Journal Article

Organ aging signatures in the plasma proteome track health and disease

Wyss-Coray, Tony, Liu, Menghan, Sathyan, Sanish, Pálovics, Róbert, Urey, Deniz Yagmur, Channappa, Divya, Greicius, Michael D., Haney, Michael, Milman, Sofiya, Oh, Hamilton Se-Hwee, Wilson, Edward N., Timsina, Jigyasha, Cruchaga, Carlos, Nachun, Daniel, Wang, Lihua, Kohlfeld, Pat, Kim, Kate, Rutledge, Jarod, Maurer, Taylor M., Western, Dan, Wagner, Anthony D., Abiose, Olamide, Andreasson, Katrin I., Barzilai, Nir, Montgomery, Stephen B., Weiss, Erica F., Guen, Yann, Budde, John, Yang, Andrew C., He, Zihuai, Lehallier, Benoit, Henderson, Victor W., Sung, Yun Ju, Mormino, Elizabeth, Moran-Losada, Patricia, Longo, Frank M.

2023

Overview

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. Blood plasma protein data was combined with machine learning models for a simple method to determine differences in organ-specific aging; the study provides a basis for the prediction of diseases and aging effects using plasma proteomics.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 38 / 38/39 / 38/91 / 59/57 / 631/114/1305 / 631/114/2784 / 692/53/2421 / 692/53/2422 / 692/53/2423 / 82 / 82/47 / 82/80 / Adult / Age differences / Age related diseases / Aging / Aging - blood / Alzheimer Disease - blood / Alzheimer's disease / Animal diseases / Biomarkers / Biomarkers - blood / Blood plasma / Brain - metabolism / Calcification / Calcification (ectopic) / Cardiac arrhythmia / Cerebrovascular disease / Cognitive ability / Cognitive Dysfunction - blood / Cohort Studies / Congestive heart failure / Diabetes / Disease / Disease Progression / Extracellular matrix / Extracellular Matrix - metabolism / Health / Health risks / Heart / Heart attacks / Heart Failure - blood / Humanities and Social Sciences / Humans / Hypertension / Kidneys / Life span / Machine Learning / Metabolism / Mortality / Mortality risk / multidisciplinary / Neurodegenerative diseases / Organ Specificity / Organs / Plasma / Plasma proteins / Proteins / Proteome - analysis / Proteomes / Proteomics / Science / Science (multidisciplinary) / Synapses - metabolism / Vascular Calcification - blood