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Introduction: Altered lipid metabolism is implicated in Alzheimer's disease (AD), but the mechanisms remain obscure. Aging‐related declines in circulating plasmalogens containing omega‐3 fatty acids may increase AD risk by reducing plasmalogen availability. Methods: We measured four ethanolamine plasmalogens (PlsEtns) and four closely related phosphatidylethanolamines (PtdEtns) from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 1547 serum) and University of Pennsylvania (UPenn; n = 112 plasma) cohorts, and derived indices reflecting PlsEtn and PtdEtn metabolism: PL‐PX (PlsEtns), PL/PE (PlsEtn/PtdEtn ratios), and PBV (plasmalogen biosynthesis value; a composite index). We tested associations with baseline diagnosis, cognition, and cerebrospinal fluid (CSF) AD biomarkers. Results: Results revealed statistically significant negative relationships in ADNI between AD versus CN with PL‐PX (P = 0.007) and PBV (P = 0.005), late mild cognitive impairment (LMCI) versus cognitively normal (CN) with PL‐PX (P = 2.89 × 10−5) and PBV (P = 1.99 × 10−4), and AD versus LMCI with PL/PE (P = 1.85 × 10−4). In the UPenn cohort, AD versus CN diagnosis associated negatively with PL/PE (P = 0.0191) and PBV (P = 0.0296). In ADNI, cognition was negatively associated with plasmalogen indices, including Alzheimer's Disease Assessment Scale 13‐item cognitive subscale (ADAS‐Cog13; PL‐PX: P = 3.24 × 10−6; PBV: P = 6.92 × 10−5) and Mini‐Mental State Examination (MMSE; PL‐PX: P = 1.28 × 10−9; PBV: P = 6.50 × 10−9). In the UPenn cohort, there was a trend toward a similar relationship of MMSE with PL/PE (P = 0.0949). In ADNI, CSF total‐tau was negatively associated with PL‐PX (P = 5.55 × 10−6) and PBV (P = 7.77 × 10−6). Additionally, CSF t‐tau/Aβ1‐42 ratio was negatively associated with these same indices (PL‐PX, P = 2.73 × 10−6; PBV, P = 4.39 × 10−6). In the UPenn cohort, PL/PE was negatively associated with CSF total‐tau (P = 0.031) and t‐tau/Aβ1‐42 (P = 0.021). CSF Aβ1‐42 was not significantly associated with any of these indices in either cohort. Discussion: These data extend previous studies by showing an association of decreased plasmalogen indices with AD, mild cognitive impairment (MCI), cognition, and CSF tau. Future studies are needed to better define mechanistic relationships, and to test the effects of interventions designed to replete serum plasmalogens. [ABSTRACT FROM AUTHOR]

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 Aβ 1–42 , 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. [ABSTRACT FROM AUTHOR]