Theoretical and Experimental Psychology
ISSN 2073-0861
eISSN 2782-5396
En Ru
ISSN 2073-0861
eISSN 2782-5396
Articles

Cognitive and biological age: current issues and new perspectives in aging research

Abstract

Background. Research in the field of healthy longevity is increasing due to the popularization of a healthy lifestyle among the population. At the same time there is a growing interest in the problem of diagnosing cognitive and physiological functioning of a person in the process of normal aging. Many scientists agree that chronological, biological, and cognitive ages often do not correspond to each other and there are many reasons for that. 

Objective. The purpose of the study was to systematize and analyze the data concerning the latest trends in determining the cognitive and biological age of a person, the possibility of correlating these parameters, to identify the main structural changes in the brain during the normal aging, to illuminate the possibilities of improving the cognitive functioning of a person. 

Results. The analysis has revealed that biological and cognitive ages are complex indicators, which are influenced by a huge number of factors, both environmental and genetic. There are different ways for measuring biological and cognitive ages. Yet there is still no single method or approach. For measuring biological age, they often resort to the methods based on the complex indices, epigenetic clocks, on the telomere division and DNA methylation. For measuring cognitive age, the most recognized are those based on the dynamics of cognitive functions, which are highly plastic and can be adjusted. 

Conclusion. Due to the increasing longevity of the population, the detailed study of the cognitive age parameters and neuroprotection methods will become one of the key research trends in the field of aging in the coming years. 

Funding. The study was implemented in the framework of the Basic Research Program at the National Research University “Higher School of Economics” (HSE University) in 2022 and as a part of the research project of the Department of Biology and Biotechnology (HSE University) “Fundamental research of topical issues of cognitive neuroscience and legal foundations of biology, medicine, and bioethics”.


References

Armanious, K., Abdulatif, S., Shi, W., Salian, S., Kustner, T., Weiskopf, D., Hepp, T., Gatidis, S., & Yang, B. (2021). Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation. IEEE Transactions on Medical Imaging, 40 (7), 1778–1791. https://doi.org/10.1109/TMI.2021.3066857 

Ashiqur Rahman, S., Giacobbi, P., Pyles, L., Mullett, C., Doretto, G., & Adjeroh, D.A. (2021). Deep learning for biological age estimation. Briefings in Bioinformatics, 22 (2), 1767–1781. https://doi.org/10.1093/bib/bbaa021 

Cho, I.H., Park, K.S., & Lim, C.J. (2010). An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI). Mechanisms of Ageing and Development, 131 (2), 69–78. https://doi.org/10.1016/j.mad.2009.12.001 

Cole, J.H., Marioni, R.E., Harris, S.E., & Deary, I.J. (2019). Brain age and other bodily ‘ages’: Implications for neuropsychiatry. Molecular Psychiatry, 24, 266–281. https://doi.org/10.1038/s41380-018-0098-1 

Deary, I.J., Corley, J., Gow, A.J., Harris, S.E., Houlihan, L.M., Marioni, R.E., Penke, L., Rafnsson, S.B., & Starr, J.M. (2009). Age-associated cognitive decline. British Medical Bulletin, 92, 135–152. https://doi.org/10.1093/bmb/ldp033 

Draganski, B., Gaser, C., Kempermann, G., Kuhn, H.G., Winkler, J., Buchel, C., May, A. (2006). Temporal and spatial dynamics of brain structure changes during extensive learning. J Neurosci, 26 (23), 6314–6317. 

Elliott, M.L., Belsky, D.W., Knodt, A.R., Ireland, D., Melzer, T.R., Poulton, R., Ramrakha, S., Caspi, A., Moffitt, T.E., Hariri, A.R. (2019). Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Molecular Psychiatry, 26, 3829–3838. https://doi.org/10.1038/s41380-019-0626-7 

Engvig, A., Fjell, A.M., Westlye, L.T., Moberget, T., Sundseth, O., Larsen, V.A., Walhovd, K.B. (2010). Effects of memory training on cortical thickness in the older adults. Neuroimage, 52 (4), 1667–1676. 

Fernández‐Ruiz, J. (2019). The biomedical challenge of neurodegenerative disorders: an opportunity for cannabinoid‐based therapies to improve on the poor current therapeutic outcomes. British Journal of Pharmacology, 176 (10), 1370–1383. 

Ferrucci, L., Gonzalez‐Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & Cabo, R. (2020). Measuring biological aging in humans: A quest. Aging Cell, 19 (2), e13080. https://doi.org/10.1111/acel.13080 

Finkel, D., Sternäng, O., & Wahlin, Å. (2017). Genetic and Environmental Influences on Longitudinal Trajectories of Functional Biological Age: Comparisons Across Gender. Behavior Genetics, 47, 375–382. https://doi.org/10.1007/s10519-017-9851-5 

Fjell, A.M., Westlye, L.T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., Dale, A.M., Walhovd, K.B., & for the Alzheimer Disease Neuroimaging Initiative. (2014). Accelerating Cortical Thinning: Unique to Dementia or Universal in Aging? Cerebral Cortex, 24 (4), 919–934. https://doi.org/10.1093/cercor/bhs379 

Frangou, S., Chitins, X., & Williams, S.C. (2004). Mapping IQ and gray matter density in healthy young people. Neuroimage, 23 (3), 800–805. 

Franke, K., & Gaser, C. (2019). Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained? Frontiers in Neurology, 10, 789. https://doi.org/10.3389/fneur.2019.00789 

Fratiglioni, L., Paillard-Borg, S., Winblad, B. (2004). An active and socially integrated lifestyle in late life might protect against dementia. Lancet Neurology, 3 (6), 343–353. 

Frol’kis, V.V. (1998). Aging: a memory of the future. Lechenie i diagnostika (Treatment anddiagnosis), 1, 14–38. (In Russ.). 

Haier, R.J., Jung, R.E., Yeo, R.A., Head, K., & Alkire, M.T. (2004). Structural brain variation and general intelligence. Neuroimage, 23 (1), 425–433. 

Hanafi, M.S., Soedarsono, N., & Auerkari, E. (2021). Biological age estimation using DNA methylation analysis: A systematic review. Scientific Dental Journal, 5 (1), 1–11. https://doi.org/10.4103/SDJ.SDJ_27_20 

Harada, C.N., Natelson Love, M.C., & Triebel, K.L. (2013). Normal cognitive aging. Clinics in Geriatric Medicine, 29 (4), 737–752. https://doi.org/10.1016/j.cger.2013.07.002 

Jia, L., Zhang, W., & Chen, X. (2017). Common methods of biological age estimation. Clinical Interventions in Aging, 12, 759–772. https://doi.org/10.2147/CIA.S134921 

Karama, S. et al. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19 (5), 555–559. https://doi.org/10.1038/mp.2013.64 

Karasik, D., Demissie, S., Cupples, L.A., & Kiel, D.P. (2005). Disentangling the Genetic Determinants of Human Aging: Biological Age as an Alternative to the Use of Survival Measures. The Journals of Gerontology Series A, 60 (5), 574–587. https://doi.org/10.1093/gerona/60.5.574 

Kaup, A.R., Mirzakhanian, H., Jeste, D.V., & Eyler, L.T. (2011). A Review of the Brain Structure Correlates of Successful Cognitive Aging. J Neuropsychiatry Clin Neurosci, 23 (1), 6–15. 

Klemera, P., & Doubal, S. (2006). A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development, 127 (3), 240–248. https://doi.org/10.1016/j.mad.2005.10.004 

Krut’ko, V.N., Doncov, V.I., Zahar’yashcheva, O.V., Kuznecov, I.A., Mamikonova, O.A., Pyrvu, V.V., Smirnova, T.M., Sokolova, L.A. (2014). Biological age as an indicator of the level of health, aging and environmental well-being of a person. Aviakosmicheskaya i ekologicheskaya medicina (Aerospace and environmental medicine), 48 (3), 12–19. (In Russ.). 

Laborda-Sánchez, F., & Cansino, S. (2021). The Effects of Neurofeedback on Aging-Associated Cognitive Decline: A Systematic Review. Applied Psychophysiology and Biofeedback, 46 (1), 1–10. https://doi.org/10.1007/s10484-020-09497-6 

Leone, A., Caroppo, A., Rescio, G., Diraco, G., & Siciliano, P. (2019). Ambient Assisted Living: Italian Forum 2018. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030- 05921-7 

MacDonald, S.W.S., Dixon, R.A., Cohen, A.-L., & Hazlitt, J.E. (2004). Biological Age and 12-Year Cognitive Change in Older Adults: Findings from the Victoria Longitudinal Study. Gerontology, 50 (2), 64–81. https://doi.org/10.1159/000075557 

Marioni, R.E., van den Hout, A., Valenzuela, M.J. et al. (2012). Active cognitive lifestyle associates with cognitive recovery and a reduced risk of cognitive decline. J Alzheimers Dis., 28 (1), 223–230. 

Narr, K.L., Woods, R.P., Thompson, P.M., Szeszko, P., Robinson, D., Dimtcheva, T., Gurbani, M., Toga, A.W., & Bilder, R.M. (2007). Relationships between IQ and Regional Cortical Gray Matter Thickness in Healthy Adults. Cerebral Cortex, 17 (9), 2163–2171. https://doi.org/10.1093/cercor/bhl125 

Nuretdinova, Z.G. (2008). Osobennosti dinamiki biologicheskogo vozrasta u sportsmenov-lyzhnikov: Diss. ... kand. med. nauk. (Features of the dynamics of biological age in athletes-skiers: dissertation). Ph.D. (Medicine). Moscow. (In Russ.). 

Petkovich, D.A., Podolskiy, D.I., Lobanov, A.V., Lee, S.-G., Miller, R.A., & Gladyshev, V.N. (2017). Using DNA Methylation Profiling to Evaluate Biological Age and Longevity Interventions. Cell Metabolism, 25 (4), 954–960, e6. https://doi.org/10.1016/j.cmet.2017.03.016 

Plakuev, A.N., Yur’eva, M.Yu., Yur’ev, Yu.Yu. (2011). Modern concepts of aging and assessment of human biological age. Ekologiya cheloveka (Human ecology), 4, 17–25. (In Russ.). 

Population of Russia for 100 years (1897–1997). (1998). In E.M. Andreev, O.I. Antonova, B.P. Bruj et al. (Eds.), Statistical compendium. M.: Goskomstat Rossii. (In Russ.). 

Prokhorov, N.I., Doncov, V.I., Krut’ko, V.N., Khodykina, T.M. (2019). Biological age as a method for assessing the level of health in the presence of environmental risks (literature review). Gigiena i sanitariya (Hygiene and sanitation), 98 (7), 761–765. (In Russ.). 

Raz, N., Rodrigue, K.M., Head, D. et al. (2004). Differential aging of the medial temporal lobe: a study of a five-year change. Neurology, 62 (3), 433–438. 

Robine, J.M., Allard, M., Herrmann, F.R., & Jeune, B. (2019). The real facts supporting Jeanne Calment as the oldest ever human. The Journals of Gerontology: Series A, 74 (1), S13–S20. 

Ryabchikova, T.V., Egorova, L.A., Kuz’micheva, E.A. (2009). Comparison of passport and biological age. Klinicheskaya gerontologiya (Clinical gerontology), 15 (12), 19–22. (In Russ.). 

Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440 (7084), 676–679. https://doi.org/10.1038/nature04513 

Smith, S.M., Elliott, L.T., Alfaro-Almagro, F., McCarthy, P., Nichols, T.E., Douaud, G., Miller, K.L. (2020). Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. eLife, 9, e52677. https://doi.org/10.7554/eLife.52677 

Solovev, I., Shaposhnikov, M., & Moskalev, A. (2020). Multi-omics approaches to human biological age estimation. Mechanisms of Ageing and Development, 185, 111192. https://doi.org/10.1016/j.mad.2019.111192 

Sowell, E.R. et al. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci, 24 (38), 8223–8231. 

Starnawska, A., Tan, Q., Lenart, A., McGue, M., Mors, O., Børglum, A.D., Christensen, K., Nyegaard, M., & Christiansen, L. (2017). Blood DNA methylation age is not associated with cognitive functioning in middle-aged monozygotic twins. Neurobiology of Aging, 50, 60–63. https://doi.org/10.1016/j.neurobiolaging.2016.10.025 

Terry, R.D., Katzman, R. (2001). Life span and synapses: will there be a primary senile dementia? Neurobiol Aging, 22 (3), 347–348. 

Vidal-Pineiro, D. et al. (2021). Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change. eLife, 10, e69995. https://doi.org/10.7554/eLife.69995 

Wilke, M., Sohn, J.H., Byars, A.W., & Holland, S.K. (2003). Bright spots: correlations of gray matter volume with IQ in a normal pediatric population. Neuroimage, 20 (1), 202–215. 

Weidner, C.I. et al. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome biology, 15 (2), R24.


PDF, ru

Received: 10/18/2022

Accepted: 10/19/2022

Accepted date: 12/18/2022

Keywords: cognitive age; biological age; brain correlates of aging; cognitive correlates of aging; enhancement of cognitive functioning

Available in the on-line version with: 18.12.2022

  • To cite this article:
Issue 4, 2022