The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 101–200 of 624
Schizophrenia and Neurodevelopment: Insights From Connectome Perspective
Sun, X., & Xia, M. (2024) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae148 · Cited by 20
Ocular ageing biomarkers and their clinical utility: a review
Liu, S., Chen, R., Hu, W., & Zhu, Z. (2025) · Visual Neuroscience · doi:10.48130/vns-0025-0009 · Cited by 19
Towards a multi-organ, multi-omics medical digital twin
Wen, J. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01474-w · Cited by 21
The exposome of brain aging across 34 countries
Legaz, A., Moguilner, S., Barttfeld, P., Cuadros, J., Rial, D. S. G., Migeot, J., Farina, F. R., González-Gómez, R., Salazar‐Londoño, S., Azzi, T. D. S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04302-z · Cited by 19
Sleep chart of biological ageing clocks in middle and late life
O’Toole, C. K., Song, Z., Anagnostakis, F., Yang, Z., Tian, Y., Duggan, M. R., Zou, C., Leng, Y., Cai, Y., Bai, W., et al. (2026) · Nature · doi:10.1038/s41586-026-10524-5 · Cited by 21
Self-citationWen, J., Skampardoni, I., Tian, Y., Yang, Z., Cui, Y., Erus, G., Hwang, G., Varol, E., Boquet-Pujadas, A., Chand, G. B., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01412-w · Cited by 21
Self-citationRevolutionizing healthcare: IoMT-enabled digital enhancement via multimodal ADL data fusion
Ghayvat, H., Awais, M. A., Geddam, R., Tiwari, P., & Löwe, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102518 · Cited by 18
Current state and perspectives of CAR T cell therapy in central nervous system diseases
Pfeffer, L. K., Fischbach, F., Heesen, C., & Friese, M. A. (2024) · Brain · doi:10.1093/brain/awae362 · Cited by 19
Divergent biological pathways linking short and long sleep durations to mental and physical health
Li, Y., Gong, W., Sahakian, B. J., Huang, S., Zhang, W., Zhao, Y., Ma, L., Naismith, S. L., Yu, J., Jia, T., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00395-6 · Cited by 18
Zhang, F., Li, R., Liu, Y., Liang, J., Gong, Y., Xiao, C., Cai, J., Wang, T., You, Q., Zhang, J., et al. (2025) · iMeta · doi:10.1002/imt2.70027 · Cited by 18
The X-Age Project to construct a Chinese aging clock
Li, J., Jiang, M., Wang, Q., Zheng, Z., Shen, J., Li, J., Xiong, M., Zheng, Y., Lu, X., Cai, Y., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00935-w · Cited by 19
Hallmarks of healthy cognitive aging: Inter-individual differences in aging trajectories
Abrous, D. N., Blin, N., Boraxbekk, C., Catheline, G., Fitzsimons, C. P., Hilscher, M. M., Lemoine, M., Lopes, L. V., Maass, A., Nilsson, M., et al. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103102 · Cited by 18
Yde, P., Jensen, M. K., Mitarai, N., & Bhatt, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-51669-5 · Cited by 21
Amoruso, L., Hernandez, H., Santamaría‐García, H. A., Moguilner, S., Legaz, A., Prado, P., Cuadros, J., Gonzalez, L., González-Gómez, R., Migeot, J., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-01000-2 · Cited by 18
Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment
Douw, L., Reijneveld, J. C., & Mandal, A. S. (2025) · Nature Reviews Neurology · doi:10.1038/s41582-025-01171-x · Cited by 18
Multimodal clocks of human aging
Li, J., Jiang, B., Zhang, W., Hao, J., Liu, Z., Ji, Q., Zheng, Y., Lu, X., Zheng, Z., Ma, S., et al. (2026) · Cell · doi:10.1016/j.cell.2026.04.025 · Cited by 19
Granell, R., Haider, S., Deliu, M., Ullah, A. A., Mahmoud, O., Fontanella, S., Lowe, L. A., Simpson, A., Dodd, J. W., Arshad, S. H., et al. (2024) · Thorax · doi:10.1136/thorax-2023-220485 · Cited by 17
Accelerated brain ageing during the COVID-19 pandemic
Mohammadi‐Nejad, A., Craig, M., Cox, E. F., Chen, X., Jenkins, G. R., Francis, S., Sotiropoulos, S. N., & Auer, D. P. (2025) · Nature Communications · doi:10.1038/s41467-025-61033-4 · Cited by 20
Lozada-Martínez, I. D., Lozada‐Martinez, L. M., & Anaya, J. (2024) · Aging Medicine · doi:10.1002/agm2.12336 · Cited by 16
Xiang, Y., Meng, Q., Huang, Z., Zhang, N., Zhang, Y., Ding, X., Yu, J., Baimakangzhuo, Liu, L., Xiao, X., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-04223-7 · Cited by 17
Biological aging and generational shifts in early-onset cancer risk
Tian, R., Zong, X., Ren, D., Tica, S., Hong, D. Y., Oduyale, O. K., Buenrostro, J. D., Govindan, R., & Cao, Y. (2026) · Nature Medicine · doi:10.1038/s41591-026-04448-w · Cited by 18
Endogenous retroviruses in multiple sclerosis: A network-based etiopathogenic model
Censi, S. T., Mariani‐Costantini, R., Granzotto, A., Tomassini, V., & Sensi, S. L. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102392 · Cited by 16
Steurer, B., Vanhaelen, Q., & Zhavoronkov, A. A. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae006 · Cited by 16
Cao, Z., Zhang, C., Lei, H., Lin, W., Yu, W.-K., Gao, X., He, Y., Li, X., Xiang, Q., Zhang, Z., et al. (2025) · Advanced Science · doi:10.1002/advs.202515794 · Cited by 16
Chen, J. Y., Ren, Y. Y., Zhou, Y., Wang, Z., Mao, K., Yu, Z., Li, J., Guo, X., Xu, H., Wang, Y., et al. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2026.03.014 · Cited by 15
Kirchner, V. A., Badshah, J. S., Hong, S. K., Martinez, O. M., Pruett, T. L., & Niedernhofer, L. J. (2023) · Transplantation · doi:10.1097/tp.0000000000004838 · Cited by 15
Shafqat, A., Masters, M. C., Tripathi, U., Tchkonia, T., Kirkland, J. L., & Hashmi, S. K. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102400 · Cited by 15
Hou, W., Wang, J., Lin, Q., Wang, X., & Huang, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3595140 · Cited by 14
Magid, H. S. A., Desjardins, M. R., & Hu, Y. (2024) · Health & Place · doi:10.1016/j.healthplace.2024.103323 · Cited by 14
Safety of solid oncology drugs in older patients: a narrative review
Rousseau, A., Géraud, A., Geiss, R., Farcet, A., Spano, J., Hamy, A., & Gougis, P. (2024) · ESMO Open · doi:10.1016/j.esmoop.2024.103965 · Cited by 14
Prediction and detection of terminal diseases using Internet of Medical Things: A review
Otapo, A. T., Othmani, A., Khodabandelou, G., & Ming, Z. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109835 · Cited by 14
Kerlikowsky, F., Krüger, K., Hahn, A., & Schuchardt, J. P. (2025) · Nutrition Research · doi:10.1016/j.nutres.2025.06.005 · Cited by 14
Senolytic Treatment Improve Small Intestine Regeneration in Aging
Luo, Q., Ye, Y., Guo, W., Zhu, Q., Wang, S.-S., Li, N., Cheng, C.-S., & Fan, G. (2023) · Aging and Disease · doi:10.14336/ad.2023.0920 · Cited by 15
Plasma proteomic signature of frailty in 50,506 adults
Jia, X., Gao, W., Hagelin, H., Zhao, Y., Zhang, J., Cao, X., Zhang, L., Wu, Y., Ma, L., Chen, L., et al. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2026.02.013 · Cited by 14
A Framework for an Effective Healthy Longevity Clinic
Mironov, S., Borysova, O., Morgunov, I., Zhou, Z., & Moskalev, A. (2024) · Aging and Disease · doi:10.14336/ad.2024.0328-1 · Cited by 14
Peng, T., Xiang, J., Tian, Y., Tang, X., Wang, L., Gao, L., Luo, O. J., Huang, L., & Chen, G. (2025) · Experimental Gerontology · doi:10.1016/j.exger.2025.112686 · Cited by 13
Self-citationDai, Q., Sun, H., Yang, X., Chen, S., Zhang, X., Yin, Z., Zhao, X., Wu, S., Cao, Z., Wu, Y., et al. (2025) · BMC Public Health · doi:10.1186/s12889-025-22114-7 · Cited by 13
Lacolley, P. J., Avril, S., Gáll, T., Balla, G., Balla, J., Michel, J., & Regnault, V. (2025) · Cardiovascular Research · doi:10.1093/cvr/cvaf137 · Cited by 15
Health Neuroscience—How the Brain/Mind and Body Affect our Health Behavior and Outcomes
Tang, Y., & Tang, R. (2024) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2304069 · Cited by 12
Huang, Y. Y., Peng, H., Wu, Y., Deng, S., Ge, F., Ma, W., Zhou, X., & Songyang, Z. (2024) · Foods · doi:10.3390/foods13111673 · Cited by 12
Dashtmian, A. R., Darvishi, F. B., & Arnold, W. D. (2024) · Cells · doi:10.3390/cells13110928 · Cited by 12
Phytonutrients in the promotion of healthspan: a new perspective
Jacquier, E. F., Kassis, A. N., Marcu, D. E., Contractor, N., Hong, J., Hu, C., Kuehn, M., Lenderink, C., & Rajgopal, A. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1409339 · Cited by 12
Wang, J., Han, X., Yang, Y., Zeng, Y., Qu, Y., Yang, H., Song, J., Qiu, C., & Song, H. (2024) · BMC Medicine · doi:10.1186/s12916-024-03578-7 · Cited by 12
Drewelies, J., Homann, J., Vetter, V. M., Düzel, S., Kühn, S., Deecke, L., Steinhagen‐Thiessen, E., Jawinski, P., Markett, S. A., Lindenberger, U., et al. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae244 · Cited by 12
Zhao, R., Lu, H., Yuan, H., Chen, S., Xu, K., Zhang, T., Liu, Z., Jiang, Y., Suo, C., & Chen, X. (2024) · GeroScience · doi:10.1007/s11357-024-01411-w · Cited by 12
Park, B., Nicaise, A. M., Tsitsipatis, D., Pirvan, L., Žucha, D., Munteanu, A., Prasad, P., Larraz, M., Bulgaru, C., Kollyfas, R., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.09.022 · Cited by 12
The nature of the relation between mental well-being and ill-being
Tamnes, C. K., Bekkhus, M., Eilertsen, M., Nes, R. B., Prydz, M. B., Ystrøm, E., Aksnes, E. R., Andersen, S. N., Ask, H., Ayorech, Z., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02319-x · Cited by 13
Gallrein, C., Meyer, D. H., Woitzat, Y., Ramirez-Ramirez, V., Vuong-Brender, T. T. K., Kirstein, J., & Schumacher, B. (2026) · Nature Aging · doi:10.1038/s43587-026-01067-5 · Cited by 12
Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm
Montella, A., Tranfa, M., Scaravilli, A., Barkhof, F., Brunetti, A., Cole, J. H., Gravina, M., Marrone, S., Riccio, D., Riccio, E., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26599 · Cited by 12
Chronic kidney disease promotes ageing in a multiorgan disease network
Benzing, T. R., & Schumacher, B. (2023) · Nature Reviews Nephrology · doi:10.1038/s41581-023-00729-6 · Cited by 12
The Longevity Med Summit: insights on healthspan from cell to society
Falshaw, N., Sagner, M., & Siow, R. C. M. (2024) · Frontiers in Aging · doi:10.3389/fragi.2024.1417455 · Cited by 11
Lin, L., Tang, R., Liu, Y., Li, Z., Li, H., & Yang, H. (2024) · Pharmacological Research · doi:10.1016/j.phrs.2024.107323 · Cited by 11
Wanner, C., Vanholder, R. C., Ortíz, A., Davenport, A., Canaud, B., Blankestijn, P. J., Masereeuw, R., Kooman, J. P., Castellano, G., Stamatialis, D. F., et al. (2024) · Frontiers in Nephrology · doi:10.3389/fneph.2024.1455260 · Cited by 11
Pan, R., Xiao, Y., Xu, Y., Song, J. G., Yi, W., Liu, J., Song, R., Li, X., Liu, L., Yuan, J., et al. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.136162 · Cited by 11
Tomkova, K., Roman, M., Adebayo, A. S., Sheikh, S., Yusoff, S., Gulston, M. K., Joel‐David, L., Lai, F. Y., Murgia, A., Eagle‐Hemming, B., et al. (2024) · npj Aging · doi:10.1038/s41514-024-00183-z · Cited by 11
AgeML: Age Modeling With Machine Learning
Condado, J. G., Elorriaga, I. T., Cortés, J. M., & Erramuzpe, A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3531017 · Cited by 11
Huang, Y., Wang, S., Tian, L., Zhang, X., Liu, S., Zhu, Z., Wang, W., Shi, D., He, M., & Shang, X. (2025) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2025.100525 · Cited by 11
Low Social Engagement and Risk of Death in Older Adults
Abugroun, A., Shah, S. J., Covinsky, K. E., Hubbard, C. C., Newman, J. C., & Fang, M. C. (2025) · Journal of the American Geriatrics Society · doi:10.1111/jgs.19511 · Cited by 12
Steatotic Liver Disease in Older Adults: Clinical Implications and Unmet Needs
Clayton‐Chubb, D., Kemp, W. W., Majeed, A., Lange, P. W., Fitzpatrick, J. A., Vaz, K., Lubel, J. S., Hodge, A. D., Ryan, J. L., McNeil, J. J., et al. (2025) · Nutrients · doi:10.3390/nu17132189 · Cited by 12
Mun, S., Ahn, J., Jin, G., Kim, H. N., & Bang, S. (2025) · BioChip Journal · doi:10.1007/s13206-025-00230-1 · Cited by 11
Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia
Coronel‐Oliveros, C., Moguilner, S., Hernandez, H., Cruzat, J., Báez, S., Medel, V., Cuadros, J., Santamaría‐García, H. A., Valdes-Sosa, P. A., Lopera, F. J., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00502-7 · Cited by 11
Brain–heart–eye axis revealed by multi-organ imaging genetics and proteomics
Zalesky, A., Tian, Y., Ferrucci, L., Bai, W., Rafii, M. S., Aisen, P. S. S., Zeng, J., Boquet-Pujadas, A., Anagnostakis, F., Duggan, M. R., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01506-5 · Cited by 11
Self-citationWang, W.-H., Peng, H., Zeng, M., Liu, J., Liang, G.-B., & He, Z. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04735-x · Cited by 12
Multiorgan biological age shows that no organ system is an island
(2024) · Nature Aging · doi:10.1038/s43587-024-00690-4 · Cited by 11
Health Galaxy: A Multi-organ Trajectory-Computing Framework in Type 2 Diabetes
Lv, H., Li, J., Li, D., Sheng, B., & Wang, Z. (2026) · Research · doi:10.34133/research.1378 · Cited by 11
Ribeiro, B. C., Silva, A. D. A. C. E., de Souza, L. B. R., Moraes, J. B. D. A., Carneiro, S. R., & Neves, L. M. T. (2024) · Experimental Gerontology · doi:10.1016/j.exger.2024.112370 · Cited by 10
Ecker, V., Früh, M., Yang, B., Gatidis, S., & Küstner, T. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447990 · Cited by 10
Dissecting unique and common variance across body and brain health indicators using age prediction
Beck, D., de Lange, A. G., Gurholt, T. P., Voldsbekk, I., Maximov, I. I., Subramaniapillai, S., Schindler, L. S., Hindley, G., Leonardsen, E. H., Rahman, Z., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26685 · Cited by 10
Zhang, C., Liu, S., & Chen, G. (2024) · Aging and Disease · doi:10.14336/ad.2024.0478 · Cited by 10
DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences
An, L., Zhang, C., Wulan, N., Zhang, S., Chen, P., Ji, F., Ng, K. K., Chen, C., Zhou, J. H., Yeo, B. T. T., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103354 · Cited by 10
Cai, J., Yu, R., Zhang, N., Zhang, H., Zhang, Y., Xiang, Y., Xu, H., Xiao, X., & Zhao, X. (2025) · Circulation Journal · doi:10.1253/circj.cj-24-0824 · Cited by 11
Nishimura, E. S., Hishikawa, A., Nakamichi, R., Akashio, R., Chikuma, S., Hashiguchi, A., Yoshimoto, N., Hama, E. Y., Maruki, T., Ito, W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59297-x · Cited by 10
Health octo tool matches personalized health with rate of aging
Salimi, S., Vehtari, A., Salive, M. E., Kaeberlein, M. R., Raftery, D., & Ferrucci, L. (2025) · Nature Communications · doi:10.1038/s41467-025-58819-x · Cited by 11
Gene therapy strategies for aging intervention
Jing, Y., Ren, J., Qu, J. T., & Liu, G. (2025) · Cell Insight · doi:10.1016/j.cellin.2025.100254 · Cited by 10
Atlas of proteomic signatures of brain structure and its links to brain disorders
Ren, P., Hou, X., Li, Z., You, J., Li, Y., Zhang, W., Gong, W., Zhang, B., Wu, B., Wang, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60185-7 · Cited by 14
Physiological aging in three dimensions
Ma, F., & Sen, R. (2025) · Trends in Cell Biology · doi:10.1016/j.tcb.2025.08.002 · Cited by 11
Cytotoxic T Cells: Kill, Memorize, and Mask to Maintain Immune Homeostasis
Kumar, V. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26188788 · Cited by 12
Patterns of Organ‐Specific Proteomic Aging in Relation to Lifestyle, Diseases, and Mortality
Wang, Q., Huang, J., He, Q., Sun, M., Snyder, M. P., & Li, L. (2025) · Aging Cell · doi:10.1111/acel.70251 · Cited by 11
Harinath, G., Lee, V., Nyquist, A., Moel, M., Wouters, M. C. A., Hagemeier, J., Verkennes, B., Tacubao, C., Nasher, S., Kauppi, K., et al. (2025) · GeroScience · doi:10.1007/s11357-025-01532-w · Cited by 10
Network modeling and topology of aging
Li, F., Yang, D., Wu, S., Xue, C., Sang, M., Liu, X., Che, J., Wu, J., Gragnoli, C., Griffin, C. H., et al. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.10.006 · Cited by 9
Fan, G., Liu, Q., Bi, J., Fang, Q., Luo, F., Huang, X., Li, H., Guo, W., Liu, B., Yan, L., et al. (2024) · Human Reproduction · doi:10.1093/humrep/deae250 · Cited by 9
Santin, Y., Chiesa, M., Alfonso, A., Doghri, Y., Kang, R., Haidar, F., Oreja-Fuentes, P., Fousset, O., Zahreddine, R., Guardia, M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adt1670 · Cited by 9
Hu, F., & Wen, J. (2025) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-11-2024-1668 · Cited by 9
Ou, Y., Wang, Z., Yuan, Y., He, Y., Li, W., Ren, H., Li, J., Chen, S., & Liu, Y. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01919-8 · Cited by 10
Randomized controlled trial of resistance exercise and brain aging clocks
González-Gómez, R., Demnitz, N., Coronel, C., Gates, A. T., Kjær, M., Siebner, H. R., Boraxbekk, C., & Ibáñez, A. M. (2026) · GeroScience · doi:10.1007/s11357-026-02141-x · Cited by 10
Sandalova, E., & Maier, A. B. (2023) · Biogerontology · doi:10.1007/s10522-023-10077-4 · Cited by 9
Self-citationVujosevic, S., Limoli, C., & Kozak, I. (2024) · Eye · doi:10.1038/s41433-024-03341-5 · Cited by 10
Liu, K. S., Wang, B., Mak, I. L., Choi, E. P. H., Lam, C. L. K., & Wan, E. Y. F. (2025) · Hypertension Research · doi:10.1038/s41440-025-02188-x · Cited by 9
Aging imaging: the future demand of health management
Zhong, Y., Jin, C., Zhang, X., Zhou, R., Dou, X., Wang, J., Tian, M., & Zhang, H. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06377-z · Cited by 10
AI analysis of chest radiographs as a biomarker of biological age
Babyn, P., & Adams, S. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00143-5 · Cited by 8
Characterizing Brain–Cardiovascular Aging Using Multiorgan Imaging and Machine Learning
Amirmoezzi, Y., Cropley, V., Sina Mansour, L., Seguin, C., Zalesky, A., & Tian, Y. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1440-24.2024 · Cited by 8
Self-citationTowards Precision Geromedicine in Singapore
Amalaraj, J. J. P., Island, L., Ong, J. Y. Y., Wang, L. Y. T., Valderas, J. M., Dunn, M., Chong, Y. S., Meij, J., & Maier, A. B. (2025) · GeroScience · doi:10.1007/s11357-025-01686-7 · Cited by 8
Self-citationOrgan Specificity and Commonality of Epigenetic Aging in Low‐ and High‐Running Capacity Rats
Kawamura, T., Kerepesi, C., Sarkar, J. P., Torma, F., Bori, Z., Zhou, L., Bakonyi, P., Kolonics, A., Balogh, L., Higuchi, M., et al. (2025) · Aging Cell · doi:10.1111/acel.70110 · Cited by 8
Khan, A. H., Ali, D., Ahmed, S., Alhumam, A., Khan, M. F., & Siddiqui, S. Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-19395-8 · Cited by 8
Wang, Z., Zhang, X., Li, W., & Yang, B. (2025) · Science China Life Sciences · doi:10.1007/s11427-024-2967-8 · Cited by 8
Li, M., Xu, S., Cai, H., Xiao, J., & Qin, Y. (2026) · Metabolism · doi:10.1016/j.metabol.2026.156547 · Cited by 8
From whole-body to organ-specific biological age clocks
Zalesky, A., Wen, J., & Tian, Y. (2026) · Nature Aging · doi:10.1038/s43587-026-01113-2 · Cited by 8
Self-citationLi, D., Ge, S., Liu, Y., Pan, M., Wang, X., Han, G., Zou, S., Liu, R., Niu, K., Zhao, C., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108558 · Cited by 8
Sol, J., Ortega, M., Portero‐Otín, M., Piñol‐Ripoll, G., Ribas, V. J., Artigues-Barberà, E., Butı́, M., Pamplona, R., & Jové, M. (2024) · BMC Medicine · doi:10.1186/s12916-023-03206-w · Cited by 9
Wannan, C., Eratne, D., Santillo, A. F., Malpas, C. B., Cilia, B.-J., Dean, O. M., Walker, A. J., Berk, M., Bousman, C., Everall, I., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116073 · Cited by 9
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 ›
Are you an author, or do you see a problem with this page? Request a review