In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98-1·11) for obesity in men and 2 ·17 (2·06-2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38-1·45 for men; 1·34, 1·28-1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21-1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality. European Commission, Swiss State Secretariat for Education, Swiss National Science Foundation, the Medical Research Council, NordForsk, Portuguese Foundation for Science and Technology.

A large-scale epigenome-wide association study identifies changes in DNA methylation associated with body mass index in blood and adipose tissue, and correlates DNA methylation sites with high risk of incident type 2 diabetes. Obesity is a major risk factor for type 2 diabetes and related metabolic disorders. Genetic association studies have identified genomic loci associated with obesity, and recent studies have also suggested associations with DNA methylation. These authors report an epigenome-wide association study for body mass index (BMI), identifying an association with DNA methylation at 187 loci in blood and adipose tissue. They find that these methylation changes are secondary to adiposity and are also associated with an increased risk of developing type 2 diabetes, independent of conventional risk factors. Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances1,2. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation3,4,5,6, a key regulator of gene expression and molecular phenotype7. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

Background: Obesity is associated with increased breast cancer risk among postmenopausal women. We examined whether this association could be explained by the relationship of body mass index (BMI) with serum sex hormone concentrations. Methods: We analyzed individual data from eight prospective studies of postmenopausal women. Data on BMI and prediagnostic estradiol levels were available for 624 case subjects and 1669 control subjects; data on the other sex hormones were available for fewer subjects. The relative risks (RRs) with 95% confidence intervals (CIs) of breast cancer associated with increasing BMI were estimated by conditional logistic regression on case–control sets, matched within each study for age and recruitment date, and adjusted for parity. All statistical tests were two-sided. Results: Breast cancer risk increased with increasing BMI (P_trend = .002), and this increase in RR was substantially reduced by adjustment for serum estrogen concentrations. Adjusting for free estradiol reduced the RR for breast cancer associated with a 5 kg/m² increase in BMI from 1.19 (95% CI = 1.05 to 1.34) to 1.02 (95% CI = 0.89 to 1.17). The increased risk was also substantially reduced after adjusting for other estrogens (total estradiol, non–sex hormone–binding globulin–bound estradiol, estrone, and estrone sulfate), and moderately reduced after adjusting for sex hormone–binding globulin, whereas adjustment for the androgens (androstenedione, dehydroepiandrosterone, dehydroepiandrosterone sulfate, and testosterone) had little effect on the excess risk. Conclusion: The results are compatible with the hypothesis that the increase in breast cancer risk with increasing BMI among postmenopausal women is largely the result of the associated increase in estrogens, particularly bioavailable estradiol.