A causal role for hyperinsulinemia in obesity
Bibliographic record
Abstract
Insulin modulates the biochemical pathways controlling lipid uptake, lipolysis and lipogenesis at multiple levels. Elevated insulin levels are associated with obesity, and conversely, dietary and pharmacological manipulations that reduce insulin have occasionally been reported to cause weight loss. However, the causal role of insulin hypersecretion in the development of mammalian obesity remained controversial in the absence of direct loss-of-function experiments. Here, we discuss theoretical considerations around the causal role of excess insulin for obesity, as well as recent studies employing mice that are genetically incapable of the rapid and sustained hyperinsulinemia that normally accompanies a high-fat diet. We also discuss new evidence demonstrating that modest reductions in circulating insulin prevent weight gain, with sustained effects that can persist after insulin levels normalize. Importantly, evidence from long-term studies reveals that a modest reduction in circulating insulin is not associated with impaired glucose homeostasis, meaning that body weight and lipid homeostasis are actually more sensitive to small changes in circulating insulin than glucose homeostasis in these models. Collectively, the evidence from new studies on genetic loss-of-function models forces a re-evaluation of current paradigms related to obesity, insulin resistance and diabetes. The potential for translation of these findings to humans is briefly discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".