复旦大学校长金力院士最新Nature子刊：利用AI精准预测表观遗传年龄与衰老相关疾病风险

Core Viewpoint - The article discusses the development of a robust computational framework called MAPLE for predicting methylation age and disease risk, which addresses the limitations of traditional epigenetic clocks and has significant potential for clinical applications in aging and health management [3][4][26]. Group 1: Background and Need for MAPLE - Aging is characterized by increased morbidity and declining quality of life, creating significant social and economic burdens [2]. - Breakthrough research indicates that interventions like caloric restriction and epigenetic reprogramming can extend lifespan and healthspan, but precise quantification of biological age and aging rate is necessary for clinical application [2]. - DNA methylation (DNAm) changes are key markers of aging, with whole-genome DNAm serving as a potential biological age assessment tool [2]. Group 2: MAPLE Development and Performance - MAPLE employs pairwise learning to determine the relative relationship between two DNA methylation profiles regarding age or disease risk, effectively reducing technical biases while identifying biological signals related to aging or disease [4][9]. - In 31 benchmark tests, MAPLE achieved a median absolute error of 1.6 years, outperforming five other competitive methods [4][12]. - MAPLE demonstrated excellent performance in disease risk assessment, with an average area under the curve (AUC) of 0.97 for disease identification and 0.85 for pre-disease state detection [4][19]. Group 3: Advantages of MAPLE - Traditional epigenetic clocks face challenges such as batch effects, which significantly hinder their clinical application [7][26]. - MAPLE's innovative approach focuses on relative relationships rather than absolute predictions, allowing for better comparability across diverse datasets [9][26]. - The two-stage training process of MAPLE enhances sample size and reduces overfitting risks, contributing to its superior performance [9][12]. Group 4: Clinical Applications and Future Prospects - MAPLE not only accurately predicts biological age but also serves as a health risk warning system, providing valuable time for early intervention [20][28]. - The framework is expected to play a crucial role in personalized anti-aging interventions, early disease risk screening, and understanding the biological mechanisms of aging [28]. - As MAPLE continues to be validated, it may become a standard component of health assessments, aiding in the management of healthy aging and offering new hope for age-related health challenges [28].