Low vs. high glycemic load diets reduce insulin‐like growth factors and inflammatory factors in overweight persons in a controlled feeding study
Bibliographic record
Abstract
Our objective was to investigate whether associations of obesity with chronic disease risk biomarkers are mediated by dietary patterns that cause unfavorable metabolic profiles. Our randomized, cross‐over, controlled feeding study tested the effect of low‐ and high‐glycemic load (GL) experimental diets on disease risk biomarkers [glucose, insulin, IGF1, IGFBP3, leptin, adiponectin, interleukin‐6 and C‐reactive protein (CRP)]. Normal weight (n=40) and overweight/obese (n=42) men and women completed two 28‐day feeding periods – low GL and high GL. All meals were prepared in a metabolic kitchen and were isocaloric; both arms had identical macronutrients but differed in GL. Fasting blood was drawn before and after each feeding period. Linear mixed models tested the intervention effect on the biomarkers; models were adjusted for baseline biomarker concentrations, diet sequence, feeding period, age, sex and body fat mass. Compared to the high‐GL diet, the low‐GL diet significantly reduced IGF1 (p=0.04) and the IGF1:IGFBP3 ratio (p=0.01). Results were more pronounced among overweight/obese participants. When stratified by body fat mass, CRP was significantly lower for the low‐ vs. the high GL diet for those with high body fat (p=0.01). A dietary change emphasizing low GL foods may improve the metabolic and inflammatory profile of overweight and obese persons. Supported by NIH U54 CA116847, NIH R03 CA132158.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".