Data from: Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population
Romero-Saldaña, Manuel et al. (2018), Data from: Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population, Dryad, Dataset, https://doi.org/10.5061/dryad.cb51t54
Objectives. A non-invasive method for the early detection of Metabolic Syndrome (NIN-MetS) using only Waist to Height Ratio (WHtR) and Blood Pressure (BP) has recently been published, with fixed cut-off values for gender and age. The aim of this study was to validate this method in a large sample of Spanish workers. Design. A diagnostic test accuracy to assess the validity of the method was performed. Setting. Occupational Health Services. Participants. The studies were conducted in 2012-2016 on a sample of 60,799 workers from the Balearic Islands (Spain). Interventions. The NCEP-ATP III criteria were used as the gold standard. NIM-MetS has been devised using classification trees (the CHAID, Chi-squared Automatic Interaction Detection method). Main outcome measures. Anthropometric and biochemical variables to diagnose MetS. Sensitivity, specificity, validity index and Youden Index were determined to analyse the accuracy of the diagnostic test (NIM-MetS). Results. Regarding the validation of the method, sensitivity was 54.7%, specificity 94.9% and validity index 91.2%. The cut-off value for WHtR was 0.54, ranging from 0.51 (lower age group) to 0.56 (higher) in the age groups. Variables more closely associated with MetS were WHtR (AUC=0.85; 95% CI: 0.84-0.86) and Systolic Blood Pressure (AUC=0.79; 95% CI: 0.78-0.80). The final cut-off values for the non-invasive method were WHtR≥0.56 and BP≥128/80 mmHg, which includes four levels of MetS risk (very low, low, moderate and high). Conclusions. The analysed method has shown a high validity index (higher than 91%) for the early detection of MetS. It is a non-invasive method easy to apply and interpret in any health care setting. This method provides a scale of MetS risk which allows a more accurate detection and a more effective intervention.