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Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank.

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  1. [1] § Methods › Assessment of biological ages and age accelerations ↔ R/table_surv.R, lines 56–111 · score 0.64 · BioAge, hazards, width, mortality, aged, men

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The authors' code

R · 111 lines · 5.3 KB · GPL-3.0 · 1 match

  1. surv_res = function (dat, agevar, covar) {
  3. covars = paste(covar, collapse = "+")
  5. cox = list()
  7. for (i in agevar) {
  9. form = formula(paste("survival::Surv(time,status)~", i, "+", covars, sep = ""))
  10. cox[[i]] = survival::coxph(form, data = dat)
  12. }; rm(i)
  14. res = lapply(cox,summary)
  16. table = as.data.frame(lapply(res, function(x)paste(round(x$conf.int[1,1],2), " (",round(x$conf.int[1,3],2),", ",round(x$conf.int[1,4],2),")",sep="")))
  17. table$sample = "BioAge"
  19. n = as.data.frame(lapply(res,function(x)x$n))
  20. n$sample = "n"
  22. table = rbind(n,table)
  24. return(table)
  26. }
  28. #' BioAge coefficients in the table are hazard ratios estimated from Cox proportional hazard regressions. KDM Biological Age and Levine Phenotypic Age measures were differenced from chronological age for analysis (i.e. values = BA-CA). These differenced values were then standardized to have M=0, SD=1 separately for men and women within the analysis sample so that effect-sizes are denominated in terms of a sex-specific 1 SD unit increase in biological age advancement. Models included covariates for chronological age and sex.
  29. #'
  30. #' @title table_surv
  31. #' @description Associations of biological aging measures with mortality.
  32. #' @param data A dataset with projected biological aging measures for analysis.
  33. #' @param agevar A character vector indicating the names of the biological aging measures.
  34. #' @param label A character vector indicating the labels of the biological aging measures.
  35. #' @note Chronological age, gender, and race/ethnicity variables need to be named "age", "gender", and "race".
  36. #' @examples
  37. #' table1 = table_surv(data,
  38. #' agevar = c("kdm_advance0","phenoage_advance0",
  39. #' "kdm_advance","phenoage_advance",
  40. #' "hd","hd_log"),
  41. #' label = c("KDM\nBiological Age\nAdvancement",
  42. #' "Levine\nPhenotypic Age\nAdvancement",
  43. #' "Modified-KDM\nBiological Age\nAdvancement",
  44. #' "Modified-Levine\nPhenotypic Age\nAdvancement",
  45. #' "Homeostatic\nDysregulation",
  46. #' "Log\nHomeostatic\nDysregulation"))
  47. #'
  48. #' table1
  49. #'
  50. #' @export
  51. #' @import dplyr
  52. #' @import survival
  53. #' @import htmlTable
  56. table_surv = function (data, agevar, label) {
  58. dat = data %>%
  59. group_by(gender) %>%
  60. mutate_at(vars(all_of(agevar)), list(~scale(.))) %>%
  61. ungroup() %>%
  62. mutate(gender = as.factor(gender),
  63. age_cat = ifelse(age<=65, "yes", "no"))
  65. #full sample
  66. table1 = surv_res(dat, agevar, covar = c("age", "gender"))
  68. #gender stratification
  69. dat_gender = split(dat, dat$gender)
  70. table2 = lapply(dat_gender, function(x) surv_res(x, agevar, covar = "age"))
  71. table2 = do.call("rbind", table2)
  73. #race stratification
  74. dat_race = split(dat, dat$race)
  75. table3 = lapply(dat_race, function(x) surv_res(x, agevar, covar = c("age","gender")))
  76. table3 = do.call("rbind", table3)
  78. #age stratification
  79. dat_age = split(dat, dat$age_cat)
  80. table4 = surv_res(dat_age$yes, agevar, covar = c("age", "gender"))
  82. #combine tables
  83. table = rbind(table1,table2,table3,table4) %>%
  84. select(sample, everything())
  86. colnames(table) = c("sample",label)
  88. #make final table
  89. htmlTable::htmlTable(table[,-1],
  90. rnames = table$sample,
  91. align = "llllll",
  92. rgroup = c("Full Sample", "Men", "Women", "White", "Black", "Other", "Aged 65 and Younger"),
  93. n.rgroup = c(2,2,2,2,2,2,2),
  94. tspanner = c("Hazard Ratio (95% CI)",
  95. "Stratified by Gender",
  96. "Stratified by Race",
  97. "People Aged 65 and Younger"),
  98. n.tspanner = c(2,4,6,2),
  99. cnames = colnames(table),
  100. css.rgroup = "font-weight: 900; text-align: left; font-size: .83em;",
  101. css.tspanner = "font-weight: 900; text-align: center; font-size: .83em;",
  102. css.cell = rbind(rep("width: 300px; font-size: .83em;", times=ncol(table)),
  103. matrix("width: 300px; font-size: .83em;", ncol=ncol(table), nrow=nrow(table))),
  104. caption = "Table 1. Associations of biological aging measures with mortality.
  105. BioAge coefficients in the table are hazard ratios estimated from Cox proportional hazard regressions.
  106. KDM Biological Age and Levine Phenotypic Age measures were differenced from chronological age for analysis (i.e. values = BA-CA).
  107. These differenced values were then standardized to have M=0, SD=1 separately for men and women within the analysis sample so that effect-sizes are denominated in terms of a sex-specific 1 SD unit increase in biological age advancement.
  108. Models included covariates for chronological age and sex.
  109. The original KDM Biological Age algorithm (left-most column) was projected onto data from NHANES 2007-2010 only because other NHANES IV waves did not include spirometry measurements.
  110. The original Levine Phenotypic Age algorithm (second column from left) was projected onto data from NHANES 1999-2010 and 2015-2018 only because the intervening waves did not include CRP measurements.")
  111. }

table_surv.R at commit b1f9fc0, under GPL-3.0 · at the source

Overview

Authors: Xiaowei Zheng1, Wenyang Han1, Yiqun Li1, Minglan Jiang1, Xiao Ren1, Pinni Yang2, Yiming Jia2, Lulu Sun2, Ruirui Wang2, Mengyao Shi2, Zhengbao Zhu2, Yonghong Zhang2
ORCID iDs: Xiaowei Zheng, Yiming Jia, Zhengbao Zhu
  1. Public Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China
  2. Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
Institutions: Jiangnan University (China); Soochow University (China)
Journal: Stroke and vascular neurology, volume 11, issue 1, article e003690
Dates: received 4 September 2024; accepted 26 March 2025; published online 3 June 2025
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1136/svn-2024-003690 · PMID 40461154 · PMCID PMC13019048 · OpenAlex W4411001834
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), stroke (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Preprocessing
Keywords: Risk Factors, Cognitive Dysfunction
MeSH: Aging*, Alzheimer Disease*, Brain*, Cognition*, Dementia*, Age Factors, Aged, Aged, 80 and over, Biological Specimen Banks, Female, Humans, Incidence, Male, Middle Aged, Prognosis, Prospective Studies, Risk Assessment, Risk Factors, Time Factors, UK Biobank, United Kingdom (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 44 references in the paper

Abstract

Background: Several small-sample studies have suggested that biological processes of ageing are implicated with dementia and cognitive function. We aimed to prospectively investigate the associations of biological age with incident dementia, cognitive functions and brain structure based on the UK Biobank.

Methods: A total of 287 846 participants without dementia at baseline (followed until November 2022) were analysed. We measured biological age from clinical traits using the Klemera-Doubal method Biological Age (KDM-BA) and PhenoAge algorithms. Cox models were applied to evaluate the risk of dementia. Logistic regression models and linear regression models were used to assess the association between cognitive functions and brain structural measures.

Results: During a median follow-up of 13.68 years, 4744 incident all-cause dementia (ACD) events (including 3013 Alzheimer’s disease (AD) and 853 vascular dementia (VaD)) were recorded. Participants with older biological age were at increased risk of incident dementia (HR=1.35, 95% CI 1.23 to 1.48 for KDM-BA acceleration, 1.71 (1.52 to 1.91) for PhenoAge acceleration). Those with the highest level of KDM-BA and PhenoAge acceleration had the highest risk of ACD risk, with the corresponding HR of 1.80 (95% CI 1.60 to 2.03) and 1.19 (1.09 to 1.29), respectively. The associations between biological ageing with AD and VaD were also significant. Furthermore, a higher level of biological age was associated with worse performance in multiple cognitive domains and brain structural measures.

Conclusion: A higher level of biological age may represent a potential risk factor for incident dementia and is associated with worse performance in multiple cognitive domains and brain structural measures.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

dayoonkwon/BioAge

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b1f9fc02f086cd4aa74185f2335ab1366082e7fe, 2 April 2026
Languages: R (13)
Size: 42 files, 13 scripts
Software Heritage: not archived
Found in: the text, “Assessment of biological ages and age accelerati”
Holds: README, license file, environment (DESCRIPTION), documentation, 1 notebook
Not found: CITATION.cff, tests, continuous integration
Tools: tidyverse (11 files), broom (2 files), ggplot2 (2 files), survival (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
15 files
At the source: github.com/dayoonkwon/BioAge

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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;
  • 13 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 21 MeSH terms, 44 references.

Cite

This paper

Zheng, X., Han, W., Li, Y., Jiang, M., Ren, X., Yang, P., Jia, Y., Sun, L., Wang, R., Shi, M., Zhu, Z., & Zhang, Y. (2026). Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank. Stroke and vascular neurology, 11(1), e003690. https://doi.org/10.1136/svn-2024-003690

BibTeX

@article{zheng2026associations,
author = {Zheng, Xiaowei and Han, Wenyang and Li, Yiqun and Jiang, Minglan and Ren, Xiao and Yang, Pinni and Jia, Yiming and Sun, Lulu and Wang, Ruirui and Shi, Mengyao and Zhu, Zhengbao and Zhang, Yonghong},
title = {{Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank}},
journal = {Stroke and vascular neurology},
year = {2026},
month = mar,
volume = {11},
number = {1},
pages = {e003690},
publisher = {BMJ Publishing Group},
issn = {2059-8688},
doi = {10.1136/svn-2024-003690},
url = {https://doi.org/10.1136/svn-2024-003690},
pmid = {40461154},
pmcid = {PMC13019048}
}

RIS

TY - JOUR
AU - Zheng, Xiaowei
AU - Han, Wenyang
AU - Li, Yiqun
AU - Jiang, Minglan
AU - Ren, Xiao
AU - Yang, Pinni
AU - Jia, Yiming
AU - Sun, Lulu
AU - Wang, Ruirui
AU - Shi, Mengyao
AU - Zhu, Zhengbao
AU - Zhang, Yonghong
TI - Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank
T2 - Stroke and vascular neurology
J2 - Stroke Vasc Neurol
PY - 2026
DA - 2026/03/04
VL - 11
IS - 1
SP - e003690
SN - 2059-8688
PB - BMJ Publishing Group
DO - 10.1136/svn-2024-003690
UR - https://doi.org/10.1136/svn-2024-003690
LA - en
ER -

CSL-JSON

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