Plasma proteomic signatures of aging at the cellular level could be associated with future disease and mortality and may help identify higher-risk patient groups. Researchers developed risk models that estimated the biological age of more than 40 cell types and found that accelerated aging signatures were linked to disease status and incident disease over a follow-up of 15 years.
In a study, the researchers analyzed plasma proteomic data from 60,542 patients across the Global Neurodegeneration Proteomics Consortium, UK Biobank, and the 1946 National Survey of Health and Development. Using measurements of more than 7,000 plasma proteins, they mapped circulating proteins to putative cellular origins and trained machine-learning models to estimate cell type–specific biological age. The researchers then examined whether those aging signatures were associated with disease and mortality outcomes.
They noted substantial variability in cellular aging across the study participants. Among healthy participants, approximately one-quarter showed accelerated aging in a single cell type, whereas 1% to 3% demonstrated accelerated aging in 10 or more cell types,suggesting that aging occurs asynchronously across cellular and organ systems rather than uniformly throughout the body.
Among neurodegenerative outcomes, astrocyte aging was the strongest cellular predictor of incident Alzheimer's disease (AD). The patient who had extreme astrocyte aging had a 12.6-fold higher likelihood of developing AD compared with patients who had youthful astrocytes. The association remained evident across apolipoprotein E (APOE) genotypes. The participants homozygous for APOE4, cumulative AD incidence during the 15 years of follow-up was about 38% among those with extreme astrocyte aging compared with 13% among those with normal astrocyte aging. No AD cases were observed among the 23 APOE4 homozygotes with youthful astrocyte aging.
The researchers also found that astrocyte aging provided risk stratification beyond established genetic risk factors. The excess risk associated with extreme astrocyte aging was comparable to APOE4 carrier status and exceeded that associated with AD polygenic risk scores and chronologic age. Youthful astrocyte profiles were associated with lower AD risk across the genotypes.
The strongest disease-specific association involved skeletal myocyte aging and amyotrophic lateral sclerosis (ALS). The patients who had extreme skeletal myocyte aging had a 12.7-fold higher likelihood of developing ALS compared with patients who had youthful skeletal myocyte aging. Extreme cardiomyocyte aging was also associated with future ALS risk, with a 6.6-fold higher likelihood among extreme vs youthful cell ages. According to the researchers, the association persisted when they restricted their analyses to cases diagnosed more than 3 years following blood collection.
Beyond neurodegenerative disease, cell-specific aging signatures were associated with future cancer and chronic disease. Extreme aging of alveolar type 2 cells and respiratory epithelial cells showed the strongest prognostic value for lung cancer. Current smokers with extreme aging in both respiratory cell populations experienced higher lung cancer risk compared with smokers alone. Myeloid lineage aging was the strongest predictor of incident type 2 diabetes, while other cellular aging signatures were associated with chronic obstructive pulmonary disease, heart failure, stroke, and lymphoma.
The researchers also reported an association between cellular aging burden and survival. The patients with extreme aging across more than 20 cell types had approximately 34% survival over 15 years compared with about 90% survival among patients without extreme cellular aging. Youthful immune and neuronal aging signatures were associated with more favorable survival outcomes. The researchers developed a polycellular aging risk score that stratified mortality risk across the cohorts and proteomic platforms.
Several limitations should be considered, according to the researchers. Cellular age estimates were inferred from plasma protein profiles rather than measured directly within tissues. Cell-type assignments were based on transcriptomic reference data and may not have fully reflected the complexity of protein production and release into circulation. In addition, the study was observational and therefore could not establish causal relationships between accelerated cellular aging and disease. The cohorts were also predominantly older and White, which may limit generalizability to younger and more diverse populations.
Overall, the findings suggested that plasma proteomics may provide a noninvasive approach for assessing cell type–specific biological aging and identifying patients at elevated risk for age-related disease.
"These findings establish a framework for quantifying human physiology at cellular resolution, revealing heterogeneous aging trajectories and their impact on disease susceptibility and resilience," wrote lead study author Daisy Yi Ding, of the Department of Neurology and Neurological Sciences at the Stanford University School of Medicine, and colleagues.
Full disclosures of the study authors are available in the article.
Source: Nature Medicine
