Changes in glucose disposal after a caloric restriction–induced weight loss program in obese postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate individual characteristics that explain interindividual variations in glucose disposal in response to a 6-month weight loss program in obese postmenopausal women. METHODS: The cohort was divided into tertiles based on changes in glucose disposal after weight loss. Only women in the upper tertile (positive responders: Δ glucose disposal ≥ 0.92 mg/kg/min; n = 19) and lower tertile (negative responders: Δ glucose disposal ≤ -0.23 mg/kg/min; n = 19) were considered for analyses. Outcome measures included body weight, lean body mass (LBM), LBM index (= LBM / height [m]), fat mass (FM), FM index (= FM / height [m]), visceral fat, subcutaneous abdominal fat, high-sensitivity C-reactive protein (hsCRP) levels, interleukin-6, lipid profile, physical activity levels, fasting blood glucose and insulin levels, glucose disposal by hyperinsulinemic-euglycemic clamp technique, and resting blood pressure. RESULTS: At baseline, positive responders had higher triglycerides and hsCRP levels and lower glucose disposal (0.01 < P < 0.05) than negative responders. Except for visceral fat, the entire cohort showed significant decreases in all measures of body composition (P < 0.005) after weight loss, with greater decreases in body weight, body mass index, and FM index in positive responders (P < 0.005). Finally, data revealed that only positive responders showed decreases in LBM, LBM index, and hsCRP levels after weight loss (P between 0.01 and 0.001). CONCLUSIONS: An important interindividual variability in changes in glucose disposal after weight loss is observed. Interestingly, participants who display improvements in glucose disposal also show significant decreases in LBM, LBM index, and hsCRP after 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) | 0.000 | 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".