Growing evidence highlights the importance of body composition (BC), including bone, muscle, and adipose tissue (AT), as a critical biomarker for cardiometabolic risk stratification. However, conventional methods for quantifying BC components using medical images are hindered by labor-intensive workflows and limited anatomical coverage. This study developed BioCompNet—an end-to-end deep learning workflow that integrates dual-parametric magnetic resonance imaging (MRI) sequences (water/fat) with a hierarchical U-Net architecture to enable fully automated quantification of 15 biomechanically critical BC components. BioCompNet targets 10 abdominal compartments (vertebral bone, psoas muscles, core muscles, subcutaneous AT [SAT], superficial SAT, deep SAT, intraperitoneal AT, retroperitoneal AT, visceral AT, and intermuscular AT [IMAT]) and 5 thigh compartments (femur, muscle, SAT, IMAT, and vessels). The workflow was developed on 8,048 MRI slices from a community-based cohort (n = 503) and independently validated on 240 MRI slices from a tertiary hospital (n = 30). The model’s performance was benchmarked against expert annotations. On internal and external validation datasets, BioCompNet achieved average Dice similarity coefficients of 0.944 and 0.938 for abdominal compartments and 0.961 and 0.936 for thigh compartments, respectively. Excellent interreader reliability was observed (intraclass correlation coefficient ≥ 0.881) across all quantified features, and IMAT quantification showed a strong linear trend (P_trend < 0.001) compared to physician-rated assessments. The workflow substantially reduced processing time from 128.8 ± 5.6 to 0.12 ± 0.001 min per case. By enabling rapid, accurate, and comprehensive volumetric analysis of BC components, BioCompNet establishes a scalable framework for precision cardiometabolic risk assessment and clinical decision support.

Non-alcoholic fatty liver disease (NAFLD) is a hepatic metabolic syndrome arising from lipid metabolic imbalance, with its prevalence increasing globally. In this study, we observed a significant up-regulation of interferon regulatory factor 8 (IRF8) in the liver of NAFLD model mice and patients. Overexpression of IRF8 induced lipid accumulation in the mouse primary hepatocytes. Mice with adeno-associated virus-mediated IRF8 overexpression exhibited hepatic steatosis due to up-regulated peroxisome proliferator-activated receptor γ (PPARγ) expression and increased fatty acid uptake and lipogenesis. In vitro, small interfering RNA-mediated IRF8 knockdown attenuated triglyceride accumulation by dampening PPARγ expression through transcriptional inhibition of brain and muscle ARNT-like 1. The PPARγ-specific antagonist GW9662 abolished the effect of IRF8 overexpression. Furthermore, adeno-associated virus-mediated IRF8 knockdown in the mouse liver markedly alleviated hepatic steatosis and obesity-related metabolic syndrome. These findings indicate that IRF8 plays a vital role in modulating hepatic lipid metabolism in a PPARγ-dependent manner and provide a previously unknown insight into NAFLD therapeutic strategies.

A number of recent studies revealed that DNA methylation plays a central role in the regulation of lipid metabolism. DNA methylation modifications are important regulators of transcriptional networks that do not affect the DNA sequence and can translate genetic variants and environmental factors into phenotypic traits. Therefore, elucidating the factors that underlie inter-individual DNA methylation variations gives us an opportunity to predict diseases and interfere with the establishment of aberrant DNA methylation early. In this review, we summarize the findings of DNA methylation-related studies focused on unravelling the potential role of genetic and environmental factors in DNA methylation and the regulatory effect of DNA methylation on gene expression in lipid metabolism.