The Effects of Coenzyme Q10 Supplementation on Lipid Profiles Among Patients with Metabolic Diseases: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Oxidative stress and inflammation are key parameters in developing metabolic disorders. Hence, antioxidant intake might be an appropriate approach. Several studies have evaluated the effect of coenzyme Q10 (CoQ10) supplementation on lipid profile among patients with metabolic diseases, though findings are controversial. The aim of this systematic review and meta-analysis was to determine the effects of CoQ10 supplementation on lipid profile in patients with metabolic disorders. METHODS: We searched PubMed, EMBASE, Web of Science and Cochrane Library databases until July 2017. Prospective clinical trials were selected assessing the effect of CoQ10 supplementation on different biomarkers. Two reviewers independently assessed the eligibility of studies, extracted data, and evaluated the risk of bias of included studies. A fixed- or random-effects model was used to pool the data, which expressed as a standardized mean difference with 95% confidence interval. Heterogeneity was measured using a Q-test and with I2 statistics. RESULTS: A total of twenty-one controlled trials (514 patients and 525 controls) were included. The meta-analysis indicated a significant reduction in serum triglycerides levels (SMD -0.28; 95% CI, -0.56, -0.005). CoQ10 supplementation also decreased total-cholesterol (SMD -0.07; 95% CI, -0.45, 0.31), increased LDL- (SMD 0.04; 95% CI, -0.27, 0.36), and HDL-cholesterol levels (SMD 0.10; 95% CI, -0.32, 0.51), not statistically significant. CONCLUSION: CoQ10 supplementation may significantly reduce serum triglycerides levels, and help to improve lipid profiles in patients with metabolic disorders. Additional prospective studies are recommended using higher supplementation doses and longer intervention period.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".