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      Association of Human Intestinal Microbiota with Lifestyle Activity, Adiposity, and Metabolic Profiles in Thai Children with Obesity

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          Abstract

          Background

          Dysbiosis of intestinal microbiota may be linked to pathogenesis of obesity and metabolic disorders.

          Objective

          This study compared the gut microbiome of obese Thai children with that of healthy controls and examined their relationships with host lifestyle, adiposity, and metabolic profiles.

          Methods

          This cross-sectional study enrolled obese children aged 7–15. Body composition was evaluated using bioelectrical impedance analysis. Stool samples were analyzed by 16S rRNA sequencing using the Illumina MiSeq platform. Relative abundance and alpha- and beta-diversity were compared with normal-weight Thai children from a previous publication using Wilcoxon rank-sum test and ANOSIM. Relationships of gut microbiota with lifestyle activity, body composition, and metabolic profiles were assessed by canonical correlation analysis (CCA) and Spearman correlation.

          Results

          The study enrolled 164 obese children with a male percentage of 59%. Mean age was 10.4 ± 2.2 years with a BMI z-score of 3.2 ± 1. The abundance of Bacteroidetes and Actinobacteria were found to be lower in obese children compared to nonobese children. Alpha-diversity indices showed no differences between groups, while beta-diversity revealed significant differences in the family and genus levels. CCA revealed significant correlations of the relative abundance of gut microbial phyla with sedentary lifestyle and certain metabolic markers. Univariate analysis revealed that Actinobacteria and Bifidobacterium were positively correlated with HDL-C and negatively correlated with body weight and screen time. Additionally, Actinobacteria was also negatively associated with fasting insulin and HOMA-IR. Lactobacillus showed positive correlation with acanthosis nigricans and adiposity. Cooccurrence analysis revealed 90 significant bacterial copresence and mutual exclusion interactions among 43 genera in obese children, whereas only 2 significant cooccurrences were found in nonobese children.

          Conclusions

          The composition and diversity of gut microbiota in obese Thai children were different from those of their normal-weight peers. Specific gut microbiota were associated with lifestyle, adiposity, and metabolic features in obese children. An interventional study is needed to support causality between specific gut microbiota and obesity.

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          Most cited references56

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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                Author and article information

                Contributors
                Journal
                J Nutr Metab
                J Nutr Metab
                JNME
                Journal of Nutrition and Metabolism
                Hindawi
                2090-0724
                2090-0732
                2022
                20 May 2022
                : 2022
                : 3029582
                Affiliations
                1Pediatric Nutrition Research Unit, Division of Nutrition, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
                2Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
                3Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
                4National Center for Genetic Engineering and Biotechnology (BIOTEC), Khlong Luang, Pathum Thani 10210, Thailand
                5Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
                6WHO-CC for Research and Training on Viral Zoonoses, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
                7Thai Red Cross Emerging Infectious Diseases Health Science Centre, Bangkok 10330, Thailand
                8Division of Nutrition, Department of Pediatrics, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
                Author notes

                Academic Editor: Karen L. Sweazea

                Author information
                https://orcid.org/0000-0003-1693-2550
                https://orcid.org/0000-0001-9712-2474
                Article
                10.1155/2022/3029582
                9146442
                35637874
                bc1eecba-6e0b-4c9c-a48b-063b2b1dc976
                Copyright © 2022 Chonnikant Visuthranukul et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 October 2021
                : 23 March 2022
                : 19 April 2022
                Funding
                Funded by: Chulalongkorn University
                Award ID: RA60/122
                Funded by: National Science and Technology Development Agency
                Award ID: FDA-CO-2561-5614-TH
                Funded by: Ministry of Higher Education, Science, Research and Innovation, Thailand
                Categories
                Research Article

                Nutrition & Dietetics
                Nutrition & Dietetics

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