Combining multiaspect factors to predict the risk of childhood hypertension incidence.

Childhood hypertension has become a global public health issue due to its increasing prevalence and association with cerebral-cardiovascular disease in adults. In this study, we developed a predictive model for childhood hypertension based on environmental and genetic factors to identify at-risk individuals. Eighty children diagnosed with childhood hypertension and 84 children in the control group matched by sex and age from an established cohort were included in a nested case-control study. We constructed a multiple logistic regression model to analyze the factors associated with hypertension and applied the 10-fold cross-validation method to verify the prediction stability of the model. Childhood hypertension was found positively correlated with triglyceride level ≥150 mg/dL; low-density lipoprotein cholesterol level ≥110 mg/dL; body mass index Z score; waist-to-height ratio Z score; and red blood cell count (all P < .01) and negatively correlated with the relative expression level of retinol acyltransferase; relative expression level of vitamin D receptor; and dietary intake of fiber, vitamin C and copper (all P < .05) in this study. BMI Z score, triglyceride ≥150 mg/dL, RBC count, VDR/β-actin and LRAT/β-actin ratios were used to construct the predictive model. The area under the receiver operating characteristic curve was 94.45% (95% CI = 89.35%∼98.65%, P < .001). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were all above 80% in both the training and validation sets. In conclusion, this model can predict the risk of childhood hypertension and could provide a theoretical basis for early prevention and intervention to improve the cardiovascular health of children.