Genome-wide association study reveals novel genetic loci: a new polygenic risk score for mitral valve prolapse.

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-ß signalling molecules and spectrin ß. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.

Lifestyle components: Self-reported physical activity, nutritional status, sleep quality and incident atrial fibrillation.

•Incident AF was detected in 249 (0.3%) individuals of the Lifelines population.•Age, sex, body mass index, heart failure and stroke were associated with incident AF.•Physical activity, nutritional status and sleep quality were not associated with incident AF.

Clinical, biomarker, and genetic predictors of specific types of atrial fibrillation in a community-based cohort: data of the PREVEND study.

Aims: Atrial fibrillation (AF) may present variously in time, and AF may progress from self-terminating to non-self-terminating AF, and is associated with impaired prognosis. However, predictors of AF types are largely unexplored. We investigate the clinical, biomarker, and genetic predictors of development of specific types of AF in a community-based cohort. Methods: We included 8042 individuals (319 with incident AF) of the PREVEND study. Types of AF were compared, and multivariate multinomial regression analysis determined associations with specific types of AF. Results: Mean age was 48.5 ± 12.4 years and 50% were men. The types of incident AF were ascertained based on electrocardiograms; 103(32%) were classified as AF without 2-year recurrence, 158(50%) as self-terminating AF, and 58(18%) as non-self-terminating AF. With multivariate multinomial logistic regression analysis, advancing age (P< 0.001 for all three types) was associated with all AF types, male sex was associated with AF without 2-year recurrence and self-terminating AF (P= 0.031 and P= 0.008, respectively). Increasing body mass index and MR-proANP were associated with both self-terminating (P= 0.009 and P< 0.001) and non-self-terminating AF (P= 0.003 and P< 0.001). The only predictor associated with solely self-terminating AF is prescribed anti-hypertensive treatment (P= 0.019). The following predictors were associated with non-self-terminating AF; lower heart rate (P= 0.018), lipid-lowering treatment prescribed (P= 0.009), and eGFR <60 mL/min/1.73 m2 (P= 0.006). Three known AF-genetic variants (rs6666258, rs6817105, and rs10821415) were associated with self-terminating AF. Conclusions: We found clinical, biomarker and genetic predictors of specific types of incident AF in a community-based cohort. The genetic background seems to play a more important role than modifiable risk factors in self-terminating AF.