We propose a new method for the early detection of metabolic syndrome in the working population, which was free of biomarkers (non-invasive) and based on anthropometric variables, and to validate it in a new working population.

Prevalence studies and diagnostic test accuracy to determine the anthropometric variables associated with metabolic syndrome, as well as the screening validity of the new method proposed, were carried out between 2013 and 2015 on 636 and 550 workers, respectively. The anthropometric variables analysed were: blood pressure, body mass index, waist circumference, waist–height ratio, body fat percentage and waist–hip ratio. We performed a multivariate logistic regression analysis and obtained receiver operating curves to determine the predictive ability of the variables. The new method for the early detection of metabolic syndrome we present is based on a decision tree using chi-squared automatic interaction detection methodology.

The overall prevalence of metabolic syndrome was 14.9%. The area under the curve for waist–height ratio and waist circumference was 0.91 and 0.90, respectively. The anthropometric variables associated with metabolic syndrome in the adjusted model were waist–height ratio, body mass index, blood pressure and body fat percentage. The decision tree was configured from the waist–height ratio (>=0.55) and hypertension (blood pressure >=128/85 mmHg), with a sensitivity of 91.6% and a specificity of 95.7% obtained.

The early detection of metabolic syndrome in a healthy population is possible through non-invasive methods, based on anthropometric indicators such as waist–height ratio and blood pressure. This method has a high degree of predictive validity and its use can be recommended in any healthcare context.