Network Modeling of Biomarker Systems in Liver Steatosis and Fibrosis.

Abstract

Metabolic dysfunction-associated fatty liver disease causes hepatic fat accumulation (steatosis) and fibrosis, which can be measured with ultrasound elastography imaging. The dependencies of elastography-derived hepatic steatosis and fibrosis measures with chronic inflammation, disease states, and physiological determinants of drug dosing (PDODD) were assessed. Liver elastography data for n = 5494 participants (50% female, 12-80 years) were obtained from the National Health and Nutrition Examination Survey. Controlled attenuation parameter (CAP) and median liver stiffness (LSM) elastography metrics were used to assess steatosis and fibrosis, and their associations with over 50 key organ systems, disease, and PDODD biomarkers were evaluated with statistical regression, ensemble, and Bayesian learning methods. CAP and LSM increased with age and were greater in males, active liver disease, active hepatitis C, and diabetes or prediabetes. LSM was greater in the presence of congestive heart failure and dialysis. The inflammatory markers C-reactive protein (CRP) and ferritin, body surface area, and hepatic R-value were greater in steatosis and fibrosis. Plasma volume, neutrophil, red blood cell, and platelet counts were greater in steatosis. Drug-induced liver injury index was lower in steatosis and greater in fibrosis. Albumin levels and platelet counts were lower, but the urine albumin-to-creatine ratio was greater in fibrosis. Ensemble learning identified interactions among BMI, age, CRP, ferritin, and liver enzymes contributing to steatosis and fibrosis. Bayesian networks were used to identify directed acyclic graph structures for steatosis and fibrosis. Elastography-derived measures may be useful for individualizing dosing regimens in the presence of metabolic comorbidities presenting dose-selection challenges.