Chia seed supplementation and disease risk factors in overweight women: a metabolomics investigation.
Mots clés
Abstrait
OBJECTIVE
This study assessed the effectiveness of milled and whole chia seed in altering disease risk factors in overweight, postmenopausal women using a metabolomics approach.
METHODS
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 10 weeks.
METHODS
Subjects included 62 overweight (body-mass index 25 kg/m(2) and higher), nondiseased, nonsmoking, postmenopausal women, ages 49-75 years, with analysis based on the 56 subjects who completed all phases of the study.
METHODS
Pre- and poststudy measures included body mass and composition, blood pressure and augmentation index, serum lipid profile, inflammation markers from fasting blood samples, plasma fatty acids, and metabolic profiling using gas chromatography-mass spectrometry with multivariate statistical methods including principal component analysis and partial least-square discriminant analysis (PLS-DA).
RESULTS
Plasma α-linolenic acid (N=ALA) increased 58% (interaction effect, p=0.002) and eicosapentaenoic acid (EPA) 39% (p=0.016) in the milled chia seed group (N=14) compared to nonsignificant changes in the whole 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 (whole or milled) and placebo groups (all interaction effects, p>0.05). Global metabolic difference scores for each group calculated through PLS-DA models were nonsignificant (Q(2)Y<0.40), and fold-changes for 28 targeted metabolites 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 by overweight women increased plasma ALA and EPA, but had no influence on inflammation or disease risk factors using both traditional and metabolomics-based measures.
