Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Issue 11 (8th October 2020)

• Record Type: Journal Article
• Authors: Lehallier, Benoit; Shokhirev, Maxim N.; Wyss‐Coray, Tony; Johnson, Adiv A.
• Is Part Of: Aging cell. Volume 19:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-08
• Subjects: age‐related disease -- aging -- aging clock -- health span -- life span -- longevity; Cells -- Aging -- Periodicals; 571.8783605
• DOI: 10.1111/acel.13256
• Languages: English
• ISSNs: 1474-9718

Abstract

ABSTRACT: We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q‐value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine‐learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra‐predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic‐exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age. Abstract : Machine learning analyses of proteins that change their expression level with age in human plasma discovered an ultra‐predictive proteomic aging clock and also unveiled widely accessible clocks that reflect different aspects of aging. For example, proteins that impact lifespan in animal models when manipulated can accurately predict age in a large human cohort comprised of 3301 individuals (aged 18–76 years).