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Repurposing drugs for the prevention of vascular dementia using evidence from drug target Mendelian randomization.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Statistics and reproducibility › Statistical analysis ↔ Cis_DTMR.R, lines 87–131 · score 0.70 · clumping threshold, kb region, genome wide, r2, IVs, cis
  2. [2] § Results › Sensitivity analyses ↔ Cis&Trans_DTMR.R, lines 2–40 · score 0.61 · weighted median, sufficient instruments, MR Egger, trans, sensitivity, cis
  3. [3] § Results › Sensitivity analyses ↔ CisLT_DTMR.R, lines 2–43 · score 0.56 · weighted median, sufficient instruments, MR Egger, sensitivity, cis, IVW
  4. [4] § Methods › Statistics and reproducibility › Statistical analysis ↔ Cis&Trans_DTMR.R, lines 2–40 · score 0.55 · weighted median, MR Egger, Wald ratio, sensitivity, cis, IVW

Paper

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The authors' code

R · 166 lines · 10 KB · MIT · 2 matches

  2. #########################################
  3. ### Taylor-Bateman et al., 2025 - Repurposing drugs for the prevention of vascular dementia: Evidence from drug target Mendelian randomization
  5. ### Cis&Trans_DTMR.R requires 2 input files
  6. ### An Exposure protein GWAS -
  7. ### An Outcome GWAS
  9. Exposure_GWAS <-
  10. Outcome_GWAS <-
  12. ### Column should be labeled as follows
  13. ## ID - rsid of snp
  14. ## CHROM - Chromosome
  15. ## POS - Base pair location in build GRCh38_hg38
  16. ## Effect_allele -
  17. ## Other_allele -
  18. ## Beta - Effect
  19. ## EAF - effect allele frequency if available
  20. ## SE - standard error
  21. ## P.value
  23. ## The following information about your gene of interest is required
  24. Gene_Name <- ##### Gene identifier
  26. ### The folling information is needed on the outcome
  27. Outcome_Name <- ##### Outcome Name
  29. library(TwoSampleMR)
  30. library(dplyr)
  31. library(plyr)
  32. library(stringr)
  33. library(ieugwasr)
  34. library(MendelianRandomization)
  35. ### A access token is required
  36. ### Further information about the token system can be found here https://api.opengwas.io/api/
  38. ### The following outputs will be generated
  39. ### A table listing the IVs selected
  40. ### A table with the results of the MR analysis including IVW or wald ratio along with sensitivity analysis of MR-egger and weighted median so long as there are sufficient instruments
  42. #### Create output tables
  43. Output_IV_Info <- data.frame(matrix(ncol = 16, nrow = 0))
  44. x <- c("beta.exposure", "beta.outcome", "effect_allele.exposure",
  45. "effect_allele.outcome", "exposure", "id.exposure", "other_allele.exposure",
  46. "other_allele.outcome", "outcome", "pval.exposure", "pval.outcome",
  47. "SNP" , "SNP_index", "Gene", "Outcome", "Analysis" )
  48. colnames(Output_IV_Info) <- x
  50. Output_Results <- data.frame(matrix(ncol = 28, nrow = 0))
  51. x <- c("Protein","Outcome", "Analysis",
  52. "IVW_SNPs", "IVW_Est", "IVW_SE" , "IVW_lCI", "IVW_uCI", "IVW_Pvalue", "FStat",
  53. "WM_SNPs", "WM_Est", "WM_SE" , "WM_lCI", "WM_uCI", "WM_Pvalue",
  54. "Egg_SNPs", "Egg_Est", "Egg_SE" , "Egg_lCI", "Egg_uCI", "Egg_Pvalue",
  55. "Egg_Int", "Egg_IntSE" , "Egg_IntlCI", "Egg_IntuCI", "Egg_IntPvalue"
  56. )
  57. colnames(Output_Results) <- x
  59. ####### Analysis
  61. Exposure_GWAS <- format_data(Exposure_GWAS,
  62. type = "exposure",
  63. snp_col = "ID",
  64. beta_col = "Beta",
  65. se_col = "SE",
  66. pval_col = "P.value",
  67. chr_col = "CHROM",
  68. pos_col = "POS",
  69. effect_allele_col = "Effect_allele",
  70. other_allele_col = "Other_allele",
  71. eaf_col = "EAF")
  73. Exp_GWAS <- subset(Exposure_GWAS, Exposure_GWAS$pval.exposure < 1e-04 ) ### subset to minimise data for clumping
  75. ### clumping, 10000kb region r2 < 0.001, genome wide significance
  76. Clumping <- 5e-08
  77. Exp_SNPS <- clump_data(Exp_GWAS, clump_kb = 10000,
  78. clump_r2 = 0.001,
  79. clump_p1 = 5e-08,
  80. clump_p2 = 5e-08,
  81. pop= "EUR")
  83. Out_GWAS <- format_data(Outcome_GWAS,
  84. type = "outcome",
  85. snp_col = "ID",
  86. beta_col = "Beta",
  87. se_col = "SE",
  88. pval_col = "P.value",
  89. chr_col = "CHROM",
  90. pos_col = "POS",
  91. effect_allele_col = "Effect_allele",
  92. other_allele_col = "Other_allele",
  93. eaf_col = "EAF")
  96. ### Match Exp_SNPS
  97. matched <- intersect( Out_GWAS$SNP, Exp_SNPS$SNP)
  98. all <- union(Out_GWAS$SNP, Exp_SNPS$SNP)
  99. non.matched <- all[!all %in% matched]
  100. Out_SNPS <- Out_GWAS[ which( ! Out_GWAS$SNP
  101. %in% non.matched) , ]
  103. non.matched <- Exp_SNPS$SNP[!Exp_SNPS$SNP
  104. %in% matched]
  106. Exp_SNPS <- Exp_SNPS[ which( Exp_SNPS$SNP
  107. %in% matched) , ]
  109. Exp_IVs <- Exp_SNPS[order(Exp_SNPS$SNP),]
  110. Out_IVs <- Out_SNPS[order(Out_SNPS$SNP),]
  112. ### Harmonise the two datasets
  113. Harmonised_dat <- harmonise_data(exposure_dat = Exp_IVs, outcome_dat = Out_IVs)
  115. #### Convert for use in MendelianRandomization Package
  116. MR_Obj <- dat_to_MRInput(Harmonised_dat, get_correlations = FALSE, pop = "EUR")
  118. MR1_pIV_pExp <- MR_Obj[[1]]
  120. ### Runs MR & generates results
  121. new.row <- data.frame(Protein = Gene_Name, Outcome = Outcome_Name, Analysis = Clumping,
  122. IVW_SNPs = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$SNPs) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$SNPs),
  123. IVW_Est = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$Estimate) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$Estimate),
  124. IVW_SE = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$StdError) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$StdError),
  125. IVW_lCI = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$CILower) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$CILower),
  126. IVW_uCI = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$CIUpper) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$CIUpper),
  127. IVW_Pvalue = if (is.numeric(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$Pvalue) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw( MR1_pIV_pExp)$Pvalue),
  128. FStat = if (is.numeric(MendelianRandomization::mr_ivw(MR1_pIV_pExp)$Fstat) == FALSE) print(NA) else print(MendelianRandomization::mr_ivw(MR1_pIV_pExp)$Fstat),
  129. WM_SNPs = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$SNPs) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$SNPs),
  130. WM_Est = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$Estimate) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$Estimate),
  131. WM_SE = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$StdError) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$StdError),
  132. WM_lCI = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$CILower) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$CILower),
  133. WM_uCI = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$CIUpper) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$CIUpper),
  134. WM_Pvalue = if (is.numeric(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$Pvalue) == FALSE) print(NA) else print(MendelianRandomization::mr_median( MR1_pIV_pExp, weighting = "weighted")$Pvalue),
  135. Egg_SNPs = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$SNPs) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$SNPs),
  136. Egg_Est = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Estimate) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Estimate),
  137. Egg_SE = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$StdError.Est) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$StdError.Est),
  138. Egg_lCI = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CILower.Est) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CILower.Est),
  139. Egg_uCI = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CIUpper.Est) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CIUpper.Est),
  140. Egg_Pvalue = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Pvalue.Est) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Pvalue.Est),
  141. Egg_Int = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Intercept) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Intercept),
  142. Egg_IntSE = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$StdError.Int) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$StdError.Int),
  143. Egg_IntlCI = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CILower.Int) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CILower.Int),
  144. Egg_IntuCI = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CIUpper.Int) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$CIUpper.Int),
  145. Egg_IntPvalue = if (is.numeric(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Pvalue.Int) == FALSE) print(NA) else print(MendelianRandomization::mr_egger( MR1_pIV_pExp)$Pvalue.Int)
  146. )
  148. #### Adds to results table
  149. Output_Results <- rbind(Output_Results, new.row)
  151. ### Saves harmonization table for reference
  152. SNP_Data <- Harmonised_dat
  153. SNP_Data <- SNP_Data %>% select(order(colnames(SNP_Data)))
  154. SNP_Data$Gene <- paste(as.character(Gene_Name))
  155. SNP_Data$Outcome <- paste(as.character(Outcome_Name))
  156. SNP_Data$Analysis <- paste(as.character(Clumping))
  158. SNP_Data<- SNP_Data[c("beta.exposure", "beta.outcome", "effect_allele.exposure",
  159. "effect_allele.outcome", "exposure", "id.exposure", "other_allele.exposure",
  160. "other_allele.outcome", "outcome", "pval.exposure", "pval.outcome",
  161. "SNP" , "SNP_index", "Gene", "Outcome", "Analysis" )]
  163. Output_IV_Info <- rbind(Output_IV_Info, SNP_Data)
  165. #### This can be repeated for all outcomes and positive controls

Cis&Trans_DTMR.R at commit 6653613, under MIT · at the source

Overview

Authors: Victoria Taylor-Bateman1, Phazha Bothongo1, Venexia Walker2,3, Patrick G Kehoe4, Liv Tybjærg Nordestgaard2,5, Yoav Ben-Shlomo6, Neil M Davies1,7,8, Dylan M Williams1,9, Emma L Anderson1
ORCID iDs: Victoria Taylor-Bateman, Phazha Bothongo, Venexia Walker, Patrick G Kehoe, Liv Tybjærg Nordestgaard, Yoav Ben-Shlomo, Neil M Davies, Dylan M Williams, Emma L Anderson
  1. Division of Psychiatry, University College London, London, UK
  2. Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
  3. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
  4. Cerebrovascular and Dementia Research Group, Bristol Medical School, University of Bristol, Learning & Research, Southmead Hospital, Bristol, UK
  5. Department of Clinical Biochemistry, Copenhagen University Hospital – Bispebjerg and Frederiksberg, Copenhagen, Denmark
  6. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
  7. Department of Statistical Science, University College London, London, UK
  8. Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
  9. Unit for lifelong health and ageing, University College London, London, UK
Institutions: University College London (United Kingdom); University of Bristol (United Kingdom); MRC Integrative Epidemiology Unit; University of Pennsylvania (United States); Southmead Hospital (United Kingdom); Frederiksberg Hospital (Denmark); Copenhagen University Hospital (Denmark); Norwegian University of Science and Technology (Norway); MRC Unit for Lifelong Health and Ageing (United Kingdom)
Journal: Nature aging, volume 6, issue 4, pages 905-915
Dates: received 6 May 2025; accepted 5 March 2026; published online 20 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s43587-026-01106-1 · PMID 42009889 · PMCID PMC13099373 · OpenAlex W7154951843
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), stroke (population)
Methods: Statistics, fMRI & imaging
Keywords: Dementia, Ageing, Drug discovery, Genetics
MeSH: Dementia, Vascular*, Drug Repositioning*, Anti-Inflammatory Agents, Antihypertensive Agents, Humans, Hypolipidemic Agents, Mendelian Randomization Analysis, Risk Factors (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Norges Forskningsråd (Research Council of Norway) (295989); Alzheimer's Research UK (ARUK) (ARUK-SRF2023B-008)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Vascular dementia (VaD) is a devastating cerebrovascular disease with no disease-modifying treatments. Repurposing drugs for known risk factors could have clinical impact. Using Mendelian randomization, we proxied 46 lipid-lowering, antihypertensive and anti-inflammatory drug effects across five VaD outcomes: clinical diagnosis (N = 7,009 cases, N = 899,672 non-cases/controls) and neuroimaging features (max N = 50,559), white matter hyperintensity volume, fractional anisotropy, mean diffusivity and lacunar stroke diagnosis. Beta-1 adrenergic receptor indicated potential benefit (clinical diagnosis: odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.80–1.01; white matter hyperintensity volume: estimated causal effect = −0.03, 95% CI = −0.07–0.00; mean diffusivity: estimated causal effect = −0.18, 95% CI = −0.37–0.00; lacunar stroke: OR = 0.91, 95% CI = 0.80–1.03). Angiotensin-converting enzyme inhibition suggested increased VaD risk (OR = 1.12, 95% CI = 1.01–1.24). Findings remained largely null after multiple-testing correction. Here we show that although little evidence supported repurposing most lipid-lowering, antihypertensive and anti-inflammatory drugs for VaD prevention or treatment, beta-1 adrenergic receptor antagonism could be a promising repurposing candidate, but replication is needed as further data becomes available. Pharmacovigilance studies should examine angiotensin-converting enzyme inhibitors’ potential to increase risk.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Zenodo 15190762

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
7 files
At the source:

victoriatb/vad_dtmr2025

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 66536134430b9062473617a901ecf49953f430af, 15 August 2025
Languages: R (5)
Size: 10 files, 5 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
7 files
At the source: github.com/VictoriaTB/VaD_DTMR2025/tree/v0.3.0

Code availability

Code used in this study is available via Zenodo at 10.5281/zenodo.15190762 (ref. 57).

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 4 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 availability

All summary statistics were obtained from publicly available datasets, with details from the original papers as described in Table 2, refs. 15,25,30,31,34–41,48,49, and from the Neale Lab UK Biobank resource (http://www.nealelab.is/uk-biobank/). Further details can also be found in Supplementary Table 1.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 8 MeSH terms, 2 funders, 54 references.

Cite

This paper

Taylor-Bateman, V., Bothongo, P., Walker, V., Kehoe, P. G., Nordestgaard, L. T., Ben-Shlomo, Y., Davies, N. M., Williams, D. M., & Anderson, E. L. (2026). Repurposing drugs for the prevention of vascular dementia using evidence from drug target Mendelian randomization. Nature aging, 6(4), 905-915. https://doi.org/10.1038/s43587-026-01106-1

BibTeX

@article{taylorbateman2026repurposing,
author = {Taylor-Bateman, Victoria and Bothongo, Phazha and Walker, Venexia and Kehoe, Patrick G and Nordestgaard, Liv Tybjærg and Ben-Shlomo, Yoav and Davies, Neil M and Williams, Dylan M and Anderson, Emma L},
title = {{Repurposing drugs for the prevention of vascular dementia using evidence from drug target Mendelian randomization}},
journal = {Nature aging},
year = {2026},
month = apr,
volume = {6},
number = {4},
pages = {905--915},
publisher = {Nature Portfolio},
issn = {2662-8465},
doi = {10.1038/s43587-026-01106-1},
url = {https://doi.org/10.1038/s43587-026-01106-1},
pmid = {42009889},
pmcid = {PMC13099373}
}

RIS

TY - JOUR
AU - Taylor-Bateman, Victoria
AU - Bothongo, Phazha
AU - Walker, Venexia
AU - Kehoe, Patrick G
AU - Nordestgaard, Liv Tybjærg
AU - Ben-Shlomo, Yoav
AU - Davies, Neil M
AU - Williams, Dylan M
AU - Anderson, Emma L
TI - Repurposing drugs for the prevention of vascular dementia using evidence from drug target Mendelian randomization
T2 - Nature aging
J2 - Nat Aging
PY - 2026
DA - 2026/04/20
VL - 6
IS - 4
SP - 905
EP - 915
SN - 2662-8465
PB - Nature Portfolio
DO - 10.1038/s43587-026-01106-1
UR - https://doi.org/10.1038/s43587-026-01106-1
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Taylor-Bateman",
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"PMCID": "PMC13099373",
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