Multi-domain Biomarkers that Characterize the Exceptional Cognition of Superagers
Poster No:
880
Submission Type:
Abstract Submission
Authors:
Yiru Yang1, Xiaolei Li1, Shudan Gao2, Yuanxu Gao3
Institutions:
1Shandong University, Jinan, China, 2Shandong Normal University, Jinan, China, 3Macau University of Science and Technology, Macau, China
First Author:
Co-Author(s):
Introduction:
There is substantial heterogeneity in cognitive aging trajectories across older adults (Lindenberger, 2014; Nyberg et al., 2020), with some experiencing significant cognitive decline while others maintain exceptional cognitive abilities well into advanced age, and the latter often termed "superagers" or demonstrating "successful cognitive aging (SCA)" (Depp et al., 2012; Islam et al., 2024). Increasing researches show interest in identifying the biological mechanisms that underlie such exceptional cognition in old age (de Godoy et al., 2021; Krivanek et al., 2021; Nyberg & Pudas, 2019), which is not clear yet. To address this critical gap in the literature regarding the biological basis of SCA, we conducted a systematic review of multi-domain biomarkers in SCA individuals following standard protocols.
Methods:
This study followed standard guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and was registered with the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42024578134). Four electronic databases (PubMed, Scopus, PsycINFO, and Web of Science) were searched and screened independently by two researchers. Data were extracted from each included study, including title, authors, published year, journal, country, data sources, demographics of participants (sample size, age, sex, et al.), definitions of SCA, neuropsychological measurements to define SCA, biomarker types, and main results, especially brain regions detected by neuroimaging studies. Six biomarker types were recorded, including genetic and epigenetic biomarkers, biofluid biomarkers, histological biomarkers, PET biomarkers, structural MRI biomarkers, and functional neuroimaging biomarkers (including EEG, ERP, and functional MRI biomarkers).
Results:
We identified 6699 records from four electronic databases, after excluding duplicated records (n=3620), extraneous records (n=2884), and 142 records that had no distinct definition of SCA, in inappropriate article forms, or without biomarkers, resulting in 53 studies that were firstly included. Nine records were further identified and included via reference screening and update checking, and 62 studies were finally included in this systematic review. The sample size of SCA individuals in the included studies ranged from 5 to 1060, with a median value of 34. We identified 14 diverse terms and 34 different SCA definitions and organized them into three distinct types, cross-sectional comparison definitions, longitudinal tracking definitions, and generational comparison definitions. The overall profile of biomarkers (Figure 1) shows a different underlying mechanism of SCA compared to pathological cognitive aging, which means SCA individuals' resistant ability to age-related pathology (like Aβ and tau, and genetic factor of APOE4) is less important when compared with their special resilience capability represented by the youthful DNA-methylation age, high density of von Economo neuron and efficient glucose metabolism et al. By summarizing neuroimaging studies, superior brain reserve, maintenance, and compensation of SCA individuals were found in various brain regions centered on a "cingulate gyrus-medial temporal lobe-frontal cortex" signature in gray matter (Figure 2A) and in multiple white matter fibers with higher integrity (Figure 2B).
Conclusions:
SCA biomarker studies remind us that the aging process is not all about decline and loss, but also a process of maintenance and adaptation with the support from distinct biological substrates. These insights contribute to our further understanding of SCA and may inspire future strategies for promoting healthy aging.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Aging 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Keywords:
Aging
Cognition
Cortex
Degenerative Disease
FUNCTIONAL MRI
MRI
Positron Emission Tomography (PET)
STRUCTURAL MRI
Other - biomarker, superager, successful cognitive aging
1|2Indicates the priority used for review
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Provide references using APA citation style.
Depp, C. A., Harmell, A., & Vahia, I. V. (2012). Successful cognitive aging. Curr Top Behav Neurosci, 10, 35-50.
Islam, M. A., Sehar, U., Sultana, O. F., Mukherjee, U., Brownell, M., Kshirsagar, S., & Reddy, P. H. (2024). SuperAgers and centenarians, dynamics of healthy ageing with cognitive resilience. Mech Ageing Dev, 219, 111936.
Krivanek, T. J., Gale, S. A., McFeeley, B. M., Nicastri, C. M., & Daffner, K. R. (2021). Promoting Successful Cognitive Aging: A Ten-Year Update. J Alzheimers Dis, 81(3), 871-920.
Lindenberger, U. (2014). Human cognitive aging: corriger la fortune? Science, 346(6209), 572-578.
Nyberg, L., Boraxbekk, C. J., Sorman, D. E., Hansson, P., Herlitz, A., Kauppi, K., Ljungberg, J. K., Lovheim, H., Lundquist, A., Adolfsson, A. N., Oudin, A., Pudas, S., Ronnlund, M., Stiernstedt, M., Sundstrom, A., & Adolfsson, R. (2020). Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res Rev, 64, 101184.
Nyberg, L., & Pudas, S. (2019). Successful Memory Aging. Annu Rev Psychol, 70, 219-243.