Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on CT scans

AimsThe distribution of abdominal adipose depots and their mechanistic links to type 2 diabetes remain incompletely understood. This study elucidated the relationship between type 2 diabetes presence and quantitative abdominal imaging traits, including hepatic steatosis, liver and spleen size, and adipose distribution, using unenhanced computed tomography (CT) scans from a large-scale, racially diverse, disease-focused medical biobank. Materials and MethodsDeep learning algorithms were applied to abdominal CT scans to automatically quantify image-derived phenotypes, including spleen-hepatic attenuation difference (SHAD) for hepatic steatosis, liver and spleen volumes (LV and SV, respectively), visceral and subcutaneous adipose tissue (VAT and SAT, respectively), and visceral-to-subcutaneous fat ratio (VSR). ResultsDiabetic individuals demonstrated a greater degree of hepatic steatosis and central adiposity than those without diabetes. Liver attenuation was lower (47.6 vs. 52.4 Hounsfield units (HU); lower values indicate greater steatosis), SHAD was higher (-5.41 vs. -8.41 HU; more positive values indicate greater steatosis), and steatosis prevalence increased (38.4% vs. 21.4%) (all p<2.2x10-{superscript 1}). VSR was also elevated (0.64 vs. 0.54, p=5.86x10-{superscript 1}3). These trends remained significant after stratification by sex. Multivariate analyses revealed independent associations of diabetes with SHAD (OR 1.04), LV (OR 1.59), SV (OR 3.95), VAT (OR 1.23), SAT (OR 1.05), and VSR (OR 2.27), after adjusting for age, sex, race, and BMI. ConclusionsHepatic steatosis, hepatomegaly, and visceral adiposity on CT imaging are predictive of type 2 diabetes presence. Notably, VSR showed a stronger association with diabetes than BMI, underscoring how body-fat distribution, rather than overall adiposity, more accurately reflects metabolic disease risk.