Xing-hua Yang1, Qiu-shan Tao2, Feng Sun2, Chun-keng Cao3, Si-yan Zhan2. 1. Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China. 2. Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Peking University. 3. MJ Health Management Organization, Taiwan.
Abstract
OBJECTIVE: This study aimed to provide an epidemiological modeling method to evaluate the risk of metabolic syndrome (MS) development in the coming 5 years among 35-74 year-olds from Taiwan. METHODS: A cohort of 13 973 subjects aged 35-74 years who did not have metabolic syndrome but took the initial testing during 1997-2006 was formed to derive a risk score which tended to predict the incidence of MS. Multivariate logistic regression was used to derive the risk functions and using the 'check-up center' (Taipei training cohort)as the overall cohort. Rules based on these risk functions were evaluated in the remaining three centers (as testing cohort). Risk functions were produced to detect the MS on a training sample using the multivariate logistic regression models. Started with those variables that could predict the MS through univariate models, we then constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables. The predictability of the model was evaluated by areas under curve (AUC) the receiver-operating characteristic (ROC) followed by the testification of its diagnostic property on the testing sample. Once the final model was defined, the next step was to establish rules to characterize 4 different degrees of risks based on the cut points of these probabilities, after being transformed into normal distribution by log-transformation. RESULTS: At baseline, the range of the proportion of MS was 23.9% and the incidence of MS in 5-years was 11.7% in the non-MS cohort. The final multivariable logistic regression model would include ten risk factors as: age, history of diabetes, contractive pressure, fasting blood-glucose, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, body mass index and blood uric acid. AUC was 0.827(95% CI: 0.814-0.839) that could predict the development of MS within the next 5 years. The curve also showed adequate performance in the three tested samples, with the AUC and 95% CI as 0.813 (0.789-0.837), 0.826 (0.800-0.852) and 0.794 (0.768-0.820), respectively. After labeling the degrees of the four risks, it was showed that over 17.6% of the incidence probability was in the population under mediate risk while over 59.0% of them was in the high risk group, respectively. CONCLUSION: Both predictability and reliability of our Metabolic Syndrome Risk Score Model, derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database, were relatively satisfactory in the testing cohort. This model was simple, with practicable predictive variables and feasible form on degrees of risk. This model not only could help individuals to assess the situation of their own risk on MS but could also provide guidance on the group surveillance programs in the community regarding the development of MS.
OBJECTIVE: This study aimed to provide an epidemiological modeling method to evaluate the risk of metabolic syndrome (MS) development in the coming 5 years among 35-74 year-olds from Taiwan. METHODS: A cohort of 13 973 subjects aged 35-74 years who did not have metabolic syndrome but took the initial testing during 1997-2006 was formed to derive a risk score which tended to predict the incidence of MS. Multivariate logistic regression was used to derive the risk functions and using the 'check-up center' (Taipei training cohort)as the overall cohort. Rules based on these risk functions were evaluated in the remaining three centers (as testing cohort). Risk functions were produced to detect the MS on a training sample using the multivariate logistic regression models. Started with those variables that could predict the MS through univariate models, we then constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables. The predictability of the model was evaluated by areas under curve (AUC) the receiver-operating characteristic (ROC) followed by the testification of its diagnostic property on the testing sample. Once the final model was defined, the next step was to establish rules to characterize 4 different degrees of risks based on the cut points of these probabilities, after being transformed into normal distribution by log-transformation. RESULTS: At baseline, the range of the proportion of MS was 23.9% and the incidence of MS in 5-years was 11.7% in the non-MS cohort. The final multivariable logistic regression model would include ten risk factors as: age, history of diabetes, contractive pressure, fasting blood-glucose, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, body mass index and blood uric acid. AUC was 0.827(95% CI: 0.814-0.839) that could predict the development of MS within the next 5 years. The curve also showed adequate performance in the three tested samples, with the AUC and 95% CI as 0.813 (0.789-0.837), 0.826 (0.800-0.852) and 0.794 (0.768-0.820), respectively. After labeling the degrees of the four risks, it was showed that over 17.6% of the incidence probability was in the population under mediate risk while over 59.0% of them was in the high risk group, respectively. CONCLUSION: Both predictability and reliability of our Metabolic Syndrome Risk Score Model, derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database, were relatively satisfactory in the testing cohort. This model was simple, with practicable predictive variables and feasible form on degrees of risk. This model not only could help individuals to assess the situation of their own risk on MS but could also provide guidance on the group surveillance programs in the community regarding the development of MS.