Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database.

OBJECTIVE: This study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population. METHODS: A cohort of 11,345 subjects aged 35-74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997-2006 to derive a risk score that could predict the occurrence of dyslipidaemia. Multivariate logistic regression was used to derive the risk functions using the check-up centre of the overall cohort. Rules based on these risk functions were evaluated in the remaining three centres as the testing cohort. We evaluated the predictability of the model using the area under the receiver operating characteristic (ROC) curve (AUC) to confirm its diagnostic property on the testing sample. We also established the degrees of risk based on the cut-off points of these probabilities after transforming them into a normal distribution by log transformation. RESULTS: The incidence of dyslipidaemia over the 5-year period was 19.1%. The final multivariable logistic regression model includes the following six risk factors: gender, history of diabetes, triglyceride level, HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and BMI (body mass index). The ROC AUC was 0.709 (95% CI: 0.693-0.725), which could predict the development of dyslipidaemia within 5 years. CONCLUSION: This model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community.

[Setting up a risk prediction model on metabolic syndrome among 35-74 year-olds based on the Taiwan MJ Health-checkup Database].

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.

[Association analysis of serum γ-glutamyltransferase with risk of metabolic syndrome in Beijing healthy population].

OBJECTIVE: To study the association of γ-glutamyltransferase (GGT) with the development of the metabolic syndrome (MS). METHODS: Subjects without MS at baseline in Beijing health-checkup database during 2003 and 2010, from MJ Health Management Centers, with complete key variables and at least two records were selected to derive a cohort, after comparison of the median trend, and analysis with Cox regression models and spline regression models, and to study the association of GGT with the development of MS and the dose-response relationship trend. RESULTS: Out of 10 076 (46.20/1 000 person-years) in the cohort, 1 181 subjects developed MS after follow-up of 2.54 years on average. With adjustment for age, gender, cigarette smoking, alcohol intake, physical activity, body mass index, family history of cardiovascular disease, systolic blood pressure, white blood cell count, high-density lipoprotein cholesterol, fasting blood glucose, triglycerides and C-reacted protein in Cox regression model, the hazard ratio for MS in quartiles 4 level of GGT was 1.60(95% confidence interval: 1.18-2.17). After adjustment with the use of spline regression model, the dose-response relationship showed an increasing curve with a degressive slope. The elevated GGT level was associated with an increased risk of MS, but the contribution of GGT augmented less when the GGT level was high. CONCLUSION: The elevated GGT level, an important risk factor and predictor, may be associated with an increased risk of MS.

[Estimation on the risk of 5-years obesity development among adults aged 30 - 59, based on the Taiwan MJ Health-checkup Database].

OBJECTIVE: This study aimed to provide an epidemiological modeling in evaluating the risk of developing obesity within 5 years in Taiwan population aged 30 - 59 years. METHODS: After excluding 918 individuals who were observed at baseline, a cohort of 14 167 non-obesity subjects aged 30 - 59 years in the initial year during 1998 - 2006, was formed to derive a Risk Score which could predict the incident obesity (IO). Multivariate logistic regression was used to derive the risk functions, using the check-up center (Taipei training cohort, n = 8104) of the overall cohort. Rules based on these risk functions were evaluated in the left three centers (testing cohort, n = 6063). Risk functions were produced to detect the IO on a training sample using the multivariate logistic regression models. Starting with variables that could predict the IO through univariate models, we constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables. We evaluated the predictability of the model by the area under the receiver-operating characteristic (ROC) curve (AUC) and to testify 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 risk based on the cut points of these probabilities after transforming into normal distribution by log-transformation. RESULTS: At baseline, the range of the proportion of normal weight, overweight and obesity were 50.00% - 60.00%, 26.47% - 31.11% and 5.76% - 7.24% respectively in four check-up centers of Taiwan. After excluding 918 obesity individuals at baseline, we ascertained 386 (2.73%, 386/14 167) cases having IO and 2.66% - 2.91% of them having centered obesity in the four check-up centers respectively. Final multivariable logistic regression model would include five risk factors: sex, age, history of diabetes, weight deduction ≥ 4 kg within 3 months and waist circumference. The area under the ROC curve (AUC) was 0.898 (95%CI, 0.884 - 0.912) that could predict the development of obesity within 5 years. The curve also had adequate performance in testing the sample [AUC = 0.881 (95%CI, 0.862 - 0.900)]. After labeling the four risk degrees, 16.0% and 2.9% of the total subjects were in the mediate and high risk populations respectively and were 7.8 and 16.6 times higher, when comparing with the population at risk in general. CONCLUSION: The predictability and reliability of our obesity risk score model, derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database, were relatively satisfactory, with its simple and practicable predictive variables and the risk degree form. This model could help individuals to self assess the situation of risk on obesity and could also guide the community caretakers to monitor the trend of obesity development.