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Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China

Hypertension Research volume 44, pages 1483–1491 (2021)Cite this article

Abstract

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.

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Acknowledgements

The authors thank all the participants, coordinators, and administrators for their support and help during the research.

Funding

This research was supported by the Henan Natural Science Foundation of China (Grant No: 182300410293), Foundation of National Key Program of Research and Development of China (Grant no: 2016YFC0900803, 2019YFC1710002), National Natural Science Foundation of China (Grant no: 81573243, 81602925), Foundation of Medical Science and Technology of Henan Province (No: 201702367, 2017T02098), and the Discipline Key Research and Development Program of Zhengzhou University (Grant no: XKZDQY202008, XKZDQY202002). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China

    Miaomiao Niu, Yikang Wang, Runqi Tu, Xiaotian Liu, Jian Hou, Wenqian Huo, Zhenxing Mao, Chongjian Wang & Ronghai Bie

  2. School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, PR China

    Liying Zhang

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  1. Miaomiao Niu
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Niu, M., Wang, Y., Zhang, L. et al. Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China. Hypertens Res 44, 1483–1491 (2021). https://doi.org/10.1038/s41440-021-00738-7

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