Insights into the role of macrophage migration inhibitory factor in obesity and insulin resistance
Bibliographic record
Abstract
High-fat diet (HFD)-induced obesity has emerged as a state of chronic low-grade inflammation characterised by a progressive infiltration of immune cells, particularly macrophages, into obese adipose tissue. Adipose tissue macrophages (ATM) present immense plasticity. In early obesity, M2 anti-inflammatory macrophages acquire an M1 pro-inflammatory phenotype. Pro-inflammatory cytokines including TNF-α, IL-6 and IL-1β produced by M1 ATM exacerbate local inflammation promoting insulin resistance (IR), which consequently, can lead to type-2 diabetes mellitus (T2DM). However, the triggers responsible for ATM recruitment and activation are not fully understood. Adipose tissue-derived chemokines are significant players in driving ATM recruitment during obesity. Macrophage migration inhibitory factor (MIF), a chemokine-like inflammatory regulator, is enhanced during obesity and is directly associated with the degree of peripheral IR. This review focuses on the functional role of macrophages in obesity-induced IR and highlights the importance of the unique inflammatory cytokine MIF in propagating obesity-induced inflammation and IR. Given MIF chemotactic properties, MIF may be a primary candidate promoting ATM recruitment during obesity. Manipulating MIF inflammatory activities in obesity, using pharmacological agents or functional foods, may be therapeutically beneficial for the treatment and prevention of obesity-related metabolic diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".