Abstract
Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ’s biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.
| Original language | English |
|---|---|
| Pages (from-to) | 1221-1231 |
| Number of pages | 11 |
| Journal | Nature Medicine |
| Volume | 29 |
| Issue number | 5 |
| Early online date | 6 Apr 2023 |
| DOIs | |
| Publication status | Published - May 2023 |
Bibliographical note
Funding Information:This research has been conducted using data from UK Biobank (https://www.ukbiobank.ac.uk/), a major biomedical database. We are grateful to UK Biobank for making the data available and to all study participants, who generously donated their time to make this resource possible. Some of the data used in the preparation of this article were obtained from the AIBL Flagship Study of Ageing, funded by the Commonwealth Scientific and Industrial Research Organisation, which was made available at the ADNI database (https://adni.loni.usc.edu/).
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.
Funding
This research has been conducted using data from UK Biobank (https://www.ukbiobank.ac.uk/), a major biomedical database. We are grateful to UK Biobank for making the data available and to all study participants, who generously donated their time to make this resource possible. Some of the data used in the preparation of this article were obtained from the AIBL Flagship Study of Ageing, funded by the Commonwealth Scientific and Industrial Research Organisation, which was made available at the ADNI database (https://adni.loni.usc.edu/).
| Funders | Funder number |
|---|---|
| Alzheimer's Disease Neuroimaging Initiative | |
| Commonwealth Scientific and Industrial Research Organisation | |
| Not added | 1142801 |
