ophthalmologyОфтальмологияOphthalmology in Russia1816-50952500-0845Ophthalmology10.18008/1816-5095-2024-4-844-849ophthalmology-2516Research ArticleСОЦИАЛЬНАЯ ОФТАЛЬМОЛОГИЯSOCIAL OPHTHALMOLOGYАспекты определения биологического возраста и его значение в офтальмологииAspects of Biological Age Assessment and Its Significance in Ophthalmologyhttps://orcid.org/0000-0003-4043-456XЮсефЮ. Н.YusefYu. N.

Юсеф Наим Юсеф, директор, доктор медицинских наук, профессор кафедры офтальмологии, почетный профессор Российской медицинской академии непрерывного профессионального образования,

ул. Россолимо, 11а, б, Москва, 119021

Yusef Naim Y., MD, director, Professor of the Department of Ophthalmology, Honorary Professor at the Russian Medical Academy of Continuous Professional Education

Rossolimo str. 11A, B, Moscow, 119021

ГусейновЮ. А.GuseynovYu. A.

Гусейнов Юсиф Азиз оглы, аспирант отдела патологии сетчатки и зрительного нерва

ул. Россолимо, 11а, б, Москва, 119021

Guseynov Yusif A., postgraduate of Retinal and Optic Nerve Pathology Department 

Rossolimo str. 11A, B, Moscow, 119021

https://orcid.org/0000-0002-3806-3985ДуржинскаяМ. Х.DurzhinskayaM. H.

Дуржинская Мадина Хикметовна, кандидат медицинских наук, научный сотрудник отдела патологии сетчатки и зрительного нерва, ассистент кафедры офтальмологии

ул. Россолимо, 11а, б, Москва, 119021

Durzhinskaya Madina H., PhD, researcher Retinal and Optic Nerve Pathology Department, assistant professor in the Ophthalmology Department

Rossolimo str. 11A, B, Moscow, 119021

m.h.efendieva@gmail.comФГБНУ «Научно-исследовательский институт глазных болезней имени М.М. Краснова»РоссияM.M. Krasnov Research Institute of Eye DiseasesRussian Federation202425122024214844849Copyright © Юсеф Ю.Н., Гусейнов Ю.А., Дуржинская М.Х., 20242024Юсеф Ю.Н., Гусейнов Ю.А., Дуржинская М.Х.Yusef Y.N., Guseynov Y.A., Durzhinskaya M.H.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.ophthalmojournal.com/opht/article/view/2516

В статье рассматриваются ключевые аспекты определения биологического возраста и потенциал изучения этого параметра в офтальмологии. Биологический возраст как показатель состояния организма отличается от календарного и позволяет более точно оценивать функциональные возможности органов и систем. В контексте офтальмологии этот параметр становится особенно актуальным, поскольку здоровье глаз и зрительная функция могут значительно варьировать в зависимости от индивидуальных особенностей пациента, включая генетические факторы, образ жизни и наличие сопутствующих заболеваний. Проанализированы современные методы оценки биологического возраста, включая лабораторные и инструментальные. Приведены данные о взаимосвязи биологического возраста и состоянии структур глазного дна. Подчеркивается важность индивидуализированного подхода к диагностике, учитывающего биологический возраст пациента, и предлагается внедрение методов оценки биологического возраста в клиническую практику для улучшения прогнозирования исходов лечения и повышения качества жизни пациентов. Необходимы дальнейшие исследования в этой области для разработки новых стратегий профилактики и лечения офтальмопатологии с учетом параметра биологического возраста.

This article explores the key aspects of biological age assessment and the potential of studying this parameter in ophthalmology. Biological age, as an indicator of the state of the organism, differs from chronological age and allows for a more accurate evaluation of the functional capabilities of organs and systems. In the context of ophthalmology, this parameter is particularly relevant, as eye health and visual function can vary significantly based on individual patient characteristics, including genetic factors, lifestyle choices, and the presence of comorbid conditions. The article analyzes contemporary methods for assessing biological age, including laboratory and instrumental approaches. It presents data on the impact of biological age on the development of various ophthalmological diseases. The importance of a personalized approach to diagnosis and therapy in ophthalmology is emphasized, taking into account the biological age of the patient. The authors propose the implementation of biological age assessment methods in clinical practice to improve treatment outcome predictions and enhance patients’ quality of life. Further research in this area is necessary to develop new strategies for the prevention and treatment of ophthalmopathologies that consider the parameter of biological age.

биологический возрастофтальмологиядиагностикаперсонализированная медицинаbiological ageophthalmologydiagnosispersonalized medicineReferencesBaker GT 3rd, Sprott RL. Biomarkers of aging. Exp Gerontol. 1988;23(4–5):223– 239. doi: 10.1016/0531-5565(88)90025-3.Baker GT 3rd, Sprott RL. Biomarkers of aging. Exp Gerontol. 1988;23(4–5):223– 239. doi: 10.1016/0531-5565(88)90025-3.Lara J, Cooper R, Nissan J, Ginty AT, Khaw KT, Deary IJ, Lord JM, Kuh D, Mathers JC. A proposed panel of biomarkers of healthy ageing. BMC Med. 2015 Sep 15;13:222. doi: 10.1186/s12916-015-0470-9.Lara J, Cooper R, Nissan J, Ginty AT, Khaw KT, Deary IJ, Lord JM, Kuh D, Mathers JC. A proposed panel of biomarkers of healthy ageing. BMC Med. 2015 Sep 15;13:222. doi: 10.1186/s12916-015-0470-9.Johnson TE. Recent results: biomarkers of aging. Exp Gerontol. 2006;41(12):1243– 1246. doi: 10.1016/j.exger.2006.09.006.Johnson TE. Recent results: biomarkers of aging. Exp Gerontol. 2006;41(12):1243– 1246. doi: 10.1016/j.exger.2006.09.006.Butler RN, Sprott R, Warner H. Biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004 Jun;59(6):B560–567. doi: 10.1093/gerona/59.6.b560.Butler RN, Sprott R, Warner H. Biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004 Jun;59(6):B560–567. doi: 10.1093/gerona/59.6.b560.Engelfriet PM, Jansen EH, Picavet HS, Dollé ME. Biochemical markers of aging for longitudinal studies in humans. Epidemiol Rev. 2013;35(1):132–151. doi: 10.1093/epirev/mxs011.Engelfriet PM, Jansen EH, Picavet HS, Dollé ME. Biochemical markers of aging for longitudinal studies in humans. Epidemiol Rev. 2013;35(1):132–151. doi: 10.1093/epirev/mxs011.Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115.Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115.Hannum G, Guinney J, Zhao L. Genomewide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013 Jan 24;49(2):359–367. doi: 10.1016/j.molcel.2012.10.016.Hannum G, Guinney J, Zhao L. Genomewide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013 Jan 24;49(2):359–367. doi: 10.1016/j.molcel.2012.10.016.Khalatyan AS, Shishparenok AN, Avetisov KS. Association of Telomere Length in T Lymphocytes, B Lymphocytes, NK Cells and Monocytes with Different Forms of AgeRelated Macular Degeneration. Biomedicines. 2024 Aug 19;12(8):1893. doi: 10.3390/biomedicines12081893.Khalatyan AS, Shishparenok AN, Avetisov KS. Association of Telomere Length in T Lymphocytes, B Lymphocytes, NK Cells and Monocytes with Different Forms of AgeRelated Macular Degeneration. Biomedicines. 2024 Aug 19;12(8):1893. doi: 10.3390/biomedicines12081893.Gardner M, Bann D, Wiley L. Gender and telomere length: systematic review and metaanalysis. Exp Gerontol. 2014 Mar;51:15–27. doi: 10.1016/j.exger.2013.12.004.Gardner M, Bann D, Wiley L. Gender and telomere length: systematic review and metaanalysis. Exp Gerontol. 2014 Mar;51:15–27. doi: 10.1016/j.exger.2013.12.004.Lapham K, Kvale MN, Lin J. Automated Assay of Telomere Length Measurement and Informatics for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics. 2015 Aug;200(4):1061–1072. doi: 10.1534/genetics.115.178624.Lapham K, Kvale MN, Lin J. Automated Assay of Telomere Length Measurement and Informatics for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics. 2015 Aug;200(4):1061–1072. doi: 10.1534/genetics.115.178624.Barrett EL, Richardson DS. Sex differences in telomeres and lifespan. Aging Cell. 2011;10(6):913–921. doi: 10.1111/j.1474-9726.2011.00741.x.Barrett EL, Richardson DS. Sex differences in telomeres and lifespan. Aging Cell. 2011;10(6):913–921. doi: 10.1111/j.1474-9726.2011.00741.x.Needham BL, Rehkopf D, Adler N. Leukocyte telomere length and mortality in the National Health and Nutrition Examination Survey, 19992002. Epidemiology. 2015 Jul;26(4):528–535. doi: 10.1097/EDE.0000000000000299.Needham BL, Rehkopf D, Adler N. Leukocyte telomere length and mortality in the National Health and Nutrition Examination Survey, 19992002. Epidemiology. 2015 Jul;26(4):528–535. doi: 10.1097/EDE.0000000000000299.Bakaysa SL, Mucci LA, Slagboom PE. Telomere length predicts survival independent of genetic influences. Aging Cell. 2007 Dec;6(6):769–774. doi: 10.1111/j.1474-9726.2007.00340.x.Bakaysa SL, Mucci LA, Slagboom PE. Telomere length predicts survival independent of genetic influences. Aging Cell. 2007 Dec;6(6):769–774. doi: 10.1111/j.1474-9726.2007.00340.x.Rode L, Nordestgaard BG, Bojesen SE. Peripheral blood leukocyte telomere length and mortality among 64,637 individuals from the general population. J Natl Cancer Inst. 2015;107(6):djv074. doi: 10.1093/jnci/djv074.Rode L, Nordestgaard BG, Bojesen SE. Peripheral blood leukocyte telomere length and mortality among 64,637 individuals from the general population. J Natl Cancer Inst. 2015;107(6):djv074. doi: 10.1093/jnci/djv074.Zhu X, Han W, Xue W. The association between telomere length and cancer risk in population studies. Sci Rep. 2016 Feb 26;6:22243. doi: 10.1038/srep22243.Zhu X, Han W, Xue W. The association between telomere length and cancer risk in population studies. Sci Rep. 2016 Feb 26;6:22243. doi: 10.1038/srep22243.Haycock PC, Heydon EE, Kaptoge S. Leucocyte telomere length and risk of cardiovascular disease: systematic review and metaanalysis. BMJ. 2014 Jul 8;349:g4227. doi: 10.1136/bmj.g4227.Haycock PC, Heydon EE, Kaptoge S. Leucocyte telomere length and risk of cardiovascular disease: systematic review and metaanalysis. BMJ. 2014 Jul 8;349:g4227. doi: 10.1136/bmj.g4227.Scheller Madrid A, Rode L, Nordestgaard BG. Short Telomere Length and Ischemic Heart Disease: Observational and Genetic Studies in 290 022 Individuals. Clin Chem. 2016 Aug;62(8):1140–1149. doi: 10.1373/clinchem.2016.258566.Scheller Madrid A, Rode L, Nordestgaard BG. Short Telomere Length and Ischemic Heart Disease: Observational and Genetic Studies in 290 022 Individuals. Clin Chem. 2016 Aug;62(8):1140–1149. doi: 10.1373/clinchem.2016.258566.Gardner MP, MartinRuiz C, Cooper R. Telomere length and physical performance at older ages: an individual participant metaanalysis. PLoS One. 2013 Jul 26;8(7):e69526. doi: 10.1371/journal.pone.0069526.Gardner MP, MartinRuiz C, Cooper R. Telomere length and physical performance at older ages: an individual participant metaanalysis. PLoS One. 2013 Jul 26;8(7):e69526. doi: 10.1371/journal.pone.0069526.Holly AC, Melzer D, Pilling LC. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013 Apr;12(2):324–326. doi: 10.1111/acel.12044.Holly AC, Melzer D, Pilling LC. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013 Apr;12(2):324–326. doi: 10.1111/acel.12044.Harries LW, Hernandez D, Henley W. Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell. 2011 Oct;10(5):868–878. doi: 10.1111/j.1474-9726.2011.00726.x.Harries LW, Hernandez D, Henley W. Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell. 2011 Oct;10(5):868–878. doi: 10.1111/j.1474-9726.2011.00726.x.Peters MJ, Joehanes R, Pilling LC. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015 Oct 22;6:8570. doi: 10.1038/ncomms9570.Peters MJ, Joehanes R, Pilling LC. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015 Oct 22;6:8570. doi: 10.1038/ncomms9570.Pucić M, Knezević A, Vidic J. High throughput isolation and glycosylation analysis of IgGvariability and heritability of the IgG glycome in three isolated human populations. Mol Cell Proteomics. 2011 Oct;10(10):M111.010090. doi: 10.1074/mcp.M111.010090. Epub 2011 Jun 8. PMID: 21653738; PMCID: PMC3205872.Pucić M, Knezević A, Vidic J. High throughput isolation and glycosylation analysis of IgGvariability and heritability of the IgG glycome in three isolated human populations. Mol Cell Proteomics. 2011 Oct;10(10):M111.010090. doi: 10.1074/mcp.M111.010090. Epub 2011 Jun 8. PMID: 21653738; PMCID: PMC3205872.Ruhaak LR, Uh HW, Beekman M. Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity. PLoS One. 2010 Sep 7;5(9):e12566. doi: 10.1371/journal.pone.0012566.Ruhaak LR, Uh HW, Beekman M. Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity. PLoS One. 2010 Sep 7;5(9):e12566. doi: 10.1371/journal.pone.0012566.Parekh R, Roitt I, Isenberg D. Agerelated galactosylation of the Nlinked oligosaccharides of human serum IgG. J Exp Med. 1988 May 1;167(5):1731–1736. doi: 10.1084/jem.167.5.1731.Parekh R, Roitt I, Isenberg D. Agerelated galactosylation of the Nlinked oligosaccharides of human serum IgG. J Exp Med. 1988 May 1;167(5):1731–1736. doi: 10.1084/jem.167.5.1731.Ruhaak LR, Uh HW, Beekman M. Plasma protein Nglycan profiles are associated with calendar age, familial longevity and health. J Proteome Res. 2011 Apr 1;10(4):1667–1674. doi: 10.1021/pr1009959.Ruhaak LR, Uh HW, Beekman M. Plasma protein Nglycan profiles are associated with calendar age, familial longevity and health. J Proteome Res. 2011 Apr 1;10(4):1667–1674. doi: 10.1021/pr1009959.Knezevic A, Gornik O, Polasek O. Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma Nglycans. Glycobiology. 2010 Aug;20(8):959– 969. doi: 10.1093/glycob/cwq051.Knezevic A, Gornik O, Polasek O. Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma Nglycans. Glycobiology. 2010 Aug;20(8):959– 969. doi: 10.1093/glycob/cwq051.Krištić J, Vučković F, Menni C. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci. 2014 Jul;69(7):779–789. doi: 10.1093/gerona/glt190.Krištić J, Vučković F, Menni C. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci. 2014 Jul;69(7):779–789. doi: 10.1093/gerona/glt190.Zhang J, Goodlett DR, Peskind ER. Quantitative proteomic analysis of agerelated changes in human cerebrospinal fluid. Neurobiol Aging. 2005 Feb;26(2):207–227. doi: 10.1016/j.neurobiolaging.2004.03.012.Zhang J, Goodlett DR, Peskind ER. Quantitative proteomic analysis of agerelated changes in human cerebrospinal fluid. Neurobiol Aging. 2005 Feb;26(2):207–227. doi: 10.1016/j.neurobiolaging.2004.03.012.Ignjatovic V, Lai C, Summerhayes R. Agerelated differences in plasma proteins: how plasma proteins change from neonates to adults. PLoS One. 2011 Feb 18;6(2):e17213. doi: 10.1371/journal.pone.0017213.Ignjatovic V, Lai C, Summerhayes R. Agerelated differences in plasma proteins: how plasma proteins change from neonates to adults. PLoS One. 2011 Feb 18;6(2):e17213. doi: 10.1371/journal.pone.0017213.Baird GS, Nelson SK, Keeney TR. Agedependent changes in the cerebrospinal fluid proteome by slow offrate modified aptamer array. Am J Pathol. 2012 Feb;180(2):446– 456. doi: 10.1016/j.ajpath.2011.10.024.Baird GS, Nelson SK, Keeney TR. Agedependent changes in the cerebrospinal fluid proteome by slow offrate modified aptamer array. Am J Pathol. 2012 Feb;180(2):446– 456. doi: 10.1016/j.ajpath.2011.10.024.Menni C, Kiddle SJ, Mangino M. Circulating Proteomic Signatures of Chronological Age. J Gerontol A Biol Sci Med Sci. 2015 Jul;70(7):809–816. doi: 10.1093/gerona/glu121.Menni C, Kiddle SJ, Mangino M. Circulating Proteomic Signatures of Chronological Age. J Gerontol A Biol Sci Med Sci. 2015 Jul;70(7):809–816. doi: 10.1093/gerona/glu121.Ishikawa M, Maekawa K, Saito K. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS One. 2014 Mar 14;9(3):e91806. doi: 10.1371/journal.pone.0091806.Ishikawa M, Maekawa K, Saito K. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS One. 2014 Mar 14;9(3):e91806. doi: 10.1371/journal.pone.0091806.Yu Z, Zhai G, Singmann P. Human serum metabolic profiles are age dependent. Aging Cell. 2012 Dec;11(6):960–967. doi: 10.1111/j.1474-9726.2012.00865.x.Yu Z, Zhai G, Singmann P. Human serum metabolic profiles are age dependent. Aging Cell. 2012 Dec;11(6):960–967. doi: 10.1111/j.1474-9726.2012.00865.x.Menni C, Kastenmüller G, Petersen AK. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013 Aug;42(4):1111–1119. doi: 10.1093/ije/dyt094.Menni C, Kastenmüller G, Petersen AK. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013 Aug;42(4):1111–1119. doi: 10.1093/ije/dyt094.Hertel J, Friedrich N, Wittfeld K. Measuring Biological Age via Metabonomics: The Metabolic Age Score. J Proteome Res. 2016 Feb 5;15(2):400–410. doi: 10.1021/acs.jproteome.5b00561.Hertel J, Friedrich N, Wittfeld K. Measuring Biological Age via Metabonomics: The Metabolic Age Score. J Proteome Res. 2016 Feb 5;15(2):400–410. doi: 10.1021/acs.jproteome.5b00561.Collino S, Montoliu I, Martin FP. Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One. 2013;8(3):e56564. doi: 10.1371/journal.pone.0056564.Collino S, Montoliu I, Martin FP. Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One. 2013;8(3):e56564. doi: 10.1371/journal.pone.0056564.Lawton KA, Berger A, Mitchell M. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008 Apr;9(4):383–397. doi: 10.2217/14622416.9.4.383.Lawton KA, Berger A, Mitchell M. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008 Apr;9(4):383–397. doi: 10.2217/14622416.9.4.383.Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013;68(6):667–674. doi: 10.1093/gerona/gls233.Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013;68(6):667–674. doi: 10.1093/gerona/gls233.Belsky DW, Caspi A, Houts R. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015 Jul 28;112(30):E4104–4110. doi: 10.1073/pnas.1506264112.Belsky DW, Caspi A, Houts R. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015 Jul 28;112(30):E4104–4110. doi: 10.1073/pnas.1506264112.Sebastiani P, Thyagarajan B, Sun F, Schupf N, Newman AB, Montano M, Perls TT. Biomarker signatures of aging. Aging Cell. 2017 Apr;16(2):329–338. doi: 10.1111/acel.12557.Sebastiani P, Thyagarajan B, Sun F, Schupf N, Newman AB, Montano M, Perls TT. Biomarker signatures of aging. Aging Cell. 2017 Apr;16(2):329–338. doi: 10.1111/acel.12557.Belsky DW, Moffitt TE, Cohen AA. Eleven Telomere, Epigenetic Clock, and BiomarkerComposite Quantifications of Biological Aging: Do They Measure the Same Thing? Am J Epidemiol. 2018 Jun 1;187(6):1220–1230. doi: 10.1093/aje/kwx346.Belsky DW, Moffitt TE, Cohen AA. Eleven Telomere, Epigenetic Clock, and BiomarkerComposite Quantifications of Biological Aging: Do They Measure the Same Thing? Am J Epidemiol. 2018 Jun 1;187(6):1220–1230. doi: 10.1093/aje/kwx346.Zierer J, Pallister T, Tsai PC. Exploring the molecular basis of agerelated disease comorbidities using a multiomics graphical model. Sci Rep. 2016 Nov 25;6:37646. doi: 10.1038/srep37646.Zierer J, Pallister T, Tsai PC. Exploring the molecular basis of agerelated disease comorbidities using a multiomics graphical model. Sci Rep. 2016 Nov 25;6:37646. doi: 10.1038/srep37646.Arsenis NC, You T, Ogawa EF. Physical activity and telomere length: Impact of aging and potential mechanisms of action. Oncotarget. 2017 Jul 4;8(27):45008–45019. doi: 10.18632/oncotarget.16726.Arsenis NC, You T, Ogawa EF. Physical activity and telomere length: Impact of aging and potential mechanisms of action. Oncotarget. 2017 Jul 4;8(27):45008–45019. doi: 10.18632/oncotarget.16726.Turner KJ, Vasu V, Griffin DK. Telomere biology and human phenotype. Cells. 2019;8(1) ):73. doi: 10.3390/cells8010073.Turner KJ, Vasu V, Griffin DK. Telomere biology and human phenotype. Cells. 2019;8(1) ):73. doi: 10.3390/cells8010073.Canela A, Klatt P, Blasco MA. Telomere length analysis. Methods Mol Biol. 2007;371:45–72. doi: 10.1007/978-1-59745-361-5_5.Canela A, Klatt P, Blasco MA. Telomere length analysis. Methods Mol Biol. 2007;371:45–72. doi: 10.1007/978-1-59745-361-5_5.Flammer J, Konieczka K, Bruno RM. The eye and the heart. Eur Heart J. 2013 May;34(17):1270–1278. doi: 10.1093/eurheartj/eht023.Flammer J, Konieczka K, Bruno RM. The eye and the heart. Eur Heart J. 2013 May;34(17):1270–1278. doi: 10.1093/eurheartj/eht023.Wong CW, Wong TY, Cheng CY. Kidney and eye diseases: common risk factors, etiological mechanisms, and pathways. Kidney Int. 2014 Jun;85(6):1290–1302. doi: 10.1038/ki.2013.491.Wong CW, Wong TY, Cheng CY. Kidney and eye diseases: common risk factors, etiological mechanisms, and pathways. Kidney Int. 2014 Jun;85(6):1290–1302. doi: 10.1038/ki.2013.491.Patton N, Aslam T, Macgillivray T. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. 2005 Apr;206(4):319–348. doi: 10.1111/j.1469-7580.2005.00395.x.Patton N, Aslam T, Macgillivray T. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. 2005 Apr;206(4):319–348. doi: 10.1111/j.1469-7580.2005.00395.x.Koronyo Y, Biggs D, Barron E. Retinal amyloid pathology and proofofconcept imaging trial in Alzheimer’s disease. JCI Insight. 2017 Aug 17;2(16):e93621. doi: 10.1172/jci.insight.93621.Koronyo Y, Biggs D, Barron E. Retinal amyloid pathology and proofofconcept imaging trial in Alzheimer’s disease. JCI Insight. 2017 Aug 17;2(16):e93621. doi: 10.1172/jci.insight.93621.Wang J, Knol MJ, Tiulpin A. Gray Matter Age Prediction as a Biomarker for Risk of Dementia. Proc Natl Acad Sci U S A. 2019 Oct 15;116(42):21213–21218. doi: 10.1073/pnas.1902376116.Wang J, Knol MJ, Tiulpin A. Gray Matter Age Prediction as a Biomarker for Risk of Dementia. Proc Natl Acad Sci U S A. 2019 Oct 15;116(42):21213–21218. doi: 10.1073/pnas.1902376116.Xia X, Chen X, Wu G. Threedimensional facialimage analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab. 2020 Sep;2(9):946–957. doi: 10.1038/s42255-020-00270-x.Xia X, Chen X, Wu G. Threedimensional facialimage analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab. 2020 Sep;2(9):946–957. doi: 10.1038/s42255-020-00270-x.Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017 Dec;40(12):681–690. doi: 10.1016/j.tins.2017.10.001.Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017 Dec;40(12):681–690. doi: 10.1016/j.tins.2017.10.001.Cole JH, Ritchie SJ, Bastin ME. Brain age predicts mortality. Mol Psychiatry. 2018 May;23(5):1385–1392. doi: 10.1038/mp.2017.62.Cole JH, Ritchie SJ, Bastin ME. Brain age predicts mortality. Mol Psychiatry. 2018 May;23(5):1385–1392. doi: 10.1038/mp.2017.62.Zhu Z, Shi D, Guankai P. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol. 2023 Apr;107(4):547–554. doi: 10.1136/bjophthalmol-2021-319807.Zhu Z, Shi D, Guankai P. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol. 2023 Apr;107(4):547–554. doi: 10.1136/bjophthalmol-2021-319807.Weidner CI, Lin Q, Koch CM. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014 Feb 3;15(2):R24. doi: 10.1186/gb-2014-15-2-r24.Weidner CI, Lin Q, Koch CM. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014 Feb 3;15(2):R24. doi: 10.1186/gb-2014-15-2-r24.Putin E, Mamoshina P, Aliper A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY). 2016 May;8(5):1021–1033. doi: 10.18632/aging.100968.Putin E, Mamoshina P, Aliper A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY). 2016 May;8(5):1021–1033. doi: 10.18632/aging.100968.Mamoshina P, Kochetov K, Putin E. Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. J Gerontol A Biol Sci Med Sci. 2018 Oct 8;73(11):1482–1490. doi: 10.1093/gerona/gly005.Mamoshina P, Kochetov K, Putin E. Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. J Gerontol A Biol Sci Med Sci. 2018 Oct 8;73(11):1482–1490. doi: 10.1093/gerona/gly005.Fleischer JG, Schulte R, Tsai HH. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 2018 Dec 20;19(1):221. doi: 10.1186/s13059-018-1599-6.Fleischer JG, Schulte R, Tsai HH. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 2018 Dec 20;19(1):221. doi: 10.1186/s13059-018-1599-6.Liem F, Varoquaux G, Kynast J. Predicting brainage from multimodal imaging data captures cognitive impairment. Neuroimage. 2017 Mar 1;148:179–188. doi: 10.1016/j.neuroimage.2016.11.005.Liem F, Varoquaux G, Kynast J. Predicting brainage from multimodal imaging data captures cognitive impairment. Neuroimage. 2017 Mar 1;148:179–188. doi: 10.1016/j.neuroimage.2016.11.005.Chen W, Qian W, Wu G. Threedimensional human facial morphologies as robust aging markers. Cell Res. 2015 May;25(5):574–587. doi: 10.1038/cr.2015.36.Chen W, Qian W, Wu G. Threedimensional human facial morphologies as robust aging markers. Cell Res. 2015 May;25(5):574–587. doi: 10.1038/cr.2015.36.Li JO, Liu H, Ting DSJ. Digital technology, telemedicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res. 2021 May;82:100900. doi: 10.1016/j.preteyeres.2020.100900.Li JO, Liu H, Ting DSJ. Digital technology, telemedicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res. 2021 May;82:100900. doi: 10.1016/j.preteyeres.2020.100900.Ho E, Wang E, Youn S. Deep Ensemble Learning for Retinal Image Classification. Transl Vis Sci Technol. 2022 Oct 3;11(10):39. doi: 10.1167/tvst.11.10.39.Ho E, Wang E, Youn S. Deep Ensemble Learning for Retinal Image Classification. Transl Vis Sci Technol. 2022 Oct 3;11(10):39. doi: 10.1167/tvst.11.10.39.Muchuchuti S, Viriri S. Retinal disease detection using deep learning techniques: a comprehensive review. J Imaging. 2023 Apr;9(4):104. doi: 10.3390/jimag-ing9040084.Muchuchuti S, Viriri S. Retinal disease detection using deep learning techniques: a comprehensive review. J Imaging. 2023 Apr;9(4):104. doi: 10.3390/jimag-ing9040084.Playout C, Duval R, Boucher MC. Focused Attention in Transformers for interpretable classification of retinal images. Med Image Anal. 2022 Nov;82:102608. doi: 10.1016/j.media.2022.102608.Playout C, Duval R, Boucher MC. Focused Attention in Transformers for interpretable classification of retinal images. Med Image Anal. 2022 Nov;82:102608. doi: 10.1016/j.media.2022.102608.

The authors declare that there are no conflicts of interest present.