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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).
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Devine, J., Monzel, A. S., Shire, D., Rosenberg, A. M., Junker, A., Cohen, A. A., & Picard, M. A. (2025) · Life Metabolism · doi:10.1093/lifemeta/loaf012 · Cited by 6
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Fang, Y., Luo, W., Huang, H. T., Ran, L., He, Y., Cheng, C., Yao, Y., Hou, Y., Zheng, H., Pan, D., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516601123 · Cited by 5
Willows, J. W., Alshahal, Z., Story, N. M., Alves, M. J., Vidal, P., Harris, H. N., Rodrigo, R., Stanford, K. I., Peng, J., Reifsnyder, P. C., et al. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.01.010 · Cited by 5
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Zeiss, C. J., Huttner, A. J., Nairn, A. C., Arnsten, A. F. T., Datta, D., Strittmatter, S. M., Wyk, B. V., & Duque, A. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251323787 · Cited by 5
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Liu, Y., Seguin, C., Sina Mansour, L., Tian, Y., Biase, M. A. D., & Zalesky, A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103761 · Cited by 5
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Egle, M. S., Groechel, R. C., Johansen, M. C., Kucharska‐Newton, A., Gottesman, R. F., & Koton, S. (2025) · Stroke · doi:10.1161/strokeaha.124.049496 · Cited by 5
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Wen, J., Davatzikos, C. A., Tian, Y., Rafii, M. S., Aisen, P., Walker, K. A., Zalesky, A., Ferrucci, L., Zeng, J., Boquet-Pujadas, A., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-025-00560-x · Cited by 5
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Akagi, K., Koizumi, K., Kadowaki, M., Kitajima, I., & Saito, S. (2023) · Cells · doi:10.3390/cells12182297 · Cited by 5
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Fruscione, S., Malta, G., Verso, M. G., Calascibetta, A., Martorana, D., & Cannizzaro, E. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1358212 · Cited by 5
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Maity, S. K., Sharma, A. D., Sarkar, J., Chaudhuri, T., Tantia, O., & Chakrabarti, P. (2024) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2024-004179 · Cited by 7
Zhu, H., Ou, X., Qian, H., & Dundrup, N. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1398916 · Cited by 5
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Maimaitiyiming, M., Yang, Y., Jia, X., & Zhang, X. (2025) · Age and Ageing · doi:10.1093/ageing/afaf132 · Cited by 4
Liao, Y., Dai, Y., Tang, Z., Ma, H., Tu, Z., Yuan, T., Li, Y., & Wang, B. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.115142 · Cited by 4
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Zhu, R., Guo, Y., Wang, J., Yu, K., Shi, W., Pan, W., Yu, X.-L., Chen, W., Dong, S., & Yang, T. (2025) · Nature Communications · doi:10.1038/s41467-025-67223-4 · Cited by 4
Xu, X., Li, Y., Wang, Y., Song, B. R., Zhan, J. J., Zong, G., Chen, X., Xu, K., Sun, L., Feng, C., et al. (2025) · npj Science of Food · doi:10.1038/s41538-025-00625-2 · Cited by 5
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Li, X., Wang, Z., Gram, L., Chen, H., Yang, Y., Liu, J., & Tao, L. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120975 · Cited by 4
Herzog, C. M. S., Poganik, J. R., Boekstein, N., Fortney, K., Peyer, J. G., Mellon, J., Starr, R., Barzilai, N., & Moqri, M. (2025) · npj Aging · doi:10.1038/s41514-025-00313-1 · Cited by 5
Wang, J., Liu, M., Liu, F., Yang, G., Yuan, Z., Huang, H., Zhang, Z., Wang, L., Wu, Y., Fan, W., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-68092-7 · Cited by 4
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Niu, Z., Liu, C., Fan, Y., & Gu, L. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2533687123 · Cited by 5
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Wu, Z., Wu, S., Song, S., Huang, Y., Yu, C. Q., Sun, D. J. Y., Pei, P., Yang, L., Chen, Y., Du, H., et al. (2026) · Aging Cell · doi:10.1111/acel.70565 · Cited by 4
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Gao, C., Wu, X., Wang, Y., Li, G., Ma, L., Wang, C., Xie, S., Chu, C., Madsen, K. H., Hou, Z., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26646 · Cited by 4
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Roberts, A. L., Ratanatharathorn, A. D., Chibnik, L. B., Zhu, Y., Jha, S. C., Kang, J. H., Wolf, E. J., Kubzansky, L. D., & Koenen, K. C. (2024) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2024.10.003 · Cited by 4
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Sun, Z., & Xu, S. (2024) · Journal of Ocean Engineering and Marine Energy · doi:10.1007/s40722-024-00369-3 · Cited by 3
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McMurran, C. E., Karlsson, I., Mostafaei, S., Wang, Y., Gerritsen, L., Pedersen, N. L., & Hägg, S. (2025) · Aging Cell · doi:10.1111/acel.70117 · Cited by 3
Wang, Y.-J., Zhou, Z., Li, Y., Kang, J., Yu, J.-T., Feng, J., Cheng, W., Pan, G., You, J., & Wang, L. (2025) · Communications Medicine · doi:10.1038/s43856-025-00957-w · Cited by 3
Laučytė-Cibulskienė, A., Nilsson, C., Jujić, A., Holm, H., & Christensson, A. G. (2025) · Clinical Kidney Journal · doi:10.1093/ckj/sfaf208 · Cited by 3
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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