Altered oral microbiome and metabolites are associated with improved lipid metabolism in HBV-infected patients with metabolic dysfunction-associated fatty liver disease

doi:10.12122/j.issn.1673-4254.2025.09.23

Abstract

Abstract:

Objective To investigate the impact of hepatitis B virus (HBV) infection on oral microbiota and metabolites in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the underlying mechanisms. Methods This prospective study was conducted in 47 MAFLD patients complicated with chronic hepatitis B (CHB) and 48 MAFLD patients without CHB enrolled from November, 2023 to January, 2024. Fasting tongue coating samples were collected from the patients for analyzing microbial community structures and metabolites using high-throughput 16S rDNA sequencing and non-targeted metabolomics techniques, and their associations with clinical indicators and biological pathways were explored using correlation analysis and functional annotation. Results The levels of fasting blood glucose, total cholesterol (TC), gamma-glutamyl transferase (GGT), and severity of fatty liver were all significantly lower in MAFLD+CHB group than in MAFLD group. Microbiota analysis showed that the abundances of Patescibacteria (at the phylum level), Hydrogenophaga, and Absconditabacteriales (at the genus level) were significantly increased, while the abundance of Megasphaera was decreased in MAFLD+CHB group. The differential microbiota were significantly correlated with TC, GGT and low-density lipoprotein (r=-0.68‒0.75). Metabolomics analysis revealed that 469 metabolites (including lipids and amino acids) were upregulated and 2306 (including organic oxygen-containing compounds and phenylpropanoids) were downregulated in MAFLD+CHB group, for which KEGG enrichment analysis suggested abnormal activation of the linoleic acid metabolism and glycerophospholipid metabolism pathways. Correlation analysis between microbiota and metabolites indicated that Patescibacteria and Megasphaera, which were positively correlated with lipid metabolites and negatively with fatty acid metabolites, respectively, jointly affected glycolipid metabolism and oxidative stress pathways. Conclusion Compared to patients with MAFLD alone, MAFLD patients with concurrent chronic HBV infection showed lower levels in some lipid metabolism indicators and the degree of hepatic steatosis, accompanied by alterations in oral microbiota structure and metabolic profiles. The precise mechanisms involved require further investigation to be fully elucidated.

Key words: hepatitis B virus, metabolic dysfunction-associated fatty liver disease, oral microbiota, metabolomics, lipid metabolism disorders, oral-liver axis

Jingjing ZHANG, Song FENG, Dali ZHANG, Jian XUE, Chao ZHOU, Pengcheng LIU, Shuangnan FU, Man GONG, Hui FENG, Ning ZHANG. Altered oral microbiome and metabolites are associated with improved lipid metabolism in HBV-infected patients with metabolic dysfunction-associated fatty liver disease[J]. Journal of Southern Medical University, 2025, 45(9): 2034-2045.

Figures/Tables 7

Tab.1 Baseline demographic and clinical characteristics of the enrolled patients

Indicator	MAFLD (n=48)	MAFLD+CHB (n=47)	Total (n=95)	P
Degree of steotosis
Mild	15 (31.3%)	31 (66.0%)	46 (48.4%)	0.0179
Medium	26 (54.2%)	14 (29.8%)	40 (42.1%)
Severe	7 (14.6%)	2 (4.3%)	9 (9.5%)
Age (years, Mean±SD)	46.8±11.2	46.6±8.03	46.7±9.70	0.976
Gender (Male)	33 (68.8%)	35 (74.5%)	68 (71.6%)	0.826
BMI (Mean±SD)	26.7±5.02	27.5±2.98	27.1±4.14	0.326
Waist (cm)	93.4±10.3	97.2±9.98	95.3±10.3	0.305
Hypertension	12 (25.0%)	9 (19.1%)	21 (22.1%)	0.79
T2DM	10 (20.8%)	3 (6.4%)	13 (13.7%)	0.123
CHD	3 (6.3%)	2 (4.3%)	5 (5.3%)	0.910
ALT (U/L)	51.6±39.8	38.7±22.0	45.2±32.7	0.586
AST (U/L)	40.7±38.2	30.3±12.7	35.6±28.9	0.221
ALP (U/L)	90.2±26.0	78.7±20.8	84.5±24.2	0.0975
GGT (U/L)	64.8±52.9	35.2±16.9	50.1±42.0	0.00116
Glucose (mmol/L)	6.77±1.91	5.79±1.45	6.28±1.76	<0.001
TG (mmol/L)	2.80±2.84	1.89±0.835	2.35±2.14	0.215
TC (mmol/L)	5.07±0.989	4.62±0.703	4.85±0.885	0.046
HDL (mmol/L)	1.18±0.246	1.12±0.235	1.15±0.242	0.541
LDL (mmol/L)	3.40±0.772	3.24±0.603	3.32±0.695	0.568
Apo-A1 (g/L)	1.23±0.206	1.18±0.191	1.21±0.199	0.419
Apo-B (g/L)	0.935±0.237	0.867±0.149	0.901±0.201	0.274
Lp-a (mg/L)	113±177	149±222	131±200	0.458
APRI	0.579±1.06	0.411±0.297	0.496±0.783	0.609
FIB-4	1.37±1.47	1.37±0.972	1.37±1.24	0.862

Fig.1 Differences in microbial flora between the two groups. A-C: Alpha diversity analysis usingthe Chao1, Shannon, and Simpson methods. D:Beta diversity analysis using the Principal Coordinates Analysis (PCoA) method. E, F:Heatmaps annotating species abundance at the phylum level, with a gradient from blue to red indicating a change in abundance from low to high, where bluer colors represent lower abundance and redder colors represent higher abundance.

Fig.2 Analysis of variances. A: Differences at the phylum level. B: Differences at the genus level. C: LEfSe analysis at all levels. Group A: MAFLD; Group B: MAFLD+CHB.

Fig.3 Correlation analysis of different bacterial groups. A: Correlation analysis between phylum-level bacteria and blood indicators. B: Correlation analysis between genus-level bacteria and biochemical indicators. C: Correlation analysis among differentially abundant genus-level bacteria. Group A: MAFLD; Group B: MAFLD+CHB. *P<0.05, **P<0.01.

Fig.4 KO functional annotation of differentially abundant bacteria between the two groups. Group A: MAFLD; Group B: MAFLD+CHB.

Fig.5 Distribution and pathway enrichment analysis of differential metabolites between the two groups. A: Scatter plot showing the differences between the sample groups (OPLS-DA model). B: Volcano plot showing the overall distribution of metabolite differences between the two groups. C: The top 10 metabolites with the highest upregulation and downregulation fold after logarithmic transformation among the differential metabolites. D: Differential abundance scores of KEGG enrichment for differential metabolites between the two groups. DA Score of 1 indicates an upregulation trend in the expression of all annotated differential metabolites within that pathway, while -1 indicates a downregulation trend. The larger the dot, the greater the number of differential metabolites in that pathway. *P<0.05, *P<0.01, ***P<0.001.

Fig.6 Correlation analysis showing statistical differences in the correlation between the differential bacteria and metabolites. Red represents positive correlation, blue represents negative correlation, and darker colors indicate stronger correlation. *P<0.05, **P<0.01.

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