Paola Sebastiani, Stefano Monti, Michael S Lustgarten, Zeyuan Song, Dylan Ellis, Qu Tian, Michaela Schwaiger-Haber, Ethan Stancliffe, Anastasia Leshchyk, Meghan I Short, Andres V Ardisson Korat, Anastasia Gurinovich, Tanya Karagiannis, Mengze Li, Hannah J Lords, Qingyan Xiang, Megan M Marron, Harold Bae, Mary F Feitosa, Mary K Wojczynski, Jeffrey R O'Connell, May E Montasser, Nicole Schupf, Konstantin Arbeev, Anatoliy Yashin, Nicholas Schork, Kaare Christensen, Stacy L Andersen, Luigi Ferrucci, Noa Rappaport, Thomas T Perls, Gary J Patti
{"title":"Metabolite signatures of chronological age, aging, survival, and longevity.","authors":"Paola Sebastiani, Stefano Monti, Michael S Lustgarten, Zeyuan Song, Dylan Ellis, Qu Tian, Michaela Schwaiger-Haber, Ethan Stancliffe, Anastasia Leshchyk, Meghan I Short, Andres V Ardisson Korat, Anastasia Gurinovich, Tanya Karagiannis, Mengze Li, Hannah J Lords, Qingyan Xiang, Megan M Marron, Harold Bae, Mary F Feitosa, Mary K Wojczynski, Jeffrey R O'Connell, May E Montasser, Nicole Schupf, Konstantin Arbeev, Anatoliy Yashin, Nicholas Schork, Kaare Christensen, Stacy L Andersen, Luigi Ferrucci, Noa Rappaport, Thomas T Perls, Gary J Patti","doi":"10.1016/j.celrep.2024.114913","DOIUrl":null,"url":null,"abstract":"<p><p>Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.</p>","PeriodicalId":9798,"journal":{"name":"Cell reports","volume":"43 11","pages":"114913"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell reports","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.celrep.2024.114913","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
Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.
期刊介绍:
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