Spirulina Supplementation with High-Intensity Interval Training Decreases Adipokines Levels and Cardiovascular Risk Factors in Men with Obesity
Bibliographic record
Abstract
Adiposity, a state characterized by excessive accumulation of body fat, is closely linked to metabolic complications and the secretion of specific adipokines. This study explores the potential of exercise and Spirulina supplementation to mitigate these complications and modulate adipokine release associated with obesity. The primary objective of this investigation was to examine the impact of a 12-week regimen of high-intensity training combined with Spirulina supplementation on adipokine concentrations and lipid profiles in male individuals with obesity (N = 44). The participants were randomly distributed into four groups, each consisting of 11 participants: a control group (CG), a supplement group (SG), a training group (TG), and a training plus supplement group (TSG). The intervention comprised a 12-week treatment involving Spirulina supplementation (6 g capsule daily), a 12-week high-intensity interval training (HIIT) protocol with three sessions per week, or a combined approach. Following the interventions, metabolic parameters, anthropometric measurements, cardiorespiratory indices, and circulating adipokines [CRP, Sema3C, TNF-α, IL-6, MCP1, IL-8] were assessed within 48 h of the before and final training session. Statistical analyses revealed significant differences across all measures among the groups (p < 0.05). Notably, post hoc analyses indicated substantial disparities between the CG and the three interventional groups regarding body weight (p < 0.05). The combined training and supplementation approach led to noteworthy reductions in low-density lipoprotein (LDL), total cholesterol (TC), and triglyceride (TGL) levels (all p < 0.0001), coupled with an elevation in high-density lipoprotein–cholesterol (HDL-C) levels (p = 0.0001). Furthermore, adipokine levels significantly declined in the three intervention groups relative to the CG (p < 0.05). The findings from this 12-week study demonstrate that Spirulina supplementation in conjunction with high-intensity interval training reduced adipokine levels, improved body weight and BMI, and enhanced lipid profiles. This investigation underscores the potential of Spirulina supplementation and high-intensity interval training as a synergistic strategy to ameliorate obesity-related complications and enhance overall cardiometabolic well-being in obese males.
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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) | 0.002 | 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".