Glucose homeostasis predicts weight gain: prospective and clinical evidence
Bibliographic record
Abstract
BACKGROUND: The potential long-term impact of low glycaemia on body fat accumulation has not been verified. Therefore, we examined the effects of low glucose concentrations on long-term energy balance and weight gain in humans. METHODS: Two sets of analyses were realized in order to verify this objective. First, Study 1 consisted of 259 participants between 20 and 65 years of age selected from Phase 2 of the Quebec Family Study (QFS). The association between glucose concentrations at the end of an oral glucose tolerance test (OGTT) and changes in body mass was analysed prospectively (mean follow-up of 6 years). In addition, Study 2 consisted of 44 obese participants (20 men and 24 women) randomly assigned to a 15-week weight loss program in either a drug therapy group (fenfluramine) or a placebo group coupled with energy intake restriction. The focus of this study was the relationship between glycaemic control at the end of the treatment and post-treatment weight regain. RESULTS: In Study 1, the glucose concentrations at 120 min of the OGTT were negatively correlated with weight gain over 6 years (r=-0.42, p<0.01). In Study 2, the weight loss program induced a mean reduction in body weight of 10 kg in the fenfluramine and placebo groups. In participants who returned for a follow-up visit (mean=81 weeks after the intervention), the glucose area below fasting values (GABF) at the end of the OGTT increased with weight loss (p<0.01) and was correlated with weight regain (r=0.74, p<0.01). CONCLUSIONS: Lower glucose concentrations at the end of an OGTT are correlated with weight gain over time. Large amounts of weight loss are associated with low glycaemia at the end of an OGTT. These low glucose concentrations are strong predictors of the amount of weight regained 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".