Association Between Metabolomics Findings and Brain Hypometabolism in Mild Alzheimer's Disease.

Abstract

Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect metabolic disturbances in biofluids, which may be advantageous for early detection of some ADrelated changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

Methods: The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal cohort study with three assessment time points to investigate the predictive ability of baseline metabolomic data for modeling longitudinal fluorodeoxyglucose-positron emission tomography (FDG-PET) trajectory changes in AD patients. A total number of 44 participants with AD were included. The cognitive abilities of participants were evaluated using the Alzheimer's Disease Assessment Scale (ADAS) and the Mini-Mental State Examination (MMSE), while the overall severity of dementia was measured by the Clinical Dementia Rating-Sum of Boxes (CDR-SB). We employed the ADNI's FDG MetaROIs (Meta Regions of Interest) dataset to identify AD-associated hypometabolism in the brain. These MetaROIs were selected based on areas frequently mentioned in FDG-PET studies of AD and MCI subjects.

Results: Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

Conclusion: This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.