Aging is a predominant risk factor for several chronic diseases that limit healthspan1. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues2,3,4,5,6,7,8,9,10, which supports a hypothesis that age-related molecular changes in blood could provide new insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18–95 years old) and developed a new bioinformatics approach that uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways that might offer potential targets for age-related diseases.
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We created a searchable web interface to mine the human INTERVAL and LonGenity datasets: https://twc-stanford.shinyapps.io/aging_plasma_proteome/.
The independent human cohorts and mouse protein data are available in Supplementary Tables 16 and 17. The INTERVAL data are available through the European Genome–Phenome Archive under accession EGAS00001002555.
An R package for DE-SWAN is available in GitHub: http://lehallib.github.io/DEswan/.
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We thank the members of the Wyss-Coray laboratory for feedback and support. We thank the clinical staff for human blood and plasma collection/coordination. We thank A. Butterworth for his help in getting access to the INTERVAL proteomics data. The AddNeuroMed data are from a public–private partnership supported by EFPIA companies and the European Union Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5. Clinical leads responsible for data collection were I. Kłoszewska (Lodz), S. Lovestone (London), P. Mecocci (Perugia), H. Soininen (Kuopio), M. Tsolaki (Thessaloniki) and B. Vellas (Toulouse); imaging leads were A. Simmons (London), L.O. Wahlund (Stockholm) and C. Spenger (Zurich); and bioinformatics leads were R. Dobson (London) and S. Newhouse (London). This work was supported by National Institutes of Health National Institute on Aging (NIA) F32 1F32AG055255 01A1 (D.G.), Hungarian Brain Research Program Grant No. 2017-1.2.1-NKP-2017-00002 (T.N.), the Fulbright Foreign Student Program (T.N.), the Cure Alzheimer’s Fund (T.W.-C.), Nan Fung Life Sciences (T.W.-C.), the NOMIS Foundation (T.W.-C.), the Stanford Brain Rejuvenation Project (an initiative of the Stanford Wu Tsai Neurosciences Institute), the Paul F. Glenn Center for Aging Research (T.W.-C.), NIA R01 AG04503 and DP1 AG053015 (T.W.-C.) and the NIA-funded Stanford Alzheimer’s Disease Research Center P50AG047366, NIA K23AG051148 (S.M.), R01AG061155 (S.M.), the American Federation for Aging Research (S.M.), R01AG044829 (J.V. and N.B.), NIA R01AG057909 (N.B.), the Nathan Shock Center of Excellence for the Basic Biology of Aging P30AG038072 (N.B.) and the Glenn Center for the Biology of Human Aging (N.B.).
The authors declare no competing interests.
Peer review information Brett Bennedetti and Jennifer Sargent were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Age (a, b), cohort (a, b) and sex distributions (c) of the 4,263 subjects from the INTERVAL and LonGenity cohorts. (d) Age and cohort distributions of the 171 subjects from the 4 independent cohorts.
(a) Age and sex effects in the INTERVAL and LonGenity studies (n = 4,263) were compared to age and sex effects in 4 independent cohorts analyzed together (n = 171) and to age effect from Tanaka et al. (n = 240, 2018). The aging plasma proteome was measured with the SomaScan assay in these cohorts and 888 proteins were measured in all studies (b) Scatter plot representing the signed -log10(q value) of the sex effect in the INTERVAL/LonGenity cohorts (x axis, n = 4,263) vs the 4 independent cohorts (y-axis, n = 171). Similar analysis for the age effect in the 4 independent cohorts (c, n = 171) and in Tanaka et al study (d, n = 240).
(a) Prediction of age in the 4 independent cohorts (n = 171) using the proteomic clock. Only 141 proteins out of the 373 constituting the clock were measured in these samples. (b) Prediction of age in the discovery cohort (n = 2,817) using the 373 plasma markers. (c) Feature reduction of the aging model in the Discovery and Validation cohorts to estimate whether a subset of the aging signature can provide similar results to the 373 aging proteins. Dashed lines represent a broken stick model and indicate the best compromise between number of variables and prediction accuracy. (d) Heatmap representing the associations between delta age and 334 clinical and functional variables. For quantitative traits, linear models adjusted for delta age, age and sex were used and significance was tested using F-test. For binary outcomes, binomial generalized linear models adjusted for delta age, age and sex were used and significance was tested using likelihood ratio chi-square test. As in (c) the analysis was performed for the top 2 to top 373 variables predicting age. The non-uniformity in the heatmaps suggests that specific subsets of proteins may best predict certain clinical and functional parameters.
(a) Trajectories of 5 selected proteins based on the INTERVAL and LonGenity cohorts (n = 4,263, left) and 4 independent human cohorts (n = 171, right). Trajectories were estimated using LOESS regression. Undulation of the 1,305 plasma proteins measured in 4 independent cohorts (b, n = 171) and in mouse (c, n = 81). Plasma proteins levels were z-scored and LOESS regression was fitted for each plasma factor.
Protein trajectories for the 8 clusters identified in the INTERVAL and LonGenity cohorts (left column). Thicker lines represent the average trajectory for each cluster. Cluster trajectories for the subset of proteins measured in the 4 independent cohorts (middle column). Corresponding cluster trajectories in 4 independent cohorts (right column).
Pathway enrichment was tested using GO, Reactome and KEGG databases (n = 4,263). Enrichment was tested using Fisher’s exact test (GO) and hypergeometric test (Reactome and KEGG). The top 4 pathways for each cluster are shown. Pathway IDs and number of plasma proteins associated are represented in the table.
Extended Data Fig. 7 DE-SWAN age effect for multiple q-values cutoffs, windows size and after phenotypes permutations.
Different Q-value cutoffs are represented in (a). Similar analysis with different after phenotype permutations (b) and different windows size in (c). The 3 local peaks identified at age 34, 60 and 78 are indicated by colored vertical lines.
Enrichment for cis-association in the waves of aging proteins identified by DE-SWAN. Aging proteins were ranked based on p-values at age 34, 60 and 78 and the cumulative number of cis-associations was counted. One-sided permutation tests (1e + 5 permutations) were used to assess significance.
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Lehallier, B., Gate, D., Schaum, N. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25, 1843–1850 (2019). https://doi.org/10.1038/s41591-019-0673-2
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