Zhengqing Yu, Yong Zhou, Kehang Mao, Bo Pang, Kai Wang, Tang Jin, Haonan Zheng, Haotian Zhai, Yiyang Wang, Xiaohan Xu, Hongxiao Liu, Yi Wang, Jing-Dong J. Han
{"title":"Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases","authors":"Zhengqing Yu, Yong Zhou, Kehang Mao, Bo Pang, Kai Wang, Tang Jin, Haonan Zheng, Haotian Zhai, Yiyang Wang, Xiaohan Xu, Hongxiao Liu, Yi Wang, Jing-Dong J. Han","doi":"10.1016/j.cmet.2024.05.012","DOIUrl":null,"url":null,"abstract":"<p>Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20–90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.</p>","PeriodicalId":9840,"journal":{"name":"Cell metabolism","volume":"63 1","pages":""},"PeriodicalIF":27.7000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell metabolism","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.cmet.2024.05.012","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20–90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.
期刊介绍:
Cell Metabolism is a top research journal established in 2005 that focuses on publishing original and impactful papers in the field of metabolic research.It covers a wide range of topics including diabetes, obesity, cardiovascular biology, aging and stress responses, circadian biology, and many others.
Cell Metabolism aims to contribute to the advancement of metabolic research by providing a platform for the publication and dissemination of high-quality research and thought-provoking articles.