Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Republic of Korea.
Center for Human and Environmental Microbiome, Institute of Health and Environment, Seoul National University, Republic of Korea.
Bio-MAX/N-Bio, Seoul National University, Seoul, Republic of Korea.
Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea.
Department of Pathology, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea.
KoBioLabs Inc, Seoul, Republic of Korea.
Abstract
Nonalcoholic fatty liver disease (NAFLD) is associated with high carbohydrate (HC) intake. We investigated whether the relationship between carbohydrate intake and NAFLD is mediated by interactions between gut microbial modulation, impaired insulin response, and hepatic de novo lipogenesis (DNL). Stool samples were collected from 204 Korean subjects with biopsy-proven NAFLD (n = 129) and without NAFLD (n = 75). The gut microbiome profiles were analyzed using 16S rRNA amplicon sequencing. Study subjects were grouped by the NAFLD activity score (NAS) and percentage energy intake from dietary carbohydrate. Hepatic DNL-related transcripts were also analyzed (n = 90). Data from the Korean healthy twin cohort (n = 682), a large sample of individuals without NAFLD, were used for comparison and validation. A HC diet rather than a low carbohydrate diet was associated with the altered gut microbiome diversity according to the NAS. Unlike individuals from the twin cohort without NAFLD, the abundances of Enterobacteriaceae and Ruminococcaceae were significantly different among the NAS subgroups in NAFLD subjects who consumed an HC diet. The addition of these two microbial families, along with Veillonellaceae, significantly improved the diagnostic performance of the predictive model, which was based on the body mass index, age, and sex to predict nonalcoholic steatohepatitis in the HC group. In the HC group, two crucial regulators of DNL (SIRT1 and SREBF2) were differentially expressed among the NAS subgroups. In particular, kernel causality analysis revealed a causal effect of the abundance of Enterobacteriaceae on SREBF2 upregulation and of the surrogate markers of insulin resistance on NAFLD activity in the HC group. Consuming an HC diet is associated with alteration in the gut microbiome, impaired glucose homeostasis, and upregulation of hepatic DNL genes, altogether contributing to NAFLD pathogenesis.
Keywords
Nonalcoholic fatty liver diseasede novo lipogenesisgut microbiomehigh carbohydrate dietliver transcriptome
Figures
Figure 1.Stratification of the study population into high carbohydrate (HC) and low carbohydrate (LC) intake groups by carbohydrate intake and associations between nutrient intake and clinical markers in these groups. (a) Percentage of energy intake from carbohydrates, fats, and proteins in the HC and LC groups. (b) Absolute amounts of nutrient intake (in kcals) in each group, stratified by carbohydrate intake (%) and the NAFLD activity score (NAS). (c) The proportion of participants classified as obese population in each group (non-obese, BMI <25; obese, BMI ≥25 kg/m2), stratified by carbohydrate intake (%) and NAS. (d) Linear regression models (with 95% confidence interval bands highlighted in gray) between the levels of alanine transaminase (ALT) and carbohydrate intake (in kcals) with and without adjustment for BMI, age, and sex (HC, upper panel, red line; LC, lower panel, blue line). (e) Heatmap displaying the significant correlations between clinical markers and the intake of nutrients (*P < .05, **P < .01, ***P < .001). Positive correlations are expressed in red and negative correlations are in blue (HC, upper panel; LC, lower panel). (f) Linear regression models (with 95% confidence interval bands highlighted in gray) between clinical markers and the intake of carbohydrates (in kcals) after adjusting for BMI, age, and sex (HC, upper panel, red line; LC, lower panel, blue line).Figure 2.Alterations in the diversity and composition of the gut microbiome according to NAFLD severity in the high carbohydrate (HC) and low carbohydrate (LC) intake groups. (a) Non-metric multidimensional scaling (NMDS) plot based on the Bray-Curtis distance showing the beta diversity of the gut microbiome composition at the genus level (HC: N0, yellow; N1, Orange; N2, red and LC: N0, light sky blue; N1, dark blue; N2, navy). (b) NMDS plot from (A) visualized as Tukey’s box and whisker plot, showing the median and upper and lower quantiles. The nonparametric Kruskal-Wallis test and Dunn’s multiple comparisons test were used for statistical analysis. (c) Alpha diversity in each group (upper left, Simpson’s diversity index; upper right, Pielou’s evenness index; lower left, Shannon diversity index; lower right, species richness). (d) Relative abundances of the top 10 family taxa in the total, HC, and LC groups stratified by the NAFLD activity score.Figure 3.Changes in the relative abundance of the gut microbial taxa according to NAFLD severity in the high carbohydrate (HC) and low carbohydrate (LC) intake groups. (a) The abundance of three representative family taxa and (b) three representative genus taxa are depicted. Nonparametric Kruskal-Wallis tests and Dunn’s multiple comparisons tests were used in the statistical analysis. (c) Ranking of the family taxa according to the mean decrease in the Gini coefficient in the random forest model to predict the NAFLD activity score. (red circle, top 3 families defined by the random forest model; blue pentagon, three representative family taxa) (d–f) Regression analysis was used to determine the abundance of the Ruminococcaceae, Enterobacteriaceae, and Veillonellaceae families after adjusting for confounding factors including BMI, age, and sex. Multivariate association analysis was performed using MaAsLin2 with adjustment for multiple comparisons. (d) Total: P = .0001, q = 0.034; P = .001, q = 0.140; P = .011, q = 0.419. (e) HC: P = .002, q = 0.035; P = .005, q = 0.202; P = .014, q = 0.341. (f) LC: P = .102, q = 0.71; P = .264, q = 0.84; P = .149, q = 0.026. (*P < .05, **P < .01, ***P < .001, †FDR<0.25).Figure 4.Alterations in the diversity and composition of the gut microbiome according to nonalcoholic fatty liver disease (NAFLD) risk in high carbohydrate (HC) and low carbohydrate (LC) intake groups from the Health Twin cohort. A hepatic steatosis index (HSI) of 30 IU/L was used as the cutoff for determining NAFLD risk in the HC and LC groups (HC, n = 382; LC, n = 300). (a) NMDS plot showing the beta diversity of the gut microbiome composition in subjects with and without liver injury. NMDS scores were calculated using Bray-Curtis distance (HC group: HSI <30, pink; HSI ≥30, purple; LC group: HSI <30, yellow-green; HSI ≥30, dark green). (b) Alpha diversity was calculated using four indices (upper left, Simpson’s diversity index; upper right, the Shannon diversity; lower left, Pielou’s evenness index; lower right, species richness). (c) Relative abundances of the top 10 families are visualized in stacked bar plots for all patients as well as the HC and LC groups, stratified by liver injury severity. (d) Univariate analysis of three key microbial families using nonparametric Mann-Whitney tests and Dunn’s multiple comparisons tests (*P < .05, **P < .01, ***P < .001).Figure 5.Changes in de novo lipogenesis (DNL)-related transcripts in the high carbohydrate (HC) intake group. (a) Heatmap displaying the correlation between DNL-related genes and the NAFLD activity score (NAS) in the HC and LC groups. Statistical analysis was performed using transformed z scores and Spearman’s rank correlation analysis. (b) Z score-transformed expression levels of predefined transcripts were compared between the HC and LC groups according to NAFLD severity using an one-way analyses of variance and the FDR correction for multiple comparisons. (c) Differentially expressed genes were defined using the R package DEseq2. The upper panel indicates log10 transformed P values and the lower panel indicates log2 transformed fold changes. (d) The linear regression model depicts the relationship between SREBF2 and Enterobacteriaceae after adjustment for BMI, age, and sex (*P < .05, **P < .01, ***P < .001).Figure 6.Noninvasive multidimensional prediction of nonalcoholic steatohepatitis (NASH) using three microbial families and potential clinical variables in all subjects as well as the high carbohydrate (HC), and low carbohydrate (LC) intake groups (non-NASH vs. NASH). The receiver-operating characteristic (ROC) curve was designed for detecting NASH among study subjects with biopsy-proven NAFLD. Area under receiver-operating characteristic curves (AUROC) analyses including clinical variables (BMI, age, and sex) (blue) vs. those also including the three microbial families (Enterobacteriaceae, Ruminococcaceae, and Veillonellaceae) in addition to clinical variables (BMI, age, and sex) (red) plotted for the diagnosis of NASH in (a)all subjects as well as, (b) the HC, and (c) LC groups. P values were calculated using the DeLong test.Figure 7.The causal relationships among nonalcoholic steatohepatitis (NASH)-related microbes, transcripts, and clinical factors in the high carbohydrate (HC) and low carbohydrate (LC) intake groups. Causal inference of inverse, normally transformed microbiome families (Ruminococcaceae and Enterobacteriaceae), transcripts (SREBF2, SIRT1, and mTOR), and clinical variables (ALT, AST, adipo-IR, HOMA-IR, insulin, FFA, and BMI) were calculated using kernel causality analysis; significant causal correlations were visualized using Cytoscape. Causality of variables in the (a) HC and (b) LC groups. Solid line indicates a positive correlation and the dashed lines indicates a negative correlation. The thickness of the line indicates the degree of statistical significance (thin: less significant P values; thick: more-significant P values).
그림 출처: PubMed Central 공개 논문 (PMC9154801)
Conflict of interest statement
G.P.K. is a founder of KoBioLabs, Inc., a company that characterizing the role of host–microbiome interactions in individuals with chronic diseases. The other authors declare no competing interests.
