Validating metabolic syndrome through principal component analysis in a medically diverse, realistic cohort
Date de publication2012
Dusseault-Bélanger, Francis; Cohen, Alan; Hivert, Marie-France; Courteau, Josiane; Vanasse, Alain
Abstract: Background: The concept of metabolic syndrome has been subject to etiological and clinical controversies in recent years. Associations among the five risk factors (obesity, high blood pressure, high blood sugar, high triglyceride levels and low HDL cholesterol) may help establish the validity of the concept and its application, but most such studies have been conducted on targeted cohorts not representative of an actual population. Methods: We used principal component analysis (PCA) to analyze the structure of the physiological components of metabolic syndrome in 7213 patients contained in an administrative database for the CHUS hospital in Sherbrooke, Quebec, a realistic cohort with diverse medical histories. We validated the results by repeating the analysis on stratified and random subgroups of patients, and on different combinations of risk factors. The first axis of the PCA was used to predict coronary heart disease (CHD) and diabetes. Results: The two first axes explained 53% of the variance. The first axis (33%) was associated in the expected direction with all five predictor variables, consistent with its interpretation as metabolic syndrome. All validation analyses strongly confirmed this interpretation. The scores from the first axis were more predictive of subsequent CHD and diabetes than the formal definition of metabolic syndrome. Conclusions: These results suggest that the concept of metabolic syndrome accurately captures an existing underlying physiological process. A continuous indicator could be constructed to identify more accurately metabolic syndrome thus improving risk assessment for CHD and diabetes mellitus. Metabolic syndrome can be measured well even without all five predictors, though measurement is improved by PCA relative to dichotomized definitions. However, discrepancies with other studies suggest that our results may not be generalizable, perhaps because our cohort tends to be sicker.
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