| Objective To build a risk predicting model of 5-year osteoporosis (OP) occurrence in 35-74-year-old males in Taiwan. Methods A total of 7081 individuals who received health check-up in Taiwan MJ Health Administration Organization for the first time from 1999 to 2005 were selected. After excluding 505 individuals who were OP patients at baseline, 7296 subjects were enrolled. Four health check-up centers were selected as the modeling cohort (Taipei center, n=3844), and the left 3 centers were selected as the testing cohort (n=3452). Multivariate logistic regression model was established based on an univariate model with variables that predicted OP incidence in 5 years and with variables that appeared on baseline. We evaluated model predictability by the area under the receiver-operating characteristic (ROC) curve (AUC) and testified its diagnostic property on the testing sample. Once final model was defined, we next established rules to characterize 4 different degrees of the risk based on cut points of these probabilities after transforming into normal distribution by log-transformation. Results At baseline, the range of OP prevalence was 4.77-7.88% in 4 check-up centers. After excluding 505 OP individuals at baseline, the incidence of OP was 2.34% (171/7296). The range of incidence was 1.52-4.89% in 4 check-up centers. Final multivariable logistic regression model included 5 risk factors: age, routine work status, waist circumference, weight, and serum creatinine. The AUC of the model consisted of modeling cohort was 0.728 (95% CI, 0.675-0.772). The AUC of testing cohorts was 0.698 (95% CI, 0.633-0.762). After labeling 4 risk degrees in modeling cohort, the risks of developing OP for subjects in moderate risk degree (11.9% of all subjects) and high risk degree (1.1% of all subjects) were 2.4 times and 8.1 times higher than those of general population, respectively. Conclusion The predictability and reliability of the model of estimated risks on developing OP within 5 years, which is based on Taiwan MJ longitudinal check-up population database, are good and satisfied. The predictive variables involved and the evaluation criterions to establish the risk degrees are simple and practicable. It will be helpful both for individual to assess the risk on developing OP and for community workers to survey the development of OP in community population. |