Artificial intelligence modeling of biomarker‐based physiological age: Impact on phase 1 drug‐metabolizing enzyme phenotypes

Abstract

Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning‐based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA‐M1 and PA‐M2. PA‐M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA‐M2 had blood and urine‐derived variables as predictors. Activity phenotypes for cytochrome‐P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N‐acetyltransferase‐2 (NAT‐2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18–79 years). The PA‐M1 and PA‐M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA‐M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA‐M2. The models also performed satisfactorily in independent validation. Model‐predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT‐2 activity. Telomere attrition was associated with greater PA‐M1 and PA‐M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug‐metabolizing enzyme phenotypes.