基于多通路候选基因构建盐敏感性高血压筛检模型的研究

Essential hypertension is a serious public health issue in China. Salt intake is one of the main risk factors of hypertension. However, the blood pressure response towards salt loading is quite different between individuals. This phenomenon is called salt sensitivity. Many studies have indicated that salt sensitive individuals are more likely to develop cardiovascular disease than ones without salt sensitivity. Based on the WHO-Luxembourg-Shandong project which was about rural health personnel training and chronic disease control, we selected 300 subjects with salt sensitivity and 300 subjects without salt sensitivity using chronic salt loading test, and we also selected and genotyped 67 genetic variants from Pubmed and Embase databases. Random forests method was used to screen about 15-20 main genetic biomarkers. The genetic risk score model, random forests model and BP artificial neural network model were used to construct the screening model for salt sensitivity. The chronic salt loading test was considered as gold criteria, ROC method was used to calculate the AUC, sensitivity and specialty values of three models, in order to choose the best screening model.This will early identify the salt sensitive subjects, ultimately, to improve the intervention and personal drug treatment, and to reduce the cardiovascular events.

原发性高血压已成为我国严重的公共卫生问题。盐是高血压的最主要危险因素之一。但是，个体间对盐负荷却表现出不同的血压反应，这种现象被称为盐敏感性。研究表明，与非盐敏感者相比，盐敏感者发生心血管疾病的风险显著增加。本课题以"卢森堡-世界卫生组织-中国山东省农村卫生人员培训与慢病控制项目"为依托，采用慢性盐负荷试验诊断300例盐敏感性高血压与300例非盐敏感性高血压作为研究样本，对国内外文献报道的67个遗传位点进行检测，采用随机森林法筛选出15-20个主要遗传标志物；采用遗传风险加权得分模型、随机森林模型和BP人工神经网络模型三种方法，构建基于多通路候选基因盐敏感高血压早期筛检模型；以慢性盐负荷试验为金标准，采用ROC曲线计算三种不同模型AUC值，综合评判模型的灵敏度和特异度等，并进行交叉验证，从而选择最优预测模型。早期识别盐敏高血压患者，加强重点干预和个体化药物治疗，从而减少心血管疾病事件。

原发性高血压已成为我国严重的公共卫生问题。盐是高血压的最主要危险因素之一。但是，个体间对盐负荷却表现出不同的血压反应，这种现象被称为盐敏感性。研究表明，与非盐敏感者相比，盐敏感者发生心血管疾病的风险显著增加。本课题采用急性盐负荷试验诊断219例血压盐敏感性与543例非盐敏感性作为研究样本，对国内外文献报道的58个遗传位点进行检测，采用单因素logistic回归分析筛选出9个主要遗传标志物；采用遗传风险加权得分模型，构建基于多通路候选基因盐敏感高血压早期筛检模型；以急性盐负荷试验为金标准，采用ROC曲线计算出模型AUC值，综合评判模型的灵敏度和特异度等，从而选择最优预测模型。遗传风险得分模型的AUC值为0.59，预测效果为中等；如果与环境因素如吸烟和BMI相结合，将能提升模型的筛检效果。总之，模型的构建有利于早期识别盐敏高血压患者，加强重点干预和个体化药物治疗，从而减少心血管疾病事件。