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What characterizes the exceptional cognition of superagers? A systematic review of multidomain biomarkers of successful cognitive aging.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 67 lines · 1.8 KB · no license

  1. # Load required libraries
  2. library("ggseg")
  3. library("ggplot2")
  4. library("dplyr")
  5. library("openxlsx")
  6. library("ggsegHO")
  7. library("showtext")
  8. library("ggseg3d")
  9. library("ggpubr")
  11. # Define color palette and mapping values
  12. fill_colors <- c("white", "#FEE7BE", "#815389")
  13. fill_values <- c(0, 0.5, 1)
  15. # Load first dataset
  16. sca_file <- "./data1.xlsx"
  17. df <- read.xlsx(sca_file)
  19. # Select hemisphere, region and p columns
  20. input <- df %>%
  21. rename(p = num) %>%
  22. select(hemi, region, p)
  24. # Disable automatic font loading
  25. showtext_auto(enable = FALSE)
  27. # Create first brain plot using hoCort atlas
  28. p1 <- ggplot(input) +
  29. geom_brain(atlas = hoCort, aes(fill = p), position = position_brain(hemi ~ side)) +
  30. guides(fill=guide_colorbar(title=stringr::str_wrap("Number of detections", width=10))) +
  31. scale_fill_gradientn(
  32. colors = fill_colors, values = fill_values,
  33. na.value = "white", limits = c(0, 8)
  34. ) +
  35. theme_void() +
  36. theme(
  37. legend.key.size = unit(0.3, 'cm'),
  38. plot.margin = margin(0, 1, 0, 0, unit = "cm")
  39. )
  41. # Load second dataset
  42. sca_file <- "./data2.xlsx"
  43. df <- read.xlsx(sca_file)
  45. # Select hemisphere, region and p columns
  46. input <- df %>%
  47. rename(p = num) %>%
  48. select(hemi, region, p)
  50. # Create second brain plot using aseg atlas, suppress legend
  51. p2 <- ggplot(input) +
  52. geom_brain(atlas = aseg, aes(fill = p)) +
  53. scale_fill_gradientn(
  54. colors = fill_colors, values = fill_values,
  55. na.value = "white", limits = c(0, 8)
  56. ) +
  57. theme_void() +
  58. theme(
  59. legend.position = "none",
  60. plot.margin = margin(0, 1, 0, 0, unit = "cm")
  61. )
  63. # Arrange and save final plot
  64. final_plot <- ggarrange(p1, p2, ncol=1, nrow=2, heights=c(2, 1), common.legend=TRUE, legend="top")
  65. showtext_auto(enable = TRUE)
  66. showtext_opts(dpi=400)
  67. ggsave("./figure2.png", height=90, width=80, units="mm", dpi=600)

code.R at commit 56311c2, no license · at the source

Overview

Authors: Yiru Yang1, Xiaolei Li1, Shudan Gao2, Yuanxu Gao3
ORCID iDs: Yiru Yang, Xiaolei Li
  1. School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
  2. Shandong Provincial Key Laboratory of Brain Science and Mental Health, Faculty of Psychology, Shandong Normal University, Jinan, China
  3. Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
Institutions: Shandong University (China); Shandong Normal University (China); Macau University of Science and Technology (Macao SAR China)
Journal: The Gerontologist, volume 66, issue 4, article gnaf277
Dates: received 18 April 2025; accepted 19 October 2025; published online 24 November 2025; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/geront/gnaf277 · PMID 41284679 · PMCID PMC13017228 · OpenAlex W4416582106
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), PET / SPECT (modality), human (organism), clinical / translational (subfield)
Methods: fMRI & imaging
Keywords: Cognitive aging heterogeneity, Resilience, Successful aging, Neuroimaging, Brain reserve
MeSH: Cognition*, Cognitive Aging*, Aged, Biomarkers, Brain, Humans, Magnetic Resonance Imaging, Positron-Emission Tomography (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32300924); Shandong Provincial Natural Science Foundation (ZR2024QC185, ZR2024QC332)
Citations: cited by 1 paper (Europe PMC); 87 references in the paper

Abstract

Background and Objectives: Substantial heterogeneity in cognitive aging trajectories has been observed among older adults, with some individuals maintaining exceptional cognitive function (“superagers” or “successful cognitive aging [SCA]”). The biological mechanisms underlying SCA remain unclear. This systematic review synthesizes current evidence on quantifiable SCA biomarkers to address this critical gap.

Research Design and Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we systematically searched PubMed, Scopus, PsycINFO, and Web of Science (up to December 2024). After screening 6,699 records, 62 studies met the inclusion criteria. Data from included studies were extracted, assessed for risk of bias, and synthesized for integrated findings.

Results: We identified 34 SCA definitions, categorized them into three types, and analyzed biomarkers across six domains: (1) genetic/epigenetic biomarkers, (2) biofluid biomarkers, (3) histological biomarkers, (4) positron emission tomography biomarkers, (5) structural magnetic resonance imaging biomarkers, and (6) functional neuroimaging biomarkers. Integrated findings suggest that SCA is driven by unique multidomain biological mechanisms (e.g., young DNA methylation age, high von Economo neuron density, and efficient glucose metabolism, etc.), not merely resistance to age-related neuropathology such as amyloid-β and tau. Neuroimaging findings highlight the role of brain reserve, maintenance, and compensation on SCA, particularly within a newly defined “cingulate gyrus–medial temporal lobe–frontal cortex” brain signature.

Discussion and Implications: This systematic review advances our understanding of SCA’s biological substrates, provides theoretical frameworks for future SCA biomarker research, and offers a foundation for future strategies to promote cognitive health in aging populations.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

ariayryang/SCA-signature

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 56311c2d9f5e552e0352c421596bfe742dfeae58, 15 July 2025
Languages: R (1)
Size: 5 files, 1 script
Software Heritage: not archived
Found in: the text, “Data synthesis and visualization”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), ggpubr (1 file), ggseg (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/ariayryang/SCA-signature

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

This systematic review is registered at PROSPERO under the following identification number: CRD42024578134 (https://www.crd.york.ac.uk/PROSPERO/view/CRD42024578134). All data supporting the findings of this study are available within the paper and its online supplementary material.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 8 MeSH terms, 2 funders, 87 references.

Cite

This paper

Yang, Y., Li, X., Gao, S., & Gao, Y. (2026). What characterizes the exceptional cognition of superagers? A systematic review of multidomain biomarkers of successful cognitive aging. The Gerontologist, 66(4), gnaf277. https://doi.org/10.1093/geront/gnaf277

BibTeX

@article{yang2026what,
author = {Yang, Yiru and Li, Xiaolei and Gao, Shudan and Gao, Yuanxu},
title = {{What characterizes the exceptional cognition of superagers? A systematic review of multidomain biomarkers of successful cognitive aging}},
journal = {The Gerontologist},
year = {2026},
month = mar,
volume = {66},
number = {4},
pages = {gnaf277},
publisher = {Oxford University Press},
issn = {0016-9013},
doi = {10.1093/geront/gnaf277},
url = {https://doi.org/10.1093/geront/gnaf277},
pmid = {41284679},
pmcid = {PMC13017228}
}

RIS

TY - JOUR
AU - Yang, Yiru
AU - Li, Xiaolei
AU - Gao, Shudan
AU - Gao, Yuanxu
TI - What characterizes the exceptional cognition of superagers? A systematic review of multidomain biomarkers of successful cognitive aging
T2 - The Gerontologist
J2 - Gerontologist
PY - 2026
DA - 2026/03/01
VL - 66
IS - 4
SP - gnaf277
SN - 0016-9013
PB - Oxford University Press
DO - 10.1093/geront/gnaf277
UR - https://doi.org/10.1093/geront/gnaf277
LA - en
ER -

CSL-JSON

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"container-title-short": "Gerontologist",
"volume": "66",
"issue": "4",
"page": "gnaf277",
"DOI": "10.1093/geront/gnaf277",
"PMID": "41284679",
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"publisher": "Oxford University Press",
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