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      Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults

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      , Dr, PhD a , * , , PhD a , , MSc a , , Prof, PhD a , , Prof, PhD a , , Prof, PhD a , b
      Lancet
      Lancet Publishing Group

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          Summary

          Background

          High body-mass index (BMI) predisposes to several site-specific cancers, but a large-scale systematic and detailed characterisation of patterns of risk across all common cancers adjusted for potential confounders has not previously been undertaken. We aimed to investigate the links between BMI and the most common site-specific cancers.

          Methods

          With primary care data from individuals in the Clinical Practice Research Datalink with BMI data, we fitted Cox models to investigate associations between BMI and 22 of the most common cancers, adjusting for potential confounders. We fitted linear then non-linear (spline) models; investigated effect modification by sex, menopausal status, smoking, and age; and calculated population effects.

          Findings

          5·24 million individuals were included; 166 955 developed cancers of interest. BMI was associated with 17 of 22 cancers, but effects varied substantially by site. Each 5 kg/m 2 increase in BMI was roughly linearly associated with cancers of the uterus (hazard ratio [HR] 1·62, 99% CI 1·56–1·69; p<0·0001), gallbladder (1·31, 1·12–1·52; p<0·0001), kidney (1·25, 1·17–1·33; p<0·0001), cervix (1·10, 1·03–1·17; p=0·00035), thyroid (1·09, 1·00–1·19; p=0·0088), and leukaemia (1·09, 1·05–1·13; p≤0·0001). BMI was positively associated with liver (1·19, 1·12–1·27), colon (1·10, 1·07–1·13), ovarian (1·09, 1.04–1.14), and postmenopausal breast cancers (1·05, 1·03–1·07) overall (all p<0·0001), but these effects varied by underlying BMI or individual-level characteristics. We estimated inverse associations with prostate and premenopausal breast cancer risk, both overall (prostate 0·98, 0·95–1·00; premenopausal breast cancer 0·89, 0·86–0·92) and in never-smokers (prostate 0·96, 0·93–0·99; premenopausal breast cancer 0·89, 0·85–0·94). By contrast, for lung and oral cavity cancer, we observed no association in never smokers (lung 0·99, 0·93–1·05; oral cavity 1·07, 0·91–1·26): inverse associations overall were driven by current smokers and ex-smokers, probably because of residual confounding by smoking amount. Assuming causality, 41% of uterine and 10% or more of gallbladder, kidney, liver, and colon cancers could be attributable to excess weight. We estimated that a 1 kg/m 2 population-wide increase in BMI would result in 3790 additional annual UK patients developing one of the ten cancers positively associated with BMI.

          Interpretation

          BMI is associated with cancer risk, with substantial population-level effects. The heterogeneity in the effects suggests that different mechanisms are associated with different cancer sites and different patient subgroups.

          Funding

          National Institute for Health Research, Wellcome Trust, and Medical Research Council.

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          Most cited references22

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          A structural approach to selection bias.

          The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.
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            Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective,

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              Body mass index, hormone replacement therapy, and endometrial cancer risk: a meta-analysis.

              Body mass index (BMI) is a risk factor for endometrial cancer. We quantified the risk and investigated whether the association differed by use of hormone replacement therapy (HRT), menopausal status, and histologic type. We searched MEDLINE and EMBASE (1966 to December 2009) to identify prospective studies of BMI and incident endometrial cancer. We did random-effects meta-analyses, meta-regressions, and generalized least square regressions for trend estimations assuming linear, and piecewise linear, relationships. Twenty-four studies (17,710 cases) were analyzed; 9 studies contributed to analyses by HRT, menopausal status, or histologic type, all published since 2003. In the linear model, the overall risk ratio (RR) per 5 kg/m(2) increase in BMI was 1.60 (95% CI, 1.52-1.68), P < 0.0001. In the piecewise model, RRs compared with a normal BMI were 1.22 (1.19-1.24), 2.09 (1.94-2.26), 4.36 (3.75-5.10), and 9.11 (7.26-11.51) for BMIs of 27, 32, 37, and 42 kg/m(2), respectively. The association was stronger in never HRT users than in ever users: RRs were 1.90 (1.57-2.31) and 1.18 (95% CI, 1.06-1.31) with P for interaction = 0.003. In the piecewise model, the RR in never users was 20.70 (8.28-51.84) at BMI 42 kg/m(2), compared with never users at normal BMI. The association was not affected by menopausal status (P = 0.34) or histologic type (P = 0.26). HRT use modifies the BMI-endometrial cancer risk association. These findings support the hypothesis that hyperestrogenia is an important mechanism underlying the BMI-endometrial cancer association, whilst the presence of residual risk in HRT users points to the role of additional systems. ©2010 AACR.
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                Author and article information

                Contributors
                Journal
                Lancet
                Lancet
                Lancet
                Lancet Publishing Group
                0140-6736
                1474-547X
                30 August 2014
                30 August 2014
                : 384
                : 9945
                : 755-765
                Affiliations
                [a ]Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
                [b ]Farr Institute of Health Informatics Research, London, UK
                Author notes
                [* ]Correspondence to: Dr Krishnan Bhaskaran, Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK krishnan.bhaskaran@ 123456lshtm.ac.uk
                Article
                S0140-6736(14)60892-8
                10.1016/S0140-6736(14)60892-8
                4151483
                25129328
                22ef6b7b-4fd0-446d-b8a6-07e4d753d2d1
                © 2014 Bhaskaran et al. Open Access article distributed under the terms of CC BY

                This document may be redistributed and reused, subject to certain conditions.

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