Distinct biological ages of organs and systems identified from a multi-omics study

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple “clocks” within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics da...

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Vydáno v:	Cell reports (Cambridge) Ročník 38; číslo 10; s. 110459
Hlavní autoři:	Nie, Chao, Li, Yan, Li, Rui, Yan, Yizhen, Zhang, Detao, Li, Tao, Li, Zhiming, Sun, Yuzhe, Zhen, Hefu, Ding, Jiahong, Wan, Ziyun, Gong, Jianping, Shi, Yanfang, Huang, Zhibo, Wu, Yiran, Cai, Kaiye, Zong, Yang, Wang, Zhen, Wang, Rong, Jian, Min, Jin, Xin, Wang, Jian, Yang, Huanming, Han, Jing-Dong J., Zhang, Xiuqing, Franceschi, Claudio, Kennedy, Brian K., Xu, Xun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 08.03.2022 Elsevier
Témata:	Aged, 80 and over Aging aging biomarker biological ages CLHLS Humans Longevity Longitudinal Studies Metabolomics multi-omics NHANES Nutrition Surveys organ aging multi-omics NHANES aging biomarker organ aging CLHLS biological ages
ISSN:	2211-1247, 2211-1247
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Abstract	Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple “clocks” within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People’s aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian. [Display omitted] •Constructing biological ages of organs/systems using multi-omics features•Organs and systems are aging at different rates•Specific biological age could predict disease of corresponding organs•Biological ages of organs and systems have diverse genetic architectures Nie et al. estimate biological ages of organs and systems using 402 multi-omics features from 4,066 individuals and demonstrate several applications. They find that organs and systems are aging at different rates, and biological ages could be utilized for population stratification, mortality prediction, and phenotypes of genetic association studies.
AbstractList	Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian. Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian. Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple “clocks” within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People’s aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian. [Display omitted] •Constructing biological ages of organs/systems using multi-omics features•Organs and systems are aging at different rates•Specific biological age could predict disease of corresponding organs•Biological ages of organs and systems have diverse genetic architectures Nie et al. estimate biological ages of organs and systems using 402 multi-omics features from 4,066 individuals and demonstrate several applications. They find that organs and systems are aging at different rates, and biological ages could be utilized for population stratification, mortality prediction, and phenotypes of genetic association studies.
ArticleNumber	110459
Author	Zong, Yang Wang, Zhen Xu, Xun Zhen, Hefu Jin, Xin Wu, Yiran Nie, Chao Li, Tao Gong, Jianping Li, Yan Yan, Yizhen Wan, Ziyun Yang, Huanming Han, Jing-Dong J. Wang, Jian Zhang, Xiuqing Franceschi, Claudio Jian, Min Huang, Zhibo Shi, Yanfang Wang, Rong Zhang, Detao Li, Zhiming Ding, Jiahong Li, Rui Sun, Yuzhe Kennedy, Brian K. Cai, Kaiye
Author_xml	– sequence: 1 givenname: Chao orcidid: 0000-0003-3536-0324 surname: Nie fullname: Nie, Chao organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 2 givenname: Yan surname: Li fullname: Li, Yan organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 3 givenname: Rui surname: Li fullname: Li, Rui organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 4 givenname: Yizhen surname: Yan fullname: Yan, Yizhen organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 5 givenname: Detao surname: Zhang fullname: Zhang, Detao organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 6 givenname: Tao surname: Li fullname: Li, Tao organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 7 givenname: Zhiming surname: Li fullname: Li, Zhiming organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 8 givenname: Yuzhe surname: Sun fullname: Sun, Yuzhe organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 9 givenname: Hefu surname: Zhen fullname: Zhen, Hefu organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 10 givenname: Jiahong surname: Ding fullname: Ding, Jiahong organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 11 givenname: Ziyun surname: Wan fullname: Wan, Ziyun organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 12 givenname: Jianping surname: Gong fullname: Gong, Jianping organization: Medical Examination Center, The Affiliated Hospital of Hebei University, Baoding 071000, China – sequence: 13 givenname: Yanfang surname: Shi fullname: Shi, Yanfang organization: Department of Neurosurgery, The Affiliated Hospital of Hebei University, Baoding 071000, China – sequence: 14 givenname: Zhibo surname: Huang fullname: Huang, Zhibo organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 15 givenname: Yiran surname: Wu fullname: Wu, Yiran organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 16 givenname: Kaiye surname: Cai fullname: Cai, Kaiye organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 17 givenname: Yang surname: Zong fullname: Zong, Yang organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 18 givenname: Zhen surname: Wang fullname: Wang, Zhen organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 19 givenname: Rong surname: Wang fullname: Wang, Rong organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 20 givenname: Min surname: Jian fullname: Jian, Min organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 21 givenname: Xin surname: Jin fullname: Jin, Xin organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 22 givenname: Jian surname: Wang fullname: Wang, Jian organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 23 givenname: Huanming surname: Yang fullname: Yang, Huanming organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 24 givenname: Jing-Dong J. surname: Han fullname: Han, Jing-Dong J. organization: Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China – sequence: 25 givenname: Xiuqing surname: Zhang fullname: Zhang, Xiuqing email: zhangxq@genomics.cn organization: BGI-Shenzhen, Shenzhen 518083, China – sequence: 26 givenname: Claudio surname: Franceschi fullname: Franceschi, Claudio email: claudio.franceschi@unibo.it organization: Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod, Russia – sequence: 27 givenname: Brian K. orcidid: 0000-0002-5754-1874 surname: Kennedy fullname: Kennedy, Brian K. email: bkennedy@nus.edu.sg organization: Healthy Longevity Translation Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore – sequence: 28 givenname: Xun surname: Xu fullname: Xu, Xun email: xuxun@genomics.cn organization: BGI-Shenzhen, Shenzhen 518083, China
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Snippet	Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple...
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StartPage	110459
SubjectTerms	Aged, 80 and over Aging aging biomarker biological ages CLHLS Humans Longevity Longitudinal Studies Metabolomics multi-omics NHANES Nutrition Surveys organ aging
Title	Distinct biological ages of organs and systems identified from a multi-omics study
URI	https://dx.doi.org/10.1016/j.celrep.2022.110459 https://www.ncbi.nlm.nih.gov/pubmed/35263580 https://www.proquest.com/docview/2638013299 https://doaj.org/article/914a56907fbd407c9a68dfc28aaa4717
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