[Epidemiological characteristics and correlated factors of habitual snoring among Chinese aged 30 to 79 year-old]

Genetic insights into the risk of snoring on stroke and ischemic stroke: A single-variable and multivariable Mendelian randomization

Background

Multiple risk factors of stroke have been identified in previous studies; however, the causal role of snoring in the onset of stroke is less investigated. To clarify the causal association of snoring on stroke and its subtypes, this study is performed.

Methods

The single nucleotide polymorphisms in relation to snoring were retrieved from the UK biobank cohort with 408,317 participants. The data for stroke and its subtypes of European ancestry (67,162 cases and 453,702 controls) were obtained from the MEGASTROKE consortium. In single-variable Mendelian randomization (SVMR) and multivariable MR (MVMR) analyses, inverse variance weighting was used as the primary estimate, complemented with sensitivity analyses more robust to pleiotropy.

Results

Genetically predicted snoring increased the risk of stroke (odds ratio [OR] = 2.69, 95% confidence interval [CI] = 1.19-6.08, P = 0.016) and ischemic stroke (IS) (OR = 2.82, 95% CI = 1.23-6.44, P = 0.013), but not large artery stroke (LAS) (OR = 3.02, 95% CI = 0.31-29.44, P = 0.339), cardioembolic stroke (CES) (OR = 1.51, 95% CI = 0.58-3.92, P = 0.395). We provide novel genetic evidence that snoring increases the risk of stroke and IS, but not LAS, CES, and SVS.

Conclusion

Our findings provide novel genetic evidence that snoring increases the risk of stroke and IS, but not LAS, CES, and SVS.

Development and Validation of a Coronary Heart Disease Risk Prediction Model in Snorers with Hypertension: A Retrospective Observed Study

Purpose

To develop and validate a risk prediction model for coronary heart disease (CHD) in snorers with hypertension, including traditional and new risk factors.

Patients and Methods

Twenty factors were evaluated in the records of 2810 snorers with hypertension. Training (70%) and validation (30%) sets were created by random allocation of data, and a new nomogram model was developed. The model's discrimination and calibration were measured by calculating the area under the receiver operating curve (AUC) and creating calibration charts. The performance of the nomogram model was compared with that of the Prediction for ASCVD Risk in China (China-PAR) and Framingham models by decision curve analysis. An optimal cutoff point for the risk score in the training set was computed to stratify patients.

Results

In the nomogram model, the AUCs for predicting CHD at 5, 7 and 9 years in the training set were 0.706 (95% confidence interval [CI] 0.649-0.763), 0.703 (95% CI 0.655-0.751) and 0.669 (95% CI 0.593-0.744), respectively. The respective AUCs were 0.682 (95% CI 0.607-0.758), 0.689 (95% CI 0.618-0.760) and 0.664 (95% CI 0.539-0.789) in the validation set. The calibration chart showed that the predicted events from the nomogram score were close to the observed events. Decision curve analysis indicated that the nomogram score was slightly better than the Prediction for ASCVD Risk in China (China-PAR) and Framingham models for predicting the risk of CHD in snorers with hypertension. A cutoff point was identified for being CHD-free (a nomogram score of ≤121), which could be helpful for the early identification of individuals at high-risk of CHD.

Conclusion

The nomogram score predicts the risk probability of CHD in snorers with hypertension at 5, 7 and 9 years, and shows good capability in terms of discrimination and calibration. It may be a useful tool for identifying individuals at high risk of CHD.