ArticleCommunications biology2021
Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 2 of them syntheses that pooled it.
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Who cites it
115 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Mendelian Randomization to Unveil Metabolic Markers inJournal of Korean medical science · 2026Pooled it
- Exploring the causal effect of genetically predicted blood and cerebrospinal fluid metabolites and metabolic pathways on intracranial aneurysm.The Journal of international medical research · 2026Pooled it
- Brain viral reservoir seeding and neurological metabolic dysregulation in early-life immunodeficiency virus infection.Brain pathology (Zurich, Switzerland) · 2026Article
- Exploratory bidirectional Mendelian randomisation analysis of cerebrospinal fluid metabolites associated with cerebral palsy and epilepsy.BMJ paediatrics open · 2026Article
- Cerebrospinal Fluid Metabolites Mediate Immune Cell Effects on Chronic Pain.Molecular neurobiology · 2026Article
- Risk of Erythritol-Associated Ischemic Stroke: Integrated Genetic, Transcriptomic, and Machine Learning Evidence.International journal of molecular sciences · 2026Article
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- Roles of cerebrospinal fluid metabolites in neuromyelitis optica spectrum disorder: Insights from Mendelian randomization.Medicine · 2026Article
- Impact of IL-6 receptor inhibitors on cerebrospinal fluid metabolites in type 2 diabetes patients.Medicine · 2026Article
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- Multi-omics pleiotropic association analyses reveal functionally relevant genes and druggable pathways for ovarian aging.Genome biology · 2026Article
- Association between cerebrospinal fluid metabolites and ischemic stroke: a Mendelian randomization analysis.Journal of thrombosis and thrombolysis · 2026Article
- Association between cerebrospinal fluid metabolites and ischemic stroke: a Mendelian randomization analysis.Journal of thrombosis and thrombolysis · 2026Article
- Untargeted and Targeted Cerebrospinal Fluid Neurometabolomics via Chromatography-Mass Spectrometry-Based Methods.Molecules (Basel, Switzerland) · 2026Review
- Dynamic Triangulation of Resting-State Networks and CSF Metabolites Reveals Bidirectional Causal Pathways in Multiple Sclerosis: A Mendelian Randomization Analysis.Journal of molecular neuroscience : MN · 2026Article
- Discovery of urinary metabolite biomarkers of psychiatric disorders using two-sample Mendelian randomization.BMC psychiatry · 2026Article
- Causal Associations of Cerebrospinal Fluid and Circulating Metabolites With Malignant Brain Neoplasms: A Mendelian Randomization Analysis.Brain and behavior · 2026Article
- Cerebrospinal fluid and plasma metabolites in Parkinson's disease: a Mendelian randomization study.Scientific reports · 2026Article
- Cell-Type-Specific WTAP and ALKBH5-Mediated mCNS neuroscience & therapeutics · 2026Article
55 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.
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