Profiling the metabolic landscape of AD. (December 2021)

• Record Type: Journal Article
• Authors: Novotny, Brenna C; Fernandez, Victoria; Budde, John P.; Bergmann, Kristy; Morris, John C.; Bateman, Randall J.; Karch, Celeste M.; Benitez, Bruno A.; Cruchaga, Carlos; Harari, Oscar
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.050086
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Metabolic dysfunction, including perturbations in lipid, neurotransmitter, and polyamine metabolism, is an early indicator of cognitive impairment and Alzheimer disease (AD) risk. Further investigation is needed to elucidate the role of genetic heterogeneity in these metabolic disturbances. Here, we interrogate metabolomic signatures in the brains of carriers of pathological mutations in APP, PSEN1, and PSEN2 (Autosomal Dominant AD; ADAD), risk variants in TREM2, non‐carrier sporadic AD cases (sAD), individuals with neuropathology but without clinical symptoms (Presymptomatic), and neuropathology‐free controls (CO). Method: Metabolomic data from parietal brain tissue of donors to the Knight Alzheimer Disease Research Center and the Dominantly Inherited Alzheimer Network (ADAD, n=25; TREM2, n=21; sAD, n=305; Presymptomatic, n=15; CO, n=27) were generated using the Metabolon global metabolomics platform. A total of 627 metabolites passed our QC process. Differential abundance between AD strata and controls was tested using linear regression corrected for sex, age, and post‐mortem interval. Age was excluded from ADAD models. Benjamini‐Hochberg multiple testing correction was applied, and pathway analysis was performed with MetaboAnalyst and IMPaLA. Result: In total, we identified 138 metabolites associated with distinct genetic strata (FDR q‐value<0.05). For sAD, these included tryptophan betaine (β=‐0.55) and N‐acetylputrescine (β=‐0.14). Metabolites associated with both sAD and ADAD were ergothioneine (β=‐0.22 and ‐0.26 respectively) and serotonin (β=‐0.34 and ‐0.57). TREM2 and ADAD showed association with α‐tocopherol (β=‐0.12 and ‐0.12). β‐citrylglutamate abundance decreased in sAD, ADAD, and TREM2 versus controls (β=‐0.14; ‐0.22; and ‐0.29). Pathways identified included glutamate, vitamin, and antioxidant metabolism. A 16‐metabolite subset showed consistent direction of effect among the genetic strata with the magnitude of effect of ADAD greater than that of TREM2, in turn greater than sAD. A representation of these (eigengene) is associated with disease duration in sAD (p=5.65x10 ‐03 ), possibly driven by tau accumulation. Hierarchical clustering identified 41 "early stage" sAD individuals with Braak tau stage similar to Presymptomatic (p=0.35), but lower than other sAD individuals (β=‐0.56, p=3.09x10 ‐04 ) (Figure 1). Conclusion: Our findings suggest distinct and characteristic metabolic perturbations in ADAD and TREM2 brains. Investigation of these differentially abundant metabolites may lead to greater insight into the metabolic etiology of AD and its impact on clinical presentation.