Alzheimer’s disease (AD) has proven remarkably refractory to proposed and approved therapies, none of which has strongly demonstrated the capability to halt, sustainedly decelerate, or reverse cognitive decline in emerging disease. Although much translational research in AD has targeted amyloid plaque and tau proteopathies, burgeoning metabolomics technologies in the past decade have enabled the large-scale survey of the peripheral plasma metabolome in these vulnerably aging individuals. This is advantageous because substantial evidence exists that AD can be described as a complex biological system of peripherally evident, metabolic dyshomeostases in the process of abnormal cognitive aging. It is substantially less clear, however, how personalized-medicine-relevant individual differences in AD etiology and cognitive staging map (as jeopardized CNS-peripheral axes) onto this diversity of interconnected and embedded metabolic networks.
To explore this question, sporadic late-onset AD (LOAD) participants at the preclinical stage of disease were profiled using genome-scale metabolic network modeling over features of the plasma metabolome altered relative to controls. This revealed a dysmetabolic signature (including lipids) which significantly overlapped with that of an independent cohort of preclinical LOAD participants. Further experiments in Down syndrome AD (DS-AD) suggested a similar alteration of lipids in manifest disease, but also central carbon metabolites vital to cellular bioenergetic homeostasis. To more closely examine this peripheral dysmetabolic heterogeneity in more comparable cognitive terms, Preclinical LOAD and preclinical familial, autosomal dominant AD (ADAD) plasma were compared and found to demonstrate modest, significant overlap. To assess the specificity of this finding, preclinical plasma was also compared to that of those with objective cognitive deficits across both LOAD and ADAD. This again demonstrated significant, modest pathway overlap, and similar metabolic pathways emerged from correlational analyses between metabolomic features and estimated mutation carrier years until diagnosis.
Because of this highly complex degree of residually non-shared, semantically dense information in the plasma metabolome across individual, clinical differences in AD, these biochemicals were mapped to inferred metabolic topics from de novo metabolic network modeling using natural language processing (NLP) approaches. Through these same topics, pairwise AD phenotypic comparisons were thus proportionally associated with clusters of biochemicals and enzymes. The fitted, metabolic Topic 4 intriguingly implicated hexosamine/aminoglycan metabolism, which was particularly pronounced in comparisons involving “supernormal,” older adults in the highest percentiles of resilient cognitive aging. In continuing to explore these clinical phenotypic- peripheral metabolic mappings in the peripheral metabolome, these efforts will afford increasingly precise, semantic level insights into the biochemical diversity of AD pathobiology. In addition to informing further, targeted mechanistic research, this will also translationally nominate contextually rich, empirically ascertained biomarker and therapeutic target candidates.
