Targeting the Metabolic Syndrome with Exercise: Evidence from the HERITAGE Family Study
Bibliographic record
Abstract
PURPOSE: To determine the efficacy of exercise training in treating the metabolic syndrome. METHODS: The sample included 621 black and white participants from the HERITAGE Family Study, identified as sedentary and apparently healthy (no chronic disease or injury). The metabolic syndrome was defined as having three or more risk factors according to the guidelines of the National Cholesterol Education Program, including elevated waist circumference, blood pressure, triglycerides, blood glucose, and low HDL cholesterol. The presence of the metabolic syndrome and component risk factors were determined before and after 20 wk of supervised aerobic exercise training. RESULTS: The prevalence of the metabolic syndrome was 16.9% in this sample (105/621) of apparently healthy participants. Of the 105 participants with the metabolic syndrome at baseline, 30.5% (32 participants) were no longer classified as having the metabolic syndrome after the exercise training. Among the 32 participants who improved their metabolic profile, 43% decreased triglycerides, 16% improved HDL cholesterol, 38% decreased blood pressure, 9% improved fasting plasma glucose, and 28% decreased their waist circumference. There were no sex or race differences in the efficacy of exercise in treating the metabolic syndrome: 32.7% of men, 28.0% of women, 29.7% of black, and 30.9% of white participants with the metabolic syndrome were no longer classified as having the syndrome after training. CONCLUSION: Aerobic exercise training in patients with the metabolic syndrome can be useful as a treatment strategy and provides support for a role for physical activity in the prevention of chronic disease.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".