Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample-A Study with 39,991,680...

Area of Science:

• Cardiology
• Public Health
• Epidemiology

Background:

• Metabolic syndrome is a precursor to serious health issues like stroke and cardiovascular disease.
• Early identification and prevention strategies are crucial for managing metabolic syndrome.
• Existing predictive tools may lack comprehensive risk factor integration.

Purpose of the Study:

• To develop and validate a novel nomogram for predicting the probability of metabolic syndrome.
• To identify significant risk factors associated with metabolic syndrome in the Korean population.
• To provide a visual tool for risk assessment and early intervention.

Main Methods:

• Analysis of data from the Korea National Health and Nutrition Examination Survey VII (KNHANES VII).
• Inclusion of 17,584 participants, weighted to represent 39,991,680 individuals (98.1% of the 2018 Korean population).
• Utilized Rao-Scott chi-squared tests and logistic regression to identify 11 key risk factors (BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history, age*sex) and construct a predictive nomogram.

Main Results:

• A nomogram was successfully constructed to predict metabolic syndrome occurrence.
• Eleven significant risk factors were identified, including demographic, lifestyle, and clinical variables.
• The nomogram demonstrated good predictive performance, validated by ROC curve analysis and calibration plots.

Conclusions:

• The developed nomogram offers a valuable tool for predicting metabolic syndrome risk.
• This visualization aids clinicians and public health officials in targeted prevention efforts.
• The study highlights the multifactorial nature of metabolic syndrome and the utility of nomograms in risk stratification.