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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Cortical Surface-Based Analysis

Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

An epigenetic biomarker of aging for lifespan and healthspan

(2018) · doi:10.1101/276162 · Cited by 3,675 · see citing works

An epigenetic biomarker of aging for lifespan and healthspan

Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675 · see citing works

Frailty: implications for clinical practice and public health

Hoogendijk, E. O., Afilalo, J., Ensrud, K. E., Kowal, P. R., Onder, G., & Fried, L. P. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31786-6 · Cited by 3,369 · see citing works

Ageing as a risk factor for neurodegenerative disease

Hou, Y., Dan, X., Babbar, M., Wei, Y., Hasselbalch, S. G., Croteau, D. L., & Bohr, V. A. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0244-7 · Cited by 3,310 · see citing works

Frailty in relation to the accumulation of deficits

Rockwood, K., & Mitnitski, A. (2007) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/62.7.722 · Cited by 3,076 · see citing works

Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works

Geroscience: Linking Aging to Chronic Disease

Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., Franceschi, C., Lithgow, G. J., Morimoto, R. I., Pessin, J. E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.039 · Cited by 2,764 · see citing works

Alzheimer's Disease Neuroimaging Initiative (ADNI)

Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181cb3e25 · Cited by 2,572 · see citing works

DNA methylation GrimAge strongly predicts lifespan and healthspan

Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., et al. (2019) · Aging · doi:10.18632/aging.101684 · Cited by 2,540 · see citing works

Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths

(2007) · The Lancet · doi:10.1016/s0140-6736(07)61778-4 · Cited by 2,331 · see citing works

Liver enzyme alteration: a guide for clinicians

Giannini, E. G. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.1040752 · Cited by 2,022 · see citing works

Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020 · see citing works

Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study

Leong, D. P., Teo, K. K., Rangarajan, S., Lopez-Jaramillo, P., Avezum, A., Orlandini, A., Seron, P., Ahmed, S. H., Rosengren, A., Kelishadi, R., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(14)62000-6 · Cited by 1,876 · see citing works

Ageing as a Risk Factor for Disease

Niccoli, T., & Partridge, L. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.024 · Cited by 1,831 · see citing works

Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection

Blackburn, E. H., Epel, E. S., & Lin, J. (2015) · Science · doi:10.1126/science.aab3389 · Cited by 1,596 · see citing works

Harmonization of cortical thickness measurements across scanners and sites

Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580 · see citing works

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

Alfaro‐Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernández-Fernández, M., Vallée, E., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.034 · Cited by 1,563 · see citing works

Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain

Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425 · see citing works

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., Roy, N., Frosch, M. P., McKee, A. C., Wald, L. L., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.042 · Cited by 1,267 · see citing works

Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Wood, A. M., Kaptoge, S., Butterworth, A. S., Willeit, P., Warnakula, S., Bolton, T., Paige, E., Paul, D. S., Sweeting, M., Burgess, S., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30134-x · Cited by 1,188 · see citing works

DunedinPACE, a DNA methylation biomarker of the pace of aging

Belsky, D. W., Caspi, A., Corcoran, D. L., Sugden, K., Poulton, R., Arseneault, L., Baccarelli, A., Chamarti, K., Gao, X., Hannon, E., et al. (2022) · eLife · doi:10.7554/elife.73420 · Cited by 1,083 · see citing works

DNA methylation aging clocks: challenges and recommendations

Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1824-y · Cited by 1,025 · see citing works

Quantification of biological aging in young adults

Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506264112 · Cited by 1,012 · see citing works

The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease

Ellis, K. A., Bush, A. I., Darby, D., Fazio, D. D., Foster, J. K., Hudson, P. J., Lautenschlager, N. T., Lenzo, N. P., Martins, R. N., Maruff, P. T., et al. (2009) · International Psychogeriatrics · doi:10.1017/s1041610209009405 · Cited by 970 · see citing works

Identification of seven loci affecting mean telomere length and their association with disease

CARDIoGRAM consortium, Codd, V., Nelson, C. P., Albrecht, E., Mangino, M., Deelen, J., Buxton, J. L., Hottenga, J. J., Fischer, K., Esko, T., et al. (2013) · Nature Genetics · doi:10.1038/ng.2528 · Cited by 933 · see citing works

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers

Cole, J. H., & Franke, K. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.10.001 · Cited by 912 · see citing works

Impaired lung function and mortality risk in men and women: findings from the Renfrew and Paisley prospective population study

Hole, D. J., Watt, G. C. M., Davey-Smith, G., Hart, C., Gillis, C. R., & Hawthorne, V. M. (1996) · BMJ · doi:10.1136/bmj.313.7059.711 · Cited by 910 · see citing works

Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies

Shan, Z., Ma, H., Xie, M., Yan, P., Guo, Y., Bao, W., Rong, Y., Jackson, C. L., Hu, F. B., & Liu, L. (2015) · Diabetes Care · doi:10.2337/dc14-2073 · Cited by 902 · see citing works

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.059 · Cited by 872 · see citing works

Brain age predicts mortality

Cole, J. H., Ritchie, S. J., Bastin, M. E., Hernández, M. C. V., Maniega, S. M., Royle, N. A., Corley, J., Pattie, A., Harris, S. E., Zhang, Q., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.62 · Cited by 828 · see citing works

Reprogramming to recover youthful epigenetic information and restore vision

Lu, Y., Brommer, B., Tian, X., Krishnan, A., Meer, M., Wang, C., Vera, D. L., Zeng, Q., Yu, D., Bonkowski, M. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2975-4 · Cited by 820 · see citing works

Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies

Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., Felton, S., Matsuyama, M., Lowe, D., Kabacik, S., et al. (2018) · Aging · doi:10.18632/aging.101508 · Cited by 815 · see citing works

Systolic Blood Pressure Reduction and Risk of Cardiovascular Disease and Mortality: A Systematic Review and Network Meta-analysis

Bundy, J. D., Li, C., Stuchlik, P., Bu, X., Kelly, T. N., Mills, K. T., He, H., Chen, J., Whelton, P. K., & He, J. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2017.1421 · Cited by 783 · see citing works

Measuring biological aging in humans: a quest

Ferrucci, L., Gonzalez‐Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & de Cabo, R. (2020) · Aging Cell · doi:10.1111/acel.13080 · Cited by 781 · see citing works

Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants

Celis-Morales, C. A., Welsh, P., Lyall, D. M., Steell, L., Petermann, F., Anderson, J., Iliodromiti, S., Sillars, A., Graham, N., Mackay, D. F., et al. (2018) · BMJ · doi:10.1136/bmj.k1651 · Cited by 772 · see citing works

A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study

Liu, Z., Kuo, P.-L., Horvath, S., Crimmins, E., Ferrucci, L., & Levine, M. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002718 · Cited by 754 · see citing works

A new approach to the concept and computation of biological age.

Klemera, P., & Ďoubal, S. (2006) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2005.10.004 · Cited by 727 · see citing works

Ageing hallmarks exhibit organ-specific temporal signatures

Schaum, N., Lehallier, B., Hahn, O., Pálovics, R., Hosseinzadeh, S., Lee, S. E., Sit, R., Lee, D. P., Losada, P. M., Zardeneta, M. E., et al. (2020) · Nature · doi:10.1038/s41586-020-2499-y · Cited by 671 · see citing works

Common brain disorders are associated with heritable patterns of apparent aging of the brain

Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0471-7 · Cited by 559 · see citing works

A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

Iglesias, J. E., Insausti, R., Lerma‐Usabiaga, G., Bocchetta, M., Leemput, K. V., Greve, D. N., van der Kouwe, A. J. W., Fischl, B. R., Caballero‐Gaudes, C., & Paz‐Alonso, P. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.012 · Cited by 558 · see citing works

Molecular and physiological manifestations and measurement of aging in humans

Khan, S. S., Singer, B. D., & Vaughan, D. E. (2017) · Aging Cell · doi:10.1111/acel.12601 · Cited by 530 · see citing works

Accurate brain age prediction with lightweight deep neural networks

Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A., & Smith, S. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101871 · Cited by 510 · see citing works

On the Law of Mortality and the Construction of Annuity Tables

Makeham, W. M. (1860) · The Assurance Magazine and Journal of the Institute of Actuaries · doi:10.1017/s204616580000126x · Cited by 458 · see citing works

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

Saygin, Z. M., Kliemann, D., Iglesias, J. E., van der Kouwe, A. J. W., Boyd, E. C., Reuter, M., Stevens, A. A., Leemput, K. V., McKee, A. C., Frosch, M. P., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.046 · Cited by 455 · see citing works

Quantitative Assessment of Structural Image Quality

Rosen, A. F. G., Roalf, D., Ruparel, K., Blake, J., Seelaus, K., Villa, L., Ciric, R., Cook, P. A., Davatzikos, C., Elliott, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.059 · Cited by 443 · see citing works

Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117)

Clarke, T.-K., Adams, M. J., Davies, G., Howard, D. M., Hall, L. S., Padmanabhan, S., Murray, A. D., Smith, B. H., Campbell, A., Hayward, C., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.153 · Cited by 440 · see citing works

Brain age and other bodily ‘ages’: implications for neuropsychiatry

Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0098-1 · Cited by 436 · see citing works

Estimation of brain age delta from brain imaging

Smith, S. M., Vidaurre, D., Alfaro‐Almagro, F., Nichols, T. E., & Miller, K. L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.017 · Cited by 432 · see citing works

Network Physiology: How Organ Systems Dynamically Interact

Bartsch, R. P., Liu, K. K. L., Bashan, A., & Ivanov, P. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142143 · Cited by 418 · see citing works

Personal aging markers and ageotypes revealed by deep longitudinal profiling

Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., & Snyder, M. (2020) · Nature Medicine · doi:10.1038/s41591-019-0719-5 · Cited by 408 · see citing works

Conceptualizations of frailty in relation to older adults

Markle‐Reid, M., & Browne, G. B. (2003) · Journal of Advanced Nursing · doi:10.1046/j.1365-2648.2003.02767.x · Cited by 401 · see citing works

Brain age prediction using deep learning uncovers associated sequence variants

Jónsson, B. A., Björnsdóttir, G., Thorgeirsson, T. E., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D. F., Stefánsson, H., Stefánsson, K., & Ulfarsson, M. O. (2019) · Nature Communications · doi:10.1038/s41467-019-13163-9 · Cited by 387 · see citing works

Recommendations for the Assessment of Carotid Arterial Plaque by Ultrasound for the Characterization of Atherosclerosis and Evaluation of Cardiovascular Risk: From the American Society of Echocardiography

Johri, A. M., Nambi, V., Naqvi, T. Z., Feinstein, S. B., Kim, E. S. H., Park, M. M., Becher, H. H., & Sillesen, H. H. (2020) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2020.04.021 · Cited by 387 · see citing works

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide

Bashyam, V., Erus, G., Doshi, J. J., Habes, M., Nasrallah, I. M., Truelove‐Hill, M., Srinivasan, D., Mamourian, L., Pomponio, R., Yong Fan, A., et al. (2020) · Brain · doi:10.1093/brain/awaa160 · Cited by 372 · see citing works

C-reactive protein: relation to total mortality, cardiovascular mortality and cardiovascular risk factors in men

Mendall, M. A. (2000) · European Heart Journal · doi:10.1053/euhj.1999.1982 · Cited by 359 · see citing works

Healthy aging: The ultimate preventative medicine

Kaeberlein, M., Rabinovitch, P. S., & Martin, G. M. (2015) · Science · doi:10.1126/science.aad3267 · Cited by 341 · see citing works

Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy

Elliott, M. L., Caspi, A., Houts, R., Ambler, A. P., Broadbent, J. M., Hancox, R. J., Harrington, H., Hogan, S., Keenan, R. J., Knodt, A. R., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00044-4 · Cited by 330 · see citing works

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Schulz, M.-A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B., & Bzdok, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18037-z · Cited by 315 · see citing works

A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images

Ramsey, J., Glymour, M., Sánchez-Romero, R., & Glymour, C. (2016) · International Journal of Data Science and Analytics · doi:10.1007/s41060-016-0032-z · Cited by 304 · see citing works

Long-term Risk of Mortality and End-Stage Renal Disease Among the Elderly After Small Increases in Serum Creatinine Level During Hospitalization for Acute Myocardial Infarction

Newsome, B. B. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.6.609 · Cited by 300 · see citing works

Bayesian segmentation of brainstem structures in MRI

Iglesias, J. E., Leemput, K. V., Bhatt, P., Casillas, C., Dutt, S., Schuff, N. W., Truran‐Sacrey, D., Boxer, A. L., & Fischl, B. R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.065 · Cited by 286 · see citing works

A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE

Le, T. T., Kuplicki, R., McKinney, B. A., Yeh, H., Thompson, W. K., Tulsa 1000 Investigators, & Paulus, M. P. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00317 · Cited by 260 · see citing works

Normative brain size variation and brain shape diversity in humans

Reardon, P. K., Seidlitz, J., Vandekar, S., Liu, S., Patel, R., Park, M. T. M., Alexander-Bloch, A., Clasen, L. S., Blumenthal, J. D., Lalonde, F. M., et al. (2018) · Science · doi:10.1126/science.aar2578 · Cited by 255 · see citing works

Genome-wide association study of medication-use and associated disease in the UK Biobank

Wu, Y., Byrne, E. M., Zheng, Z., Kemper, K. E., Yengo, L., Mallett, A. J., Yang, J., Visscher, P. M., & Wray, N. R. (2019) · Nature Communications · doi:10.1038/s41467-019-09572-5 · Cited by 243 · see citing works

Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank

Codd, V., Denniff, M. J., Swinfield, C. E., Warner, S., Papakonstantinou, M., Sheth, S., Nanus, D. E., Budgeon, C. A., Musicha, C., Bountziouka, V. P., et al. (2022) · Nature Aging · doi:10.1038/s43587-021-00166-9 · Cited by 237 · see citing works

Premature mortality attributable to socioeconomic inequality in England between 2003 and 2018: an observational study

Lewer, D., Jayatunga, W., Aldridge, R. W., Edge, C., Marmot, M. G., Story, A., & Hayward, A. C. (2019) · The Lancet Public Health · doi:10.1016/s2468-2667(19)30219-1 · Cited by 225 · see citing works

Distinct biological ages of organs and systems identified from a multi-omics study

Nie, C., Li, Y., Li, R., Yan, Y., Zhang, D., Li, T., Li, Z., Sun, Y., Zhen, H., Ding, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110459 · Cited by 225 · see citing works

Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans

Mangino, M., Hwang, S., Spector, T. D., Hunt, S. C., Kimura, M., Fitzpatrick, A. L., Christiansen, L., Petersen, I., Elbers, C. C., Harris, T. B., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds382 · Cited by 224 · see citing works

How should we define light or intermittent smoking? Does it matter?

Husten, C. G. (2009) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntp010 · Cited by 223 · see citing works

Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233 110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes

Cassidy, S., Chau, J. Y., Catt, M., Bauman, A. E., & Trenell, M. I. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010038 · Cited by 223 · see citing works

Systolic blood pressure and mortality

Port, S. C., Demer, L. L., Jennrich, R. I., Walter, D. O., & Garfinkel, A. (2000) · The Lancet · doi:10.1016/s0140-6736(99)07051-8 · Cited by 214 · see citing works

Cellular senescence and chronological age in various human tissues: A systematic review and meta‐analysis

Tuttle, C. S. L., Waaijer, M. E. C., Slee‐Valentijn, M. S., Stijnen, T., Westendorp, R., & Maier, A. B. (2020) · Aging Cell · doi:10.1111/acel.13083 · Cited by 211 · see citing works

Mortality and cardiovascular and respiratory morbidity in individuals with impaired FEV1 (PURE): an international, community-based cohort study

Duong, M., Islam, S., Rangarajan, S., Leong, D. P., Kurmi, O., Teo, K. K., Killian, K. J., Dagenais, G. R., Lear, S. A., Wielgosz, A. T., et al. (2019) · The Lancet Global Health · doi:10.1016/s2214-109x(19)30070-1 · Cited by 209 · see citing works

Serum aminotransferase activity and mortality risk in a United States community

Lee, T. H., Kim, W. R., Benson, J. T., Therneau, T. M., & Melton, J. L. (2008) · Hepatology · doi:10.1002/hep.22090 · Cited by 208 · see citing works

Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception

Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0191-3 · Cited by 207 · see citing works

Spirometric “lung age” estimation for motivating smoking cessation

Morris, J. F., & Temple, W. P. (1985) · Preventive Medicine · doi:10.1016/0091-7435(85)90085-4 · Cited by 171 · see citing works

Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change

Vidal-Piñeiro, D., Wang, Y.-P., Krogsrud, S. K., Amlien, I., Baaré, W., Bartrés-Faz, D., Bertram, L., Brandmaier, A., Drevon, C. A., Düzel, S., et al. (2021) · bioRxiv · doi:10.7554/elife.69995 · Cited by 149 · see citing works

Age trajectories of functional activation under conditions of low and high processing demands: An adult lifespan fMRI study of the aging brain

Kennedy, K. M., Rodrigue, K. M., Bischof, G. N., Hebrank, A. C., Reuter‐Lorenz, P. A., & Park, D. C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.056 · Cited by 147 · see citing works

Learning patterns of the ageing brain in MRI using deep convolutional networks

Dinsdale, N. K., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., & Namburete, A. I. L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117401 · Cited by 138 · see citing works

Longevity and aging

Kaeberlein, M. R. (2013) · F1000Prime Reports · doi:10.12703/p5-5 · Cited by 131 · see citing works

Assessment of biological age by principal component analysis

Nakamura, E., Miyao, K., & Ozeki, T. (1988) · Mechanisms of Ageing and Development · doi:10.1016/0047-6374(88)90109-1 · Cited by 127 · see citing works

C-reactive protein and all-cause mortality--the Copenhagen City Heart Study

Zacho, J., Tybjærg–Hansen, A., & Nordestgaard, B. G. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq103 · Cited by 112 · see citing works

Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Wood, A., Kaptoge, S. K., Butterworth, A. S., et al. (2018) · UEF eRepo (University of Eastern Finland) · doi:10.17863/cam.20236 · Cited by 97 · see citing works

Harmonization of cortical thickness measurements across scanners and sitesPreprint

Fortin, J., Cullen, N. C., Sheline, Y. I., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/148502 · Cited by 88 · see citing works

DCAF4, a novel gene associated with leucocyte telomere length

Mangino, M., Christiansen, L., Stone, R. C., Hunt, S. C., Horvath, K., Eisenberg, D. T. A., Kimura, M., Petersen, I., Kark, J. D., Herbig, U., et al. (2015) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102681 · Cited by 77 · see citing works

Estimating Biological Age in the Singapore Longitudinal Aging Study

Zhong, X., Lu, Y., Gao, Q., Nyunt, M. S. Z., Fülöp, T., Monterola, C. P., Tong, J. C., Larbi, A., & Ng, T. P. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz146 · Cited by 76 · see citing works

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Chan, M. S., Arnold, M., Offer, A. R., Hammami, I., Mafham, M. M., Armitage, J., Perera, R., & Parish, S. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab069 · Cited by 71 · see citing works

The assessment of biological age and sex differences of human aging

Voitenko, V. P., & Tokar, A. V. (1983) · Experimental Aging Research · doi:10.1080/03610738308258458 · Cited by 70 · see citing works

Sex Differences in Longevity and Aging

Austad, S. N. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-378638-8.00023-3 · Cited by 66 · see citing works

‘Your Heart Forecast’: a new approach for describing and communicating cardiovascular risk?

Wells, S. R., Kerr, A., Eadie, S., Wiltshire, C., & Jackson, R. (2010) · Heart · doi:10.1136/hrt.2009.191320 · Cited by 60 · see citing works

Study of Longitudinal Aging in Mice: Presentation of Experimental Techniques

Palliyaguru, D. L., Teixeira, C. V. L., Duregon, E., Germanio, C. D., Alfaras, I., Mitchell, S. J., Navas‐Enamorado, I., Shiroma, E. J., Studenski, S. A., Bernier, M., et al. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa285 · Cited by 58 · see citing works

Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

Kuo, C., Pilling, L. C., Atkins, J. L., Masoli, J. A. H., Delgado, J., Tignanelli, C. J., Kuchel, G. A., Melzer, D. R., Beckman, K. B., & Levine, M. E. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab060 · Cited by 58 · see citing works

On the use of regression analysis for the estimation of human biological age

Krøll, J., & Saxtrup, O. (2000) · Biogerontology · doi:10.1023/a:1026594602252 · Cited by 49 · see citing works

Assessing the rate of aging to monitor aging itself

Xia, X., Wang, Y., Yu, Z., Chen, J., & Han, J. J. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101350 · Cited by 46 · see citing works

A nonlinear simulation framework supports adjusting for age when analyzing BrainAGEPreprint

Le, T. T., Kuplicki, R., McKinney, B. A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/377648 · Cited by 38 · see citing works

Covid-19: UK records first death, as world’s cases exceed 100 000

Mahase, E. (2020) · BMJ · doi:10.1136/bmj.m943 · Cited by 33 · see citing works

Genetically Increased Telomere Length and Aging-Related Traits in the U.K. Biobank

Demanelis, K., Lin, T., & Pierce, B. L. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz240 · Cited by 24 · see citing works

A study on the basic nature of human biological aging processes based upon a hierarchical factor solution of the age-related physiological variables

Nakamura, E. (1991) · Mechanisms of Ageing and Development · doi:10.1016/0047-6374(91)90128-m · Cited by 19 · see citing works

Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policyPreprint

Elliott, M. L., Caspi, A., Houts, R., et al. (2021) · medRxiv · doi:10.1101/2021.03.09.21252473 · Cited by 19 · see citing works

Kidney Age Index (KAI): A novel age-related biomarker to estimate kidney function in patients with diabetic kidney disease using machine learning

Nagaraj, S. B., Kieneker, L. M., & Pena, M. J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106434 · Cited by 13 · see citing works

Learning Patterns of the Ageing Brain in MRI using Deep Convolutional NetworksPreprint

Dinsdale, N. K., Bluemke, E., Smith, S. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.17.253732 · Cited by 8 · see citing works

The Role and Regulation of the Immune Responses

Dembić, Z. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-419998-9.00004-3 · Cited by 5 · see citing works

Organ aging signatures in the plasma proteome track health and disease

Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634 · see citing works

From geroscience to precision geromedicine: Understanding and managing aging

Kroemer, G., Maier, A. B., Cuervo, A. M., Gladyshev, V. N., Ferrucci, L., Gorbunova, V., Kennedy, B. K., Rando, T. A., Seluanov, A., Sierra, F., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.011 · Cited by 395 · see citing works

Validation of biomarkers of aging

Moqri, M., Herzog, C. M. S., Poganik, J. R., Ying, K., Justice, J. N., Belsky, D. W., Higgins‐Chen, A., Chen, B. H., Cohen, A. A., Fuellen, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02784-9 · Cited by 348 · see citing works

Aging Hallmarks and Progression and Age-Related Diseases: A Landscape View of Research Advancement

Tenchov, R., Sasso, J. M., Wang, X., & Zhou, Q. A. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.3c00531 · Cited by 276 · see citing works

Arterial stiffness and vascular aging: mechanisms, prevention, and therapy

Herzog, M. J., Müller, P., Lechner, K., Stiebler, M., Arndt, P., Kunz, M., Ahrens, D., Schmeißer, A., Schreiber, S., & Braun-Dullaeus, R. C. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02346-0 · Cited by 246 · see citing works

Epigenetic ageing clocks: statistical methods and emerging computational challenges

Teschendorff, A. E., & Horvath, S. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00807-w · Cited by 220 · see citing works

The impact of population ageing on the burden of chronic kidney disease

Chesnaye, N. C., Ortíz, A., Zoccali, C., Stel, V. S., & Jager, K. J. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00863-9 · Cited by 185 · see citing works

Comprehensive human proteome profiles across a 50-year lifespan reveal aging trajectories and signatures

Ding, Y., Zuo, Y., Zhang, B., Fan, Y., Xu, G., Cheng, Z., Ma, S., Fang, S., Tian, A., Gao, D., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.047 · Cited by 182 · see citing works

Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances

Jiang, Q., Liu, J., Huang, S., Wang, X.-Y., Chen, X., Liu, G.-H., Ye, K., Song, W., Masters, C. L., Wang, J., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02145-7 · Cited by 165 · see citing works

Stress, epigenetics, and aging: Unraveling the intricate crosstalk

Wu, Z., Qu, J. T., Zhang, W., & Liu, G. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.10.006 · Cited by 142 · see citing works

Plasma proteomics links brain and immune system aging with healthspan and longevity

Oh, H. S., Guen, Y. L., Rappoport, N., Urey, D. Y., Farinas, A., Rutledge, J., Channappa, D., Wagner, A. D., Mormino, E. C., Brunet, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03798-1 · Cited by 138 · see citing works

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Moguilner, S., Baez, S., Hernandez, H., Migeot, J., Legaz, A., Gonzalez-Gomez, R., Farina, F. R., Prado, P., Cuadros, J., Tagliazucchi, E., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03209-x · Cited by 128 · see citing works

Neuroimaging and fluid biomarkers in Parkinson’s disease in an era of targeted interventions

Zarkali, A., Thomas, G. E. C., Zetterberg, H., & Weil, R. S. (2024) · Nature Communications · doi:10.1038/s41467-024-49949-9 · Cited by 128 · see citing works

A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

Zhang, S., Wang, Z., Wang, Y., Zhu, Y., Zhou, Q., Jian, X., Zhao, G., Qiu, J., Xia, K., Tang, B., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52310-9 · Cited by 126 · see citing works

Systems Age: a single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Sehgal, R., Markov, Y., Qin, C., Meer, M. V., Hadley, C., Shadyab, A. H., Casanova, R., Manson, J. E., Bhatti, P., Moore, A. Z., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00958-3 · Cited by 105 · see citing works

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Oh, S., Jekal, J., Jia Liu, B., Kim, J., Park, J., Lee, T., & Jang, K. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202403562 · Cited by 103 · see citing works

The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression

Jiang, R., Noble, S., Rosenblatt, M., Dai, W., Ye, J., Liu, S., Qi, S., Calhoun, V. D., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-48827-8 · Cited by 102 · see citing works

The 'middle-aging' brain

Dohm-Hansen, S., English, J. A., Lavelle, A., Fitzsimons, C. P., Lucassen, P. J., & Nolan, Y. M. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.02.001 · Cited by 101 · see citing works

Pathological mechanisms of kidney disease in ageing

Yamamoto, T., & Isaka, Y. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00868-4 · Cited by 94 · see citing works

The pathobiology of neurovascular aging

Santisteban, M. M., & Iadecola, C. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.014 · Cited by 94 · see citing works

Brain aging patterns in a large and diverse cohort of 49,482 individuals

Yang, Z.-J., Wen, J.-H., Erus, G., Govindarajan, S. T., Melhem, R., Mamourian, E., Cui, Y.-H., Srinivasan, D., Abdulkadir, A., Parmpi, P., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03144-x · Cited by 92 · see citing works

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Fong, S., Pabis, K., Latumalea, D., Dugersuren, N., Unfried, M., Tolwinski, N. S., Kennedy, B. K., & Gruber, J. (2024) · Nature Aging · doi:10.1038/s43587-024-00646-8 · Cited by 86 · see citing works

Plasma proteomics identify biomarkers and undulating changes of brain aging

Liu, W., You, J., Chen, S.-D., Zhang, Y., Feng, J., Xu, Y., Yu, J., & Cheng, W. (2024) · Nature Aging · doi:10.1038/s43587-024-00753-6 · Cited by 84 · see citing works

Aging promotes metabolic dysfunction-associated steatotic liver disease by inducing ferroptotic stress

Du, K., Wang, L., Jun, J. H., Dutta, R. K., Maeso‐Díaz, R., Oh, S., Ko, D. C., & Diehl, A. M. (2024) · Nature Aging · doi:10.1038/s43587-024-00652-w · Cited by 80 · see citing works

The metabolic costs of cognition

Jamadar, S. D., Behler, A., Deery, H. A., & Breakspear, M. J. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.11.010 · Cited by 76 · see citing works

Extracellular vesicles set the stage for brain plasticity and recovery by multimodal signalling

Hermann, D. M., Peruzzotti‐Jametti, L., Giebel, B., & Pluchino, S. (2023) · Brain · doi:10.1093/brain/awad332 · Cited by 75 · see citing works

Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine

Prattichizzo, F., Frigé, C., Pellegrini, V., Scisciola, L., Santoro, A., Monti, D., Rippo, M. R., Ivanchenko, M., Olivieri, F., & Franceschi, C. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102253 · Cited by 75 · see citing works

Pathological and Inflammatory Consequences of Aging

García‐Domínguez, M. (2025) · Biomolecules · doi:10.3390/biom15030404 · Cited by 75 · see citing works

The genetic architecture of biological age in nine human organ systems

Wen, J., Tian, Y., Skampardoni, I., Yang, Z., Cui, Y., Anagnostakis, F., Mamourian, E., Zhao, B., Toga, A. W., Zalesky, A., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00662-8 · Cited by 73 · see citing works

The exposome of healthy and accelerated aging across 40 countries

Hernandez, H., Santamaria-Garcia, H., Moguilner, S., Farina, F. R., Legaz, A., Prado, P., Cuadros, J., Gonzalez, L., Gonzalez-Gomez, R., Migeot, J., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03808-2 · Cited by 71 · see citing works

Preventing chronic kidney disease and maintaining kidney health: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

Ortíz, A., Guerra, J. M. A., Chan, J. C. N., Jha, V., Kramer, H., Nicholas, S. B., Pavkov, M. E., Wanner, C., Wong, L. P., Cheung, M., et al. (2025) · Kidney International · doi:10.1016/j.kint.2025.04.005 · Cited by 70 · see citing works

An integrated multi-omics analysis reveals osteokines involved in global regulation

Liang, W., Wei, T., Hu, L., Chen, M., Tong, L., Zhou, W., Duan, X., Zhao, X., Zhou, W., Jiang, Q., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.006 · Cited by 67 · see citing works

A synergetic turn in cognitive neuroscience of brain diseases

Ibanez, A., Kringelbach, M. L., & Deco, G. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.12.006 · Cited by 65 · see citing works

Interactions between mitochondrial dysfunction and other hallmarks of aging: Paving a path toward interventions that promote healthy old age

Li, Y., Berliocchi, L., Li, Z., & Rasmussen, L. J. (2023) · Aging Cell · doi:10.1111/acel.13942 · Cited by 65 · see citing works

The genetic architecture of multimodal human brain age

Wen, J., Zhao, B., Yang, Z., Erus, G., Skampardoni, I., Mamourian, E., Cui, Y., Hwang, G., Bao, J., Boquet-Pujadas, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46796-6 · Cited by 64 · see citing works

A perspective on brain-age estimation and its clinical promise

Gaser, C., Kalc, P., & Cole, J. H. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00659-8 · Cited by 62 · see citing works

Environmental and genetic predictors of human cardiovascular ageing

Shah, M., Inácio, M. H. D. A., Lu, C., Schiratti, P.-R., Zheng, S. L., Clement, A., de Marvao, A., Bai, W., King, A. P., Ware, J. S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40566-6 · Cited by 61 · see citing works

Roadmap for alleviating the manifestations of ageing in the cardiovascular system

Liberale, L., Tual-Chalot, S., Sedej, S., Ministrini, S., Georgiopoulos, G., Grunewald, M., Bäck, M., Bochaton-Piallat, M.-L., Boon, R. A., Ramos, G. C., et al. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01130-5 · Cited by 59 · see citing works

Proteomic organ-specific ageing signatures and 20-year risk of age-related diseases: the Whitehall II observational cohort study

Mika Kivimaki, B., Frank, P., Pentti, J., Jokela, M. M., Nyberg, S. T., Blake, A., Lindbohm, J. V., Oh, H. S., Singh‐Manoux, A., Wyss‐Coray, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.01.006 · Cited by 57 · see citing works

Association of biological age with health outcomes and its modifiable factors

Liu, W., You, J., Ge, Y., Wu, B., Zhang, Y., Chen, S., Zhang, Y.-R., Huang, S., Ma, L., Feng, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13995 · Cited by 56 · see citing works

Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity

Salinas‐Rodríguez, A., Fernández-Niño, J. A., Rivera‐Almaraz, A., & Manrique-Espinoza, B. S. (2024) · International Journal for Equity in Health · doi:10.1186/s12939-024-02136-0 · Cited by 52 · see citing works

Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review

Liu, Y., & Wang, B. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1510456 · Cited by 51 · see citing works

Aging in Heart Failure

Goyal, P., Maurer, M. S., & Roh, J. D. (2024) · JACC Heart Failure · doi:10.1016/j.jchf.2024.02.021 · Cited by 50 · see citing works

Deep phenotyping of health–disease continuum in the Human Phenotype Project

Reicher, L., Shilo, S., Godneva, A., Lutsker, G., Zahavi, L., Shoer, S., Krongauz, D. L., Rein, M., Kohn, S., Segev, T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03790-9 · Cited by 50 · see citing works

Cellular reprogramming as a tool to model human aging in a dish

Pitrez, P. R., Monteiro, L. M., Borgogno, O., Nissan, X., Mertens, J., & Ferreira, L. (2024) · Nature Communications · doi:10.1038/s41467-024-46004-5 · Cited by 49 · see citing works

Brain structure ages—A new biomarker for multi‐disease classification

Nguyen, H., Clément, M., Mansencal, B., & Coupé, P. (2024) · Human Brain Mapping · doi:10.1002/hbm.26558 · Cited by 48 · see citing works

A deep learning model for brain age prediction using minimally preprocessed T1w images as input

Dartora, C. M., Marseglia, A., Mårtensson, G., Rukh, G., Dang, J., Muehlboeck, J., Wahlund, L., Moreno, R., Barroso, J., Ferreira, D., et al. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1303036 · Cited by 46 · see citing works

Mitochondria-driven inflammation: a new frontier in ovarian ageing

Ju, W., Yan, B., Li, D., Lian, F., & Xiang, S. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06966-6 · Cited by 44 · see citing works

Organ-specific proteomic aging clocks predict disease and longevity across diverse populations

Wang, Y., Xiao, S., Liu, B., Jiang, R., Liu, Y., Hang, Y., Chen, L., Chen, R., Vitiello, M. V., Bennett, D., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-01016-8 · Cited by 44 · see citing works

A biomarker framework for liver aging: the Aging Biomarker Consortium consensus statement

Jiang, M., Zheng, Z., Wang, X., Chen, Y., Qu, J. T., Ding, Q., Zhang, W., Liu, Y., Yang, J., Tang, W., et al. (2024) · Life Medicine · doi:10.1093/lifemedi/lnae004 · Cited by 43 · see citing works

Inter-organ communication is a critical machinery to regulate metabolism and aging

Tokizane, K., & Imai, S. (2024) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2024.11.013 · Cited by 42 · see citing works

Sex-specific body fat distribution predicts cardiovascular ageing

Losev, V., Lu, C., Tahasildar, S., Senevirathne, D. S., Inglese, P., Bai, W., King, A. P., Shah, M., de Marvao, A., & O’Regan, D. P. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf553 · Cited by 42 · see citing works

Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health

Zhang, R., Fan, Y., Mao, H., Huang, Z., Wang, K., & Zhang, J. (2025) · Communications Medicine · doi:10.1038/s43856-025-01100-5 · Cited by 42 · see citing works

Unveiling mechanisms of lung aging in COPD: A promising target for therapeutics development

Devulder, J. V. (2024) · Chinese Medical Journal - Pulmonary and Critical Care Medicine · doi:10.1016/j.pccm.2024.08.007 · Cited by 41 · see citing works

Functional connectome through the human life span

Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., et al. (2024) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2024/1294 · Cited by 41 · see citing works

Cross‐tissue comparison of epigenetic aging clocks in humans

Apsley, A. T., Ye, Q., Caspi, A., Chiaro, C. R., Etzel, L. C., Hastings, W. J., Heim, C. C., Kozlosky, J. T., Noll, J. G., Schreier, H. M. C., et al. (2025) · Aging Cell · doi:10.1111/acel.14451 · Cited by 41 · see citing works

Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics

Pu, F., Chen, W., Li, C., Fu, J., Gao, W., Ma, C., Cao, X., Zhang, L., Hao, M., Zhou, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49283-0 · Cited by 39 · see citing works

Mitochondrial respiration atlas reveals differential changes in mitochondrial function across sex and agePreprint

Sarver, D. C., Saqib, M., Chen, F., & Wong, G. W. (2024) · eLife · doi:10.7554/elife.96926.4 · Cited by 39 · see citing works

Temporal trends in obesity defined by the relative fat mass (RFM) index among adults in the United States from 1999 to 2020: a population-based study

Woolcott, O. O., & Seuring, T. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-071295 · Cited by 38 · see citing works

BrainAGE, brain health, and mental disorders: A systematic review

Seitz‐Holland, J., Haas, S. S., Penzel, N., Reichenberg, A., & Pasternak, O. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105581 · Cited by 38 · see citing works

Nutrition in the prevention and treatment of skeletal muscle ageing and sarcopenia: a single nutrient, a whole food and a whole diet approach

Granic, A., Sayer, A. A., Cooper, R., & Robinson, S. M. (2024) · Proceedings of The Nutrition Society · doi:10.1017/s0029665124007432 · Cited by 38 · see citing works

Refining the generation, interpretation and application of multi-organ, multi-omics biological aging clocks

Wen, J. (2025) · Nature Aging · doi:10.1038/s43587-025-00928-9 · Cited by 38 · see citing works

Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance

Li, J., Zhang, C., Meng, Y., Yang, S., Xia, J., Chen, H., & Liao, W. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002647 · Cited by 37 · see citing works

Large language model-based biological age prediction in large-scale populations

Li, Y., Huang, Q. Z., Jiang, J., Du, X., Xiang, W., Zhang, S., Pan, Z., Zhao, L., Cui, Y., Ke, L., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03856-8 · Cited by 37 · see citing works

Brain, lifestyle and environmental pathways linking physical and mental health

Tian, Y., Cole, J. H., Bullmore, E. T., & Zalesky, A. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00303-4 · Cited by 36 · see citing works

MRI-based multi-organ clocks for healthy aging and disease assessment

Cao, H., Song, Z., Duggan, M. R., Erus, G., Srinivasan, D., Tian, Y., Bai, W., Rafii, M. S., Aisen, P. S. S., Belsky, D. W., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03999-8 · Cited by 36 · see citing works

A full life cycle biological clock based on routine clinical data and its impact in health and diseases

Wang, K., Liu, F., Wu, W., Hu, C., Shen, X., Wang, M., Li, G., Zeng, F., Liu, L., Wong, I. N., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-04006-w · Cited by 35 · see citing works

Increased cholesterol synthesis drives neurotoxicity in patient stem cell-derived model of multiple sclerosis

Ionescu, R.-B., Nicaise, A. M., Reisz, J. A., Williams, E. C., Prasad, P., Willis, C. M., Simões-Abade, M. B. C., Sbarro, L., Dzieciatkowska, M., Stephenson, D., et al. (2024) · Cell Stem Cell · doi:10.1016/j.stem.2024.09.014 · Cited by 34 · see citing works

Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

Zalesky, A., Tian, Y., Ferrucci, L., Walker, K. A., Bai, W., Rafii, M. S., Aisen, P. S. S., Anagnostakis, F., Ko, S., Saadatinia, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59964-z · Cited by 34 · see citing works

A blood biomarker of accelerated aging in the body associates with worse structural integrity in the brain: replication across three cohorts

Whitman, E. T., Ryan, C. P., Abraham, W. C., Addae, A., Corcoran, D. L., Elliott, M. L., Hogan, S., Ireland, D. R., Keenan, R. J., Knodt, A. R., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.01.008 · Cited by 33 · see citing works

Hypoxia and aging: molecular mechanisms, diseases, and therapeutic targets

Nisar, A., Khan, S., Li, W., Hu, L., Samarawickrama, P. N., Gold, N. M., Zi, M., Mehmood, S. A., Miao, J., & He, Y. (2024) · MedComm · doi:10.1002/mco2.786 · Cited by 32 · see citing works

Digital biomarkers of ageing for monitoring physiological systems in community-dwelling adults

Lu, J. K., Wang, W., Mahadzir, M. D. A., Poganik, J. R., Moqri, M., Herzog, C. M. S., Verdin, E. M., Sebastiano, V., Gladyshev, V. N., Maier, A. B., et al. (2025) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2025.100725 · Cited by 30 · see citing works

Gompertz Law‐Based Biological Age (GOLD BioAge): A Simple and Practical Measurement of Biological Ageing to Capture Morbidity and Mortality Risks

Hao, M., Zhang, H., Wu, J., Huang, Y., Li, X., Wang, M., Wang, S., Wang, J., Chen, J., Bao, Z., et al. (2025) · Advanced Science · doi:10.1002/advs.202501765 · Cited by 30 · see citing works

Creative experiences and brain clocks

Coronel‐Oliveros, C., Migeot, J., Lehue, F., Amoruso, L., Kowalczyk‐Grębska, N., Jakubowska, N., Mandke, K. N., Seabra, J. P., Orio, P., Campbell, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64173-9 · Cited by 30 · see citing works

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Wen, J., Yang, Z., Nasrallah, I. M., Cui, Y., Erus, G., Srinivasan, D., Abdulkadir, A., Mamourian, E., Skampardoni, I., Hwang, G., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03121-5 · Cited by 30 · see citing works

Epigenetic clocks indicate that kidney transplantation and not dialysis mitigate the effects of renal ageing

Neytchev, O., Erlandsson, H., Witasp, A., Nordfors, L., Qureshi, A. R., Iseri, K., Morohoshi, H., Selman, C., Ebert, T., Kublickiene, K. R., et al. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13724 · Cited by 29 · see citing works

Harnessing BDNF Signaling to Promote Resilience in Aging

Faraji, J., & Metz, G. A. S. (2024) · Aging and Disease · doi:10.14336/ad.2024.0961 · Cited by 28 · see citing works

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine

Wilczok, D. (2025) · Aging · doi:10.18632/aging.206190 · Cited by 28 · see citing works

Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank

Fan, Y., Yuan, J., Somekh, J., Mor Peleg, B., Zhu, Y., Jia, Z., Wu, F. L., & Huang, Z. (2025) · Science Advances · doi:10.1126/sciadv.adr3757 · Cited by 28 · see citing works

Variation in mesenchymal KITL/SCF and IGF1 expression in middle age underlies steady-state hematopoietic stem cell aging

Young, K. A., Telpoukhovskaia, M. A., Hofmann, J., Mistry, J. J., Kokkaliaris, K. D., & Trowbridge, J. J. (2024) · Blood · doi:10.1182/blood.2024024275 · Cited by 27 · see citing works

Organism-wide cellular dynamics and epigenomic remodeling in mammalian aging

Lu, Z., Zhang, Z., Xu, Z., Abdulraouf, A., Zhou, W. Y., & Cao, J. (2026) · Science · doi:10.1126/science.adw6273 · Cited by 27 · see citing works

Estimating biological age from retinal imaging: a scoping review

Grimbly, M. J., Koopowitz, S., Chen, R., Sun, Z., Foster, P. J., He, M., Stein, D. J., Ipser, J., & Zhu, Z. (2024) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2024-001794 · Cited by 26 · see citing works

Chronic Obstructive Pulmonary Disease Airway Epithelial Cell–derived Extracellular Vesicles Spread Cellular Senescence via MicroRNA-34a

Devulder, J. V., Baker, J. R., Fenwick, P. S., Odqvist, L., Donnelly, L., & Barnes, P. J. (2025) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2024-0183oc · Cited by 26 · see citing works

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts

Wang, S., Rao, Z., Cao, R., Blaes, A. H., Coresh, J., Deo, R., Dubin, R. F., Joshu, C. E., Lehallier, B., Lutsey, P. L., et al. (2024) · PLoS Medicine · doi:10.1371/journal.pmed.1004464 · Cited by 26 · see citing works

Establishing healthy longevity clinics in publicly funded hospitals

Bonnes, S. L. R., Strauss, T., Palmer, A. K., Hurt, R. T., Island, L., Goshen, A., Wang, L. Y. T., Kirkland, J. L., Bischof, E. Y., & Maier, A. B. (2024) · GeroScience · doi:10.1007/s11357-024-01132-0 · Cited by 25 · see citing works

Systems Human Immunology and AI: Immune Setpoint and Immune Health

Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 25 · see citing works

Transforming mental health research and care through artificial intelligence

Opel, N., & Breakspear, M. J. (2026) · Science · doi:10.1126/science.adz9193 · Cited by 25 · see citing works

Deciphering RNA m 6 A regulation in aging: Perspectives on current advances and future directions

Wu, Z., Ren, J., & Liu, G. (2023) · Aging Cell · doi:10.1111/acel.13972 · Cited by 25 · see citing works

Imaging biomarkers of ageing: a review of artificial intelligence-based approaches for age estimation

Haugg, F., Lee, G. C., He, J., Johnson, J., Zapaishchykova, A., Bitterman, D. S., Kann, B. H., Aerts, H. J. W. L., & Mak, R. H. (2025) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2025.100728 · Cited by 24 · see citing works

Multi-omic underpinnings of heterogeneous aging across multiple organ systems

Xiong, J., Zhu, X., Guo, Y., Tang, H., Dong, C., Wang, B., Liu, M., Li, Z., & Tu, Y. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101032 · Cited by 24 · see citing works

A wearable-based aging clock associates with disease and behavior

Miller, A. C., Futoma, J., Abbaspourazad, S., Heinze‐Deml, C., Emrani, S., Shapiro, I., & Sapiro, G. (2025) · Nature Communications · doi:10.1038/s41467-025-64275-4 · Cited by 24 · see citing works

Cognitive impairments in chronic pain: a brain aging framework

Zhao, L., Zhang, L., Tang, Y., & Tu, Y. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.12.004 · Cited by 23 · see citing works

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Zhang, X., Wang, C., Pi, X., Li, B., Ding, Y., Yu, H., Sun, J., Wang, P., Chen, Y., Wang, Q., et al. (2025) · Advanced Materials · doi:10.1002/adma.202418108 · Cited by 23 · see citing works

Using RNA therapeutics to promote healthy aging

Chen, S., Chen, Q., You, X., Zhou, Z., Kong, N., Ambrosio, F., Cao, Y., Abdi, R., & Tao, W. (2025) · Nature Aging · doi:10.1038/s43587-025-00895-1 · Cited by 23 · see citing works

Spatial and single-cell transcriptomics reveal the reorganization of cerebellar microglia with aging

Tsai, A. P., Henze, D. E., Lopez, E. R., Haberberger, J., Dong, C., Lu, N., Atkins, M., Costa, E. K., Farinas, A., Oh, H. S., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116624 · Cited by 22 · see citing works

Maintaining kidney health in aging societies: a JSN and ERA call to action

Ortíz, A., Kramer, A., Rychlík, I., Nangaku, M., Yanagita, M., Jager, K. J., Caskey, F. J., Stel, V. S., Kashihara, N., Kuragano, T., et al. (2025) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaf068 · Cited by 21 · see citing works

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Yusri, K., Kumar, S., Fong, S., Gruber, J., & Sorrentino, V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126793 · Cited by 21 · see citing works

Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging

Bartolomucci, A., Kane, A. E., Gaydosh, L. M., Razzoli, M., McCoy, B. M., Ehninger, D., Chen, B. H., Howlett, S. E., & Snyder‐Mackler, N. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae135 · Cited by 21 · see citing works

An Expert Opinion on the Management of Frailty in Heart Failure from the Australian Cardiovascular Alliance National Taskforce

McDonagh, J., Ferguson, C., Hilmer, S. N., Hubbard, R. E., Lindley, R. I., Driscoll, A. L., Maiorana, A., Wu, L. E., Atherton, J. J., Bajorek, B. V., et al. (2025) · Heart Lung and Circulation · doi:10.1016/j.hlc.2025.01.012 · Cited by 20 · see citing works

Aging at the Crossroads of Organ Interactions: Implications for the Heart

Sen, I., Trzaskalski, N. A., Hsiao, Y., Liu, P. P., Shimizu, I., & Dérumeaux, G. (2025) · Circulation Research · doi:10.1161/circresaha.125.325637 · Cited by 20 · see citing works

Meta-analyses: sources of bias

Sedgwick, P. M. (2011) · BMJ · doi:10.1136/bmj.d5085

Organ-specific aging and the risk of chronic diseases

Moqri, M., & Snyder, M. P. (2023) · Nature Medicine · doi:10.1038/s41591-023-02338-z · Cited by 8 · see citing works

Distinct biological ages of organs and systems identified from a multi-omics study

Nie, C., Li, Y., Li, R., Yan, Y., Zhang, D., Li, T., Li, Z., Sun, Y., Zhen, H., Ding, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110459 · Cited by 225 · see citing works

From ageing clocks to organ networks: Biological age-driven organ asynchrony and inter-organ interactions shaping mortality risk

Li, Z., Liu, J., Dai, Y., Zhu, H., Gao, Y., Ning, A., Zhang, X., Hou, Y., Xu, J., Wu, Y., et al. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103056 · Cited by 3 · see citing works

Blood Omics Models for System-Specific Mortality Risk EstimationPreprint

Fuentealba, M., Arpawong, T. E., Schneider, K., Crimmins, E. M., & Furman, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.27.625723 · Cited by 2 · see citing works