Deficiency in Interferon-γ Results in Reduced Body Weight and Better Glucose Tolerance in Mice
Bibliographic record
Abstract
Obesity is a chronic low-grade inflammatory disease caused by increased energy intake and reduced energy expenditure. Studies using animal models with deletion of inflammatory cytokines have produced conflicting results with some showing increased weight gain and others showing no effect or even reduced body weights. Clearly, more work is necessary to understand the role of cytokines on body weight control. The aim of this study was to determine the effect of interferon-γ deletion (IFNγ(-/-)) on body weight regulation and glucose metabolism. Male IFNγ(-/-) and wild-type C57BL/6 mice were fed a low-fat chow diet, and body weight, food intake, and energy expenditure were monitored over 20 wk. At the end of the study, ip glucose tolerance test, insulin tolerance test, basal glucose turnover, and hyperinsulinemic/euglycemic clamps were performed. Expression levels of arcuate nucleus neuropeptide Y, Agouti-related peptide, and proopiomelanocortin mRNA as well as circulating leptin levels were also determined. IFNγ(-/-) mice had improved glucose tolerance with reduced rate of glucose appearance and increased insulin sensitivity due to greater suppression of endogenous glucose output, which was associated with decreased hepatic glucose-6-phosphatase activity. In addition, we also observed reduced body weight associated with decreased food intake and increased physical activity. Neuropeptide Y and Agouti-related peptide mRNA expression was reduced, whereas proopiomelanocortin mRNA expression was increased, as were plasma leptin levels. Global deletion of IFNγ in mice resulted in reduced body weight associated with negative energy balance, improved glucose tolerance, and hepatic insulin sensitivity. Our findings demonstrate that IFNγ plays a critical role in the regulation of body weight and glucose metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".