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Objective: To assess whether prediagnostic levels of plasma branched-chain amino acids (BCAAs) are associated with amyotrophic lateral sclerosis (ALS) risk., Methods: We included participants from 5 large cohort studies-The Nurses' Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition, the Multiethnic Cohort Study, and the Women's Health Initiative-and identified 275 individuals who developed ALS during follow-up. Two controls were randomly selected for each case, matched on cohort, age, sex, fasting status, and time of blood draw. We measured metabolites using liquid chromatography-mass spectrometry and used conditional logistic regression to estimate rate ratios (RRs) and 95% confidence intervals (CIs) for the association of individual BCAAs with ALS risk., Results: None of the 3 BCAAs was associated with a higher ALS risk. The risk estimates were similar for leucine (RR top vs bottom quartile: 0.87, 95% CI 0.57-1.33), isoleucine (RR top vs bottom quartile: 0.81, 95% CI 0.52-1.24), and valine (RR top vs bottom quartile: 0.80, 95% CI 0.52-1.23) in a multivariable analysis adjusted for body mass index, smoking, level of education, and physical activity. The estimates did not vary significantly by sex, fasting status, or time interval between blood draw and disease onset., Conclusion: The results from this study do not support the hypothesis that BCAAs are risk factors for ALS., (© 2018 American Academy of Neurology.)

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., (© 2019 American Academy of Neurology.)