MicroRNAs associated with chronic kidney disease in the general population and high-risk subgroups: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) is a significant health and economic burden, owing to its ever-increasing global prevalence. Due to the limitations in the current diagnostic methods, CKD is frequently diagnosed at advanced stages, where there is an increased risk of cardiovascular complications and end-stage kidney disease. As such, there has been considerable interest in microRNAs (miRNAs) as potential markers for CKD detection. This review seeks to identify all miRNAs associated with CKD and/or markers of kidney function or kidney damage in the general population and high-risk subgroups, and explore their expression profiles in these populations. METHODS AND ANALYSIS: A systematic search of published literature will be conducted for observational studies that report on miRNAs associated with CKD or kidney function or kidney damage markers (serum creatinine and cystatin C, estimated glomerular filtration rate and urinary albumin excretion) in adult humans. The electronic database search will be restricted to English and French publications up to 31 October 2021. Two investigators will independently screen and identify studies for inclusion, as well as extract data from eligible studies. Risk-of-bias and methodological quality will be assessed by the Newcastle-Ottawa Quality Assessment Scale for observational studies and Grading of Recommendations Assessment, Development and Evaluation tools. Appropriate meta-analytic techniques will be used to pool estimates from studies with similar miRNAs, overall and by major characteristics, including by country or region, sample size, gender and risk-of-bias score. Heterogeneity of the estimates across studies will be quantified and publication bias investigated. This protocol is reported according to Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 guidelines. ETHICS AND DISSEMINATION: This study design does not require formal ethical clearance and findings will be published in a peer-reviewed journal. CONCLUSION: This review will provide the expression pattern of miRNAs associated with CKD. This will allow for further research into the identified miRNAs, which could later be used as biomarkers for prediction and early detection of CKD, monitoring of disease progression to advanced stages and as potential therapeutic targets. PROSPERO REGISTRATION NUMBER: CRD42021270028.
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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 | 0.054 | 0.075 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".