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Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
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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.
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“All included biomarkers/studies met the following inclusion criteria: 1) studies that propose new ways to quantify biological aging (predicting aging outcomes better than chronological age 27); 2) biomarkers measured entirely or primarily in blood; 3) biomarkers that are applicable to the general population and not specific to certain subpopulations with specific diseases; 4) biomarkers that”
From Validation of biomarkers of aging (2024) · cites this paper as [27]
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“A recent study established the biological age (BA) of multiple human organ systems using data from the UK Biobank and revealed that cardiovascular age has the strongest influence on brain age, with a 1-year increase in the cardiovascular age increasing the brain BA by 27 days.207”
From Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances (2025) · cites this paper as [207]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Vos, T., Lim, S. S., Abbafati, C., Abbas, K. M., Abbasi, M., Abbasifard, M., Abbasi-Kangevari, M., Abbastabar, H., Abd-Allah, F., Abdelalim, A., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30925-9 · Cited by 19,358
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
An epigenetic biomarker of aging for lifespan and healthspan
(2018) · doi:10.1101/276162 · Cited by 3,675
Research works printed but not linked: 1
- Austad, S.N. in Handbook of the Biology of Aging 7th edn (eds Masoro, E.J. & Austad, S.N.) Ch. 23 (Academic Press, 2011).
Cited by
Sorted by citations
Items 601–624 of 624
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Li, Y., Feng, G., Yan, S., Huang, Q., Jin, J., Wang, Y., Pei, Z., Tay, L. X., Li, K., Yuan, Z., et al. (2026) · medRxiv · doi:10.64898/2026.09.26.26364091
Acute Kidney Injury in the Elderly
KIYKIM, A. A., Turgutalp, K., & Demir, S. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017745
Speech clocks decode dementia phenotypes, social exposome, and biological aging
Hernandez, H., Pedraza, L. K., Santamaría‐García, H. A., Moguilner, S., Legaz, A., Prado, P., Cuadros, J., Amoruso, L., Gonzalez, L., Dellavale, D., et al. (2026) · Science Advances · doi:10.1126/sciadv.aef9864 · Cited by 1
Igumnov, D., Melnikova, E., Matrenok, S., Denisov, K. A., Kostiuk, K., Nikolskaya, A., Lopatina, L., Tuna, S., Avchaciov, K., Feizi, A., et al. (2026) · medRxiv · doi:10.64898/2026.10.02.26364618
Brain care score and retinal health: structural and metabolic insights from the UK Biobank
Yu, J., Zhang, Y., Gao, Y. L., Ho, M., Kam, K. W., Gong, B., Young, A. L., Pang, C. P., Tham, C. C., Yam, J. C. S., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02579-z
Hu, G., Liu, Y., & Zhang, Y. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261494123
Shared genetic architecture between constipation and aging-related phenotypes
Liao, Y., Liu, W., Liu, X., & Xiao, Y. (2026) · Medicine · doi:10.1097/md.0000000000051036
The concept of biological age and prospects for its application in clinical practice
OSLOPOV, V. N., & GRISHINA, Y. D. (2026) · Kazan medical journal · doi:10.17816/kmj703616
Birthweight and Breastfeeding Shape Patterns of Biological Aging Across Adult Organs
Wei, W., Qi, X., Cheng, B., Zhao, B., He, D., Hui, J., Feng, J., Cheng, S., Yang, X., Pan, C., et al. (2026) · Aging Cell · doi:10.1111/acel.70762
Lee, M., Garza-Gonzalez, S. M., Schaefer, S. M., Bourassa, K., & Brown, C. K. (2026) · Research Square · doi:10.21203/rs.3.rs-11292832/v1
Huang, H., Zhang, W., Hou, C., Lu, X., Ruan, H., Liu, J., Lei, T., Chen, S., Chen, M., & Liu, Y. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103419
Iacovantuono, M., Hukara, A., Sauer, G. M., Distler, O., Chimenti, M. S., Kania, G., Bruni, C., & Elhaï, M. (2026) · EULAR Rheumatology Open · doi:10.1016/j.ero.2026.100256
Cao, L., Chen, Y., Zhang, W., Jiang, H., Huang, H., Xu, Y., Li, X., Xu, W. L., Qian, K., & Pu, J. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adz4815
Majumdar, V., Palanisamy, A., Yadav, A., Dwivedi, R., Tiwari, P., & Dada, R. (2026) · Advances in biogerontology · doi:10.1016/bs.abger.2026.09.002
Bello‐Chavolla, O. Y., & Fermín‐Martínez, C. A. (2026) · Advances in biogerontology · doi:10.1016/bs.abger.2026.08.002
Selective vulnerability of human basal forebrain subsystems in ageing and neuropsychiatric disordersPreprint
Eisenstein, T., Hu, M. T., & Klein, J. C. (2026) · medRxiv · doi:10.64898/2026.10.13.26365385
Multi-organ imaging genetics: towards an integrated view of brain, body and brain diseases
Fan, Z., & Zhao, B. (2026) · Nature Reviews Neurology · doi:10.1038/s41582-026-01276-x
Epigenetics in development, disease, and therapy: from mechanisms to clinical interventions
Ren, Y., Yang, L., Fei, B., Guo, R., Huang, J., Xu, Z., Wang, R., Zhang, P., Cao, D., Chen, P., et al. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3356-1
Qi, X., Liu, S., Zheng, Q., Li, B., Dai, P., & Su, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10712456/v1
Kim, Y., Kim, D. W., Park, Y. R., Kim, W. J., Kim, J., Chang, Y., Kwon, H., & Yoon, D. (2026) · Journal of Medical Internet Research · doi:10.2196/108392
Zhang, J., Lin, S., Zhang, Z., Yang, S., Yang, Y., & Zhao, H. (2026) · Age and Ageing · doi:10.1093/ageing/afag309
Kojima, M., Sato, Y., Hirayama, A., Ryuge, A., Imaizumi, T., Maeda, K., Kato, N., Kozaki, Y., Sugimura, M., Noda, Y., et al. (2026) · iScience · doi:10.1016/j.isci.2026.118058
Yoon, M.-G., Park, S., Kim, Y., 이동규, Lee, K., Lee, Y.-S., & Lee, Y. (2026) · npj Aging · doi:10.1038/s41514-026-00555-7
Re-timing mortality risk through individualized modelling of lifespan trajectories
Xu, T., Peng, Q., Li, H., Wang, M., Zhang, J., Yu, M., Chong, C. C. Y., Xue, C. C., Zhang, X., He, H., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01822-4
Metadata
- Authors
- Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A.
- Affiliations
- The University of Melbourne (AU)
- National University of Singapore (SG)
- The Royal Melbourne Hospital (AU)
- University of Newcastle Australia (AU)
- Corresponding author
- Tian, Ye Ella
- Zalesky, Andrew
- ORCID
- Venue
- Nature Medicine
- Published
- 2023-04-06
- Volume
- 29
- Issue
- 5
- Pages
- 1221-1231
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4362674168
- Cited by
- 618
- References
- 106
- Citations by year
- 2026: 174 · 2025: 224 · 2024: 180 · 2023: 39
- FWCI
- 41.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, other-oa)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- VU Research Portal (submittedVersion, other-oa)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
- Foundation for the National Institutes of Health (W81XWH-12-2-0012, U01AG024904, AG024904)
- Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University of Southern California (W81XWH-12-2-0012, U01 AG024904)
- F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
- University of Oxford
- Commonwealth Scientific and Industrial Research Organisation
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Universitetet i Oslo
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, AG024904)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- Medical Research Council (U01 AG024904, MC_PC_17228)
- National Health and Medical Research Council (1095227, 1142801, 2008612, APP118153, 1177370, 1152623)
- National Institute on Aging (U01-AG-024904, W81XWH-12-2–0012, AG 024904)
- National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Keywords
- biological aging, biological age prediction, chronic disease, mortality prediction, brain aging, telomere length, lifestyle factors, normative model, survival prediction, premature mortality
- MeSH terms
- Aging, Brain, Chronic Disease, Humans, Leukocytes, Risk Factors
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