Prevalence of nonalcoholic fatty liver disease in patients with hepatitis B: A meta-analysis

Abstract

BACKGROUND

The prevalence of nonalcoholic fatty liver disease (NAFLD) in patients with chronic hepatitis B (CHB) has increased in recent clinical practice; however, the relationship between CHB and hepatic steatosis (HS) remains controversial.

AIM

To shed light on the potential association between NAFLD and hepatitis B virus (HBV) infection.

METHODS

We conducted a systematic literature search using multiple databases, including PubMed, the Cochrane Library, Web of Science, and EMBASE, to identify relevant studies. Predefined inclusion criteria were used to determine the eligibility of the studies for further analysis.

RESULTS

Comprehensive meta-analysis software was used for statistical analysis, which covered 20 studies. The results indicated a lower NAFLD susceptibility in HBV-infected individuals (pooled OR = 0.87; 95%CI = 0.69-1.08; I² = 91.1%), with diabetes (P = 0.015), body mass index (BMI; P = 0.010), and possibly age (P = 0.061) as heterogeneity sources. Of note, in four studies (6197 HBV patients), HBV-infected individuals had a reduced NAFLD risk (OR = 0.68, 95%CI = 0.51-0.89, P = 0.006). A positive link between hyperlipidemia and metabolic syndrome emerged in hepatitis B patients, along with specific biochemical indicators, including BMI, creatinine, uric acid, fasting blood glucose, and homeostasis model assessment of insulin resistance.

CONCLUSION

HBV infection may provide protection against HS; however, the occurrence of HS in patients with HBV infection is associated with metabolic syndrome and specific biochemical parameters.

Key Words: Nonalcoholic fatty liver disease; Hepatitis B; Prevalence; Meta-analysis; Infectious

Core Tip: Investigating chronic hepatitis B (CHB) and nonalcoholic fatty liver disease (NAFLD) was reviewed in 20 studies. The results suggest reduced NAFLD susceptibility in hepatitis B virus (HBV)-infected individuals (OR = 0.87, 95%CI = 0.69-1.08, I² = 91.1%). In HBV patients, a positive correlation emerged between hyperlipidemia, metabolic syndrome, and indicators, such as body mass index, creatinine, uric acid, fasting blood glucose, and homeostasis model assessment of insulin resistance. HBV may protect from hepatic steatosis, but its occurrence is associated with metabolic syndrome and specific factors. This study describes the complex interplay between CHB, NAFLD, and its associated factors.

INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) is an acquired metabolic liver injury in adults. It is characterized by hepatic steatosis (HS), except alcohol and other definite liver damage factors, and is commonly associated with obesity, hyperlipidemia, and insulin resistance[1,2]. The global incidence of NAFLD is increasing, in part, because of improved living standards and lifestyle changes. It has emerged as a leading cause of chronic liver disease worldwide[3]. The prevalence of NAFLD ranges from 6% to 35% globally, with a median of 20%[4]. In clinical practice, there is an increasing occurrence of NAFLD and chronic hepatitis B (CHB) presenting together[5], which affects approximately 29.6% of CHB patients worldwide[6]. Because both hepatitis B virus (HBV) infection and NAFLD independently contribute to chronic liver injury, exacerbated liver damage, and an increased risk of cirrhosis and hepatocellular carcinoma, understanding their relationship is of paramount importance[7-9].

The association between CHB and HS has been a subject of debate[10]. Although a previous meta-analysis indicated a negative association between hepatitis B and the risk of NAFLD[11], a subsequent study by Azarkar et al[12] demonstrated a higher prevalence of mild fatty liver in HBV carriers than that in healthy individuals. Moreover, recent studies have indicated that HBV may promote the development of NAFLD in vitro by increasing mitochondrial production[13]. Nonetheless, epidemiologic and in vitro findings of a link between fatty liver and HBV remain controversial[14]. We aimed to shed light on the potential associations between NAFLD and HBV infection through a meta-analysis.

MATERIALS AND METHODS

Database search

This study was conducted according to the Meta-analysis of Observational Studies in Epidemiology criteria[15]. To ensure transparency and credibility, we prospectively registered the study protocol at PROSPERO (CRD42022376132). The PubMed, the Cochrane Library, Web of Science, and EMBASE databases were searched through December 30, 2022. The search terms included “hepatitis b virus” OR “b virus, hepatitis” OR “hepatitis b viruses” OR “viruses, hepatitis b” OR “HBV,” and “nonalcoholic fatty liver disease” OR “NAFLD” OR “fatty live, nonalcoholic” OR “live, nonalcoholic fatty” OR “nonalcoholic fatty live” OR “nonalcoholic steatohepatitis” OR “steatohepatitis, nonalcoholic.” In addition, we reviewed the references of the retrieved articles to ensure the inclusion of all relevant studies.

Inclusion and exclusion criteria

For inclusion in the meta-analysis, studies had to meet the following criteria: (1) Written in English or Chinese; (2) Cross-sectional, case-control, or cohort study design; (3) involved adult participants aged 18 years and older; and (4) Provided data that allowed an assessment of NAFLD risk (e.g., number of NAFLD cases in the HBV and non-HBV groups, or hazard ratios with their 95%CIs). Studies were excluded if they (1) investigated alcoholic liver disease; (2) were letters, review articles, case reports, case series, in vitro studies, or animal studies; (3) were duplicate publications; or (4) provided ambiguous or insufficient information.

Data collection and quality assessment

To ensure accuracy, two researchers independently collected data, including details regarding study design, location, author names, year of publication, the number of NAFLD cases, age and gender distribution, follow-up duration, and RR or OR with 95%CI in the HBV-positive and HBV-negative groups. A positive HBsAg test was used for the identification of HBV infection.

The risk of bias across the included studies was assessed using the Newcastle-Ottawa Scale (NOS)[16], and the results were categorized as low quality (NOS score < 4), medium quality (NOS score 4-6), and high quality (NOS score 7-9)[17]. Any discrepancies were resolved through consensus.

Statistical analyses

Dichotomous variables were expressed as OR RR with 95%CI. Continuous variables were presented as the weighted mean difference (SMD) with its CI. The heterogeneity between included studies was assessed using the Q statistic and I² index, where I² 25% indicated a small degree of heterogeneity and I² 50% indicated significant heterogeneity[18]. In the presence of statistically significant heterogeneity, meta-regression was used to analyze potential sources of heterogeneity, such as age, year, study quality, study design, diagnosis of fatty liver, body mass index (BMI), diabetes, and study location. A factor with a meta-regression coefficient of P < 0.05 was considered a source of heterogeneity. Because of the observed significant heterogeneity, random-effects models were preferred over fixed-effects models. The use of random-effects models allows for possible variations in study procedures and settings, making the findings more generalizable[19].

Egger’s and Begg’s P values were utilized for the assessment of publication bias, along with funnel plot asymmetry, where P < 0.05 pointed to a significant publication bias. Symmetrical funnel plots upon visual inspection suggest no publication bias. Statistical analysis was conducted using STATA software version 16 (Stata Corp., College Station, TX, United States), with a threshold of P < 0.05 indicating statistical significance.

RESULTS

Literature selection and patient demographics

Figure 1 shows the strategy for selecting the studies. An initial search of the databases yielded 1008 articles, including 40 from Cochrane, 279 from Web of Science, 115 from PubMed, and 574 from EMBASE. After removing 151 duplicates, we screened the titles and abstracts of the remaining articles, leading to the identification of 298 full-text articles. A final set of 20 articles[10,12,13,20-36] were considered eligible for the meta-analysis after excluding studies that did not meet the eligibility criteria.

Figure 1  Literature screening strategy.

All of the included studies were observational and consisted of seven cross-sectional[22,23,25,31,34-36], five case-control[12,20,21,24,28], and eight cohort studies[10,13,26,27,29,30,32,33]. The characteristics of the studies are summarized in Table 1. In total, data from 157734 patients were evaluated, with 50.05% being male. Of these patients, 49598 (31.44%) were diagnosed with NAFLD. The studies were conducted in various regions of the world, including China, Iran, Israel, South Korea, Pakistan, and Turkey. The NOS scores of the analyzed studies ranged from 6 to 9, with 18 studies rated as high quality and two as medium quality (Table 2).

Table 1 Study characteristics.

Ref.	Study location	Number of NAFLD	Sample size (HBV+ /HBV-)	Age (year)	Gender (F/M)	Subtype of study	Diagnosis of fatty liver	Follow	Adjusted OR/RR (95%CI)
Wang et al[20], 2008	Taiwan	257	50/457	44.6 ± 1.4/46.8 ± 0.4	264/243	Case-control	Ultrasonography	NA	0.97 (0.48, 1.95)
Yun et al[21], 2009	Korea	44	68/18	21.0 (20-26)	0/86	Case-control	Liver biopsy	2005-2006	1.19 (1.17, 2.83)
Wong et al[22], 2012	Hong Kong	273	91/922	49 ± 10/48 ± 11	580/433	Cross-sectional	MRS	2008-2010	0.42 (0.20, 0.88)
Cheng et al[23], 2013	Taiwan	14671	3642/29797	51.9 ± 13.1	15182/18257	Cross-sectional	Ultrasonography	2002-2009	0.66 (0.59, 0.72)
Peng et al[24], 2013	China	527	253/922	20-74	438/1131	Case-control	Ultrasonography	2007-2008	3.96 (2.10, 7.48)
Yilmaz et al[25], 2015	Turkey	28	88/0	31 ± 1.1	27/61	Cross-sectional	Liver biopsy	NA	0.21 (0.11, 0.41)
Chan et al[26], 2017	Hong Kong	107	270/0	43.6 ± 11.3	67/203	Cohort	Liver biopsy	2006-2009	0.43 (0.30, 0.60)
Joo et al[27], 2017	Korea	20200	3926/79413	38.5 (7.2)/ 37.5 (7.6)	40856/42483	Cohort	Ultrasonography	2002-2014	0.78 (0.72, 0.84)
Zhong et al[28], 2018	China	631	291/2697	45-66	1849/1139	Case-control	Ultrasonography	2015-2016	0.64 (0.42, 0.95)
Azarkar et al[12], 2019	Iran	336	373/447	41.1 ± 12.9/39.8 ± 13.9	403/420	Case-control	Ultrasonography	2013-2014	0.62 (0.455, 0.845)
Lee et al[29], 2019	Korea	70	321/0	41 (33-49)	125/196	Cohort	Liver biopsy	2007-2015	1.56 (0.90, 2.71)
Peleg et al[30], 2019	Israel	241	524/0	42.32 (18.5-83.5)	209/315	Cohort	Ultrasonography	2007-2017	0.08 (0.02, 0.28)
Sharif et al[31], 2019	Pakistan	166	230/0	36.8 ± 10.84	27/203	Cross-sectional	Transient elastography	2018-2019	5.4 (3.62, 8.03)
Wang et al[13], 2019	China	308	152/1714	46.42 ± 10.291	1866/0	Cohort	Ultrasonography	2011-2014	0.656 (0.379, 1.134)
Zhu et al[32], 2019	China	283	2393/0	50.7 ± 13.2	1767/626	Cohort	Ultrasonography	2012-2015	0.89 (0.69, 1.15)
Huang et al[33], 2020	China	4917	2110/12342	43.84 ± 13.03	6710/7742	Cohort	Ultrasonography	2016-2018	0.717 (0.608, 0.846)
Su et al[34], 2020	Taiwan	104	30/74	49.97 ± 14.76	47/57	Cross-sectional	Liver biopsy	2009-2018	3.55 (1.46, 8.58)
Zhuang et al[35], 2020	China	88	46/42	37.37 ± 10.11/39.64 ± 12.31	13/75	Cross-sectional	MRS	2013-2016	0.322 (0.095, 0.550)
Lv et al[10], 2021	China	6315	5680/10771	42.6 ± 11.2/46.9 ± 10.6	6519/9932	Cohort	Ultrasonography	2013-2017	NA
Zhou et al[36], 2022	China	32	78/105	45.41 ± 11.59	54/129	Cross-sectional	Ultrasonography	2019-2020	1.95 (0.90, 4.22)

NAFLD: Nonalcoholic fatty liver disease; HBV: Hepatitis B virus; F: Female; M: Male; NA: Not applicable; MRS: Magnetic resonance spectroscopy.

Table 2 Scores using modified Newcastle-Ottawa scale for included studies.

Ref.	Selection				Comparability	Exposure			Total score
	Fully defined cases	Define the study design	Selection of controls	Described the general characteristics	Controlling the important factors or confounding factors	List inclusion and exclusion criteria for all the participants	Provided Enrollment duration for all the participants	Indicate study period and follow-up duration
Wang et al[20], 2008	*	*		*	2	1	1		7
Yun et al[21], 2009	1		1	1	2	1		1	7
Wong et al[22], 2012	1	1		1	2	1	1	1	8
Cheng et al[23], 2013	1	1		1	2	1		1	7
Peng et al[24], 2013	1	1	1	1		1		1	6
Yilmaz et al[25], 2015	1		1	1	2	1		1	7
Chan et al[26], 2017	1	1	1	1	1	1	1	1	8
Joo et al[27], 2017	1	1	1	1	2	1	1	1	9
Zhong et al[28], 2018	1	1	1	1	2	1		1	8
Azarkar et al[12], 2019	1	1	1	1	2		1	1	8
Lee et al[29], 2019	1	1	1	1	1	1		1	7
Peleg et al[30], 2019	1	1		1	1	1	1	1	7
Sharif et al[31], 2019	1	1		1	2	1		1	7
Wang et al[13], 2019	1	1	1	1	2	1	1	1	9
Zhu et al[32], 2019	1	1	1		2	1	1	1	8
Huang et al[33], 2020	1		1	1	2			1	6
Su et al[34], 2020	1	1		1	1	1	1	1	7
Zhuang et al[35], 2020	1	1	1	1	1	1		1	7
Lv et al[10], 2021	1		1	1	2	1		1	7
Zhou et al[36], 2022	1	1	1	1	1	1		1	7

Analysis of the relationship between NAFLD and hepatitis B infection

We performed a meta-analysis on 20 studies to determine a link between NAFLD and hepatitis B infection. Of these studies, 13[10,12,13,22,23,25-28,30,32,33] indicated that hepatitis B infection was associated with a lower risk of NAFLD, whereas seven[20,21,24,29,31,34,36] showed a slightly higher risk of NAFLD among patients with HBV infection compared with those without. A meta-analysis of 19 of the studies[10,12,13,20-24,26-36] revealed that the OR/RR with 95%CIs using a random-effects model and a Q and I² test, suggested significant heterogeneity among them (P = 0.000, I² = 91.1%). The analysis revealed that patients with CHB did not show significant differences in the risk for NAFLD compared with those without CHB, but there was a possible reduction of 12.7% in the risk of NAFLD in patients with CHB (OR = 0.87, 95%CI = 0.69-1.08, P = 0.209; Figure 2A). Nex, the incidence of NAFLD was compared between patients with and without hepatitis B infection by analyzing four of the included studies[10,12,20,22] consisting of 6197 patients with CHB (including 1947 patients with NAFLD) and 12597 non-CHB patients (including 2534 patients with NAFLD). Because of the heterogeneity across the four studies in terms of the incidence of NAFLD, a random-effects model was used (P = 0.028, I² = 67.0%) for data analysis, which indicated a lower incidence of NAFLD in patients with CHB compared with non-CHB controls (OR = 0.68, 95%CI = 0.51-0.89, P = 0.006; Figure 2B). This suggests that HBV may have a protective effect against HS.

Figure 2 Forest plot. A: The risk relationship between hepatitis B infection and nonalcoholic fatty liver disease based on OR/RR (95%CI) analysis; B: Comparison of nonalcoholic fatty liver disease incidence between the chronic hepatitis B group and control group. Each point on the X-axis (the horizontal axis) represents the risk estimate for each study. Horizontal lines represent a 95%CI; diamonds represent the summary risk estimate with 95%CI.

Subgroup and meta-regression analyses

Univariate meta-regression was used to identify potential causes of heterogeneity among factors that could influence the results, including age, year, study quality, study design, diagnosis of fatty liver, BMI, diabetes, study location, and follow-up duration. To ensure comprehensive consideration of potential influential factors, the test level α was relaxed to 0.1. The results indicated that BMI (P = 0.010) and diabetes (P = 0.015) were significant contributors to heterogeneity, whereas age (P = 0.061) was identified as a possible factor (Table 3). A further subgroup analysis based on age revealed a higher risk of developing NAFLD in patients with CHB older than 45 years (RR = 1.04, 95%CI = 0.72-1.48) compared with those with CHB under 45 years (RR = 0.72, 95%CI = 0.50-1.05).

Table 3 Results of meta-regression analysis of the risk association between hepatitis B infection and nonalcoholic fatty liver disease.

Heterogeneous factors	Coef.	SE	95%CI		P value
			Lower	Upper
Age	0.477	0.391	-0.347	1.302	0.061
Year	-0.020	0.055	-0.136	0.096	0.723
Study quality	-0.527	0.734	-2.075	1.021	0.718
Study design	-0.226	0.265	-0.784	0.332	0.959
Diagnosis of fatty liver	-0.095	0.223	-0.565	0.375	0.827
BMI	0.848	0.389	0.233	1.673	0.010
Diabetes	0.790	0.417	-0.112	1.691	0.015
Study location	0.992	0.082	0.832	1.182	0.208
Follow	-0.068	0.057	-0.191	0.054	0.603

BMI: Body mass index.

Publication bias

An assessment of publication bias was performed using a funnel plot, which displayed a largely symmetrical pattern (Figure 3). The results of Egger’s linear regression (P = 0.486) and Begg’s rank correlation (P = 0.944) tests suggested no significant publication bias.

Figure 3  Funnel plot of the relationship between hepatitis B infection and the risk of developing nonalcoholic fatty liver disease.

Factors behind HS in patients with hepatitis B

The evaluation of risk factors for HS in patients with CHB revealed a strong association between BMI and the incidence of HS (in five studies: Pooled SMD = 2.40, 95%CI = 1.20-3.61, P < 0.001; in seven studies: OR = 1.24, 95%CI = 1.21-1.27, P < 0.001). In addition, metabolic disorders indicated by abnormal levels of fasting blood glucose (FBG), homeostatic model assessment-insulin resistance (HOMA-IR), and low-density lipoprotein cholesterol (LDL-C) were also contributing factors to HS in these patients. A statistical analysis revealed that these biochemical abnormalities were significant risk factors for liver steatosis among patients with CHB (Table 4).

Table 4 Factors associated with hepatic steatosis in patients with hepatitis B infection.

Variable	Number of studies	Random effects model		Heterogeneity			Publication bias
		OR or SMD (95%CI)	P value	Q value	P value	I2	Egger’s P value	Begg’s P value
BMI (kg/m2)	7	OR: 1.24 (1.21, 1.27)	< 0.01	27.79	< 0.01	78.40	0.69	0.88
	5	SMD: 2.40 (1.20, 3.61)	< 0.01	34.11	< 0.01	88.30	0.94	0.73
ALT	5	OR: 1.01 (1.00, 1.03)	0.05	140.35	< 0.01	97.10	0.38	0.81
	5	SMD: 6.76 (0.92, 12.60)	0.02	24.28	< 0.01	83.50	0.01	0.46
Creatinine	2	SMD: 4.73 (4.15, 5.31)	< 0.01	0.17	0.68	0.00	NA	0.32
UA	3	SMD: 64.24 (60.75, 67.74)	< 0.01	0.59	0.74	0.00	0.16	0.60
FBG	6	OR: 1.58 (1.13, 1.77)	< 0.01	114.14	< 0.01	95.60	0.49	0.85
	5	SMD: 1.55 (1.43, 1.68)	< 0.01	9.86	0.04	59.40	0.92	0.81
Cholesterol	7	SMD: 0.37 (0.12, 0.62)	< 0.01	37.01	< 0.01	83.80	0.01	0.76
LDL-C	6	SMD: 0.28 (0.03, 0.52)	0.03	66.65	< 0.01	92.50	0.06	0.71
Triglyceride	6	OR: 1.71 (1.14, 2.55)	0.01	59.19	< 0.01	91.60	0.04	0.45
HOMA-IR	3	OR: 1.46 (1.17, 1.83)	< 0.01	6.68	0.03	70.90	0.40	0.60

BMI: Body mass index; ALT: Alanine aminotransaminase; UA: Uric acid; FBG: Fasting blood glucose; LDL-C: Low-density lipoprotein cholesterol; HOMA-IR: Homeostasis model assessment of insulin resistance; SMD: Standardized mean differences.

DISCUSSION

Nearly 350 million people have chronic HBV infection worldwide[37] and approximately 25% of the population is affected by NAFLD[7]. When these two conditions coexist, they exert significant damage to the liver, accelerating the progression of liver fibrosis and liver cancer. To systematically examine the correlation between CHB and NAFLD, we used a meta-analysis approach. Interestingly, the results suggest that CHB infection may reduce the risk of NAFLD.

A meta-analysis published in 2017, which examined how CHB affects the occurrence of NAFLD, reported similar results to our study; however, only five publications were included. It found that CHB infection was linked to a decreased incidence of NAFLD (OR = 0.71, 95%CI = 0.53-0.90, I² = 75.20%, P < 0.05), but did not confirm a negative correlation in case-control studies[11]. In contrast, our analysis of 20 studies confirmed a negative correlation between CHB infection and the risk of NAFLD in cross-sectional studies (OR = 0.96, 95%CI = 0.40-2.29) and cohort studies (OR = 0.71, 95%CI = 0.56-0.89). However, in case-control studies, this negative correlation was not confirmed (OR = 1.09, 95%CI = 0.61-1.93), possibly because of recall and selection biases inherent in case-control designs, leading to insufficient data to determine the association between CHB and the risk of NAFLD. The majority of the included studies were cross-sectional studies and cohort studies, which may also contribute to the discrepancy in the results.

The results of our analysis suggest that having an HBV infection may decrease the chances of developing NAFLD, although the precise underlying mechanisms remain unclear. Several possible biological explanations could account for this phenomenon. First, HBV infection appears to affect fat distribution. Studies have found associations between HBV infection and alterations in cholesterol, triglycerides, high-density lipoprotein cholesterol, and LDL-C[38-40]. Second, HBV infection can affect fat metabolism, possibly by reducing the levels of apolipoprotein A5[41]. Decreased expression of the apolipoprotein A5 gene is associated with reduced intrahepatic triglyceride levels[42], potentially affecting the development of NAFLD. This may result in a minor degree of hepatocyte steatosis that is insufficient to form a fatty liver because of an inability to accumulate more hepatocytes[39]. Third, HBV-X protein may also be involved in the development of NAFLD by hindering the synthesis of apolipoprotein B, which is essential for the formation of (very) low-density lipoprotein[43]. The HBV-X protein interacts with liver X receptor-a and TNF receptor 1, leading to lipid accumulation, impaired apolipoprotein secretion, and inflammation induction, all of which increase the risk of NAFLD in HBV-infected patients[44]. Finally, some studies have reported associations from various perspectives. For example, HBV infection was found to be linked to a lower risk of other health problems, including heart disease and hypertension[45,46], which are positively correlated with NAFLD[47]. This suggests that HBV infection may not only lower the risk of NAFLD but also reduce the risk of cardiovascular diseases.

A meta-regression analysis was performed to determine the source of heterogeneity among the studies. BMI and diabetes were identified as significant factors contributing to the observed heterogeneity, whereas age may also have a role in variation between studies. Several studies have highlighted the importance of host factors, such as BMI and fasting glucose, in the development of NAFLD in CHB patients[23,25,48-50]. For example, Viganò et al[51] found that 62% of CHB patients with steatosis exhibited high BMI and hyperglycemia. A subgroup analysis based on age indicated that patients with CHB over the age of 45 years had an increased risk of developing NAFLD compared with those younger than 45 (RR = 1.04, 95%CI = 0.72-1.48). This may be the result of a higher risk of complications, such as cardiovascular disease[47] and metabolic syndrome[45], which are positively correlated with NAFLD and tend to increase with age. The observed heterogeneity across the included studies may have been caused by differences in study design and demographic characteristics, such as age, BMI, and glycemia.

Although we used a statistical analysis to explore the sources of heterogeneity, there may still be other factors that we did not consider, such as differences in sample size (ranging from 88 to 83339 cases) and variations in the study population (including differences in ethnicity, physical activity, and dietary factors) in the literature.

Interestingly, our findings suggest that BMI is a significant factor for HS in patients with HBV infection (OR 1.24; SMD 2.40). A similar study found that BMI was a significant factor for HS in people with both HBV infection and NAFLD compared with those with HBV infection alone[52]. Metabolic factors also contribute to a higher risk of HS. For example, FBG and HOMA-IR conferred a 58% and 46% increased risk of HS, respectively. Moreover, our results showed that viral hepatitis-infected patients had significantly higher levels of creatinine, cholesterol, LDL-C, and triglycerides, which are risk factors for HS, compared with those without viral hepatitis. These results are consistent with the importance of metabolic factors in the development of NAFLD and metabolic syndrome[53]. Furthermore, we found that uric acid and alanine transaminase levels were significantly increased in patients with both HS and CHB. An earlier study[30] indicated that the co-occurrence of liver steatosis and CHB had a synergistic impact, leading to a quicker advancement of advanced liver disease and related complications, compared with the presence of NAFLD alone. This finding may help to clarify our findings. Nevertheless, additional studies comparing the long-term outcomes of patients with CHB and liver steatosis with those who have NAFLD, but no CHB, are necessary to confirm this finding.

The present study has some limitations. First, only Chinese and English literature studies were included. Second, the sample sizes varied widely among the included studies. Third, the studies used different methods to diagnose NAFLD, ranging from ultrasound to magnetic resonance imaging and pathological examination. In addition, the studies varied in type, including cross-sectional studies, cohort studies, and case-control studies. As a result of these differences in diagnostic methods and study design, we assessed the heterogeneity across the various studies. Although we considered and analyzed multiple factors for the heterogeneity, some potential risk factors, including those related to physical activity and diet, besides alcohol, were not considered. Studies have demonstrated that individuals infected with HBV may prioritize physical activity to enhance their dietary habits, which may affect the incidence and progression of NAFLD in humans[54,55].

CONCLUSION

To summarize, our meta-analysis indicated that hepatitis B infection protects against the occurrence of NAFLD; however, this association was observed only in cross-sectional and cohort studies, not in case-control studies. To further examine this relationship and understand the underlying reasons, additional prospective studies and basic research are needed. Specifically, if it can be established that a substance produced during HBV infection reduces the incidence of NAFLD, drugs containing this substance could be developed to prevent or treat NAFLD.