A novel predicted model for hypertension based on a large cross-sectional study

Abstract

Hypertension is a global public health issue and leading risk for death and disability. It is urgent to search novel methods predicting hypertension. Herein, we chose 73158 samples of physical examiners in central China from June 2008 to June 2018. After strict exclusion processes, 33570 participants with hypertension and 35410 healthy controls were included. We randomly chose 70% samples as the train set and the remaining 30% as the test set. Clinical parameters including age, gender, height, weight, body mass index, triglyceride, total cholesterol, low-density lipoprotein, blood urea nitrogen, uric acid, and creatinine were significantly increased, while high-density lipoprotein was decreased in the hypertension group versus controls. Nine optimal markers were identified by a logistic regression model, and achieved AUC value of 76.52% in the train set and 75.81% in the test set for hypertension. In conclusions, this study is the first to establish predicted models for hypertension using the logistic regression model in Central China, which provide risk factors and novel prediction method to predict and prevent hypertension.

Figure 1

Study design and flow diagram. A total of 73158 participants were randomly enrolled. After removing 4165 participants with nulls values and 13 participants with outliers, the remaining 68980 participants, including 33570 participants and 35410 healthy controls, were used for further statistical analysis. We randomly chose 70% of the samples as the train set (n = 48404) and the remaining 30% as the test set (n = 20576) from both groups.

Figure 2

The difference and comparison of clinical parameters between the hypertension group (n = 33570) and healthy controls (n = 35410). The student T test was used to analyze the significant differences of clinical parameters between the hypertension group (case) and healthy controls (control). Clinical parameters included age, gender, height, weight, body mass index (BMI), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), blood urea nitrogen (BUN), uric acid (UA), and creatinine (Cr).

Figure 3

Identification and validation of the predicted model for hypertension by a logistic regression model. (a) The POD index was calculated based on the selected 9 optimal markers for each sample, and the mean POD value was significantly increased in the hypertension group versus healthy controls in the train set (p < 0.001). (b) The AUC value of the POD index achieved 76.52% (95% CI: 76.11–76.94%) between the hypertension group and healthy controls in the train set. (c) The mean POD value was significantly increased in the hypertension group versus healthy controls in the test set (p < 0.001). (d) The AUC value of the POD index achieved 75.81% (95% CI: 75.16–76.46%) between the hypertension group and healthy controls in the test set.