Preprint
Cellular Aging Signatures in the Plasma Proteome Record Human Health and Disease
Daisy Yi Ding2026
Veronica Augustina BotKenneth Chen
Low Citations
1 citations · Physiology
Open Access

TLDR

A blood test can reveal how different parts of your body are aging, and these patterns can help predict your risk for diseases and how long you might live.

Summary

1 Study Aim

The study aims to find out if proteins in blood plasma can be used to measure how different types of cells in the human body age. The researchers want to see if these cell-specific aging patterns can predict who is more likely to get certain diseases or die earlier. They also hope to create a new way to track aging in people by looking at many cell types at once. This research wants to see if a blood test can show how fast different parts of your body are getting older and if that can help predict health problems.

2 Study Design

The researchers measured over 7,000 proteins in the blood plasma of more than 60,000 people from three large groups. They used machine learning (a type of computer modeling) to estimate the biological age of over 40 different cell types, such as nerve, immune, and muscle cells. The team compared these cell-specific aging estimates to people's actual ages and tracked their health outcomes, including disease and death, over up to 15 years. They also checked how stable these aging patterns were over time and tested their findings in different groups and with different protein measurement technologies. The study looked at blood samples from thousands of people to see if computer models could spot which body parts are aging faster and if that links to future health.

3 Findings

The study reveals that people age differently at the cellular level, with some cell types aging faster than others in each person. About a quarter of people had accelerated aging in just one cell type, while a small percentage showed widespread fast aging across many cell types. Certain genetic factors, like APOE gene variants, affected aging in opposite ways in brain and immune cells. The researchers found that these cell-specific aging patterns could predict who would develop diseases like Alzheimer's, ALS, lung cancer, and diabetes, sometimes years before symptoms appeared. For example, fast aging in muscle cells strongly predicted ALS, and fast aging in astrocytes (a type of brain cell) greatly increased Alzheimer's risk, especially in people with high genetic risk. Having youthful immune or nerve cells was linked to longer life. The team created a combined risk score that accurately predicted mortality across different groups and testing methods. The study suggests that tracking cellular aging through blood proteins could help identify people at risk for disease and guide prevention or treatment strategies. The research found that measuring how fast different cell types age in your blood can help predict your chances of getting sick or dying, and keeping some cells young may help you live longer.

Abstract

Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types-spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins-using over 7,000 plasma proteins measured in 60,000 individuals across three cohorts, comprising the largest human plasma proteomics aging study to date. Individuals showed heterogeneous aging profiles, with 20-25% exhibiting accelerated aging in a single cell type and 1-3% across ten or more cell types. APOE genotype showed antagonistic aging effects in different cell types: APOE4 carriers exhibited older astrocytes but younger macrophages, while APOE2 carriers showed the inverse. Cellular aging signatures were uniquely associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Amyotrophic lateral sclerosis (ALS) showed the strongest association with skeletal myocyte aging (hazard ratio = 12.7 for extreme accelerated versus youthful aging). In Alzheimer's disease (AD), prevalent cases showed accelerated aging across multiple neural and peripheral cell types, with extreme astrocyte aging conferring AD risk comparable to APOE4 carrier status. Moreover, extreme astrocyte aging increased AD risk in APOE4/4 carriers threefold, while youthful astrocytes strikingly reduced risk. Beyond neurodegeneration, respiratory cell aging identified smokers at 58% higher lung cancer risk, and myeloid aging identified normoglycemic individuals at higher diabetes risk. Both specific cellular vulnerabilities and cumulative aging burden influenced survival, wherein youthful immune or neuronal profiles were protective. A polycellular aging risk score provided robust mortality risk stratification across platforms and cohorts. These findings establish a framework for quantifying biological aging at the cellular resolution using plasma proteomics, revealing heterogeneity in aging trajectories and their impact on disease susceptibility and resilience.