Detraining-induced alterations in adipokines and cardiometabolic risk factors after nonlinear periodized resistance and aerobic interval training in obese men
Bibliographic record
Abstract
This study compared the effects of nonlinear resistance training (NRT), aerobic interval training (AIT), and detraining on adipokines and cardiometabolic risk factors in middle-aged obese men. Thirty-three obese men were randomly allocated to NRT (n = 12), AIT (n = 10), and control (CON, n = 11) groups. Subjects in experimental groups performed exercise protocols 3 days per week for 12 weeks followed by a 4-week detraining period. The NRT involved 55 min of weight training with flexible periodization. The AIT consisted of running on a treadmill (4 × 4-min intervals at 90% of maximal heart rate, with each interval separated by 3 min at 65%). Peak oxygen consumption increased significantly after training compared with CON (P < 0.01), but it increased more in the AIT group than in the NRT group (P = 0.004). After detraining, peak oxygen consumption decreased significantly in both training groups (P < 0.001); however, the value in the AIT group was still higher than that in the CON group (P = 0.003). No significant changes were observed in serum levels of omentin-1 and interleukin (IL)-18 after training (P > 0.05), but omentin-1 decreased significantly in both training groups and IL-18 increased significantly in the NRT group after detraining (P < 0.05). High-density lipoprotein cholesterol (HDL-C) increased significantly after training in the AIT group compared with the CON group (P < 0.05) and returned to the pre-training level after detraining. Conversely, apelin-13 increased significantly in response to training, compared with baseline (P < 0.05), and remained unchanged after detraining. Both training regimens had similar effects on most markers; however, AIT seems to have stronger anti-coronary disease effects (as indicated by HDL-C and peak oxygen consumption) than NRT.
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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 | 0.000 |
| 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".