Fig 1.
MetS: metabolic syndrome.
Table 1.
Baseline Characteristics of the Development Cohort and Crude Association of Potential Risk Factors with Incident Metabolic Syndrome.
Table 2.
Multivariate Logistic Regression Analysis for the Prediction of Metabolic Syndrome and Risk Scoring System in the Derivation cohort.
Fig 2.
Receiver-operating characteristics curves and correlation of predicted versus observed risk of outcomes of an initial risk score for predicting Metabolic Syndrome in the derivation and validation cohorts.
The initial score accurately predicted incident MetS both in the derivation (A) and validation cohorts (B). Calibration plots for prediction of incident MetS in the derivation cohort (C) (Intercept of 0.12, a slope of 0.99, and an R2 of 99%, p <0.001) and validation cohorts (D) (Intercept of 0.48, a slope of 0.97, and an R2 of 99%, p <0.001). Abbreviations as in Fig 1.
Table 3.
Baseline Characteristics of the MetS Population and Crude Association of Potential Risk Factors with Recovery from Metabolic Syndrome.
Table 4.
Multivariate Logistic Regression Analysis for the Recovery from Metabolic Syndrome and Risk Scoring System in individuals with Prevalent Metabolic Syndrome.
Fig 3.
Receiver-operating characteristics curves and predicted risk versus observed risk of outcomes of a final risk score for predicting MetS and recovery from it.
(A) In MetS cohort, the final risk model predicted the recovery from MetS. (B) The final score predicted subsequent MetS in the whole population without MetS at enrollment. (C) Calibration plots for prediction of recovery from MetS in the prevalent MetS population at enrollment were good with an intercept of 0.95, a slope of 0.96, and an R2 of 97% (p <0.001). (D) The final score demonstrated good calibration for predicting incident MetS in the whole population with an intercept of 0.69, a slope of 0.95, and an R2 of 98% (p <0.001). Abbreviations as in Fig 1.
Fig 4.
Comparison of area under the curves between final risk score and model derived from only the five MetS diagnostic components.
The final score had a significantly larger area under the curve compared with the area obtained using the model that was derived from only the five MetS diagnostic components (0.80 vs. 0.79, p < 0.0001). Abbreviations as in Fig 1.
Fig 5.
Final score predicts incidence of MetS in the entire Non-MetS population.
Final risk scores for incident MetS were calculated for each individual participant in the entire population without MetS at enrollment (derivation and validation cohorts combined, n = 13,634) as described in Table 4. Incidence (%) in the bottom table represents the developed MetS cases in the population (n = 1,635). I bars represent 95% confidential interval (CI). Other abbreviations as in Fig 1.