An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Nazish Sayed, Yingxiang Huang, Khiem Nguyen, Zuzana Krejciova-Rajaniemi, Anissa P. Grawe, Tianxiang Gao, Robert Tibshirani, Trevor Hastie, Ayelet Alpert, Lu Cui, Tatiana Kuznetsova, Yael Rosenberg-Hasson, Rita Ostan, Daniela Monti, Benoit Lehallier, Shai S. Shen-Orr, Holden T. Maecker, Cornelia L. Dekker, Tony Wyss-Coray, Claudio FranceschiVladimir Jojic, François Haddad, José G. Montoya, Joseph C. Wu, Mark M. Davis, David Furman

Research output: Contribution to journalArticlepeer-review

Abstract

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.

Original languageEnglish
Pages (from-to)598-615
Number of pages18
JournalNature aging
Volume1
Issue number7
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Geriatrics and Gerontology
  • Ageing
  • Neuroscience (miscellaneous)

Fingerprint

Dive into the research topics of 'An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging'. Together they form a unique fingerprint.

Cite this