Hispanic ethnicity as a moderator of the effects of aerobic and resistance exercise in survivors of breast cancer
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome (MSY) is associated with an increased risk of cardiovascular disease, type 2 diabetes, and recurrence in breast cancer survivors (BCS). MSY is 1.5 times more common in Hispanic women compared with non-Hispanic women. Although exercise mitigates MSY in BCS, to the best of the authors' knowledge, few studies to date have focused on minorities. This secondary analysis examined ethnicity as a moderator of the effects of a 16-week aerobic and resistance exercise intervention on MSY, sarcopenic obesity, and serum biomarkers in BCS. METHODS: A total of 100 eligible BCS were randomized to exercise (50 BCS) or usual care (50 BCS). The exercise intervention promoted moderate to vigorous aerobic and resistance exercise 3 times a week for 16 weeks. MSY z scores, sarcopenic obesity, and serum biomarkers were measured at baseline, after the intervention, and at the 28-week follow-up (exercise group only). Linear mixed models adjusted for baseline values of the outcome, age, disease stage, adjuvant treatment, and recent physical activity were used to evaluate effect modification by ethnicity. RESULTS: The study sample was 57% Hispanic BCS (HBCS) and 43% non-Hispanic BCS (NHBCS). HBCS were younger, of greater adiposity, and had been diagnosed with more advanced cancers compared with NHBCS (P<.001). Ethnicity was found to moderate the mean differences in exercise training on triglycerides (-36.4 mg/dL; 95% confidence interval [95% CI],-64.1 to -18.8 mg/dL), glucose (-8.6 mg/dL; 95% CI, -19.1 to -3.0 mg/dL), and C-reactive protein (-3.3 mg/L; 95% CI, -7.3 to -0.9 mg/L). CONCLUSIONS: HBCS appear to have poorer metabolic profiles and therefore may derive relatively larger metabolic changes from exercise compared with NHBCS. Clinical exercise interventions may attenuate existing health disparities across diverse groups of BCS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".