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

Abstract

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.

Figure 1

Flow diagram showing the creation of the main dataset, reasons for exclusions, and assignment of body-mass index (BMI) at study entry CPRD= Clinical Practice Research Datalink. *When the first available BMI was after start of CPRD follow-up, the patient was late-entered into the risk set.

Figure 2

Forest plot of hazard ratios (HR) for each cancer per 5 kg/m² increase in body-mass index (BMI), from models with BMI fitted as a linear effect Number of incident cancer cases in never smokers only were: oral cavity (302); oesophagus (1858); stomach (1320); colon (6115); rectum (2623); liver (699); gallbladder (133); pancreas (1525); lung (2674); malignant melanoma (4477); breast—premenopausal (3109); breast—postmenopausal (14 833); cervix (535); uterus (1555); ovaries (1864); prostate (10 634); kidney (776); bladder (2687); brain and central nervous system (CNS) (1359); thyroid (478); non-Hodgkin lymphoma (3212); multiple myeloma (1441); and leukaemia (2685). HRs estimated using a separate model for each cancer with linear BMI term, adjusted for age, diabetes status, smoking, alcohol use, socioeconomic status, calendar year, and stratified by sex; p values from Wald tests on the linear BMI term in each adjusted model.

Figure 3

Association between body-mass index (BMI) and specific cancers, allowing for non-linear effects, with 99% CIs The reference BMI for these plots (with HR fixed as 1·0) was 22 kg/m². Separate models were fitted for each cancer type, each with a restricted cubic spline for BMI (knots placed at equal percentiles of BMI), adjusted for age, calendar year, diabetes status, alcohol use, smoking (all at time of BMI recording), socioeconomic status (index of multiple deprivation), and stratified by sex. HR=hazard ratio.

Figure 4

Modelled associations between body-mass index (BMI) and colon, liver, breast, ovarian, and prostate cancers and malignant melanoma, including detected non-linearities and effect modification Curves for each cancer type estimated from models with BMI fitted as a spline, adjusted for age, calendar year, diabetes status, smoking, alcohol use, socioeconomic status (index of multiple deprivation). Stratified curves were produced by adding interaction terms with the BMI spline basis. For estimated effect modification by sex, smoking, menopausal status, and present age for all cancer types, see appendix pp 9–12. Estimated HRs per 5 kg/m² derived from best fitting piecewise linear or linear model (with Akaike information criterion used to select optimal knots or thresholds). HR=hazard ratio.

Figure 5

Associations between body-mass index and oral, stomach, and lung cancers with effect modification by smoking status Curves for each cancer type estimated from models with BMI fitted as a spline, interaction terms between smoking status and spline basis, adjusted for age, calendar year, diabetes status, alcohol use, socioeconomic status (index of multiple deprivation), and stratified by sex. p values for effect of BMI in never smokers=0·62 for oral cavity cancer, 0·16 for stomach cancer, and 0·18 for lung cancer. Estimated curves by smoking status for all cancer types are presented in appendix p 10. P_interaction=p value for interaction. HR=hazard ratio.