Markers of aging: Unsupervised integrated analyses of the human plasma proteome

Abstract

Aging associates with an increased susceptibility for disease and decreased quality of life. To date, processes underlying aging are still not well understood, leading to limited interventions with unknown mechanisms to promote healthy aging. Previous research suggests that changes in the blood proteome are reflective of age-associated phenotypes such as frailty. Moreover, experimentally induced changes in the blood proteome composition can accelerate or decelerate underlying aging processes. The aim of this study is to identify a set of proteins in the human plasma associated with aging by integration of the data of four independent, large-scaled datasets using the aptamer-based SomaScan platform on the human aging plasma proteome. Using this approach, we identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions. We validated the age-associated effects in an independent study using a centenarian population, showing highly concordant effects. Our results suggest that APs are more associated to diseases than other plasma proteins. Plasma levels of APs can predict chronological age, and a reduced selection of 15 APs can still predict individuals’ age accurately, highlighting their potential as biomarkers of aging processes. Furthermore, we show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in the understanding of the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging.

Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations

“…Recently, there has been growing interest in using plasma proteins to study biological aging and create proteomic aging clocks. We specifically compared our findings to three of the largest published studies: (1) a systematic review of studies (n=32) reporting protein associations with age (Johnson et al 2020 9 ), in which the authors developed a proteomic age clock using 85 proteins associated with age in at least three previous studies and validated it in the INTERVAL cohort (n=3,301); (2) a recent study that identified 273 APs across several cohorts (Coenen et al 2023 8 ) (n=37,650); and (3) a clock consisting of 373 APs developed in the INTERVAL and LonGenity cohorts (Lehallier et al 2019 10 ) (n=4,263). To our knowledge, neither these nor any previous study has directly tested associations between proteomic aging and disease or multimorbidity in the comprehensive manner described here.…”

“…5,6 Loss of proteostasis is another primary hallmark of aging, 7 and protein expression levels may provide a more direct mechanistic and functional insight into aging biology compared with DNAm. 6 While several previous studies have systematically examined aging-related proteins (APs) and developed proteomic aging clocks, [8][9][10][11] these studies have been constrained by smaller sample sizes (typically < 10k samples), lack of geographical diversity in their study populations, and inability to systematically evaluate associations between proteomic aging, biological markers of aging, aging-related physical and cognitive decline, and incidence of common diseases. To date, no studies have been reported that comprehensively assess the associations between accelerated biological aging (either DNAm or proteomic) and incidence of common diseases or major causes of death.…”

Distribution of Copy Number Variants and Impact of Chromosome Arm Call Thresholds for Meningioma

“…CNV pro les were generated from this DNA methylation data using the conumee package in R, using whole blood methylation pro le data as a copy number normal control. 25 CNV intensity value distributions were then processed according to previously published mean segment intensities: segments with a mean intensity value of less than − 0.1 were de ned as copy number losses and segments with a mean intensity value of greater than 0.15 were de ned as copy number gains. 3 The size of each CNV segment, determined using the probe start and end location, was used to calculate the percent of the chromosome arm affected by each CNV.…”

Healthy lifestyle linked to innate immunity and lipoprotein metabolism: a cross-sectional comparison using untargeted proteomics

“…Proteomics involves the large-scale measurement of the structure and function of proteins and is useful in the identification of potential biomarkers for health, various disease processes, and treatment effects 9 , 10 . Previous studies focused on plasma proteome profiles associated with cardiorespiratory fitness status and acute and chronic exercise training 11 – 16 , the aging process 17 , 18 , disease prediction 19 – 21 , body composition, obesity, and weight loss 20 – 27 , and dietary intake patterns 24 .…”