Current Alzheimer research · 2024
Associations Between Metabolomics Findings and Brain Hypometabolism in Mild Cognitive Impairment and Alzheimer's Disease.
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Mir Moein, Khosravani Parinaz, Ramezannezhad Elham, Saadabad Fatemeh Pourali, Falahati Marjan, Ghanbarian Mahsa, Saberian Parsa, Sadeghi Mohammad, Niknam Nafise, Ghejelou Sanaz Eskandari, Jafari Masoumeh, Gulisashvili David, Mayeli Mahsa
Аннотация
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease 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 disturbances in biofluids, which may be advantageous for early detection of some AD-related 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 study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.
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.
METHODS: The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.
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.