Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Sehgal, Raghav; Meer, Margarita; Shadyab, Aladdin H.; Casanova, Ramon; Manson, Jo Ann E.; Bhatti, Parveen; Crimmins, Eileen M.; Assimes, Themistocles L.; Whitsel, Eric A.; Higgins‐Chen, Albert; Levine, Morgan E.

doi:10.1101/2023.07.13.548904

Cited by 23 publications

(7 citation statements)

References 79 publications

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“…However, PhenoAge was used to train DNAm PhenoAge, thus providing an indirect comparison between PAC and DNAm PhenoAge clock. Third, while PAC is robustly associated with mortality and major chronic diseases, disease-specific (You et al 2023) or organ-specific clocks (Oh et al 2023;Sehgal et al 2023) address heterogeneity within individuals and may thus be more favorable in certain contexts.…”

Section: Discussionmentioning

confidence: 99%

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Kuo,

Chen,

Liu

et al. 2023

Preprint

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SummaryBackgroundBeyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might therefore offer useful information for the testing and ultimately clinical use of gerotherapeutics. Using a large proteomic cohort in the UK Biobank (UKB), we aimed to develop a proteomic aging clock for all-cause mortality risk as a proxy of biological age (BA).MethodsParticipants in the UK Biobank Pharma Proteomics Project (UKB PPP) were included with ages between 39 and 70 years (n=53,021). Data were split into a training (70%, n=37,115) and a test set (30%, n=15,906), including 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of Least Absolute Shrinkage and Selection Operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. The validation for PAC included assessing its age-adjusted associations with, and predictions for all-cause mortality and 18 incident diseases, and head-to-head comparisons with two biological age measures (PhenoAge and BioAge) and leukocyte telomere length (LTL). Additionally, a functional analysis was performed to identify gene sets and tissues enriched with genes associated with BA deviation, based on different BA measures.FindingsThe Spearman correlation between PAC proteomic age and chronological age was 0.76. 10.9% of the combined training and test samples died during a mean follow-up of 13.3 years (SD=2.2), with the mean age at death 70.1 years (SD=8.1). PAC proteomic age, after controlling for age and other covariates, showed stronger associations than PhenoAge, BioAge, and LTL, with mortality and multiple incident diseases in the test set sample and in disease-free participants, such as mortality, heart failure, pneumonia, delirium, Chronic Obstructive Pulmonary Disease (COPD), and dementia. Additionally, PAC proteomic age showed higher predictive power for the conditions above compared to chronological age, PhenoAge, and BioAge, based on Harrell’s C-statistics. Proteins associated with PAC proteomic age deviation (from chronological age) are enriched in various hallmarks of biological aging, including immunoinflammatory responses, cellular senescence, extracellular matrix remodeling, cellular response to stressors, and vascular biology.InterpretationPAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases. The diverse hallmark gene sets linked with PAC proteomic age deviation highlight the potential efficacy of geroscience-guided interventions. Further validation is essential to ascertain the use of PAC across different settings.FundingAccess to UK Biobank data was granted under application no. 92647 “Research to Inform the Field of Precision Gerontology” (PI: Richard H. Fortinsky), funded by the Claude D. Pepper Older American Independence Centers (OAIC) program: P30AG067988 (MPIs: George A. Kuchel and Richard H. Fortinsky). CLK, BSD, RHF, and GAK are partially supported by P30AG067988. JLA has a UK National Institute for Health and Care Research (NIHR) Advanced Fellowship (NIHR301844).Research in contextProteomic aging clocks have been developed using the BA surrogate of chronological age, but their validation remains limited. None had been developed using the surrogate of all-cause mortality risk, which is believed more sensitive to changes in biological aging processes. We relied on two recent comprehensive review papers1,2and expanded our search on PubMed and Google Scholar for English articles, using keywords including “biomarkers of aging”, “biological age predictors”, “biological age deviation”, “accelerated biological aging”, “methylation clocks”, “proteomic clocks”, “UK Biobank biomarkers”, “UK Biobank Pharma Proteomics Project”, “PhenoAge”, “BioAge”, “telomere length”, “SASP index”, and “composite aging biomarkers”.Added value of this studyTo the best of our knowledge, PAC is the first proteomic aging clock developed for all-cause mortality risk as a surrogate of BA, using the largest dataset of proteins and individuals in the world. Our results expand previous findings by showing that PAC age acceleration strongly predicts not only all-cause mortality but also several incident disease outcomes, with a follow-up exceeding a decade and a substantial sample size to ensure adequate statistical power.Implications of all the available evidenceDue to its associations and predictive value for all-cause mortality and multiple incident diseases, PAC has the potential to serve as a valuable tool for assessing the effects of geoscience-guided interventions. It facilitates the evaluation of risk for multiple conditions in a disease-free population; thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

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Section: Discussionmentioning

confidence: 99%

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Kuo,

Chen,

Liu

et al. 2023

Preprint

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“…Recent studies show that human organs age at different rates [2][3][4][5] similar to what has been reported in animals [6][7][8] , which suggests a need for organ-specific measures of biological age. Previously developed organ age estimates include those developed from clinical metrics of organ function (glomerular filtration rate, blood pressure, etc), clinical blood chemistry, brain MRI scans, immune cell DNA methylation profiles, and the levels of organ-specific proteins in blood plasma 2-5 .…”

Section: Mainmentioning

confidence: 62%

Plasma proteomics in the UK Biobank reveals youthful brains and immune systems promote healthspan and longevity

Oh,

Le Guen,

Rappoport

et al. 2024

Preprint

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Organ-derived plasma protein signatures derived from aptamer protein arrays track organ-specific aging, disease, and mortality in humans, but the robustness and clinical utility of these models and their biological underpinnings remain unknown. Here, we estimate biological age of 11 organs from 44,526 individuals in the UK Biobank using an antibody-based proteomics platform to model disease and mortality risk. Organ age estimates are associated with future onset of heart failure (heart age HR=1.83), chronic obstructive pulmonary disease (lung age HR=1.39), type II diabetes (kidney age HR=1.58), and Alzheimer’s disease (brain age HR=1.81) and sensitive to lifestyle factors such as smoking and exercise, hormone replacement therapy, or supplements. Remarkably, the accrual of aged organs progressively increases mortality risk while a youthful brain and immune system are uniquely associated with disease-free longevity. These findings support the use of plasma proteins for monitoring organ health and the efficacy of drugs targeting organ aging disease.

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“…These data provide a background against which to evaluate proposed novel indices of biological aging, as we did in our prior analysis of epigenetic clocks 17 . The CALERIE™ Genomic Data Resource will provide a platform for analysis of the many new blood-based clocks recently published and others still forthcoming [45][46][47][48] . Furthermore, DNAm and RNA from muscle and adipose data from the CALERIE™ Genomic Data Resource will provide researchers with the rare opportunity to study the relationship between new and existing blood-based measures and molecular responses to CR in other tissues.…”

Section: Discussionmentioning

confidence: 99%

The CALERIE™ Genomic Data Resource

Ryan,

Corcoran,

Banskota

et al. 2024

Preprint

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Caloric restriction (CR) slows biological aging and prolongs healthy lifespan in model organisms. Findings from CALERIE-2™ – the first ever randomized, controlled trial of long-term CR in healthy, non-obese humans – broadly supports a similar pattern of effects in humans. To expand our understanding of the molecular pathways and biological processes underpinning CR effects in humans, we generated a series of genomic datasets from stored biospecimens collected from participants during the trial. These data constitute the first publicly-accessible genomic data resource for a randomized controlled trial of an intervention targeting the biology of aging. Datasets include whole-genome SNP genotypes, and three-timepoint-longitudinal DNA methylation, mRNA, and small RNA datasets generated from blood, skeletal muscle, and adipose tissue samples. The CALERIE Genomic Data Resource described in this article is available from the Aging Research Biobank. This multi-tissue, multi-omic, longitudinal data resource has great potential to advance translational geroscience.

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Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Cited by 23 publications

References 79 publications

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Plasma proteomics in the UK Biobank reveals youthful brains and immune systems promote healthspan and longevity

The CALERIE™ Genomic Data Resource

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