Comparative analysis of gut microbiota associated with body mass index in a large Korean cohort
- Yeojun Yun†1,
- Han-Na Kim†1,
- Song E. Kim1,
- Seong Gu Heo2,
- Yoosoo Chang3,
- Seungho Ryu3,
- Hocheol Shin3 and
- Hyung-Lae Kim1Email author
© The Author(s). 2017
Received: 23 December 2016
Accepted: 18 June 2017
Published: 4 July 2017
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.
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.
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.
The growing incidence of obesity and obesity-associated complications, including diabetes, cardiovascular disease, and stroke, is a major public health concern worldwide . The etiology of obesity, which implies an energy imbalance between calories consumed and expended, is complicated by biological and environmental factors [2, 3]. Recently, a large number of studies have demonstrated that gastrointestinal bacteria can interplay with diet in the development and propagation of obesity .
There have been considerable advances in determining possible mechanisms underlying gut microbiota-induced obesity [5–7]. These mechanisms contain a key feature of increased energy production/absorption; for example, short-chain fatty acid (SCFA)-producing bacteria can ferment indigestible dietary fiber and hydrogentrophs with the importance of H2 removal, an end product of bacterial fermentation [8–11]. Moreover, changes in metabolic pathways caused by intestinal dysbiosis, such as de novo lipogenesis in liver [12–14], can induce increased adiposity by host gene suppression . In addition, the induction of low-grade inflammation by increased endotoxin exposure through gut leakage  and the effects of appetite and satiety regulation by leptin signaling on gut-brain axis  have been proposed as candidate pathways leading to obesogenic environments [15, 16].
However, the results of many articles speculating on the potential associations between gut microbiota and obesity are conflicting and have not been replicated in clinical studies. These shortcomings prevent designation of a consistent pattern of human gut microbiota that correlates with obesity [4, 17, 18]. Therefore, the challenge to incorporate assessment of microbiomes into epidemiologic studies remains and is critical. Surprisingly, there is a lack of statistically significant study with a large sample size in gut microbiota studies. The large sample sizes in epidemiologic studies will provide increased statistical power and help to reveal significant findings involved with human-associated microbiota .
Here we examined the correlation between the gut microbiota and body mass index (BMI) in relatively large sample size of Asian population. This study could contribute to further population-based association study using microbiota data.
Characteristics of the study population categorized by BMI
Fat mass (kg)
16 .9 (3.7)
HDL cholesterol (mg/dl)
Systolic BP (mmHg)
Diastolic BP (mmHg)
Type 2 Diabetes (T2DM)b
Med. Of T2DMb
Subjects with nutrient informationb
Total calorie (kcal/day)
DNA extraction and sequence data generation
The 16S rRNA genes were extracted and amplified from stool specimens using the MO-BIO PowerSoil DNA Isolation Kit (MO-BIO Laboratories) according to the manufacturer’s instructions. Amplification and sequencing were performed as described previously for analysis of bacterial communities . Briefly, the V3-V4 domain of bacterial 16S rRNA genes was amplified using primers F319 (5′- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG) and R806 (5′–GGACTACHVGGGTWTCTAAT–3′). Each primer was modified with Nextera® XT kit (Illumina, Inc.) to contain a unique 8-nt barcode index by combination (16 of S and 24 of N). Polymerase chain reactions (PCRs) comprised a 5 ng/μL DNA template, 2 × KAPA HiFi HotStart Ready Mix (KAPA Biosystems), and 2 pmol of each primer. Reaction conditions were an initial 95 °C for 3 min, followed by 25 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and a final extension of 72 °C for 5 min. After PCR cleanup and indexing, sequencing was performed on the Illumina MiSeq platform (Illumina, Inc.) according to the manufacturer’s specifications. The 100 bp of overlapping paired-end reads were merged using pandaseq (version 2.7). Only data from Illumina reads with a length of >300 bp were retained for further analysis. Chimeric sequences were filtered out by UCHIME algorithm in USEARCH platform which performs both de novo chimera and reference based detection (USEARCH v6.1.544).
Microbial operational taxonomic units (OTUs) and their taxonomic assignments were obtained using default settings in the QIIME version 1.9 and by closed reference mapping at 97% similarity against representative sequences of Greengenes (version 13_8). We used all default settings in QIIME 1.9 for OTU mapping and the pre-assigned taxonomy for the Greengenes OTU representative sequences. As recommended for Illumina-generated data , we removed OTUs comprised <0.005% of reads in the total data set. Samples with <1000 sequences per sample (n = 2) were considered failures and filtered out (Fig. 1). Finally, total 1274 subjects with a mean of 26,024 (+/−18,528) sequences per sample were included for the QIIME analysis. Alpha and beta diversity on Cumulative Sum Scaling (CSS) normalized OTU tables to assess phylogenetic diversity (PD) metrics were calculated by QIIME . The PD metrics provide a measure of alpha diversity of taxa present based on phylogenetic tree within subjects, while the weighted UniFrac distance metrics reflects the similarity between bacterial communities between subjects, so called beta diversity. The significant difference between categories in alpha diversity (PD) and beta diversity (weighted UniFrac) was compared by creating boxplots with a two-sided Student’s two-sample t-test. The analysis of similarities (ANOSIM) on beta diversity was applied to test the difference of distance metrics by grouping, and a P value was calculated by 999 Monte Carlo permutation non-parametric tests.
The PICRUSt approach was used to evaluate the functional potential of microbial communities . Since this is a following process after QIIME analysis, we included the same samples with QIIME (n = 1274; Fig. 1). The BIOM format of data from QIIME 1.9 was processed with the PICRUSt version 1.0.0 using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis module. Total 328 predicted KOs (KEGG orthology terms) were grouped into the levels of categorization, hierarchical levels 1, 2 and 3. The results were further analyzed with the STAMP version 2.1.3 as a graphical tool , extended error bar plot for two-group analysis module of normal/obese groups or box plots for multiple group analysis module of normal/overweight/obese groups. Welch’s t-test for two groups and Kruskal-Walis H-test for multiple groups without controlling of confounding factors were applied. An adjusted P value of <0.05 was considered statistically significant after Bonferroni multiple test correction for all analyses.
Statistical analysis of microbiome data
The zero-inflated Gaussian mixture (fitZIG) model of metagenomeSeq package version 1.14.2 was used for correlation analysis between bacterial normalized count data (as dependent variables) and BMI (as independent categorical variables) . Besides age and sex covariates, dietary components with the strongest impact (Additional file 1: Table S1), and total energy intake were chosen for adjustment according to the residual nutrient model for regression analysis [25, 31]. Since this analysis needs conditions with or without nutrient adjustment, final sample size was 940 after exclusion of missing data on FFQ (Fig. 1). Bacterial count data from QIIME were aggregated to genus level. The genera that were abundant (>50 normalized counts per sample) and prevalent (present in 10% of samples) were applied to the fitZIG model with Bonferroni multiple correction (an adjusted P value <0.05 is significant). This analysis was performed using R software package version 3.2.3.
Gut bacterial diversity differentiated by BMI category
Table 1 shows descriptive statistics by BMI category. As Fig. 1 presents, final 1274 subjects were included for a basic metagenomic analysis. The relative abundance of gut taxa in each BMI group (normal, overweight, and obese) is considerably even throughout the phylum-to-order level (Additional file 2: Figure S1). The phylum Firmicutes:Bacteroidetes ratio has no significant difference between BMI groups. At family and genus levels, however, bacterial compositional change is seen while processing from normal weight to obese status.
Distance matrix analysis from Principal Coordinates analysis (PCoA) of weighted UniFrac also identified significant differences between three BMI groups (Fig. 2b) and the statistical significance of sample clustering (ANOSIM; R = 0.020, P = 0.001).
These results suggest that the diversity according to BMI descends stepwise and the cluster of each BMI group contains unique bacterial components. The distance from normal group was significant greater in obese than overweight group.
Functional differences of gut microbiota in BMI groups
PICRUSt predicted functions of KEGG categories represented in obese group compared to normal group
KO Functional Categories
Difference between means (95% CI)a
obese vs. normal
Adj. P valuesb
Metabolism of Cofactors and Vitamins
Porphyrin and chlorophyll metabolism
7.42 × 10−3
2.36 × 10−3
1.33 × 10−7
3.73 × 10−6
5.64 × 10−6
3.29 × 10−3
Amino Acid Metabolism
1.39 × 10−4
Arginine and proline metabolism
9.83 × 10−3
Valine, leucine and isoleucine biosynthesis
2.72 × 10−5
Glycolysis / Gluconeogenesis
6.39 × 10−6
1.23 × 10−6
Immune System Diseases
3.21 × 10−7
Antigen Processing and Presentation
7.26 × 10−6
NOD-like receptor signaling pathway
8.71 × 10−13
Taxonomic comparison by BMI
A genus-level representation of the three BMI categories was assessed by metagenomeSeq, with sequence count data as a dependent variable and BMI as a categorical independent variable, and with controlling of confounding factors.
Statistical analysis using sequence-counting data is challenged by the assumption of normal distribution. This challenge becomes a critical issue when the dependent variable is over-dispersed and contains many instances of zero microbiome count data. We therefore used the zero-inflated Gaussian mixture model in a metagenomeSeq package, which is a recently described and relevant statistical model that is purported to be able to overcome this limitation .
Regression analysis between gut microbiota and BMI levels
Overweight vs. Normal
Age- and sex- adjusted
Adj. P valueb
Adj. P valueb
2.63 × 10−9
9.08 × 10−5
Bacteroidales unknown family unknown genusd
2.48 × 10−4
4.58 × 10−4
2.13 × 10 −7
1.51 × 10 −5
Lactobacillales unknown family unknown genusc
Obese vs. Normal
3.90 × 10−8
2.89 × 10 −5
7.32 × 10−6
1.79 × 10−5
Christensenellaceae unknown genusd
Clostridiales unknown family unknown genus
5.24 × 10 −5
Obese vs. Overweight
2.64 × 10 −8
1.87 × 10 −6
1.10 × 10 −6
2.61 × 10 −5
Christensenellaceae unknown genusd
9.34 × 10 −5
Remarkably, in comparisons of the obese vs. overweight groups, nutrient adjustment had little effect on the significance; i.e. bacterial components related to the obese group were not influenced by the diet confounding factor compared with overweight group. This suggests there is a signature bacterium for the obesity that has no relation with dietary intake.
Recent human microbiome project studies have linked human gut microbes to obesity, proving the evidence that gut microbiota plays an important role in the harvesting, storage, and expenditure of energy obtained from diet [4, 34]. Our cross-sectional study aimed to identify differences in human gut microbiota associated with BMI in a large-scale metagenome cohort controlled by diet intake information.
Our results, like those of many others, do not support the hypothesis that an increased ratio of Firmicutes to Bacteroidetes may make a significant contribution to the pathophysiology of obesity. However, there is a consistent alpha diversity trend in previous reports that obese individuals have less diverse gut microbiota than normal weight individuals . Clustering of three groups showed a significant difference between each other, with the obese group showing the greatest differences from normal and overweight groups.
The theory of increased energy harvesting by an obesogenic microbiome is supported by the finding of increased production of SCFAs in the obese subjects [10, 13]. Our PICRUSt results indicate that gut microbial function in the obese group involves oxidative phosphorylation which can stimulate lipogenesis or gluconeogenesis  while decreasing carbohydrate metabolism. SCFA can increase oxidative phosphorylation, glycolysis, and fatty acid synthesis, which contribute the energy production . SCFAs are generated by microbial fermentation of indigestible dietary polysaccharides into absorbable monosaccharides, which are converted to more complex lipids in the liver . The major SCFAs are acetate, propionate, and butyrate, and the rate and amount of their production depends on the species and amounts of microbes present in colon . Firmicutes, including Clostridium and Lactobacilli, are major producers of acetate and butyrate. Whereas Bacteroidetes can ferment carbohydrate to produce propionate, Acidaminococcus, Megasphaera, and Mitsuokella from the Veillonellaceae family cannot digest a carbohydrate, but can utilize lactate to produce propionate . Our results showed that carbohydrate metabolism in the KEGG pathway was less predicted in the obese group compared with the normal group, which can be speculated by the positive association of Veillonellaceae in the obese group. Paraprevotellaceae (Bacteroidetes) in the overweight group and Veillonellaceae in the obese group contribute to propionate formation but via different pathways, which suggests that substrates or conditions specific to the obese group influence this switch of propionate producers. The mechanism behind this phenomenon will need to be further studied.
Additional mechanisms involving perturbation of the intestinal microbiota and changes in intestinal permeability as potential triggers of inflammation contribute to the risk of obesity and associated diseases . A reduced abundance of Akkermansia may reflect a thin mucus layer and thus an impaired gut barrier function with increased translocation of pro-inflammatory bacterial toxins that potentially lead to metabolic disturbances . Lately, Akkermansia was proposed to increase body thermogenesis and energy expenditure in cold temperatures . One longitudinal study showed that successful weight reduction in obese human individuals is accompanied by increased Akkermansia numbers in feces . A significant negative correlation of Akkermansia in the obese group was a consistent feature in our results as well. Thus, this microbe would need to be considered in relation with obesity in future studies.
Eggerthella and Adlercreutzia in the Coriobacteriia group within Actinobacteria have been repeatedly linked to positive effects in host lipid metabolism and involved in the stimulation of a major hepatic detoxification activity . In addition, these strains have been shown to play a role in the transformation from soy compound to equol, which has higher binding affinity to human estrogen receptors and induces transcription more strongly than any other isoflavone . Our results showed the negative correlation of Eggerthella with overweight and obese groups compared to the normal group, but the negative correlation was not significant when adjustment was made for carbohydrate intake. In contrast, the increase of Adlercreutzia was significantly correlated with the obese group compared with the overweight group and was not influenced by any nutrients. It can be speculated that Adlercreutzia may be replaced in the same niche as Eggerthella, but the meaning of this exchange in the obese group will need further study.
We have several limitations from 16S amplicon-based sequencing data which can introduce biases through PCR amplification steps, and resolute only genus level as a maximum . Another limitation of our study could be that our functional approach is represented only by using 16S rRNA gene. However, previous report showed this phylogenetic marker gene, 16S rRNA gene, is sufficiently linked with PICRUSt functional data, which accuracy already reached a maximum with around 100 sequence depth of 16S sequencing . Nevertheless, further studies on the correlation between significant bacteria and their predicted function will be required to define the role related with obesity.
Although there are a lot of gut microbiota studies regarding obesity, only recently have there been studies using large-scale epidemiologic data with significant statistical power and long-term diet confounding factors. The results of this study will contribute to establishment of a consistent theory on the extent of the influence of intestinal microbiota on obesity. The expectation is that a huge dataset affords the new possibility to discover a novel microbial component with impact on the human health.
We thank the subjects for providing samples. We also thank to the support of the computing resources by Global Science experimental Data hub Center (GSDC) Project and Korea Research Environment Open NETwork (KREONET) in Korea Institute of Science and Technology Information (KISTI).
This research was supported by the National Research Foundation of Korea (NRF), with funding by ICT & Future Planning (NRF-2014R1A2A2A04006291) and the Ministry of Education (NRF-2016R1A6A3A11932719). Additional support was received from the Intramural Research Support Program for Research Professor 2015/2016 of Ewha Womans University School of Medicine and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), with funding by the Ministry of Health & Welfare (HI14C0072).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request, and planning to be shared in the public repository, CODA (Clinical and Omics data archives) in KNIH (Korea National Institute of Health).
YY designed the analytic strategy and analyzed the actual dataset. HLK designed the study. HNK directed quality assurance and control of the study. YC, SR, and HS helped supervise and process the entire cohort. SK and SH contributed computational expertise. HLK and HNK helped conduct the literature review. All authors were involved in writing the paper and had final approval of the submitted and published versions.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The Institutional Review Board of Kangbuk Samsung Hospital approved this study (KBSMC 2013–01–245-008), and written informed consent was obtained from all participants. All applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during our research.
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