A Novel Longitudinal Proteomic Aging Index Predicts Mortality, Multimorbidity, and Frailty in Older Adults

Previous studies have developed proteomic aging clocks to estimate biological age and predict mortality and age-related diseases. However, these earlier clocks were based on cross-sectional data, capturing only the cumulative aging burden at a single time point but were unable to reflect the dynamic trajectory of biological aging over time. We constructed a longitudinal proteomic aging index (LPAI) using data from 4684 plasma proteins measured by the SomaScan 5K Array across three visits in the Atherosclerosis Risk in Communities (ARIC) study (ages 67–90 at last visit). Our two-step approach applied functional principal component analysis (FPCA) to capture protein-level change patterns over time, followed by elastic net penalized Cox regression for protein selection. LPAI was constructed in a randomly selected training set of ARIC participants (N = 2954), tested among the remaining ARIC participants (N = 1267), and validated externally in Multi-Ethnic Study of Atherosclerosis (MESA) participants (N = 3726, ages 53–94 at last exam). Using Cox proportional hazards model, higher LPAI was associated with increased all-cause mortality (HR = 2.50, 95% CI: [2.15, 2.92] per SD), CVD mortality (HR = 1.79, 95% CI: [1.34, 2.39] per SD), and cancer mortality (HR = 1.96, 95% CI: [1.45, 2.64] per SD) risk in ARIC, with statistically significant and directionally consistent associations also observed in MESA. Additionally, higher LPAI was associated with increased multimorbidity and frailty. This study demonstrates the feasibility of developing biological aging measures from longitudinal proteomics data and supports LPAI as a biomarker for aging-related health risks.