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DNA methylation signatures of bilateral hippocampal volume, asymmetry and atrophy: a cross-omics analysis in the general population.

Code ↔ Paper

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  1. [1] § Methods › Statistics › Cohort-level epigenome-wide association analyses (EWAS) ↔ Run_EWAS_script.R, lines 50–128 · score 0.72 · smoking status, eTIV, variable, Covariates, technical, education
  2. [2] § Methods › Statistics ↔ Run_EWAS_script.R, lines 1–48 · score 0.56 · identifying DNA methylation, bilateral hippocampal volume, signatures associated, omics, EWAS, asymmetry

Paper

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The authors' code

R · 309 lines · 9.2 KB · CC-BY-4.0 · 2 matches

  1. #!/usr/bin/env Rscript
  3. ## =========================================================
  4. ##Integrative cross-omics analysis identifies DNA methylation signatures associated with bilateral hippocampal volume, asymmetry and atrophy rate in the general population
  5. ##
  6. ## Usage:
  7. ## Rscript run_ewas_per_study.R <MODEL> <PHENOTYPE> <STRATA>
  8. ##
  9. ## Example:
  10. ## Rscript run_ewas_per_study.R M2 LHCV All
  11. ##
  12. ## MODEL options:
  13. ## M1, M2, M3
  14. ##
  15. ## PHENOTYPE options:
  16. ## Example: LHCV, RHCV, HCasy
  17. ##
  18. ## STRATA options:
  19. ## All, f, m
  20. ## =========================================================
  22. args <- commandArgs(trailingOnly = TRUE)
  24. if (length(args) < 3) {
  25. stop("Usage: Rscript run_ewas_per_study.R <MODEL> <PHENOTYPE> <STRATA>")
  26. }
  28. Model <- args[1]
  29. Pheno <- args[2]
  30. Strata <- args[3]
  32. if (!Model %in% c("M1", "M2", "M3")) {
  33. stop("MODEL must be one of: M1, M2, M3")
  34. }
  36. if (!Strata %in% c("All", "f", "m")) {
  37. stop("STRATA must be one of: All, f, m")
  38. }
  40. ## =========================================================
  41. ## Load packages
  42. ## =========================================================
  43. suppressPackageStartupMessages({
  44. library(parallel)
  45. library(doParallel)
  46. library(data.table)
  47. library(dplyr)
  48. })
  50. ## =========================================================
  51. ## Parallel environment
  52. ## =========================================================
  53. nnodes <- as.integer(Sys.getenv("SLURM_NNODES", unset = "1"))
  54. nid <- as.integer(Sys.getenv("SLURM_NODEID", unset = "0"))
  55. ncpus <- as.integer(Sys.getenv("SLURM_CPUS_PER_TASK", unset = parallel::detectCores()))
  57. message("Model: ", Model)
  58. message("Phenotype: ", Pheno)
  59. message("Strata: ", Strata)
  60. message("Nodes: ", nnodes, " | Node ID: ", nid, " | CPUs: ", ncpus)
  62. ## =========================================================
  63. ## User-defined paths
  64. ## =========================================================
  65. data_dir <- "path/to/data"
  66. output_dir <- "path/to/output"
  68. if (!dir.exists(output_dir)) {
  69. dir.create(output_dir, recursive = TRUE)
  70. }
  72. ## =========================================================
  73. ## Input files
  74. ## =========================================================
  75. main_data_file <- file.path(data_dir, "main_dataset.RData")
  77. ## =========================================================
  78. ## Load data
  79. ##
  80. ## Expected objects:
  81. ## phenotype_data : data.frame with one row per participant
  82. ## methylation_mat : matrix/data.frame of CpGs x samples
  83. ##
  84. ## Replace the object names below as needed.
  85. ## =========================================================
  86. load(main_data_file)
  88. ## ---------------------------------------------------------
  89. ## Rename loaded objects here
  90. ## Replace these with the actual object names in your .RData
  91. ## ---------------------------------------------------------
  92. phenotype_data <- pheno_data
  93. methylation_mat <- beta_data
  95. ## Optional cleanup
  96. # rm(pheno_data, beta_data)
  98. ## =========================================================
  99. ## Study metadata
  100. ## Replace as needed
  101. ## =========================================================
  102. study_name <- "STUDY"
  103. array_type <- "ARRAY"
  104. ancestry <- "ANCESTRY"
  105. tissue <- "TISSUE"
  106. run_date <- Sys.Date()
  108. ## =========================================================
  109. ## Generic variable definitions
  110. ##
  111. ## Replace these variable names with those in your dataset
  112. ## =========================================================
  114. ## Sample ID column in phenotype_data matching methylation matrix columns
  115. sample_id_var <- "sample_id"
  117. ## Outcome/covariate variable names
  118. sex_var <- "sex"
  119. age_var <- "age"
  120. etiv_var <- "eTIV"
  121. smoking_var <- "smoking_status"
  122. education_var <- "education"
  123. handedness_var <- "handedness"
  125. ## Optional covariate groups
  126. cell_type_vars <- c("celltype1", "celltype2", "celltype3", "celltype4", "celltype5", "celltype6")
  127. technical_vars <- c("technical_covariate1", "technical_covariate2")
  128. genetic_pc_vars <- paste0("genetic_PC", 1:10)
  130. ## =========================================================
  131. ## Helper: keep only covariates that exist in the dataset
  132. ## =========================================================
  133. keep_existing <- function(vars, dat) {
  134. vars[vars %in% colnames(dat)]
  135. }
  137. cell_type_vars <- keep_existing(cell_type_vars, phenotype_data)
  138. technical_vars <- keep_existing(technical_vars, phenotype_data)
  139. genetic_pc_vars <- keep_existing(genetic_pc_vars, phenotype_data)
  141. ## =========================================================
  142. ## Build model formula
  143. ## =========================================================
  144. base_terms <- c("meth_beta", age_var, etiv_var)
  146. if (length(cell_type_vars) > 0) {
  147. base_terms <- c(base_terms, cell_type_vars)
  148. }
  149. if (length(technical_vars) > 0) {
  150. base_terms <- c(base_terms, technical_vars)
  151. }
  152. if (length(genetic_pc_vars) > 0) {
  153. base_terms <- c(base_terms, genetic_pc_vars)
  154. }
  156. if (Model == "M1") {
  157. model_terms <- c(base_terms, paste0("as.factor(", sex_var, ")"))
  158. } else if (Model == "M2") {
  159. model_terms <- c(
  160. base_terms,
  161. paste0("as.factor(", sex_var, ")"),
  162. paste0("as.factor(", smoking_var, ")"),
  163. paste0("as.factor(", education_var, ")")
  164. )
  165. } else if (Model == "M3") {
  166. model_terms <- c(
  167. base_terms,
  168. paste0("as.factor(", sex_var, ")"),
  169. paste0("as.factor(", smoking_var, ")"),
  170. paste0("as.factor(", education_var, ")"),
  171. paste0("as.factor(", handedness_var, ")")
  172. )
  173. }
  175. model_formula <- paste("outcome_var ~", paste(model_terms, collapse = " + "))
  176. message("Model formula: ", model_formula)
  178. ## =========================================================
  179. ## Subset by strata
  180. ## =========================================================
  181. if (Strata == "All") {
  182. cdat <- phenotype_data
  183. } else if (Strata == "f") {
  184. cdat <- phenotype_data[phenotype_data[[sex_var]] == "f", , drop = FALSE]
  185. } else if (Strata == "m") {
  186. cdat <- phenotype_data[phenotype_data[[sex_var]] == "m", , drop = FALSE]
  187. }
  189. samples <- cdat[[sample_id_var]]
  191. ## Keep only samples present in both datasets
  192. samples <- intersect(samples, colnames(methylation_mat))
  193. cdat <- cdat[cdat[[sample_id_var]] %in% samples, , drop = FALSE]
  194. cdat <- cdat[match(samples, cdat[[sample_id_var]]), , drop = FALSE]
  195. methylation_mat <- methylation_mat[, samples, drop = FALSE]
  197. stopifnot(identical(colnames(methylation_mat), cdat[[sample_id_var]]))
  199. ## =========================================================
  200. ## Split CpGs across nodes
  201. ## =========================================================
  202. n_rows <- nrow(methylation_mat)
  204. node_indices <- lapply(seq_len(nnodes), function(node) {
  205. start <- ((node - 1) * floor(n_rows / nnodes)) + 1
  206. end <- if (node == nnodes) n_rows else start + floor(n_rows / nnodes) - 1
  207. start:end
  208. })
  210. current_idx <- unlist(node_indices[nid + 1])
  211. meth_chunk <- methylation_mat[current_idx, , drop = FALSE]
  213. message("Running on node ", nid, ": ", nrow(meth_chunk), " CpGs x ", ncol(meth_chunk), " samples")
  215. ## =========================================================
  216. ## Set up parallel cluster
  217. ## =========================================================
  218. cl <- makeCluster(ncpus)
  219. registerDoParallel(cl)
  221. ## =========================================================
  222. ## EWAS function
  223. ## =========================================================
  224. run_mod <- function(outcome, clindat, methdat, filename, cl, model_formula) {
  226. stopifnot(identical(colnames(methdat), clindat[[sample_id_var]]))
  228. clindat$outcome_var <- clindat[[outcome]]
  230. clusterEvalQ(cl, {
  231. library(stats)
  232. NULL
  233. })
  235. clusterExport(
  236. cl,
  237. varlist = c("methdat", "clindat", "model_formula"),
  238. envir = environment()
  239. )
  241. all_cpgs <- parApply(cl, data.frame(methdat), 1, function(mrow) {
  243. tmp_dat <- clindat
  244. tmp_dat$meth_beta <- as.numeric(mrow)
  246. mean_cpg <- mean(tmp_dat$meth_beta, na.rm = TRUE)
  247. sd_cpg <- sd(tmp_dat$meth_beta, na.rm = TRUE)
  249. mod <- tryCatch(
  250. lm(as.formula(model_formula), data = tmp_dat),
  251. error = function(e) NULL
  252. )
  254. if (is.null(mod)) {
  255. res <- rep(NA, 5)
  256. } else {
  257. coef_tab <- summary(mod)$coefficients
  258. res <- c(coef_tab[2, ], nobs(mod))
  259. }
  261. res <- c(res, mean_cpg, sd_cpg)
  262. names(res) <- c("beta", "se", "t_val", "p_val", "N", "mean", "sd")
  263. res
  264. })
  266. all_cpgs <- t(all_cpgs)
  267. all_cpgs <- as.data.frame(all_cpgs, stringsAsFactors = FALSE)
  268. all_cpgs$cpg <- rownames(methdat)
  270. write.table(all_cpgs, file = filename, sep = "\t", quote = FALSE, row.names = FALSE)
  272. return("Success")
  273. }
  275. ## =========================================================
  276. ## Output filename
  277. ## =========================================================
  278. outfile <- file.path(
  279. output_dir,
  280. paste0(
  281. study_name, "_", array_type, "_", ancestry, "_",
  282. Pheno, "_", Model, "_", Strata, "_",
  283. tissue, "_", run_date, "_Node_", nid, ".txt"
  284. )
  285. )
  287. ## =========================================================
  288. ## Run EWAS
  289. ## =========================================================
  290. message("Starting EWAS on node ", nid)
  292. result <- run_mod(
  293. outcome = Pheno,
  294. clindat = cdat,
  295. methdat = meth_chunk,
  296. filename = outfile,
  297. cl = cl,
  298. model_formula = model_formula
  299. )
  301. message("Finished EWAS on node ", nid, " | Result: ", result)
  303. ## =========================================================
  304. ## Clean up
  305. ## =========================================================
  306. stopCluster(cl)
  308. message("Output written to: ", outfile)
  309. message("Combine node-level output files after all jobs finish.")

Run_EWAS_script.R, under CC-BY-4.0 · at the source

Overview

Authors: Dan Liu1, Valentina Talevi1, Juliana F. Tavares1, Ruiqi Wang2, Mohammed A. Imtiaz1, Konstantinos Melas1, Alexander Teumer3,4, Katharina Wittfeld3, Robert F. Hillary5, Dina Vojinovic6, Marian Beekman6, Nicola J. Armstrong7, Santiago Estrada1,8, Henry Völzke4,9, Robin Bülow10, Natalie A. Royle5,11, Joanna M. Wardlaw5,11, Wei Wen12, Perminder S. Sachdev12, Karen A. Mather12
and 6 other authorsP. Eline Slagboom6,13, Simon R. Cox5, Hans Jörgen Grabe3,14, Qiong Yang2,15, N. Ahmad Aziz1,16, Monique M.B. Breteler1,17
ORCID iDs: Dan Liu, Konstantinos Melas, Alexander Teumer, Katharina Wittfeld, Nicola J. Armstrong, Santiago Estrada, Perminder S. Sachdev, Karen A. Mather, N. Ahmad Aziz, Monique M.B. Breteler
17 affiliations
  1. Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
  2. Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
  3. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17489, Greifswald, Germany
  4. German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, 17475, Germany
  5. Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
  6. Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre, 233 ZA, Leiden, the Netherlands
  7. Mathematics and Statistics, Curtin University, 6845, Perth, Australia
  8. Artificial Intelligence in Medical Imaging, German Centre for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
  9. Institute for Community Medicine, University Medicine Greifswald, 17489, Greifswald, Germany
  10. Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17489, Greifswald, Germany
  11. Brain Research Imaging Centre, The University of Edinburgh, Edinburgh, EH8 9AB, UK
  12. Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, 2052, Australia
  13. Max Planck Institute for Biology of Ageing, 50931, Cologne, Germany
  14. German Centre for Neurodegenerative Diseases (DZNE), Partner Site Rostock/Greifswald, 17489, Greifswald, Germany
  15. The Framingham Heart Study, Framingham, MA, 01701, USA
  16. Department of Neurology, Faculty of Medicine, University of Bonn, 53127, Bonn, Germany
  17. Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53127, Bonn, Germany
Institutions: German Center for Neurodegenerative Diseases (Germany); Boston University (United States); Universitätsmedizin Greifswald (Germany); Universität Greifswald (Germany); German Centre for Cardiovascular Research (Germany); University of Edinburgh (United Kingdom); Leiden University (Netherlands); Leiden University Medical Center (Netherlands); Curtin University (Australia); UNSW Sydney (Australia); Centre for Healthy Brain Ageing (Australia); Max Planck Institute for Biology of Ageing (Germany); Framingham Heart Study (United States); University of Bonn (Germany); Institut für Medizinische Biometrie, Informatik und Epidemiologie (Germany)
Journal: EBioMedicine, volume 128, article 106289
Dates: received 23 June 2025; accepted 24 April 2026; published online 11 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ebiom.2026.106289 · PMID 42114417 · PMCID PMC13191633 · OpenAlex W7160767739
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Biomarker, DNA methylation, Hippocampal volume, Hippocampal asymmetry, Diet, Population-based
MeSH: DNA Methylation*, Hippocampus*, Aged, Atrophy, CpG Islands, Epigenesis, Genetic, Epigenomics, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Multiomics, Organ Size (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG); Germany’s Excellence Strategy; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation); Diet–Body–Brain Competence Cluster in Nutrition Research; Helmholtz Association; Deutsches Zentrum für Neurodegenerative Erkrankungen (German Center for Neurodegenerative Diseases); Alzheimer’s Association; Agence Nationale de la Recherche (French National Research Agency); UK Medical Research Council; RCUK | Biotechnology and Biological Sciences Research Council (BBSRC); Row Fogo Charitable Trust; Desert Research Institute; British Heart Foundation; Economic and Social Research Council; Wellcome Trust; Royal Society (The Royal Society)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Background: Left-right hippocampal volumetric asymmetry and atrophy are implicated in neurodegenerative and neuropsychiatric disorders, yet their molecular basis in healthy adults remains poorly understood.

Methods: We conducted a meta-analysis of epigenome-wide association studies across six population-based cohorts (n = 8156; 53% women; mean age = 60.7 years) to identify DNA methylation signatures associated with left and right hippocampal volumes (LHCV, RHCV) and hippocampal asymmetry (i.e, differences between left and right volumes divided by their sums).

Findings: We identified five CpGs and 262 differentially methylated regions associated with LHCV, nine CpGs and 246 regions with RHCV, one CpG and 16 regions with asymmetry. Cross-omics integration uncovered 15 LHCV-related and 13 RHCV-related methylation-gene expression pairs, with five overlapping genes primarily involved in immune regulation. LHCV-specific genes were involved in cellular signalling, and Mendelian randomisation (MR) analyses supported a potential causal association between brain expression of DIP2C and increased risk of major depressive disorder. RHCV-specific genes were involved in neuronal differentiation pathways, with MR analyses suggesting that brain-tissue expression of BAIAP2, MACF1, SLC16A5, and CORO1B was associated with neuropsychiatric disorders. We also identified sex-specific patterns with hippocampal asymmetry. Notably, baseline methylation at these sites predicted hippocampal atrophy rates, explaining >10% of the variation. Associations with multiple healthy dietary patterns suggest modifiable influences on hippocampal structure.

Interpretation: These findings highlight distinct methylation profiles as potential biomarkers or therapeutic targets for neuropsychiatric and neurodegenerative conditions.

Funding: Institutional funds , 10.13039/501100002347Federal Ministry of Education and Research of Germany , 10.13039/100000957Alzheimer's Association .

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 2 matches between paragraphs and lines of code.

Zenodo 18926492

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 8 files, 1 script
Software Heritage: not checked
Found in: “Data sharing statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source:

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

Tracing map

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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;
  • 1 script, each with its path and the digest of its content;
  • 2 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 statement

The data supporting the findings of this study are included in the manuscript and its supplementary materials. The complete EWAS summary statistics and analysis scripts are available via Zenodo (https://doi.org/10.5281/zenodo.18926492). Individual-level dataset is not openly accessible due to data protection regulations and participant privacy constraints. Access to these data can be granted to qualified researchers in accordance with each cohort's Data Use and Access Policy. Access requests should be directed to the relevant cohort data access committee.

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 2, 28 September 2026

  • Authors: added Katharina Wittfeld (0000-0003-4383-5043); Nicola J. Armstrong (0000-0002-4477-293X); Santiago Estrada (0000-0003-0339-8870); N. Ahmad Aziz (0000-0001-6184-458X); Monique M.B. Breteler (0000-0002-0626-9305); removed Katharina Wittfeld; Nicola J. Armstrong; Santiago Estrada; N. Ahmad Aziz; Monique M.B. Breteler

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 26 authors, 6 keywords, 14 MeSH terms, 16 funders, 72 references.

Cite

This paper

Liu, D., Talevi, V., Tavares, J. F., Wang, R., Imtiaz, M. A., Melas, K., Teumer, A., Wittfeld, K., Hillary, R. F., Vojinovic, D., Beekman, M., Armstrong, N. J., Estrada, S., Völzke, H., Bülow, R., Royle, N. A., Wardlaw, J. M., Wen, W., Sachdev, P. S., . . . Breteler, M. M. (2026). DNA methylation signatures of bilateral hippocampal volume, asymmetry and atrophy: a cross-omics analysis in the general population. EBioMedicine, 128, 106289. https://doi.org/10.1016/j.ebiom.2026.106289

BibTeX

@article{liu2026dna,
author = {Liu, Dan and Talevi, Valentina and Tavares, Juliana F. and Wang, Ruiqi and Imtiaz, Mohammed A. and Melas, Konstantinos and Teumer, Alexander and Wittfeld, Katharina and Hillary, Robert F. and Vojinovic, Dina and Beekman, Marian and Armstrong, Nicola J. and Estrada, Santiago and Völzke, Henry and Bülow, Robin and Royle, Natalie A. and Wardlaw, Joanna M. and Wen, Wei and Sachdev, Perminder S. and Mather, Karen A. and Slagboom, P. Eline and Cox, Simon R. and Grabe, Hans Jörgen and Yang, Qiong and Aziz, N. Ahmad and Breteler, Monique M.B.},
title = {{DNA methylation signatures of bilateral hippocampal volume, asymmetry and atrophy: a cross-omics analysis in the general population}},
journal = {EBioMedicine},
year = {2026},
month = may,
volume = {128},
pages = {106289},
publisher = {Elsevier},
issn = {2352-3964},
doi = {10.1016/j.ebiom.2026.106289},
url = {https://doi.org/10.1016/j.ebiom.2026.106289},
pmid = {42114417},
pmcid = {PMC13191633}
}

RIS

TY - JOUR
AU - Liu, Dan
AU - Talevi, Valentina
AU - Tavares, Juliana F.
AU - Wang, Ruiqi
AU - Imtiaz, Mohammed A.
AU - Melas, Konstantinos
AU - Teumer, Alexander
AU - Wittfeld, Katharina
AU - Hillary, Robert F.
AU - Vojinovic, Dina
AU - Beekman, Marian
AU - Armstrong, Nicola J.
AU - Estrada, Santiago
AU - Völzke, Henry
AU - Bülow, Robin
AU - Royle, Natalie A.
AU - Wardlaw, Joanna M.
AU - Wen, Wei
AU - Sachdev, Perminder S.
AU - Mather, Karen A.
AU - Slagboom, P. Eline
AU - Cox, Simon R.
AU - Grabe, Hans Jörgen
AU - Yang, Qiong
AU - Aziz, N. Ahmad
AU - Breteler, Monique M.B.
TI - DNA methylation signatures of bilateral hippocampal volume, asymmetry and atrophy: a cross-omics analysis in the general population
T2 - EBioMedicine
J2 - EBioMedicine
PY - 2026
DA - 2026/05/11
VL - 128
SP - 106289
SN - 2352-3964
PB - Elsevier
DO - 10.1016/j.ebiom.2026.106289
UR - https://doi.org/10.1016/j.ebiom.2026.106289
LA - en
ER -

CSL-JSON

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"given": "N. Ahmad"
},
{
"family": "Breteler",
"given": "Monique M.B."
}
],
"container-title-short": "EBioMedicine",
"volume": "128",
"page": "106289",
"DOI": "10.1016/j.ebiom.2026.106289",
"PMID": "42114417",
"PMCID": "PMC13191633",
"ISSN": "2352-3964",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.ebiom.2026.106289",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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