Many studies have demonstrated that biological age (BA) varies significantly among individuals of similar chronological age. A recent study by Ahadi et al. used longitudinal and deep multi-omic profiling to identify individuals with distinct BA phenotypes or 'ageotypes'. These ageotypes open new avenues to creating diagnostic and treatment strategies that may slow the aging process based on the unique biochemistry of each individual.
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Ageotypes revisited: The brain and central nervous system dysfunction as a major nutritional and lifestyle target for healthy aging.
Maturitas
April 2023
Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Mikras Asias 75, Athens GR-11527, Greece; Embiodiagnostics Biology Research Company, 1 Melissinon and Damvergidon Street, Heraklion GR-71305, Crete, Greece. Electronic address:
Undeniably, biological age can significantly differ between individuals of similar chronological age. Longitudinal, deep multi-omic profiling has recently enabled the identification of individuals with distinct aging phenotypes, termed 'ageotypes'. This effort has provided a plethora of data and new insights into the diverse molecular mechanisms presumed to drive aging.
View Article and Find Full Text PDFAgeotypes: Distinct Biomolecular Trajectories in Human Aging.
Trends Pharmacol Sci
May 2020
Institute for Systems Biology, Seattle, WA, USA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Many studies have demonstrated that biological age (BA) varies significantly among individuals of similar chronological age. A recent study by Ahadi et al. used longitudinal and deep multi-omic profiling to identify individuals with distinct BA phenotypes or 'ageotypes'.
View Article and Find Full Text PDFPersonal aging markers and ageotypes revealed by deep longitudinal profiling.
Nat Med
January 2020
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
The molecular changes that occur with aging are not well understood. 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.
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