Metabolic flux in macrophages in obesity and type-2 diabetes
Bibliographic record
Abstract
Recent literature extensively investigates the crucial role of energy metabolism in determining the inflammatory response and polarization status of macrophages. This rapidly expanding area of research highlights the importance of understanding the link between energy metabolism and macrophage function. The metabolic pathways in macrophages are intricate and interdependent, and they can affect the polarization of macrophages. Previous studies suggested that glucose flux through cytosolic glycolysis is necessary to trigger pro-inflammatory phenotypes of macrophages, and fatty acid oxidation is crucial to support anti-inflammatory responses. However, recent studies demonstrated that this understanding is oversimplified and that the metabolic control of macrophage polarization is highly complex and not fully understood yet. How the metabolic flux through different metabolic pathways (glycolysis, glucose oxidation, fatty acid oxidation, ketone oxidation, and amino acid oxidation) is altered by obesity- and type 2 diabetes (T2D)-associated insulin resistance is also not fully defined. This mini-review focuses on the impact of insulin resistance in obesity and T2D on the metabolic flux through the main metabolic pathways in macrophages, which might be linked to changes in their inflammatory responses. We closely evaluated the experimental studies and methodologies used in the published research and highlighted priority research areas for future investigations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".