Meta analysis of glucose metabolism across Alzheimer's, Parkinson's and ALS Reveals emergence of adaptive brain glucometabolic responses and associated neurological functional profiles.
Meta-analysis of 130 FDG‑PET studies across Alzheimer’s, Parkinson’s, ALS and MS identifies common bidirectional (hypo- and hyper-) glucose metabolic alterations with distinct, disease-specific neuroanatomical signatures suggesting adaptive/remodeling bioenergetic responses.
What the AI sees
Meta-analysis of 130 FDG‑PET studies across Alzheimer’s, Parkinson’s, ALS and MS identifies common bidirectional (hypo- and hyper-) glucose metabolic alterations with distinct, disease-specific neuroanatomical signatures suggesting adaptive/remodeling bioenergetic responses.
Research significance
For Parkinson’s research, this supports prioritizing brain energy metabolism as a translational axis—FDG‑PET signatures could serve as biomarkers for patient stratification and for testing metabolic or anti‑inflammatory therapeutic strategies, though the study is correlative and not mechanistic.
Source abstract
IMPORTANCE: Glucose metabolic dysregulation in brain is a common feature of late-onset age-associated neurodegenerative disease (A 2 ND). Prior meta-analyses have identified disease-specific effects compared to healthy, unimpaired individuals. Yet, a unifying A 2 ND glucose dysregulation spatial signature remains undescribed. OBJECTIVE: To determine the common signature of dysregulated glucose metabolism on FDG-PET using activation likelihood estimation (ALE) meta-analyses across A 2 ND. DATA SOURCES: Searches were conducted using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane from inception through July 2025. The search terms included controlled vocabulary and keywords for four neurodegenerative diseases Parkinson Disease, Amyotrophic Lateral Sclerosis, Alzheimer Disease, and Multiple Sclerosis, Fluorodeoxyglucose F18, glucose, and positron-emission tomography (PET). STUDY SELECTION: Studies comparing adults with late-onset neurodegenerative diseases to non-diseased controls using FDG-PET to quantify brain glucose uptake and reporting whole-brain coordinate findings in either Talairach or Montreal Neurological Institute space were included. DATA EXTRACTION AND SYNTHESIS: Three researchers, assisted by an AI screening tool, screened 7275 potential titles and abstracts for inclusion. Full texts were then retrieved for potentially relevant articles and were evaluated by three researchers using prespecified inclusion/exclusion criteria. MAIN OUTCOMES AND MEASURES: Cluster peak and subpeak coordinates, cluster-wise t-or Z-values, and annotations indicating the disease of interest, whether the outcome was for hyper-(disease group > control) or hypometabolism (disease group < control), were extracted from included texts and analyzed using ALE. RESULTS: A total of 130 FDG-PET studies were included in the meta-analysis, with a combined sample of 5298 individuals with A 2 ND and 3499 controls. Meta-analyses revealed dysregulated glucose metabolism as a unifying feature across A 2 ND which included both hypo-and hypermetabolic patterns. Neuroanatomical metabolic pattern was unique and disease specific. Each A 2 ND metabolic phenotype was associated with unique and complex patterns of neurological functionalities. CONCLUSIONS AND RELEVANCE: These data demonstrate dysregulated glucose metabolism as a common A 2 ND feature, suggesting responsive remodeling of neural bioenergetics. While hypometabolism is a common research focus, due to functional relevance, hypermetabolism may reflect a compensatory, maladaptive, or neuroinflammatory signal, that requires focused investigation. A 2 ND prevention and treatment efficacy may depend on addressing bidirectional metabolic dysregulation in addition to disease-specific drivers of pathology.