Correlation between osteoprotegerin and coronary artery calcification in diabetic subjects: a systematic review of observational studies
Bibliographic record
Abstract
Coronary artery calcification (CAC) is one of the critical cardiovascular complications that lead to elevated morbidity and mortality among patients with type 2 diabetes (T2M). The association between osteoprotegerin (OPG) and CAC could potentially provide a reasonable chance for preventive therapy in type 2 diabetic patients and benefit the rate of mortality. Since measurement of CAC score is relatively expensive and requires radiation exposure, the current systematic review aims to provide clinical evidence for evaluating the prognostic role of OPG in determining CAC risk among subjects with T2M. Web of Science, PubMed, Embase, and Scopus, were investigated until July 2022. We assessed human studies investigating the association of OPG with CAC in type 2 diabetic patients. Quality assessment was performed by Newcastle-Ottawa quality assessment scales (NOS). Out of 459 records, 7 studies remained eligible to be included. Observational studies that provided odds ratio (OR) estimates with 95% confidence intervals (CIs) for the association between OPG and the risk of CAC were analyzed by random-effects model. In order to provide a visual summary of our findings, the estimation of pooled OR from cross-sectional studies was reported as 2.86 [95% CI 1.49-5.49], which is consistent with the findings of the cohort study. Results revealed that the association between OPG and CAC was significant among diabetic patients. OPG is hypothesized to be a potential marker in predicting the presence of high coronary calcium score among subjects with T2M that could be recognized as a novel target for further pharmacological investigations.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".