Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis

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dc.contributor.author Bjornevik, Kjetil
dc.contributor.author Zhang, Zhongli
dc.contributor.author O'Reilly, Éilis J.
dc.contributor.author Berry, James D.
dc.contributor.author Clish, Clary B.
dc.contributor.author Deik, Amy
dc.contributor.author Jeanfavre, Sarah
dc.contributor.author Kato, Ikuko
dc.contributor.author Kelly, Rachel S.
dc.contributor.author Kolonel, Laurence N.
dc.contributor.author Liang, Liming
dc.contributor.author Le Marchand, Loic
dc.contributor.author McCullough, Marjorie L.
dc.contributor.author Paganoni, Sabrina
dc.contributor.author Pierce, Kerry A.
dc.contributor.author Schwarzschild, Michael A.
dc.contributor.author Shadyab, Aladdin H.
dc.contributor.author Wactawski-Wende, Jean
dc.contributor.author Wang, Dong D.
dc.contributor.author Wang, Ying
dc.contributor.author Manson, JoAnn E.
dc.contributor.author Ascherio, Alberto
dc.date.accessioned 2019-05-28T11:03:07Z
dc.date.available 2019-05-28T11:03:07Z
dc.date.issued 2019-03-29
dc.identifier.citation Bjornevik, K., Zhang, Z., O'Reilly, É. J., Berry, J. D., Clish, C. B., Deik, A., Jeanfavre, S., Kato, I., Kelly, R. S., Kolonel, L. N., Liang, L., Le Marchand, L., McCullough, M. L., Paganoni, S., Pierce, K. A., Schwarzschild, M. A., Shadyab, A. H., Wactawski-Wende, J., Wang, D. D., Wang, Y., Manson, J. E. and Ascherio, A. (2019) 'Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis'. Neurology, 92(18), pp. 2089-2100. doi: 10.1212/WNL.0000000000007401 en
dc.identifier.volume 92 en
dc.identifier.issued 18 en
dc.identifier.startpage 2089 en
dc.identifier.endpage 2100 en
dc.identifier.issn 0028-3878
dc.identifier.uri http://hdl.handle.net/10468/7988
dc.identifier.doi 10.1212/WNL.0000000000007401 en
dc.description.abstract Objective: To identify prediagnostic plasma metabolomic biomarkers associated with amyotrophic lateral sclerosis (ALS). Methods: We conducted a global metabolomic study using a nested case-control study design within 5 prospective cohorts and identified 275 individuals who developed ALS during follow-up. We profiled plasma metabolites using liquid chromatography–mass spectrometry and identified 404 known metabolites. We used conditional logistic regression to evaluate the associations between metabolites and ALS risk. Further, we used machine learning analyses to determine whether the prediagnostic metabolomic profile could discriminate ALS cases from controls. Results: A total of 31 out of 404 identified metabolites were associated with ALS risk (p < 0.05). We observed inverse associations (n = 27) with plasma levels of diacylglycerides and triacylglycerides, urate, purine nucleosides, and some organic acids and derivatives, while we found positive associations for a cholesteryl ester, 2 phosphatidylcholines, and a sphingomyelin. The number of significant associations increased to 67 (63 inverse) in analyses restricted to cases with blood samples collected within 5 years of onset. None of these associations remained significant after multiple comparison adjustment. Further, we were not able to reliably distinguish individuals who became cases from controls based on their metabolomic profile using partial least squares discriminant analysis, elastic net regression, random forest, support vector machine, or weighted correlation network analyses. Conclusions: Although the metabolomic profile in blood samples collected years before ALS diagnosis did not reliably separate presymptomatic ALS cases from controls, our results suggest that ALS is preceded by a broad, but poorly defined, metabolic dysregulation years before the disease onset. en
dc.description.sponsorship National Institute of Neurologic Disorders and Stroke (R01 NS045893); National Institutes of Health (Grants UM1 CA186107; R01 CA49449; UM1 CA167552; U01 CA164973); U.S. Department of Health and Human Services (contracts HHSN268201600018C; HHSN268201600001C; HHSN268201600002C; HHSN268201600003C; HHSN268201600004C) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher American Academy of Neurology en
dc.relation.uri http://n.neurology.org/content/92/18/e2089.abstract
dc.rights © 2019, American Academy of Neurology. All rights reserved. en
dc.subject Plasma en
dc.subject Metabolomic biomarker en
dc.subject Amyotrophic lateral sclerosis en
dc.subject ALS en
dc.subject Plasma metabolites en
dc.subject Metabolic dysregulation en
dc.title Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Eilis O'Reilly, Public Health, University College Cork, Cork, Ireland. +353-21-490-3000 Email: eilis.oreilly@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher. en
dc.check.date 2020-03-29
dc.date.updated 2019-05-28T10:45:23Z
dc.description.version Published Version en
dc.internal.rssid 486903176
dc.contributor.funder National Institute of Neurological Disorders and Stroke en
dc.contributor.funder National Institutes of Health en
dc.contributor.funder U.S. Department of Health and Human Services en
dc.contributor.funder American Cancer Society en
dc.contributor.funder National Heart, Lung, and Blood Institute en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Neurology en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress eilis.oreilly@ucc.ie en
dc.identifier.eissn 1526-632X


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