Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Abstract

Advances in technology have allowed for the study of metabolomics in the context of disease, enabling the discovery of new potential risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular biomedical importance as it is in direct contact with the brain and spinal cord. 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 compared to blood or urine studies. Here, we present a metabolome-wide association study (MWAS), an analysis using individual-level genetic and metabolomic data from two cohorts to impute metabolites into large samples with genome-wide association summary statistics. We conducted a metabolome-wide genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations, 6 of which were novel. Using these results, we then built prediction models for all available CSF metabolites and tested for associations with 27 neurological and psychiatric phenotypes in large cohorts, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the potential of MWAS to overcome the logistic challenges inherent in cerebrospinal fluid research to study the role of metabolomics in brain-related phenotypes and the feasibility of this framework for similar studies of omic data in scarce sample types.

Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

“…Consistent with previous mQTL studies(citations 40,41,64–67), the top SNP associated with levels of methyl-lysine, rs4539242, is in high linkage disequilibrium (LD) with both the missense mutation M461T (R 2 =1.0 in Europeans) and synonymous mutation F484F (R 2 =0.94 in Europeans), observations that represent an internal quality control for the present analysis (Manhattan plots and LocusZoom in Figure 2.B-C , respectively). Both mutations are themselves associated with levels of methyl-lysine (p=4.22×10 −13 and p=1.28×10 −44 , respectively; Supplementary Table 1 ).…”

“…In A , for the top GWA hits we generated box and whisker plots based on metabolite abundances as a function of allele variance across all genetic ancestries in this study. Consistently with previous mQTL studies, 40,41,65–67 polymorphisms in the exonic region coding for the enzyme PYROXD2 were associated with variance in the levels of methyl-lysine, an observation that represents a sort of internal quality control for the present analysis compared to the literature. Manhattan plots and LocusZoom are shown in panels B-C , respectively.…”

“…The study builds on previous mQTL reports in the context of cardiovascular diseases, asthma, or neurodegenerative diseases, including Alzheimer's disease. [38][39][40][41][42] Given the importance of RBC metabolism as a window into systems homeostasis, findings reported here could be relevant not just for transfusion medicine research, but also for diverse areas of physiology where RBC metabolism is impaired (e.g., exercise, 43 aging, 44 adaptation to high-altitude hypoxia, 45 pathological hypoxia upon hemorrhage, 46 COVID-19, 47,48 cardiovascular 49 and kidney diseases 50,51 ). Blood collection, sample processing and other aspects of the screening and recall phases of the RBC-Omics Study have been extensively described.…”

Metabolic Sex Dimorphism of the Brain at the Gene, Cell, and Tissue Level

“…Among methods to investigate brain metabolism, metabolomics remains a powerful tool to offer insights at the tissue and cellular level by measuring hundreds of metabolites. Metabolomics studies revealed the association of brain metabolism with physiological activities and disease conditions (118122), and when combined with genome-wide association studies can be particularly useful in predicting specific diseaseassociated metabolites (123). Fluxomics, the measurement of flow of metabolites between different pathways, better enables investigation into the cellular metabolic state.…”

CSF metabolites associated with biomarkers of Alzheimer’s disease pathology

“…The genome-wide significant SNPs (p < 5 × 10 −8 ) from a previous genome-wide meta-analysis of CSF metabolites [19] were extracted for each elastic net-selected metabolite (5863 SNPs for 52 metabolites). These SNPs (or the top 100 SNPs if there were more than 100 genome-wide significant SNPs for a metabolite) were used as instrumental variables (IV) for the metabolite in an MR analysis for each elastic net-selected metabolite-NTK biomarker association pair in the combined WRAP and Wisconsin ADRC cohort.…”