Effects of dynamic and isometric resistance training protocols on metabolic profile in hemodialysis patients: a randomized controlled trial
Bibliographic record
Abstract
The aim of this study was to compare the effect of dynamic (DRT) and isometric (IRT) resistance training on glycemic homeostasis, lipid profile, and nitric oxide (NO) in hemodialysis (HD) patients. Patients were randomly distributed into 3 groups: control (n = 65), DRT (n = 65), and IRT (n = 67). Patients assessed before and after the intervention period were tested for fasting blood glucose, glycated hemoglobin, oral glucose tolerance test, insulin resistance, lipid profile, leptin, insulin, adiponectin, C-reactive protein, and NO . Patients underwent to strength and body composition assessments. Subjects allocated in both DRT and IRT groups took part in a 24-week resistance training program, 3 times per week. Each training session was approximately 1 hour before dialysis and consisted of 3 sets of 8–12 repetitions at low intensity. Total workload was higher in the DRT as compared with the IRT. This heightened workload related to better glycemic homeostasis in HD patients as measured by regulation of insulin, adiponectin, and leptin, while improveing triglycerides, free-fat mass, and muscle strength. Additionally, NO levels were increased in the DRT group. NO was significantly correlated with glucose intolerance (r = −0.42, p = 0.0155) and workload (r = 0.46, p = 0.0022). The IRT group only improved strength (p < 0.05). Twenty-four weeks of DRT improved glycemic homeostasis, lipid profile, and NO in HD patients. Although IRT seems to play an important role in increasing strength, DRT might be a better choice to promote metabolic adjustments in HD patients. Clinical trial: http://www.ensaiosclinicos.gov.br/rg/RBR-3gpg5w . Novelty: DRT might be a better choice for metabolic improvements in patients with chronic kidney disease (CKD). Exercise-training might treat metabolic imbalance in CKD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".