Association Between Body Composition and Cardiometabolic Outcomes

作者：Matthias Jung, Marco Reisert, H. Rieder, Susanne Rospleszcz, Michael T. Lu, Fabian Bamberg, Vineet K. Raghu, Jakob Weiss · 发表于：Annals of Internal Medicine · 年份：2025 · DOI：10.7326/annals-24-01863 · 被引用次数：4 · 研究领域：Nutrition and Health in Aging、Body Composition Measurement Techniques、Cardiovascular Disease and Adiposity

BACKGROUND: Current measures of adiposity have limitations. Artificial intelligence (AI) models may accurately and efficiently estimate body composition (BC) from routine imaging. OBJECTIVE: To assess the association of AI-derived BC compartments from magnetic resonance imaging (MRI) with cardiometabolic outcomes. DESIGN: Prospective cohort study. SETTING: UK Biobank (UKB) observational cohort study. PARTICIPANTS: [SD, 4.2]; 52.8% female) who underwent whole-body MRI. MEASUREMENTS: An AI tool was applied to MRI to derive 3-dimensional (3D) BC measures, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SM), and SM fat fraction (SMFF), and then calculate their relative distribution. Sex-stratified associations of these relative compartments with incident diabetes mellitus (DM) and major adverse cardiovascular events (MACE) were assessed using restricted cubic splines. RESULTS: Adipose tissue compartments and SMFF increased and SM decreased with age. After adjustment for age, smoking, and hypertension, greater adiposity and lower SM proportion were associated with higher incidence of DM and MACE after a median follow-up of 4.2 years in sex-stratified analyses; however, after additional adjustment for BMI and waist circumference (WC), only elevated VAT proportions and high SMFF (top fifth percentile in the cohort for each) were associated with increased risk for DM (respective adjusted hazard ratios [aHRs], 2.16 [95% CI, 1.59 to 2.94] an...