Including selective metabolic components in current diagnostic criteria does not improve discriminative validity for metabolic syndrome: a risk score approach. Issue 3 (March 2019)

• Record Type: Journal Article
• Authors: Chang, Huan-Cheng; Chen, Sheng-Pyng; Yang, Hao-Jan
• Is Part Of: Journal of international medical research. Volume 47:Issue 3(2019)
• Page Start: 1298
• Page End: 1311
• Publication Date: 2019-03
• Subjects: Metabolic syndrome -- diagnostic criteria -- risk score -- receiver operating characteristic curve analysis; Medicine -- Periodicals; Pharmacology -- Periodicals; 610.5
• DOI: 10.1177/0300060518822919
• Languages: English
• ISSNs: 0300-0605

Abstract

Objective: To examine whether including additional metabolic components to the current five-marker system can improve the discriminative validity for diagnosing metabolic syndrome (MetS). Methods: This longitudinal cohort study included data from subjects that had completed at least three health examinations during a 5-year period. The study outcome was the onset of MetS. Sociodemographic and biochemical variables were recorded for all subjects so that the adjusted relative risks (ARRs) could be calculated for 11 metabolic components. Risk scores for the development of MetS based on the ARR values were determined. The sums of the risk scores of different component combinations were used to conduct a receiver operating characteristic (ROC) curve analysis of MetS diagnosis. Results: A total of 3368 individuals with complete data was analysed. The ARRs of the 11 metabolic components were all statistically significant. According to ROC analysis, although good discriminative validity (area under the curve [AUC] range, 0.954–0.976) could be achieved for MetS diagnosis by using either all 11 or combinations of six metabolic components (the five current components plus one extra component), the current five metabolic components used for diagnosis had the best discriminative validity (AUC = 0.977). Conclusion: The current five metabolic components used for the diagnosis of MetS still represent the best combination with the highest discriminative validity.