Postprandial insulin sensitivity and thermogenesis in frail elderly women
Bibliographic record
Abstract
The frailty syndrome is associated with inflammation, hypercortisolemia, and cardiovascular diseases, all of which are linked with insulin resistance. But whether frailty is characterized by insulin resistance is unclear, especially in the postprandial state. The prevalence of underweight with frailty is high. We wondered whether hypermetabolism associated with inflammation and hypercortisolemia could increase the thermic effect of food (TEF) and contribute to the frailty-associated body weight loss. In this study, we determined whether insulin sensitivity and TEF responses differ between frail and healthy elderly persons following a meal. Ten healthy and 13 frail elderly women were recruited and studied during the 5 h following the ingestion of a standardized liquid mixed-meal test. Areas under the curve (AUC) for glucose and insulin, and the product of AUC glucosexAUC insulinx10(−6) (PGI) were used as indices of insulin sensitivity. TEF was measured by indirect calorimetry. Following the meal, glucose and insulin AUCs and PGI were significantly higher in frail than in healthy elderly women and, except for the insulin AUC; these differences remained significant after adjustment for age, body weight, and physical activity. Physical activity, determined by questionnaire, was the single best predictor of PGI, explaining 27% of its variance. There was no difference in TEF between groups, and it did not correlate with any significant variable measured. Our results suggest that postprandial insulin resistance is higher in frail than in healthy elderly women, and TEF is similar, indicating that both processes do not contribute to the propensity for body weight loss.
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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".