An Evaluation of Multivariate Data Analysis Models for Lipidomic Parameters from Patients with Metabolic Syndrome Undergoing Remedial Treatment

Multivariate data methods have been applied in analysis of parameters derived from patients with metabolic syndrome undergoing a remedial regime. In an example involving parameters derived from the fatty acid composition of serum lipids multivariate modeling is challenged to identify potential biomarkers for prediction during the intervention. Multivariate methods also reveal useful applications to monitor compliance to the prescribed exercise and diet regime, a critical feature in a lifestyle intervention conducted over a long time period.