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.

The possible therapeutic impact of dietary changes on existing mental illness is largely unknown. Using a randomised controlled trial design, we aimed to investigate the efficacy of a dietary improvement program for the treatment of major depressive episodes. ‘SMILES’ was a 12-week, parallel-group, single blind, randomised controlled trial of an adjunctive dietary intervention in the treatment of moderate to severe depression. The intervention consisted of seven individual nutritional consulting sessions delivered by a clinical dietician. The control condition comprised a social support protocol to the same visit schedule and length. Depression symptomatology was the primary endpoint, assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS) at 12 weeks. Secondary outcomes included remission and change of symptoms, mood and anxiety. Analyses utilised a likelihood-based mixed-effects model repeated measures (MMRM) approach. The robustness of estimates was investigated through sensitivity analyses. We assessed 166 individuals for eligibility, of whom 67 were enrolled (diet intervention, n = 33; control, n = 34). Of these, 55 were utilising some form of therapy: 21 were using psychotherapy and pharmacotherapy combined; 9 were using exclusively psychotherapy; and 25 were using only pharmacotherapy. There were 31 in the diet support group and 25 in the social support control group who had complete data at 12 weeks. The dietary support group demonstrated significantly greater improvement between baseline and 12 weeks on the MADRS than the social support control group, t(60.7) = 4.38, p < 0.001, Cohen’s d = –1.16. Remission, defined as a MADRS score <10, was achieved for 32.3% (n = 10) and 8.0% (n = 2) of the intervention and control groups, respectively (χ 2 (1) = 4.84, p = 0.028); number needed to treat (NNT) based on remission scores was 4.1 (95% CI of NNT 2.3–27.8). A sensitivity analysis, testing departures from the missing at random (MAR) assumption for dropouts, indicated that the impact of the intervention was robust to violations of MAR assumptions. These results indicate that dietary improvement may provide an efficacious and accessible treatment strategy for the management of this highly prevalent mental disorder, the benefits of which could extend to the management of common co-morbidities. Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000251820 . Registered on 29 February 2012.

A review¹ of experimental studies and randomized clinical trials revealed a protective effect of ω-3 polyunsaturated fatty acids (PUFAs) on coronary heart disease (CHD) risk pathways and clinical risk factors. However, key controversies remain. First, randomized clinical trials using fish oil supplements have found mixed effects on CHD events.^2-8 However, most trials provided supplements for a few years or less and were conducted in patients with preexisting CHD or at high risk for CHD and taking multiple cardiovascular drugs. Furthermore, the background dietary intake in these trials was not usually measured, and many participants may have had adequate intake from diet alone. Thus, their generalizability and relevance for long-term effects of dietary ω-3 PUFAs for primary CHD prevention are uncertain. Second, although a review⁹ of several prior observational studies found inverse associations between seafood-derived ω-3 PUFAs and CHD death, potential effects on nonfatal myocardial infarction (MI) or total CHD are less clear. This review⁹ additionally found that most studies also relied on self-reported dietary questionnaires, which may produce errors or bias in recall. A previous review¹ also found that a handful of studies have measured objective circulating or tissue ω-3 PUFA levels, but such findings could be limited by publication bias. In addition, prior individual biomarker studies were generally underpowered to explore potentially relevant differences in effects depending on underlying participant characteristics, medication use, or genetic variation. Thus, uncertainties remain about the effects of ω-3 PUFAs on CHD.

OBJECTIVE—To examine associations between type 2 diabetes and fiber, glycemic load (GL), dietary glycemic index (GI), and fiber-rich foods. RESEARCH DESIGN AND METHODS—This was a prospective study of 36,787 men and women aged 40–69 years without diabetes. For all self-reported cases of diabetes at 4-year follow-up, confirmation of diagnosis was sought from medical practitioners. Case subjects were those who reported diabetes at follow-up and for whom there was no evidence that they did not have type 2 diabetes. Data were analyzed with logistic regression, adjusting for country of birth, physical activity, family history of diabetes, alcohol and energy intake, education, 5-year weight change, sex, and age. RESULTS—Follow-up was completed by 31,641 (86%) participants, and 365 cases were identified. The odds ratio (OR) for the highest quartile of white bread intake compared with the lowest was 1.37 (95% CI 1.04–1.81; P for trend = 0.001). Intakes of carbohydrate (OR per 200 g/day 0.58, 0.36–0.95), sugars (OR per 100 g/day 0.61, 0.47–0.79), and magnesium (OR per 500 mg/day 0.62, 0.43–0.90) were inversely associated with incidence of diabetes, whereas intake of starch (OR per 100 g/day 1.47, 1.06–2.05) and dietary GI (OR per 10 units 1.32, 1.05–1.66) were positively associated with diabetes. These relationships were attenuated after adjustment for BMI and waist-to-hip ratio. CONCLUSIONS—Reducing dietary GI while maintaining a high carbohydrate intake may reduce the risk of type 2 diabetes. One way to achieve this would be to substitute white bread with low-GI breads.