Exercise and the metabolic syndrome with weight regain
Bibliographic record
Abstract
Weight loss improves metabolic syndrome (MetS) factors, but risk may return with weight regain. This study was designed to determine if exercise training can maintain improvements in MetS risk factors during weight regain. In a randomized control trial,102 overweight or obese (body mass index 25.0-39.9 kg/m(2)) men and women (age 21-52 yr), with characteristics of the MetS, lost 10% of body weight with supervised walking/jogging at 60% of maximal oxygen consumption (Vo(2 max)) (-400 kcal/session), 5 days/wk, and caloric restriction (-600 kcal/day) over a 4- to 6-mo period. After weight loss, 77 remaining subjects underwent programmed weight regain (+50% of lost weight) for 4-6 mo with random assignment to two groups: no exercise (NoEX) or continued supervised exercise (EX). Blood pressure, regional fat, glucose homeostasis, lipids, and inflammatory markers were assessed at baseline, post-weight loss, and post-weight regain. Groups were compared by two-way repeated-measures ANOVA on the 67 subjects. After weight loss (9.7 +/- 0.2% of body weight), significant (P < 0.05) improvements were observed in almost all parameters assessed. Following weight regain (54.4 +/- 1.6% of lost weight), the NoEX group exhibited deterioration in most metabolic markers, while the EX group maintained improvements in Vo(2 max), blood pressures, glucose homeostasis, high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), oxidized LDL, and other markers of inflammation, but did not maintain improvements in triglyceride and cholesterol concentrations or abdominal fat. Results of this design of controlled human weight regain suggest that aerobic exercise can counter the detrimental effects of partial weight regain on many markers of disease risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".