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WITHDRAWN: Can glucagon like peptide-1 receptor agonists be beneficial for brain health?

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › Statistical analysis › MR methods ↔ mr_script.r, lines 104–151 · score 0.71 · horizontal pleiotropy, MR PRESSO, MR Egger, instruments
  2. [2] § Methods › Statistical analysis › MR methods ↔ mr_script.r, lines 104–151 · score 0.52 · odds ratios, beta, regression, instruments, exposure, MR
  3. [3] § Methods › Statistical analysis ↔ mr_script.r, lines 1–55 · score 0.50 · TwoSampleMR, models, linear, allele, logistic, Exposure

Paper

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The authors' code

R · 151 lines · 4.6 KB · no license · 3 matches

  1. # load the library
  2. # remotes::install_github("MRCIEU/TwoSampleMR") # nolint: commented_code_linter.
  3. library(TwoSampleMR)
  4. library(ggplot2)
  5. library(dplyr)
  7. args <- commandArgs(trailingOnly = TRUE)
  8. outcome <- args[1]
  9. exposure_file <- args[2]
  10. outcome_base <- args[3]
  11. output_dir <- args[4]
  12. use_or_col <- as.logical(as.integer(args[5]))
  14. # Pick the right outcome file extension
  15. # if (use_or_col) {
  16. # outcome_file <- paste0(outcome_base, ".glm.logistic.hybrid")
  17. # message("Detected logistic model > using file: ", outcome_file)
  18. # } else {
  19. # outcome_file <- paste0(outcome_base, ".glm.linear")
  20. # message("Detected linear model > using file: ", outcome_file)
  21. # }
  22. outcome_file <- outcome_base
  24. # Load outcome
  25. if (use_or_col) {
  26. outcome_dat <- read_outcome_data(
  27. outcome_file,
  28. sep = "\t",
  29. snp_col = "ID",
  30. beta_col = "OR",
  31. se_col = "LOG(OR)_SE",
  32. eaf_col = "A1_FREQ",
  33. effect_allele_col = "A1",
  34. other_allele_col = "OMITTED",
  35. pval_col = "P",
  36. samplesize_col = "OBS_CT"
  37. )
  38. outcome_dat <- outcome_dat %>% mutate(beta.outcome = log(beta.outcome))
  39. } else {
  40. outcome_dat <- read_outcome_data(
  41. outcome_file,
  42. sep = "\t",
  43. snp_col = "ID",
  44. beta_col = "BETA",
  45. se_col = "SE",
  46. eaf_col = "A1_FREQ",
  47. effect_allele_col = "A1",
  48. other_allele_col = "OMITTED",
  49. pval_col = "P",
  50. samplesize_col = "OBS_CT"
  51. )
  52. }
  54. outcome_dat$outcome <- outcome
  55. outcome_dat$id.outcome <- outcome
  57. # Load exposure
  58. exposure_dat <- read_exposure_data(
  59. exposure_file,
  60. clump = FALSE,
  61. sep = "\t",
  62. snp_col = "SNP",
  63. beta_col = "beta",
  64. se_col = "se",
  65. eaf_col = "eaf",
  66. effect_allele_col = "effect_allele",
  67. other_allele_col = "other_allele",
  68. pval_col = "pval"
  69. )
  71. # exposure_dat <- read_exposure_data(
  72. # exposure_file,
  73. # clump = FALSE,
  74. # sep = "\t",
  75. # snp_col = "RSID",
  76. # beta_col = "BETA",
  77. # se_col = "SE",
  78. # eaf_col = "EFFECT_ALLELE_FREQ",
  79. # effect_allele_col = "EFFECT_ALLELE",
  80. # other_allele_col = "OTHER_ALLELE",
  81. # pval_col = "P"
  82. # )
  84. # Add phenotype label for clarity in output
  85. exposure_dat$exposure <- "GLP1"
  86. exposure_dat$id.exposure <- "GLP1"
  88. # Harmonise
  89. dat <- harmonise_data(exposure_dat, outcome_dat)
  90. write.csv(dat, sprintf("%s/mr_harmonised.csv",
  91. output_dir), row.names = FALSE)
  93. # Run MR
  94. mr_results <- mr(dat)
  95. # , method_list = c("mr_simple_mode",
  96. # "mr_ivw",
  97. # "mr_weighted_mode",
  98. # "mr_egger_regression")
  99. # Save results in a file
  100. write.csv(mr_results, sprintf("%s/mr_results.csv",
  101. output_dir), row.names = FALSE)
  104. # Approximate F-statistic for each instrument
  105. exposure_dat <- exposure_dat %>%
  106. mutate(F_statistic = (beta.exposure^2) / (se.exposure^2))
  108. stats <- summary(exposure_dat$F_statistic)
  109. write.csv(as.data.frame(t(stats)), sprintf("%s/mr_exposure_Fstats.csv",
  110. output_dir), row.names = TRUE)
  112. # odds_ratio <- mr_results %>% mutate(
  113. # or = exp(mr_results$b),
  114. # up_ci = exp(mr_results$b + 1.96 * mr_results$se),
  115. # lo_ci = exp(mr_results$b - 1.96 * mr_results$se)
  116. # )
  117. # write.csv(odds_ratio, sprintf("%s/mr_odds_ratio.csv",
  118. # output_dir), row.names = FALSE)
  120. odds_ratio <- generate_odds_ratios(mr_results)
  121. write.csv(odds_ratio, sprintf("%s/mr_odds_ratio.csv",
  122. output_dir), row.names = FALSE)
  124. print("------------- Odds Ratios -------------")
  125. print(odds_ratio)
  126. # lower_level <- min(exp(mr_results$b - 1.96*mr_results$se)) # nolint: commented_code_linter, line_length_linter.
  127. # upper_level <- max(exp(mr_results$b + 1.96*mr_results$se)) # nolint: commented_code_linter, line_length_linter.
  128. print("---------------------------------------")
  130. # leave one out method
  131. mr_leaveoneout <- mr_leaveoneout(dat, method = mr_egger_regression)
  132. write.csv(mr_leaveoneout, sprintf("%s/mr_leaveoneout.csv",
  133. output_dir), row.names = FALSE)
  135. # heterogeneity test
  136. mr_heterogeneity <- mr_heterogeneity(dat)
  137. write.csv(mr_heterogeneity, sprintf("%s/mr_heterogeneity.csv",
  138. output_dir), row.names = FALSE)
  140. # test for horizontal pleiotropy
  141. mr_pleiotropy_test <- mr_pleiotropy_test(dat)
  142. write.csv(mr_pleiotropy_test, sprintf("%s/mr_pleiotropy_test.csv",
  143. output_dir), row.names = FALSE)
  145. res_single <- mr_singlesnp(dat)
  146. write.csv(res_single, sprintf("%s/mr_singlesnp.csv",
  147. output_dir), row.names = FALSE)
  149. presso_results <- run_mr_presso(dat)
  150. write.csv(presso_results, sprintf("%s/mr_presso.csv",
  151. output_dir), row.names = FALSE)

mr_script.r at commit 2c27edd, no license · at the source

Overview

Authors: Giulia Ballabio1,2, Andrew Mason2, Valentina Paz2,3, Caroline Dale2, Reecha Sofat2, Victoria Garfield2
ORCID iDs: Giulia Ballabio, Andrew Mason, Valentina Paz, Caroline Dale, Reecha Sofat, Victoria Garfield
  1. University College London Hospital, NHS Foundation Trust, London, UK
  2. Department of Pharmacology and Therapeutics, University of Liverpool, UK
  3. Facultad de Psicología, Universidad de la República, Uruguay
Institutions: University College London (United Kingdom); University of Liverpool (United Kingdom); Universidad de la República de Uruguay (Uruguay)
Journal: medRxiv (preprint)
Dates: published online 17 May 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.05.13.26353120 · OpenAlex W7161488341
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), stroke (population), cellular / molecular (subfield)
Methods: Machine learning
Topic: Diabetes Treatment and Management (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR303160)
Citations: not cited yet (Europe PMC); 23 references in the paper

Abstract

Background: GLP-1 receptor agonists (GLP-1 RAs) are widely used for treatment of type 2 diabetes and obesity and have demonstrated cardiovascular benefits, but their effect on dementia risk is uncertain. We used a drug-target Mendelian randomisation (MR) framework to estimate the causal effect of GLP-1 receptor (GLP-1R) agonism on dementia risk and brain structure.

Methods: We used single nucleotide polymorphisms (SNPs) in GLP1R to recapitulate the effect of GLP1-RAs against circulating markers of glucose, glycated haemoglobin and outcomes of type 2 diabetes and obesity, to understand if we can reliably proxy agonism at GLP1R using gene variants. We then considered clinical outcomes, including all-cause dementia, vascular dementia, Alzheimer’s disease, and neuroimaging outcomes. Analyses were conducted in UK Biobank and replicated in FinnGen and All of Us and results were combined using meta-analysis.

Findings: Genetically proxied GLP-1R agonism was associated with a 17% lower risk of vascular dementia, with directional consistency across the two largest cohorts. No consistent association was observed for all-cause dementia. An elevated risk of Alzheimer’s disease was observed in UK Biobank but was not replicated in FinnGen or All of Us, and the pooled estimate across cohorts was null. There was no consistent evidence of an effect on neuroimaging outcomes, though higher GLP-1R agonism was associated with greater total brain volume.

Interpretation: Genetic evidence supports a potential protective effect of GLP-1R agonism on vascular dementia, consistent with the established cardio-metabolic benefits of this drug class. The null pooled finding for Alzheimer’s disease suggests that GLP-1R agonism does not meaningfully modify neurodegenerative pathways specific to this disease, though results from ongoing clinical trials are awaited. These findings highlight the importance of distinguishing between dementia subtypes when evaluating the cognitive effects of cardiometabolic therapies.

Funding: This work is funded by Diabetes Research & Wellness Foundation, Professor David Matthews Non-Clinical Fellowship to VG (ref: SCA/01/NCF/22). RS is an NIHR Research Professor, NIHR303160 is funded by the NIHR for this research project. GB is funded by an MRC grant.

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 3 matches between paragraphs and lines of code.

MetRXlab/spectra

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2c27edd80fd0fd18d34ca4408a6e89d3d01f93c5, 5 January 2026
Languages: Python (6), R (1)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Data sharing”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (3 files), NumPy (2 files), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/MetRXlab/spectra.git

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.

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;
  • 7 scripts, each with its path and the digest of its content;
  • 3 matches 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 sharing

No additional data is available. The software has been developed by the authors and has been made publicly available at https://github.com/MetRXlab/spectra.git.

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

Versions

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Version 2, 28 September 2026

  • Authors: added Andrew Mason (0000-0003-1502-2088); Victoria Garfield (0000-0002-5127-0890); removed Andrew Mason; Victoria Garfield

Version 1, 28 September 2026: the first record

Recorded: type, journal, dates, 6 authors, 1 funder, 22 references.

Cite

This paper

Ballabio, G., Mason, A., Paz, V., Dale, C., Sofat, R., & Garfield, V. (2026). WITHDRAWN: Can glucagon like peptide-1 receptor agonists be beneficial for brain health? medRxiv (preprint). https://doi.org/10.64898/2026.05.13.26353120

BibTeX

@article{ballabio2026withdrawn,
author = {Ballabio, Giulia and Mason, Andrew and Paz, Valentina and Dale, Caroline and Sofat, Reecha and Garfield, Victoria},
title = {{WITHDRAWN: Can glucagon like peptide-1 receptor agonists be beneficial for brain health?}},
journal = {medRxiv (preprint)},
year = {2026},
month = may,
publisher = {medRxiv},
doi = {10.64898/2026.05.13.26353120},
url = {https://doi.org/10.64898/2026.05.13.26353120}
}

RIS

TY - JOUR
AU - Ballabio, Giulia
AU - Mason, Andrew
AU - Paz, Valentina
AU - Dale, Caroline
AU - Sofat, Reecha
AU - Garfield, Victoria
TI - WITHDRAWN: Can glucagon like peptide-1 receptor agonists be beneficial for brain health?
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/05/17
PB - medRxiv
DO - 10.64898/2026.05.13.26353120
UR - https://doi.org/10.64898/2026.05.13.26353120
ER -

CSL-JSON

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