Effects of HIIT training and HIIT combined with circuit resistance training on measures of physical fitness, miRNA expression, and metabolic risk factors in overweight/obese middle-aged women
Bibliographic record
Abstract
Abstract Objective The purpose of this study was to examine the effects of 10 weeks of high-intensity interval training (HIIT) and HIIT combined with circuit resistance training (HCRT) on selected measures of physical fitness, the expression of miR-9, -15a, -34a, -145, and − 155 as well as metabolic risk factors including lipid profiles and insulin resistance in middle-aged overweight/obese women. Methods Twenty-seven overweight/obese women aged 35–50 yrs. were randomized to HIIT (n = 14) or HCRT (n = 13) groups. The HIIT group performed running exercises (5 reps x 4 min per session) with active recovery between repetitions for 10 weeks with 5 weekly sessions. The HCRT group performed 10 weeks of HIIT and resistance training with 3 weekly HIIT sessions and 2 weekly HCRT sessions. Anthropometric measures (e.g., body mass), selected components of physical fitness (cardiovascular fitness, muscle strength), levels of miRNAs (miR-9, -15a, -34a, -145, and − 155), lipid profiles (total cholesterol; TC, Triglycerides; TG, low-density lipoprotein cholesterol; LDL-C and high-density lipoprotein cholesterol; HDL-C), and insulin resistance; HOMA-IR index, were measured at baseline and week 10. Results An ANOVA analysis indicated no significant group by time interactions (p > 0.05) for all anthropometric measures, and maximum oxygen consumption (VO2max). A significant group by time interaction, however, was found for the one-repetition maximum (IRM; p < 0.001, ES= 0.751 , moderate). A post-hoc test indicated an increase in the pre-to-post mean 1RM for HCRT (p = 0.001, ES = 1.83, large). There was a significant group by time interaction for miR-155 (p = 0.05, ES = 0.014, trivial). Levels for miR-155 underwent pre-to-post HIIT increases (p = 0.045, ES = 1.232, large). Moreover, there were also significant group by time interactions for TC (p = 0.035, ES = 0.187, trivial), TG (p < 0.001, ES = 0.586, small), LDL-C (p = 0.029, ES = 0.200, small) and HDL-C (p = 0.009, ES = 0.273, small). Post-hoc tests indicated pre-post HCRT decreases for TC (p = 0.001, ES = 1.44, large) and HDL-C (p = 0.001, ES = 1.407, large). HIIT caused pre-to-post decreases in TG (p = 0.001, ES = 0.599, small), and LDL-C (p = 0.001, ES = 0.926, moderate). Conclusions Both training regimes did not improve cardiovascular fitness. But, HCRT improved lower/upper limb muscle strength, and HIIT resulted in an increase in miR-155 expression in peripheral blood mononuclear cells. Furthermore, HIIT and HCRT each improved selected metabolic risk factors including lipid profiles and glucose and insulin metabolism in overweight/obese middle-aged women. Trial registration OSF, October, 4th 2023. Registration DOI: https://doi.org/10.17605/OSF.IO/UZ92E . osf.io/tc5ky . “Retrospectively registered”.
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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) | 0.001 | 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 | 0.001 |
| 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".