The Effect of Honey Intake on Lipid Risk Factors: a Systematic Review and Meta‐Analysis of Controlled Trials
Bibliographic record
Abstract
Objective Excessive fructose intake has an adverse effect on established lipid risk factors when added to existing diets. It is not known whether honey, a naturally occurring fructose‐containing sugar, exerts a different effect on lipid risk factors. We undertook a systematic review and meta‐analysis of controlled trials to assess the effects of honey intake on lipid risk factors. Methods MEDLINE, EMBASE and the Cochrane Library were searched through September 27, 2016 for controlled trials with follow‐up of ≥7 days, which investigated the effect of oral honey intake compared to a sugar equivalent (or usual diet) on lipid outcomes including LDL‐Cholesterol (LDL‐C), HDL‐C and fasting triglycerides in participants from all health backgrounds (normal, overweight‐obese, high cholesterol, pre‐diabetes, diabetes). Two reviewers independently extracted relevant data. Data were pooled using inverse variance random effects model and expressed as mean difference (MD) with 95% Confidence Intervals (CI). Inter‐study heterogeneity was assessed (Cochran Q statistic) and quantified (I 2 statistic). The overall quality of the evidence was assessed using the Grading of recommendations assessment, development, and evaluation (GRADE). Results Eligibility criteria were met by 10 trials (n=444, median length=5 weeks) with an average honey dose of 70 grams/day (range 40–100 grams/day). Only 3 trials used monofloral honey. Regular honey intake reduced LDL‐C (n=9; MD −0.29 mmol/L [95% CIs, −0.52, −0.05]; P=0.02), fasting triglycerides (n=10; MD −0.29 mmol/L [95% CIs, −0.35, −0.22]; P<0.001), and increased HDL‐C (n=9; MD 0.08 mmol/L [95% CIs, 0.05, 0.11]; P<0.001). There was evidence of substantial inter‐study heterogeneity for LDL‐C (I 2 =92.5%, p<0.001), and non‐significant heterogeneity for fasting triglycerides and HDL‐C (P>0.10 for both). The overall quality of the evidence was graded as “low quality” for LDL‐C owing to downgrades for serious inconsistency and serious imprecision, “moderate quality” for fasting triglycerides owing to downgrade for strongly suspected publication bias, and of “moderate‐quality” for HDL‐C owing to downgrade for serious imprecision. Conclusion Pooled analyses show a beneficial effect of honey intake on lipid risk factors including LDL‐C, triglycerides, and HDL‐C in people from all health backgrounds. There is a need for larger, longer and higher quality trials, as our confidence in the results is low. Registered at PROSPERO 2015= CRD42015023580 Support or Funding Information Canadian Diabetes Association, PSI Foundation, Banting and Best Diabetes Centre, and Bee Maid Honey Ltd (unrestricted travel donation).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.011 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".