The effects of saffron (<i><scp>Crocus sativus</scp> L</i>.) in conjunction with concurrent training on body composition, glycaemic status, and inflammatory markers in obese men with type 2 diabetes mellitus: A randomized double‐blind clinical trial
Bibliographic record
Abstract
AIMS: Chronic inflammation is one of the major challenges in the management of obesity and type 2 diabetes mellitus (T2DM). Our primary aim was to assess the anti-inflammatory effects of Saffron (Crocus sativus L.) supplementation and concurrent training in obese men with T2DM. METHODS: Sixty obese men with T2DM (age = 39 ± 5 years; body mass = 93.9 ± 6 kg) were randomly assigned to four groups; concurrent training + placebo (CT; n = 15), saffron supplementation (S; n = 15), concurrent training + saffron supplementation (CTS; n = 15), or control (CON; n = 15). The participants in the CT group performed concurrent training (resistance + aerobic) three times per week for 12 weeks and received daily one pill of placebo (maltodextrin); the participants in the S group supplemented with one pill of 100 mg of saffron daily, and the participants in the CTS group participated in both saffron and training intervention while CON group continued regular lifestyle (no training and no supplementation). Inflammatory markers, body composition (evaluated by a multi-frequency bioelectrical impedance device; Jawon X-Contact 356), and metabolic profile were evaluated before and after interventions. RESULTS: All three interventions significantly (P < .05) decreased TNF-α (CT = -4.22, S = -1.91, CTS = -9.69 pg/mL), hs-CRP (CT = -0.13, S = -0.1, CTS = -0.32 ng/mL), IL-6 (CT = -6.84, S = -6.36, CTS = -13.55 pg/mL), IL-1β (CT = -8.85, S = -6.46, CTS = -19.8 pg/mL), FBG (CT = -6.97, S = -2.45, CTS = -13.86 mg/dL), insulin (CT = -0.13, S = -0.03, CTS = -0.21 mU/L), HOMA-IR (CT = -0.12, S = -0.04, CTS = -0.21), HbA1c (CT = -0.17, S = -0.11, CTS = -0.26%), and increased IL-10 (CT = 1.09, S = 0.53, CTS = 2.27 pg/mL) concentrations. There was a positive correlation between changes in BFP with hs-CRP, IL-6, IL-1β and TNF-α, and IL-10 concentrations across the intervention groups. Additionally, significant differences were observed between the changes for all variables in the CTS group compared to CT, S and CON groups (P < .05). CONCLUSION: It seems that an interaction of saffron supplementation and concurrent training has more efficient effects on anti-inflammatory status compared to saffron supplementation or concurrent training alone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".