-04-10 18:16:53","publishedOnDateReadable":"April 10th, 2024"},"versionCreatedAt":"2024-01-17 07:32:07","video":"","vorDoi":"10.1007/s44197-024-00226-4","vorDoiUrl":"https://doi.org/10.1007/s44197-024-00226-4","workflowStages":[]},"version":"v1","identity":"rs-3850301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3850301","identity":"rs-3850301","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}