Diabetes mellitus (DM) is a well-known risk factor for atrial fibrillation (AF), but the mechanism(s) by which DM affects AF prevalence remains unclear. This study aims to evaluate the impact of diabetes mellitus severity (expressed as its known duration), antihyperglycemic treatment regimen and glycaemic control on AF prevalence. From the representative sample of 3014 participants (mean age 77.5, 49.1% female) from the cross-sectional NOMED-AF study, 881 participants (mean age 77.6 ± 0.25, 46.4% female) with concomitant DM were involved in the analysis. AF was screened using a telemonitoring vest for a mean of 21.9 ± 9.1 days. The mean DM duration was 12 ± 0.35 years, but no significant impact of DM timespan on AF prevalence was observed. No differences in the treatment pattern (oral medication vs insulin vs both oral + insulin) among the study population with and without AF were shown (p = 0.106). Metabolic control reflected by HbA1c levels showed no significant association with AF and silent AF prevalence (p = 0.635; p = 0.094). On multivariate analyses, age (Odds Ratio (OR) 1.35, 95%CI: 1.18-1.53, p < 0.001), p = 0.042), body mass index (BMI; OR 1.043, 95%CI: 1.01-1.08, p = 0.027) and LDL < 100 mg/dl (OR 0.64, 95%CI: 0.42-0.97, p = 0.037) were independent risk factors for AF prevalence, while age (OR 1.45, 95%CI: 1.20-1.75, p < 0.001), LDL < 100 mg/dl (OR 0.43, 95%CI 0.23-0.82, p = 0.011), use of statins (OR 0.51, 95%CI: 0.28-0.94, p = 0.031) and HbA1c ≤ 6.5 (OR 0.46, 95%CI: 0.25-0.85, p = 0.013) were associated with silent AF prevalence. Diabetes duration, diabetic treatment pattern or metabolic control per se did not significantly impact the prevalence of AF, including silent AF detected by prospective continuous monitoring. Independent predictors of AF were age, BMI and low LDL levels, with statins and HbA1c ≤ 6.5 being additional independent predictors for silent AF.Trial registration: NCT03243474.
Rivaroxaban was associated with similar or lower stroke rates, but higher bleeding and mortality rates. Channeling of rivaroxaban towards elderly and less healthy patients may have generated residual confounding. In particular, our findings cannot stand alone when deciding which oral anticoagulant to prescribe. Copyright © 2016 John Wiley & Sons, Ltd.
A combination of low socioeconomic status and disability contributes to AF incidence and complications, elevating risks to higher levels relative to the general population. ML algorithms can be used to identify AF patients at high risk of clinical events. While further research is definitely in need on this socially important issue, the reported investigation is unique in which it integrates the general case about the subject due to the different ethnic groups around the world under a unified culture stemming from residing in the US.