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. 2018 Oct;17(5):e12799.
doi: 10.1111/acel.12799. Epub 2018 Jul 11.

Plasma proteomic signature of age in healthy humans

Toshiko Tanaka  1 , Angelique Biancotto  2 , Ruin Moaddel  3 , Ann Zenobia Moore  1 , Marta Gonzalez-Freire  1 , Miguel A Aon  4 , Julián Candia  2 , Pingbo Zhang  5 , Foo Cheung  2 , Giovanna Fantoni  2 ; CHI consortium  2 ; Richard D Semba  5 , Luigi Ferrucci  1
Collaborators, Affiliations

Plasma proteomic signature of age in healthy humans

Toshiko Tanaka et al. Aging Cell. 2018 Oct.

Abstract

To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22-93 years old, who were disease- and treatment-free and had no physical and cognitive impairment. Using a p ≤ 3.83 × 10-5 significance threshold, 197 proteins were positively associated, and 20 proteins were negatively associated with age. Growth differentiation factor 15 (GDF15) had the strongest, positive association with age (GDF15; 0.018 ± 0.001, p = 7.49 × 10-56 ). In our sample, GDF15 was not associated with other cardiovascular risk factors such as cholesterol or inflammatory markers. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. Using elastic net regression models, we created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age (r = 0.94). The generalizability of our findings needs replication in an independent cohort.

Keywords: aging; aptamers; healthy aging; humans; plasma; proteomics.

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Figures

Figure 1
Figure 1
Associations of proteins with age. Volcano plot displaying the association of 1,301 proteins with chronological age. Protein values were log‐transformed and associations with age were tested using a linear model adjusting for sex, race, study (BLSA or GESTALT), and batch. The figure displays the effect size (beta coefficient from the linear model), against significance presented as the −log10 (p‐value)
Figure 2
Figure 2
Correlation of GDF15 with age and validation with ELISA assay. (a) The most significant age association was observed for growth differentiation factor 15 (GDF15), which was positively associated with age (β = 0.018 ± 0.001, p = 7.5 × 10−56). To validate association of GDF15 using an independent assay, GDF15 abundance was measured with ELISA on a subset of 88 subjects. (b) GDF15 abundance measured with ELISA correlated with age (β = 0.018 ± 0.002, p = 3.83 × 10−20). (c) Plasma GDF15 measured by ELISA assay was correlated with the measure from SOMAscan, and a correlation of 0.821 was found
Figure 3
Figure 3
Proteomic signature of age. Using elastic net regression model, proteomic predictors of age were created with variable numbers of predictor proteins in the model. This graphs show the correlation between the predicted age on the y‐axis and chronological age on the x‐axis for proteomic predictors with 76 predictor proteins. The correlation between predicted age using the proteomic signature and observed age was 0.94
Figure 4
Figure 4
Age‐associated proteins by sex. Association between protein abundance and age differed by sex for eight proteins: (a) Follicle‐stimulating hormone (FSH), (b) sex hormone‐binding globulins (SHBG), (c) tissue factor pathway inhibitor (TFPI), (d) luteinizing hormone (CGA/LHB), (e) vitamin K‐dependent protein 5 (PROS1), (f) human chorionic gonadotropin (CGA/CGB), (g) netrin 4 (NTN4), and (h) insulin‐like growth factor binding protein 7 (IGFBP7). Observations from women are displayed by open triangles and men in closed circles. Regression lines within women (dotted line) and men (solid line) are also displayed
Figure 5
Figure 5
Functional annotation clustering using Database for Annotation, Visualization and Integrated Discovery (DAVID). Pathway enrichment analysis was conducted using DAVID, and to better visualize the shared proteins between the top GO annotation terms, functional annotation clustering was conducted on GO “biological processes,” “molecular function,” and “cellular component” terms. The GO terms and proteins shared among the terms for the top five clusters are displayed
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