Changes in HDL-c concentrations after 16 weeks of combined training in postmenopausal women: characteristics of positive and negative responders
Bibliographic record
Abstract
This study aimed to investigate the individual characteristics of body composition and metabolic profile that could explain interindividual variation in high-density lipoprotein cholesterol (HDL-c) concentrations in response to 16 weeks of combined strength plus aerobic (combined) training in postmenopausal women. The participants were divided into tertiles based on percentage of changes in HDL-c concentrations after combined training. Only women in the upper tertile (positive responders: Δ > 10.4%; n = 19) and lower tertile (negative responders: Δ < -1.4%; n = 19) were considered for analyses. The total body fat (BF), trunk fat (TF), android fat (AF), gynoid fat, and lean body mass were estimated by dual-energy X-ray absorptiometry. The metabolic profile - glucose, triacylglycerol, total cholesterol, HDL-c, low-density lipoprotein cholesterol, and very-low-density lipoprotein (VLDL) - were assessed. After 16 weeks, both positive and negative responders presented similar improvement in body composition, such as a decrease in percentage and kilograms of BF, TF, and AF, and increase in lean body mass (p value for time < 0.05). As expected, there was an effect of time and also a significant interaction (time vs. group) (p value < 0.001) in the improvement of HDL-c, with higher values for positive responders. Regarding metabolic profile, there were significant interactions (time vs. group) for triacylglycerol (p value = 0.032) and VLDL (p value = 0.027) concentrations, with lower values for positive responders. Our results suggests there is heterogeneity in combined training-induced HDL-c changes in postmenopausal women, and the positive responders were those who presented more pronounced decreases in triacylglycerol and VLDL concentrations.
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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".