Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis

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dc.check.date2020-03-29
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorBjornevik, Kjetil
dc.contributor.authorZhang, Zhongli
dc.contributor.authorO'Reilly, Éilis J.
dc.contributor.authorBerry, James D.
dc.contributor.authorClish, Clary B.
dc.contributor.authorDeik, Amy
dc.contributor.authorJeanfavre, Sarah
dc.contributor.authorKato, Ikuko
dc.contributor.authorKelly, Rachel S.
dc.contributor.authorKolonel, Laurence N.
dc.contributor.authorLiang, Liming
dc.contributor.authorLe Marchand, Loic
dc.contributor.authorMcCullough, Marjorie L.
dc.contributor.authorPaganoni, Sabrina
dc.contributor.authorPierce, Kerry A.
dc.contributor.authorSchwarzschild, Michael A.
dc.contributor.authorShadyab, Aladdin H.
dc.contributor.authorWactawski-Wende, Jean
dc.contributor.authorWang, Dong D.
dc.contributor.authorWang, Ying
dc.contributor.authorManson, JoAnn E.
dc.contributor.authorAscherio, Alberto
dc.contributor.funderNational Institute of Neurological Disorders and Strokeen
dc.contributor.funderNational Institutes of Healthen
dc.contributor.funderU.S. Department of Health and Human Servicesen
dc.contributor.funderAmerican Cancer Societyen
dc.contributor.funderNational Heart, Lung, and Blood Instituteen
dc.date.accessioned2019-05-28T11:03:07Z
dc.date.available2019-05-28T11:03:07Z
dc.date.issued2019-03-29
dc.date.updated2019-05-28T10:45:23Z
dc.description.abstractObjective: 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.sponsorshipNational 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.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBjornevik, 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.0000000000007401en
dc.identifier.doi10.1212/WNL.0000000000007401en
dc.identifier.eissn1526-632X
dc.identifier.endpage2100en
dc.identifier.issn0028-3878
dc.identifier.issued18en
dc.identifier.journaltitleNeurologyen
dc.identifier.startpage2089en
dc.identifier.urihttps://hdl.handle.net/10468/7988
dc.identifier.volume92en
dc.language.isoenen
dc.publisherAmerican Academy of Neurologyen
dc.relation.urihttp://n.neurology.org/content/92/18/e2089.abstract
dc.rights© 2019, American Academy of Neurology. All rights reserved.en
dc.subjectPlasmaen
dc.subjectMetabolomic biomarkeren
dc.subjectAmyotrophic lateral sclerosisen
dc.subjectALSen
dc.subjectPlasma metabolitesen
dc.subjectMetabolic dysregulationen
dc.titlePrediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosisen
dc.typeArticle (peer-reviewed)en
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