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Open AccessArticle

Obesity Measures and Dietary Parameters as Predictors of Gut Microbiota Phyla in Healthy Individuals

1
Faculty of Health Sciences, University of Primorska, Polje 42, SI-6310 Izola, Slovenia
2
Faculty of Mathematics, University of Primorska, Natural Sciences and Information Technologies, Glagoljaška 8, SI-6000 Koper, Slovenia
3
Immunonutrition Group, Department of Metabolism and Nutrition, Institute of Food Science, Technology and Nutrition (ICTAN), Spanish National Research Council (CSIC), Jose Antonio Novais, St.10., 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(9), 2695; https://doi.org/10.3390/nu12092695
Received: 13 July 2020 / Revised: 27 August 2020 / Accepted: 1 September 2020 / Published: 3 September 2020
(This article belongs to the Section Nutrition and Metabolism)
The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host’s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25–50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition. View Full-Text
Keywords: gut microbiota; nutrition; obesity measures; lifestyle parameters; clustering; machine learning gut microbiota; nutrition; obesity measures; lifestyle parameters; clustering; machine learning
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MDPI and ACS Style

Bezek, K.; Petelin, A.; Pražnikar, J.; Nova, E.; Redondo, N.; Marcos, A.; Jenko Pražnikar, Z. Obesity Measures and Dietary Parameters as Predictors of Gut Microbiota Phyla in Healthy Individuals. Nutrients 2020, 12, 2695.

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