Dysfunctional Astrocyte Metabolism: A Driver of Imbalanced Excitatory/Inhibitory Tone and Support for Therapeutic Intervention Targets
Bibliographic record
Abstract
A balanced excitatory/inhibitory (E/I) tone is crucial for proper brain function, and disruptions can lead to neurological disorders. This review explores the role of astrocytes in maintaining a balanced E/I tone in the brain, which is crucial for proper functioning. It highlights the potential for dysfunctional astrocyte metabolism to disrupt E/I balance, leading to neuronal dysfunction and potentially causing neurological disease pathogenesis. The review focuses on glucose, lactate shuttling, and glutamate metabolism. This review synthesizes findings from in vitro, in vivo, and human studies examining the interplay between astrocyte metabolism, neuronal activity, and E/I balance. Literature searches were conducted using keywords including “astrocyte metabolism”, “excitatory/inhibitory balance”, “glutamate”, “lactate shuttle”, “neurometabolic coupling”, and “neurological disorders” in databases such as PubMed and Web of Science. Disruptions in astrocyte glucose uptake or glycolysis can impair lactate production, reducing neuronal energy supply and affecting neuronal excitability. Impaired glutamate uptake and conversion to glutamine within astrocytes leads to elevated extracellular glutamate, promoting excitotoxicity. Altered glycogen metabolism and other metabolic impairments within astrocytes can also affect neuronal health and contribute to imbalances between excitation and inhibition. Dysfunctional astrocyte metabolism represents a significant contributor to E/I imbalance in the brain. Understanding the specific metabolic vulnerabilities of astrocytes and their impact on neuronal function provides potential therapeutic targets for neurological disorders characterized by E/I dysregulation. Targeting astrocyte metabolism may offer a novel approach to restoring E/I balance and improving neurological outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".