INTRODUCTION: Research suggests that targeting high-risk, nonhypertensive @entity1 for preventive intervention may delay the onset of @entity101 . We aimed to develop a biomarker-based risk prediction model for assessing @entity101 risk in an urban Han Chinese population. METHODS: We analyzed data from 26,496 @entity1 with @entity101 to extract factors from 11 check-up biomarkers. Then, depending on a 5-year follow-up cohort, a Cox model for predicting @entity101 development was built by using extracted factors as predictors. Finally, we created a @entity101 synthetic predictor (HSP) by weighting each factor with its risk for @entity101 to develop a risk assessment matrix. RESULTS: After factor analysis, 5 risk factors were extracted from data for both @entity1 and @entity1 . After a 5-year follow-up, the cohort of @entity1 had an area under receiver operating characteristic curve (area under the curve [AUC]) with an odds ratio (OR) of 0.755 (95% confidence interval [CI], 0.746-0.763) for @entity1 and an OR of 0.801 (95% CI, 0.792-0.810) for @entity1 . After tenfold cross validation, the AUC was still high, with 0.755 (95% CI, 0.746-0.763) for @entity1 and 0.800 (95% CI, 0.791-0.810) for @entity1 . An HSP-based 5-year risk matrix provided a convenient tool for risk appraisal. CONCLUSION: @entity101 could be explained by 5 factors in a population sample of Chinese urban Han. The HSP may be useful in predicting @entity101 .

Identification of @entity101 Predictors and Application to XXXX Prediction in an Urban Han Chinese Population: A Longitudinal Study, 2005-2010.

@entity1 :: ('9606', 'Species') :: ['participants', 'women', 'patients', 'people', 'men'] @entity101 :: ('MESH:D006973', 'Disease') :: ['hypertension', 'Hypertension']
@entity101 :: (MESH:D006973,Disease) :: ['Hypertension']
