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The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome.

Code ↔ Paper

22 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 22 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Statistics and reproducibility › EV of metabolites ↔ EV_estimationLightGBM.py, lines 24–35 · score 0.98 · bagging_fraction, bagging_freq, early_stopping_rounds, feature_fraction, learning_rate, max_depth
  2. [2] § Methods › Statistics and reproducibility › EV of metabolites ↔ EV_estimationLightGBM_genetics.py, lines 23–29 · score 0.98 · bagging_fraction, bagging_freq, early_stopping_rounds, feature_fraction, learning_rate, max_depth
  3. [3] § Results › General cognition and brain MRI markers are associated with distinct blood metabolites ↔ Figures/script_main_figure3.R, lines 1–74 · score 0.86 · naphthol sulfate, acetylphenol sulfate, methylpyridine sulfate, vinylcatechol sulfate, vinylguaiacol sulfate, cresol sulfate
  4. [4] § Results › General cognition and brain MRI markers are associated with distinct blood metabolites ↔ Figures/script_main_figure1.R, lines 44–113 · score 0.86 · naphthol sulfate, acetylphenol sulfate, methylpyridine sulfate, vinylcatechol sulfate, vinylguaiacol sulfate, cresol sulfate
  5. [5] § Results › Exposome has strongest influences on metabolites associated with brain health ↔ Figures/script_main_figure3.R, lines 1–74 · score 0.79 · naphthol sulfate, methylpyridine sulfate, vinylcatechol sulfate, vinylguaiacol sulfate, cresol sulfate, deoxyuridine
  6. [6] § Methods › Statistics and reproducibility › Association of metabolites with general cognition and MRI markers ↔ script_for_association_mmetabolites_MRIvariables.R, lines 1–67 · score 0.79 · log transformation, intracranial volume, hippocampal volume, brain volume, blood collection, hypertension
  7. [7] § Results › Exposome has strongest influences on metabolites associated with brain health ↔ Figures/script_main_figure1.R, lines 44–113 · score 0.78 · naphthol sulfate, methylpyridine sulfate, vinylcatechol sulfate, vinylguaiacol sulfate, cresol sulfate, deoxyuridine
  8. [8] § Methods › Study population › Assessment of lifestyle, clinical factors and medication intake ↔ Script_univariate_Lifestyle_factors.R, lines 1–54 · score 0.71 · primary education, higher education, lifestyle factors, clinical factors, alcohol, home
  9. [9] § Methods › Study population › MRI features ↔ script_for_association_mmetabolites_MRIvariables.R, lines 69–150 · score 0.70 · intracranial volume, hippocampal volume, FreeSurfer, brain volume, blood collection, head
  10. [10] § Methods › Study population › Metabolomics profiling ↔ Figures/script_main_figure2.R, lines 47–94 · score 0.68 · amino acids, carbohydrates, cofactors, energy, peptides, nucleotides
  11. [11] § Results › Ergothioneine mediates the association between antacid medication and cognition ↔ Script_Mediation_analysis_Medications_Ergothioneine.R, lines 41–106 · score 0.68 · confidence interval, 15.5 %, 71 %, mediating, antacid, ergothioneine
  12. [12] § Methods › Study population › Metabolomics profiling ↔ Figures/Extended_Figure2_overlap_findings_cog_MRI.R, lines 171–215 · score 0.67 · amino acids, carbohydrates, cofactors, energy, peptides, nucleotides
  13. [13] § Methods › Study population › Assessment of lifestyle, clinical factors and medication intake ↔ Script_univariate_clinical_factors.R, lines 1–54 · score 0.66 · primary education, higher education, clinical factors, alcohol, home, cohorts
  14. [14] § Methods › Statistics and reproducibility › Sex-stratified association analysis ↔ script_for_association_mmetabolites_MRIvariables.R, lines 200–262 · score 0.61 · interaction term, sex interaction, error, age, BMI, RSI
  15. [15] § Results › Link of brain health-associated metabolites and individual microbial or exposomal features ↔ Figures/script_main_figure3.R, lines 76–159 · score 0.61 · glutamine conjugates, C6H10O2, MRI associated, gut microbiota, caffeine, sphingomyelins
  16. [16] § Results › Metabolite signatures of cognition and MRI markers are concordant ↔ Figures/Extended_Figure2_overlap_findings_cog_MRI.R, lines 83–169 · score 0.59 · white matter lesion, hippocampal volume, regression coefficients, brain volume, Scatter, HCV
  17. [17] § Methods › Statistics and reproducibility › Association of metabolites with gut microbial and exposomal features ↔ script_for_association_mmetabolites_MRIvariables.R, lines 1–67 · score 0.59 · log transformation, blood collection, covariates, hypertension, age, sex
  18. [18] § Methods › Statistics and reproducibility › Association of metabolites with general cognition and MRI markers ↔ script_ElasticNet_multi_variateModel_MRI.R, lines 92–146 · score 0.58 · cross validation, elastic, RMSE, glmnet, squared, zero
  19. [19] § Methods › Statistics and reproducibility › Mediation analysis between blood metabolite levels and drug intake ↔ Script_Mediation_analysis_Medications_Ergothioneine.R, lines 41–106 · score 0.57 · thyroid therapy, mediation, psychoanaleptics, mediators, antacids, ergothioneine
  20. [20] § Results › Exposome has strongest influences on metabolites associated with brain health ↔ Figures/Extended_Figure3_sex_stratified_forestplot.R, the whole file · a weak match · score 0.53 · metabolites strongly influenced, explained variance, bars, gut microbiome, CI, nominal
  21. [21] § Results › Link of brain health-associated metabolites and individual microbial or exposomal features ↔ Figures/Extended_Figure4_Heatmap_Univariate_Cognition.R, lines 78–150 · score 0.53 · alcohol intake, methylcatechol sulfate, cognition associated metabolites, lifestyle factors, matching, education
  22. [22] § Results › General cognition and brain MRI markers are associated with distinct blood metabolites ↔ Figures/Source_data_files_main_Figures.R, lines 46–90 · score 0.51 · GPC, adenosylhomocysteine, bromotryptophan, argininate, SAH, caffeine

Paper

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The authors' code

R · 265 lines · 11 KB · no license · 4 matches

  1. ### script to perform association of metabolites with MRI variables (Total brain volume, total hippocampal volume and Total white matter lesions)
  2. ### Results are provided in supplementary table 2
  3. ### Load packages
  4. .libPaths("/home/sahmad/R/x86_64-pc-linux-gnu-library/4.1")
  5. library("dplyr")
  6. ### Loading the MRI and metabolomics combined dataset for analysis
  7. mri_metabolomics_df<-read.table('RS1_5_Metabolon_MRIdata_2Jun2021.txt',head=T)
  8. covariates<-readRDS(file ="Study_RSI_IV_RSIII_2_covars.rds")
  9. m<-merge(mri_metabolomics_df,covariates,by.all="ergoid",all.x=T)
  10. ########################################################
  11. ########################################################
  12. ### Model 1: adjusted for Age_blood_collection + sex + BMI + Lipilowering medication + ICV_from_mask
  13. ########################################################
  14. ########################################################
  15. results <- data.frame(
  16. endo_pheno=as.character(),
  17. Metabolite=as.character(),
  18. Beta=as.numeric(),
  19. Se=as.numeric(),
  20. p=as.numeric(),
  21. n=as.numeric(),
  22. lower=as.numeric(),
  23. upper=as.numeric(),
  24. stringsAsFactors=FALSE)
  26. # List of tested phenotype: Total hippocampal volume, total brain volume (total_par_ml), and total white matter lesions
  27. endo_pheno<-c("total_Hippocampus","Total_par_ml","Total_wml")
  29. for (j in 1:length(endo_pheno)) {
  30. for ( i in 1:length(metabolites)){
  31. # each metabolite is transformed to z-value before association
  32. m$metabo<-scale(m[,metabolites[i]],center = TRUE, scale = TRUE)
  33. # all MRI variables are log transformed and scaled, ICV_from_mask is intracranial volume variable
  34. m$mri_variable<-scale(log(m[,endo_pheno[j]]),center = TRUE, scale = TRUE)
  35. test<- summary(lm (paste("mri_variable ~ metabo + Age_blood_collection + sex + BMI + Lipilower + ICV_from_mask",sep=""), data=m))
  36. tablerow <- data.frame(
  37. endo_pheno=endo_pheno[j],
  38. Metabolite=metabolites[i],
  39. Beta=test$coefficients[2,1],
  40. Se=test$coefficients[2,2],
  41. p=test$coefficients[2,4],
  42. n=dim(na.omit(m[,c("metabo","mri_variable")]))[[1]],
  43. lower=test$coef[2,1] - qt(0.975, df = test$df[2]) * test$coef[2, 2],
  44. upper=test$coef[2,1] + qt(0.975, df = test$df[2]) * test$coef[2, 2],
  45. stringsAsFactors=FALSE)
  46. results <- rbind(results, tablerow)
  47. }
  48. }
  49. head (results[order(results$p),])
  50. results$FDR<-p.adjust(results[,5], method = 'fdr', n = length(results[,4]))
  52. write.table(results,file="Association_regression_MRI_M1_RSIII_2.csv",sep="\t",row.names=F,col.names=T,quote=F)
  53. ########################################################
  54. ########################################################
  55. ### Model 2: adjusted for Age_blood_collection + sex + BMI + Lipilower + Smoke + Diabetes2 + Hypertension + ICV_from_mask
  56. ########################################################
  57. ########################################################
  58. results <- data.frame(
  59. endo_pheno=as.character(),
  60. Metabolite=as.character(),
  61. Beta=as.numeric(),
  62. Se=as.numeric(),
  63. p=as.numeric(),
  64. n=as.numeric(),
  65. lower=as.numeric(),
  66. upper=as.numeric(),
  67. stringsAsFactors=FALSE)
  69. # List of tested phenotype: Total hippocampal volume, total brain volume (total_par_ml), and total white matter lesions
  70. endo_pheno<-c("total_Hippocampus","Total_par_ml","Total_wml")
  72. for (j in 1:length(endo_pheno)) {
  73. for ( i in 1:length(metabolites)){
  74. # each metabolite is transformed to z-value before association
  75. m$metabo<-scale(m[,metabolites[i]],center = TRUE, scale = TRUE)
  76. # all MRI variables are log transformed and scaled, ICV_from_mask is intracranial volume variable
  77. m$mri_variable<-scale(log(m[,endo_pheno[j]]),center = TRUE, scale = TRUE)
  78. test<- summary(lm (paste("mri_variable ~ metabo + Age_blood_collection + sex + BMI + Lipilower + Smoke + Diabetes2 + Hypertension + ICV_from_mask",sep=""), data=m))
  79. tablerow <- data.frame(
  80. endo_pheno=endo_pheno[j],
  81. Metabolite=metabolites[i],
  82. Beta=test$coefficients[2,1],
  83. Se=test$coefficients[2,2],
  84. p=test$coefficients[2,4],
  85. n=dim(na.omit(m[,c("metabo","mri_variable")]))[[1]],
  86. lower=test$coef[2,1] - qt(0.975, df = test$df[2]) * test$coef[2, 2],
  87. upper=test$coef[2,1] + qt(0.975, df = test$df[2]) * test$coef[2, 2],
  88. stringsAsFactors=FALSE)
  89. results <- rbind(results, tablerow)
  90. }
  91. }
  92. head (results[order(results$p),])
  93. results$FDR<-p.adjust(results[,5], method = 'fdr', n = length(results[,4]))
  95. write.table(results,file="Association_regression_MRI_M2_RSIII_2.csv",sep="\t",row.names=F,col.names=T,quote=F)
  97. ########################################################
  98. ########################################################
  99. ## Association of metabolites with MRI variables in only participants which have less than equal to 1 year difference between MRI and blood collection
  100. ########################################################
  101. ########################################################
  103. MRI_data<-read.table("RS1_5_Metabolon_MRIdata_2Jun2021.txt",head=T)
  104. covariates<-readRDS(file ="Study_RSI_IV_RSIII_2_covars.rds")
  105. covariates<-as.data.frame(covariates)
  106. m<-left_join(MRI_data,covariates,by="ergoid")
  108. # abs_difference variable defines the absolute time in years between MRI and blood collection for metabolomics
  109. m<-m %>% filter(abs_difference<=1)
  110. #########################################################
  111. #########################################################
  112. results <- data.frame(
  113. endo_pheno=as.character(),
  114. Metabolite=as.character(),
  115. Beta=as.numeric(),
  116. Se=as.numeric(),
  117. p=as.numeric(),
  118. n=as.numeric(),
  119. lower=as.numeric(),
  120. upper=as.numeric(),
  121. stringsAsFactors=FALSE)
  124. endo_pheno<-c("total_Hippocampus","Total_par_ml","Total_wml")
  125. metabolites<-grep("metab_",colnames(m),value = T)
  127. for (j in 1:length(endo_pheno)) {
  128. for ( i in 1:length(metabolites)){
  130. m$metabo<-scale(m[,metabolites[i]],center = TRUE, scale = TRUE)
  131. m$mri_variable<-scale(log(m[,endo_pheno[j]]),center = TRUE, scale = TRUE)
  132. test<- summary(lm (paste("mri_variable ~ metabo + Age_blood_collection + sex.y + BMI + Lipilower + ICV_from_mask",sep=""), data=m))
  133. #test<- summary(lm (paste("mri_variable ~ metabo + Age_blood_collection + sex + BMI + Lipilower + Smoke + Diabetes2 + Hypertension + ICV_from_mask",sep=""), data=m))
  134. tablerow <- data.frame(
  135. endo_pheno=endo_pheno[j],
  136. Metabolite=metabolites[i],
  137. Beta=test$coefficients[2,1],
  138. Se=test$coefficients[2,2],
  139. p=test$coefficients[2,4],
  140. n=dim(na.omit(m[,c("metabo","mri_variable")]))[[1]],
  141. lower=test$coef[2,1] - qt(0.975, df = test$df[2]) * test$coef[2, 2],
  142. upper=test$coef[2,1] + qt(0.975, df = test$df[2]) * test$coef[2, 2],
  143. stringsAsFactors=FALSE)
  144. results <- rbind(results, tablerow)
  145. }
  146. }
  147. head (results[order(results$p),])
  148. results$FDR<-p.adjust(results[,5], method = 'fdr', n = length(results[,4]))
  150. write.table(results,file="Association_regression_MRI_M1_RSIII_2_YearLessthan1.csv",sep="\t",row.names=F,col.names=T,quote=F)
  152. ########################################################
  153. ########################################################
  154. ### Sex stratified analysis
  155. ########################################################
  156. ########################################################
  157. m<-merge(mri_metabolomics_df,covariates,by.all="ergoid",all.x=T)
  158. m<-m[m$sex=='female',]
  159. #m<-m[m$sex=='male',]
  160. # Model 1 was run separately for females and males
  161. results <- data.frame(
  162. endo_pheno=as.character(),
  163. Metabolite=as.character(),
  164. Beta=as.numeric(),
  165. Se=as.numeric(),
  166. p=as.numeric(),
  167. n=as.numeric(),
  168. lower=as.numeric(),
  169. upper=as.numeric(),
  170. stringsAsFactors=FALSE)
  173. endo_pheno<-c("total_Hippocampus","Total_par_ml","Total_wml")
  175. for (j in 1:length(endo_pheno)) {
  176. for ( i in 1:length(metabolites)){
  178. m$metabo<-scale(m[,metabolites[i]],center = TRUE, scale = TRUE)
  179. m$mri_variable<-scale(log(m[,endo_pheno[j]]),center = TRUE, scale = TRUE)
  180. test<- summary(lm (paste("mri_variable ~ metabo + Age_blood_collection + BMI + Lipilower + ICV_from_mask",sep=""), data=m))
  181. tablerow <- data.frame(
  182. endo_pheno=endo_pheno[j],
  183. Metabolite=metabolites[i],
  184. Beta=test$coefficients[2,1],
  185. Se=test$coefficients[2,2],
  186. p=test$coefficients[2,4],
  187. n=dim(na.omit(m[,c("metabo","mri_variable")]))[[1]],
  188. lower=test$coef[2,1] - qt(0.975, df = test$df[2]) * test$coef[2, 2],
  189. upper=test$coef[2,1] + qt(0.975, df = test$df[2]) * test$coef[2, 2],
  190. stringsAsFactors=FALSE)
  191. results <- rbind(results, tablerow)
  192. }
  193. }
  194. head (results[order(results$p),])
  195. results$FDR<-p.adjust(results[,5], method = 'fdr', n = length(results[,4]))
  196. write.table(results,file="Association_regression_MRI_M1_RSIII_2_female.csv",sep="\t",row.names=F,col.names=T,quote=F)
  197. #write.table(results,file="Association_regression_MRI_M1_RSIII_2_male.csv",sep="\t",row.names=F,col.names=T,quote=F)
  200. ###############################################################################################
  201. ## Sex interaction term in the model analysis: Supplementary Table 3
  202. ###############################################################################################
  203. MRI_data<-read.table("RS1_5_Metabolon_MRIdata_2Jun2021.txt",head=T)
  204. covariates<-readRDS(file ="Study_RSI_IV_RSIII_2_covars.rds")
  205. covariates<-as.data.frame(covariates)
  206. m<-left_join(MRI_data,covariates,by="ergoid")
  208. #########################################################
  209. #########################################################
  210. results <- data.frame(
  211. endo_pheno = as.character(),
  212. Metabolite = as.character(),
  213. Beta = as.numeric(),
  214. Se = as.numeric(),
  215. p = as.numeric(),
  216. n = as.numeric(),
  217. lower = as.numeric(),
  218. upper = as.numeric(),
  219. stringsAsFactors = FALSE
  220. )
  222. endo_pheno <- c("total_Hippocampus", "Total_par_ml", "Total_wml")
  223. metabolites <- grep("metab_", colnames(m), value = TRUE)
  225. # convert sex value into factor
  226. m$sex.y <- factor(m$sex.y, levels = c("female", "male"))
  228. for (j in 1:length(endo_pheno)) {
  229. for (i in 1:length(metabolites)) {
  231. m$metabo <- scale(m[, metabolites[i]], center = TRUE, scale = TRUE)
  232. m$mri_variable <- scale(log(m[, endo_pheno[j]]), center = TRUE, scale = TRUE)
  234. model <- lm(mri_variable ~ metabo * sex.y + Age_blood_collection + BMI + Lipilower + ICV_from_mask, data = m)
  235. test <- summary(model)
  237. # Get interaction term row: metabo:sex.ymale
  238. interaction_row <- grep("metabo:sex\\.ymale", rownames(test$coefficients))
  240. # Check in case interaction term is missing due to singularity or coding error
  241. if (length(interaction_row) == 1) {
  242. tablerow <- data.frame(
  243. endo_pheno = endo_pheno[j],
  244. Metabolite = metabolites[i],
  245. Beta = test$coefficients[interaction_row, 1],
  246. Se = test$coefficients[interaction_row, 2],
  247. p = test$coefficients[interaction_row, 4],
  248. n = nrow(na.omit(m[, c("metabo", "mri_variable", "sex.y")])),
  249. lower = test$coefficients[interaction_row, 1] - qt(0.975, df = test$df[2]) * test$coefficients[interaction_row, 2],
  250. upper = test$coefficients[interaction_row, 1] + qt(0.975, df = test$df[2]) * test$coefficients[interaction_row, 2],
  251. stringsAsFactors = FALSE
  252. )
  253. results <- rbind(results, tablerow)
  254. }
  255. }
  256. }
  257. results <- results[order(results), ]
  258. # Write to file
  259. write.table(results,
  260. file = "MRI_Interaction_Metabolite_Sex.csv",
  261. sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE
  262. )

script_for_association_mmetabolites_MRIvariables.R at commit bb8e2f6, no license · at the source

Overview

Authors: Shahzad Ahmad1,2,3,4, Tong Wu5, Matthias Arnold5,6, Thomas Hankemeier2, Mohsen Ghanbari1, Gennady Roshchupkin1, André G Uitterlinden7, Kamil Borkowski8, Julia Neitzel1,9, Robert Kraaij7, The Alzheimer’s Disease Metabolomics Consortium, Cornelia M van Duijn1,10, M Arfan Ikram1, Rima Kaddurah-Daouk6,11,12, Gabi Kastenmüller5
ORCID iDs: Shahzad Ahmad, Tong Wu, Matthias Arnold, Thomas Hankemeier, Mohsen Ghanbari, Julia Neitzel, Robert Kraaij, Cornelia M van Duijn, M Arfan Ikram, Rima Kaddurah-Daouk, Gabi Kastenmüller
  1. Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
  2. Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
  3. Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
  4. Sidra Medicine, Doha, Qatar
  5. Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  6. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC USA
  7. Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
  8. West Coast Metabolomics Center, Genome Center, University of California, Davis, Davis, CA USA
  9. Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
  10. Nuffield Department of Population Health, University of Oxford, Oxford, UK
  11. Duke Institute of Brain Sciences, Duke University, Durham, NC USA
  12. Department of Medicine, Duke University, Durham, NC USA
Institutions: Leiden University (Netherlands); Centre for Human Genetics (United Kingdom); Erasmus MC (Netherlands); University of Oxford (United Kingdom); Helmholtz Munich (Germany); Institute of Computational Biology (Germany); Duke University (United States); University of California, Davis (United States)
Journal: Nature aging, volume 6, issue 7, pages 1452-1467
Dates: received 24 March 2025; accepted 21 May 2026; published online 24 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s43587-026-01149-4 · PMID 42342913 · PMCID PMC13375541 · OpenAlex W7165797643
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Systems analysis, Metabolomics, Data integration, Alzheimer's disease, Ageing
MeSH: Alzheimer Disease*, Brain*, Cognition*, Exposome*, Gastrointestinal Microbiome*, Metabolome*, Aged, Cross-Sectional Studies, Ergothioneine, Female, Humans, Life Style, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Metabolomics and Mass Spectrometry Studies (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIA NIH HHS (P30 AG049638, P30 AG072973, RF1 AG059093, U24 AG072122, RF1 AG058942, P30 AG066512, RF1 AG057452, P30 AG062715, RF1 AG051550, U01 AG061359, P30 AG086401, R01 AG046171, U24 AG021886, P30 AG072976, U19 AG063744, P30 AG062429); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01AG059093, RF1AG051550, RF1AG057452, RF1AG058942, R01AG046171, U01AG061359, U19AG063744, RF1AG059093); U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (U19AG063744, U01AG061359, R01AG059093, RF1AG058942, RF1AG057452, R01AG046171, RF1AG051550, RF1AG059093); FNIH #DAOU16AMPA; ZonMw (733050814, #733050814); Alzheimer Nederland; Deutsche Forschungsgemeinschaft (German Research Foundation) (536691227); Oxford-GSK Institute of Molecular and Computational Medicine; National Institute for Health Research (NIHR)
Citations: cited by 3 papers (Europe PMC); 91 references in the paper

Abstract

Metabolic alterations are increasingly implicated in neurological disorders, including Alzheimer’s disease (AD), highlighting the relevance of the peripheral metabolome, shaped by genetic and environmental exposures, for brain health. We examined the relation of 991 blood metabolites with cognition and magnetic resonance imaging (MRI) measures cross-sectionally in 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study and quantified contributions of genetic variation, lifestyle, comorbidities, medication and gut microbiota to metabolite variance. Cognition-associated metabolites were replicated in two independent cohorts of older adults and tested for associations with incident AD longitudinally in one cohort. Twenty-two metabolites were associated with MRI measures. Fourteen metabolites showed replicated associations with cognition, with ergothioneine exhibiting the largest effect. The metabolite signature of cognition mirrored that of incident AD. Lifestyle, clinical variables and medication were the strongest determinants of cognition-associated and MRI-associated metabolites, explaining up to 28.6% of their variance. Antacid use was associated with worse cognition and lower ergothioneine levels, which mediated 31.5% of the negative medication effect, suggesting implications for AD prevention.

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 22 matches between paragraphs and lines of code.

omics-x/BloodMetabolomics_Genes_Gut_Exposome

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bb8e2f6f484f80b38d570c8e4f8af86134db5539, 21 May 2026
Languages: R (23), Python (2)
Size: 27 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (16 files), ggplot2 (8 files), caret (2 files), glmnet (2 files), LightGBM (2 files), NumPy (2 files), pandas (2 files), scikit-learn (2 files), SciPy (2 files), cowplot (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files
At the source: github.com/omics-x/BloodMetabolomics_Genes_Gut_Exposome.git

Code availability

Code is available via the GitHub repository: https://github.com/omics-x/BloodMetabolomics_Genes_Gut_Exposome.git.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 25 scripts, each with its path and the digest of its content;
  • 22 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

Rotterdam Study data (including RSI and RSIII) can be made available to interested researchers upon request. Requests can be directed to data manager F. J. A. van Rooij. We are unable to place data in a public repository due to legal and ethical restraints. Sharing of individual participant data was not included in the informed consent of the study, and there is potential risk of revealing participants’ identities as it is not possible to completely anonymize the data. This is of particular concern given the sensitive personal nature of much of the data collected as part of the Rotterdam Study. ADRC clinical data are available through the NACC at https://www.naccdata.org/. Access requires a NACC data request using https://www.naccdata.org/data-request-process/. ADRC biochemical data will be shared via the AD Knowledge Portal, https://adknowledgeportal.synapse.org/, and requires Synapse registration to download data.

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

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 5 keywords, 15 MeSH terms, 9 funders, 88 references.

Cite

This paper

Ahmad, S., Wu, T., Arnold, M., Hankemeier, T., Ghanbari, M., Roshchupkin, G., Uitterlinden, A. G., Borkowski, K., Neitzel, J., Kraaij, R., The Alzheimer’s Disease Metabolomics Consortium, van Duijn, C. M., Ikram, M. A., Kaddurah-Daouk, R., & Kastenmüller, G. (2026). The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome. Nature aging, 6(7), 1452-1467. https://doi.org/10.1038/s43587-026-01149-4

BibTeX

@article{ahmad2026blood,
author = {Ahmad, Shahzad and Wu, Tong and Arnold, Matthias and Hankemeier, Thomas and Ghanbari, Mohsen and Roshchupkin, Gennady and Uitterlinden, André G and Borkowski, Kamil and Neitzel, Julia and Kraaij, Robert and {The Alzheimer’s Disease Metabolomics Consortium} and van Duijn, Cornelia M and Ikram, M Arfan and Kaddurah-Daouk, Rima and Kastenmüller, Gabi},
title = {{The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome}},
journal = {Nature aging},
year = {2026},
month = jun,
volume = {6},
number = {7},
pages = {1452--1467},
publisher = {Nature Portfolio},
issn = {2662-8465},
doi = {10.1038/s43587-026-01149-4},
url = {https://doi.org/10.1038/s43587-026-01149-4},
pmid = {42342913},
pmcid = {PMC13375541}
}

RIS

TY - JOUR
AU - Ahmad, Shahzad
AU - Wu, Tong
AU - Arnold, Matthias
AU - Hankemeier, Thomas
AU - Ghanbari, Mohsen
AU - Roshchupkin, Gennady
AU - Uitterlinden, André G
AU - Borkowski, Kamil
AU - Neitzel, Julia
AU - Kraaij, Robert
AU - The Alzheimer’s Disease Metabolomics Consortium
AU - van Duijn, Cornelia M
AU - Ikram, M Arfan
AU - Kaddurah-Daouk, Rima
AU - Kastenmüller, Gabi
TI - The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome
T2 - Nature aging
J2 - Nat Aging
PY - 2026
DA - 2026/06/24
VL - 6
IS - 7
SP - 1452
EP - 1467
SN - 2662-8465
PB - Nature Portfolio
DO - 10.1038/s43587-026-01149-4
UR - https://doi.org/10.1038/s43587-026-01149-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s43587-026-01149-4",
"type": "article-journal",
"title": "The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome",
"container-title": "Nature aging",
"author": [
{
"family": "Ahmad",
"given": "Shahzad"
},
{
"family": "Wu",
"given": "Tong"
},
{
"family": "Arnold",
"given": "Matthias"
},
{
"family": "Hankemeier",
"given": "Thomas"
},
{
"family": "Ghanbari",
"given": "Mohsen"
},
{
"family": "Roshchupkin",
"given": "Gennady"
},
{
"family": "Uitterlinden",
"given": "André G"
},
{
"family": "Borkowski",
"given": "Kamil"
},
{
"family": "Neitzel",
"given": "Julia"
},
{
"family": "Kraaij",
"given": "Robert"
},
{
"literal": "The Alzheimer’s Disease Metabolomics Consortium"
},
{
"family": "van Duijn",
"given": "Cornelia M"
},
{
"family": "Ikram",
"given": "M Arfan"
},
{
"family": "Kaddurah-Daouk",
"given": "Rima"
},
{
"family": "Kastenmüller",
"given": "Gabi"
}
],
"container-title-short": "Nat Aging",
"volume": "6",
"issue": "7",
"page": "1452-1467",
"DOI": "10.1038/s43587-026-01149-4",
"PMID": "42342913",
"PMCID": "PMC13375541",
"ISSN": "2662-8465",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s43587-026-01149-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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