Association of Acylated and Nonacylated Ghrelin with Insulin Sensitivity in Overweight and Obese Postmenopausal Women
Bibliographic record
Abstract
OBJECTIVE: Ghrelin [acylated (AG) and nonacylated (NAG)] has been shown to play a pivotal role in the regulation of food intake and insulin sensitivity. It is presently unclear whether variation in insulin sensitivity is related to AG and NAG levels in obese individuals. To address this issue, we determined whether insulin-sensitive overweight or obese (ISO) and insulin-resistant overweight or obese (IRO) individuals display different total ghrelin (TotG), AG, and NAG profiles during a euglycemic/hyperinsulinemic clamp (EHC). DESIGN: Eighty-nine nondiabetic overweight and obese postmenopausal women underwent EHC to evaluate insulin sensitivity. Body composition and blood lipid profiles were assessed. Subjects within the highest tertile of insulin sensitivity were described as ISO (n = 31), whereas those within the lowest tertile of insulin sensitivity were considered as IRO (n = 29). Plasma TotG, AG, and NAG profiles were assessed by RIA at 0, 60, 160, 170, and 180 min during the EHC. RESULTS: TotG and NAG levels were significantly decreased for ISO and IRO individuals during the EHC, whereas only ISO subjects displayed a significant reduction of AG concentrations (P < 0.05). AG area under the curve value and the ratio of AG/NAG (fasting and area under the curve) were significantly decreased in ISO individuals. Furthermore, maximal reduction of TotG and AG concentrations was greater in ISO compared with IRO individuals (P < 0.05). Insulin sensitivity was significantly correlated with maximal reduction of TotG (r = 0.36; P < 0.01) and AG (r = 0.36; P < 0.05) concentrations. CONCLUSION: The dysregulation of ghrelin secretion profiles during EHC is associated with insulin resistance. AG may contribute to the variation of insulin sensitivity in overweight or obese postmenopausal women.
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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 | 0.001 |
| 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".