Key result
New nomogram predicts metabolic syndrome using 11 standard clinical risk factors.
Why the study?
Metabolic syndrome can cause stroke and cardiovascular disease, prompting the need for a nomogram to identify risk factors and predict its probability of occurrence for prevention and recognition.
Cross-Sectional (n=17,584)
A novel nomogram incorporating 11 clinical and demographic risk factors can visualize and predict the probability of metabolic syndrome in the Korean population.
May aid Korean metabolic syndrome risk visualization; hypothesis-generating pending prospective validation.
Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.
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Shin et al. (2022) conducted a cross-sectional in Metabolic syndrome (n=17,584). Nomogram for metabolic syndrome was evaluated on Prediction of metabolic syndrome occurrence. A nomogram to predict metabolic syndrome was constructed and verified using 11 risk factors (including BMI, age, and sex) identified from 17,584 participants in a Korean national survey.
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