A large study of plasma proteins has shown that patterns of aging in certain types of cells may help determine which people are more susceptible to disease and who are more resilient.
He studies: Plasma proteomic signatures of cellular senescence predict human disease. Image credit: Katrina Kuhn/Shutterstock
A recent study published in the journal Natural medicine He suggests that estimating cell type-specific signs of aging from plasma proteins in the blood could help scientists predict disease risks.
By analyzing more than 7,000 plasma proteins in more than 60,000 individuals, they found that signs of accelerated aging in certain types of cells were linked to an increased risk of disease. For example, extreme astrocyte senescence has increased the risk of developing Alzheimer’s disease (advertisement) among people with the APOE4 genotype. On the other hand, young nerve and immune cells appear to have a protective effect, improving survival outcomes. These findings suggest that identifying the protein could one day help researchers stratify risk and explore more personalized approaches to prevention and treatment.
As we age, the body’s cells undergo several changes that may alter their structure and function. These changes may also predispose individuals to chronic diseases. Scientists are now developing strategies that could enable early identification of individuals at risk. If detected early, patients can get timely treatment before the disease progresses to advanced stages. Such proactive approaches may improve disease prognosis and ultimately enhance overall well-being.
By exploring the biological mechanisms that drive aging, researchers can identify molecular targets for interventions that can reduce the risk of age-related diseases. Cellular-resolution epigenetic and transcriptional clocks can measure cellular aging by analyzing gene activity and DNA modifications, but they often require tissue biopsies, laboratory samples, or animal experiments.
About the study
In this study, researchers investigated whether plasma protein analysis could be used to estimate which senescent cell populations are associated with disease. To do this, they analyzed more than 7,000 proteins in plasma samples from 60,542 individuals using machine learning models. They then linked the cell types to their corresponding plasma proteins using the Human Protein Atlas. The analysis helped researchers estimate the biological age of more than 40 cell types across the nervous, immune, endocrine and skeletal muscle systems. They also estimated the risk of death over 15 years of follow-up.
To confirm the reliability of the results, the researchers used aging clocks derived from two separate plasma protein analysis platforms. The SomaScan platform measured 7,289 proteins, while Olink evaluated 2,923 protein markers. They then evaluated these models across three large populations: the World Neurodegeneration Proteomics Consortium (GNBC14,281 respondents), 1946 National Health and Development Survey (chant1,803 participants), and the UK Biobank (KB44,458 participants).
The researchers used the Knight Alzheimer’s Disease Research Center (Alzheimer’s disease research Center”>KADRC) and UKB data to train models for SomaScan and Olink analysis, respectively. After quality assessment, 43 SomaScan and 48 Olink cell models were retained for final analysis. They also developed a multicellular senescence risk score (Pars) to stratify mortality risks based on cellular senescence identified using these proteomic datasets and platforms.
The team calculated “age gaps” for individual cell types based on the difference between the expected biological age of a given cell and the model-estimated biological age expected for an average individual at the same chronological age. They then assessed whether these age gaps were associated with disease status. They also compared cell type-specific biological age with clinical dementia classification (Council for Development and Reconstruction) and the preclinical Alzheimer’s cognitive complex (PACC) scores to identify cell types with the strongest relationships with dementia severity and cognitive decline.
results
The researchers found that cell type-specific signs of aging were associated with disease risk. Cellular senescence also predicted risk of death over the 15-year follow-up period. Compared with apolipoprotein E3 (APOE3) carriers, subjects with the APOE4 genotype showed older astrocytes and younger macrophages. The presence of old-age astrocytes tripled the risk of developing Alzheimer’s disease among participants with two APOE4 alleles. In contrast, young astrocyte populations had reduced disease risk.
Alzheimer’s disease has been associated with accelerated aging in many types of cells throughout the body, including brain cells involved in insulating and supporting nerves, cells lining the intestines, and pancreatic cells involved in metabolism and insulin production. Accelerated senescence of oligodendrocyte precursor cells and inhibitory neurons showed the strongest correlations with CDR scores. The team observed similar associations using blood pTau-217 levels and PACC scores, particularly for oligodendrocyte precursors.
The researchers observed similar results related to the musculoskeletal and respiratory systems. Individuals with old skeletal muscle cells were 12.7 times more likely to develop amyotrophic lateral sclerosis (if) than those with young skeletal muscle cells. Among current smokers, severe senescence in both alveolar type 2 cells and the broader respiratory epithelial lineage increased lung cancer risk by 58% compared with current smoking alone.
Cellular senescence also influenced survival outcomes, with younger immune and neural cells associated with better survival. Individuals with normal cellular senescence had a survival rate of about 90% over 15 years. People with more than 20 very aged cell types had much lower survival rates, about 34%.
However, the authors note that the findings require validation in a broader population, as the models relied on cell type annotations in the Human Protein Atlas, plasma proteins may not always directly reflect cellular gene activity, and the study populations were predominantly older and Caucasian.
Conclusions
The results suggest that plasma proteomic signatures of cellular aging could help scientists determine disease susceptibility and survival outcomes. This could help researchers improve risk stratification in the future, study disease mechanisms, and identify groups at risk for further monitoring or research.
Based on the findings, strategies that can prevent or halt cell aging could help reduce the burden of disease and improve longevity. If confirmed across broader and more diverse populations, clinicians could eventually incorporate protein identification tests into future disease risk stratification or targeted surveillance strategies to enable risk stratification and ultimately improve the standard of care for affected individuals.
