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Aging is a complex process that affects various organs differently within an individual, leading to a wide range of age-related diseases. Traditional methods of measuring aging often provide a single measure for the whole body, which can be difficult to interpret given the complexity of human aging trajectories. This study introduces a novel framework for modeling organ health and biological aging using plasma proteomics, offering a more nuanced understanding of aging at the organ level.

Methodology

The researchers utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, they analyzed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. The study discovered that nearly 20% of the population shows strongly accelerated aging in one organ, and 1.7% are multi-organ agers. Accelerated organ aging confers a 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs.

Key Findings

The study found that individuals with accelerated heart aging have a 250% increased risk of heart failure, and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181, the current best blood-based biomarker for AD. The models linked vascular calcification, extracellular matrix alterations, and synaptic protein shedding to early cognitive decline. The researchers introduced a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Future Directions

The study opens several avenues for future research. Expanding proteomic coverage could provide more biological information, including cell and organ-specific splice isoforms and posttranslational modifications. Future genomic studies should identify which organ-specific aging proteins are causal drivers of aging, given that multiple plasma proteins have been shown to modulate aging phenotypes directly. Additionally, multimodal aging and disease prediction models may have similar impacts on other diseases, as the study demonstrated that the approach added value to established biomarkers of AD. The minimally invasive nature of the approach makes it suitable for understanding the effects of health interventions, such as lifestyle modifications and drug therapies, at the organ level.

Conclusion

This study provides a novel framework for modeling organ health and biological aging using plasma proteomics. The findings highlight the importance of organ-specific aging in predicting disease risk and mortality. The approach offers a comprehensive resource for understanding the molecular changes associated with aging across multiple organs, paving the way for improved patient care, preventative medicine, and drug development. By leveraging large-scale plasma proteomics and machine learning, this method enables noninvasive measurement of organ health and aging in living people, offering significant potential for future research and clinical applications.

Read Paper│Nature