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. 2024 Jun;23(6):e14136.
doi: 10.1111/acel.14136. Epub 2024 Mar 5.

Plasma proteomic signature of human longevity

Xiaojuan Liu  1 , Gisli Thor Axelsson  2   3 , Anne B Newman  4 , Bruce M Psaty  5   6   7 , Robert M Boudreau  4 , Chenkai Wu  8 , Alice M Arnold  9 , Thor Aspelund  2   3 , Thomas R Austin  10 , Julius M Gardin  11 , Kristin Siggeirsdottir  3   12 , Russell P Tracy  13   14 , Robert E Gerszten  15 , Lenore J Launer  16 , Lori L Jennings  17 , Vilmundur Gudnason  2   3 , Jason L Sanders  18 , Michelle C Odden  1   19
Affiliations

Plasma proteomic signature of human longevity

Xiaojuan Liu et al. Aging Cell. 2024 Jun.

Abstract

The identification of protein targets that exhibit anti-aging clinical potential could inform interventions to lengthen the human health span. Most previous proteomics research has been focused on chronological age instead of longevity. We leveraged two large population-based prospective cohorts with long follow-ups to evaluate the proteomic signature of longevity defined by survival to 90 years of age. Plasma proteomics was measured using a SOMAscan assay in 3067 participants from the Cardiovascular Health Study (discovery cohort) and 4690 participants from the Age Gene/Environment Susceptibility-Reykjavik Study (replication cohort). Logistic regression identified 211 significant proteins in the CHS cohort using a Bonferroni-adjusted threshold, of which 168 were available in the replication cohort and 105 were replicated (corrected p value <0.05). The most significant proteins were GDF-15 and N-terminal pro-BNP in both cohorts. A parsimonious protein-based prediction model was built using 33 proteins selected by LASSO with 10-fold cross-validation and validated using 27 available proteins in the validation cohort. This protein model outperformed a basic model using traditional factors (demographics, height, weight, and smoking) by improving the AUC from 0.658 to 0.748 in the discovery cohort and from 0.755 to 0.802 in the validation cohort. We also found that the associations of 169 out of 211 proteins were partially mediated by physical and/or cognitive function. These findings could contribute to the identification of biomarkers and pathways of aging and potential therapeutic targets to delay aging and age-related diseases.

Keywords: aging; longevity; proteomics.

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Conflict of interest statement

BMP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. JLS is an employee of Vertex Pharmaceuticals at the time of publication. LLJ is an employee and stockholder of Novartis.

Figures

FIGURE 1
FIGURE 1
Logistic regression of proteins associated with survival to 90. (a) Odds ratios of top 30 significant proteins associated with survival to 90. (b) Volcano plot summarizing associations of all proteins (n = 4985) with survival to 90 in CHS. (c) Odds ratios of top 30 significant proteins associated with survival to 90 in AGES‐Reykjavik. (d) Volcano plot summarizing associations of all proteins (n = 4783) with survival to 90 in AGES‐Reykjavik.
FIGURE 2
FIGURE 2
Venn diagram showing the difference of the significant proteins for survival to 90 and overall survival in CHS (a) and AGES‐Reykjavik (b). Labeled proteins are those significantly associated with survival to 90 but not with overall survival.
FIGURE 3
FIGURE 3
Prognostic analysis of proteins for survival to 90 (a) Proteins chosen by LASSO with 10‐fold cross‐validation using within one standard error of the minimum criteria (lambda.1se). (b) ROC curves verified the prognostic performance of the proteins chosen by LASSO in CHS. (c) ROC curves validated the prognostic performance of the proteins model in AGES‐Reykjavik. Proteins are colored according to the magnitude of the coefficients. The basic model included age, sex, race (in CHS only), height, weight, waist circumstances in CHS or abdominal circumstances in AGES‐Reykjavik, smoking status, and pack‐years. The protein model was the basic model plus 36 proteins in (a) and 27 out of 36 proteins were available and used in the AGES‐Reykjavik model.
FIGURE 4
FIGURE 4
Venn diagram showing protein associations mediated (partially) by four functional measurements including grip strength, gait speed, digit symbol substitution test (DSST), and modified mini‐mental status examination (3MSE) in CHS. A total of 211 proteins that are significantly associated with survival to 90 by logistic regression were included in this analysis and 169 of them were mediated by at least one mediator (2 mediated by 4, 16 mediated by 3, 70 mediated by 2, 81 mediated by 1). The mediating model was fitted using linear regression with each of the four mediators as outcome, and protein levels as exposures and adjusted for age, sex, race, height, weight, waist, smoking status, and pack‐years. The outcome model was fitted using logistic regression with survival to 90 as outcome, protein levels, and each of the four mediators as exposures, and adjusted for the same covariates set. An interaction term of protein*mediators was included if the p value for interaction <0.1.
FIGURE 5
FIGURE 5
Heatmap depicting the percentage mediated of each functional measurement on the protein‐longevity associations (n = 88 mediated by 2+ mediators). Statistically significant mediation is annotated by asteroid *. Repeated targets are distinguished by SeqId.
See this image and copyright information in PMC

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