Comparative analysis of gut microbiota associated with body mass index in a large Korean cohort

Yeojun Yun, Han-Na Kim, Song E Kim, Seong Gu Heo, Yoosoo Chang, Seungho Ryu, Hocheol Shin, Hyung-Lae Kim, Yeojun Yun, Han-Na Kim, Song E Kim, Seong Gu Heo, Yoosoo Chang, Seungho Ryu, Hocheol Shin, Hyung-Lae Kim

Abstract

Background: Gut microbiota plays an important role in the harvesting, storage, and expenditure of energy obtained from one's diet. Our cross-sectional study aimed to identify differences in gut microbiota according to body mass index (BMI) in a Korean population. 16S rRNA gene sequence data from 1463 subjects were categorized by BMI into normal, overweight, and obese groups. Fecal microbiotas were compared to determine differences in diversity and functional inference analysis related with BMI. The correlation between genus-level microbiota and BMI was tested using zero-inflated Gaussian mixture models, with or without covariate adjustment of nutrient intake.

Results: We confirmed differences between 16Sr RNA gene sequencing data of each BMI group, with decreasing diversity in the obese compared with the normal group. According to analysis of inferred metagenomic functional content using PICRUSt algorithm, a highly significant discrepancy in metabolism and immune functions (P < 0.0001) was predicted in the obese group. Differential taxonomic components in each BMI group were greatly affected by nutrient adjustment, whereas signature bacteria were not influenced by nutrients in the obese compared with the overweight group.

Conclusions: We found highly significant statistical differences between normal, overweight and obese groups using a large sample size with or without diet confounding factors. Our informative dataset sheds light on the epidemiological study on population microbiome.

Keywords: Body mass index (BMI); Gut microbiota; Obesity.

Figures

Fig. 1
Fig. 1
Flow chart of study subjects
Fig. 2
Fig. 2
Comparison of (a) phylogenetic diversity (PD) across BMI categories, and (b) weighted UniFrac distant metrics of each BMI category (**P < 0.01, ***P < 0.001)
Fig. 3
Fig. 3
Comparison of PICRUSt predicted KEGG function data based on BMI categories. a An extended error bar plot for the comparison of normal vs obese groups. Only functions with P < 0.05 are shown. b–dBox plots for multiple group analysis of normal/overweight/obese groups. b ‘Metabolism of Cofactors and Vitamins’ (P value, 5.04 × 10−13), c ‘Energy Metabolism’ (2.15 × 10−5), d ‘Lipid Metabolism’ (2.86 × 10−7). P value was calculated by Bonferroni multiple test correction methods

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Source: PubMed

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