Pathophysiological background in different phenotypes of nonalcoholic fatty liver disease (NAFLD) remains to be elucidated. The aim was to investigate the association between fecal and blood ...microbiota profiles and the presence of NAFLD in obese versus lean subjects. Demographic and clinical data were reviewed in 268 health checkup examinees, whose fecal and blood samples were available for microbiota analysis. NAFLD was diagnosed with ultrasonography, and subjects with NAFLD were further categorized as obese (body mass index (BMI) ≥25) or lean (BMI <25). Fecal and blood microbiota communities were analyzed by sequencing of the V3-V4 domains of the 16S rRNA genes. Correlation between microbiota taxa and NAFLD was assessed using zero-inflated Gaussian mixture models, with adjustment of age, sex, and BMI, and Bonferroni correction. The NAFLD group (n = 76) showed a distinct bacterial community with a lower biodiversity and a far distant phylotype compared with the control group (n = 192). In the gut microbiota, the decrease in Desulfovibrionaceae was associated with NAFLD in the lean NAFLD group (log2 coefficient (coeff.) = -2.107, P = 1.60E-18), but not in the obese NAFLD group (log2 coeff. = 1.440, P = 1.36E-04). In the blood microbiota, Succinivibrionaceae showed opposite correlations in the lean (log2 coeff. = -1.349, P = 5.34E-06) and obese NAFLD groups (log2 coeff. = 2.215, P = 0.003). Notably, Leuconostocaceae was associated with the obese NAFLD in the gut (log2 coeff. = -1.168, P = 0.041) and blood (log2 coeff. = -2.250, P = 1.28E-10). In conclusion, fecal and blood microbiota profiles showed different patterns between subjects with obese and lean NAFLD, which might be potential biomarkers to discriminate diverse phenotypes of NAFLD.
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.
A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease ...using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.
Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. ...To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data.
HLAscan performs alignment of reads to HLA sequences from the international ImMunoGeneTics project/human leukocyte antigen (IMGT/HLA) database. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods such as HLAreporter and PHLAT. In addition, the results of HLA-A, -B, and -DRB1 typing by HLAscan using data generated by NextGen were identical to those obtained using a Sanger sequencing-based method. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, -B, -C, -DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%.
HLAscan, an alignment-based program that takes read distribution into account to determine true allele types, outperformed previously developed HLA typing tools. Therefore, HLAscan can be reliably applied for determination of HLA type across the whole-genome, exome, and target sequences.
There have been few large-scale studies on the relationship between smoking and gut microbiota. We investigated the relationship between smoking status and the composition of gut microbiota. This was ...a population-based cross-sectional study using Healthcare Screening Center cohort data. A total of 758 men were selected and divided into three groups: never (
= 288), former (
= 267), and current smokers (
= 203). Among the three groups, there was no difference in alpha diversity, however, Jaccard-based beta diversity showed significant difference (
= 0.015). Pairwise permutational multivariate analysis of variance (PERMANOVA) tests between never and former smokers did not show a difference; however, there was significant difference between never and current smokers (
= 0.017) and between former and current smokers (
= 0.011). Weighted UniFrac-based beta diversity also showed significant difference among the three groups (
= 0.038), and pairwise PERMANOVA analysis of never and current smokers showed significant difference (
= 0.01). In the analysis of bacterial composition, current smokers had an increased proportion of the phylum Bacteroidetes with decreased Firmicutes and Proteobacteria compared with never smokers, whereas there were no differences between former and never smokers. In conclusion, gut microbiota composition of current smokers was significantly different from that of never smokers. Additionally, there was no difference in gut microbiota composition between never and former smokers.
Although obesity is associated with numerous diseases, the risks of disease may depend on metabolic health. Associations between the gut microbiota, obesity, and metabolic syndrome have been ...reported, but differences in microbiomes according to metabolic health in the obese population have not been explored in previous studies. Here, we investigated the composition of gut microbiota according to metabolic health status in obese and overweight subjects. A total of 747 overweight or obese adults were categorized by metabolic health status, and their fecal microbiota were profiled using 16S ribosomal RNA gene sequencing. We classified these adults into a metabolically healthy group (MH, N = 317) without any components of metabolic syndrome or a metabolically unhealthy group (MU, N = 430) defined as having at least one metabolic abnormality. The phylogenetic and non-phylogenetic alpha diversity for gut microbiota were lower in the MU group than the MH group, and there were significant differences in gut microbiota bacterial composition between the two groups. We found that the genus Oscillospira and the family Coriobacteriaceae were associated with good metabolic health in the overweight and obese populations. This is the first report to describe gut microbial diversity and composition in metabolically healthy and unhealthy overweight and obese individuals. Modulation of the gut microbiome may help prevent metabolic abnormalities in the obese population.
To identify genetic factors influencing quantitative traits of biomedical importance, we conducted a genome-wide association study in 8,842 samples from population-based cohorts recruited in Korea. ...For height and body mass index, most variants detected overlapped those reported in European samples. For the other traits examined, replication of promising GWAS signals in 7,861 independent Korean samples identified six previously unknown loci. For pulse rate, signals reaching genome-wide significance mapped to chromosomes 1q32 (rs12731740, P = 2.9 x 10(-9)) and 6q22 (rs12110693, P = 1.6 x 10(-9)), with the latter approximately 400 kb from the coding sequence of GJA1. For systolic blood pressure, the most compelling association involved chromosome 12q21 and variants near the ATP2B1 gene (rs17249754, P = 1.3 x 10(-7)). For waist-hip ratio, variants on chromosome 12q24 (rs2074356, P = 7.8 x 10(-12)) showed convincing associations, although no regional transcript has strong biological candidacy. Finally, we identified two loci influencing bone mineral density at multiple sites. On chromosome 7q31, rs7776725 (within the FAM3C gene) was associated with bone density at the radius (P = 1.0 x 10(-11)), tibia (P = 1.6 x 10(-6)) and heel (P = 1.9 x 10(-10)). On chromosome 7p14, rs1721400 (mapping close to SFRP4, a frizzled protein gene) showed consistent associations at the same three sites (P = 2.2 x 10(-3), P = 1.4 x 10(-7) and P = 6.0 x 10(-4), respectively). This large-scale GWA analysis of well-characterized Korean population-based samples highlights previously unknown biological pathways.
•Personality traits were correlated with gut microbiota composition.•Gammaproteobacteria was increased in high neuroticism group.•Low conscientiousness group showed increased abundance of ...Proteobacteria.•The low conscientiousness group showed decreased abundance of Lachnospiraceae.
Personality affects fundamental behavior patterns and has been related with health outcomes and mental disorders. Recent evidence has emerged supporting a relationship between the microbiota and behavior, referred to as brain-gut relationships. Here, we first report correlations between personality traits and gut microbiota. This research was performed using the Revised NEO Personality Inventory and the sequencing data of the 16S rRNA gene in 672 adults. The diversity and the composition of the human gut microbiota exhibited significant difference when stratified by personality traits. We found that personality traits were significantly correlated with diversity of gut microbiota, while their differences were extremely subtle. High neuroticism and low conscientiousness groups were correlated with high abundance of Gammaproteobacteria and Proteobacteria, respectively when covariates, including age, sex, BMI and nutrient intake, were controlled. Additionally, high conscientiousness group also showed increased abundance of some universal butyrate-producing bacteria including Lachnospiraceae. This study was of observational and cross-sectional design and our findings must be further validated through metagenomic or metatranscriptomic methodologies, or metabolomics-based analyses. Our findings will contribute to elucidating potential links between the gut microbiota and personality, and provide useful insights toward developing and testing personality- and microbiota-based interventions for promoting health.
Rosacea is a chronic inflammatory dermatosis affecting the face and eyes. An association between systemic comorbidities and rosacea has been reported, but the link to enteral microbiota is uncertain. ...We aimed to investigate the link between rosacea and enteral microbiota. A cross‐sectional study was performed in a sample of Korean women who participated in a health check‐up programme at the Kangbuk Samsung Hospital Health Screening Center between 23 June 2014 and 5 September 2014. The gut microbiome was evaluated by 16S rRNA gene and metagenome sequence analyses. A total of 12 rosacea patients and 251 controls were enrolled. We identified links between rosacea and several changes in gut microbiota: reduced abundance of Peptococcaceae family unknown genus, Methanobrevibacter (genus), Slackia (genus), Coprobacillus (genus), Citrobacter (genus), and Desulfovibrio (genus), and increased abundance of Acidaminococcus (genus), Megasphaera (genus), and Lactobacillales order unknown family unknown genus. A link between rosacea and enteral microbiota was observed in this metagenomic study. A large and elaborate study is needed to confirm these findings and to elucidate the mechanisms involved.
STUDY QUESTION
Are there any novel genetic markers of susceptibility to polycystic ovary syndrome (PCOS)?
SUMMARY ANSWER
We identified a novel susceptibility locus on chromosome 8q24.2 and several ...moderately associated loci for PCOS in Korean women.
WHAT IS KNOWN ALREADY
PCOS is a highly complex disorder with significant contributions from both genetic and environmental factors. Previous genome-wide association studies (GWAS) in the Han Chinese population identified several risk loci for PCOS. However, GWAS studies on PCOS remain very few. The aim of this study was to identify novel markers of susceptibility to PCOS through GWAS.
STUDY DESIGN, SIZE, DURATION
A two-stage GWAS was conducted. The initial discovery set for GWAS consisted of 976 PCOS cases and 946 controls. The second stage (replication study) included 249 PCOS cases and 778 controls.
PARTICIPANTS/MATERIALS, SETTING, METHODS
Patients were diagnosed according to the Rotterdam criteria. Genomic DNAs were genotyped using the HumanOmni1-Quad v1 array. In the replication stage, the 21 most promising signals selected from the discovery stage were tested for their association with PCOS.
MAIN RESULTS AND THE ROLE OF CHANCE
One novel locus with genome-wide significance and seven moderately associated loci for PCOS were identified. The strongest association was on chromosome 8q24.2 (rs10505648, OR = 0.52, P = 5.46 × 10−8), and other association signals were located at 4q35.2, 16p13.3, 4p12, 3q26.33, 9q21.32, 11p13 and 1p22 (P = 5.72 × 10−6–6.43 × 10−5). The strongest signal was located upstream of KHDRBS3, which is associated with telomerase activity, and could drive PCOS and related phenotypes.
LIMITATIONS, REASONS FOR CAUTION
The limitation of our study is the modest sample size used in the replication cohort. The limited sample size may contribute to a lack of statistical power to detect an association or show a trend in severity.
WIDER IMPLICATIONS OF THE FINDINGS
Our findings provide new insight into the genetics and biological pathways of PCOS and could contribute to the early diagnosis and prevention of metabolic and reproductive morbidities.
STUDY FUNDING/COMPETING INTEREST(S)
This work was supported in part by the grant from the Korea Centers for Disease Control and Prevention (2009-E00591-00). The work was also supported by the Ewha Global Top5 Grant 2013 of Ewha Womans University. None of the authors has any conflict of interest to declare.
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