The effect of flaxseed supplementation on body weight and body composition: a systematic review and meta‐analysis of 45 randomized placebo‐controlled trials
Bibliographic record
Abstract
Summary Flaxseed consumption may be inversely associated with obesity; however, findings of available randomized controlled trials (RCTs) are conflicting. The present study aimed to systematically review and analyse RCTs assessing the effects of flaxseed consumption on body weight and body composition. PubMed, Medline via Ovid, SCOPUS, EMBASE and ISI Web of Sciences databases were searched up to November 2016. Mean changes in body composition indices including body weight, body mass index (BMI) and waist circumference were extracted. Effect sizes were expressed as weighted mean difference (WMD) and 95% confidence intervals (CI). Heterogeneity between studies was assessed with the I2 test. Publication bias and subgroup analyses were also performed. The quality of articles was assessed via the Jadad scale. A total of 45 RCTs were included. Meta‐analyses suggested a significant reduction in body weight (WMD: −0.99 kg, 95% CI: −1.67, −0.31, p = 0.004), BMI (WMD: −0.30 kg m−2, 95% CI: −0.53, −0.08, p = 0.008) and waist circumference (WMD: −0.80 cm, 95% CI: −1.40, −0.20, p = 0.008) following flaxseed supplementation. Subgroup analyses showed that using whole flaxseed in doses ≥30 g d−1, longer‐term interventions (≥12 weeks) and studies including participants with higher BMI (≥ 27 kg m−2) had positive effects on body composition. Whole flaxseed is a good choice for weight management particularly for weight reduction in overweight and obese participants.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".