Elevated glycated hemoglobin levels may increase the risk of atrial fibrillation in patients with diabetes mellitus.
Bibliographic record
Abstract
BACKGROUND: As the most common cardiac arrhythmia, atrial fibrillation (AF) is always accompanied with various complications if without detection and treatment timely. Blood-based pleiotropic molecule biomarkers have now been popularly applied in clinical detection. We hence performed this meta-analysis to evaluate the correlation of serum glycated hemoglobin (HbA1c) levels with the risk of AF in patients with diabetes mellitus (DM). METHODS: Covering myriads of computerized databases, we identified potential relevant studies for statistical analysis. We used a standard reporting form to extract data from each included study. Newcastle-Ottawa Scale (NOS) criteria was used for methodological quality assessment. Statistical analyses were conducted with the STATA statistical software. RESULTS: Six cohort studies in full text fulfilled our inclusion criteria, and following overestimation indicated that serum levels of HbA1c in DM patients with AF was higher than that in DM patients without AF (SMD = 0.67, 95% CI: 0.39-0.94, P < 0.001). Subgroup analyses by sample size and detection method implicated that elevated serum HbA1c levels exhibited significant correlations with an increased risk of AF in DM patients in the large-size subgroup (n ≥ 200), the small-size subgroup (n < 200), the high performance liquid chromatography (HPLC) subgroup and the non-HPLC subgroup (Large-size: SMD = 0.70, 95% CI: 0.38-1.03, P < 0.001; Small-size: SMD = 0.64, 95% CI: 0.09-1.19, P = 0.023; HPLC: SMD = 0.81, 95% CI: 0.49-1.12, P < 0.001; Non-HPLC: SMD = 0.36, 95% CI: 0.04-0.68, P = 0.029; respectively). CONCLUSION: Elevated serum HbA1c levels may be associated with an increased risk of AF in DM patients, possibly reflecting that serum HbA1c level might be a potential biomarker in the prediction of AF in DM patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".