Gut microbiota of obese and diabetic Thai subjects and interplay with dietary habits and blood profiles

Obesity and type 2 diabetes mellitus (T2DM) have become major public health issues globally. Recent research indicates that intestinal microbiota play roles in metabolic disorders. Though there are numerous studies focusing on gut microbiota of health and obesity states, those are primarily focused on Western countries. Comparatively, only a few investigations exist on gut microbiota of people from Asian countries. In this study, the fecal microbiota of 30 adult volunteers living in Chiang Rai Province, Thailand were examined using next-generation sequencing (NGS) in association with blood profiles and dietary habits. Subjects were categorized by body mass index (BMI) and health status as follows; lean (L) = 8, overweight (OV) = 8, obese (OB) = 7 and diagnosed T2DM = 7. Members of T2DM group showed differences in dietary consumption and fasting glucose level compared to BMI groups. A low level of high-density cholesterol (HDL) was observed in the OB group. Principal coordinate analysis (PCoA) revealed that microbial communities of T2DM subjects were clearly distinct from those of OB. An analogous pattern was additionally illustrated by multiple factor analysis (MFA) based on dietary habits, blood profiles, and fecal gut microbiota in BMI and T2DM groups. In all four groups, Bacteroidetes and Firmicutes were the predominant phyla. Abundance of Faecalibacterium prausnitzii, a butyrate-producing bacterium, was significantly higher in OB than that in other groups. This study is the first to examine the gut microbiota of adult Thais in association with dietary intake and blood profiles and will provide the platform for future investigations.

149 yogurt/ cheese/ fermented milk, and fruits were missing for one subject (lean group). 150 Frequencies were categorized into the following six levels: every day, 5-6 days a week, 3-4 days 151 a week, 1-2 days a week, less than once a week, and never. The statistical significance of 152 differences in the mean ranks among groups was determined using Kruskal-Wallis rank sum test 153 with post-hoc analysis (Dunn's test of multiple comparisons, p-value adjusted with the 154 Benjamini-Hochberg method (hereafter referred to as q-value)). The frequencies of dietary 155 consumption of each group are summarized in Table S1. 156 Fecal sample collection and DNA extraction 157 Fecal samples of all volunteers were collected in a sterilized container and immediately stored at 158 -20 o C until further use. Total genomic DNA from fecal samples was extracted using the 159 innuPREP Stool DNA Kit (Analytik Jena Biometra, Germany) following the manufacturer's 160 guidelines. Concentration and purity of DNA were evaluated on 1% agarose gels. 161 Spectrophotometry was applied to determine the DNA concentration (ng/µl) by the Take 3 162 Micro-Volume Plate (Biotek, USA). Total DNA per gram of fecal wet weight was calculated and 163 recorded.
164 Amplicon generation, library preparation and sequencing 165 The hypervariable region V3-V4 of the 16S rRNA gene was amplified using specific primers 166  Manuscript to be reviewed 262 beverage with some exceptions: significant differences were noted regarding consumption of 263 chicken (OB-T2DM comparison, q < 0.01), rice vermicelli (L-T2DM comparison, q < 0.05) and 264 fermented fruits or vegetables (L-OV and L-T2DM comparisons, p < 0.05 for both).

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The results of MFA revealed that individuals with T2DM displayed a notable variation on 266 frequency of food consumption from the rest of the groups. This was discernible on the factor 267 map indicating the first two dimensions accounting for 31.6% of variance (Fig. S1) (Fig. 1A). Regarding the number of non-shared OTUs, non-diabetic subjects (merging 293 OTUs of L, OV, and OB groups) had four times more than diabetic subjects (T2DM).

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Fecal microbiome community diversity (richness and evenness) in the four groups was 299 characterized using ACE, Chao1, observe-species, Shannon, Simpson, and Good's coverage.
300 Sequencing data and alpha diversity indices in each sample are presented in Table S2 and S3. 301 Significant differences of overall bacterial community structure across the four groups were 302 found in the ACE, Chao1, and observe-species indices (Fig. 2)  Manuscript to be reviewed 533 drawn from such differences marked in OB and T2DM may be the specificity of metabolic 534 diseases in gut bacteria associations (Festi et al., 2014;Gurung et al., 2020). Considering all 535 blood profiles in association with fecal gut microbiota (irrespective of gender and age), neither 536 the blood profiles nor the gut microbiome influenced on a specific group of subjects. Besides, the 537 individual differences shown on the MFA were resulted from some bacterial genera 538 (Bacteroides, Prevotella, and Faecalibacterium) and blood profiles (total cholesterol, 539 triglyceride, and diastolic blood pressure, systolic blood pressure, and HDL cholesterol) that only