Objective To investigate the association of predicted lean body mass, fat mass, and body mass index (BMI) with all cause and cause specific mortality in men. Design Prospective cohort study. Setting Health professionals in the United States Participants 38 006 men (aged 40-75 years) from the Health Professionals Follow-up Study, followed up for death (1987-2012). Main outcome measures All cause and cause specific mortality. Results Using validated anthropometric prediction equations previously developed from the National Health and Nutrition Examination Survey, lean body mass and fat mass were estimated for all participants. During a mean of 21.4 years of follow-up, 12 356 deaths were identified. A J shaped association was consistently observed between BMI and all cause mortality. Multivariable adjusted Cox models including predicted fat mass and lean body mass showed a strong positive monotonic association between predicted fat mass and all cause mortality. Compared with those in the lowest fifth of predicted fat mass, men in the highest fifth had a hazard ratio of 1.35 (95% confidence interval 1.26 to 1.46) for mortality from all causes. In contrast, a U shaped association was found between predicted lean body mass and all cause mortality. Compared with those in the lowest fifth of predicted lean body mass, men in the second to fourth fifths had 8-10% lower risk of mortality from all causes. In the restricted cubic spline models, the risk of all cause mortality was relatively flat until 21 kg of predicted fat mass and increased rapidly afterwards, with a hazard ratio of 1.22 (1.18 to 1.26) per standard deviation. For predicted lean body mass, a large reduction of the risk was seen within the lower range until 56 kg, with a hazard ratio of 0.87 (0.82 to 0.92) per standard deviation, which increased thereafter (P for non-linearity <0.001). For cause specific mortality, men in the highest fifth of predicted fat mass had hazard ratios of 1.67 (1.47 to 1.89) for cardiovascular disease, 1.24 (1.09 to 1.43) for cancer, and 1.26 (0.97 to 1.64) for respiratory disease. On the other hand, a U shaped association was found between predicted lean body mass and mortality from cardiovascular disease and cancer. However, a strong inverse association existed between predicted lean body mass and mortality from respiratory disease (P for trend <0.001). Conclusions The shape of the association between BMI and mortality was determined by the relation between two body components (lean body mass and fat mass) and mortality. This finding suggests that the “obesity paradox” controversy may be largely explained by low lean body mass, rather than low fat mass, in the lower range of BMI.

Previous meta-analyses of randomized controlled trials (RCTs) of vitamin D supplementation and total cancer incidence and mortality found inconsistent results, and most included trials administered generally low doses of vitamin D (≤ 1100 IU/day). We updated the meta-analysis by incorporating recent RCTs that have tested higher doses of vitamin D supplements. PubMed and Embase were searched from the inception to November, 2018. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated using a random-effects model. For total cancer incidence, 10 trials were included (6,547 cases; 3-10 years of follow-up; 54-135 nmol/L of attained levels of circulating 25(OH)vitamin D [25(OH)D] in the intervention group). The summary RR was 0.98 (95% CI, 0.93 to 1.03; P=.42; I²=0%). The results remained null across subgroups tested, including even when attained 25(OH)D levels exceeded 100 nmol/L (RR, 0.95; 95% CI, 0.83 to 1.09; P=.48; I²=26%). For total cancer mortality, 5 trials were included (1,591 deaths; 3-10 years of follow-up; 54-135 nmol/L of attained levels of circulating 25(OH)D in the intervention group). The summary RR was 0.87 (95% CI, 0.79 to 0.96; P=.005; I²=0%), which was largely attributable to interventions with daily dosing (as opposed to infrequent bolus dosing). No statistically significant heterogeneity was observed by attained levels of circulating 25(OH)D (P_{heterogeneity}=.83), with RR being 0.88 (95% CI, 0.78 to 0.98; P=.02; I²=0%) for ≤ 100 nmol/L and 0.85 (95% CI, 0.70-1.03; P=.11; I²=0%) for > 100 nmol/L. In an updated meta-analysis of RCTs, vitamin D supplementation significantly reduced total cancer mortality but did not reduce total cancer incidence.

Adiposity, measured by body mass index, is implicated in carcinogenesis. While adult weight gain has diverse advantages over body mass index in measuring adiposity, systematic reviews on adult weight gain in relation to adiposity-related cancers are lacking. PubMed and Embase were searched through September 2014 for prospective observational studies investigating the relationship between adult weight gain and the risk of 10 adiposity-related cancers. Dose-response meta-analyses were performed using a random-effects model to estimate summary relative risk (RR) and 95% confidence interval (CI) for each cancer type. All statistical tests were two-sided. A total of 50 studies were included. For each 5kg increase in adult weight gain, the summary relative risk was 1.11 (95% CI = 1.08 to 1.13) for postmenopausal breast cancer among no- or low-hormone replacement therapy (HRT) users, 1.39 (95% CI = 1.29 to 1.49) and 1.09 (95% CI = 1.02 to 1.16) for postmenopausal endometrial cancer among HRT nonusers and users, respectively, 1.13 (95% CI = 1.03 to 1.23) for postmenopausal ovarian cancer among no or low HRT users, 1.09 (95% CI = 1.04 to 1.13) for colon cancer in men. The relative risk of kidney cancer comparing highest and lowest level of adult weight gain was 1.42 (95% CI = 1.11 to 1.81). Adult weight gain was unrelated to cancers of the breast (premenopausal women, postmenopausal HRT users), prostate, colon (women), pancreas, and thyroid. An increase in risk associated with adult weight gain for breast cancer was statistically significantly greater among postmenopausal women (P_{heterogeneity} = .001) and HRT nonusers (P_{heterogeneity} = .001); that for endometrial cancer was alike among HRT nonusers (P_{heterogeneity} = .04). Avoiding adult weight gain itself may confer protection against certain types of cancers, particularly among HRT nonusers.

Background: The 2018 physical activity guidelines for Americans recommend a minimum of 150 to 300 min/wk of moderate physical activity (MPA), 75 to 150 min/wk of vigorous physical activity (VPA), or an equivalent combination of both. However, it remains unclear whether higher levels of long-term VPA and MPA are, independently and jointly, associated with lower mortality. Methods: A total of 116221 adults from 2 large prospective US cohorts (Nurses' Health Study and Health Professionals Follow-up Study, 1988–2018) were analyzed. Detailed self-reported leisure-time physical activity was assessed with a validated questionnaire, repeated up to 15 times during the follow-up. Cox regression was used to estimate the hazard ratio and 95% CI of the association between long-term leisure-time physical activity intensity and all-cause and cause-specific mortality. Results: During 30 years of follow-up, we identified 47596 deaths. In analyses mutually adjusted for MPA and VPA, hazard ratios comparing individuals meeting the long-term leisure-time VPA guideline (75–149 min/wk) versus no VPA were 0.81 (95% CI, 0.76–0.87) for all-cause mortality, 0.69 (95% CI, 0.60–0.78) for cardiovascular disease (CVD) mortality, and 0.85 (95% CI, 0.79–0.92) for non-CVD mortality. Meeting the long-term leisure-time MPA guideline (150–299 min/wk) was similarly associated with lower mortality: 19% to 25% lower risk of all-cause, CVD, and non-CVD mortality. Compared with those meeting the long-term leisure-time physical activity guidelines, participants who reported 2 to 4 times above the recommended minimum of long-term leisure-time VPA (150–299 min/wk) or MPA (300–599 min/wk) showed 2% to 4% and 3% to 13% lower mortality, respectively. Higher levels of either long-term leisure-time VPA (≥300 min/wk) or MPA (≥600 min/wk) did not clearly show further lower all-cause, CVD, and non-CVD mortality or harm. In joint analyses, for individuals who reported <300 min/wk of long-term leisure-time MPA, additional leisure-time VPA was associated with lower mortality; however, among those who reported ≥300 min/wk of long-term leisure-time MPA, additional leisure-time VPA did not appear to be associated with lower mortality beyond MPA. Conclusions: The nearly maximum association with lower mortality was achieved by performing ≈150 to 300 min/wk of long-term leisure-time VPA, 300 to 600 min/wk of long-term leisure-time MPA, or an equivalent combination of both.

Quantification of lean body mass and fat mass can provide important insight into epidemiological research. However, there is no consensus on generalisable anthropometric prediction equations to validly estimate body composition. We aimed to develop and validate practical anthropometric prediction equations for lean body mass, fat mass and percent fat in adults (men, n 7531; women, n 6534) from the National Health and Nutrition Examination Survey 1999–2006. Using a prediction sample, we predicted each of dual-energy X-ray absorptiometry (DXA)-measured lean body mass, fat mass and percent fat based on different combinations of anthropometric measures. The proposed equations were validated using a validation sample and obesity-related biomarkers. The practical equation including age, race, height, weight and waist circumference had high predictive ability for lean body mass (men: R²=0·91, standard error of estimate (SEE)=2·6 kg; women: R²=0·85, SEE=2·4 kg) and fat mass (men: R²=0·90, SEE=2·6 kg; women: R²=0·93, SEE=2·4 kg). Waist circumference was a strong predictor in men only. Addition of other circumference and skinfold measures slightly improved the prediction model. For percent fat, R² were generally lower but the trend in variation explained was similar. Our validation tests showed robust and consistent results with no evidence of substantial bias. Additional validation using biomarkers demonstrated comparable abilities to predict obesity-related biomarkers between direct DXA measurements and predicted scores. Moreover, predicted fat mass and percent fat had significantly stronger associations with obesity-related biomarkers than BMI did. Our findings suggest the potential application of the proposed equations in various epidemiological settings.