title: Identification of Hypertension Predictors and Application to Hypertension Prediction in an Urban Han Chinese Population: A Longitudinal Study, 2005-2010.
abstract: BACKGROUND
  Research suggests that targeting high-risk, nonhypertensive patients for preventive intervention may delay the onset of hypertension. We aimed to develop a biomarker-based risk prediction model for assessing hypertension risk in an urban Han Chinese population.
  
  
  METHODS
  We analyzed data from 26,496 people with hypertension to extract factors from 11 check-up biomarkers. Then, depending on a 5-year follow-up cohort, a Cox model for predicting hypertension development was built by using extracted factors as predictors. Finally, we created a hypertension synthetic predictor (HSP) by weighting each factor with its risk for hypertension to develop a risk assessment matrix.
  
  
  RESULTS
  After factor analysis, 5 risk factors were extracted from data for both men and women. After a 5-year follow-up, the cohort of participants 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 men and an OR of 0.801 (95% CI, 0.792-0.810) for women. After tenfold cross validation, the AUC was still high, with 0.755 (95% CI, 0.746-0.763) for men and 0.800 (95% CI, 0.791-0.810) for women. An HSP-based 5-year risk matrix provided a convenient tool for risk appraisal.
  
  
  CONCLUSIONS
  Hypertension could be explained by 5 factors in a population sample of Chinese urban Han. The HSP may be useful in predicting hypertension.
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Wenchao
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, China.
authors.forename: Linping
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Yafei
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Tang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, China>
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan 250014, China. Email: chengqizhangsd@126.com.
authors.forename: Chengqi
authors.initials: C
authors.lastname: Zhang
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015415:Biomarkers / Q000032:analysis*; D002681:China; D005163:Factor Analysis, Statistical; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D006973:Hypertension / Q000175:diagnosis* / Q000208:ethnology*; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D017678:Sex Distribution; D014505:Urban Population; D055815:Young Adult
chemical_list: D015415:Biomarkers