Fueling neurodegeneration: metabolic insights into microglia functions
Bibliographic record
Abstract
Microglia, the resident immune cells of the central nervous system, emerge in the brain during early embryonic development and persist throughout life. They play essential roles in brain homeostasis, and their dysfunction contributes to neuroinflammation and the progression of neurodegenerative diseases. Recent studies have uncovered an intricate relationship between microglia functions and metabolic processes, offering fresh perspectives on disease mechanisms and possible treatments. Despite these advancements, there are still significant gaps in our understanding of how metabolic dysregulation affects microglial phenotypes in these disorders. This review aims to address these gaps, laying the groundwork for future research on the topic. We specifically examine how metabolic shifts in microglia, such as the transition from oxidative phosphorylation and mitochondrial metabolism to heightened glycolysis during proinflammatory states, impact the disease progression in Alzheimer's disease, multiple sclerosis, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. Additionally, we explore the role of iron, fatty and amino acid metabolism in microglial homeostasis and repair. Identifying both distinct and shared metabolic adaptations in microglia across neurodegenerative diseases could reveal common therapeutic targets and provide a deeper understanding of disease-specific mechanisms underlying multiple CNS disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".