• Users Online: 218
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Contacts Login 

 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 42  |  Issue : 4  |  Page : 166-174

Gut microbiome profiling in nonalcoholic fatty liver disease and healthy individuals in Indonesian population


1 Gastroentero-hepatology Division, Department of Internal Medicine, Faculty of Medicine, Universitas Hasanuddin; Gastroenterology and Hepatology Centre, Dr. Wahidin Sudirohusodo Hospital, Makassar, Indonesia
2 Postgraduate Program, Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia
3 Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia; Fondazione Italiana Fegato-Onlus, Bldg. Q, AREA Science Park, ss14, Km 163.5, Basovizza, Trieste, Italy
4 Postgraduate Program; Department of Clinical Pathology, Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia
5 Department of Clinical Microbiology, Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia

Date of Submission24-Jan-2021
Date of Decision22-Sep-2021
Date of Acceptance06-Oct-2021
Date of Web Publication08-Dec-2021

Correspondence Address:
Muhammad Nasrum Massi
Department of Clinical Microbiology, Faculty of Medicine, Universitas Hasanuddin, Jalan Perintis Kemerdekaan Km. 10 Tamalanrea, Makassar 90245, South Sulawesi
Indonesia
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmedsci.jmedsci_25_21

Rights and Permissions
  Abstract 


Background: The gut microbiome is thought to produce metabolites that are widely investigated to play a role in various disease pathophysiologies. Aim: This study aims to identify the differences in gut microbiome diversity and profile between nonalcoholic fatty liver disease (NAFLD) and healthy individuals. Methods: This was a cross-sectional study. We collected 21 fecal specimens from NAFLD subjects and 13 controls. The gut microbiota from all samples were profiled by using 16s ribosomal RNA next-generation sequencing. Statistical analysis was done using SPSS version 25.0 software. Results: NAFLD subjects had a greater body mass index. Hypertension, diabetes, and dyslipidemia were found in 19%, 28.6%, and 81%, respectively, in NAFLD subjects. There was a lower diversity of gut microbiota in NAFLD compared to the control group. At the phylum level, Firmicutes was found more in the control than the NAFLD group (42.24% vs. 54.01%, P = 0.037). At the genus level, the percentage of Enterobacter was more abundant in the NAFLD group compared to the control group (0.517% vs. 0%, P = 0.001). At the genus level, there was a negative correlation between Bifidobacterium and NAFLD fibrosis score (NFS) (r = −0.532, P = 0.013). Conclusion: The diversity of the gut microbiota in NAFLD group was less than in control group. Firmicutes was found to be less prevalent in NAFLD patients compared to control. Enterobacter was found to be more abundant in NAFLD patients. The amount of Bifidobacterium was inversely correlated to the severity of NAFLD based on NFS.

Keywords: Nonalcoholic fatty liver disease, gut microbiome, microbiome diversity


How to cite this article:
S Daud NA, Akram NH, Hidayah N, Jayanti S, Handayani I, Massi MN. Gut microbiome profiling in nonalcoholic fatty liver disease and healthy individuals in Indonesian population. J Med Sci 2022;42:166-74

How to cite this URL:
S Daud NA, Akram NH, Hidayah N, Jayanti S, Handayani I, Massi MN. Gut microbiome profiling in nonalcoholic fatty liver disease and healthy individuals in Indonesian population. J Med Sci [serial online] 2022 [cited 2022 Aug 10];42:166-74. Available from: https://www.jmedscindmc.com/text.asp?2022/42/4/166/353044




  Introduction Top


Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, worldwide. The incidence of NAFLD is found in one-third of the adult population, especially in obese people with a high-fat diet and lack of exercise. Histologically, NAFLD is categorized into simple intrahepatic fat accumulation (simple steatosis) to various stages of necrotic inflammation (nonalcoholic steatohepatitis [NASH]).[1],[2],[3] NASH has the potential to develop fibrosis and cirrhosis and can even become hepatocellular carcinoma, in contrast to simple steatosis which rarely develops into an advanced disease.[4],[5]

A growing body of evidence in recent years suggests that the gut microbiome plays an important role in the health of the host and contributes to the development of a disease. The regulation of immune system development, inflammation, and metabolism is known to be influenced by the gut microbiota.[6],[7] It has been known for decades that there has been a substantial change in the structure of gut bacteria in patients with chronic liver disease. Furthermore, overgrowth of bacteria in the small bowel is associated with the level of cirrhosis. At present, dysbiosis, a condition that describes an imbalance in the amount of gut microbiota, is found to be involved in a variety of metabolic diseases such as obesity, the resistance of insulin, NAFLD, and NASH.[8]

Based on some of the results of previous studies, the gut microbiota is thought to be involved in the pathological process of NAFLD in ways such as enhancing the generation and absorption of short chain fatty acids (SCFAs), changing dietary choline action, changing the total amount of bile acids circulating in the enterohepatic circulation, the upward transmission of ethanol from microbiota to the liver, and changes in intestinal permeability and endotoxins secretion.[9] SCFA is the main fermentation product of intestinal microbes. The main components of SCFA produced by the gut microbiota, including butyrate, propionate, and acetate, propionate, play an important role in maintaining intestinal homeostasis.[10] The possible role of SCFA in obesity also adds to the explanation for its role in the incidence of NAFLD. Previous studies in both animals and humans demonstrated a change in the composition of the gut microbiota in obesity, i.e., Bacteroidetes was found to be decreased and Firmicutes was found to be abundant.[11],[12],[13] Changes in the composition of the gut microbiota have also been shown in other studies but with different results.[14],[15],[16]

Looking at the possible role of gut microbiota in the pathogenesis of NAFLD from the above, we were interested in identifying the diversity of gut microbiota in NAFLD patients. Previous studies have succeeded in recognizing the gut composition of the microbiota; however, with varying results, thus, it is still inconclusive.[17],[18],[19],[20] We hope that this study will enrich our knowledge and understanding of the role of the gut microbiome, which plays a role in the incidence and protection against NAFLD.


  Materials and Methods Top


Research design and sampling

Consecutive patients (aged ≥18 years) who met the diagnostic criteria of NAFLD based on ultrasonography and abdominal computed tomographic scan were prospectively recruited between February 2018 and October 2018 from Wahidin Sudirohusodo Hospital, Makassar, South Sulawesi, Indonesia. Conventional ultrasonography is the most commonly used imaging method for the diagnosis of hepatic steatosis because it is widely available, well established, well tolerated, and cheap. European guidelines for the management of NAFLD recommend using ultrasonography as the first-choice imaging in adults at risk for NAFLD (noninvasive assessment of liver diseases in patients with NAFLD).[21],[22] We excluded patients with secondary causes of hepatic steatosis (such as the use of systemic corticosteroids or total parenteral nutrition), infection with the hepatitis B or C virus, or decompensated cirrhosis. In addition, we included control subjects who had no clinical signs or symptoms of NAFLD and metabolic syndromes, such as hypertension, obesity, diabetes mellitus, and high serum cholesterol. This study was approved by the Ethics Committees of the Medical Faculty of Hasanuddin University (No. 587/H4.8.4.5.3.1/pp36-KOMETIK/2018). After providing an adequate explanation, consent for participation in this study was provided by all subjects before sample collection.

Demographic information was collected, including age, gender, history of drinking, hypertension, dyslipidemia, and type 2 diabetes mellitus. Body mass index (BMI, kg/m2) and laboratory tests results were recorded. Complete blood count, serum fasting plasma glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lipid profile, albumin, and the serum HBsAg levels and anti-HCV levels were determined using a standard testing kit or automatic system.

Fecal and sample collection

Stool samples were obtained at the homes of each participant and were immediately delivered to Gastroenterology and Hepatology Center in Wahidin Sudirohusodo Hospital, and then, there are be transferred into Hasanuddin Unit Medical Research Center Laboratory and stored at −80°C until analysis. The period from sampling to delivery to the Wahidin Sudirohusodo Hospital was intended to be as short as possible with a maximum of 24 h.

Determination of the degree of liver fibrosis with nonalcoholic fatty liver disease fibrosis score

The NAFLD fibrosis score (NFS) was used to differentiate NAFLD patients with and without advanced liver fibrosis.[23] We used NFS because it has been externally validated more than once and shows consistent results. It was accurate and had a high negative predictive value (>90%). It was based on six clinical parameters such as age, BMI, the presence of diabetes or impaired fasting glucose, the AST/ALT ratio, platelet count, and albumin. A score below −1.455 excludes advanced fibrosis, whereas a score >−1.455 predicts advanced fibrosis.[24]

DNA extraction

DNA extraction was performed using Stool DNA Isolation Kit Cat. 27600 (Norgen Biotek Corp, ON, Canada) based on the protocol kit. Briefly, a frozen aliquot (200 mg) of each fecal sample and lysis buffer L along with lysis additive A were mixed in the bead tube that was available in the kit. Then, the lysate was added by binding buffer I and incubated on ice. After that, the lysate was transferred and added with ethanol. Then, the clarified lysate was centrifuged to allow the DNA to bind to the column. The washing procedure was performed then using binding buffer C and wash solution A sequentially. Finally, elution buffer was added into the center of the column and incubated at room temperature for 1 min to obtain the purified genomic DNA. Quality control was performing by measuring the concentration and the purity of each sample using BioDrop μLITE (BioDrop Ltd., UK). After performing quality control, qualified samples proceed to library construction.

Library construction, next-generation sequencing, and microbiome analysis

The sequencing library is prepared by random fragmentation of the DNA samples. The DNA is converted into the library by ligation to 5' and 3' sequencing adapters designed to interact with the surface of the flow-cell as an next-generation sequencing (NGS) platform. Alternatively, “tagmentation” combines the fragmentation and ligation reactions into a single step that greatly increases the efficiency of the library preparation process. Adapter-ligated fragments are then PCR amplified and gel purified. PCR was performed to produce V3–V4 hypervariable regions of the 16s rRNA gene using Hercules II Fusion DNA Polymerase Nextera XT Index Kit V2 Library Kit. Sequencing was carried out by Macrogen Asia Pacific Pte Ltd. with the Illumina platform (Roche) under 16S Metagenomic Sequencing Library Preparation Part # 15044223 Rev. B Library protocol. Analysis of sequencing results with NGS (Illumina) was performed using the QIIME2 program. The pipelines from the analysis stage are as follows: importing and demultlipexing raw data sequences, quality control using DADA2 (denoizing and chimera removals), and taxonomical classification and diversity analysis.

Statistical analysis

The numerical data that have normal distribution are presented as mean ± standard deviation, and those without normal distribution are presented as the median (minimum–maximum). The categorical data were analyzed using Chi-square test. Mann–Whitney test was carried out to analyze the comparison between two groups with P < 0.05 being considered statistically significant. The statistical analysis in this study was conducted using IBM Statistical Package for the Social Sciences (SPSS) for Windows ver. 25.0 software (IBM Corp., Armonk, N.Y., USA).


  Results Top


Sample characteristics

During the study period, we screened 73 patients with NAFLD who met the inclusion criteria [Figure 1] at Wahidin Sudirohusodo Hospital, Makassar, South Sulawesi, Indonesia. A total of 58 patients gave consent and collected stool samples. Among them, 14 patients infected with hepatitis B and hepatitis C were excluded. A total of 23 patients were excluded because of incomplete medical record data (medical record number, BMI, and laboratory results). Thirty controls who matched the criteria participated in this study; however, only 13 of them agreed to take part in this study and collected their stool samples. A total of 34 eligible samples were enrolled in this study, consisting of 21 NAFLD samples and 13 control samples. The characterization of the samples is described in [Table 1]. In both the NAFLD and control groups, the sample was dominated by men, 71.4% and 53.8% in the NAFLD and the control group, respectively, but there was no substantial difference in gender between the two groups (P = 0.297). BMI in the NAFLD group was higher significantly than in the control group (P = 0.001). In the NAFLD group, it was found that 19% of the patients had a history of hypertension, 28.6% presented with type II diabetes and 81% of patients had dyslipidemia. The AST and ALT values in NAFLD patients were found to be significantly higher than in the control group (P = 0.02 and P = 0.007, respectively). Based on NFS, there were eight samples of advanced fibrosis and 13 samples of nonadvanced fibrosis in the NAFLD group.
Figure 1: Study profile

Click here to view
Table 1: Samples characterization

Click here to view


Gut microbiota

A total of 920,286 readings were obtained after screening for three ambiguous, 8724 low quality, 114,761 chimaera, 1,102,306 merged, and 1,214,772 denoized sequences. OTU was identified as a sequence with a similarity of more than 97%. The average OTU obtained was 2272 OTU and 7750 OTU in the NAFLD and the control samples, respectively, and there was a significant difference in OTU between the two groups (P = 0.002).

To determine alpha diversity or taxa diversity in each sample, several indexes can be used such as Chao1, Shannon, and Simpson's index. The average Chao1 index in the NAFLD group was 184 while the control was 207.08 (P = 0.249). While the Shannon index in the NAFLD group was 4.39 and the control group was 5.9 (P = 0.002). Meanwhile, the Simpson index in the NAFLD group was 0.86 and the control group was 0.96 (P = 0.001).

Differences in the composition of the gut microbiota in nonalcoholic fatty liver disease and control

Based on the 16S reading, the results were classified into six dominant phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, and Verrucobicrobia. Other bacterial phyla such as Desulfobacteriota, Cyanobacteria, Synergistota, and Spirochaetes were also detected, but their population averaged <0.01% of the total collective readings. Phylum-level analysis showed a significantly higher relative abundance of Firmicutes in the control group than in the NAFLD group (42.24% vs. 54.01%, P = 0.037). Besides, it was found that the abundance of Bacteroidetes, Fusobacteria, and Verrucomicrobial was higher in the NAFLD than in the control group, but the difference between the two groups was not significant. The reading results are shown in [Table 2] and [Figure 2].
Table 2: Differences in relative abundance of dominant gut microbiome in nonalcoholic fatty liver disease patients compared to control at the phylum level

Click here to view
Figure 2: Proportion of gut microbiota in the nonalcoholic fatty liver disease and control groups (phylum level)

Click here to view


At the level of genus analysis, 11 major genera were identified, i.e., Bifidobacterium, Bacteroides, Prevotella, Lactobacillus, Streptococcus, Clostridium, Faecalibacterium,  Escherichia More Details, Dialister, Enterobacter, and Alistipes. The results showed a significant difference in the genus Enterobacter, where this genus was found more in NAFLD than in the control group (P = 0.001). Meanwhile, the abundance of other genera did not differ significantly [Table 3]. The differences in the composition of the gut microbiota at the genus level between the NAFLD and control groups are seen in [Figure 3].
Table 3: Differences in relative abundance of gut microbiome proportion in non-alcoholic fatty liver disease patients compared to control at the genus level

Click here to view
Figure 3: Proportion of gut microbiota in the nonalcoholic fatty liver disease and control groups (genus level)

Click here to view


At the species level, 31 OTUs were predominantly found in NAFLD (P < 0.05), including Bacteroides caccae, Bacteroides corocola, Bacteroides dorei, Bacteroides fragilis, Faecalibacterium prausnitzii, and Enterobacter cloacae. Meanwhile, six OTUs were found to be more significantly dominant in the control than in the NAFLD group, including unknown (Agathobacter genus), unknown (Coprococcus genus), unknown (Faecalibacterium genus), and unknown (Subdoliggranulum genus) with a P < 0.05. Unknown here means that the OTU is not classified into any identified species in its genus, or in other words, based on its 16s rRNA sequence, the OTU is different from any known species in its genus. The differences in the gut microbiota at the species level between the NAFLD and control groups are seen in [Table 4].
Table 4: Differences in gut microbiome proportion in nonalcoholic fatty liver disease patients compared to control at the species level

Click here to view


Correlation of gut microbiota with the degree of liver fibrosis based on nonalcoholic fatty liver disease fibrosis score

At the phylum level, there was no correlation between a particular phylum and the degree of fibrosis using NFS, as shown in [Table 5].
Table 5: Correlation of gut microbiota with degree of fibrosis based on nonalcoholic fatty liver disease fibrosis score at phylum level

Click here to view


At the genus level, there was a negative correlation between Bifidobacterium and NFS (r = −0.532, P = 0.013). The other genera did not show any correlation with the degree of liver fibrosis using NFS, as listed in [Table 6].
Table 6: Correlation of the gut microbiota with the degree of liver fibrosis was based on nonalcoholic fatty liver disease fibrosis score at the genus level

Click here to view


In addition, there was a positive correlation between age and NFS values in NAFLD patients (r = 0.764, P = 0.000), as presented in [Figure 4].
Figure 4: Positive correlation of age and nonalcoholic fatty liver disease fibrosis score in nonalcoholic fatty liver disease group (r = 0.764, P = 0.000)

Click here to view


Apart from the degree of fibrosis, the correlation between the gut microbiota in NAFLD and other variables was also found, namely a positive correlation between Bacteroides genus and age (r = 0.52, P = 0.016) and a negative correlation between Bifidobacterium genus and BMI (r = −0.474, P = 0.03), as shown in [Figure 5].
Figure 5: Correlation of gut microbiota in nonalcoholic fatty liver disease and other variables: (a) a positive correlation between Bacteroides genus and age (r = 0.52, P = 0.016); (b) a negative correlation between Bifidobacterium genus and body mass index (r = −0.474, P = 0.03)

Click here to view



  Discussion Top


The link between NAFLD and gut microbiota has been identified by the relationship between bacterial overgrowth in the intestine and NASH. The liver is continuously exposed to intestine-derived bacteria and bacterial elements since 70% of its circulation comes from the vena portae, which drains blood out of the intestines.[25] In this study, the diversity of intestinal microbiota in NAFLD was lower than the control, and it can be seen from the OTUs value, as well as the alpha diversity, particularly shown from the Shannon and Simpson's index. Our result was consistent with Sobhonslidsuk et al.'s study which revealed that at the genus and species levels, alpha diversity was inferior in patients with NASH than in healthy controls, but they did not reach statistical significance.[17]

In this study, the Firmicutes was found more in control than the NAFLD group (42.24% vs. 54.01%, P = 0.037). This result is in line with the previous study that found lower Firmicutes composition in the NASH group than in healthy controls.[17] In the NAFLD group, the abundance of Bacteroidetes was higher than in the control group, although it was not statistically different (38.34% vs. 32.04%, P = 0.39). This is also similar to the prior research using 16s metagenomics sequencing that the phylum Bacteroidetes was found to be more abundant in the NASH group.[17] However, another study obtained the different results that the percentage of Bacteroidetes was lower in the NASH group compared to simple steatosis and controls by using the different method, quantitative real-time PCR.[19]

Dysbiosis predominant by Gram-negative bacteria such as Bacteroidetes and Proteobacteria might contribute to the pathogenesis of NAFLD. Increased levels of lipopolysaccharide (LPS) by Gram-negative bacteria can activate an inflammatory response that contributes to the initiation and progression of NAFLD. The accumulation of Bacteroides is also associated with increased levels of branched-chain fatty acids, resulting from amino acid fermentation, a factor that is involved in insulin resistance, thereby increasing the risk of NAFLD progression.

At the genus level, the percentage of Enterobacter was more abundant in the NAFLD group compared to the control group (0.517% vs. 0% P = 0.001). This result was in concordance with another study with a different method.[26] Specific genera of Gammaproteobacteria, including Enterobacter spp., have been suggested as notable bacterial predictors of susceptibility to fatty liver disease due to lack of choline.[27] Enterobacter spp. are natural commensals of the human gut microbiota. Several species belonging to Enterobacter have been known as pathogenic species and have also been reported as opportunistic pathogen.[28] Endotoxins produced by these bacteria penetrate the portal vein and decrease fasting-induced adipose factor (FIAF) secretion by increasing lipoprotein lipase (LPL) activity, promoting de novo fatty acid synthesis and triglyceride production, and activating inflammatory Toll-like receptors in hepatocytes. A permeable intestinal barrier and a small intestinal bacterial overgrowth, which is a condition in which elevated levels of small intestinal bacteria cause abdominal distension and other common symptoms associated with irritable bowel syndrome, are common in obese patients and cause liver injury by increasing Lipopolysaccharides (LPS) production from Gram-negative bacteria in the gut, which activates the production of NF-kβ and TNF-α. This suggests that intestinal microbiota (IM) increases hepatic exposure to endotoxins, which play an important role in the development of NASH.[29]

Several things could affect the amount of microbiota, such as age and BMI. In this study, we found the genus Bacteroides was correlated with age (r = 0.52, P = 0.016). The previous study has revealed that Bacteroides will reduce with age, but this study recruited centenarians,[30] while our study involved adults and the elderly (>65 years) with a maximum recorded age of 72 years. Besides, the proportion of other bacteria such as Bifidobacteria, Faecalibacterium prausnitzii, and some members of Firmicutes will also decrease.[30] In addition, Bifidobacterium was correlated to BMI in this study (r = −0.474, P = 0.03). It has been reported that the Bifidobacterium genus representatives may have a critical role in weight regulation as an antiobesity effect, or as a growth-promoter effect in agriculture, depending on the strains. A study conducted by Million et al. showed that Bifidobacterium animalis were associated with normal weight. Therefore, gut microbiota composition at the species level is related to body weight and obesity, which might be of relevance for further studies and the management of obesity.[14] Sobhonslidsuk et al. also reported that age and BMI were the important factors for the separation of the gut microbiota pattern between NASH and normal subjects in addition to the Bacteroidetes/Firmicutes ratio.[17]

In addition, there was a negative correlation between Bifidobacterium and NFS (r = −0.532, P = 0.013). This finding emphasized the possibility of gut microbiota-targeted therapy to reduce the severity of NAFLD as has been demonstrated in the previous study. Patients with the nonsignificant fibrosis group had higher abundances of Bifidobacteria consistent with the study by Bastian et al. and following the literature which states that the role of modulation of the gut microbiota with probiotics can affect the degree of liver fibrosis.[31] Administration of probiotics containing Bifidobacteria to NAFLD patients can improve serum transaminases and reduce levels of inflammatory cytokines and endotoxins that play a role in NAFLD progression.[32]

This study was also found a positive correlation between age and NFS values in NAFLD patients (r = 0.764, P = 0.000). This is in accordance with data from the American Association for the Study of Liver Disease, which states that the prevalence of NAFLD can vary with age, but the degree of liver fibrosis will increase with age.[21]

The difference in research findings may be due to different methods in determining the composition of the gut microbiome, as in several other studies using the quick PCR or quantitative real-time PCR method. Moreover, several other factors can affect microbiota levels, such as BMI, age, history of antibiotic use, components of the metabolic syndrome present in the sample, ethnicity variations, and diet. Regarding ethnic issues, participants in this study have the same ethnicity, so it is hoped that ethnic factors will not bias the results of this study. Researches on different ethnicities have been carried out in Indonesia and show that there are differences in the composition of the microbiota between different ethnicities and are thought to be influenced by differences in lifestyles and diet between the ethnic groups studied.[33] The degree of severity of NAFLD could also affect the shifting of microbiota.[34]

The limitation of this study is the small number of samples and the absence of histopathological findings to confirm the NAFLD diagnosis. Further research needs to be carried out by involving the large size of the population and by investigating other factors that can influence the gut composition of the microbiota.


  Conclusion Top


The diversity of the gut microbiota in NAFLD patients was less than in control group. Firmicutes was found to be less prevalent in NAFLD patients compared to control while Enterobacter was found to be more abundant in NAFLD patients. Interestingly, the amount of Bifidobacterium was inversely correlated to the severity of NAFLD based on NFS.

Financial support and sponsorship

This study was supported by grant from the Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi dan Pendidikan Tinggi (No. Grant 123/SP2H/PTNBH/DRPM/2018 and 1603/UN4.21/PL.00.00/2018).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Demir M, Lang S, Steffen HM. Nonalcoholic fatty liver disease – Current status and future directions. J Dig Dis 2015;16:541-57.  Back to cited text no. 1
    
2.
Tiniakos DG, Vos MB, Brunt EM. Nonalcoholic fatty liver disease: Pathology and pathogenesis. Annu Rev Pathol 2010;5:145-71.  Back to cited text no. 2
    
3.
Abu-Shanab A, Quigley EM. The role of the gut microbiota in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol 2010;7:691-701.  Back to cited text no. 3
    
4.
Angulo P. Long-term mortality in nonalcoholic fatty liver disease: Is liver histology of any prognostic significance? Hepatology 2010;51:373-5.  Back to cited text no. 4
    
5.
Söderberg C, Stål P, Askling J, Glaumann H, Lindberg G, Marmur J, et al. Decreased survival of subjects with elevated liver function tests during a 28-year follow-up. Hepatology 2010;51:595-602.  Back to cited text no. 5
    
6.
Spencer SP, Fragiadakis GK, Sonnenburg JL. Pursuing human-relevant gut microbiota-immune interactions. Immunity 2019;51:225-39.  Back to cited text no. 6
    
7.
Kamada N, Seo SU, Chen GY, Núñez G. Role of the gut microbiota in immunity and inflammatory disease. Nat Rev Immunol 2013;13:321-35.  Back to cited text no. 7
    
8.
Ma J, Zhou Q, Li H. Gut microbiota and nonalcoholic fatty liver disease: Insights on mechanisms and therapy. Nutrients 2017;9:E1124.  Back to cited text no. 8
    
9.
Yu J, Marsh S, Hu J, Feng W, Wu C. The pathogenesis of nonalcoholic fatty liver disease: Interplay between diet, gut microbiota, and genetic background. Gastroenterol Res Pract 2016;2016:2862173.  Back to cited text no. 9
    
10.
Venegas DP, De Fuente MK, Landskron G, González MJ, Quera R, Dijkstra G, et al. Short chain fatty acids (SCFAs)-mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. Front Immunol 2019;10:277.  Back to cited text no. 10
    
11.
Zhu L, Baker RD, Baker SS. Gut microbiome and nonalcoholic fatty liver diseases. Pediatr Res 2015;77:245-51.  Back to cited text no. 11
    
12.
Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A 2005;102:11070-5.  Back to cited text no. 12
    
13.
Kasai C, Sugimoto K, Moritani I, Tanaka J, Oya Y, Inoue H, et al. Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC Gastroenterol 2015;15:100.  Back to cited text no. 13
    
14.
Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P, et al. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Int J Obes (Lond) 2012;36:817-25.  Back to cited text no. 14
    
15.
Haro C, Rangel-Zúñiga OA, Alcalá-Díaz JF, Gómez-Delgado F, Pérez-Martínez P, Delgado-Lista J, et al. Intestinal microbiota is influenced by gender and body mass index. PLoS One 2016;11:e0154090.  Back to cited text no. 15
    
16.
Angelakis E, Armougom F, Carrière F, Bachar D, Laugier R, Lagier JC, et al. A Metagenomic investigation of the duodenal microbiota reveals links with obesity. PLoS One 2015;10:e0137784.  Back to cited text no. 16
    
17.
Sobhonslidsuk A, Chanprasertyothin S, Pongrujikorn T, Kaewduang P, Promson K, Petraksa S, et al. The association of gut microbiota with nonalcoholic steatohepatitis in thais. Biomed Res Int 2018;2018:9340316.  Back to cited text no. 17
    
18.
Zhu L, Baker SS, Gill C, Liu W, Alkhouri R, Baker RD, et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: A connection between endogenous alcohol and NASH. Hepatology 2013;57:601-9.  Back to cited text no. 18
    
19.
Mouzaki M, Comelli EM, Arendt BM, Bonengel J, Fung SK, Fischer SE, et al. Intestinal microbiota in patients with nonalcoholic fatty liver disease. Hepatology 2013;58:120-7.  Back to cited text no. 19
    
20.
Wang B, Jiang X, Cao M, Ge J, Bao Q, Tang L, et al. Altered fecal microbiota correlates with liver biochemistry in nonobese patients with non-alcoholic fatty liver disease. Sci Rep 2016;6:32002.  Back to cited text no. 20
    
21.
Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, et al. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American association for the study of liver diseases. Hepatology 2018;67:328-57.  Back to cited text no. 21
    
22.
Aller R, Fernández-Rodríguez C, Lo Iacono O, Bañares R, Abad J, Carrión JA, et al. Consensus document. Management of non-alcoholic fatty liver disease (NAFLD). Clinical practice guideline. Gastroenterol Hepatol 2018;41:328-49.  Back to cited text no. 22
    
23.
Treeprasertsuk S, Björnsson E, Enders F, Suwanwalaikorn S, Lindor KD. NAFLD fibrosis score: A prognostic predictor for mortality and liver complications among NAFLD patients. World J Gastroenterol 2013;19:1219-29.  Back to cited text no. 23
    
24.
Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology 2019;156:1264-81.e4.  Back to cited text no. 24
    
25.
Son G, Kremer M, Hines IN. Contribution of gut bacteria to liver pathobiology. Gastroenterol Res Pract 2010;2010:453563.  Back to cited text no. 25
    
26.
Zhang J, Wang C, Wang J, Zhang F. Relationship between intestinal flora and inflammatory factors in patients with nonalcoholic steatohepatitis. Exp Ther Med 2018;15:723-6.  Back to cited text no. 26
    
27.
Spencer MD, Hamp TJ, Reid RW, Fischer LM, Zeisel SH, Fodor AA. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology 2011;140:976-86.  Back to cited text no. 27
    
28.
Davin-Regli A, Lavigne JP, Pagès JM. Enterobacter spp.: Update on taxonomy, clinical aspects, and emerging antimicrobial resistance. Clin Microbiol Rev 2019;32:1-32.  Back to cited text no. 28
    
29.
Duarte SM, Stefano JT, Oliveira CP. Microbiota and nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH). Ann Hepatol 2019;18:416-21.  Back to cited text no. 29
    
30.
Hollister EB, Gao C, Versalovic J. Compositional and functional features of the gastrointestinal microbiome and their effects on human health. Gastroenterology 2014;146:1449-58.  Back to cited text no. 30
    
31.
Bastian WP, Hasan I, Lesmana CR, Rinaldi I, Gani RA. Gut microbiota profiles in nonalcoholic fatty liver disease and its possible impact on disease progression evaluated with transient elastography: Lesson learnt from 60 cases. Case Rep Gastroenterol 2019;13:125-33.  Back to cited text no. 31
    
32.
He X, Ji G, Jia W, Li H. Gut microbiota and nonalcoholic fatty liver disease: Insights on mechanism and application of metabolomics. Int J Mol Sci 2016;17:300.  Back to cited text no. 32
    
33.
Rahayu ES, Utami T, Mariyatun M, Hasan PN, Kamil RZ, Setyawan RH, et al. Gut microbiota profile in healthy Indonesians. World J Gastroenterol 2019;25:1478-91.  Back to cited text no. 33
    
34.
Liu G, Zhao Q, Wei H. Characteristics of intestinal bacteria with fatty liver diseases and cirrhosis. Ann Hepatol 2019;18:796-803.  Back to cited text no. 34
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed152    
    Printed2    
    Emailed0    
    PDF Downloaded27    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]