Obesity-associated improvements in metabolic profile through expansion of adipose tissue
Bibliographic record
Abstract
Excess caloric intake can lead to insulin resistance. The underlying reasons are complex but likely related to ectopic lipid deposition in nonadipose tissue. We hypothesized that the inability to appropriately expand subcutaneous adipose tissue may be an underlying reason for insulin resistance and beta cell failure. Mice lacking leptin while overexpressing adiponectin showed normalized glucose and insulin levels and dramatically improved glucose as well as positively affected serum triglyceride levels. Therefore, modestly increasing the levels of circulating full-length adiponectin completely rescued the diabetic phenotype in ob/ob mice. They displayed increased expression of PPARgamma target genes and a reduction in macrophage infiltration in adipose tissue and systemic inflammation. As a result, the transgenic mice were morbidly obese, with significantly higher levels of adipose tissue than their ob/ob littermates, leading to an interesting dichotomy of increased fat mass associated with improvement in insulin sensitivity. Based on these data, we propose that adiponectin acts as a peripheral "starvation" signal promoting the storage of triglycerides preferentially in adipose tissue. As a consequence, reduced triglyceride levels in the liver and muscle convey improved systemic insulin sensitivity. These mice therefore represent what we believe is a novel model of morbid obesity associated with an improved metabolic profile.
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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) | 0.000 | 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) | 0.001 | 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".