Oh, Hamilton Se-Hwee and Rutledge, Jarod and Nachun, Daniel and Pálovics, Róbert and Abiose, Olamide and Moran-Losada, Patricia and Channappa, Divya and Urey, Deniz Yagmur and Kim, Kate and Sung, Yun Ju and Wang, Lihua and Timsina, Jigyasha and Western, Dan and Liu, Menghan and Kohlfeld, Pat and Budde, John and Wilson, Edward N. and Guen, Yann and Maurer, Taylor M. and Haney, Michael and Yang, Andrew C. and He, Zihuai and Greicius, Michael D. and Andreasson, Katrin I. and Sathyan, Sanish and Weiss, Erica F. and Milman, Sofiya and Barzilai, Nir and Cruchaga, Carlos and Wagner, Anthony D. and Mormino, Elizabeth and Lehallier, Benoit and Henderson, Victor W. and Longo, Frank M. and Montgomery, Stephen B. and Wyss-Coray, Tony (2023) Organ aging signatures in the plasma proteome track health and disease. Nature, 624 (7990). pp. 164-172. ISSN 0028-0836
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Abstract
Animal studies show aging varies between individuals as well as between organs within an individual1,2,3,4, 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.
Item Type: | Article |
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Subjects: | Pustakas > Geological Science |
Depositing User: | Unnamed user with email support@pustakas.com |
Date Deposited: | 14 Dec 2023 11:09 |
Last Modified: | 14 Dec 2023 11:09 |
URI: | http://archive.pcbmb.org/id/eprint/1762 |