Personal aging markers and ageotypes revealed by deep longitudinal profiling

• Published in: Nature medicine Vol. 26; no. 1; pp. 83 - 90
• Main Authors: Ahadi, Sara, Zhou, Wenyu, Schüssler-Fiorenza Rose, Sophia Miryam, Sailani, M. Reza, Contrepois, Kévin, Avina, Monika, Ashland, Melanie, Brunet, Anne, Snyder, Michael
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2020; Nature Publishing Group
• Subjects: 692/53/2422; 692/700/459/284; Adult; Age; Aged; Aging; Aging - physiology; Biological markers; Biomarkers - metabolism; Biomedical and Life Sciences; Biomedicine; Cancer Research; Cohort Studies; Cytokines; Depth profiling; Female; Genetic aspects; Genomics; Humans; Identification and classification; Infectious Diseases; Insulin; Letter; Longitudinal Studies; Male; Markers; Metabolic Diseases; Metabolites; Middle Aged; Molecular Medicine; Neurosciences; Physiological aspects; Statistics, Nonparametric; Young Adult; United States
• ISSN: 1078-8956; 1546-170X; 1546-170X
• DOI: 10.1038/s41591-019-0719-5

The molecular changes that occur with aging are not well understood 1 – 4 . Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns—termed ageotypes—in different individuals.