Coffee Intake and Obesity: A Meta-Analysis.
Ariel LeeWoobin LimSeoyeon KimHayeong KhilEugene CheonSoobin AnSungEun HongDong Hoon LeeSeok-Seong KangHannah OhNaNa KeumChung-Cheng HsiehPublished in: Nutrients (2019)
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.
Keyphrases
- weight gain
- body mass index
- insulin resistance
- case control
- systematic review
- weight loss
- metabolic syndrome
- type diabetes
- physical activity
- adipose tissue
- high fat diet induced
- magnetic resonance imaging
- skeletal muscle
- electronic health record
- computed tomography
- machine learning
- middle aged
- ionic liquid
- deep learning
- open label
- big data
- glycemic control
- artificial intelligence