FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2920798634> ?p ?o ?g. }

• W2920798634 endingPage "21" @default.
• W2920798634 startingPage "12" @default.
• W2920798634 abstract "Background Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis. Methods To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites. Results 12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites. Conclusion Our analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment." @default.
• W2920798634 created "2019-03-22" @default.
• W2920798634 creator A5013089268 @default.
• W2920798634 creator A5015648982 @default.
• W2920798634 creator A5030006277 @default.
• W2920798634 creator A5035845108 @default.
• W2920798634 creator A5041230435 @default.
• W2920798634 creator A5041446600 @default.
• W2920798634 creator A5064296151 @default.
• W2920798634 creator A5078899376 @default.
• W2920798634 date "2019-06-01" @default.
• W2920798634 modified "2023-10-18" @default.
• W2920798634 title "Metabolome-based signature of disease pathology in MS" @default.
• W2920798634 cites W1572318341 @default.
• W2920798634 cites W1768577517 @default.
• W2920798634 cites W1941562583 @default.
• W2920798634 cites W1961658070 @default.
• W2920798634 cites W1967362739 @default.
• W2920798634 cites W1968681109 @default.
• W2920798634 cites W1984533345 @default.
• W2920798634 cites W1990235699 @default.
• W2920798634 cites W1990348466 @default.
• W2920798634 cites W1991493014 @default.
• W2920798634 cites W1998115145 @default.
• W2920798634 cites W2011269657 @default.
• W2920798634 cites W2015292648 @default.
• W2920798634 cites W2027145809 @default.
• W2920798634 cites W2044863747 @default.
• W2920798634 cites W2047200592 @default.
• W2920798634 cites W2051274614 @default.
• W2920798634 cites W2058409970 @default.
• W2920798634 cites W2062364627 @default.
• W2920798634 cites W2076580441 @default.
• W2920798634 cites W2080859771 @default.
• W2920798634 cites W2084332644 @default.
• W2920798634 cites W2088626603 @default.
• W2920798634 cites W2092298118 @default.
• W2920798634 cites W2106419962 @default.
• W2920798634 cites W2107665951 @default.
• W2920798634 cites W2109879418 @default.
• W2920798634 cites W2112390338 @default.
• W2920798634 cites W2113679292 @default.
• W2920798634 cites W2115801316 @default.
• W2920798634 cites W2122963571 @default.
• W2920798634 cites W2133727193 @default.
• W2920798634 cites W2137421386 @default.
• W2920798634 cites W2148778080 @default.
• W2920798634 cites W2153118028 @default.
• W2920798634 cites W2160427694 @default.
• W2920798634 cites W2510100774 @default.
• W2920798634 cites W2518684363 @default.
• W2920798634 cites W2593219710 @default.
• W2920798634 cites W2606675097 @default.
• W2920798634 cites W2624866309 @default.
• W2920798634 cites W2626907267 @default.
• W2920798634 cites W2630986599 @default.
• W2920798634 cites W2734745440 @default.
• W2920798634 cites W2755864943 @default.
• W2920798634 cites W2764306416 @default.
• W2920798634 cites W2766371671 @default.
• W2920798634 cites W2783943140 @default.
• W2920798634 cites W2785916925 @default.
• W2920798634 cites W2790748698 @default.
• W2920798634 cites W2793503965 @default.
• W2920798634 cites W2795635227 @default.
• W2920798634 cites W2803490859 @default.
• W2920798634 cites W2803664483 @default.
• W2920798634 cites W4256316091 @default.
• W2920798634 cites W843693155 @default.
• W2920798634 doi "https://doi.org/10.1016/j.msard.2019.03.006" @default.
• W2920798634 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6548586" @default.
• W2920798634 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30877925" @default.
• W2920798634 hasPublicationYear "2019" @default.
• W2920798634 type Work @default.
• W2920798634 sameAs 2920798634 @default.
• W2920798634 citedByCount "39" @default.
• W2920798634 countsByYear W29207986342013 @default.
• W2920798634 countsByYear W29207986342019 @default.
• W2920798634 countsByYear W29207986342020 @default.
• W2920798634 countsByYear W29207986342021 @default.
• W2920798634 countsByYear W29207986342022 @default.
• W2920798634 countsByYear W29207986342023 @default.
• W2920798634 crossrefType "journal-article" @default.
• W2920798634 hasAuthorship W2920798634A5013089268 @default.
• W2920798634 hasAuthorship W2920798634A5015648982 @default.
• W2920798634 hasAuthorship W2920798634A5030006277 @default.
• W2920798634 hasAuthorship W2920798634A5035845108 @default.
• W2920798634 hasAuthorship W2920798634A5041230435 @default.
• W2920798634 hasAuthorship W2920798634A5041446600 @default.
• W2920798634 hasAuthorship W2920798634A5064296151 @default.
• W2920798634 hasAuthorship W2920798634A5078899376 @default.
• W2920798634 hasBestOaLocation W29207986341 @default.
• W2920798634 hasConcept C104317684 @default.
• W2920798634 hasConcept C124535831 @default.
• W2920798634 hasConcept C126322002 @default.
• W2920798634 hasConcept C135870905 @default.
• W2920798634 hasConcept C142724271 @default.
• W2920798634 hasConcept C203014093 @default.

• next