100<sup>th</sup> anniversary of the discovery of insulin perspective: insulin and adipose tissue fatty acid metabolism
Bibliographic record
Abstract
Insulin inhibits systemic nonesterified fatty acid (NEFA) flux to a greater degree than glucose or any other metabolite. This remarkable effect is mainly due to insulin-mediated inhibition of intracellular triglyceride (TG) lipolysis in adipose tissues and is essential to prevent diabetic ketoacidosis, but also to limit the potential lipotoxic effects of NEFA in lean tissues that contribute to the development of diabetes complications. Insulin also regulates adipose tissue fatty acid esterification, glycerol and TG synthesis, lipogenesis, and possibly oxidation, contributing to the trapping of dietary fatty acids in the postprandial state. Excess NEFA flux at a given insulin level has been used to define in vivo adipose tissue insulin resistance. Adipose tissue insulin resistance defined in this fashion has been associated with several dysmetabolic features and complications of diabetes, but the mechanistic significance of this concept is not fully understood. This review focusses on the in vivo regulation of adipose tissue fatty acid metabolism by insulin and the mechanistic significance of the current definition of adipose tissue insulin resistance. One hundred years after the discovery of insulin and despite decades of investigations, much is still to be understood about the multifaceted in vivo actions of this hormone on adipose tissue fatty acid metabolism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.022 |
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".