Organ aging signatures in the plasma proteome track health and disease

TLDR

Animal studies show aging varies between individuals and organs, but it is unclear whether this holds in humans or how it affects age‑related diseases. The authors aim to develop a simple, interpretable plasma‑proteomics method to study organ aging and predict associated diseases. They measured organ‑specific plasma protein levels and applied machine‑learning models to estimate organ age across 5,676 adults in five independent cohorts, covering 11 major organs. They found that ~20 % of people exhibit accelerated aging in a single organ and 1.7 % are multi‑organ agers, with accelerated organ aging linked to 20–50 % higher mortality, increased heart‑failure risk (250 %) and independent prediction of Alzheimer’s progression comparable to plasma pTau‑181, while their models associate vascular calcification, extracellular‑matrix changes and synaptic protein shedding with early cognitive decline.

Abstract

Abstract Animal studies show aging varies between individuals as well as between organs within an individual 1–4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.