Metabolic network failures in Alzheimer's disease: A biochemical road map.
Toledo JB., Arnold M., Kastenmüller G., Chang R., Baillie RA., Han X., Thambisetty M., Tenenbaum JD., Suhre K., Thompson JW., John-Williams LS., MahmoudianDehkordi S., Rotroff DM., Jack JR., Motsinger-Reif A., Risacher SL., Blach C., Lucas JE., Massaro T., Louie G., Zhu H., Dallmann G., Klavins K., Koal T., Kim S., Nho K., Shen L., Casanova R., Varma S., Legido-Quigley C., Moseley MA., Zhu K., Henrion MYR., van der Lee SJ., Harms AC., Demirkan A., Hankemeier T., van Duijn CM., Trojanowski JQ., Shaw LM., Saykin AJ., Weiner MW., Doraiswamy PM., Kaddurah-Daouk R., Alzheimer's Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium None.
INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.