Fructose overconsumption-induced reprogramming of microglia metabolism and function
Bibliographic record
Abstract
The overconsumption of dietary fructose has been proposed as a major culprit for the rise of many metabolic diseases in recent years, yet the relationship between a high fructose diet and neurological dysfunction remains to be explored. Although fructose metabolism mainly takes place in the liver and intestine, recent studies have shown that a hyperglycemic condition could induce fructose metabolism in the brain. Notably, microglia, which are tissue-resident macrophages (Mφs) that confer innate immunity in the brain, also express fructose transporters (GLUT5) and are capable of utilizing fructose as a carbon fuel. Together, these studies suggest the possibility that a high fructose diet can regulate the activation and inflammatory response of microglia by metabolic reprogramming, thereby altering the susceptibility of developing neurological dysfunction. In this review, the recent advances in the understanding of microglia metabolism and how it supports its functions will be summarized. The results from both in vivo and in vitro studies that have investigated the mechanistic link between fructose-induced metabolic reprogramming of microglia and its function will then be reviewed. Finally, areas of controversies and their associated implications, as well as directions that warrant future research will be highlighted.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".