Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, PR China.
Hypertens Res. 2021 Nov;44(11):1483-1491. doi: 10.1038/s41440-021-00738-7. Epub 2021 Sep 3.
Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to determine whether genetic information can improve the prediction of incident hypertension using machine learning approaches in a prospective study. The study recruited 4592 subjects without hypertension at baseline from a cohort study conducted in rural China. A polygenic risk score (PGGRS) was calculated using 13 SNPs. According to a ratio of 7:3, subjects were randomly allocated to the train and test datasets. Models with and without the PGGRS were established using the train dataset with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) methods. The discrimination and reclassification of models were estimated using the test dataset. The PGGRS showed a significant association with the risk of incident hypertension (HR (95% CI), 1.046 (1.004, 1.090), P = 0.031) irrespective of baseline blood pressure. Models that did not include the PGGRS achieved AUCs (95% CI) of 0.785 (0.763, 0.807), 0.790 (0.768, 0.811), 0.838 (0.817, 0.857), and 0.854 (0.835, 0.873) for the Cox, ANN, RF, and GBM methods, respectively. The addition of the PGGRS led to the improvement of the AUC by 0.001, 0.008, 0.023, and 0.017; IDI by 1.39%, 2.86%, 4.73%, and 4.68%; and NRI by 25.05%, 13.01%, 44.87%, and 22.94%, respectively. Incident hypertension risk was better predicted by the traditional+PGGRS model, especially when machine learning approaches were used, suggesting that genetic information may have the potential to identify new hypertension cases using machine learning methods in resource-limited areas. CLINICAL TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375 .
目前的研究表明，遗传风险评分（GRS）在高血压预测中的作用存在争议。机器学习方法在医学领域得到了广泛应用，但在挖掘遗传信息方面却很少应用。本研究旨在确定遗传信息是否可以通过机器学习方法在一项前瞻性研究中提高偶发性高血压的预测能力。
该研究从中国农村进行的一项队列研究中招募了 4592 名基线时无高血压的受试者。使用 13 个 SNP 计算多基因风险评分（PGGRS）。根据 7:3 的比例，将受试者随机分配到训练和测试数据集。使用 Cox 回归、人工神经网络（ANN）、随机森林（RF）和梯度提升机（GBM）方法，在训练数据集中建立了包含和不包含 PGGRS 的模型。使用测试数据集估计模型的区分度和再分类。PGGRS 与偶发性高血压的风险呈显著相关（HR（95%CI），1.046（1.004，1.090），P=0.031），与基线血压无关。不包含 PGGRS 的模型的 AUC（95%CI）分别为 Cox 法 0.785（0.763，0.807）、ANN 法 0.790（0.768，0.811）、RF 法 0.838（0.817，0.857）和 GBM 法 0.854（0.835，0.873）。加入 PGGRS 后，AUC 分别提高了 0.001、0.008、0.023 和 0.017；IDI 提高了 1.39%、2.86%、4.73%和 4.68%；NRI 提高了 25.05%、13.01%、44.87%和 22.94%。传统+PGGRS 模型对偶发性高血压风险的预测更好，尤其是在使用机器学习方法时，这表明遗传信息可能有潜力利用机器学习方法在资源有限的地区识别新的高血压病例。
河南农村队列研究已在中国临床试验注册中心（注册号：ChiCTR-OOC-15006699）注册。http://www.chictr.org.cn/showproj.aspx?proj=11375。
