Novel machine learning framework for multidimensional biological age estimation reveals heterogeneous aging of organ systems.

Qi Yu, Lijuan Da, Qian Ma, Yushu Huang, Yue Dong, Yuan Liu, Xiaoyu Li, Xifeng Wu, Zilin Li, Wenyuan Li

Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. · pubmed

Paper

Abstract

Existing biological age (BA) models often oversimplify aging's complexity, offering single-dimensional metrics. However, these fail to capture the critical heterogeneity of aging across organs.. This study aims to develop a machine learning-based unified framework to assess and interpret multi-organ biological aging comprehensively.

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Research summary

The paper proposes a machine learning framework to assess multi-organ biological aging. This research is relevant as it addresses the complexity of aging and aims to provide a more comprehensive understanding of biological age, which is crucial for advancing longevity research.

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