Machine learning approaches can be used to create aging clocks from near any set of biological data collected from people of various ages. The techniques are well established and many new clocks are published every year. A clock is really an age predictor (or a mortality predictor, or a predictor of some other outcome) trained on a single dataset. When the clock algorithm is applied to any given individual not in that data set, it is thought that the predicted age or mortality risk or other outcome is some reflection of biological age. It is hard to validate this proposition, as there is very little concrete connection between any easily measured biomarker and mechanisms of aging, and indeed all too little consensus on how to measure biological age in the first place. To my eyes more effort should go towards understanding the clocks we have and less to producing new clocks.
Biological aging is a key determinant of liver disease and mortality, but there is little evidence on noninvasive index for assessment of liver biological aging. We developed the Liver Aging Index (LAI) in the China Kadoorie Biobank (CKB, N = 21,629) using Cox-Gompertz proportional hazards model. The LAI incorporated three clinical factors (body mass index, systolic and diastolic blood pressure), eight plasma biomarkers (glucose, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, and γ-glutamyl transpeptidase), and two imaging biomarkers (fat attenuation parameter and liver stiffness measurement).
External validation was conducted in the National Health and Nutrition Examination Survey (NHANES; N = 3412) and the VCTE-Prognosis cohort (N = 12,170, 16 global centers). Across all cohorts, the LAI demonstrated strong discrimination for all-cause mortality (area under the receiver operating characteristic curve: 0.764 in NHANES; 0.759 in VCTE-Prognosis), outperforming chronological age. Liver aging acceleration (LAA), defined as the difference between LAI and chronological age, was associated with substantially elevated risks: each 1 standard deviation increase in LAA conferred a 22%-85% higher risk of all-cause mortality and a 34%-170% higher risk of liver-related event or mortality.
Using genetic instruments identified in CKB, we found genetic predisposition to accelerated liver aging was associated with higher risks of cirrhosis and liver cancer (hazard ratios = 3.94 and 7.82), further validated in Biobank Japan. Integrating genetics and proteomics revealed novel pathophysiological involvement of amyloid-beta clearance pathway and amyloid precursor protein in liver aging. These findings demonstrate the feasibility of a noninvasive, liver-specific biological aging index and provide new insights into mechanisms underlying liver aging.
Link: https://doi.org/10.1111/acel.70565
View the full article at FightAging
