Using compositional principal component analysis to describe children’s gut microbiota in relation to diet and body composition

Gut microbiota data obtained by DNA sequencing are complex and compositional because of large numbers of detectable taxa, and because microbiota characteristics are described in relative terms. Nutrition researchers use principal component analysis (PCA) to derive dietary patterns from food data. Al...

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Veröffentlicht in:The American journal of clinical nutrition 2020-01, Vol.111 (1), p.70-78
Hauptverfasser: Leong, Claudia, Haszard, Jillian J, Heath, Anne-Louise M, Tannock, Gerald W, Lawley, Blair, Cameron, Sonya L, Szymlek-Gay, Ewa A, Gray, Andrew R, Taylor, Barry J, Galland, Barbara C, Lawrence, Julie A, Otal, Anna, Hughes, Alan, Taylor, Rachael W
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container_issue 1
container_start_page 70
container_title The American journal of clinical nutrition
container_volume 111
creator Leong, Claudia
Haszard, Jillian J
Heath, Anne-Louise M
Tannock, Gerald W
Lawley, Blair
Cameron, Sonya L
Szymlek-Gay, Ewa A
Gray, Andrew R
Taylor, Barry J
Galland, Barbara C
Lawrence, Julie A
Otal, Anna
Hughes, Alan
Taylor, Rachael W
description Gut microbiota data obtained by DNA sequencing are complex and compositional because of large numbers of detectable taxa, and because microbiota characteristics are described in relative terms. Nutrition researchers use principal component analysis (PCA) to derive dietary patterns from food data. Although compositional PCA methods are not commonly used to describe patterns from complex microbiota data, this approach would be useful for identifying gut microbiota patterns associated with diet and body composition. To use compositional PCA to describe the principal components (PCs) of gut microbiota in 5-y-old children and explore associations between microbiota components, diet, and BMI z-score. A fecal sample was provided by 319 children aged 5 y. Their primary caregiver completed a validated 123-item quantitative FFQ. Body composition was determined using DXA, and a BMI z-score was calculated. Compositional PCA identified characterizing taxa and weightings for calculation of gut microbiota PC scores at the genus level, and was examined in relation to diet and body size. Three gut microbiota PCs were found. PC1 (negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was related to lower BMI z-scores and longer duration of breastfeeding (per month) (β = −0.14; 95% CI: −0.26, −0.02; and β = 0.02; 95% CI: 0.003, 0.34, respectively). PC2 (positive loadings on Fusicatenibacter and Bifidobacterium; negative loadings on Bacteroides) was associated with a lower intake of nuts, seeds, and legumes (β = −0.05 per gram; 95% CI: −0.09, −0.01). When adjusted for fiber intake, PC2 was also associated with higher BMI z-scores (β = 0.12; 95% CI: 0.01, 0.24). PC3 (positive loadings on Faecalibacterium, Eubacterium, and Roseburia) was associated with higher intakes of fiber (β = 0.02 per gram; 95% CI: 0.003, 0.04) and total nonstarch polysaccharides (β = 0.02 per gram; 95% CI: 0.003, 0.04). Our results suggest that specific gut microbiota components determined using compositional PCA are associated with diet and BMI z-score. This trial was registered at clinicaltrials.gov as NCT00892983.
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Nutrition researchers use principal component analysis (PCA) to derive dietary patterns from food data. Although compositional PCA methods are not commonly used to describe patterns from complex microbiota data, this approach would be useful for identifying gut microbiota patterns associated with diet and body composition. To use compositional PCA to describe the principal components (PCs) of gut microbiota in 5-y-old children and explore associations between microbiota components, diet, and BMI z-score. A fecal sample was provided by 319 children aged 5 y. Their primary caregiver completed a validated 123-item quantitative FFQ. Body composition was determined using DXA, and a BMI z-score was calculated. Compositional PCA identified characterizing taxa and weightings for calculation of gut microbiota PC scores at the genus level, and was examined in relation to diet and body size. Three gut microbiota PCs were found. PC1 (negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was related to lower BMI z-scores and longer duration of breastfeeding (per month) (β = −0.14; 95% CI: −0.26, −0.02; and β = 0.02; 95% CI: 0.003, 0.34, respectively). PC2 (positive loadings on Fusicatenibacter and Bifidobacterium; negative loadings on Bacteroides) was associated with a lower intake of nuts, seeds, and legumes (β = −0.05 per gram; 95% CI: −0.09, −0.01). When adjusted for fiber intake, PC2 was also associated with higher BMI z-scores (β = 0.12; 95% CI: 0.01, 0.24). PC3 (positive loadings on Faecalibacterium, Eubacterium, and Roseburia) was associated with higher intakes of fiber (β = 0.02 per gram; 95% CI: 0.003, 0.04) and total nonstarch polysaccharides (β = 0.02 per gram; 95% CI: 0.003, 0.04). Our results suggest that specific gut microbiota components determined using compositional PCA are associated with diet and BMI z-score. 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PC1 (negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was related to lower BMI z-scores and longer duration of breastfeeding (per month) (β = −0.14; 95% CI: −0.26, −0.02; and β = 0.02; 95% CI: 0.003, 0.34, respectively). PC2 (positive loadings on Fusicatenibacter and Bifidobacterium; negative loadings on Bacteroides) was associated with a lower intake of nuts, seeds, and legumes (β = −0.05 per gram; 95% CI: −0.09, −0.01). When adjusted for fiber intake, PC2 was also associated with higher BMI z-scores (β = 0.12; 95% CI: 0.01, 0.24). PC3 (positive loadings on Faecalibacterium, Eubacterium, and Roseburia) was associated with higher intakes of fiber (β = 0.02 per gram; 95% CI: 0.003, 0.04) and total nonstarch polysaccharides (β = 0.02 per gram; 95% CI: 0.003, 0.04). Our results suggest that specific gut microbiota components determined using compositional PCA are associated with diet and BMI z-score. 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subjects Bacteria - classification
Bacteria - genetics
Bacteria - isolation & purification
Body Composition
Body size
Body Weight
Breast feeding
Child, Preschool
Children
compositional analysis
Cross-Sectional Studies
Diet
Dietary Fiber - metabolism
DNA sequencing
Dual energy X-ray absorptiometry
Feces - microbiology
Female
Food composition
Gastrointestinal Microbiome
gut microbiota
Humans
Intestinal microflora
Legumes
Male
Mathematical analysis
Microbiota
Non-starch polysaccharides
Nutrition
Nutrition research
Nuts
Nuts - metabolism
Polysaccharides
Principal Component Analysis
Principal components analysis
Saccharides
Seeds
Taxa
Vegetables - metabolism
title Using compositional principal component analysis to describe children’s gut microbiota in relation to diet and body composition
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