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
Why this work is in the frame
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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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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it