METRNL Rescues Glucose Intolerance and Ameliorates Obesity Phenotype in a Diet‐Induced Model of Obesity
Bibliographic record
Abstract
Physical inactivity is a primary modifiable risk factor for obesity and type 2 diabetes (T2D), metabolic diseases that are rampant in both pediatric/adult populations. Endurance exercise has been shown to prevent/attenuate the onset and progression of obesity/T2D. We postulated that exercise mediated these pro‐metabolic effects on distal fat depots and other organs via secretory myokines. Our lab has identified two such myokines: interleukin‐15 (IL‐15) and meteorin‐like protein METRNL. Here we administered these two myokines, singularly and in combination, to deduce the respective potency in treating obesity by utilizing a diet‐induced model of obesity. C57Bl/6 mice were fed high‐fat diet (HFD; 60% kcal from fat) for 6 months until the animals were hyperglycemic and glucose intolerant. Subsequently the animals were divided into HFD control, endurance exercise (15 m/min for 60 min, 5 days/week, END), or given intravenous injections of recombinant METRNL (0.4 ng/kg/day, 3x/week, MET), IL‐15 (25 ng/kg/day, 3x/week), or a combination group of both IL‐15 and METRNL (COMBO) for 8 weeks. Treatment with METRNL or COMBO normalized serum glucose levels, improved glucose tolerance, and reduced body and fat weight, increased pancreas weight as effectively as the mice in END group (all P < 0.05). Inguinal fat analyses showed a marked improvement in gene networks involved in mitochondrial biogenesis and browning of the fat (increased ucp1 , prdm16 , and cidea expression), and COX activity in END, MET, and COMBO groups in tandem. IL‐15 treatment alone had no effect on these indices. Our data clearly establishes treatment with METRNL as an effective therapeutic approach to counteract diet‐induced obesity. Funded by NSERC and CIHR.
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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 | 0.001 | 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 | 0.002 |
| 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".