Uric acid and incident atrial fibrillation of 14 years population-based cohort study: The Suita Study.
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ABSTRACT: Background
Higher baseline uric acid (UA) was significantly associated with higher atrial fibrillation (AF) incidence in Japanese women. However, no prospective study is evident in the association between UA and incident AF in Japanese urban residents.Methods
A total of 6863 participants (aged 30-79 years; 47% men) without prior AF were followed for 13.9 years on average in the Suita Study. According to the UA categories, cox proportional hazards regression models were used to estimating the Hazard Ratios (HRs) and 95% confidence intervals (CIs) for incident AF.Results
During 95178 person-years of follow-up, we observed 311 cases of incident AF (204 cases in men and 107 cases in women). Compared to the subjects with UA of 4.0-4.9 mg/dL, multivariable-adjusted HR (95% CIs) of incident AF was 1.50 (1.01-2.25) (P = .047) for the subjects with UA ≥7.0 mg/dL.Conclusion
High UA was associated with an increased risk for incident AF in the Japanese population.
SUBMITTER: Kobayashi T
PROVIDER: S-EPMC8485815 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivesAtrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population.Design, setting, participants, & measurementsWe studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated C-index; calibration was evaluated graphically and with the Grønnesby and Borgan test.ResultsMean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m2; 259 incident AF events occurred during a median of 8 years of follow-up. The CHARGE-AF prediction equation using original and re-estimated coefficients had C-indices of 0.67 (95% confidence interval, 0.64 to 0.71) and 0.67 (95% confidence interval, 0.64 to 0.70), respectively. A likelihood-based boosting model using clinical variables only had a C-index of 0.67 (95% confidence interval, 0.64 to 0.70); adding N-terminal pro-B-type natriuretic peptide, high-sensitivity troponin T, or both biomarkers improved the C-index by 0.04, 0.01, and 0.04, respectively. In addition to N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T, the final model included age, non-Hispanic Black race/ethnicity, Hispanic race/ethnicity, cardiovascular disease, chronic obstructive pulmonary disease, myocardial infarction, peripheral vascular disease, use of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blockers, diuretics, height, and weight.ConclusionsUsing machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.
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