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Neurology. 2019 Apr 30;92(18):e2089-e2100. doi: 10.1212/WNL.0000000000007401. Epub 2019 Mar 29.

Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis.

Author information

1
From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA. kbjorne@hsph.harvard.edu.
2
From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

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.

PMID:
30926684
PMCID:
PMC6512888
[Available on 2020-04-30]
DOI:
10.1212/WNL.0000000000007401

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