The Effects of Coenzyme Q10 Supplementation on Metabolic Profiles of Patients with Chronic Kidney Disease: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis of Randomized Controlled Trials (RCTs) were conducted to determine the effects of coenzyme Q10 (CoQ10) supplementation on metabolic profiles of patients diagnosed with Chronic Kidney Disease (CKD). METHODS: Two independent reviewers systematically searched online databases including PubMed, Cochrane Library, and Web of Science databases, Scopus, EMBASE until July 2018 to identify eligible clinical trials. The heterogeneity across included trials was assessed using Cochran's Q test and I-square (I2) statistic. Cochrane Collaboration risk of bias tool was applied to evaluate the quality of selected RCTs. Standardized mean difference (SMD) and 95% Confidence Interval (CI) between two groups of intervention were used to determine pooled effect sizes. RESULTS: Out of 721 potential papers, 7 RCTs were appropriate to be included in our meta-analysis. The pooled results revealed that CoQ10 supplementation significantly reduced total-cholesterol (SMD=-0.58; CI, -0.94, - 0.21; P=0.002; I2: 54.9), LDL-cholesterol (SMD=-0.47; 95% CI, -0.78, -0.17; P=0.003; I2:00.0), malondialdehyde (MDA) (SMD=-3.0; 95% CI, -5.10, -0.90; P=0.005; I2: 95.4) and creatinine levels (SMD=-1.65; 95% CI, - 2.75, -0.54; P=0.003; I2: 95.0) in patients diagnosed with CKD. Triglycerides, HDL-cholesterol, fasting glucose, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), and C-reactive protein (CRP) concentrations did not affect following CoQ10 supplementation. CONCLUSION: Overall, the current meta-analysis demonstrated that CoQ10 supplementation significantly improved metabolic profile in patients with CKD by reducing total cholesterol, LDL-cholesterol, MDA and creatinine levels, yet it did not affect fasting glucose, insulin, HOMA-IR, and CRP concentrations.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.043 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".