An anthropometry-based nomogram for predicting metabolic syndrome in the working population

Area of Science:

• Public Health
• Epidemiology
• Biostatistics

Background:

• Metabolic syndrome (MetS) poses significant health risks, necessitating early detection for effective prevention and management.
• Current diagnostic methods can be complex; simpler, accessible tools are needed for widespread screening.

Purpose of the Study:

• To develop and validate an anthropometry-based nomogram for predicting metabolic syndrome in a working population.
• To provide a tool for early risk assessment and health counseling.

Main Methods:

• Secondary analysis of a cross-sectional study involving 60,799 Spanish workers (2012-2016).
• Variable selection using LASSO regression, followed by multivariable logistic regression to build the predictive model and nomogram.
• Internal validation included discrimination (Area Under Curve) and calibration analyses.

Main Results:

• A nomogram was developed incorporating six variables: age, smoking, body fat percentage, waist circumference, systolic blood pressure, and diastolic blood pressure.
• The nomogram demonstrated strong predictive accuracy with an Area Under Curve of 0.901 in the derivation cohort and 0.899 in the validation cohort.
• Decision curve analysis indicated clinical utility when the threshold probability for metabolic syndrome is below 72.0%.

Conclusions:

• An anthropometry-based nomogram was successfully developed and validated for predicting metabolic syndrome in a working population.
• The nomogram utilizes reliable, non-invasive anthropometric features, facilitating health counseling and self-risk assessment.
• This tool can aid in the early identification of individuals at risk for metabolic syndrome.