Microglia Mediate Metabolic Dysfunction From Common Air Pollutants Through NF-κB Signaling
Bibliographic record
Abstract
The prevalence of type 2 diabetes (T2D) poses a significant health challenge, yet the contribution of air pollutants to T2D epidemics remains under-studied. Several studies demonstrated a correlation between exposure to volatile organic compounds (VOCs) in indoor/outdoor environments and T2D. Here, we conducted the first meta-analysis, establishing a robust association between exposure to benzene, a prevalent airborne VOC, and insulin resistance in humans across all ages. We used a controlled benzene exposure system, continuous glucose monitoring approach, and indirect calorimetry in mice, to investigate the underlying mechanisms. Following exposure, disruptions in energy homeostasis, accompanied by modifications in the hypothalamic transcriptome and alterations in insulin and immune signaling, were observed exclusively in males, leading to a surge in blood glucose levels. In agreement, RNA sequencing of microglia revealed increased expression of genes associated with immune response and NF-κB signaling. Selective ablation of IKKβ in immune cells (Cx3cr1GFPΔIKK) or exclusively in microglia (Tmem119ERΔIKK) in adult mice alleviated benzene-induced gliosis, restored energy homeostasis and hypothalamic gene expression, and protected against hyperglycemia. We conclude that the microglial NF-κB pathway plays a critical role in chemical-induced metabolic disturbances, revealing a vital pathophysiological mechanism linking exposure to airborne toxicants and the onset of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".