<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Goutman SA</submitter><funding>National ALS Registry/CDC/ATSDR</funding><funding>ATSDR CDC HHS</funding><funding>NCATS NIH HHS</funding><funding>NCI</funding><funding>NIEHS NIH HHS</funding><funding>NIEHS</funding><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><pagination>4425-4439</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9762943</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>145(12)</volume><pubmed_abstract>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P &lt; 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.</pubmed_abstract><journal>Brain : a journal of neurology</journal><pubmed_title>Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.</pubmed_title><pmcid>PMC9762943</pmcid><funding_grant_id>U01 CA235487</funding_grant_id><funding_grant_id>K23 ES027221</funding_grant_id><funding_grant_id>200-2013-56856</funding_grant_id><funding_grant_id>UL1 TR002240</funding_grant_id><funding_grant_id>K23ES027221</funding_grant_id><funding_grant_id>UL1TR002240</funding_grant_id><funding_grant_id>R01 ES030049</funding_grant_id><funding_grant_id>R01ES030049</funding_grant_id><funding_grant_id>1U01CA235487</funding_grant_id><funding_grant_id>1R01TS000289</funding_grant_id><funding_grant_id>R01 TS000289</funding_grant_id><pubmed_authors>Habra H</pubmed_authors><pubmed_authors>Savelieff MG</pubmed_authors><pubmed_authors>Hur J</pubmed_authors><pubmed_authors>Guo K</pubmed_authors><pubmed_authors>Feldman EL</pubmed_authors><pubmed_authors>Patterson A</pubmed_authors><pubmed_authors>Goutman SA</pubmed_authors><pubmed_authors>Karnovsky A</pubmed_authors><pubmed_authors>Sakowski SA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.</name><description>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P &lt; 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Dec</publication><modification>2026-06-02T16:19:49.268Z</modification><creation>2025-04-07T01:56:26.419Z</creation></dates><accession>S-EPMC9762943</accession><cross_references><pubmed>35088843</pubmed><doi>10.1093/brain/awac025</doi></cross_references></HashMap>