Towards a gene expression biomarker set for human biological age |
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| Authors: | Luke C Pilling William Henley Dena G Hernandez Andrew B Singleton Stefania Bandinelli Jack M Guralnik Luigi Ferrucci Lorna W Harries |
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| Institution: | 1. Epidemiology and Public Health, Exeter Medical School, University of Exeter, , Exeter, EX2 5DW UK;2. Institute of Health Service Research, Exeter Medical School, University of Exeter, , Exeter, EX2 4SG UK;3. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, , Bethesda, MD, USA;4. Geriatric Unit, Azienda Sanitaria di Firenze, , Florence, Italy;5. Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, , Bethesda, MD, USA;6. National Institute on Aging, , Baltimore, MD, USA;7. Institute of Biomedical and Clinical Science, Exeter Medical School, University of Exeter, , Exeter, EX2 5DW UK |
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| Abstract: | We have previously described a statistical model capable of distinguishing young (age <65 years) from old (age ≥75 years) individuals. Here we studied the performance of a modified model in three populations and determined whether individuals predicted to be biologically younger than their chronological age had biochemical and functional measures consistent with a younger biological age. Those with ‘younger’ gene expression patterns demonstrated higher muscle strength and serum albumin, and lower interleukin‐6 and blood urea concentrations relative to ‘biologically older’ individuals (odds ratios 2.09, 1.64, 0.74, 0.74; P = 2.4 × 10?2, 3.5 × 10?4, 1.8 × 10?2, 1.5 × 10?2, respectively). We conclude that our expression signature of age is robust across three populations and may have utility for estimation of biological age. |
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| Keywords: | biological aging mRNA expression cell senescence predictive model |
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