A full life cycle biological clock based on routine clinical data and its impact in health and diseases
Kai Wang, Fei Liu, Wei Wu, Changxi Hu, Xian Shen, Meihao Wang, Gen Li, Fanxin Zeng, Li Liu, Io Nam Wong, Sian Liu, Zixing Zou, B Li, Jinghang Li, Xiaoying Huang, Shengwei Jin, Zhenqi Li, Huiyan Xu, Gang Chen, Xiaodong Chen, Ying‐Jie Zhu, Ping Li, Zhe Feng, Winston Wang, Linling Cheng, Mingqi Yang, Qiang Hou, Wen-Yang Lu, Yiwen Sun, Kun Li, Tian Zhong, Zhuo Sun, Yun Yin, Alexandre Loupy, Eric K. Oermann, Xiangmei Chen, Kang Zhang, Xiangmei Chen, Kang Zhang, Manson Fok, Taiwa Hou, Lei Guo, Miaosang Xu, Dan Yao, Chengjing Wang, Pingzhen Yang, Caiwen Ou, Xueqiang Wang, Aimin Wu, Xiaodong Chen, Xiaodong Chen, Winston Wang, Xiangmei Chen, Xiangmei Chen
Abstract
Aging research has primarily focused on adult aging clocks, leaving a critical gap in understanding a biological clock across the full life cycle, particularly during infancy and childhood. Here we introduce LifeClock, a biological clock model that predicts biological age across all life stages using routine electronic health records and laboratory test data. To enhance individualized predictions, we integrated virtual patient representations from 24,633,025 heterogeneous longitudinal clinical visits across 9,680,764 individuals and projected them into a latent space. Our approach leverages EHRFormer, a time-series transformer-based model, to analyze developmental and aging dynamics with high precision and develop accurate biological age clocks spanning infancy to old age. Our findings reveal distinct biological clock patterns across different life stages. The pediatric clock is strongly associated with children's development and accurately predicts current and future risks of major pediatric diseases, including malnutrition, growth and developmental abnormalities. The adult clock is strongly associated with aging and accurately predicts current and future risks of major age-related diseases, such as diabetes, renal failure, stroke and cardiovascular diseases. This work therefore distinguishes pediatric development from adult aging, establishing a novel framework to advance precision health by leveraging routine clinical data across the entire lifespan.