Effect of yoga on adipokine levels among overweight and obese people: A systematic review
Bibliographic record
Abstract
BACKGROUND: Adipokines have an important role in the pathophysiology of overweight and obesity and associated inflammatory diseases. OBJECTIVE: The present review aims to evaluate the role of Yoga on adipokines among people with overweight and obesity. METHODS: Authors performed a systematic search for relevant research studies as per the PRISMA guidelines in Google Scholar, Medline/Pubmed, Scopus, Web of Science, PsychInfo electronic databases. Two independent authors conducted the selection of articles, data extraction, assessment of the risk of bias for individual studies. Any disagreements were resolved by discussion with the third author. RESULTS: Eight randomized trials and four uncontrolled trials involving a total of 1054 participants were included. Yoga with varying frequencies was administered for different durations. The studied adipokines among overweight and obese were leptin, adiponectin, interleukin-6 (IL-6), Tumor necrosis factor-alpha (TNF-α), chemerin, visfatin, plasminogen activator inhibitor-1 (PAI-1), and transforming growth factor-beta (TGF-β). The methodological quality of the included studies was low to moderate on the Cochrane risk of bias tool and Newcastle-Ottawa Quality Assessment Scale. The higher the frequency and duration of Yoga practice, the more significant changes in the adipokine levels were seen. CONCLUSION: The present review indicates that Yoga practices positively impacts adipokines among people with overweight and obesity. However, the present study precludes the generalizability of results due to the methodological heterogeneity, the type of Yoga intervention, and settings.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".