Blood DNA methylation and type 2 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Epidemiology of type 2 diabetes (T2D) is affected by genetic and environmental factors. Deoxyribonucleic acid (DNA) methylation as a kind of epigenetic mechanism has been reported to affect the pathogenesis of T2D. Both global DNA methylation and candidate genes were observed differentially methylated and identified changes. Peripheral blood biomarkers could have significant clinical utility due to the noninvasive operation and showed efficacy in measuring T2D-related DNA methylation. Moreover, due to the reversible feature of DNA methylation, it can be an effective therapy for T2D. METHODS: This study will be conducted according to the preferred reporting items for systematic review and meta-analysis protocols 2015 statement. The protocol of this systematic review and meta-analysis was registered on international platform of registered systematic review and meta-analysis protocols (No. 202040136). The following electronic bibliographic databases will be searched from inception: EMBASE, MEDLINE, Web of Science, Cochrane Central, China National Knowledge Infrastructure, Wanfang and China Science and Technology Journal Database, and Clinical Trials (ClinicalTrials.gov). A search strategy will be developed using a combination of medical subheadings words and keywords related to T2D, human peripheral blood, and DNA methylation. Two reviewers will screen the literature, and a third reviewer will adjudicate in case of disagreements. All the data will be extracted by 2 independent reviewers. The Cochrane Risk of Bias Assessment Tool will be used to assess the bias of randomized controlled studies, and the Newcastle-Ottawa scale for nonrandomized controlled studies. All the assessment will be investigated by 2 independent reviewers. The overall quality of extracted data will be assessed by using the grading of recommendations, assessment, development, and evaluation assessment tool. Disagreements and conflicts will be resolved by discussing it with a third reviewer. The narrative synthesis and meta-analysis of all evidence will be performed as the main findings analyzed.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".