Saibin Wang1, Sujiao Wang2, Shuzhen Jiang2, Qian Ye2,3. 1. Department of Respiratory Medicine, Jinhua Municipal Central Hospital, China. 2. Department of Nursing, Jinhua Municipal Central Hospital, China. 3. Department of Medical Records Quality Management, Jinhua Municipal Central Hospital, China.
Abstract
BACKGROUND: Early detection of metabolic syndrome is highly desirable for the prevention and treatment of various diseases. Therefore, this study aimed to develop and validate an anthropometry-based nomogram for predicting metabolic syndrome in a working population. METHODS: The present study was a secondary analysis of a cross-sectional study. A total of 60,799 workers in Spain were enrolled between 2012 and 2016, of which 50% were randomly assigned to the derivation cohort and the remainder to the validation cohort. Participants' demographics and anthropometric variables were entered into least absolute shrinkage and selection operator (LASSO) regression for the selection of variables. Subsequently, multivariable logistic regression was performed to develop the predictive model and a nomogram. The discrimination ability, calibration curve analysis and decision curve analysis of the nomogram was evaluated. Internal validation of the model was also performed. RESULTS: There were 2725 (9.0%) participants diagnosed with metabolic syndrome in the derivation cohort and 2762 (9.1%) participants in the validation cohort. Six variables (age, smoking, body fat percentage, waist circumference, systolic blood pressure and diastolic blood pressure were included in the nomogram. The area under the curve was 0.901 (95% confidence interval (CI) 0.895-0.906) and 0.899 (95% CI 0.894-0.905) for the predictive and internal validation, respectively. Furthermore, decision curve analysis showed that if the threshold probability of metabolic syndrome is less than 72.0%, application of this nomogram can benefit more than either the treat-all or treat-none strategies. CONCLUSIONS: An anthropometry-based nomogram for predicting metabolic syndrome in a working population was developed that incorporates reliable non-invasive anthropometric features to facilitate health counselling and self-risk assessment of developing metabolic syndrome.
BACKGROUND: Early detection of metabolic syndrome is highly desirable for the prevention and treatment of various diseases. Therefore, this study aimed to develop and validate an anthropometry-based nomogram for predicting metabolic syndrome in a working population. METHODS: The present study was a secondary analysis of a cross-sectional study. A total of 60,799 workers in Spain were enrolled between 2012 and 2016, of which 50% were randomly assigned to the derivation cohort and the remainder to the validation cohort. Participants' demographics and anthropometric variables were entered into least absolute shrinkage and selection operator (LASSO) regression for the selection of variables. Subsequently, multivariable logistic regression was performed to develop the predictive model and a nomogram. The discrimination ability, calibration curve analysis and decision curve analysis of the nomogram was evaluated. Internal validation of the model was also performed. RESULTS: There were 2725 (9.0%) participants diagnosed with metabolic syndrome in the derivation cohort and 2762 (9.1%) participants in the validation cohort. Six variables (age, smoking, body fat percentage, waist circumference, systolic blood pressure and diastolic blood pressure were included in the nomogram. The area under the curve was 0.901 (95% confidence interval (CI) 0.895-0.906) and 0.899 (95% CI 0.894-0.905) for the predictive and internal validation, respectively. Furthermore, decision curve analysis showed that if the threshold probability of metabolic syndrome is less than 72.0%, application of this nomogram can benefit more than either the treat-all or treat-none strategies. CONCLUSIONS: An anthropometry-based nomogram for predicting metabolic syndrome in a working population was developed that incorporates reliable non-invasive anthropometric features to facilitate health counselling and self-risk assessment of developing metabolic syndrome.