Cardiometabolic risk factors and disease trends for atrial fibrillation in individuals with type 1 diabetes: a nationwide registry study
Bibliographic record
Abstract
Abstract Objective To investigate standardized incidence of atrial fibrillation (AF) in individuals with type 1 diabetes (T1DMM) compared with matched controls from the general population. Additionally, to examine optimal levels- and relative importance of risk factors associated with AF and numbers of risk factors necessary to reduce excess risk in individuals with T1DM. Research design and methods The study included individuals with T1DM between 2001 and 2019 and matched controls without T1DM. The outcome of interest was the first occurrence of AF. Standardized incidence rates and Cox regression were used for analyzing incidence and risk associations. Results The study comprises analyses of data from 36,069 persons with T1DM and 165,705 matched controls; average age 34.1; 43.2% women. Incidence rates per 100,000 person years for AF in persons with T1DM declined between 2001 and 2019 from 671 to 494; also in controls from 568 to 317. However, results shows that those without cardiovascular disease at baseline, did not display a similar rate reduction over time. During this period, people with T1DM had a 1.34-fold (95% CI 1.24–1.46) higher adjusted hazard for incident AF than controls when adjusting for sociodemographic factors. This hazard was attenuated to 0.95 (95% CI 0.87–1.03) after also accounting for coronary, cerebrovascular, kidney disease and heart failure; among those with T1DM. In those, with several risk factors at baseline, we observed a hazard ratio from 1.61 (95%, 1.07–2.43), and there was also an indication of clear risk reduction in those with zero risk factors, albeit non-significant (HR 0.60, 95% CI 0.35–1.04). In the T1DM cohort, the first available value of hemoglobin A1c, systolic blood pressure, body mass index and estimated glomerular filtration rate were each independently associated with incident AF and we noticed a clear linear risk increase for several cardiometabolic risk factors. Conclusions The crude incidence of AF was higher for persons with versus without T1DM, and declined significantly in both groups. Adjusting for data-derived predictors of AF attenuated higher risks, suggesting that the higher AF risk for persons with T1DM is driven by its common comorbidities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".