Organ aging signatures in the plasma proteome track health and disease

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

doi:10.1038/s41586-023-06802-1

Organ aging signatures in the plasma proteome track health and disease

Nature. 2023 Dec;624(7990):164-172. doi: 10.1038/s41586-023-06802-1. Epub 2023 Dec 6.

Authors

Hamilton Se-Hwee Oh^#^{1

2

3}, Jarod Rutledge^#^{2

3

4}, Daniel Nachun⁵, Róbert Pálovics^{2

3

6}, Olamide Abiose^{3

6}, Patricia Moran-Losada^{2

3

6}, Divya Channappa^{2

3

6}, Deniz Yagmur Urey^{2

7}, Kate Kim^{2

3

6}, Yun Ju Sung^{8

9}, Lihua Wang^{8

9}, Jigyasha Timsina^{8

9}, Dan Western^{8

9

10}, Menghan Liu^{8

9}, Pat Kohlfeld^{8

9}, John Budde^{8

9}, Edward N Wilson^{3

6}, Yann Guen^{6

11}, Taylor M Maurer⁵, Michael Haney^{2

3

6}, Andrew C Yang^{12

13

14}, Zihuai He⁶, Michael D Greicius⁶, Katrin I Andreasson^{3

6

15}, Sanish Sathyan¹⁶, Erica F Weiss¹⁷, Sofiya Milman¹⁶, Nir Barzilai¹⁶, Carlos Cruchaga^{8

9}, Anthony D Wagner^{3

18}, Elizabeth Mormino⁶, Benoit Lehallier⁶, Victor W Henderson^{3

6

19}, Frank M Longo^{3

6}, Stephen B Montgomery^{5

20

21}, Tony Wyss-Coray^{22

23

24}

Affiliations

¹ Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA.
² The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA.
³ Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
⁴ Graduate Program in Genetics, Stanford University, Stanford, CA, USA.
⁵ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, USA.
⁸ Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA.
⁹ NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
¹⁰ Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹² Departments of Neurology and Anatomy, University of California San Francisco, San Francisco, CA, USA.
¹³ Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
¹⁴ Bakar Aging Research Institute, University of California San Francisco, San Francisco, CA, USA.
¹⁵ Chan Zuckerberg Biohub, San Francisco, CA, USA.
¹⁶ Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA.
¹⁷ Department of Neurology, Montefiore Medical Center, New York, NY, USA.
¹⁸ Department of Psychology, Stanford University, Stanford, CA, USA.
¹⁹ Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
²⁰ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
²¹ Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
²² The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA. twc@stanford.edu.
²³ Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. twc@stanford.edu.
²⁴ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. twc@stanford.edu.

^# Contributed equally.

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.

MeSH terms

Adult
Aging* / blood
Alzheimer Disease / blood
Biomarkers* / blood
Brain / metabolism
Cognitive Dysfunction / blood
Cohort Studies
Disease Progression
Disease*
Extracellular Matrix / metabolism
Health*
Heart
Heart Failure / blood
Humans
Machine Learning
Organ Specificity*
Proteome* / analysis
Proteomics*
Synapses / metabolism
Vascular Calcification / blood

Substances

Biomarkers
Proteome
MAPT protein, human

Abstract

MeSH terms

Substances

Grants and funding