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The effect of physical activity on brain structure and cognitive function in the population-based cohort of LIFE-Adult Study.

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  1. [1] § Materials and methods › Statistical analysis ↔ Follow-up.R, lines 45–125 · score 0.51 · quasi cross lagged, scores, BrainAGE

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The authors' code

R · 125 lines · 4.9 KB · AGPL-3.0 · 1 match

  1. #Follow-up
  3. #follow up dataset curation
  4. life_join_fu <- read_xlsx("~/Life-ADULT/pv801/data/data_follow-up/PV0801_datajoin.xlsx")
  5. colnames(life_join_fu) <- paste0("fu_", colnames(life_join_fu))
  6. colnames(life_join_fu)[1] <- c("SIC")
  8. neuromorph_gm_fu <- left_join(neuromorph_gm_fu, pseudonyms, by = "ID")
  9. neuromorph_gm_fu <- neuromorph_gm_fu%>% dplyr::distinct(ID, .keep_all = TRUE)
  11. colnames(neuromorph_gm_fu) <- paste0("fu_", colnames(neuromorph_gm_fu))
  12. colnames(neuromorph_gm_fu)[132] <- c("SIC")
  14. #merge the datasets
  15. life_join_fu <- left_join(life_join_fu, neuromorph_gm_fu, by = "SIC")
  17. bl_fu <- life_main
  18. bl_fu <- left_join(bl_fu, life_join_fu, by = "SIC")
  20. actigraphy_fu <- left_join(final, neuromorph_gm_fu, by = "SIC")
  22. # calculate METs for the follow up from the VSAQ questionnaire
  23. VSAQ <- read_xlsx('~/Life-ADULT/pv801/data/data_follow-up/PV0801_T01221_NODUP.xlsx')
  24. life_demo <- read_xlsx('~/Life-ADULT/pv801/data/data_basis/PV0801_R00001.xlsx')
  26. #derive the birth date of the participants. Take the middle of the month as a day of birth
  27. life_demo$birth_date <- paste0(substr(life_demo$TEILNEHMER_GEB_JJJJMM, 1,4), "-", substr(life_demo$TEILNEHMER_GEB_JJJJMM,5,6), "-15")
  28. life_demo$birth_date <- as.Date(life_demo$birth_date)
  29. names(life_demo)[1] <- c("SIC")
  32. #calculate the approximate age at the time of VSAQ questionnaire
  33. VSAQ <- left_join(VSAQ, life_demo, by = "SIC")
  34. VSAQ$VSAQ_EDAT <- as.Date(VSAQ$VSAQ_EDAT)
  35. VSAQ <- VSAQ %>%
  36. dplyr::mutate(age = (VSAQ_EDAT - birth_date)/365)
  38. VSAQ$age <- as.numeric(VSAQ$age)
  40. #calculate the VSAQ score
  41. VSAQ$erste <- as.numeric(str_sub(VSAQ$VSAQ_F8, 4, 5)) #I'm taking the first naming of activity when person feels uncomfortable
  42. VSAQ <- VSAQ %>%
  43. dplyr::mutate(predicted_MET = 4.47 + (0.97 * erste) - (0.06 *age)) # the formula from Gawecki et al. (2019) 10.1136/bmjresp-2018-000351
  45. #merge the VSAQ score with the main file
  46. VSAQ_merge <-VSAQ %>% dplyr::select(SIC, predicted_MET)
  48. bl_fu <- left_join(bl_fu, VSAQ_merge, by = "SIC")
  49. rm(VSAQ_merge)
  51. #get the follow-up BrainAGE
  52. BA_fu <- BA_global %>%
  53. dplyr::filter(ses == "fu" & pat == T) %>%
  54. dplyr::select(-ses, - pat, -filename, -qualityratings_IQR, -basename)
  56. colnames(BA_fu) <- c("BA_fu", "ID")
  58. BA_fu$BA_fu <- as.numeric(BA_fu$BA_fu)
  60. #add the actigraphy data
  61. actigraphy4fu <- actigraphy_fu%>%dplyr::select(SIC, TSA_PP_METs_mean_all)
  63. #prepare the relevant variables for the model
  64. bl_fu <- left_join(bl_fu, BA_fu, by ="ID")
  65. bl_fu <- left_join(bl_fu, actigraphy4fu, by="SIC")
  67. #I exclude the same people from this dataset as from the bl ones
  68. exclude_IPAQ <- IPAQ_wf$SIC[which(!IPAQ_wf$SIC%in% IPAQ_wf1$SIC)]
  69. bl_fu_wf <- bl_fu_wf[!bl_fu_wf$SIC %in% exclude_IPAQ,]
  70. bl_fu_wf <- bl_fu_wf[!bl_fu_wf$ID %in% brain_lesions,]
  72. bl_fu_wf <- bl_fu_wf%>%
  73. dplyr::select(ID, IPAQ_AKT_TOTAL_METMNWK, predicted_MET,
  74. ADULT_PROB_AGE, ADULT_PROB_GENDER, TMT_TIMEA, fu_TMT_TIMEA,
  75. TMT_TIMEB, fu_TMT_TIMEB,BA, BA_fu, TSA_PP_METs_mean_all,
  76. fu_TMT_DATUM, TMT_DATUM, SES2_SES3, ADULT_PROB_GENDER)%>%
  77. dplyr::mutate(IPAQ_AKT_TOTAL_METHWK = (IPAQ_AKT_TOTAL_METMNWK/60)/7,,
  78. SES2 = ifelse(SES2_SES3 == 2, 1, 0),#recode into dummy vars
  79. SES3 = ifelse(SES2_SES3 == 3, 1, 0),
  80. ADULT_PROB_GENDER = ifelse(ADULT_PROB_GENDER==2, 0,1))
  82. clean_cog <- bl_fu_wf1%>%dplyr::select(fu_TMT_TIMEB, TMT_TIMEB, IPAQ_AKT_TOTAL_METHWK, predicted_MET, ADULT_PROB_AGE)
  83. clean_cog <- na.omit(clean_cog)
  85. clean_ba <- bl_fu_wf1%>%dplyr::select(SIC, BA_fu, BA, IPAQ_AKT_TOTAL_METHWK, predicted_MET, ADULT_PROB_AGE)
  86. clean_ba <- na.omit(clean_ba)
  89. #quasi cross-lagged models
  90. pa_model_BA <- '#quasi autoregressive paths
  91. BA_fu ~ BA
  92. predicted_MET ~ IPAQ_AKT_TOTAL_METHWK
  94. #quasi cross-lagged paths
  95. predicted_MET ~ BA
  96. BA_fu ~ IPAQ_AKT_TOTAL_METHWK
  98. #age as covariate
  99. IPAQ_AKT_TOTAL_METHWK ~ ADULT_PROB_AGE
  100. BA ~ ADULT_PROB_AGE
  101. predicted_MET ~ ADULT_PROB_AGE
  102. BA_fu ~ ADULT_PROB_AGE'
  104. pa_model_cog <- '#quasi autoregressive paths
  105. fu_TMT_TIMEB ~ TMT_TIMEB
  106. predicted_MET ~ IPAQ_AKT_TOTAL_METHWK
  108. # quasi cross-lagged paths
  109. predicted_MET ~ TMT_TIMEB
  110. fu_TMT_TIMEB ~ IPAQ_AKT_TOTAL_METHWK
  112. #age as covariate
  113. IPAQ_AKT_TOTAL_METHWK ~ ADULT_PROB_AGE
  114. TMT_TIMEB ~ ADULT_PROB_AGE
  115. predicted_MET ~ ADULT_PROB_AGE
  116. fu_TMT_TIMEB ~ ADULT_PROB_AGE'
  118. sem_ba <- sem(pa_model_BA, data = clean_ba, se = "robust", estimator = "MLR")
  119. summary(sem_ba, fit.measures = T, standardized = TRUE)
  121. sem_cog <- sem(pa_model_cog , data = clean_cog, se = "robust", estimator = "MLR")
  122. summary(sem_cog, fit.measures = T, standardized = TRUE)
  124. #semPaths(sem_ba, 'std', layout = 'tree2', intercepts = FALSE)
  125. #semPaths(sem_cog, 'std', layout = 'tree2', intercepts = FALSE)

Follow-up.R at commit 37099bc, under AGPL-3.0 · at the source

Overview

Authors: Polona Kalc1,2, Robert Dahnke1,2,3, Christian Sanders4,5, Frauke Beyer6,7, Andrea Zülke8, Steffi Riedel-Heller8, A Veronica Witte6,7, Christian Gaser1,2,3
ORCID iDs: Polona Kalc, Christian Sanders, Frauke Beyer
  1. Jena University Hospital, Department of Psychiatry and Psychotherapy, Jena, Germany
  2. Jena University Hospital, Department of Neurology, Jena, Germany
  3. German Center for Mental Health (DZPG), Jena-Halle-Magdeburg, Germany
  4. LIFE – Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
  5. Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany
  6. University of Leipzig Medical Center, Cognitive Neurology, Leipzig, Germany
  7. Max-Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany
  8. Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
Institutions: Jena University Hospital (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); University Hospital Leipzig (Germany); Leipzig University (Germany); Max Planck Institute for Human Cognitive and Brain Sciences (Germany)
Journal: eLife, volume 15, article RP109461
Dates: published online 15 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109461 · PMID 42454378 · PMCID PMC13372311 · OpenAlex W7134939251
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Statistics
Keywords: Human
MeSH: Brain*, Cognition*, Exercise*, Adult, Aged, Cohort Studies, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Male, Middle Aged (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Carl-Zeiss-Stiftung (IMPULS P2019-01-006); HORIZON EUROPE Marie Sklodowska-Curie Actions (10.3030/859890); Bundesministerium für Forschung, Technologie und Raumfahrt (Pattern-Cog ERAPERMED2021-127)
Citations: not cited yet (Europe PMC); 75 references in the paper
Research resources: SPM12 (r7771) RRID:SCR_007037, CAT12 (12.9) RRID:SCR_019184, DAGitty RRID:SCR_024509, laavan 0.6–19 RRID:SCR_027663

Abstract

Physical activity is believed to positively influence brain health and cognition and is considered a modifiable lifestyle factor that may protect against cognitive decline and neurodegeneration. In this observational study, we investigated the cross-sectional and longitudinal effects of self-reported total and moderate-to-vigorous physical activity on cognitive scores on the Trail Making Test (TMT-A and TMT-B), hippocampal volume, and Brain Age Gap Estimate (BrainAGE) in a large population-based cohort from the LIFE-Adult Study (n=2576). Furthermore, we examined the effect of objectively measured physical activity on brain structure in a subgroup with available accelerometry data (n=227). Multiple linear regression analyses did not show any positive effects of self-reported or objectively measured physical activity on hippocampal volume or processing speed and executive function. Longitudinal path analyses suggested a potential for reverse causation, where a higher BrainAGE at baseline was associated with lower physical capacity at follow-up. Additionally, we observed an age-related bias in the self-reporting of physical activity, indicating that older individuals tend to overestimate their level of activity. Future interventions targeting middle-aged adults may be necessary to raise awareness of potential misperception and encourage increased physical activity.

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

PolonaCa/PA_LIFE

License: AGPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 37099bc5d53a4e93a6cf29aaa378ded0a60580a5, 10 July 2026
Languages: R (3)
Size: 5 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), lavaan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/PolonaCa/PA_LIFE

The paper's code and data availability statement is in the Data section.

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 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);
  • 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

We used data from the Leipzig Research Centre for Civilization Diseases (LIFE) under project agreement PV-801. All original data will be exclusively shared by LIFE (https://www.uniklinikum-leipzig.de/einrichtungen/life) based on a project proposal in accordance with the University of Leipzig's data protection rules. The other publicly available neuroimaging datasets used in this study are openly accessible via the following websites: IXI (https://brain-development.org/ixi-dataset/), Cam-CAN (https://opendata.mrc-cbu.cam.ac.uk/projects/camcan/), Wayne State studies 10, 11, & EF datasets (https://fcon_1000.projects.nitrc.org/indi/retro/wayne_10.html, https://fcon_1000.projects.nitrc.org/indi/retro/wayne_11.html, https://fcon_1000.projects.nitrc.org/indi/retro/wayne_EF.html), the Enhanced NKI-Rockland sample (https://rocklandsample.org/for-researchers/step-3-accessing-the-data), Southwest University adult lifespan dataset (SALD; https://fcon_1000.projects.nitrc.org/indi/retro/sald.html). The Australian Imaging Biomarkers and Lifestyle study (AIBL; https://aibl.org.au/) is available upon request and after an approval process at: https://ida.loni.usc.edu/login.jsp?project=AIBL. The code used for this study is available on https://github.com/PolonaCa/PA_LIFE (copy archived at Kalc, 2026).

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 1 keyword, 12 MeSH terms, 3 funders, 72 references, 4 RRIDs.

Cite

This paper

Kalc, P., Dahnke, R., Sanders, C., Beyer, F., Zülke, A., Riedel-Heller, S., Witte, A. V., & Gaser, C. (2026). The effect of physical activity on brain structure and cognitive function in the population-based cohort of LIFE-Adult Study. eLife, 15, RP109461. https://doi.org/10.7554/elife.109461

BibTeX

@article{kalc2026effect,
author = {Kalc, Polona and Dahnke, Robert and Sanders, Christian and Beyer, Frauke and Zülke, Andrea and Riedel-Heller, Steffi and Witte, A Veronica and Gaser, Christian},
title = {{The effect of physical activity on brain structure and cognitive function in the population-based cohort of LIFE-Adult Study}},
journal = {eLife},
year = {2026},
month = jul,
volume = {15},
pages = {RP109461},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109461},
url = {https://doi.org/10.7554/elife.109461},
pmid = {42454378},
pmcid = {PMC13372311}
}

RIS

TY - JOUR
AU - Kalc, Polona
AU - Dahnke, Robert
AU - Sanders, Christian
AU - Beyer, Frauke
AU - Zülke, Andrea
AU - Riedel-Heller, Steffi
AU - Witte, A Veronica
AU - Gaser, Christian
TI - The effect of physical activity on brain structure and cognitive function in the population-based cohort of LIFE-Adult Study
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/07/15
VL - 15
SP - RP109461
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109461
UR - https://doi.org/10.7554/elife.109461
LA - en
ER -

CSL-JSON

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"author": [
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