Date: September 2020.
Source: Nature Metabolism, Volume 2, 946–957 (2020). https://doi.org/10.1038/s42255-020-00270-x.
Abstract: Not all individuals age at the same rate. Methods such as the ‘methylation clock’ are invasive, rely on expensive assays of tissue samples and infer the ageing rate by training on chronological age, which is used as a reference for prediction errors. Here, we develop models based on convoluted neural networks through training on non-invasive three-dimensional (3D) facial images of approximately 5,000 Han Chinese individuals that achieve an average difference between chronological or perceived age and predicted age of ±2.8 and 2.9 yr, respectively. We further profile blood transcriptomes from 280 individuals and infer the molecular regulators mediating the impact of lifestyle on the facial-ageing rate through a causal-inference model. These relationships have been deposited and visualized in the Human Blood Gene Expression—3D Facial Image (HuB-Fi) database. Overall, we find that humans age at different rates both in the blood and in the face, but do so coherently and with heterogeneity peaking at middle age. Our study provides an example of how artificial intelligence can be leveraged to determine the perceived age of humans as a marker of biological age, while no longer relying on prediction errors of chronological age, and to estimate the heterogeneity of ageing rates within a population.
Article: Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle.
Authors: Xian Xia, Xingwei Chen, Gang Wu, Fang Li, Yiyang Wang, Yang Chen, Mingxu Chen, Xinyu Wang, Weiyang Chen, Bo Xian, Weizhong Chen, Yaqiang Cao, Chi Xu, Wenxuan Gong, Guoyu Chen, Donghong Cai, Wenxin Wei, Yizhen Yan, Kangping Liu, Nan Qiao, Xiaohui Zhao, Jin Jia, Wei Wang, Brian K. Kennedy, Kang Zhang, Carlo V. Cannistraci, Yong Zhou & Jing-Dong J. Han. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
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