{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:18:40Z","timestamp":1783005520181,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program for Basic Research in the Russian Federation for a long-term period","award":["122030100168-2"],"award-info":[{"award-number":["122030100168-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>Aging is a major risk factor for numerous chronic diseases and a leading contributor to global mortality. Slowing the rate of aging would have revolutionary implications for health and longevity. A fundamental barrier to achieving this goal, however, is the difficulty of accurately measuring the effects of rejuvenating interventions. The development of precise gerontometric methods, therefore, is a priority for both science and preventive medicine. In this opinion article, the authors suggest the principles and discuss the implementation of precision gerontometry using recent advances in metabolomics. Although metabolomic approaches have limited accuracy in determining biological age, the described approach, which averages multiple metabolites from a large metabolomic signature of aging, circumvents this limitation. It allows for measurement of biological age change with an accuracy of approximately one month. Such precision gerontometry enables accelerated testing of candidate anti-aging interventions, helping to eliminate ineffective ones, speed the development of effective ones, and ultimately extend the duration of healthy human life, with profound social and humanitarian benefits.<\/jats:p>","DOI":"10.3390\/metabo16070463","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:43:14Z","timestamp":1783003394000},"page":"463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Precision Gerontometry: Introduction, Fundamentals and Areas of Application"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0730-1725","authenticated-orcid":false,"given":"Petr G.","family":"Lokhov","sequence":"first","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5314-5727","authenticated-orcid":false,"given":"Elena E.","family":"Balashova","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.cell.2022.11.001","article-title":"Hallmarks of aging: An expanding universe","volume":"186","author":"Blasco","year":"2023","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.tem.2022.01.007","article-title":"Targeting aging mechanisms: Pharmacological perspectives","volume":"33","author":"Moskalev","year":"2022","journal-title":"Trends Endocrinol. Metab."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10522-019-09851-0","article-title":"Naive extrapolations, overhyped claims and empty promises in ageing research and interventions need avoidance","volume":"21","author":"Rattan","year":"2020","journal-title":"Biogerontology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vaiserman, A.M. (2014). Aging-modulating treatments: From reductionism to a system-oriented perspective. Front. Genet., 5.","DOI":"10.3389\/fgene.2014.00446"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1111\/acel.12463","article-title":"Developing criteria for evaluation of geroprotectors as a key stage toward translation to the clinic","volume":"15","author":"Moskalev","year":"2016","journal-title":"Aging Cell"},{"key":"ref_6","first-page":"564","article-title":"Geroprotectors: Specific action or hormesis?","volume":"21","year":"2008","journal-title":"Adv. Gerontol."},{"key":"ref_7","first-page":"e220002","article-title":"Does Modulation of an Epigenetic Clock Define a Geroprotector?","volume":"4","author":"Schork","year":"2022","journal-title":"Adv. Geriatr. Med. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41576-022-00511-7","article-title":"Measuring biological age using omics data","volume":"23","author":"Rutledge","year":"2022","journal-title":"Nat. Rev. Genet."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E4104","DOI":"10.1073\/pnas.1506264112","article-title":"Quantification of biological aging in young adults","volume":"112","author":"Belsky","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1038\/s41591-025-04006-w","article-title":"A full life cycle biological clock based on routine clinical data and its impact in health and diseases","volume":"31","author":"Wang","year":"2025","journal-title":"Nat. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nayak, D., Galkin, F., and Ghosh, S.K. (2025). Biometric aging clock for practical utility in clinical settings. bioRxiv, bioRxiv:2025.04.14.648594.","DOI":"10.1101\/2025.04.14.648594"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"573","DOI":"10.18632\/aging.101414","article-title":"An epigenetic biomarker of aging for lifespan and healthspan","volume":"10","author":"Levine","year":"2018","journal-title":"Aging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"R115","DOI":"10.1186\/gb-2013-14-10-r115","article-title":"DNA methylation age of human tissues and cell types","volume":"14","author":"Horvath","year":"2013","journal-title":"Genome Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.18632\/aging.101684","article-title":"DNA methylation GrimAge strongly predicts lifespan and healthspan","volume":"11","author":"Lu","year":"2019","journal-title":"Aging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e73420","DOI":"10.7554\/eLife.73420","article-title":"DunedinPACE, a DNA methylation biomarker of the pace of aging","volume":"11","author":"Belsky","year":"2022","journal-title":"eLife"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Quill, Q., Agarwal, V., Li, O., Patil, A., Kassis, T., and Gopinath, A. (2025). Transcriptomic age prediction using mixture-of-experts models reveals tissue-specific aging signatures in large-scale human RNA-sequencing data. medRxiv, medRxiv:2025.06.28.25330474.","DOI":"10.1101\/2025.06.28.25330474"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ali, M., Li, F., and Katari, M. (2025). SkeletAge: Transcriptomics-based Aging Clock Identifies 26 New Targets in Skeletal Muscle Aging. bioRxiv, bioRxiv:2025.07.28.667277.","DOI":"10.1101\/2025.07.28.667277"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e13799","DOI":"10.1111\/acel.13799","article-title":"Measuring biological age using a functionally interpretable multi-tissue RNA clock","volume":"22","author":"Jung","year":"2023","journal-title":"Aging Cell"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108538","DOI":"10.1016\/j.isci.2023.108538","article-title":"An accurate aging clock developed from large-scale gut microbiome and human gene expression data","volume":"27","author":"Gopu","year":"2024","journal-title":"iScience"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e13320","DOI":"10.1111\/acel.13320","article-title":"BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy","volume":"20","author":"Meyer","year":"2021","journal-title":"Aging Cell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8570","DOI":"10.1038\/ncomms9570","article-title":"The transcriptional landscape of age in human peripheral blood","volume":"6","author":"Peters","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1038\/s41591-024-03164-7","article-title":"Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations","volume":"30","author":"Argentieri","year":"2024","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Neirynck, R.E., Chirinos, J.A., Van Damme, M., Coussement, L., Segers, P., De Buyzere, M.L., Rietzschel, E.R., and De Meyer, T. (2026). Longitudinal dynamics of organ-specific proteomic aging clocks over a decade of midlife. bioRxiv, bioRxiv:2026.02.17.706320.","DOI":"10.64898\/2026.02.17.706320"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Yang, F., Li, M., Qi, T., Lu, Z., Wang, Q., Bu, Q., Sun, L., and Wo, H. (J. Adv. Res., 2026). ProtPhenoAge: Integrating plasma proteomics to predict Aging-Related disease Risks, J. Adv. Res., in press.","DOI":"10.1016\/j.jare.2026.05.022"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e12799","DOI":"10.1111\/acel.12799","article-title":"Plasma proteomic signature of age in healthy humans","volume":"17","author":"Tanaka","year":"2018","journal-title":"Aging Cell"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1038\/s41591-019-0673-2","article-title":"Undulating changes in human plasma proteome profiles across the lifespan","volume":"25","author":"Lehallier","year":"2019","journal-title":"Nat. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1093\/gerona\/glab212","article-title":"A Metabolomic Aging Clock Using Human Cerebrospinal Fluid","volume":"77","author":"Hwangbo","year":"2022","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lau, C.-H.E., Chekmeneva, E., Pinto, R., O\u2019Halloran, A.M., Chu, D.K.H., Dehghan, A., Tzoulaki, I., Elliott, P., Kenny, R.A., and McCrory, C. (2026). Assessment of ageing using global mass-spectrometry based metabolomics: A cross-cohort longitudinal study in the UK and Ireland. medRxiv, medRxiv:2026.02.09.26345895.","DOI":"10.64898\/2026.02.09.26345895"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.cmet.2024.11.007","article-title":"Decoding aging clocks: New insights from metabolomics","volume":"37","author":"Huang","year":"2025","journal-title":"Cell Metab."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"eadp3743","DOI":"10.1126\/sciadv.adp3743","article-title":"Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms","volume":"10","author":"Mutz","year":"2026","journal-title":"Sci. Adv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156467","DOI":"10.1016\/j.metabol.2025.156467","article-title":"Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank","volume":"176","author":"You","year":"2026","journal-title":"Metabolism"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e14164","DOI":"10.1111\/acel.14164","article-title":"NMR metabolomic modelling of age and lifespan: A multi-cohort analysis","volume":"23","author":"Lau","year":"2024","journal-title":"Aging Cell"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ding, K., Xu, R., Chao, X., Li, M., Sun, T., Yang, T., Zhu, M., Peng, X., Liu, R., and Luo, P. (Nat. Commun., 2026). Amino acid-based biological age clock and its implications for human health and aging, Nat. Commun., in press.","DOI":"10.1038\/s41467-026-73371-y"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"glad202","DOI":"10.1093\/gerona\/glad202","article-title":"Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study","volume":"79","author":"Smit","year":"2024","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1021\/acs.jproteome.5b00561","article-title":"Measuring Biological Age via Metabonomics: The Metabolic Age Score","volume":"15","author":"Hertel","year":"2016","journal-title":"J. Proteome Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"bbag271","DOI":"10.1093\/bib\/bbag271","article-title":"StackAge: An ensemble-based clock for precise quantification of biological age using multi-omics data","volume":"27","author":"Jiang","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_37","unstructured":"Li, H., Tang, F., Haochen, X., Li, Y., Zhuang, X., Zhang, B., Segal, E., and Razzak, I. (2025). Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2226915","DOI":"10.1080\/19490976.2023.2226915","article-title":"Machine learning model for predicting age in healthy individuals using age-related gut microbes and urine metabolites","volume":"15","author":"Seo","year":"2023","journal-title":"Gut Microbes"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e13149","DOI":"10.1111\/acel.13149","article-title":"Determinants of accelerated metabolomic and epigenetic aging in a UK cohort","volume":"19","author":"Robinson","year":"2020","journal-title":"Aging Cell"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s41591-025-03999-8","article-title":"MRI-based multi-organ clocks for healthy aging and disease assessment","volume":"32","author":"Cao","year":"2026","journal-title":"Nat. Med."},{"key":"ref_41","unstructured":"Li, J., Jiang, B., Zhang, W., Hao, J., Liu, Z., Ji, Q., Zheng, Y., Lu, X., Zheng, Z., and Ma, S. (Cell, 2026). Multimodal clocks of human aging, Cell, in press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101430","DOI":"10.1016\/j.measen.2024.101430","article-title":"Evaluation of the long-term drift of high precision resistance measures","volume":"38","author":"Velychko","year":"2025","journal-title":"Meas. Sens."},{"key":"ref_43","unstructured":"(2008). Evaluation of Measurement Data\u2014Guide to the Expression of Uncertainty in Measurement (Standard No. JCGM 100:2008). Available online: http:\/\/www.bipm.org\/utils\/common\/documents\/jcgm\/JCGM_100_2008_E.pdf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/19315775.2020.1774945","article-title":"Calculating Interval Uncertainties for Calibration Standards That Drift with Time","volume":"12","author":"Delker","year":"2020","journal-title":"NCSLI Meas."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s10522-020-09892-w","article-title":"MetaboAge DB: A repository of known ageing-related changes in the human metabolome","volume":"21","author":"Anghel","year":"2020","journal-title":"Biogerontology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"593","DOI":"10.3390\/metabo14110593","article-title":"Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway","volume":"14","author":"Lokhov","year":"2024","journal-title":"Metabolites"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e2307430121","DOI":"10.1073\/pnas.2307430121","article-title":"Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts","volume":"121","author":"Liu","year":"2024","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"156195","DOI":"10.1016\/j.metabol.2025.156195","article-title":"A multi-metabolite signature robustly predicts long-term mortality in the PREDIMED trial and several US cohorts","volume":"170","author":"Razquin","year":"2025","journal-title":"Metabolism"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"A0106","DOI":"10.5702\/massspectrometry.A0106","article-title":"Data Processing of Product Ion Spectra: Quality Improvement by Averaging Multiple Similar Spectra of Small Molecules","volume":"11","author":"Matsuda","year":"2022","journal-title":"Mass Spectrom."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10768","DOI":"10.1021\/ac302748b","article-title":"Normalizing and Integrating Metabolomics Data","volume":"84","author":"Dias","year":"2012","journal-title":"Anal. Chem."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, S.-G., and Yoo, H.J. (2025). A Scoring Model Using Multi-Metabolites Based on Untargeted Metabolomics for Assessing Dyslipidemia in Korean Individuals with Obesity. Metabolites, 15.","DOI":"10.3390\/metabo15040279"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1002\/bit.29008","article-title":"A Comprehensive Model for Separating Systematic Bias and Noise in Metabolomic Timecourse Data\u2014A Nonlinear B-Spline Mixed-Effects Approach","volume":"122","author":"Isaac","year":"2025","journal-title":"Biotechnol. Bioeng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lokhov, P.G., Balashova, E.E., Maslov, D.L., Trifonova, O.P., Lokhov, A.P., and Archakov, A.I. (2026). Towards Metabolomics-Guided Healthy and Anti-Aging Nutrition. Metabolites, 16.","DOI":"10.3390\/metabo16040241"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.medj.2023.06.010","article-title":"Determining a multimodal aging clock in a cohort of Chinese women","volume":"4","author":"Li","year":"2023","journal-title":"Med"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1038\/s43587-024-00692-2","article-title":"Nonlinear dynamics of multi-omics profiles during human aging","volume":"4","author":"Shen","year":"2024","journal-title":"Nat. Aging"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1080\/10408347.2017.1298986","article-title":"Direct Analysis of Samples of Various Origin and Composition Using Specific Types of Mass Spectrometry","volume":"47","author":"Dymerski","year":"2017","journal-title":"Crit. Rev. Anal. Chem."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/s41586-020-2896-2","article-title":"A reference map of potential determinants for the human serum metabolome","volume":"588","author":"Bar","year":"2020","journal-title":"Nature"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"423","DOI":"10.3390\/metabo11070423","article-title":"Metabolomic Laboratory-Developed Tests: Current Statusand Perspectives","volume":"11","author":"Lichtenberg","year":"2021","journal-title":"Metabolites"},{"key":"ref_59","unstructured":"Omenn, G.S., DeAngelis, C.D., DeMets, D.L., Fleming, T.R., Geller, G., Gray, J., Hayes, D.F., Henderson, G.I., Kessler, L., and Lapidus, S. (2012). Evolution of Translational Omics: Lessons Learned and the Path Forward\u2014Institute of Medicine, National Academies Press."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1373\/clinchem.2014.223339","article-title":"Where are all the new omics-based tests?","volume":"60","author":"Bossuyt","year":"2014","journal-title":"Clin. Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e14451","DOI":"10.1111\/acel.14451","article-title":"Cross-tissue comparison of epigenetic aging clocks in humans","volume":"24","author":"Apsley","year":"2025","journal-title":"Aging Cell"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Balashova, E.E., Maslov, D.L., Trifonova, O.P., Lokhov, P.G., and Archakov, A.I. (2022). Metabolome Profiling in Aging Studies. Biology, 11.","DOI":"10.3390\/biology11111570"}],"container-title":["Metabolites"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-1989\/16\/7\/463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:57:05Z","timestamp":1783004225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-1989\/16\/7\/463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":62,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["metabo16070463"],"URL":"https:\/\/doi.org\/10.3390\/metabo16070463","relation":{},"ISSN":["2218-1989"],"issn-type":[{"value":"2218-1989","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}