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Functional foods for coronary heart disease risk reduction: a meta-analysis using a multivariate approach


Background: It has been difficult to identify the adequate bioactive substance for the development of new functional foods associated with coronary heart disease, since many results from clinical studies are contradictory. Objective: The objective of this study was to present the multivariate statistical approach known as Principal Component Analysis (PCA) followed by a mixed model to process data obtained from a meta-analysis aimed at evaluating simultaneously the effect of ingestion of one of three types of bioactive substances (n-3 fatty acids, soluble fibers and phytosterols) on one or more of four biomarkers (plasma total cholesterol, triglyceride, low-density-lipoprotein cholesterol and high-density-lipoprotein cholesterol). Design: Five independent variables (number of patients per study, dose, age, body mass index and treatment length) and four dependent variables (blood % change in total cholesterol, low density lipoprotein, high density lipoprotein and triacylglycerol) from 159 studies and sub-studies were organized into a matrix. The original values were converted to linear correlation units, resulting in a new matrix. Results: Two Principal Components were enough to explain 63.73% and 84.27% of independent and dependent variables, respectively. Phytosterols and soluble fibers presented a hypocholesterolemic effect while n-3 fatty acids reduced TG, and increased CHOL, LDLc and HDLc. The PCA and mixed model were able to show that this behavior was independent of dose, number of patients per study, age and body mass index but was associated with treatment length. Conclusions: The PCA can be useful in summarizing the available scientific information when examining health claims for foods and supplements. (AU)