Is Insulin Action in the Brain Clinically Relevant?
Bibliographic record
Abstract
Last year marked the ninetieth anniversary of the discovery of insulin by Nobel Laureates Frederick Banting and John Macleod, as well as Charles Best and James Bertram Collip. The initial success of insulin’s ability to lower glucose levels in type 1 diabetes is now shadowed by the urgent need to characterize insulin resistance and secretion defects in type 2 diabetes and obesity. Insulin triggers signaling pathways in liver, muscle, and fat that inhibit glucose production and increase glucose uptake (1). However, it has only been in recent years that the brain has received attention for being an insulin-sensitive organ that regulates food intake (2) and glucose (3) and lipid (4) homeostasis in animals (Fig. 1). FIG. 1. A working hypothesis for insulin action in the brain. Insulin triggers signaling cascades in the brain to regulate food intake and modulate the reward-related hedonic food response. In addition, brain insulin action alters glucose and lipid metabolism. The hypothalamus (blue) is the main effector of the metabolic changes induced by insulin and can coordinate the prefrontal cortex (green) to regulate the hedonic assessment of appetite. WAT, white adipose tissue. Circulating insulin crosses the blood-brain barrier and acts on its receptor in the hypothalamus to lower food intake and body weight (2). Infusion of insulin into the central nervous system (CNS) lowers food intake in rats (5), mice (6), and baboons (7), whereas hyperphagia is detected in neuronal insulin receptor knockout mice (8). Further, high-fat feeding induces hypothalamic insulin resistance in rodents (9). Although these animal studies expand the role of insulin to regulate appetite and suggest that characterizing insulin signaling pathways may reveal novel targets that reverse brain insulin resistance in obesity, a central question remains, Is …
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".