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This app can Access your Internet connection. Permissions info. Third, on the basis of the cohort design, the Cox proportional hazards regression model was built again between the hazard function of hypertension and the calculated HSP :. The predictive probability of hypertension at year t was calculated by the following formula:.
Finally, for each participant from the 5-year cohort study, we calculated relative absolute risk RAR using the following equation:. All data analyses in this study were conducted for both men and women. We used ArcGIS 9. All statistical analyses were performed using SAS version 9. In our study, 26, of 95, participants had hypertension at baseline, a prevalence of Although hypertension prevalence increased with age in both men and women Figure 1 , it was higher in men than women before age 60 and was similar after age Of the 3, participants 2, men and women who did not have hypertension at baseline but had hypertension at the end of year 5, the cumulative incidence was We calculated the distribution of age and 11 biomarkers among participants with and without baseline hypertension Table 1 , and all variables differed significantly for participants with and without baseline hypertension.
Results of the analysis were used to create a correlation matrix for the 11 biomarkers Appendix A. Figure 1. Prevalence of hypertension, by age, at baseline, in an urban Han population, China, — After exploring factor analysis EFA , 5 latent factors were extracted from 11 biomarkers. Combined with explained variance and cumulative variance, factor loadings by principal component analysis with varimax rotation Table 2. Five latent risk-related factors could explain The AUC was up to These matrices provide a convenient tool for hypertension prediction in clinical and health management.
AR shows his predictive probabilities for hypertension are more than 5, and RAR shows his hypertension risk compared with his peers people aged Figure 2. The 5-year risk matrix for risk appraisal of hypertension by sex. Graphs A1 and B1 are absolute risk matrices for men and women, respectively; graphs A2 and B2 are relative absolute risk matrices for men and women, respectively.
The dashed lines indicate discrimination criteria of absolute risk for predicting hypertension; the curved lines indicate mean absolute risk in the population. Abbreviation: HSP, hypertension synthetic predictor. In our study, the prevalence of hypertension was higher among men However, hypertension prevalence changes with age. Hypertension prevalence rises more steeply in aging women than in men, perhaps because of hormonal changes during menopause 24— Distribution of age and 11 routine health check-up biomarkers of participants with and without baseline hypertension differed significantly.
After factor analysis, 5 latent factors were extracted from 11 biomarkers, not only eliminating the multicollinearity, but also explaining the specific pathogenesis of hypertension. The 5 factors were used to predict hypertension in the following prediction model.
The cumulative explained variances of the 5 latent factors were Similar results have been found in other studies. Evidence from human and animal studies suggests that inflammation leads to the development of hypertension and that oxidative stress and endothelial dysfunction are involved in the inflammatory cascade
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