Resistance training and cardiometabolic risk in women with metabolically healthy and unhealthy obesity
Bibliographic record
Abstract
Despite some reported benefits, there is a low quality of evidence for resistance training (RT) improving metabolic health of individuals with overweight or obesity. We evaluated the impact of RT on body composition, cardiorespiratory fitness (CRF) and physical performance, lipid–lipoprotein profile, inflammation, and glucose–insulin homeostasis in 51 postmenopausal women versus 29 controls matched for age, obesity, and physical activity. Exercised women were further subdivided for comparison of RT effects into those presenting metabolically healthy obesity (MHO) and those with metabolically unhealthy obesity (MUHO) classified according to Karelis and Rabasa-Lhoret or an approach based on adipose tissue secretory dysfunction using the plasma adiponectin(A)/leptin (L) ratio. Participants followed a 4-month weekly RT program targeting major muscle groups (3 × 10 repetitions at 80% one repetition maximum (1-RM)). Percent fat marginally decreased and lean body mass increased (0.01 < p < 0.05) while CRF and muscular strength improved in all women, after RT (effect size (ES): 0.11–1.21 (trivial to large effects), p ˂ 0.01). Fasting plasma triacylglycerol and high-density lipoprotein-cholesterol levels slightly increased and decreased, respectively, in participants with MHO using the A/L ratio approach (ES: −0.47 to 1.07 (small to large effects), p ˂ 0.05). Circulating interleukin-6 soluble receptor decreased in both groups and soluble tumor necrosis factor receptor-1/soluble tumor necrosis factor receptor-2 in women with MUHO only, irrespective of definition (ES: −0.42 to −0.84 (small to large effects), p ˂ 0.05). Glucose–insulin homeostasis was unchanged regardless of group or definition. RT improved physical performance and body composition but had a lesser impact on cardiometabolic risk in women with obesity, irrespective of their metabolic phenotype.
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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".