Organ aging signatures in the plasma proteome track health and disease

Hamilton Oh(Neurosciences Institute), Jarod Rutledge(Neurosciences Institute), Daniel Nachun(Stanford University), Róbert Pálovics(Neurosciences Institute), Olamide Abiose(Neurosciences Institute), Patricia Moran‐Losada(Neurosciences Institute), Divya Channappa(Neurosciences Institute), Deniz Yagmur Urey(Stanford University), K. Kim(Neurosciences Institute), Yun Ju Sung(Washington University in St. Louis), Lihua Wang(Washington University in St. Louis), Jigyasha Timsina(Washington University in St. Louis), Daniel Western(Washington University in St. Louis), Menghan Liu(Washington University in St. Louis), Pat Kohlfeld(Washington University in St. Louis), John Budde(Washington University in St. Louis), Edward N. Wilson(Neurosciences Institute), Yann Le Guen(Stanford University), Taylor Maurer(Stanford University), Michael S. Haney(Neurosciences Institute), Andrew C. Yang(Gladstone Institutes), Zihuai He(Stanford University), Michael D. Greicius(Stanford University), Katrin I. Andreasson(Neurosciences Institute), Sanish Sathyan(Albert Einstein College of Medicine), Erica Weiss(Montefiore Medical Center), Sofiya Milman(Albert Einstein College of Medicine), Nir Barzilai(Albert Einstein College of Medicine), Carlos Cruchaga(Washington University in St. Louis), Anthony D. Wagner(Neurosciences Institute), Elizabeth C. Mormino(Stanford University), Benoit Lehallier(Stanford University), Victor W. Henderson(Neurosciences Institute), Frank M. Longo(Neurosciences Institute), Stephen B. Montgomery(Stanford University), Tony Wyss‐Coray(Neurosciences Institute)
Nature
December 6, 2023
Cited by 552Open Access
Full Text

Abstract

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.


Related Papers