An integrative study of five biological clocks in somatic and mental health
Open accessPublished version of a preprint
Abstract
Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one’s biological age is best reflected by combining aging measures from multiple cellular levels.
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
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?
Epigenetic age gaps showed no correlations with proteomic and metabolomic age gaps.
“Interestingly, epigenetic age gaps showed no correlations with proteomic and metabolomic age gaps, emphasizing that different omics technologies may capture different ageing signals.”
From Measuring biological age using omics data (2022) · cites this paper as [155]
In one multi-omics study, epigenetic and transcriptomic clock age gaps were mildly correlated, and proteomic and metabolomic clock age gaps were mildly correlated.
“One multi-omics study wherein the authors built their own telomere, epigenetic, proteomic and metabolomics clocks within a single cohort found mild correlations between epigenetic and transcriptomic clock age gaps and between proteomic and metabolomic clock age gaps155.”
From Measuring biological age using omics data (2022) · cites this paper as [155]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
International physical activity questionnaire: 12-country reliability and validity
CRAIG, C. L., MARSHALL, A. L., SJ??STR??M, M., BAUMAN, A. E., BOOTH, M. L., AINSWORTH, B. E., PRATT, M., EKELUND, U., YNGVE, A., SALLIS, J. F., et al. (2003) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000078924.61453.fb · Cited by 21,619 · see citing works
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
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
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
Inflammaging: a new immune–metabolic viewpoint for age-related diseases
Franceschi, C., Garagnani, P., Parini, P., Giuliani, C., & Santoro, A. (2018) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0059-4 · Cited by 3,586 · see citing works
Telomere measurement by quantitative PCR
Cawthon, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.10.e47 · Cited by 3,492 · see citing works
DNA methylation-based biomarkers and the epigenetic clock theory of ageing
Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095 · see citing works
The Inventory of Depressive Symptomatology (IDS): Psychometric properties
Rush, A. J., Gullion, C. M., Basco, M. R., Jarrett, R. B., & Trivedi, M. H. (1996) · Psychological Medicine · doi:10.1017/s0033291700035558 · Cited by 2,177 · see citing works
DNA methylation age of blood predicts all-cause mortality in later life
Marioni, R. E., Shah, S., McRae, A. F., Chen, B. H., Colicino, E., Harris, S. E., Gibson, J., Henders, A. K., Redmond, P., Cox, S. R., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0584-6 · Cited by 1,261 · see citing works
Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240 · see citing works
DNA methylation-based measures of biological age: meta-analysis predicting time to death
Chen, B. H., Marioni, R. E., Colicino, E., Peters, M. J., Ward-Caviness, C. K., Tsai, P.-C., Roetker, N. S., Just, A. C., Demerath, E. W., Guan, W., et al. (2016) · Aging · doi:10.18632/aging.101020 · Cited by 1,126 · see citing works
The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods
Penninx, B. W. J. H., Beekman, A. T. F., Smit, J. H., Zitman, F. G., Nolen, W. A., Spinhoven, P., Cuijpers, P. J., de Jong, P. J., van Marwijk, H., Assendelft, W. J., et al. (2008) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.256 · Cited by 918 · see citing works
Self-citationSoininen, P., Kangas, A. J., Würtz, P., Suna, T., & Ala‐Korpela, M. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000216 · Cited by 890 · see citing works
Keeping It All Going—Complement Meets Metabolism
Kolev, M. V., & Kemper, C. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00001 · Cited by 819 · see citing works
The transcriptional landscape of age in human peripheral blood
Peters, M. J., Joehanes, R., Pilling, L. C., Schurmann, C., Conneely, K. N., Powell, J., Reinmaa, E., Sutphin, G. L., Zhernakova, A., Schramm, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms9570 · Cited by 725 · see citing works
Integrating neuroimmune systems in the neurobiology of depression
Wohleb, E. S., Franklin, T., Iwata, M., & Duman, R. S. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.69 · Cited by 668 · see citing works
Metabolite Profiling and Cardiovascular Event Risk
Würtz, P., Havulinna, A. S., Soininen, P., Tynkkynen, T., Prieto‐Merino, D., Tillin, T., Ghorbani, A., Artati, A., Wang, Q., Tiainen, M., et al. (2015) · Circulation · doi:10.1161/circulationaha.114.013116 · Cited by 667 · see citing works
Belsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., Hannon, E., Harrington, H. L., Rasmussen, L. J., Houts, R., et al. (2020) · eLife · doi:10.7554/elife.54870 · Cited by 652 · see citing works
Aging effects on DNA methylation modules in human brain and blood tissue
Horvath, S., Zhang, Y., Langfelder, P., Kahn, R., Boks, M., van Eijk, K., van den Berg, L. H., & Ophoff, R. (2012) · Genome Biology · doi:10.1186/gb-2012-13-10-r97 · Cited by 635 · see citing works
Austad, S. N., & Fischer, K. E. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.05.019 · Cited by 591 · see citing works
Perna, L., Zhang, Y., Mons, U., Holleczek, B., Saum, K.-U., & Brenner, H. (2016) · Clinical Epigenetics · doi:10.1186/s13148-016-0228-z · Cited by 572 · see citing works
Disability as a Public Health Outcome in the Aging Population
Guralnik, J. M., Fried, L. P., & Salive, M. E. (1996) · Annual Review of Public Health · doi:10.1146/annurev.pu.17.050196.000325 · Cited by 457 · 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 435 · see citing works
Belsky, D. W., Moffitt, T. E., Cohen, A. A., Corcoran, D. L., Levine, M. E., Prinz, J. A., Schaefer, J. D., Sugden, K., Williams, B. S., Poulton, R., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx346 · Cited by 419 · see citing works
de Graaf, R., Bijl, R., Smit, F., Vollebergh, W., & Spijker, J. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.4.620 · Cited by 406 · see citing works
Li, X., Ploner, A., Wang, Y., Magnusson, P. K., Reynolds, C., Finkel, D., Pedersen, N. L., Jylhävä, J., & Hägg, S. (2020) · eLife · doi:10.7554/elife.51507 · Cited by 390 · see citing works
Aging, obesity, and inflammatory age-related diseases
Frasca, D., Blomberg, B. B., & Paganelli, R. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.01745 · Cited by 371 · see citing works
Heritability and genomics of gene expression in peripheral blood
Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. (2014) · Nature Genetics · doi:10.1038/ng.2951 · Cited by 362 · see citing works
Self-citationFischer, K., Kettunen, J., Würtz, P., Haller, T., Havulinna, A. S., Kangas, A. J., Soininen, P., Esko, T., Tammesoo, M.-L., Mägi, R., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001606 · Cited by 344 · see citing works
Fehrmann, R. S. N., Jansen, R. C., Veldink, J. H., Westra, H.-J., Arends, D., Bonder, M. J., Fu, J., Deelen, P., Groen, H. J. M., Smolonska, A., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002197 · Cited by 330 · see citing works
Self-citationTelomere Length and All-Cause Mortality: A Meta-analysis
Wang, Q., Zhan, Y., Pedersen, N. L., Fang, F., & Hägg, S. (2018) · Ageing Research Reviews · doi:10.1016/j.arr.2018.09.002 · Cited by 322 · see citing works
DNA methylation age is associated with mortality in a longitudinal Danish twin study
Christiansen, L., Lenart, A. G., Tan, Q., Vaupel, J. W., Aviv, A., McGue, M. K., & Christensen, K. (2015) · Aging Cell · doi:10.1111/acel.12421 · Cited by 306 · see citing works
Metabolic Signatures of Insulin Resistance in 7,098 Young Adults
Würtz, P., Mäkinen, V., Soininen, P., Kangas, A. J., Tukiainen, T., Kettunen, J., Savolainen, M. J., Tammelin, T., Viikari, J. S. A., Rönnemaa, T., et al. (2012) · Diabetes · doi:10.2337/db11-1355 · Cited by 304 · see citing works
Frailty is associated with the epigenetic clock but not with telomere length in a German cohort
Breitling, L. P., Saum, K.-U., Perna, L., Schöttker, B., Holleczek, B., & Brenner, H. (2016) · Clinical Epigenetics · doi:10.1186/s13148-016-0186-5 · Cited by 296 · see citing works
Lindqvist, D., Epel, E. S., Mellon, S. H., Penninx, B. W., Révész, D., Verhoeven, J. E., Reus, V. I., Lin, J., Mahan, L., Hough, C. M., et al. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.05.007 · Cited by 285 · see citing works
Self-citationMarioni, R. E., Harris, S. E., Shah, S. H., McRae, A. F., von Zglinicki, T., Martin-Ruiz, C. M., Wray, N. R., Visscher, P. M., & Deary, I. J. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw041 · Cited by 285 · see citing works
Verhoeven, J. E., Révész, D., Epel, E. S., Lin, J., Wolkowitz, O. M., & Penninx, B. W. J. H. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.151 · Cited by 270 · see citing works
Self-citationEpigenetic aging in major depressive disorder
Han, L. K. M., Aghajani, M., Clark, S. L., Chan, R. F., Hattab, M. W., Shabalin, A. A., Zhao, M., Kumar, G., Xie, L. Y., Jansen, R., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17060595 · Cited by 260 · see citing works
Self-citationBabor, T., Kranzler, H., & Lauerman, R. (1989) · Addictive Behaviours · doi:10.1016/0306-4603(89)90043-9 · Cited by 251 · see citing works
Traumatic stress and accelerated DNA methylation age: a meta-analysis
Wolf, E. J., Maniates, H., Nugent, N., Maihofer, A. X., Armstrong, D., Ratanatharathorn, A., Ashley-Koch, A. E., Garrett, M., Kimbrel, N. A., Lori, A., et al. (2018) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2017.12.007 · Cited by 245 · see citing works
Bot, M., Milaneschi, Y., Alshehri, T., Amin, N., Garmaeva, S., Onderwater, G. L. J., Pool, R., Thesing, C. S., Vijfhuizen, L. S., Vogelzangs, N., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.08.016 · Cited by 241 · see citing works
Self-citationBoks, M. P., Mierlo, H. C. V., Rutten, B. P. F., Radstake, T. R. D. J., De Witte, L., Geuze, E., Horvath, S., Schalkwyk, L. C., Vinkers, C. H., Broen, J. C. A., et al. (2015) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2014.07.011 · Cited by 231 · see citing works
Darrow, S. M., Verhoeven, J. E., Révész, D., Lindqvist, D., Penninx, B. W. J. H., Delucchi, K. L., Wolkowitz, O. M., & Mathews, C. A. (2016) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000356 · Cited by 229 · see citing works
Self-citationVillagra-Blanco, R., Silva, L. M. R., Muñoz-Caro, T., Yang, Z., Li, J., Gärtner, U., Taubert, A., Zhang, X., & Hermosilla, C. R. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00606 · Cited by 192 · see citing works
Molecular and phenotypic biomarkers of aging
Xia, X., Chen, W., McDermott, J., & Han, J.-D. J. (2017) · F1000Research · doi:10.12688/f1000research.10692.1 · Cited by 187 · see citing works
Gielen, M., Hageman, G. J., Antoniou, E. E., Nordfjäll, K., Mangino, M., Balasubramanyam, M., Meyer, T. D., Hendricks, A. E., Giltay, E. J., Hunt, S. C., et al. (2018) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy107 · Cited by 168 · see citing works
Self-citationSex differences in the human peripheral blood transcriptome
Jansen, R., Batista, S., Brooks, A. I., Tischfield, J. A., Willemsen, G. A. H. M., van Grootheest, G., Hottenga, J., Milaneschi, Y., Mbarek, H., Madar, V., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-33 · Cited by 166 · see citing works
Self-citationHastings, W. J., Shalev, I., & Belsky, D. W. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.03.012 · Cited by 142 · see citing works
Conditional eQTL analysis reveals allelic heterogeneity of gene expression
Jansen, R., Hottenga, J.-J., Nivard, M. G., Abdellaoui, A., Laport, B., de Geus, E. J., Wright, F. A., Penninx, B. W. J. H., & Boomsma, D. I. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx043 · Cited by 141 · see citing works
An epigenetic predictor of death captures multi-modal measures of brain health
Hillary, R. F., Stevenson, A. J., Cox, S. R., McCartney, D. L., Harris, S. E., Seeboth, A., Higham, J., Sproul, D., Taylor, A. M., Redmond, P., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-019-0616-9 · Cited by 139 · see citing works
Metabolic profiling of alcohol consumption in 9778 young adults
Würtz, P., Cook, S., Wang, Q., Tiainen, M., Tynkkynen, T., Kangas, A. J., Soininen, P., Laitinen, J. H., Viikari, J. S. A., Kähönen, M. A. P., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw175 · Cited by 138 · see citing works
Révész, D., Milaneschi, Y., Verhoeven, J. E., & Penninx, B. W. J. H. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-1851 · Cited by 134 · see citing works
Self-citationMons, U., Müezzinler, A., Schöttker, B., Dieffenbach, A. K., Butterbach, K., Schick, M., Peasey, A., Vivo, I. D., Trichopoulou, A., Boffetta, P., et al. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kww210 · Cited by 113 · see citing works
van den Akker, E. B., Trompet, S., Barkey Wolf, J. J. H., Beekman, M., Suchiman, H. E. D., Deelen, J., Asselbergs, F. W., Boersma, E., Cats, D., Elders, P. M., et al. (2020) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.119.002610 · Cited by 109 · see citing works
Self-citationHan, L. K. M., Verhoeven, J. E., Tyrka, A. R., Penninx, B. W. J. H., Wolkowitz, O. M., Månsson, K. N. T., Lindqvist, D., Boks, M. P., Révész, D., Mellon, S. H., et al. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.04.004 · Cited by 104 · see citing works
Longitudinal Associations Between Metabolic Syndrome Components and Telomere Shortening
Révész, D., Milaneschi, Y., Verhoeven, J. E., Lin, J., & Penninx, B. W. J. H. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-1995 · Cited by 84 · see citing works
Self-citationSerum proteomic profiles of depressive subtypes
Lamers, F., Bot, M., Jansen, R., Chan, M. K., Cooper, J. D., Bahn, S., & Penninx, B. W. J. H. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.115 · Cited by 84 · see citing works
Self-citationLi, Z., He, Y., Wang, D., Tang, J., & Chen, X. (2017) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.06.002 · Cited by 84 · see citing works
Murabito, J. M., Zhao, Q., Larson, M. G., Rong, J., Lin, H., Benjamin, E. J., Levy, D. A., & Lunetta, K. L. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx144 · Cited by 84 · see citing works
Depression in Older Persons with Mobility Limitations
Milaneschi, Y., & Penninx, B. W. J. H. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113196660060 · Cited by 81 · see citing works
Self-citationA methylation study of long-term depression risk
Clark, S. L., Hattab, M. W., Chan, R. F., Shabalin, A. A., Han, L. K. M., Zhao, M., Smit, J. H., Jansen, R., Milaneschi, Y., Xie, L. Y., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0516-z · Cited by 77 · see citing works
Self-citationAberg, K. A., Dean, B., Shabalin, A. A., Chan, R. F., Han, L. K. M., Zhao, M., van Grootheest, G., Xie, L. Y., Milaneschi, Y., Clark, S. L., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-018-0247-6 · Cited by 73 · see citing works
Self-citationBiomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative
Nelson, P. G., Promislow, D. E. L., & Masel, J. (2020) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glz174 · Cited by 65 · see citing works
RaMWAS: fast methylome-wide association study pipeline for enrichment platforms
Shabalin, A. A., Hattab, M. W., Clark, S. L., Chan, R. F., Kumar, G., Åberg, K. A., & van den Oord, E. J. C. G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty069 · Cited by 56 · see citing works
Leukocyte telomere length, T cell composition and DNA methylation age
Chen, B. H., Carty, C. L., Kimura, M., Kark, J. D., Chen, W., Li, S., Zhang, T., Kooperberg, C. L., Levy, D. A., Assimes, T. L., et al. (2017) · Aging · doi:10.18632/aging.101293 · Cited by 55 · see citing works
Associations Between Cellular Aging Markers and Metabolic Syndrome: Findings From the CARDIA Study
Révész, D., Verhoeven, J. E., Picard, M. A., Lin, J., Sidney, S., Epel, E. S., Penninx, B. W. J. H., & Puterman, E. M. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-01625 · Cited by 55 · see citing works
Self-citationVerhoeven, J. E., Yang, R., Wolkowitz, O. M., Bersani, F. S., Lindqvist, D., Mellon, S. H., Yehuda, R., Flory, J. D., Lin, J., Amara, D. A., et al. (2018) · Complex Psychiatry · doi:10.1159/000491431 · Cited by 50 · see citing works
Self-citationThe Impact of Chronic Somatic Diseases on the Course of Depressive and Anxiety Disorders
Gerrits, M. M. J. G., van Oppen, P., van Marwijk, H., van der Horst, H. E., & Penninx, B. W. J. H. (2012) · Psychotherapy and Psychosomatics · doi:10.1159/000338636 · Cited by 45 · see citing works
Self-citationRelationship between short telomere length and stroke
Jin, X., Biqi, P., Dang, X., Wu, H., & Xu, D. (2018) · Medicine · doi:10.1097/md.0000000000012489 · Cited by 37 · see citing works
The Impact of Depressive Disorder Symptoms and Subtypes on 6-Year Incidence of Somatic Diseases
Gaspersz, R., Lamers, F., Beekman, A. T. F., et al. (2018) · Psychotherapy and Psychosomatics · doi:10.1159/000491933 · Cited by 25 · see citing works
Accelerated Epigenetic Ageing in Major Depressive DisorderPreprint
Whalley, H. C., Gibson, J., Marioni, R. E., Walker, R. M., Clarke, T., Howard, D. M., Adams, M. J., Hall, L. S., Morris, S. W., Deary, I. J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/210666 · Cited by 20 · see citing works
Belsky, D. W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518878112 · Cited by 17 · see citing works
Belsky, D. W., Moffitt, T. E., Cohen, A. A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/071373 · Cited by 17 · see citing works
Stress, Inflammation, and Aging: An Association Beyond Chance
Diniz, B. S., & Vieira, É. L. M. (2018) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2018.06.013 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Akker E Trompet S Wolf J Beekman M Suchiman HED 2019 Predicting biological age based on the BBMRI-NL 1 H-NMR metabolomics repository bioRxiv 10.1101/632919 · DOI
Cited by
Sorted by citations
Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
Perret, L. C., Geoffroy, M.-C., Barr, E., Parnet, F., Provencal, N., Boivin, M., O’Donnell, K. J., Suderman, M., Power, C., Turecki, G., et al. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1051556 · Cited by 19 · see citing works
- Authors
- Jansen, R., Han, L. K., Verhoeven, J. E., Aberg, K. A., van den Oord, E. C., Milaneschi, Y., & Penninx, B. W.
- Affiliations
- Amsterdam Neuroscience (NL)
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, United States
- Corresponding author
- Jansen, Rick
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2021-02-09
- Volume
- 10
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC7872513
- OpenAlex
- W3127104885
- Cited by
- 127
- References
- 76
- Citations by year
- 2026: 22 · 2025: 28 · 2024: 25 · 2023: 24 · 2022: 16 · 2021: 11 · 2020: 1
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Universiteit Leiden (10‐000‐1002)
- Rijksuniversiteit Groningen (10‐000‐1002)
- ZonMw (184.021.007, 10‐000‐1002, 91811602)
- Vrije Universiteit Amsterdam
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (184021007, 10-000-1002, 91811602, 184.033.111)
- GGZ inGeest
- Rivierduinen
- GGZ Friesland
- GGZ Drenthe
- Leids Universitair Medisch Centrum
- Universitair Medisch Centrum Groningen
- National Institute of Mental Health (R01MH099110, RC2MH089951, R01MH109525)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
- Keywords
- biological clock, biological aging, telomere length, epigenetic clocks, physical health, mental health, depression, metabolic syndrome, body mass index, smoking, sex differences
- MeSH terms
- Biological Clocks, Humans, Mental Health, Telomere, Proteome, Epigenesis, Genetic, Metabolome, Transcriptome
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
