Association between exercise-induced change in body composition and change in cardiometabolic risk factors in postmenopausal South Asian women
Bibliographic record
Abstract
The South Asian population suffers from a high prevalence of type 2 diabetes and cardiovascular disease (CVD). A unique obesity phenotype of elevated visceral adipose tissue (VAT) is associated with CVD risk among South Asians. Exercise-induced reduction in VAT and body fat is an effective mechanism to improve cardiometabolic risk factors but this has not been shown in South Asians. Whether exercise-induced changes in measurements such as waist circumference (WC) are independently related to changes in cardiometabolic risk factors in South Asians is unknown. Multi-slice computed tomography scanning was used to assess VAT, cardiometabolic risk factors through a fasting blood sample, and body fat using dual-energy X-ray absorptiometry. Forty- nine postmenopausal South Asian women who participated in two 12-week aerobic exercise programs were included. Bivariate correlations were used to assess associations between change in cardiometabolic risk factors and change in body composition. Regression analyses were conducted with change in glucose, insulin, and homeostatic model assessment of insulin resistance (HOMA-IR) as dependent variables and change in body composition as independent variables of interest. There were significant associations between changes in fasting insulin, glucose, and HOMA-IR with change in VAT. The association between change in VAT and these cardiometabolic risk factors was independent of change in other body composition variables of interest. South Asian women should be encouraged to engage in aerobic activity to reduce their risk of type 2 diabetes and CVD, and physicians should be aware of improvements in glucose regulation with exercise training not observed through reductions in WC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".