Effect of eicosapentaenoic/docosahexaenoic fatty acids and soluble fibers on blood lipids of individuals classified into different levels of lipidemia.

OBJECTIVE: This study evaluated the effect of a formulation containing eicosapentaenoic acid and docosahexaenoic acid combined with soluble fibers (beta-glucan and guar gum) on fasting blood lipids used as coronary heart disease biomarkers of individuals classified into different levels of lipidemia by multivariate techniques.

METHODS: Serum total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triacylglycerol, plasma glucose concentrations, body mass index, age, and waist circumference were determined in 99 healthy volunteers. Three clusters or subgroups were identified according to coronary heart disease risk levels. Within each cluster, individuals were randomly assigned to one of four experimental groups, with each group receiving samples of a functional formulation containing 460 mg of omega-3 polyunsaturated fatty acids and/or 580 mg of soluble fibers, and placebo to be consumed in one bottle per day (200 mL) for 6 wk.

RESULTS: No significant changes were observed for triacylglycerol (P = 0.281) and total cholesterol (P = 0.082) concentrations across the three subgroups. Soluble dietary fibers improved the sensory quality of the formulation containing eicosapentaenoic acid and docosahexaenoic acid. The efficiency of cluster analysis to discriminate individuals in subgroups was confirmed by one-way analysis of variance (P < 0.003).

CONCLUSION: The omega-3 polyunsaturated fatty acid supplementation equivalent to fish consumed 2.5 to 3 times per week by a functional food-containing soluble dietary fiber showed no beneficial result in terms of changes in blood lipids in individuals classified according to different levels of lipidemia. Small numbers of patients in each cluster and possibly the low dose of fish oil and soluble dietary fibers used in this study may have also contributed to the lack of these differences. Multivariate techniques proved to be a very efficient tool to solve the heterogeneity problem usually observed in human designs and to evaluate the results within subgroups categorized by n variables extracted from the same population.