Plasma MicroRNAs as noninvasive diagnostic biomarkers in patients with Brugada syndrome.

BACKGROUND: Brugada syndrome (BrS) can be diagnosed by a type 1 BrS tracing in a 12-lead electrocardiogram (ECG). However, there are daily variations in the ECGs of BrS patients, which presents a challenge when diagnosing BrS. Although many susceptibility genes have been identified, the SCN5A gene is reportedly the main causative gene of BrS. However, most patients do not have an evidence of genetic predisposition to develop BrS. In addition, the diagnosis and risk stratification for ventricular fibrillation (VF) in patients with BrS presents some problems. Meanwhile, circulating micro RNAs (miRNAs) have drawn increased attention as potential biomarkers of various diseases. We hypothesize that circulating miRNAs may be potential diagnostic biomarkers for BrS. METHODS: We enrolled 70 Japanese BrS patients and 34 controls for the screening cohort. A total of 2,555 miRNA sequences were detected using the 3D-Gene miRNAs labeling kit and 3D-Gene Human miRNAs Oligo Chip. We compared the expression of the miRNAs between the BrS patients and the controls. We validated whether the miRNA were significantly up- or downregulated in the screening cohort using RT-PCR. We also enrolled 72 Japanese BrS patients and 56 controls to replicate these miRNAs. RESULTS: Eight miRNAs (hsa-miR-223-3p, hsa-miR-22-3p, hsa-miR-221-3p, hsa-miR-4485-5p, hsa-miR-550a-5p, hsa-miR-423-3p, hsa-miR-23a-3p, and hsa-miR-30d-5p) were downregulated, and one miRNA (hsa-miR-873-3p) was upregulated by more than 3-fold in BrS patients. The multivariate logistic regression analysis determined that hsa-miR-423-3p, hsa-miR-223-3p, and hsa-miR-23a-3p were independently associated with BrS (P < 0.0001). The AUC based on cross validation was 0.871 with a sensitivity and specificity of 83.5% and 81.1%, respectively. CONCLUSIONS: The plasma miRNAs are potential noninvasive biomarkers of BrS, and the constructed logistic model was useful for discriminating BrS.

Predicting atrial fibrillation using a combination of genetic risk score and clinical risk factors.

BACKGROUND: Atrial fibrillation (AF) has a genetic basis, and environmental factors can modify its actual pathogenesis. OBJECTIVE: The purpose of this study was to construct a combined risk assessment method including both genetic and clinical factors in the Japanese population. METHODS: We screened a cohort of 540 AF patients and 520 non-AF controls for single nucleotide polymorphisms (SNPs) previously associated with AF by genome-wide association studies. The most strongly associated SNPs after propensity score analysis were then used to calculate a weighted genetic risk score (WGRS). We also enrolled 1018 non-AF Japanese subjects as a validation cohort and monitored AF emergence over several years. Finally, we constructed a logistic model for AF prediction combining WGRS and clinical risk factors. RESULTS: We identified 5 SNPs (in PRRX1, ZFHX3, PITX2, HAND2, and NEURL1) associated with AF after Bonferroni correction. There was a 4.92-fold difference in AF risk between the highest and lowest WGRS calculated using these 5 SNPs (P = 2.32 × 10-10). Receiver operating characteristic analysis of WGRS yielded an area under the curve (AUC) of 0.73 for the screening cohort and 0.72 for the validation cohort. The predictive logistic model constructed using a combination of WGRS and AF clinical risk factors (age, body mass index, sex, and hypertension) demonstrated better discrimination of AF than WGRS alone (AUC = 0.84; sensitivity 75.4%; specificity 80.2%). CONCLUSION: This novel predictive model of combined AF-associated SNPs and known clinical risk factors can accurately stratify AF risk in the Japanese population.