Enhanced glycolysis and HIF-1α activation in adipose tissue macrophages sustains local and systemic interleukin-1β production in obesity
Bibliographic record
Abstract
During obesity, macrophages infiltrate the visceral adipose tissue and promote inflammation that contributes to type II diabetes. Evidence suggests that the rewiring of cellular metabolism can regulate macrophage function. However, the metabolic programs that characterize adipose tissue macrophages (ATM) in obesity are poorly defined. Here, we demonstrate that ATM from obese mice exhibit metabolic profiles characterized by elevated glycolysis and oxidative phosphorylation, distinct from ATM from lean mice. Increased activation of HIF-1α in ATM of obese visceral adipose tissue resulted in induction of IL-1β and genes in the glycolytic pathway. Using a hypoxia-tracer, we show that HIF-1α nuclear translocation occurred both in hypoxic and non-hypoxic ATM suggesting that both hypoxic and pseudohypoxic stimuli activate HIF-1α and its target genes in ATM during diet-induced obesity. Exposure of macrophages to the saturated fatty acid palmitate increased glycolysis and HIF-1α expression, which culminated in IL-1β induction thereby simulating pseudohypoxia. Using mice with macrophage-specific targeted deletion of HIF-1α, we demonstrate the critical role of HIF-1α-derived from macrophages in regulating ATM accumulation, and local and systemic IL-1β production, but not in modulating systemic metabolic responses. Collectively, our data identify enhanced glycolysis and HIF-1α activation as drivers of low-grade inflammation in obesity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".