Altered brain energy metabolism related to astrocytes in Alzheimer’s disease
Bibliographic record
Abstract
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 energy metabolism. It is hypothesized that lactate, a glucose metabolite, is produced in astrocytes and subsequently shuttled to neurons as an energy substrate. This study aimed to examine alterations in brain lactate levels and their association with astrocytic activities in AD. Methods 30 AD and 30 cognitively unimpaired (CU) subjects were enrolled. For AD subjects, amyloid and tau depositions were confirmed by positron emission tomography using [ 11 C]PiB and [ 18 F]florzolotau, respectively. Lactate and myo-inositol, an astroglial marker, in the posterior cingulate cortex (PCC) were quantified by magnetic resonance spectroscopy (MRS). These MRS metabolites were compared with plasma biomarkers, including glial fibrillary acidic protein (GFAP) as another astrocytic marker. Results Lactate and myo-inositol levels were higher in AD than in CU ( p < 0.05). Lactate levels correlated with myo-inositol levels ( r = 0.272, p = 0.047). Lactate and myo-inositol 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 GFAP and tau phosphorylated at threonine 181 levels ( p < 0.05). Interpretation We found high lactate levels accompanied by an increased astrocytic marker in the PCC in AD. Thus, impaired lactate shuttle of reactive astrocytes may disrupt energy regulation, resulting in surplus lactate levels. Myo-inositol and plasma GFAP may reflect similar astrocytic changes.
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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".