OSCR

Multisite Chronic Pain Reveals Neuro-Immune-Metabolic Dysregulation across Rheumatoid Arthritis and Depression.

Code ↔ Paper

7 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 7 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Causal inference using MR ↔ Codes/4_protein_MR_analysis.R, lines 1–58 · score 0.98 · genetically predicted MCP, genome wide, MR Egger, MR PRESSO, weighted median, conducted bidirectional
  2. [2] § Results › Causal associations of the identified multi-omic signatures with depression and RA ↔ Codes/4_protein_MR_analysis.R, lines 1–58 · score 0.94 · inverse variance weighted, Mendelian randomization, MR Egger, MR PRESSO, weighted median, odds ratio
  3. [3] § Materials and Methods › Associations with the incidence of depression and RA ↔ Codes/3_mediation_loadings.R, the whole file · a weak match · score 0.88 · household income, alcohol consumption, incident depression, metabolic syndrome, bidirectional association, sex
  4. [4] § Results › Prospective association between the identified signatures and incidence of depression and RA ↔ Codes/3_mediation_loadings.R, the whole file · a weak match · score 0.88 · household income, alcohol consumption, confidence interval, baseline RA, incident depression, metabolic syndrome
  5. [5] § Results › Linear gradients of MCP-related multi-omic signatures across disease burden ↔ Codes/0_anova_linear_analysis.R, the whole file · a weak match · score 0.70 · linear gradient, polynomial trend, disease burden, ANOVA, identified multi omic, MCP related multi
  6. [6] § Materials and Methods › Multi-omic fusion with reference: MCP ↔ Codes/0_anova_linear_analysis.R, the whole file · a weak match · score 0.62 · way ANOVA, polynomial trend, healthy, linearly, multi omic signatures, identified multi omic
  7. [7] § Materials and Methods › Associations with the incidence of depression and RA ↔ Codes/1_cox.R, lines 117–166 · score 0.54 · metabolic syndrome, Cox, BMI, alcohol, deprivation, education

Paper

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The authors' code

R · 234 lines · 8.3 KB · no license · 2 matches

  1. ###############################################################################
  2. ## Script: 4_protein_MR_analysis.R
  4. ## Purpose:
  5. ## Causal inference using Mendelian Randomization (MR).
  6. ## This script conducts bidirectional two-sample MR analyses to examine
  7. ## whether genetically predicted MCP-related multi-omic signatures exert
  8. ## causal effects on RA or depression. The section shown here corresponds to
  9. ## the direction: RA → protein traits, iterating over 119 plasma proteins.
  10. ##
  11. ## Methods overview:
  12. ## – Summary statistics for RA (exposure) and plasma protein GWAS (outcomes)
  13. ## are imported from independent datasets as described in Supplementary
  14. ## Table 11.
  15. ## – Exposure and outcome datasets are harmonised to ensure allele alignment,
  16. ## allowing automatic strand flips when required.
  17. ## – MR is performed using:
  18. ## * Inverse-variance weighted (IVW, primary)
  19. ## * Weighted median
  20. ## * Simple median
  21. ## * MR-Egger regression
  22. ## * Simple mode / weighted mode
  23. ## Odds ratios (ORs) and confidence intervals are extracted for all methods.
  24. ##
  25. ## – For each of the 119 proteins, the script:
  26. ## * Reads GWAS summary statistics,
  27. ## * Harmonises exposure–outcome SNPs,
  28. ## * Performs MR and heterogeneity tests,
  29. ## * Optionally runs MR-PRESSO,
  30. ## * Saves all MR results into protein-specific CSV files,
  31. ## * Updates a summary table recording final IVW p-values and any
  32. ## associated error or pleiotropy flags.
  33. ## Notes:
  34. ## – All analyses follow the same framework as described in the Methods:
  35. ## (1) SNP extraction at genome-wide significance,
  36. ## (2) LD clumping,
  37. ## (3) harmonisation,
  38. ## (4) IVW plus complementary MR estimators,
  39. ## (5) heterogeneity & pleiotropy assessment,
  40. ## (6) MR-PRESSO correction when applicable.
  41. ## – This script corresponds to the RA → protein direction; parallel scripts
  42. ## are used for depression → proteins and multi-omic layers (metabolites,
  43. ## blood cell traits, biochemistry markers).
  44. # Reference:
  45. # Multi-Site Chronic Pain Reveals Shared Neuro-Immune-Metabolic Alterations Underlying Rheumatoid Arthritis and Depression.
  46. ###############################################################################
  51. rm(list = ls())
  52. library(data.table)
  53. library(TwoSampleMR)
  54. library(dplyr)
  55. library(MRPRESSO)
  56. protein_names <- fread("E:/Project_R/R_code/UKB_project_RA/RA_MR_analysis/protein_name.csv",
  57. header = TRUE)
  58. names(protein_names) <- "protein"
  60. results_summary <- data.table(
  61. protein = character(),
  62. pval_method3 = numeric(),
  63. error_type = character()
  64. )
  66. exposure <- fread("E:/Gene_Dataset/RA_MR/finngen_R11_M13_RHEUMA/finngen_R11_M13_RHEUMA",
  67. header = TRUE)
  68. exposure <- subset(exposure, pval < 5e-8)
  70. write.csv(
  71. exposure,
  72. 'C:/Users/qianw/AppData/Local/R/win-library/4.4/TwoSampleMR/exposure.csv'
  73. )
  74. exposure <- system.file('exposure.csv', package = "TwoSampleMR")
  76. exposure_exp_dat <- read_exposure_data(
  77. filename = exposure,
  78. sep = ",",
  79. snp_col = "rsids",
  80. beta_col = "beta",
  81. se_col = "sebeta",
  82. effect_allele_col = "alt",
  83. other_allele_col = "ref",
  84. eaf_col = "af_alt",
  85. pval_col = "pval"
  86. )
  88. exposure_dat_clumped <- clump_data(
  89. exposure_exp_dat,
  90. clump_kb = 1000,
  91. clump_r2 = 0.01,
  92. clump_p1 = 1,
  93. clump_p2 = 1,
  94. pop = "EUR"
  95. )
  96. rm(exposure, exposure_exp_dat)
  99. for (current_protein in protein_names$protein) {
  100. tryCatch({
  101. outcome_file <- sprintf("merged_%s_with_rsid.regenie", current_protein)
  102. outcome_path <- file.path("E:/Gene_Dataset/UKB_protein_GWAS/protein_merge",
  103. outcome_file)
  104. outcome <- fread(outcome_path, header = TRUE)
  105. outcome$P <- 10 ^ (-outcome$LOG10P)
  106. c <- merge(exposure_dat_clumped,
  107. outcome,
  108. by.x = "SNP",
  109. by.y = "ID")
  111. write.csv(c,
  112. 'C:/Users/qianw/AppData/Local/R/win-library/4.4/TwoSampleMR/outcome.csv')
  113. rm(c)
  114. outcome <- system.file('outcome.csv', package = "TwoSampleMR")
  115. outcome_dat <- read_outcome_data(
  116. snps = exposure_dat_clumped$SNP,
  117. filename = outcome,
  118. sep = ",",
  119. snp_col = "SNP",
  120. beta_col = "BETA",
  121. se_col = "SE",
  122. effect_allele_col = "ALLELE1",
  123. other_allele_col = "ALLELE0",
  124. eaf_col = "A1FREQ",
  125. pval_col = "P"
  126. )
  128. dat <- harmonise_data(exposure_dat = exposure_dat_clumped,
  129. outcome_dat = outcome_dat)
  130. result =mr(dat, method_list = c("mr_egger_regression","mr_ivw_mre","mr_ivw_fe","mr_egger_regression_bootstrap",
  131. "mr_simple_median","mr_weighted_median","mr_simple_mode","mr_weighted_mode"))
  132. or <- generate_odds_ratios(result)
  133. output_path <- sprintf(
  134. "E:/Project_R/R_code/UKB_project_RA/RA_MR_analysis/results_protein2/%s_RA.csv",
  135. current_protein
  136. )
  137. write.csv(or, output_path, row.names = FALSE)
  140. hetegeneity<-mr_heterogeneity(dat)
  141. q_pval <- hetegeneity %>%
  142. filter(method == "Inverse variance weighted") %>%
  143. pull(Q_pval) %>%
  144. signif(3)
  146. if (length(q_pval) == 1 && !is.na(q_pval)) {
  147. ivw_method <- ifelse(q_pval < 0.05,
  148. "Inverse variance weighted (multiplicative random effects)",
  149. "Inverse variance weighted (fixed effects)")
  151. ivw_pval <- or %>%
  152. filter(method == ivw_method) %>%
  153. pull(pval)
  155. if (length(ivw_pval) == 0) {
  156. ivw_pval <- NA
  157. error_reason <- paste0("OR 表中未找到 ", ivw_method)
  158. } else {
  159. error_reason <- ""
  160. }
  162. } else {
  163. ivw_pval <- NA
  164. error_reason <- "异质性检验 Q 值缺失或无效"
  165. }
  167. pleitropy<-mr_pleiotropy_test(dat)
  169. if (!is.na(ivw_pval) && ivw_pval < 0.05 && pleitropy$pval < 0.05) {
  170. mr_presso_results <- mr_presso(BetaOutcome = "beta.outcome",
  171. BetaExposure = "beta.exposure",
  172. SdOutcome = "se.outcome",
  173. SdExposure = "se.exposure",
  174. OUTLIERtest = TRUE,
  175. DISTORTIONtest = TRUE,
  176. data = dat,
  177. NbDistribution = 5000,
  178. SignifThreshold = 0.05)
  180. outlier_indices <- mr_presso_results$`MR-PRESSO results`$`Distortion Test`$`Outliers Indices`
  181. outlier_snps <- dat$SNP[outlier_indices]
  183. dat_clean <- subset(dat, !(SNP %in% outlier_snps))
  185. result_clean <- mr(dat_clean, method_list = c("mr_egger_regression","mr_ivw_mre","mr_ivw_fe","mr_egger_regression_bootstrap",
  186. "mr_simple_median","mr_weighted_median","mr_simple_mode","mr_weighted_mode"))
  187. or_clean <- generate_odds_ratios(result_clean)
  188. output_path <- sprintf(
  189. "E:/Project_R/R_code/UKB_project_RA/RA_MR_analysis/results_protein2/upgrade_according to Preitropy/%s_RA.csv",
  190. current_protein
  191. )
  192. write.csv(or_clean, output_path, row.names = FALSE)
  194. ivw_pval <- or_clean %>%
  195. filter(method == ivw_method) %>%
  196. pull(pval)
  198. } else {
  199. message("未触发 MR-PRESSO（因 IVW 或 pleiotropy 不显著）")
  200. }
  202. # 更新汇总表
  203. results_summary <- rbindlist(list(
  204. results_summary,
  205. list(
  206. protein = current_protein,
  207. pval_method3 = ifelse(length(ivw_pval) == 1, ivw_pval, NA),
  208. error_type = error_reason
  209. )
  210. ))
  211. }, error = function(e) {
  212. results_summary <<- rbindlist(list(
  213. results_summary,
  214. list(
  215. protein = current_protein,
  216. pval_method3 = NA,
  217. error_type = e$message
  218. )
  219. ))
  220. message(sprintf("[错误] %s: %s", current_protein, e$message))
  222. })
  223. }
  225. output_summary_path <- "E:/Project_R/R_code/UKB_project_RA/RA_MR_analysis/results_protein2/summary_pvals_RA_protein.csv"
  227. write.csv(
  228. results_summary,
  229. file = output_summary_path,
  230. row.names = FALSE,
  231. fileEncoding = "GBK"
  232. )

4_protein_MR_analysis.R at commit 171e76e, no license · at the source

Overview

Authors: Qian Wang1, Ye Ella Tian2, Andrew Zalesky2,3, Peng Wang1, Zening Fu4, Guozheng Feng4, Dongmei Zhi5,6, Ming Xu1,7, Chunyang Wang1,8, Xiaoli Wang9, Xizhen Wang10, Peiwu Qin11, Vince D. Calhoun4, Rongtao Jiang1, Jing Sui1,12
ORCID iDs: Guozheng Feng, Jing Sui
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  2. Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia
  3. Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Australia
  4. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
  5. Center for Addiction Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
  6. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
  7. China Mobile Research Institute, Beijing 100032, China
  8. School of Education, Ludong University, Yantai 264025, China
  9. School of Medical Imaging, Shandong Second Medical University, Weifang 261053, China
  10. Medical Imaging Center Affiliated Hospital of Shandong Second Medical University, Weifang 261031, China
  11. Hengqin Lab, Zhuhai, Guangdong, China
  12. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
Institutions: Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; The University of Melbourne (Australia); Georgia Institute of Technology (United States); Emory University (United States); Georgia State University (United States); Center for Translational Research in Neuroimaging and Data Science (United States); Harvard University (United States); Massachusetts General Hospital (United States); China Mobile Research Institute (China); Ludong University (China)
Journal: Research (Washington, D.C.), volume 9, article 1298
Dates: received 9 March 2026; accepted 11 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.34133/research.1298 · PMID 42317795 · PMCID PMC13272895 · OpenAlex W7160930072
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), depression (population), pain (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Journal subjects: Health Science
Topic: Pain Mechanisms and Treatments (Physiology, Medicine), according to OpenAlex
Funding: Major Project of Brain Science and Brain-Inspired Intelligence Technology (2021ZD0200500); National Science Foundation (62373062)
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Multisite chronic pain (MCP) frequently co-occurs with immune and depressive disorders, yet whether it reflects coordinated cross-domain multi-omic dysregulation remains unknown. Using UK Biobank data (19,484 baseline participants; 32,870 to 399,476 for 15.9-year follow-up), we identified MCP-related multi-omic signatures spanning 59 biochemical measures, 168 metabolites, and 2,920 proteins. Notably, these signatures showed graded dysregulation [controls < depression < rheumatoid arthritis (RA) < comorbidity] with increasing disease burden, were associated with increased risk of incident RA and depression, and partially mediated their bidirectional association. We further identified HNMT as a depression risk factor, FGF21 as an RA risk factor, MME as a depression protective factor, and platelet count/FGF21/HNMT as shared factors using Mendelian randomization. Beyond disease outcomes, the signatures were associated with brain structural impairment and health-related behaviors (smoking/fish intake/physical activity), and aligned with polygenic liability for immune–metabolic–psychiatric traits. Together, these findings demonstrate that MCP reflects coordinated neuro–immune–metabolic dysregulation underlying RA–depression comorbidity and functions as a systems-level phenotype linking immune processes and depression.

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 7 matches between paragraphs and lines of code.

qianwang-brain/Pain_RheumatoidArthritic_Depression_Fusion

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 171e76e44b9f36fb7aadc44c9e364f3ff3cbc9fd, 11 December 2025
Languages: R (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), data.table (2 files), survival (2 files), car (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/qianwang-brain/Pain_RheumatoidArthritic_Depression_Fusion

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;
  • 6 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Data Availability

The data used in this study were obtained from the UK Biobank under an approved application and are available through the UK Biobank Access Management System (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Software packages used in this study include R v4.4.1 with the survival, survminer, and mediation packages (https://www.r-project.org), TwoSampleMR v2.0, STRING (STRING: functional protein association networks (https://string-db.org/)), Cytoscape v3.10.2 (https://cytoscape.org), Fusion ICA Toolbox (https://trendscenter.org/software/fit), MATLAB R2020b (https://www.mathworks.com/products/matlab.html), BrainNet Viewer v20191031 (https://www.nitrc.org/projects/bnv), Python v3.8.13 (https://www.python.org), and ggplot2 v3.4.2 (https://ggplot2.tidyverse.org). The analysis code used in this study is available at https://github.com/qianwang-brain/Pain_RheumatoidArthritic_Depression_Fusion.

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, 15 authors, 2 funders, 74 references.

Cite

This paper

Wang, Q., Tian, Y. E., Zalesky, A., Wang, P., Fu, Z., Feng, G., Zhi, D., Xu, M., Wang, C., Wang, X., Wang, X., Qin, P., Calhoun, V. D., Jiang, R., & Sui, J. (2026). Multisite Chronic Pain Reveals Neuro-Immune-Metabolic Dysregulation across Rheumatoid Arthritis and Depression. Research (Washington, D.C.), 9, 1298. https://doi.org/10.34133/research.1298

BibTeX

@article{wang2026multisite,
author = {Wang, Qian and Tian, Ye Ella and Zalesky, Andrew and Wang, Peng and Fu, Zening and Feng, Guozheng and Zhi, Dongmei and Xu, Ming and Wang, Chunyang and Wang, Xiaoli and Wang, Xizhen and Qin, Peiwu and Calhoun, Vince D. and Jiang, Rongtao and Sui, Jing},
title = {{Multisite Chronic Pain Reveals Neuro-Immune-Metabolic Dysregulation across Rheumatoid Arthritis and Depression}},
journal = {Research (Washington, D.C.)},
year = {2026},
month = jun,
volume = {9},
pages = {1298},
publisher = {AAAS Science Partner Journal Program},
issn = {2639-5274},
doi = {10.34133/research.1298},
url = {https://doi.org/10.34133/research.1298},
pmid = {42317795},
pmcid = {PMC13272895}
}

RIS

TY - JOUR
AU - Wang, Qian
AU - Tian, Ye Ella
AU - Zalesky, Andrew
AU - Wang, Peng
AU - Fu, Zening
AU - Feng, Guozheng
AU - Zhi, Dongmei
AU - Xu, Ming
AU - Wang, Chunyang
AU - Wang, Xiaoli
AU - Wang, Xizhen
AU - Qin, Peiwu
AU - Calhoun, Vince D.
AU - Jiang, Rongtao
AU - Sui, Jing
TI - Multisite Chronic Pain Reveals Neuro-Immune-Metabolic Dysregulation across Rheumatoid Arthritis and Depression
T2 - Research (Washington, D.C.)
J2 - Research (Wash D C)
PY - 2026
DA - 2026/06/17
VL - 9
SP - 1298
SN - 2639-5274
PB - AAAS Science Partner Journal Program
DO - 10.34133/research.1298
UR - https://doi.org/10.34133/research.1298
LA - en
ER -

CSL-JSON

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"id": "10.34133/research.1298",
"type": "article-journal",
"title": "Multisite Chronic Pain Reveals Neuro-Immune-Metabolic Dysregulation across Rheumatoid Arthritis and Depression",
"container-title": "Research (Washington, D.C.)",
"author": [
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"family": "Wang",
"given": "Qian"
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{
"family": "Fu",
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{
"family": "Feng",
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{
"family": "Zhi",
"given": "Dongmei"
},
{
"family": "Xu",
"given": "Ming"
},
{
"family": "Wang",
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{
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{
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{
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"container-title-short": "Research (Wash D C)",
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"issued": {
"date-parts": [
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17
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}
}

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