Altered Brain Energy Metabolism Related to Astrocytes in Alzheimer's Disease
Bibliographic record
Abstract
OBJECTIVE: Increasing evidence suggests that reactive astrocytes are associated with Alzheimer's disease (AD). However, its underlying pathogenesis remains unknown. Given the role of astrocytes in energy metabolism, reactive astrocytes may contribute to altered brain energy metabolism. Astrocytes are primarily considered glycolytic cells, suggesting a preference for lactate production. This study aimed to examine alterations in astrocytic activities and their association with brain lactate levels in AD. METHODS: F]florzolotau, respectively. Myo-inositol, an astroglial marker, and lactate in the posterior cingulate cortex were quantified by magnetic resonance spectroscopy. These magnetic resonance spectroscopy metabolites were compared with plasma biomarkers, including glial fibrillary acidic protein as another astrocytic marker, and amyloid and tau positron emission tomography. RESULTS: Myo-inositol and lactate levels were higher in AD patients than in cognitively unimpaired participants (p < 0.05). Myo-inositol levels correlated with lactate levels (r = 0.272, p = 0.047). Myo-inositol and lactate levels were positively associated with the Clinical Dementia Rating sum-of-boxes scores (p < 0.05). Significant correlations were noted between myo-inositol levels and plasma glial fibrillary acidic protein, tau phosphorylated at threonine 181 levels, and amyloid and tau positron emission tomography accumulation in the posterior cingulate cortex (p < 0.05). INTERPRETATION: We found high myo-inositol levels accompanied by increased lactate levels in the posterior cingulate cortex in AD patients, indicating a link between reactive astrocytes and altered brain energy metabolism. Myo-inositol and plasma glial fibrillary acidic protein may reflect similar astrocytic changes as biomarkers of AD. ANN NEUROL 2023.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".