Year: 2019
Type: article
Study design: Observational Study
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: < 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.
Source: Neurology
Authors: Kjetil Bjornevik, Zhongli Zhang, Éilis J. O'Reilly, James D. Berry, Clary B. Clish, Amy Deik, Sarah Jeanfavre, Ikuko Kato, Rachel S. Kelly, Laurence N. Kolonel, Liming Liang, Loic Le Marchand, Marjorie L. McCullough, Sabrina Paganoni, Kerry A. Pierce, Michael A. Schwarzschild, Aladdin H. Shadyab, Jean Wactawski-Wende, Dong D. Wang, Ying Wang, JoAnn E. Manson, Alberto Ascherio
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funders: U.S. Department of Health and Human Services, American Cancer Society, Ministerio de Economía y Competitividad, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke
Awards: G1050918383, G11984311, G1471908156, G1848253584, G1851278524, G2209176514, G3062349281, G3069585042, G3643850837, G3900787601, G4102267556, G4346707325, G4546180501, Mediterranean Diet, Polyphenol-Rich Foods, Gut Microbiota and Type 2 Diabetes, Understanding Ethnic Differences in Cancer: The Multiethnic Cohort Study, Prospective study of biomarkers and risk factors for ALS incidence and progressio, G6147499578, Multi-Omic Endotyping of Asthma, G6694253514, G6995773045, G700120080, G7085675193, G7332203415, Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health Study, G757750085, Cancer Epidemiology Cohort in Male Health Professionals, G7978929429, G8606882599, G9000019617, G9000028950, G9000084571, G9000122174, Understanding Population Differences in Cancer: The MEC Study, G915959914
OpenAlex ID: W2927472751