Abstract
Introduction
Limited data are available on the correlation between microbial communities and metabolic dysfunction-associated fatty liver disease (MAFLD). This study aimed to evaluate the influence of MAFLD on diverse microbial communities.
Methods
We recruited 43 patients with a nonviral liver disease. Enrolled patients were divided into two groups according to MAFLD criteria. The fecal microbial composition was evaluated using the variable V3-V4 region of the 16S ribosomal RNA region, which was amplified using polymerase chain reaction. First, we assessed the influence of MAFLD on distinct microbial communities at the bacterial phylum level. Next, the correlation between the microbial communities and diversity in patients with MAFLD was evaluated.
Results
Among the enrolled participants, the non-MAFLD and MAFLD groups consisted of 21 and 22 patients, respectively. Sequences were distributed among ten bacterial phyla. The relative abundance of Firmicutes was significantly higher in the MAFLD group than in the non-MAFLD group (p = 0.014). The microbial diversity was not significantly influenced by the presence of MAFLD (Chao-1 index: p = 0.215 and Shannon index: p = 0.174, respectively); nonetheless, the correlation coefficient between the abundances of Firmicutes and microbial diversity was higher in the non-MAFLD group than in the MAFLD group.
Conclusion
The presence of MAFLD increased the relative abundances of Firmicutes at the bacterial phylum level, which may cause the discrepancy between the abundances of Firmicutes and diversity in patients with MAFLD.
Keywords: Liver cirrhosis, Metabolic dysfunction-associated fatty liver disease, Fecal microbial communities, Dysbiosis, Firmicutes
Introduction
The human microbiome represents all microbes living in the human body [1]. Notably, the gut has the richest human microbiome that plays a great role in intestinal physiology, basal metabolism, and the development of the immune system [2]. Under normal health conditions, the gut lumen has an extensive network of commensal microbes, including Firmicutes, Bacteroidetes, and other phyla (such as Proteobacteria and Actinobacteria) [3]. However, if a disorder of the gut microbiome occurs, the vital function of this organ, which depends on normal microbiota, will collapse, leading to disease development. For example, compared with healthy individuals, those with nonalcoholic steatohepatitis have a specific pattern of gut microbiome and community diversity of the colonic microbiome, characterized by a lower diversity and higher abundance of Bacteroidetes [4]. Imbalance in gut microbiota composition, known as dysbiosis, is responsible for an increase in inflammatory immune responses and loss of gut barrier integrity, which contribute to the pathogenesis and progression of steatohepatitis [5, 6].
Similar pathological events have been described for alterations in progression of the liver fibrosis on the intestinal microbiome [7, 8]. In this regard, accumulating evidence suggests that dysbiosis is closely associated with cirrhotic complications, such as hepatic encephalopathy (HE) and ascites (HA) [9]. For liver cirrhosis (LC) patients, the gut microbiota plays a great role in leaky gut and bacterial translocation, which contribute to the progression of liver fibrosis [10]. Increased gut permeability to bacteria has been observed in patients with inadequate gut barrier function due to LC. Elevated levels of endotoxins or bacteria, which occur across the intestinal epithelium to the portal vein, induce the expression of Toll-like receptor 4 and inflammatory cytokines [11]. Therefore, the inflammatory reaction resulting from imbalances in gut microbiome contributes to the development of cirrhotic complications [12].
As maintaining a normal microbial community and preventing gut microbiota dysbiosis may lead to new treatment strategies for diseases [13], it is necessary to detect prebiotic or symbiotic markers that can be used in clinical practice. Changes in the Firmicutes/Bacteroidetes ratio (FBR), a well-known marker of microbial dysbiosis, were previously identified in several metabolic disorders [14]. In fact, the relative abundance of Firmicutes is negatively correlated with disease severity in patients with hepatitis and fibrosis [15]. However, another report showed that the FBR is positively correlated with the severity of obesity and that the relative proportion of Firmicutes decreases with weight loss. Epidemiological evidence also suggests that obesity is a major risk for the steatohepatitis [16] (e.g., an inverse correlation of obesity and dysbiosis during the progression of steatohepatitis and fibrosis) (Fig. 1).
Fig. 1.
Changes in the Firmicutes/Bacteroidetes ratio (FBR). Inverse correlation of obesity and the FBR during the progression of steatohepatitis and fibrosis.
Recently, an international consensus panel proposed a novel concept of fatty liver disease, termed metabolic dysfunction-associated fatty liver disease (MAFLD) [17]. This new concept can identify the risk of disease progression owing to fatty liver disease compared with that owing to nonalcoholic fatty liver disease [18]. Moreover, the MAFLD criteria can identify patients with risk factors for intrahepatic and extrahepatic events, which lead to a poor prognosis [19]. Many clinical trials in different fields have been conducted to evaluate this new definition [20]. Verification of the clinical usefulness of MAFLD for fatty liver disease has been suggested. However, limited data are available on the impact of MAFLD on dysbiosis. MAFLD is characterized by the inclusion criteria of metabolic abnormalities with obesity [17]. It has been suggested that microbial diversity and communities are correlated with the risk of metabolic abnormalities other than that associated with evidence of fatty liver. As it is necessary to assess the correlation between dysbiosis and MAFLD, we aimed to evaluate the influence of MAFLD in microbial communities.
Patients and Methods
Study Design and Patients
The patients who met the following requirements were enrolled in the present study: (1) the presence of chronic liver disease and (2) age >20 years. Patients with a malignant tumor other than hepatocellular carcinoma and those administered antibiotics other than nonabsorbable antibiotics within 3 months were excluded. Enrolled patients were divided into two groups according to MAFLD criteria [17]. MAFLD was defined as fatty liver with one of the following conditions: overweight/obesity, the presence of type 2 diabetes mellitus (DM), or lean/normal weight with evidence for a risk of metabolic abnormalities. The fatty liver was diagnosed by MRI, which indicated the presence of intrahepatic triglyceride content of approximately 5% of liver weight [21]. The assessment of steatosis using MRI-based proton density fat fraction was referred to the previous report [22]. Definitions of DM, hypertension, hyperlipidemia, and obesity are provided in online supplementary File 1 (for all online suppl. material, see https://doi.org/10.1159/000534284).
Analysis of Gut Microbiome
Fecal samples from enrolled patients were collected using collection kits. The samples were stored as soon as possible at 4 and −80°C for short- and long-term storage, respectively, to maintain the appropriate conditions and preserve the microbial community after collection.
The gut microbiome was evaluated based on fecal microbial composition using molecular methods [23]. Microbial DNA was extracted from fecal samples using the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany). Amplicon sequencing by polymerase chain reaction with primers targeting the variable V3-V4 region of the 16S rRNA gene was performed using the MiSeq system (Illumina Inc., San Diego, CA, USA) with a 2 × 300-cycle MiSeq Reagent Kit v3. Data processing was performed using QIIME2 pipeline (version 2019.4.0). The phylogenetic assignment of each operational taxonomic unit (OTU) was carried out using the Greengenes 16S rRNA gene database (version 13_8). The Hokkaido System Science Corporation assisted in the analysis of the gut microbiome.
Clinical, Demographic, and Laboratory Data
We assessed clinical factors, demographic information, and laboratory data within 1 month of obtaining fecal samples. Fibrosis stage comprised chronic hepatitis, compensated LC, and decompensated LC (DLC). LC was defined as a liver stiffness measurement (LSM) value ≥4 using magnetic resonance elastography (MRE). LC was also classified into compensated and decompensated phases; the decompensated phase is associated with varix ruptures, HE, and/or HA [24]. Clinical factors related to fatty liver were investigated as follows: platelets, prothrombin time, total bilirubin, alanine aminotransferase, aspartate aminotransferase, Mac-2 binding protein glycosylation isomer (M2BPGi), and branched-chain amino acid and tyrosine ratio.
Endpoint Measurements
First, we assessed the influence of MAFLD on distinct microbial communities at the bacterial phylum level. Next, the correlation between the microbial communities and diversity in MAFLD patients was evaluated. Bacterial diversity was determined using sampling-based analysis of OTUs and rarefaction curves. Alpha diversity was defined as the mean diversity of species in different subjects. Comparison of bacterial richness and diversity across samples was analyzed using the Chao-1 and Shannon indices, which were calculated with QIIME (version 1.7.0).
Statistical Analyses
SPSS software version 23.0 (IBM Corp NY) was used for data analysis. Analysis between MAFLD and non-MAFLD groups was performed using the χ2 test or Mann-Whitney test to detect differences in proportions. Analysis between groups regarding the microbial communities was performed using the Mann-Whitney test. Pearson’s r coefficient was used to measure the linear correlation between variables. Spearman’s rank correlation coefficient was used as an index to measure the relationship between variables.
Results
Subjects
Forty-three patients were assessed between December 2021 and September 2022. Table 1 shows the characteristics of the enrolled patients. The median age was 68.0 years (24–83), and 22 patients were men. The number of patients with LC was 25 (58.1%) based on MRE. Seven patients had hepatocellular carcinoma. Varix ruptures, HA, and HE were present in 1, 6, and 12 patients, respectively. For LC, Child-Pugh classes A, B, and C were present in 13, 10, and 2 patients, respectively. Medications, such as gastric acid secretion inhibitors and nonabsorbable antibiotics, were administered to 17 patients and 6 patients, respectively.
Table 1.
Baseline clinical characteristics
| Clinical characteristics | All patients | Non-MAFLD | MAFLD | p value |
|---|---|---|---|---|
| Number of patients | 43 | 21 | 22 | |
| Age, years | 68.0 (24–83) | 68.0 (31–83) | 67.0 (24–79) | 0.237 |
| Gender: male, n (%) | 22 (51.2) | 11 (52.4) | 11 (50.0) | 0.559 |
| Weight, kg | 64.0 (37–115) | 59.0 (37–83) | 68.0 (54–115) | 0.001 |
| BMI, kg/m2 | 25.2 (15.6–38.8) | 23.0 (15.6–28.6) | 28.5 (24–38.8) | 0.001 |
| Alcohol, n (%) | 6 (14.0) | 5 (23.8) | 1 (4.5) | 0.095 |
| Hepatocellular carcinoma, n (%) | 6 (14.0) | 4 (19.0) | 2 (9.1) | 0.412 |
| LC, n (%) | 25 (58.1) | 14(66.7) | 11(50.0) | 0.358 |
| HA, n (%) | 6 (14.0) | 5 (23.8) | 1 (4.5) | 0.095 |
| HE, n (%) | 12 (27.9) | 7 (33.3) | 5 (22.7) | 0.510 |
| DM, n (%) | 18 (43.8) | 4 (19.0) | 14 (63.6) | 0.005 |
| Dyslipidemia, n (%) | 12 (43.8) | 1 (4.8) | 11 (50.0) | 0.002 |
| Hypertension, n (%) | 15 (43.8) | 7 (33.3) | 8 (36.3) | 1,000 |
| Inhibitor of gastric acid secretion, n (%) | 17 (39.3) | 11 (52.4) | 6 (27.3) | 0.124 |
| Nonabsorbable antimicrobials, n (%) | 6 (12.5) | 4 (19.0) | 1 (4.5) | 0.185 |
| M2BPGi, C.O.I | 1.98 (0.4–17.4) | 2.36 (0.34–16.0) | 1.50 (0.53–17.4) | 0.330 |
| LSMs | 5.5 (1.5–13.6) | 5.83 (1.60–13.6) | 5.15 (1.5–12.6) | 0.089 |
| White blood cells/μL | 5,400 (1,700–11,500) | 4,800 (3,100–8,000) | 5,200 (1,700–6,500) | 0.325 |
| Hemoglobin, g/dL | 13.7 (7–18.2) | 13.1 (7.0–18.2) | 14.2 (8.8–17.7) | 0.089 |
| Platelets ×104/μL | 12.3 (3.2–31.9) | 11.9 (5.8–23.6) | 10.9 (3.2–31.9) | 0.132 |
| Prothrombin time, % | 87 (32–104) | 76.6 (44–100) | 78.0 (32–104) | 0.129 |
| Serum albumin, g/dL | 4.0 (2.0–5.0) | 3.50 (2.0–4.5) | 3.75 (2.7–5.0) | 0.122 |
| AST, IU/L | 34.0 (12–115) | 39.0 (17–113) | 34.5 (18–84) | 0.961 |
| ALT, IU/L | 28.0 (10–151) | 26.5 (13–104) | 37.5 (14–137) | 0.096 |
| Total bilirubin, mg/dL | 0.9 (0.3–4.4) | 1.10 (0.3–4.4) | 0.9 (0.5–3.1) | 0.147 |
| HbA1c, % | 5.7 (4.7–9.2) | 5.56 (4.7–8.0) | 6.0 (4.7–8.3) | 0.036 |
| BTR | 3.55 (1.16–8.63) | 3.25 (1.91–8.63) | 5.12 (1.16–8.39) | 0.360 |
Data are expressed as median, min-max, or number (%).
M2BPGi, Mac-2 binding protein glycosylation isomer; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HbA1c, hemoglobin A1c; BTR, branched-chain amino acid and tyrosine molar ratio.
Characteristics of MAFLD Groups
Among the enrolled patients, MAFLD and non-MAFLD groups had 22 and 21 patients, respectively. Body weight and body mass index (BMI) were significantly higher in the MAFLD group than in the non-MAFLD group. The frequency of dyslipidemia and DM was higher in the MAFLD group than in the non-MAFLD group. Differences regarding age, sex, alcohol, hepatocellular carcinoma, LC, hypertension, inhibitors of gastric acid secretion, and nonabsorbable antimicrobials between groups were not statistically significant.
Sequencing Data from Stool Samples
In total, 783,304 read pairs were obtained from 43 stool samples. The mean numbers of sequencing tags and OTUs for the enrolled patients were 16,272 and 67, respectively. Community richness and diversity were compared after equalizing the sample sizes to 20,000 using random subtraction. The median Chao-1 and Shannon indices were 68.0 and 4.8, respectively. Online supplementary File 2 presents rarefaction curves of the Chao-1 and Shannon index methods, showing the consistently saturated alpha diversity for enrolled patients.
Distinct Microbial Communities at the Bacterial Phylum Level
Figure 2 depicts distinctive microbial communities at the bacterial phylum level. Sequences were distributed among ten bacterial phyla, including Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, Actinobacteria, Verrucomicrobia, Synergistetes, Cyanobacteria, TM7, and Spirochaetes. The relative abundance of Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria in all enrolled patients was 42%, 44%, 6%, and 6%, respectively.
Fig. 2.
Distinct microbial communities at the bacterial phylum level. Sequences were distributed among ten bacterial phyla, including Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria, Verrucomicrobia, TM7, Synergistetes, Cyanobacteria, and Spirochaetes.
The Influence of the Microbial Communities in MAFLD Patients
Body weight and BMI were associated with the relative abundances of Firmicutes (body weight, r = 0.489, p = 0.001, and BMI, r = 0.393, p = 0.009) and Bacteroidetes (body weight, r = −0.468, p = 0.002b and BMI, r = −0.409, p = 0.006). However, LSM and M2BPGi were not associated with the relative abundance of Firmicutes (LSM, r = 0.130, p = 0.405; M2BPGi, r = 0.107, p = 0.512, respectively) (online supplementary File 3).
Table 2 shows the analysis of microbial communities and clinical characteristics using the Mann-Whitney test. The relative abundance of Firmicutes was significantly higher in MAFLD patients than in non-MAFLD patients (p = 0.014). The relative abundance of Bacteroidetes was significantly lower in patients treated with an inhibitor of gastric acid secretion than in patients without that treatment (p = 0.033).
Table 2.
Microbial communities and clinical characteristics
| Variable | Firmicutes (%) | p value | Bacteroidetes (%) | p value | Proteobacteria (%) | p value | Actinobacteria (%) | p value | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age >65/<65 | 36.5 | 47.1 | 0.285 | 46.7 | 40.4 | 0.264 | 3.1 | 2.5 | 0.552 | 1.4 | 6.3 | 0.152 |
| BMI >25/<25 | 51.3 | 33.9 | 0.076 | 32.6 | 52.0 | 0.020 | 3.4 | 2.5 | 0.473 | 5.8 | 1.12 | 0.076 |
| Gender: male/female | 52.9 | 38.8 | 0.080 | 32.5 | 49.1 | 0.061 | 2.8 | 3.1 | 0.481 | 2.5 | 2.2 | 0.846 |
| MAFLD: +/− | 51.4 | 28.1 | 0.014 | 35.4 | 51.3 | 0.044 | 2.3 | 3.5 | 0.269 | 4.7 | 1.1 | 0.055 |
| Alcohol: +/− | 33.9 | 42.6 | 0.462 | 55.8 | 44.2 | 0.528 | 3.3 | 3.1 | 0.505 | 3.75 | 2.2 | 0.551 |
| Hepatocellular carcinoma: +/− | 49.5 | 42.5 | 0.833 | 35.3 | 44.4 | 0.420 | 3.8 | 2.1 | 0.207 | 5.3 | 1.8 | 0.277 |
| LC: +/− | 44.8 | 40.2 | 0.961 | 40.7 | 48.4 | 0.431 | 2.7 | 3.5 | 0.721 | 2.8 | 1.6 | 0.676 |
| DLC: +/− | 44.3 | 41.9 | 0.978 | 44.2 | 44.1 | 0.656 | 3.5 | 1.9 | 0.164 | 2.2 | 2.5 | 0.911 |
| DM: +/− | 52.8 | 38.8 | 0.065 | 32.6 | 49.2 | 0.146 | 2.8 | 3.5 | 0.538 | 4.7 | 1.4 | 0.168 |
| Dyslipidemia: +/− | 45.2 | 41.3 | 0.850 | 38.9 | 47.8 | 0.871 | 3.6 | 2.5 | 0.542 | 2.0 | 2.8 | 1,000 |
| Hypertension: +/− | 45.8 | 36.8 | 0.464 | 38.4 | 44.3 | 0.595 | 4.0 | 1.5 | 0.029 | 2.0 | 3.2 | 0.527 |
| Inhibitor of gastric acid secretion: +/− | 27.8 | 49.9 | 0.033 | 52.0 | 38.9 | 0.062 | 3.0 | 2.7 | 0.378 | 0.9 | 6.7 | 0.003 |
| Nonabsorbable antimicrobials: +/− | 44.3 | 41.9 | 0.985 | 52.0 | 42.3 | 0.520 | 3.5 | 2.6 | 0.289 | 1.2 | 3.0 | 0.363 |
Bacterial Diversity of the Microbial Communities in MAFLD Patients
Table 3 shows the analysis of bacterial diversity and clinical characteristics using the Mann-Whitney test. The alpha diversity was higher in MAFLD patients than in non-MAFLD patients, although the difference was not statistically significant (Chao-1 index: p = 0.215 and Shannon index: p = 0.174). The alpha diversity was higher with DM than without DM, although the difference was not statistically significant (Chao-1 index: p = 0.086 and Shannon index: p = 0.052).
Table 3.
Alpha diversity and clinical characteristics
| Clinical characteristics | Chao 1 index | p value | Shannon index | p value | ||
|---|---|---|---|---|---|---|
| Age >65/<65 | 67.0 | 68.0 | 0.644 | 4.81 | 4.79 | 0.846 |
| BMI >25/<25 | 68.5 | 66.0 | 0.923 | 4.80 | 4.79 | 0.903 |
| Gender: male/female | 59.5 | 69.0 | 0.158 | 4.85 | 4.77 | 0.846 |
| MAFLD: +/− | 69.0 | 63.0 | 0.215 | 4.89 | 4.60 | 0.174 |
| Alcohol: +/− | 44.0 | 68.0 | 0.146 | 4.34 | 4.80 | 0.262 |
| Hepatocellular carcinoma: +/− | 52.0 | 69.0 | 0.177 | 4.49 | 4.80 | 0.247 |
| LC: +/− | 59.0 | 68.5 | 0.409 | 4.77 | 4.85 | 0.622 |
| DLC: +/− | 45.0 | 74.0 | 0.001 | 4.11 | 4.95 | 0.010 |
| DM: +/− | 72.0 | 61.5 | 0.086 | 5.07 | 4.60 | 0.052 |
| Dyslipidemia: +/− | 68.5 | 66.0 | 0.978 | 4.78 | 4.80 | 0.372 |
| Hypertension: +/− | 67.0 | 75.0 | 0.242 | 4.68 | 4.85 | 0.098 |
| Inhibitor of gastric acid secretion: +/− | 68.0 | 67.0 | 0.673 | 4.85 | 4.78 | 1,000 |
| Nonabsorbable antimicrobials: +/− | 45.0 | 68.5 | 0.041 | 4.57 | 4.79 | 0.384 |
Table 4 shows the correlation between the microbial communities and alpha diversity using Spearman’s rank correlation. In the MAFLD group, the correlation coefficient between the abundance of Firmicutes and alpha diversity was very low (Chao-1 index: r = 0018, p = 0.935, and Shannon index: r = 0.182, p = 0.416). In the non-MAFLD group, the correlation coefficient between the abundance of Firmicutes and alpha diversity was relatively high (Chao-1 index: r = 0.369, p = 0.085, and Shannon index: r = 0.530, p = 0.013). The correlation coefficient between the abundance of Firmicutes and alpha diversity was higher in non-MAFLD patients than in MAFLD patients.
Table 4.
Correlation between clinical factors and α diversity using Spearman’s rank correlation
| Variable | Firmicutes (%) | Bacteroidetes (%) | Proteobacteria (%) | Actinobacteria (%) | |||||
|---|---|---|---|---|---|---|---|---|---|
| r value | p value | r value | p value | r value | p value | r value | p value | ||
| Chao 1 index | |||||||||
| All enrolled patients | n = 43 | 0.204 | 0.188 | −0.258 | 0.112 | −0.386 | 0.011 | −0.004 | 0.981 |
| MAFLD patients | n = 22 | 0.018 | 0.935 | −0.121 | 0.264 | −0.244 | 0.038 | −0.469 | 0.028 |
| Non-MAFLD patients | n = 21 | 0.369 | 0.085 | −0.352 | 0.038 | −0.437 | 0.047 | 0.273 | 0.232 |
| Shannon index | |||||||||
| All enrolled patients | n = 43 | 0.398 | 0.008 | −0.289 | 0.225 | −0.539 | 0.001 | 0.029 | 0.853 |
| MAFLD | n = 22 | 0.182 | 0.416 | −0.210 | 0.281 | −0.444 | 0.038 | 0.192 | 0.391 |
| Non-MAFLD | n = 21 | 0.530 | 0.013 | −0.496 | 0.022 | −0.526 | 0.014 | 0.161 | 0.487 |
Correlation between the Clinical Factors and the Bacterial Diversity of Gut Microbiota
The correlation between clinical factors and the Chao-1 index using Spearman’s rank correlation is as follows: total bilirubin was associated with the Chao-1 index (p = 0.017). The LSM was considerably negatively correlated with the Chao-1 index, although the difference was not statistically significant (r = −0.255, p = 0.098). No significant correlations were found between the Chao-1 index for M2BPGi, white blood cells, hemoglobin, platelets, prothrombin time, serum albumin, BUN, serum creatinine, AST, alanine aminotransferase, hemoglobin A1c, and branched-chain amino acid and tyrosine molar ratio (online supplementary File 4).
DLC was associated with a significantly lower diversity (Chao-1 index: p = 0.001 and Shannon index: p = 0.010). The presence of hypertension was associated with a considerably lower diversity compared with its absence, although the difference was not statistically significant (Chao-1 index: p = 0.242 and Shannon index: p = 0.098). No significant correlations were found between the Chao-1 index for age, obesity, alcohol, hepatocellular carcinoma, HA, inhibitor of gastric acid secretion, and nonabsorbable antimicrobials (Table 3).
Discussion
This is the first report to evaluate the distinct microbial communities at the bacterial phylum level in MAFLD patients. Our study revealed that the presence of MAFLD increased the relative abundance of Firmicutes and decreased the relative abundance of Bacteroidetes, regardless of the presence of LC. MAFLD is characterized by the presence of fatty liver with obesity and other chronic comorbidities [17]. The pathophysiology of MAFLD comprises multiple factors, including age, sex, ethnicity, host genetics, body composition, food intake, and the presence of chronic metabolic disease [19, 25]. Notably, the gut microbiome can be associated with these factors and is involved in MAFLD development [26]. A link between gut microbiome and obesity has been reliably established from animal and human studies [27]. Obesity is positively correlated with the abundance of Firmicutes at the phylum level. In the present study, the frequency of obese patients in the MAFLD and non-MAFLD groups was 90% and 24%, respectively. The presence of obesity greatly affected the high frequency of Firmicutes in MAFLD patients.
Our analysis of microbial diversity in patients with MALFD suggested that the relative abundances of Bacteroides and Firmicutes were not symbiotic markers for gut microbiota dysbiosis in MAFLD patients. The community diversity was not significantly correlated with the relative abundances of Bacteroidetes and Firmicutes in MAFLD patients. However, the diversity in the non-MAFLD group was associated with the relative abundances of Firmicutes. This result may cause a discrepancy between the microbial communities and diversity in patients with MAFLD. Furthermore, the increased frequency of Firmicutes could presumably balance the FBR and consequentially treat the progression of fibrosis and hepatitis [28]. Balancing the gut and intestinal microbial ecosystem maintains a living organism, and normalization of the FBR is a useful and well-established therapeutic strategy [29]. The presence of MAFLD may help balance the gut ecosystem to increase the FBR in patients with LC, which causes FBR decrease and dysbiosis. In fact, elevated BMI in LC patients is associated with prolonged survival compared with normal-weight patients [30]. In the present study, patients with DLC had lower alpha diversity than those without DLC. Regarding the underlying mechanisms of HE in DLC, dysbiosis is thought to be the major source of both ammonia and the systemic pro-inflammatory milieu in humans [31]. The administration of fecal transplantation was examined as a new therapy to balance the FBR for DLC [32]. Unexpectedly, there was no statistically significant difference in the distinct microbial communities between patients having LC and those without LC unless the frequency of LC in the enrolled patients was sufficient to influence the gut microbiome. The relative abundance of Firmicutes was negatively correlated with the progression of fibrosis and steatohepatitis [33, 34]. The reason for these conflicting results may be selection biases for enrolled patients. Previous reports showed that fecal microbial communities in LC patients are distinct from those in healthy individuals [35]. In contrast, the present study compared the LC and CH groups. The early stage of steatohepatitis occurs owing to imbalances in the composition of microbiota, which contribute to the increase in inflammatory immune responses [36].
Our findings also revealed that total bilirubin was a useful predictor for the change in the bacterial diversity according to the development of liver dysfunction. High serum bilirubin levels are correlated with disturbances in liver function in various diseases [37]. The most popular algorithm-based score model for the severity of LC is the Child-Turcotte-Pugh score, which is composed of the serum bilirubin, albumin, and prothrombin time [38]. However, total bilirubin is not always a sensitive indicator of liver dysfunction. Even in patients with moderate to severe hepatic parenchymal injury, a change in the bilirubin level may be slight. MRE was more useful than total serum bilirubin for predicting fibrosis and portal hypertension. To predict bacterial diversity in chronic liver disease, biomarkers are required not only for the evaluation of liver function but also for the biliary-gut system. Bilirubin, which is a metabolic end-product of heme, is taken up from the blood by liver cells and secreted with bile into the gut for excretion [39]. Thus, serum bilirubin perfectly meets the symbiotic biomarkers to predict dysbiosis in chronic liver disease in clinical practice.
The present study had some limitations. First, the diagnosis of steatohepatitis and liver fibrosis was based on MRE. Histological evaluations were not performed. Second, the sample size was too small to detect a correlation between MAFLD and microbial communities. Third, the microbiota was influenced by various factors, such as age, diet, race, and treatments. As such, the role of gut microbiome in MAFLD requires further investigation to compare the gut microbiota among individuals with different diets and from countries and generations. Particularly, targeting the influence of MAFLD on the gut microbiota of patients who take medications, such as gastric acid secretion inhibitors and nonabsorbable agents, would be a valuable area of research. Fourth, this study did not consider the influence of burn-out NASH on MRE. Lastly, molecular methods to evaluate the gut microbiome were different among facilities. Particularly, the method of DNA extraction and the selection of the primers to amplify the 16S rRNA region might have led to discrepancies in the results.
Summary
In conclusion, the presence of MAFLD increased the relative abundance of Firmicutes, regardless of liver fibrosis. This result may cause the discrepancy between the microbial communities and diversity in MAFLD patients.
Acknowledgments
We thank the Hokkaido System Science Corporation for assistance with the analysis of the gut microbiome. We also thank Robert E. Brandt, Founder, CEO, and CME of MedEd, Japan, for editing and formatting the manuscript.
Statement of Ethics
The protocol for this research project has been approved by the Ethics Committee of Kitasato University Hospital (Number: C21-212), and it conforms to the provisions of the Declaration of Helsinki. All informed consent was obtained from the subjects. All patients were informed about the significance of this clinical research and gave written informed consent.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
No funding was received to assist with the preparation of this manuscript.
Author Contributions
Uojima H., Yoshihiko S., Gotoh K., Satoh T., Hidaka H., Akira T., Horio K., Shunji H., and Kusano C. contributed equally to this work; Uojima H. collected and analyzed the data; Uojima H. drafted the manuscript; Hidaka H. and Sakaguchi Y designed and supervised the study; Gotoh K., Satoh T., Take A., Horio K., Hayashi S., and Kusanoa C. offered technical or material support. All the authors discussed the results and approved the final manuscript for publication.
Funding Statement
No funding was received to assist with the preparation of this manuscript.
Data Availability Statement
All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.
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Associated Data
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Data Availability Statement
All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.
