A meta-analysis of HDL cholesterol efflux capacity and concentration in patients with rheumatoid arthritis
Bibliographic record
Abstract
Abstract Background Poor cholesterol efflux capacity (CEC) has been proposed to be an independent risk factor for cardiovascular diseases. However, current evidence is inconsistent, especially in rheumatoid arthritis (RA) patients. This meta-analysis aims to identify whether CEC is impaired or altered by drug therapy in RA. Methods The PubMed/MEDLINE, Embase, Cochrane Library and ClinicalTrials.gov databases were browsed to identify studies on CEC in RA patients. The searches mainly focused on studies in human subjects that were published before November 14, 2020, without any language restrictions. The effect size was pooled by the standardized mean differences and mean differences (SMD & MD) as well as the corresponding 95% confidence intervals (CIs) in a random or fixed effect model. Heterogeneity across the studies was tested using Cochran’s Q test and I2 statistic. Newcastle-Ottawa Scale and the Downs and Black scale (D&B) were applied to evaluate the quality of included studies. The GRADE-system with its 4-grade evidence scale was used to assess the quality of evidence. Results A total of 11 eligible articles, including 6 observational and 5 interventional studies, were retrieved. The pooled results showed that in patients with RA, CEC was not significantly different than in healthy controls (SMD: -0.34, 95% CI: − 0.83 to 0.14), whereas the plasma HDL-C levels was significantly lower (MD: -3.91, 95% CI: − 7.15 to − 0.68). Furthermore, in the before-after studies, the CEC of RA patients (SMD: 0.20, 95% CI: 0.02 to 0.37) increased, but the plasma HDL-C levels (MD: 3.63, 95% CI: − 0.13 to 7.39) remained at a comparable quantity after anti-rheumatic treatment comparing with the baseline. In addition, the funnel plot of included studies displayed a lightly asymmetry, while Egger’s and Begg’s test did not suggest the existence of publication bias. The quality of evidence was rated according to GRADE as moderate to very low. Conclusion The current meta-analysis demonstrated that HDL-mediated CEC can be improved by the early control of inflammation and anti-rheumatic treatment in RA patients, which is independent of the plasma HDL-C levels. However, the results should be interpreted with caution because of low-quality and limited quantity of evidence. Future randomized controlled trials are needed to determine whether therapeutic strategies to enhance CEC in RA patients have beneficial effects for preventing CVD.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.064 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".