Mary Ann Liebert, Journal of Alternative and Complementary Medicine, 7(18), p. 700-708, 2012
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Objective/setting: This study assessed the effectiveness of milled and whole chia seed in altering disease riskfactors in overweight, postmenopausal women using a metabolomics approach.Design/intervention: Subjects were randomized to chia seed (whole or milled) and placebo (poppy seed)groups, and under double-blinded procedures ingested 25 g chia seed or placebo supplements each day for 10weeks. Subjects: Subjects included 62 overweight (body–mass index 25 kg/m2 and higher), nondiseased,nonsmoking, postmenopausal women, ages 49–75 years, with analysis based on the 56 subjects who completedall phases of the study.Outcome measures: Pre- and poststudy measures included body mass and composition, blood pressure andaugmentation index, serum lipid profile, inflammation markers from fasting blood samples, plasma fattyacids, and metabolic profiling using gas chromatography–mass spectrometry with multivariate statisticalmethods including principal component analysis and partial least-square discriminant analysis (PLS-DA).Results: Plasma a-linolenic acid (N = ALA) increased 58% (interaction effect, p = 0.002) and eicosapentaenoicacid (EPA) 39% ( p = 0.016) in the milled chia seed group (N = 14) compared to nonsignificant changes in thewhole chia seed (N = 16) and placebo (N = 26) groups. Pre-to-post measures of body composition,inflammation, blood pressure, augmentation index, and lipoproteins did not differ between chia seed (wholeor milled) and placebo groups (all interaction effects, p > 0.05). Global metabolic difference scores for eachgroup calculated through PLS-DA models were nonsignificant (Q2Y < 0.40), and fold-changes for 28 targetedmetabolites associated with inflammation and disease risk factors did not differ between groups.Conclusions: Ingestion of 25 g/day milled chia seed compared to whole chia seed or placebo for 10 weeks byoverweight women increased plasma ALA and EPA, but had no influence on inflammation or disease riskfactors using both traditional and metabolomics-based measures.