A new multi-omics metric called PAAG (Personalized-context-Aware Age Gap) accounts for age-stratified variations in aging patterns, improving prediction of chronic disease risk and clinical outcomes beyond conventional biological age measurements. The underlying model, AOE-Net, uses contrastive learning on healthy population data to distinguish true biological aging signals from technical noise in omics data.
Key Points
- PAAG outperforms conventional age gap metrics across cancer, atherosclerosis, osteoporosis outcomes
- AOE-Net reconstructs individual aging trajectories by separating biological from technical variation
- Immune-response pathways emerge as shared molecular drivers of accelerated aging and disease
Longevity Analysis
Accurate measurement of individual aging trajectories is foundational to understanding who needs intervention and when. Traditional biological age clocks fail to account for how aging patterns diverge across age groups, producing misleading risk assessments. By contextualizing aging acceleration within age-stratified patterns and identifying immune dysfunction as a mechanistic hub, PAAG enables more precise decoding of the body's aging signals—the prerequisite for effective optimization. This approach also reveals that chronic disease risk flows from detectable molecular patterns in immune regulation, suggesting that interventions targeting immune-response pathways may address multiple disease pathways simultaneously rather than treating conditions in isolation.
Original published by Wiley Aging Cell, by Feng‐Ao Wang, Tao Zeng, Chunchun Yuan, Hongyu Wang, Yule Yu, Enjin Deng, Yao Wang, Jiangxun Ji, Jiarui Cui, Dezhi Tang, Ruikun He, Yongjun Wang, Yixue Li .