Many studies have explored the relationship between coffee—one of the most commonly consumed beverages today—and obesity. Despite inconsistent results, the relationship has not been systematically summarized. Thus, we conducted a meta-analysis by compiling data from 12 epidemiologic studies identified from PubMed and Embase through February 2019. The included studies assessed obesity by body mass index (BMI, a measure of overall adiposity) or waist circumference (WC, a measure of central adiposity); analyzed the measure as a continuous outcome or binary outcome. Using random effects model, weighted mean difference (WMD) and 95% confidence interval (CI) were obtained for continuous outcomes; summary relative risk (RR) and 95% CI for the highest vs. lowest categories of coffee intake were estimated for binary outcome. For BMI, WMD was −0.08 (95% CI −0.14, −0.02); RR was 1.49 (95% CI 0.97, 2.29). For WC, WMD was −0.27 (95% CI −0.51, −0.02) and RR was 1.07 (95% CI 0.84, 1.36). In subgroup analysis by sex, evidence for an inverse association was more evident in men, specifically for continuous outcome, with WMD −0.05 (95% CI −0.09, −0.02) for BMI and −0.21 (95% CI −0.35, −0.08) for WC. Our meta-analysis suggests that higher coffee intake might be modestly associated with reduced adiposity, particularly in men.

Alcohol is widely consumed and is known as a major risk factor for several types of cancers. Yet, it is unclear whether alcohol consumption is associated with the risk of prostate cancer (PCa) or not. We conducted linear and non-linear dose–response meta-analyses of cohort studies on alcohol consumption and PCa risk by types of alcohol (total, wine, beer, and liquor) and PCa (non-aggressive and aggressive). Pubmed and Embase were searched through April 2020 to identify relevant studies. Summary relative risk (RR) and 95% confidence interval (CI) were estimated using a random-effects model. For non-aggressive PCa, by alcohol type, the risk increased linearly with liquor (RR per 14 g/day intake (alcohol content in standard drink) being 1.04 (95% CI = 1.02–1.06, I² = 0%, three studies) and non-linearly with beer (P_{non-linearity} = 0.045, four studies), with increased risk observed in the lower range (RR = 1.03, 95% CI = 1.01–1.05; 14 g/day), with 1.05 (95% CI = 1.01–1.08) at 28 g/day. Wine was not significantly associated with the risk of non-aggressive PCa. For aggressive PCa, a non-linear relationship of diverse shapes was indicated for all types of alcohol in the sensitivity analysis. Compared to non-drinking, a significant positive association was more apparent at lower dose for liquor (RR = 1.12, 95% CI = 1.04–1.20 at 14 g/day; RR = 1.16, 95% CI = 1.03–1.31 at 28 g/day; P_{non-linearity} = 0.005, three studies) but at higher doses for wine (RR = 1.02, 95% CI = 0.90–1.16 at 28 g/day, RR = 1.35, 95% CI = 1.08–1.67 at 56 g/day; P_{non-linearity} = 0.01, four studies). In contrast, decreased risks were indicated at lower doses of beer (RR = 0.85, 95% CI = 0.79–0.92 at 14 g/day; RR = 0.79, 95% CI = 0.70–0.90 at 28 g/day, P_{non-linearity} < 0.001, four studies). Total alcohol consumption was not associated with both types of PCa. In this study, we found heterogeneous associations between alcohol intake and PCa by types of alcohol and PCa.

Dairy consumption in adulthood has been demonstrated to influence cancer risk. Although childhood and adolescence represent critical periods of rapid growth, the relationship between milk intake in early life and later cancer risk is unclear. Thus, we examined this relationship by conducting a meta-analysis of the observational studies. PubMed and Embase were searched for relevant articles that were published throughout December 2021. The summary relative risk (RR) and 95% confidence interval (CI) were estimated using the DerSimonian-Laird random-effects model. The summary RR for the highest vs. lowest milk intake was 0.83 (95% CI = 0.69–1.00; p = 0.05; I² = 60%; seven studies) for breast cancer, 0.98 (95% CI = 0.72–1.32; p = 0.88; I² = 51%; four studies) for prostate cancer, and 0.90 (95% CI = 0.42–1.93; p = 0.78; I² = 83%; three studies) for colorectal cancer. No evidence of an association emerged in subgroup analyses of menopausal status, cancer stage, fat content of milk, life stage of milk intake, or study design. Consistent results were observed in the meta-analyses using total dairy intake. In conclusion, milk intake during childhood and adolescence might not be associated with risks of breast, prostate, and colorectal cancer later in life. Given the small number of studies that were included in our meta-analysis, and the high heterogeneity, more studies are warranted for a definitive conclusion.

Background: Water consumption is believed to be a key factor in weight management strategies, yet the existing literature on the subject yields inconsistent findings. To systematically assess the scientific evidence regarding the effect of water intake on adiposity, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) among overweight and obese populations. Methods: PubMed and Embase were searched for relevant articles published up to December 2023. The summary weighted mean difference (WMD) and 95% confidence interval (CI) were estimated using the DerSimonian–Laird random-effects model. Results: In this meta-analysis of eight RCTs, interventions to promote water intake or to substitute water for other beverages as compared to the control group resulted in a summary WMD of −0.33 kg (95% CI = −1.75–1.08, I² = 78%) for body weight, −0.23 kg/m² (95% CI = −0.55–0.09, I² = 0%) for body mass index (BMI), and 0.05 cm (95% CI = −1.20–1.30, I² = 40%) for waist circumference (WC). Among RCTs substituting water for artificially sweetened beverages, summary WMD was 1.82 kg (95% CI = 0.97–2.67, I² = 0%) for body weight and 1.23 cm (95% CI = −0.03–2.48, I² = 0%) for WC. Conversely, among RCTs substituting water for sugar-sweetened beverages, summary WMD was −0.81 kg (95% CI = −1.66–0.03, I² = 2%) for body weight and −0.96 cm (95% CI = −2.06–0.13, I² = 0%) for WC. Conclusions: In conclusion, water intake may not significantly impact adiposity among overweight and obese individuals. However, replacing sugar-sweetened beverages with water might offer a modest benefit in inducing weight loss.

Background/Objectives: Communicating genetic risk has been proposed as a motivational tool for weight control, but evidence remains limited. Methods: We conducted a randomized controlled trial among 53 overweight or obese young adults in South Korea. Participants were randomized to receive either their obesity-related genetic results with tailored lifestyle recommendations (intervention, n = 27) or genetic results limited to beauty traits (control, n = 26). Body weight and body composition were measured at baseline, 6 months, and 18 months. Primary outcome was change in body weight; secondary outcomes included body fat mass, body fat percentage, and skeletal muscle mass. Analyses used general linear and mixed models. Exploratory analyses examined effects among participants carrying ≥2 risk alleles across FTO, MC4R, and BDNF. Results: Overall, body weight and composition changes did not differ significantly between groups at 6 or 18 months. In exploratory subgroup analysis conducted among participants with obesity-related genetic risk, body weight increased in the intervention group (model-adjusted mean change, 2.68 kg; 95% CI, 2.28 to 3.09) but decreased in the control group (model-adjusted mean change, −11.58 kg; 95% CI, −1.99 to −1.18) over 18 months. Among participants with obesity-related genetic risk in the intervention group, those who reported behavior changes after receiving their genetic test results experienced modest weight reductions over 6 months compared with non-changers. Conclusions: Disclosure of obesity-related genetic information with tailored advice did not improve weight outcomes overall, but may benefit a subset of genetically susceptible individuals. Larger trials are warranted.