Background: The relation between sugar-sweetened beverages (SSBs) and body weight remains controversial.Objective: We conducted a systematic review a

The global prevalence of obesity has increased substantially over the past 40 years, from less than 1% in 1975, to 6-8% in 2016, among girls and boys, and from 3% to 11% among men and from 6% to 15% among women over the same time period. Our aim was to consolidate the evidence on the epidemiology of obesity into a conceptual model of the so-called obesity transition. We used illustrative examples from the 30 most populous countries, representing 77·5% of the world's population to propose a four stage model. Stage 1 of the obesity transition is characterised by a higher prevalence of obesity in women than in men, in those with higher socioeconomic status than in those with lower socioeconomic status, and in adults than in children. Many countries in south Asia and sub-Saharan Africa are presently in this stage. In countries in stage 2 of the transition, there has been a large increase in the prevalence among adults, a smaller increase among children, and a narrowing of the gap between sexes and in socioeconomic differences among women. Many Latin American and Middle Eastern countries are presently at this stage. High-income east Asian countries are also at this stage, albeit with a much lower prevalence of obesity. In stage 3 of the transition, the prevalence of obesity among those with lower socioeconomic status surpasses that of those with higher socioeconomic status, and plateaus in prevalence can be observed in women with high socioeconomic status and in children. Most European countries are presently at this stage. There are too few signs of countries entering into the proposed fourth stage of the transition, during which obesity prevalence declines, to establish demographic patterns. This conceptual model is intended to provide guidance to researchers and policy makers in identifying the current stage of the obesity transition in a population, anticipating subpopulations that will develop obesity in the future, and enacting proactive measures to attenuate the transition, taking into consideration local contextual factors.

Objective To examine whether overall lifestyles mediate associations of socioeconomic status (SES) with mortality and incident cardiovascular disease (CVD) and the extent of interaction or joint relations of lifestyles and SES with health outcomes. Design Population based cohort study. Setting US National Health and Nutrition Examination Survey (US NHANES, 1988-94 and 1999-2014) and UK Biobank. Participants 44 462 US adults aged 20 years or older and 399 537 UK adults aged 37-73 years. Exposures SES was derived by latent class analysis using family income, occupation or employment status, education level, and health insurance (US NHANES only), and three levels (low, medium, and high) were defined according to item response probabilities. A healthy lifestyle score was constructed using information on never smoking, no heavy alcohol consumption (women ≤1 drink/day; men ≤2 drinks/day; one drink contains 14 g of ethanol in the US and 8 g in the UK), top third of physical activity, and higher dietary quality. Main outcome measures All cause mortality was the primary outcome in both studies, and CVD mortality and morbidity in UK Biobank, which were obtained through linkage to registries. Results US NHANES documented 8906 deaths over a mean follow-up of 11.2 years, and UK Biobank documented 22 309 deaths and 6903 incident CVD cases over a mean follow-up of 8.8-11.0 years. Among adults of low SES, age adjusted risk of death was 22.5 (95% confidence interval 21.7 to 23.3) and 7.4 (7.3 to 7.6) per 1000 person years in US NHANES and UK Biobank, respectively, and age adjusted risk of CVD was 2.5 (2.4 to 2.6) per 1000 person years in UK Biobank. The corresponding risks among adults of high SES were 11.4 (10.6 to 12.1), 3.3 (3.1 to 3.5), and 1.4 (1.3 to 1.5) per 1000 person years. Compared with adults of high SES, those of low SES had higher risks of all cause mortality (hazard ratio 2.13, 95% confidence interval 1.90 to 2.38 in US NHANES; 1.96, 1.87 to 2.06 in UK Biobank), CVD mortality (2.25, 2.00 to 2.53), and incident CVD (1.65, 1.52 to 1.79) in UK Biobank, and the proportions mediated by lifestyle were 12.3% (10.7% to 13.9%), 4.0% (3.5% to 4.4%), 3.0% (2.5% to 3.6%), and 3.7% (3.1% to 4.5%), respectively. No significant interaction was observed between lifestyle and SES in US NHANES, whereas associations between lifestyle and outcomes were stronger among those of low SES in UK Biobank. Compared with adults of high SES and three or four healthy lifestyle factors, those with low SES and no or one healthy lifestyle factor had higher risks of all cause mortality (3.53, 3.01 to 4.14 in US NHANES; 2.65, 2.39 to 2.94 in UK Biobank), CVD mortality (2.65, 2.09 to 3.38), and incident CVD (2.09, 1.78 to 2.46) in UK Biobank. Conclusions Unhealthy lifestyles mediated a small proportion of the socioeconomic inequity in health in both US and UK adults; therefore, healthy lifestyle promotion alone might not substantially reduce the socioeconomic inequity in health, and other measures tackling social determinants of health are warranted. Nevertheless, healthy lifestyles were associated with lower mortality and CVD risk in different SES subgroups, supporting an important role of healthy lifestyles in reducing disease burden.

Background: Americans have a shorter life expectancy compared with residents of almost all other high-income countries. We aim to estimate the impact of lifestyle factors on premature mortality and life expectancy in the US population. Methods: Using data from the Nurses’ Health Study (1980–2014; n=78 865) and the Health Professionals Follow-up Study (1986–2014, n=44 354), we defined 5 low-risk lifestyle factors as never smoking, body mass index of 18.5 to 24.9 kg/m ² , ≥30 min/d of moderate to vigorous physical activity, moderate alcohol intake, and a high diet quality score (upper 40%), and estimated hazard ratios for the association of total lifestyle score (0–5 scale) with mortality. We used data from the NHANES (National Health and Nutrition Examination Surveys; 2013–2014) to estimate the distribution of the lifestyle score and the US Centers for Disease Control and Prevention WONDER database to derive the age-specific death rates of Americans. We applied the life table method to estimate life expectancy by levels of the lifestyle score. Results: During up to 34 years of follow-up, we documented 42 167 deaths. The multivariable-adjusted hazard ratios for mortality in adults with 5 compared with zero low-risk factors were 0.26 (95% confidence interval [CI], 0.22–0.31) for all-cause mortality, 0.35 (95% CI, 0.27–0.45) for cancer mortality, and 0.18 (95% CI, 0.12–0.26) for cardiovascular disease mortality. The population-attributable risk of nonadherence to 5 low-risk factors was 60.7% (95% CI, 53.6–66.7) for all-cause mortality, 51.7% (95% CI, 37.1–62.9) for cancer mortality, and 71.7% (95% CI, 58.1–81.0) for cardiovascular disease mortality. We estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3–29.8) for women and 25.5 years (95% CI, 24.7–26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3–44.9) for women and 37.6 years (95% CI, 35.8–39.4) for men. The projected life expectancy at age 50 years was on average 14.0 years (95% CI, 11.8–16.2) longer among female Americans with 5 low-risk factors compared with those with zero low-risk factors; for men, the difference was 12.2 years (95% CI, 10.1–14.2). Conclusions: Adopting a healthy lifestyle could substantially reduce premature mortality and prolong life expectancy in US adults.