Astaxanthin Supplemented with High-Intensity Functional Training Decreases Adipokines Levels and Cardiovascular Risk Factors in Men with Obesity
Bibliographic record
Abstract
The aim of this study was to investigate the effects of 12 weeks of high-intensity training with astaxanthin supplementation on adipokine levels, insulin resistance and lipid profiles in males with obesity. Sixty-eight males with obesity were randomly stratified into four groups of seventeen subjects each: control group (CG), supplement group (SG), training group (TG), and training plus supplement group (TSG). Participants underwent 12 weeks of treatment with astaxanthin or placebo (20 mg/d capsule daily). The training protocol consisted of 36 sessions of high-intensity functional training (HIFT), 60 min/sessions, and three sessions/week. Metabolic profiles, body composition, anthropometrical measurements, cardio-respiratory indices and adipokine [Cq1/TNF-related protein 9 and 2 (CTRP9 and CTRP2) levels, and growth differentiation factors 8 and 15 (GDF8 and GDF15)] were measured. There were significant differences for all indicators between the groups (p < 0.05). Post-hoc analysis indicated that the levels of CTRP9, CTRP2, and GDF8 were different from CG (p < 0.05), although levels of GDF15 were similar to CG (p > 0.05). Levels of GDF8 were similar in the SG and TG groups (p > 0.05), with reductions of GDF15 levels in both training groups (p < 0.05). A total of 12 weeks of astaxanthin supplementation and exercise training decreased adipokines levels, body composition (weight, %fat), anthropometrical factors (BMI), and improved lipid and metabolic profiles. These benefits were greater for men with obesity in the TSG group.
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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 | 0.000 |
| 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".