Circulating serum and plasma levels of micro‐RNA in type‐1 diabetes in children and adolescents: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes mellitus (T1DM) is a complex metabolic disorder characterized by hyperglycaemia, with constantly increasing incidence in paediatric population. The discovery of new molecules, such as microRNAs, and their possible interactions with T1DM create novel aspects in the diagnosis of the disease. METHODS: This systematic review and meta-analysis adhered to PRISMA guidelines. MEDLINE, SCOPUS, Cochrane CENTRAL and Clinicaltrials.gov. were searched up to 20 April 2020. Inclusion criteria for individual studies were quantification of microRNAs in serum/plasma samples and study groups consisting of children and adolescents with T1DM and healthy controls. Primary outcome of the study was the qualitative expression of microRNAs between the two groups. Statistical analysis was performed with Comprehensive Meta-Analysis Software v3.0. Methodological quality of included studies was assessed using Newcastle-Ottawa scale. RESULTS: A total of 484 studies were retrieved from the initial search of the databases. These were subsequently limited to seven included studies. Seven microRNAs demonstrated contrasting expression between the two groups, with two of them showing significant overexpression in T1DM group (miR-181:95% CI: 0.429 to 1.341 P < .001, miR-210:95% CI: 0.381 to 0.852, P < .001) and one micro-RNA being significantly overexpressed in control group (miR-375:95% CI: 0.293 to 1.459, P = .003). CONCLUSION: A total of three micro-RNA molecules appeared to have a significantly different expression in T1DM patients, serving as a possible diagnostic panel of biomarkers. These findings may contribute as reference for future research to further support the use of microRNAs as a novel diagnostic tool in T1DM.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".