<scp>GABA</scp> ameliorates diet‐induced hepatic steatosis and insulin resistance by inhibiting macrophage activation
Bibliographic record
Abstract
Abstract Background Obesity‐associated fatty liver and hepatic insulin resistance are risk factors for impaired glucose tolerance and type 2 diabetes. In this study, we investigated the effects of GABA on high‐fat diet (HFD)‐induced hepatic metabolic abnormalities and liver insulin resistance in mice. Methods Male C57BL/6J mice were fed a normal chow diet, HFD or HFD combined with GABA in drinking water for 14 weeks; the insulin sensitivity and inflammation signalling molecules such as toll‐like receptor 4 (TLR4), nuclear factor kappa B (NF‐κB) and TNF‐α were evaluated. Moreover, the effect of GABA on the above inflammatory signalling molecules and electrophysiology was explored in macrophages. Results Oral GABA treatment attenuated HFD‐induced liver steatosis and increased hepatic insulin sensitivity. This was associated with inactivation of Kupffer cells (KCs) and decreased production of hepatic TNF‐α. GABA treatment significantly suppressed TLR4/NF‐κB activation in livers of HFD‐fed mice. Consistently, in vitro studies showed that GABA‐attenuated palmitate‐induced upregulation of TLR4 signalling and TNF‐α production in the macrophage. Furthermore, conditioned medium from macrophage cells treated with palmitate significantly attenuated insulin‐stimulated protein kinase B (Akt) activation in HepG2 cells. However, this did not occur in the conditioned medium from palmitate‐treated macrophages in the presence of GABA. Electrophysiological studies in macrophages showed that GABA evokes GABA currents and hyperpolarized the membrane potential. This membrane hyperpolarizing inhibitory effect is associated with decreased palmitate‐induced Ca 2+ influx and TNF‐α production. Conclusions GABA ameliorates HFD‐induced hepatic insulin resistance by suppressing KCs activation and its production and secretion of inflammatory cytokines.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".