Is Insulin Action in the Brain Relevant in Regulating Blood Glucose in Humans?
Bibliographic record
Abstract
PURPOSE: In addition to its direct action on the liver to lower hepatic glucose production, insulin action in the central nervous system (CNS) also lowers hepatic glucose production in rodents after 4 hours. Although CNS insulin action (CNSIA) modulates hepatic glycogen synthesis in dogs, it has no net effect on hepatic glucose output over a 4-hour period. The role of CNSIA in regulating plasma glucose has recently been examined in humans and is the focus of this review. METHODS AND RESULTS: Intransal insulin (INI) administration increases CNS insulin concentration. Hence, INI can address whether CNSIA regulates plasma glucose concentration in humans. We and three other groups have sought to answer this question, with differing conclusions. Here we will review the critical aspects of each study, including its design, which may explain these discordant conclusions. CONCLUSIONS: The early glucose-lowering effect of INI is likely due to spillover of insulin into the systemic circulation. In the presence of simultaneous portal and CNS hyperinsulinemia, portal insulin action is dominant. INI administration does lower plasma glucose independent of peripheral insulin concentration (between ∼3 and 6 h after administration), suggesting that CNSIA may play a role in glucose homeostasis in the late postprandial period when its action is likely greatest and portal insulin concentration is at baseline. The potential physiological role and purpose of this pathway are discussed in this review. Because the effects of INI are attenuated in patients with type 2 diabetes and obesity, this is unlikely to be of therapeutic utility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".