<i>Moo1</i> obesity quantitative trait locus in BTBR T<sup>+</sup> <i>Itpr3</i><sup>tf</sup>/J mice increases food intake
Bibliographic record
Abstract
The rising prevalence of obesity is one of the greatest health challenges facing the world today. Discovery of genetic factors affecting obesity risk will provide important insight to its etiology that could suggest new therapeutic approaches. We have previously identified the Modifier of obese 1 (Moo1) quantitative trait locus (QTL) in a cross between leptin-deficient BTBR T(+) Itpr3(tf)/J (BTBR) and C57BL/6J (B6) mice. Understanding the mechanism by which this locus acts will aid in the identification of candidate genes. Here we refined the location of this QTL and sought to determine the mechanism by which Moo1 affects body weight. We found that the effects of Moo1 also alter high fat diet-induced obesity in mice having functional leptin. In detailed metabolic analyses we determined that this locus acts by increasing food intake in BTBR mice, without affecting energy expenditure. The expression levels of the main molecular mediators of food intake in the hypothalamus were not altered, suggesting this locus affects an independent pathway, consistent with its identification in mice lacking functional leptin. Finally, we show that the increased adiposity resulting from Moo1 is sufficient to affect glucose tolerance. These studies show that the Moo1 obesity QTL affects food intake, likely through a novel mechanism, and indicate that modulation of the underlying pathway may not only ameliorate obesity but also its clinical consequences.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".