Introduction
With a global prevalence of 25%,1 nonalcoholic fatty liver disease (NAFLD), the liver component of a group of disorders linked to metabolic dysfunction,2 is defined as the presence of steatosis in more than 5% of hepatocytes in the absence of heavy alcohol intake or other chronic liver illnesses.3 NAFLD is currently slowly being recognized as a chronic disease. However, due to limited medical knowledge, most NAFLD patients ignore treatment of the disease.4,5 NAFLD is known to have a close, bilateral association with metabolic syndrome.6 Additionally, lipid abnormalities are linked to an increased risk of liver7 as well as cardiovascular disease.8 Furthermore, some studies have conclusively shown cardiovascular disease to be the leading cause of death in NAFLD patients.9,10 Therefore, it is imperative to investigate the link between dyslipidemia and NAFLD.
Collectively, data from rodent studies support the hypothesis that gut microbiota plays a role in the development of NAFLD.11–14 Additionally, changes in the gut microbiota can affect the gut–liver axis and are linked to the development of cirrhosis and NAFLD in human patients.15–19 Therefore, characterizing the bacterial populations implicated in dysbiosis is critical as it may aid in the development of alternative disease management techniques. In most cases, NAFLD is diagnosed by imaging, and in routine practice, the most commonly used imaging technique is abdominal ultrasound.2 However, whether the effects changes of the gut microbiota in FL can be diagnosed by ultrasound is up for debate. The controlled attenuation parameter (CAP) has been widely used to assess steatosis20,21 and reportedly has outstanding performance in diagnosing more than 10% of hepatic steatosis instances.22,23 In this study, we used abdominal ultrasound and CAP to estimate the level of FL. We also noted that how the gut microbiota changes in patients with NAFLD and hyperlipidemia remains an open question.
The primary objective of this investigation was two-fold, firstly, to establish a classification framework for distinguishing individuals afflicted with FL through the combined use of ultrasound and CAP, and secondly, to dissect differences in the gut microbial composition in subjects with normal liver function and in those diagnosed with NAFLD, using 16S rDNA gene amplicon sequencing. Furthermore, this study delved into alterations of the gut microbiota of patients concurrently affected by NAFLD and hyperlipidemia. We used biochemical parameters such as alanine transaminase (ALT) and aspartate aminotransferase (AST) to identify significantly differences between NAFLD patients and healthy individuals.
Methods
Human patients
A total of 60 patients were enrolled in our study between January 1 and November 30, 2021. The study was approved by the institutional ethics committee of Shenzhen People's Hospital. The approval number is LL-KY-2021637. The inclusion criteria for NAFLD patients were: (1) >18 years of age; (2) newly diagnosed with NAFLD during the selection period, and confirmed by abdominal ultrasound and FibroScan; (3) not being treated with any medication and had not gained any significant weight in the preceding 6 months; (4) abdominal ultrasound and biochemical indexes were evaluated on the same day. The inclusion criteria of normal patients were (1) >18 years of age; (2). abdominal ultrasound was normal; (3) abdominal ultrasound and biochemical indexes were evaluated on the same day. The exclusion criteria for both NAFLD and normal patients were: (1) the presence of autoimmune hepatitis, primary biliary cholangitis, or primary sclerosing cholangitis; (2) hepatitis B or C viral infection; (3) antibiotics and other commonly used nonantibiotic medications, such as PPIs, laxatives, statins, antidepressants, and opioids used within the preceding month; (4) a malignancy diagnosis (<5 years); (5) human immunodeficiency virus infection; (6) chronic disorders associated with lipodystrophy or immunosuppression; (7) drug-induced steatosis or liver injury; or (8) diabetes, gout, and/or other metabolic disease.
Ultrasound and FibroScan detection
All abdominal ultrasound examinations were performed using a Mindray Resona 7A (Mindray, Shenzhen, China) convex array probe at a frequency of 1–6MHz. We also assessed liver disease severity using a FibroScan 502 Touch model (M Probe; XL Probe; Echosens, Paris, France), which included two functional examinations of liver stiffness and fat content, i.e., CAP and vibration-controlled transient elastography. Patients, were divided into three groups, of 20 each, those with a normal liver (NL), mild FL (FL1), and moderate-to-severe FL (FL2) according to the liver ultrasound performance and CAP value based on the following criteria.24–26 (1) Patients with a normal liver echogenic structure were included in the NL group. (2). When the diaphragm and the portal vein wall could be normally observed, but there was a small and generalized increase in liver echogenicity, the patients were included in the FL1 group. (3). Patients with moderate or markedly increased liver echogenicity and mild or severe impairment in the appearance of the portal vein wall, diaphragm, and posterior right hepatic lobe were included in the FL2 group. Patients were divided into three groups according to the CAP value as follows:27 (1) NL group, CAP<240 dB/m; (2) FL1 group, 240 dB/m<CAP<265 dB/m; and (3) FL2 group, CAP>265 dB/m.
Baseline assessment
Low-density lipoprotein, high-density lipoprotein, triglycerides (TGs), total cholesterol, gamma-glutamyl transpeptidase, ALT, AST, alkaline phosphatase, albumin, direct bilirubin, total bilirubin, and white blood cell (WBC) and platelet counts were measured after an 8 h overnight fast. Body mass index (commonly known as BMI) was defined as the weight divided by height squared (kg/m2). Abdominal circumference was defined as the horizontal abdominal girth through the point of the iliac crest. Additionally, age and sex characteristics were also collected in our study.
Hyperlipidemia definition
In this study, hyperlipidemia (HL) was defined as (1) total cholesterol >6.5 mmol/L] and (2) TGs>2.3 mmol/L.28 If the levels of the subject's blood lipid indicators were consistent with the above definitions, we classified the study participants as having hyperlipidemia.
Microbiome analysis by 16S rDNA sequencing
Stool samples of the patients in our study were collected and stored in a freezer at −40°C. Following the extraction of 16S rDNA and sample quality checks, variable regions V3–V4 of bacterial 16S rRNA genes were amplified with degenerate PCR primers. An Agilent 2100 Bioanalyzer was used for quality inspection and the qualified library was sequenced by selecting the corresponding Illumina sequencing HiSeq 2500 platform and PE300 (San Diego, CA, USA). Raw reads were filtered to remove adaptors and low-quality and ambiguous bases. Paired-end reads were then added to tags using the Fast Length Adjustment of Short Reads program (FLASH, v.1.2.11)29 to derive the tags. The tags were clustered into operational taxonomic units (OTUs) with a cutoff value of 97% using UPARSE software (v7.0.1090),30 and chimera sequences were compared with the Gold database using UCHIME (v4.2.40)31 to detect. Representative OTU sequences were taxonomically classified using Ribosomal Database Project Classifier v.2.2 with a minimum confidence threshold of 0.6 and trained on the Greengenes database (v.201305) using QIIME (v.1.8.0).32 USEARCH_global33 was used to compare all of the tags back to the OTU to derive the OTU abundance statistics table for each sample. Details of the microbiome analysis are shown in the Supplementary File 1.
Statistical analysis
The statistical analysis was performed using R (https://www.r-project.org ). Differences in normally distributed numerical variables were compared with t-tests, rank sum tests were used for non-normally distributed numerical variables, and a chi-squared tests were used for disordered classification variables. P-values <0.05 was used to determine whether the clinical indicators were associated with NAFLD or HL. The ace index was used to access the alpha diversity. Principal coordinate analysis was conducted to access beta diversity. Specificity–occupancy plots were used to identify potential keystone species.
Detrended correspondence analysis (DCA) was employed to identify broad structural changes in microbial communities. In correspondence analysis, the arch effect, where the data points are arranged in a horseshoe-like pattern, is removed using detrending. The ordination method is known as DCA. Canonical correspondence analysis (CCA) was performed to determine the most important biochemical index shaping microbial community composition and organization. Thus, DCA and CCA were employed to select the most ideal biochemical indexes. Finally, areas under the receiver operating characteristic curve (AUCs) were compared to determine the efficacy of the gut microbiota and biochemical index for identifying significant FL.
Results
Baseline patient characteristics
This study included 20 patients with ultrasound-proven NL, 20 patients with ultrasound-proven FL1, and 20 patients with ultrasound-proven FL2. The FL1 and FL2 patients were divided into two groups, an HL and a non-HL (NHL) group. Finally, there were a total of 40 NHL patients and 20 HL patients in our study. Table 1 and Supplementary Tables 1 and 2 summarize the characteristics of each group. Among all the HL and NHL patients, only ALT was statistically significant among the NL, FL1, and FL2 patients. Meanwhile, whether in all patients or the NHL patients, TGs, gamma-glutamyl transpeptidase, direct bilirubin, platelet, and abdominal circumference were statistically significant among the NL, FL1, and FL2 patients. Furthermore, only BMI was statistically significant among the NL, FL1, and FL2 patients, both in the HL and NHL groups. Age and weight were only statistically significant among the NL, FL1, and FL2 patients in the HL group.
Table 1Baseline information of all the study patients
| Variables | Total (n=60) | NL (n=20) | FL1 (n=20) | FL2 (n=20) | pa | pb | pc | pd | pe |
|---|
| Ageg, year | 47.07±11.43 | 45.9±11.25 | 50.65±10.36 | 44.65±12.27 | 0.22 | 0.58 | 0.1 | 0.17 | 0.74 |
| Sexh | | | | | 0.82 | 1 | 0.75 | 1 | 1 |
| Female | 27 (45) | 9 (45) | 8 (40) | 10 (50) | | | | | |
| Male | 33 (55) | 11 (55) | 12 (60) | 10 (50) | | | | | |
| HLh | | | | | <0.01 | <0.01 | 1 | <0.01 | <0.01 |
| HL | 40 (67) | 20 (100) | 10 (50) | 10 (50) | | | | | |
| NHL | 20 (33) | 0 (0) | 10 (50) | 10 (50) | | | | | |
| LDLg, mmol/L | 2.95±0.97 | 3.18±0.55 | 2.53±1.14 | 3.14±1 | 0.05 | 0.11 | 0.08 | 0.03 | 0.88 |
| TGf, mmol/L | 1.41 (1, 2.71) | 0.9 (0.76, 1.19) | 1.83 (1.33, 3.41) | 2.24 (1.78, 4.46) | <0.01 | <0.01 | 0.27 | <0.01 | <0.01 |
| HDLf, mmol/L | 1.1 (0.9, 1.33) | 1.33 (1.06, 1.54) | 1.06 (0.88, 1.18) | 1 (0.88, 1.19) | 0.06 | 0.02 | 0.88 | 0.1 | 0.05 |
| TCg, mmol/L | 5.03±1.28 | 4.75±0.88 | 4.77±1.43 | 5.57±1.34 | 0.07 | 0.17 | 0.08 | 0.96 | 0.03 |
| GGTf, U/L | 27 (16, 44.25) | 16 (13.75, 18.5) | 32.5 (25.08, 46) | 38 (28.75, 51.5) | <0.01 | <0.01 | 0.4 | <0.01 | <0.01 |
| ALTf, U/L | 20 (14.9, 28) | 15.5 (13.5, 18) | 20 (14.82, 28) | 29 (20.75, 51.25) | <0.01 | <0.01 | 0.02 | 0.04 | <0.01 |
| ASTf, U/L | 22 (17, 42.7) | 18 (14, 24.5) | 22 (18.75, 41.4) | 36.65 (22.75, 46.6) | <0.01 | 0.01 | 0.08 | 0.14 | <0.01 |
| ALPf, U/L | 67 (60.75, 69.93) | 64.6 (58, 68.12) | 67.35 (57.75, 68.62) | 68.6 (62.9, 76.5) | 0.08 | 0.1 | 0.07 | 0.42 | 0.06 |
| ALBf, g/L | 42.63 (14.88, 45.65) | 40.88 (16.2, 46.15) | 41.85 (12.4, 44.7) | 43.2 (16.35, 44.75) | 0.82 | 0.72 | 0.6 | 0.59 | 0.96 |
| TPf, g/L | 68.25 (62.25, 71.83) | 65.8 (62.08, 71.3) | 70 (62.32, 71.48) | 68.55 (66.15, 74.12) | 0.54 | 0.29 | 0.84 | 0.51 | 0.25 |
| DBf, µmol/L | 3.4 (2.2, 4.7) | 2.25 (1.9, 2.8) | 4.07 (2.52, 4.74) | 3.95 (3.25, 4.78) | <0.01 | <0.01 | 0.88 | 0.03 | <0.01 |
| TBILf, µmol/L | 11.15 (8.05, 15.82) | 11.1 (8.67, 15.45) | 11.1 (7.92, 16.52) | 11.45 (7.75, 15.5) | 0.99 | 0.96 | 0.94 | 0.91 | 1 |
| WBCf, ×109/L | 6.59 (5.9, 8.02) | 6.53 (6.15, 7.62) | 7.11 (5.84, 8.58) | 6.56 (5.7, 7.58) | 0.8 | 0.86 | 0.54 | 0.5 | 0.86 |
| PLTf, ×109/L | 239.5 (218, 267.5) | 221 (201.75, 239.5) | 250 (231, 273) | 248 (235.25, 270.25) | 0.03 | <0.01 | 0.94 | 0.02 | 0.03 |
| Abdominal circumferenceg, cm | 93.13±11.58 | 84.95±10.63 | 93.85±9.24 | 100.6±9.39 | <0.01 | <0.01 | 0.03 | <0.01 | <0.01 |
| Heightg, cm | 165.72±8.85 | 167.3±7.69 | 164.5±9.16 | 165.35±9.78 | 0.6 | 0.3 | 0.78 | 0.45 | 0.49 |
| Weightf, kg | 71 (64.5, 75.5) | 72 (61.5, 77.25) | 67.5 (59.5, 75) | 74 (69.5, 79.75) | 0.1 | 0.68 | 0.04 | 0.97 | 0.05 |
| BMIf | 25.39 (24.1, 27.34) | 24.49 (22.72, 25.99) | 25.08 (23.6, 25.83) | 27.37 (25.69, 28.66) | <0.01 | 0.02 | 0.02 | 0.59 | <0.01 |
Decreased gut bacterial diversity is present in patients with NAFLD
We compared the microbial diversity using data from 16S rDNA gene amplicon sequencing, based on patient liver ultrasound results and blood lipid levels. For comparison, the beta diversity, based on the Bray–Curtis distance, and the alpha diversity based on the ace metric were both plotted (Fig. 1). In all patients, 6% lower ace alpha diversity was found in cases of NAFLD; in NHL patients, 5% lower ace alpha diversity was found in NAFLD cases. In terms of beta diversity, no significant differences were found in all patients (p=0.26), hyperlipidemia patients (p=0.356), or nonhyperlipidemic patients (p=0.337).
Top 10 bacteria in terms of OTU abundance differed slightly among the three groups
We evaluated the relative abundance of NL, FL1, and FL2 based on various groupings and selected the 10 most abundant levels (Fig. 2 and Supplementary Table 3). In the three groups, the top 10 phyla were Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Actinobacteria, Verrucomicrobia, unclassified, Synergistetes, Candidatus_Saccharibacteria, and Cyanobacteria. However, the relative order of abundance of Fusobacteria, Actinobacteria, and Verrucomicrobia were different. Among the three groups, from NL, FL1, and FL2, the proportion of Firmicutes gradually decreased while the proportion of Bacteroidetes gradually increased. That is, Firmicutes/Bacteroidetes decreased as NAFLD progressed.
Changes in FL-related microbiota are more prevalent in all patients and NHL patients than in HL patients
Univariate analysis was used to assess differences in various microbial taxa by fatty severity in all, NHL, and HL patients (Table 2 and Supplementary Tables 4–6). Comparison of NL, FL1, and FL2 patients, found Faecalibacterium, Lachnospira, and Lachnospiracea_incertae_sedis to differ in all patients and the NHL patient group. Fusicatenibacter, Lachnoanaerobaculum, and Victivallis were only found different in the all-patients group. Whether in the all-patients group or the NHL patients group, the relative abundance of Faecalibacterium, Lachnospira, and Lachnospiracea_incertae_sedis in NL patients was the highest, indicating that the abundance of these three gut bacteria was reduced in patients with NAFLD, as well as in patients with NAFLD but without HL. Interestingly, in both the all-patients group and the NHL patients group, the abundance of Faecalibacterium for FL2 patients was higher than that of FL1. This indicated that although the abundance of these bacteria was lower in NAFLD, the degree of reduction was inversely proportional to the severity of the disease. Conversely, the abundance of Lachnospiracea_incertae_sedis gradually decreased between the NL, FL1, and FL2 patients, signaling that the reduction in its abundance positively correlated with NAFLD progression.
Table 2Differences in gut microbiome in different groups
| All patients (n=60)
| NHL patients (n=40)
| HL patients (n=20)
|
|---|
| NL vs. FL1 vs. FL2 | NL vs. FL | NL vs. FL1 | NL vs. FL2 | FL1 vs. FL2 | NL vs. FL1 vs.FL2 | NL vs. FL | NL vs. FL1 | NL vs. FL2 | FL1 vs. FL2 | FL1 vs. FL2 |
|---|
| Cyanobacteria | 0.08 | 0.36 | 0.81 | 0.09 | 0.05 | 0.02 | 0.8 | 0.1 | 0.09 | 0.01 | 0.55 |
| Anaerostipes | 0.05 | 0.04 | 0.27 | 0.01 | 0.24 | 0.05 | 0.04 | 0.37 | 0.02 | 0.25 | 0.48 |
| Bilophila | 0.51 | 0.25 | 0.32 | 0.32 | 1 | 0.1 | 0.04 | 0.06 | 0.15 | 0.53 | 0.57 |
| Butyricicoccus | 0.1 | 0.38 | 0.06 | 0.7 | 0.08 | 0.09 | 0.19 | 0.03 | 0.98 | 0.13 | 0.39 |
| Christensenella | 0.51 | 0.31 | 0.5 | 0.27 | 0.58 | 0.09 | 0.03 | 0.08 | 0.06 | 0.85 | 0.48 |
| Cloacibacillus | 0.16 | 0.06 | 0.07 | 0.11 | 0.78 | 0.08 | 0.03 | 0.09 | 0.04 | 0.57 | 0.8 |
| Enterococcus | 0.07 | 0.02 | 0.07 | 0.02 | 0.76 | 0.05 | 0.01 | 0.05 | 0.01 | 1 | 0.73 |
| Faecalibacterium | <0.01 | <0.01 | <0.01 | <0.01 | 0.19 | <0.01 | <0.01 | <0.01 | 0.04 | 0.06 | 0.97 |
| Faecalicoccus | 0.15 | 0.1 | 0.39 | 0.05 | 0.33 | 0.1 | 0.07 | 0.41 | 0.03 | 0.34 | 0.73 |
| Fusicatenibacter | 0.03 | 0.11 | 0.02 | 0.74 | 0.03 | 0.09 | 0.14 | 0.03 | 0.78 | 0.11 | 0.17 |
| Lachnoanaerobaculum | 0.02 | 0.09 | 0.63 | <0.01 | <0.01 | 0.15 | 0.17 | 0.68 | 0.06 | 0.08 | 0.08 |
| Lachnospira | <0.01 | <0.01 | <0.01 | <0.01 | 0.94 | <0.01 | <0.01 | 0.01 | 0.02 | 0.84 | 0.85 |
| Lachnospiracea_incertae_sedis | 0.04 | 0.01 | 0.06 | 0.02 | 0.46 | <0.01 | <0.01 | 0.02 | <0.01 | 0.53 | 0.8 |
| Mogibacterium | 0.08 | 0.91 | 0.21 | 0.36 | 0.03 | 0.09 | 0.75 | 0.31 | 0.15 | 0.03 | 0.4 |
| Roseburia | 0.08 | 0.33 | 0.05 | 0.8 | 0.06 | 0.06 | 0.15 | 0.02 | 0.98 | 0.09 | 0.48 |
| Ruminococcus 2 | 0.06 | 0.64 | 0.49 | 0.13 | 0.02 | 0.52 | 0.97 | 0.5 | 0.56 | 0.28 | 0.03 |
| Solobacterium | 0.56 | 0.66 | 0.93 | 0.36 | 0.33 | 0.09 | 0.56 | 0.51 | 0.06 | 0.03 | 0.58 |
| Victivallis | 0.04 | 0.22 | 1 | 0.08 | 0.08 | 0.05 | 0.16 | 1 | 0.05 | 0.17 | 1 |
Comparing FL1 and FL2 patients, Mogibacterium abundance was different in the all-patients group and the NHL patients group, Ruminococcus 2 differed in the all-patients group and the HL patients group, Fusicatenibacter and Lachnoanaerobaculum abundance only differed among the all-patients group, and Cyanobacteria and Solobacterium only differed in abundance among the NHL patients group. In the three groups, the abundance of Cyanobacteria, Mogibacterium, Lachnoanaerobaculum, and Solobacterium was close to 0. As such, the differences in abundance of the three bacteria may be unreliable. The abundance of Ruminococcus 2 in the FL2 group was significantly higher than in the FL1 group both in the all-patient group and in the HL patients group, indicating that Ruminococcus 2 was associated with severe FL and HL. The abundance of Fusicatenibacter gradually decreased in NL, FL2, and FL1 patients. Moreover, Fusicatenibacter differed significantly in abundance between FL1 and FL2 in all patients as well as between the NL and FL1 in the all-patient group and the NHL patient group. But there was no difference between FL1 and FL2 in the NHL patient group and the HL patient group. This phenomenon may indicate that the change in abundance of Fusicatenibacter may be more strongly related to patients with mild rather than severe NAFLD.
Potential keystone species within the group, identified using a specificity–occupancy plot
The OTU of species and phyla with total relative abundance above 0.01% were retained from the OTU table; then, the specificity and occupancy of each group were calculated separately according to the retained OTU table. Specificity was defined as the mean abundance of an OTU in the samples of a group. Occupancy was defined as the relative frequency of occurrence of the OTU in the samples of a group. These values were calculated as follows:34,35
Specificity=Average relative abundance of an OTU across samples in a subgroupSum of the average relative abundance of the OTU across all study subgroupsOccupancy=Number of samples detected by an OTU in a subgroupNumber of samples detected across all study samples
To locate potential keystone species attributed to each group, we selected the specie showing an OTU with specificity and occupancy equal to or greater than 0.7 for each specific group. Firmicutes were found in the three groups, while Proteobacteria was found only in the FL1 group (Fig. 3). At the genus level, there were a total of six specific genus species in the NL group, none of which were observed in the other two groups (Fig. 4). The six genus species were Faecalibacterium, Clostridium_XIVa, Streptococcus, Ruminococcus, Oscillibacter, and Flavonifractor. Klebsiella, and Veillonella found in the FL1 group. Only Lachnospiracea_incertae_sedis was found in the FL2 group.
ALT and AST levels and WBC count were significant biochemical indexes for gut microbial community changes in all-patients group
To explore which clinical factors influenced changes in the gut microbial community, we first used DCA to calculate the values of four gradient lengths (Supplementary Table 7). We found that the largest value was greater than 4 and, accordingly, decided to use CCA (Fig. 5). The significance of clinical factors associated with gut microbial community composition was then assessed using Monte Carlo permutation tests (Supplementary Table 8). Finally, we found that ALT (p=0.001), AST (p=0.001), and WBC count (p=0.019) were significant biochemical indexes for microbial community changes in the all-patient group.
Microbiome combined with biochemical index reflects FL severity in the all-patient group
Based on the above results, Faecalibacterium and Ruminococcus 2 were selected as the most ideal and symbolic fatty-related bacteria taxa. Furthermore, ALT, AST, and WBC count were chosen as the most significant biochemical indexes. To ascertain the discriminatory capability between NAFLD patients and healthy individuals, we opted for AUCs (Fig. 6), confirming the distinct classification potential of the two gut microbiota taxa and the three biochemical indices. While diagnosing NL patients, the AUCs of the biochemical index, bacteria taxa, and (bacterial + biochemical) were found to be 0.78, 0.77, and 0.86, respectively. When distinguishing FL1, the AUCs of the biochemical index, bacteria taxa, and (bacterial + biochemical) were 0.50, 0.61, and 0.51, respectively. For diagnosing FL2, the AUCs of the biochemical index, bacteria taxa, and (bacteria + biochemical) were 0.78, 0.66, and 0.85, respectively.
Discussion
In this study, we found substantial variations in gut microbiota modifications based on NAFLD severity in all, NHL, and HL patients. We focused on two microorganisms, Faecalibacterium and Ruminococcus 2, as potential targets for distinguishing between FL and healthy individuals using univariate analysis and a specificity–occupancy plot. We also found that ALT, AST, and WBC count were the best biochemical indexes related to FL, based on the result of the CCA. Using the two microbiomes and the three biochemical indexes, NL and FL2 patients could be easily identified. However, this approach performed poorly in diagnosing FL1.
Previous studies showed that the alpha diversity of gut microbiota was lower in FL cases,36,37 which was also found in this study. Whether with or without HL, the alpha diversity of NAFLD decreased and the ace index in the NHL patient group was 5% higher than in the all-patient group. That is, HL would further decrease the abundance of gut microbial communities in NAFLD patients. The results of this study are in accord with existing results indicating the influence of environmental variables over genetic traits in determining human intestinal microbiota.38
It was reported that an increased proportion of Firmicutes was dominantly linked with NAFLD in mice as well as humans.39–41 In this study, among the three groups, the specificity and occupancy of Firmicutes were ≥ 0.7. Remarkably, prominent associations with NAFLD in our study all derived from the Firmicutes phylum and, diversely, included Faecalibacterium, Lachnospiracea_incertae_sedis, Ruminococcus 2, Fusicatenibacter, Gemmiger, and Roseburia. These results were also found to be the case in a large population sample.36
Much evidence suggests that the presence of Faecalibacterium is significantly lower in NAFLD than in non-NAFLD patients.15,42–45Faecalibacterium prausnitzii, a species of the Faecalibacterium genus,46 as well as an oxygen-sensitive, butyrate-producing bacterium, plays a significant part in maintaining a healthy gut.47Faecalibacterium prausnitzii levels have been reported to be lower in patients with intestinal and metabolic disorders, such as inflammatory bowel disease, irritable bowel syndrome, and celiac disease.48,49 It is therefore not surprising that Faecalibacterium decreased not only in NAFLD but also in HL patients in our study. Furthermore, Faecalibacterium produce short-chain fatty acids50 that have an anti-inflammatory function by regulating immune cell chemotaxis, reactive oxygen species release, and cytokine release.51 Additionally, a clinical study demonstrated the direct anti-inflammatory activity of butyrate at the site of inflammation.52 Accordingly, a decrease in the amount of Faecalibacterium may lessen short-chain fatty acid levels in the gut, intensifying gut inflammation involved in the pathogenesis of NAFLD.
Ruminococcus 2 has the genetic potential to worsen the onset of FL disease because it was more prevalent in the FL2 patients in our study. More importantly, the abundance of Ruminococcus 2 in FL2 patients was higher than in both all FL1 and HL patients, but not in NHL patients. A Chinese population study found that Ruminococcus 2 was positively correlated with serum lipids,53 and a survey showed that dietary fiber intervention for 4 days inhibited the growth of Ruminococcus 2.54 That is, Ruminococcus 2 may through influence human lipid metabolism rather than another way to aggravate FL. Furthermore, NAFLD patients may be able to adjust their diet to include more dietary fiber to combat the disease; however, further research is needed to establish whether this is possible.
In this study, oddly, Fusicatenibacter decreased in NAFLD cases but its abundance in FL1 patients was lower than that in FL2 patients. A study showed that the abundance of Fusicatenibacter decreased in the presence of NAFLD.55 Furthermore, Fusicatenibacter saccharivorans, the lone species in the Fusicatenibacter genus,56 decreased in cases of NAFLD with coronary artery disease57 but increased in cases of NAFLD in obese youth.58 An early-life nutrition study found this species to be associated with a diet high in processed foods.59 Although the difference in the ages of FL1 and FL2 patients was not statistically significant (p=0.1), the mean age of FL1 patients was 50.65 and that of FL2 patients was 44.65 years. Younger people tend to eat more processed foods. Therefore, we hold the hypothesis that although Fusicatenibacter decreased in NAFLD patients, those with severe NAFLD tend to be more obese or eat more processed foods, thereby increasing its abundance to a higher level than in FL1 patients. Further study is needed to support this.
ALT and AST levels are common biomarkers of liver injury.60 Although both were increased in the patients with NAFLD among the three groups in our study, the changes between them can be considered mild increases in aminotransferase levels (increases of <5 times the upper reference limit).61–63 In the Western world, NAFLD is the most common cause of minor changes in liver enzyme levels.60 Furthermore, the liver enzyme levels in HL patients were higher than in NHL patients. It was reported that metabolic syndromes, such as HL, increased the suspicion of the presence of NAFLD.64,65 Thus, in our study, the levels of ALT and AST in NAFLD cases with HL participants were pronouncedly increased.
White blood cell count is a reliable, easily accessible, and low-cost inflammatory marker,66 as well as an important predictor of NAFLD in Chinese people.67,68 There are two possible avenues for WBC involvement at the start of NAFLD.69 NAFLD is viewed as a liver-based manifestation of metabolic syndrome.70,71 As a relationship between WBC count and metabolic syndrome components has been documented in previous studies,71,72 metabolic syndrome may link WBC count and NAFLD. In our study, WBC count was selected as one of the most significant biochemical indexes associated with NAFLD. That may help reduce the medical burden on society because of its low cost, but further research on this is necessary. Furthermore, WBC count is commonly used to determine inflammatory state73 and, accordingly, may be related to NAFLD. There are several limitations to our study. First, this was a small case study that included only one Chinese population, which may have led to bias. Secondly, the levels of NAFLD were classified by ultrasound but not pathology.