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Compositional principal component analysis generates gut microbiota profiles that associate with children's diet and body composition
- Claudia Leong, Jillian J. Haszard, Anne-Louise M. Heath, Gerald W. Tannock, Blair Lawley, Sonya L. Cameron, Ewa A. Szymlek-Gay, Andrew R. Gray, Barry J. Taylor, Barbara C. Galland, Julie A. Lawrence, Anna Otal, Alan Hughes, Rachael W Taylor
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- Journal:
- Proceedings of the Nutrition Society / Volume 79 / Issue OCE2 / 2020
- Published online by Cambridge University Press:
- 10 June 2020, E284
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- Article
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Gut microbiota data obtained by DNA sequencing are not only complex because of the number of taxa that may be detected within human cohorts, but also compositional because characteristics of the microbiota are described in relative terms (e.g., “relative abundance” of particular bacterial taxa expressed as a proportion of the total abundance of taxa). Nutrition researchers often use standard principal component analysis (PCA) to derive dietary patterns from complex food data, enabling each participant's diet to be described in terms of the extent to which it fits their cohort's dietary patterns. However, compositional PCA methods are not commonly used to describe patterns of microbiota in the way that dietary patterns are used to describe diets. This approach would be useful for identifying microbiota patterns that are associated with diet and body composition. The aim of this study is to use compositional PCA to describe gut microbiota profiles in 5 year old children and explore associations between microbiota profiles, diet, body mass index (BMI) z-score, and fat mass index (FMI) z-score. This study uses a cross-sectional data for 319 children who provided a faecal sample at 5 year of age. Their primary caregiver completed a 123-item quantitative food frequency questionnaire validated for foods of relevance to the gut microbiota. Body composition was determined using dual-energy x-ray absorptiometry, and BMI and FMI z-scores calculated. Compositional PCA identified and described gut microbiota profiles at the genus level, and profiles were examined in relation to diet and body size. Three gut microbiota profiles were found. Profile 1 (positive loadings on Blautia and Bifidobacterium; negative loadings on Bacteroides) was not related to diet or body size. Profile 2 (positive loadings on Bacteroides; negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was associated with a lower BMI z-score (r = -0.16, P = 0.003). Profile 3 (positive loadings on Faecalibacterium, Eubacterium and Roseburia) was associated with higher intakes of fibre (r = 0.15, P = 0.007); total (r = 0.15, P = 0.009), and insoluble (r = 0.13, P = 0.021) non-starch polysaccharides; protein (r = 0.12, P = 0.036); meat (r = 0.15, P = 0.010); and nuts, seeds and legumes (r = 0.11, P = 0.047). Further regression analyses found that profile 2 and profile 3 were independently associated with BMI z-score and diet respectively. We encourage fellow researchers to use compositional PCA as a method for identifying further links between the gut, diet and obesity, and for developing the next generation of research in which the impact on body composition of dietary interventions that modify the gut microbiota is determined.
Body composition of New Zealand European and Pacific women is associated with lower dietary fibre intake and gut microbiota diversity
- Nikki Renall, Benedikt Merz, Blair Lawley, Gerald W. Tannock, Marine Corbin, Jeroen Douwes, Rozanne Kruger, Bernhard H. Breier
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- Journal:
- Proceedings of the Nutrition Society / Volume 79 / Issue OCE2 / 2020
- Published online by Cambridge University Press:
- 10 June 2020, E685
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- Article
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- You have access Access
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Diet is considered one of the key drivers of the world-wide obesity epidemic, and the gut microbiota may play a role in this multifaceted disease due to their mutualistic relationship. This study investigated relationships between habitual dietary intake of New Zealand European and Pacific women and their gut microbiota and body fat content. Pacific (44%) and NZ European (NZE; 56%) women (n = 287) aged 18–45 years were recruited based on body mass index (normal versus obese) and stratified as low (< 35%) or high (≥ 35%) body fat percentage (BF%). Dietary intake was assessed with a 5-day estimated food record and a semi-quantitative food frequency questionnaire, which were used to calculate habitual dietary intake using the National Cancer Institute (NCI) method. BF% was assessed by dual-energy x-ray absorptiometry (DXA). Fasting blood samples were analysed for markers of insulin sensitivity. The DNA from faecal samples was analysed following shotgun sequencing. There were no significant differences in BF% between Pacific and NZE women (p = 0.498). Significant differences in homeostasis model assessment of insulin resistance (HOMA-IR) index were observed between Pacific (3.4 [2.3, 5.9]) and NZE (2.1 [1.5, 3.1], p ≤ 0.001) women, and between; low-BF% (1.9 [1.3, 2.7]) and high-BF% (3.4 [2.5, 5.9], p ≤ 0.001) groups. The highest (27.6g/d [24.9, 30.6]) compared to the lowest tertile (16g/d [13.3, 17.6]) of habitual total dietary fibre (DF) intake was associated with a significantly lower HOMA-IR (2.1 [1.3, 3.1] versus 3.3 [2.1, 5.3] p ≤ 0.001) respectively. Higher DF intake was also associated with significantly lower BF% (β -0.35, p ≤ 0.001), and this relationship became stronger when considering the intake of other macronutrients (β -0.47, p ≤ 0.001). Alpha diversity; observed taxonomic units (OTU's; rs = -0.15, p = 0.011), Pielou's evenness (rs = -0.20, p = 0.001), and Shannon index (rs = -0.22, p ≤ 0.001), were all negatively correlated with BF%. In contrast BF% was positively correlated with the Firmicutes:Bacteroidetes ratio (rs = 0.26, p ≤ 0.001). HOMA-IR index was significantly higher in Pacific and women in the higher BF% group, indicating an increased metabolic disease risk. Higher habitual DF intake was associated with lower BF% and HOMA-IR, suggesting a potential metabolically protective effect. The positive effects of higher DF intake may be associated with microbiota diversity, as higher BF% was associated with reduced alpha diversity and an increased Firmicutes:Bacteroidetes ratio. Further analysis will explore which foods contributed to the higher DF intake, and associations with body composition, microbiota and biomarkers of metabolic health.