Dataset Information

Organ aging signatures in the plasma proteome track health and disease.

ABSTRACT: Animal studies show aging varies between individuals as well as between organs within an individual^1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. ⁵), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

SUBMITTER: Oh HS

PROVIDER: S-EPMC10700136 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Organ aging signatures in the plasma proteome track health and disease.

Oh Hamilton Se-Hwee HS Rutledge Jarod J Nachun Daniel D Pálovics Róbert R Abiose Olamide O Moran-Losada Patricia P Channappa Divya D Urey Deniz Yagmur DY Kim Kate K Sung Yun Ju YJ Wang Lihua L Timsina Jigyasha J Western Dan D Liu Menghan M Kohlfeld Pat P Budde John J Wilson Edward N EN Guen Yann Y Maurer Taylor M TM Haney Michael M Yang Andrew C AC He Zihuai Z Greicius Michael D MD Andreasson Katrin I KI Sathyan Sanish S Weiss Erica F EF Milman Sofiya S Barzilai Nir N Cruchaga Carlos C Wagner Anthony D AD Mormino Elizabeth E Lehallier Benoit B Henderson Victor W VW Longo Frank M FM Montgomery Stephen B SB Wyss-Coray Tony T

Nature 20231206 7990

Animal studies show aging varies between individuals as well as between organs within an individual<sup>1-4</sup>, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,6 ...[more]

PMID: 38057571

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Project description:BackgroundRecent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.Methods and findingsWe focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((?,?)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.ConclusionsApplying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages.

Dataset Information

Organ aging signatures in the plasma proteome track health and disease.

Publications

Organ aging signatures in the plasma proteome track health and disease.

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