Cited by 624Genetics, Aging, and Longevity in Model OrganismsDementia and Cognitive Impairment ResearchFrailty in Older Adults

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Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

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Tian, Ye Ella, Cropley, Vanessa, Maier, Andrea B., Lautenschlager, Nicola T., Breakspear, Michael, Zalesky, Andrew·2023·Nature Medicine

DOI 10.1038/s41591-023-02296-6

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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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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
Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019
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
The Hallmarks of Aging
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
UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age
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
Whole Brain Segmentation
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

Show all 106 references

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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Items 501–600 of 624

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Ng, B. (2025) · Springer eBooks · doi:10.1007/978-3-031-94431-4_8
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Ong, C. K. Y., Lai, J. Y., Narayanan, S. S., Yousof, N. S. A. M., Shakrin, N. M., Yap, Z. Y., Daud, Z. A. M., Rahman, S. A., Noh, M. F. M., Chan, S. W., et al. (2025) · Malaysian Applied Biology · doi:10.55230/mabjournal.v54i4.3587
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Zuo, L., Zhu, N., Wang, B., Li, D., Fan, J., Fan, Z., Shang, Y., Wang, Y., Xu, L., Zhou, P., et al. (2026) · JMIR Aging · doi:10.2196/78243
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Gautier, S. V., Chebotareva, N. V., Khomyakov, S. M., Tsirulniкova, O. M., & Volkova, E. A. (2026) · Russian Journal of Transplantology and Artificial Organs · doi:10.15825/1995-1191-2026-3-238-265
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Zábó, V., Ceglédi, A., Varga, P., Buda, A., Fekete, M., Csípő, T., Fazekas‐Pongor, V., Espinosa, D.-C.-I., Grüber, M., Giovannetti, G., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02526-y
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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: Tian, Ye Ella
Cropley, Vanessa
Maier, Andrea B.
Lautenschlager, Nicola T.
Breakspear, Michael
Zalesky, Andrew
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2023-04-06
Volume: 29
Issue: 5
Pages: 1221-1231
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-023-02296-6
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: other-oa
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

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