Effects of Dietary Weight Loss and Exercise on Insulin-Like Growth Factor-I and Insulin-Like Growth Factor-Binding Protein-3 in Postmenopausal Women: A Randomized Controlled Trial
Bibliographic record
Abstract
High levels of insulin-like growth factor (IGF)-I may increase the risk of common cancers in humans. We hypothesized that weight loss induced by diet and/or exercise would reduce IGF-I in postmenopausal women. Four hundred and thirty nine overweight or obese [body mass index (BMI) ≥ 25 kg/m(2)] women (50-75 years) were randomly assigned to: (i) exercise (N = 117), (ii) dietary weight loss (N = 118), (iii) diet + exercise (N = 117), or (iv) control (N = 87). The diet intervention was a group-based program with a 10% weight loss goal. The exercise intervention was 45 minutes/day, 5 days/week of moderate-to-vigorous intensity activity. Fasting serum IGF-I and IGF-binding protein (IGFBP)-3 were measured at baseline and 12 months by radioimmunoassay. Higher baseline BMI was associated with lower IGF-I and IGF-I/IGFBP-3 molar ratio. Although no significant changes in either IGF-I or IGFBP-3 were detected in any intervention arm compared with control, the IGF-I/IGFBP-3 ratio increased significantly in the diet (+5.0%, P < 0.01) and diet + exercise (+5.4%, P < 0.01) groups compared with control. Greater weight loss was positively associated with change in both IGF-I (P(trend) = 0.017) and IGF-I/IGFBP-3 ratio (P(trend) < 0.001) in the diet group, but inversely with change in IGFBP-3 in the diet + exercise group (P(trend) = 0.01). No consistent interaction effects with baseline BMI were detected. Modified IGF-I bioavailability is unlikely to be a mechanism through which caloric restriction reduces cancer risk in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".