# Relationship ofHelicobacter pylori Infection with Nonalcoholic Fatty Liver Disease: A Meta-Analysis

Authors: Guangqin Xu; Shaoze Ma; Liyan Dong; Nahum Mendez-Sanchez; Hongyu Li; Xingshun Qi
Journal: Canadian Journal of Gastroenterology and Hepatology
 (2023)

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## Abstract

Background and Aims. Helicobacter pylori (H. pylori) and nonalcoholic fatty liver disease (NAFLD) have become increasingly recognized, both of which affect human health globally. The association of H. pylori infection with NAFLD remains unclear. Methods. PubMed, EMBASE, and Cochrane Library databases were searched. Only a random-effects model was used. Odds ratios (ORs) and risk ratios (RRs) with 95% confidence intervals (CIs) were calculated for the combined estimates of raw data. Adjusted ORs (aORs) and hazard ratios (aHRs) with 95% CIs were calculated for the combined estimates of data adjusted for confounders. Results. Thirty-four studies with 218573 participants were included. Based on unadjusted data from 26 cross-sectional studies and 3 case-control studies, H. pylori infection was significantly associated with the presence of NAFLD (OR = 1.26, 95% CI = 1.17–1.36, P<0.001). Based on adjusted data from 15 cross-sectional studies and 1 case-control study, H. pylori infection was significantly associated with the presence of NAFLD (aOR = 1.25, 95% CI = 1.08–1.44, P<0.001). Compared with control subjects without NAFLD, patients with moderate (OR = 1.67, 95% CI = 1.17–2.39, P=0.005) and severe (OR = 1.71, 95% CI = 1.30–2.24, P<0.001) NAFLD, but not those with mild NAFLD (OR = 1.14, 95% CI = 0.9–1.45, P=0.286), had significantly higher proportions of H. pylori infection. The association of H. pylori infection with the occurrence of NAFLD was statistically significant based on adjusted data from 3 cohort studies (aHR = 1.18, 95% CI = 1.05–1.34, P=0.007), but not based on unadjusted data from 3 cohort studies (RR = 1.41, 95% CI = 0.80–2.48, P=0.237). Conclusion. H. pylori infection is associated with NAFLD, especially moderate and severe NAFLD. The impact of H. pylori eradication on the prevention of NAFLD should be further explored.


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## Body

## 1. Introduction

Helicobacter pylori (H. pylori) infects about half of the world’s population, especially people living in developing countries and poor socioeconomic countries [1–4]. Considering such a high infection rate, it is recognized as a major public health problem worldwide [2]. H. pylori is a main pathogenic factor for chronic gastritis, peptic ulcers, gastric cancer, and gastric mucosa-associated lymphoid tissue (MALT) lymphoma [5]. H. pylori infection may also disturb a series of biological processes and determine or influence the development and severity of various extragastric diseases [6], such as insulin resistance, metabolic syndrome, diabetes, nonalcoholic fatty liver disease (NAFLD), vitamin B12 deficiency, cardiovascular, neurological, dermatological, and ophthalmic diseases [7].NAFLD is considered a major hepatic manifestation of metabolic syndrome [8] and includes a full spectrum of fatty liver disease from simple hepatic steatosis or nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) and cirrhosis [9]. NAFLD has become the most common type of chronic liver disease, with a global prevalence of approximately 25% [10]. Recently, a new nomenclature of metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed to replace NAFLD with updated diagnostic criteria and recognized by worldwide experts [11, 12]. Regardless, there is now growing evidence that the development of NAFLD is associated with gut microbiota imbalance [13]. Some studies suggested an association between H. pylori infection and NAFLD, and the presence of H. pylori or Helicobacter species has been observed in liver specimens from patients with various liver diseases [14–16]. However, others indicated no correlation between them [17–19]. Considering the importance of understanding potential risk factors for NAFLD on its management, we have conducted an updated meta-analysis of studies published to date to explore the association between H. pylori infection with NAFLD.


## 2. Methods

### 2.1. Registration

This study was registered on the PROSPERO with a registration number CRD42021247307.


### 2.2. Literature Search

The relevant publications were searched viaPubMed, Cochrane library, and EMBASE databases. The search terms were as follows: (“HP” or “H. pylori” or “Helicobacter pylori” or “Helicobacter infection” or “Helicobacter”) and (“Nonalcoholic fatty liver disease” or “Fatty liver” or “Nonalcoholic fatty liver” or “Nonalcoholic steatohepatitis” or “NAFLD” or “NASH” or “NAFL”). There was no language restriction. The last search was conducted on July 14, 2022.


### 2.3. Eligibility Criteria

Inclusion criteria were as follows: (1) eligible studies should include patients who were diagnosed with NAFLD and detected theH. pylori infection, (2) eligible studies should clearly report the diagnostic methods of H. pylori infection and NAFLD, (3) eligible studies should provide the number of patients with positive and negative H. pylori infection in NAFLD patients and control subjects without NAFLD or report the odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) to evaluate the association between H. pylori infection and NAFLD, and (4) age >18 years old. If multiple publications were available for the same study, only the publication with the most complete data would be included.Exclusion criteria were as follows: (1) duplicated studies, (2) consensus, notes, guidelines, editorials, or letters, (3) meta-analyses, reviews, or case reports, and (4) experimental or animal studies.


### 2.4. Data Extraction

The following data were extracted from each study: first author, publication year, study country, study design, publication form (abstract or full text), number of positive and negativeH. pylori infection in the NAFLD patients and control subjects without NAFLD, and diagnostic methods of H. pylori infection and NAFLD. Adjusted ORs (aORs) and adjusted HRs (aHRs) with 95% CIs with confounders adjusted were extracted from the studies where multivariate regression analyses were performed to evaluate the association of H. pylori infection with NAFLD. If studies had multiple adjustment models, only the models that reflected the greatest degree of adjustment for confounders and its corresponding aOR and aHR would be further considered in our meta-analysis.


### 2.5. Diagnosis

H. pylori infection can be diagnosed by invasive (i.e., endoscopic biopsy) and noninvasive tests (i.e., serology, 13C or 14C urea breath test, and fecal antigen test). NAFLD can be diagnosed by histology, ultrasonography, and/or surrogate markers of NAFLD, which include hepatic steatosis index (HSI), NAFLD-liver fat score (NAFLD-LFS), and/or fatty liver index (FLI).


### 2.6. Study Quality Assessment

The quality of cohort and case-control studies was assessed by the Newcastle-Ottawa Scale (NOS), a widely used tool for assessing the quality of observational/nonrandomized studies. It has three major domains: (1) selection, (2) comparability, and (3) exposure/outcome. The maximum score is 9. A score of 0–3, 4–6, and 7–9 represents low, moderate, and high quality, respectively. The quality of cross-sectional studies was evaluated by the Agency for Healthcare Research and Quality (AHRQ) with an 11-item checklist. An item would be scored “0,” if its answer was “NO” or “UNCLEAR”; and an item would be scored “1,” if its answer was “YES.” The maximum score is 11. A score of 0–3, 4–7, and 8–11 represents low, moderate, and high quality, respectively.


### 2.7. Statistical Analyses

All statistical analyses were performed using the Stata software version 12.0 (Stata Corp, College Station, USA) and Review Manager software version 5.4 (Cochrane collaboration, the Nordic Cochrane Centre, Copenhagen, Denmark). Only a random-effects model was employed. ORs and RRs with 95% CIs were calculated for the combined estimation of raw data, and aORs and aHRs with 95% CIs were calculated for the combined estimates of data adjusted for confounders. TheI2 statistics and Cochran Q test were used to evaluate the heterogeneity, and P<0.1 and/or I2 > 50% were considered to indicate statistically significant heterogeneity. Subgroup and meta-regression analyses were performed to explore the sources of heterogeneity among the studies with and without adjustment for confounders. They were grouped according to the study design, region, study quality, diagnostic methods of H. pylori infection and NAFLD, sample size, adjustment for confounders, and publication form. The interaction between subgroups was tested. Leave-one-out sensitivity analyses were assessed by sequentially omitting one study each time. Publication bias was evaluated by Egger test. P<0.1 was considered as a statistically significant publication bias. In addition, the proportion of H. pylori infection was compared according to the severity of NAFLD (i.e., mild, moderate, and severe).


## 2.1. Registration

This study was registered on the PROSPERO with a registration number CRD42021247307.


## 2.2. Literature Search

The relevant publications were searched viaPubMed, Cochrane library, and EMBASE databases. The search terms were as follows: (“HP” or “H. pylori” or “Helicobacter pylori” or “Helicobacter infection” or “Helicobacter”) and (“Nonalcoholic fatty liver disease” or “Fatty liver” or “Nonalcoholic fatty liver” or “Nonalcoholic steatohepatitis” or “NAFLD” or “NASH” or “NAFL”). There was no language restriction. The last search was conducted on July 14, 2022.


## 2.3. Eligibility Criteria

Inclusion criteria were as follows: (1) eligible studies should include patients who were diagnosed with NAFLD and detected theH. pylori infection, (2) eligible studies should clearly report the diagnostic methods of H. pylori infection and NAFLD, (3) eligible studies should provide the number of patients with positive and negative H. pylori infection in NAFLD patients and control subjects without NAFLD or report the odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) to evaluate the association between H. pylori infection and NAFLD, and (4) age >18 years old. If multiple publications were available for the same study, only the publication with the most complete data would be included.Exclusion criteria were as follows: (1) duplicated studies, (2) consensus, notes, guidelines, editorials, or letters, (3) meta-analyses, reviews, or case reports, and (4) experimental or animal studies.


## 2.4. Data Extraction

The following data were extracted from each study: first author, publication year, study country, study design, publication form (abstract or full text), number of positive and negativeH. pylori infection in the NAFLD patients and control subjects without NAFLD, and diagnostic methods of H. pylori infection and NAFLD. Adjusted ORs (aORs) and adjusted HRs (aHRs) with 95% CIs with confounders adjusted were extracted from the studies where multivariate regression analyses were performed to evaluate the association of H. pylori infection with NAFLD. If studies had multiple adjustment models, only the models that reflected the greatest degree of adjustment for confounders and its corresponding aOR and aHR would be further considered in our meta-analysis.


## 2.5. Diagnosis

H. pylori infection can be diagnosed by invasive (i.e., endoscopic biopsy) and noninvasive tests (i.e., serology, 13C or 14C urea breath test, and fecal antigen test). NAFLD can be diagnosed by histology, ultrasonography, and/or surrogate markers of NAFLD, which include hepatic steatosis index (HSI), NAFLD-liver fat score (NAFLD-LFS), and/or fatty liver index (FLI).


## 2.6. Study Quality Assessment

The quality of cohort and case-control studies was assessed by the Newcastle-Ottawa Scale (NOS), a widely used tool for assessing the quality of observational/nonrandomized studies. It has three major domains: (1) selection, (2) comparability, and (3) exposure/outcome. The maximum score is 9. A score of 0–3, 4–6, and 7–9 represents low, moderate, and high quality, respectively. The quality of cross-sectional studies was evaluated by the Agency for Healthcare Research and Quality (AHRQ) with an 11-item checklist. An item would be scored “0,” if its answer was “NO” or “UNCLEAR”; and an item would be scored “1,” if its answer was “YES.” The maximum score is 11. A score of 0–3, 4–7, and 8–11 represents low, moderate, and high quality, respectively.


## 2.7. Statistical Analyses

All statistical analyses were performed using the Stata software version 12.0 (Stata Corp, College Station, USA) and Review Manager software version 5.4 (Cochrane collaboration, the Nordic Cochrane Centre, Copenhagen, Denmark). Only a random-effects model was employed. ORs and RRs with 95% CIs were calculated for the combined estimation of raw data, and aORs and aHRs with 95% CIs were calculated for the combined estimates of data adjusted for confounders. TheI2 statistics and Cochran Q test were used to evaluate the heterogeneity, and P<0.1 and/or I2 > 50% were considered to indicate statistically significant heterogeneity. Subgroup and meta-regression analyses were performed to explore the sources of heterogeneity among the studies with and without adjustment for confounders. They were grouped according to the study design, region, study quality, diagnostic methods of H. pylori infection and NAFLD, sample size, adjustment for confounders, and publication form. The interaction between subgroups was tested. Leave-one-out sensitivity analyses were assessed by sequentially omitting one study each time. Publication bias was evaluated by Egger test. P<0.1 was considered as a statistically significant publication bias. In addition, the proportion of H. pylori infection was compared according to the severity of NAFLD (i.e., mild, moderate, and severe).


## 3. Results

### 3.1. Study Characteristics

We initially searched 2025 papers. Finally, 34 studies with 218573 participants were included (Figure1). Characteristics of included studies are shown in Table 1. Among them, 4 were cohort studies, 3 were case-control studies, and 27 were cross-sectional studies; 3 studies were published as abstracts and 31 as full texts; 25 studies were performed in Asia [17–41], 3 in North America [42–44], 2 in Africa [45, 46], and 4 in Europe [47–50]. The publication date ranged from 2013 to 2022.Figure 1
Flowchart of the study selection process.Table 1
Characteristics of studies regarding the association ofH. pylori infection with NAFLD.
First author (year)CountryStudy designPublication formDiagnostic methods ofH. pyloriDiagnostic methods of NAFLDNAFLDControlAdjusted OR/HR (95% CI)Adjusted confoundersHP+HP−HP+HP−Wernly (2022)AustriaCross-sectionalFull textRUTUS487194053223790.96 (0.82, 1.13)Age, gender, type 2 diabetes, and LDLWang (2022)ChinaCross-sectionalFull text13C-UBTUS86171467816128322101.02 (0.97, 1.08)Age, gender, BMI, SBP, DBP, FBG, HbA1C, LDL-C, HDL-C, TG, AST, ALT, GGT, Scr, and BUNZhao (2022)ChinaCohortFull text13C-UBTUS3773169396NANAKim (2022)South KoreaCohortFull textSerologyUSNANANANA1.36 (1.18, 1.56)SBP, FPG, TG, LDL-C, HDL-C, ALT, GGT, and HS-CRPChoi (2022)South KoreaCross-sectionalFull textSerologyUS6604457045481.36 (1.04, 1.78)BMI, HTN, diabetes, dyslipidemia, and smokingHan (2021)South KoreaCross-sectionalFull textSerologyUS3435283655480.96 (0.78, 1.19)Age, gender, HTN, diabetes, BMI, fasting glucose, TG, HDL-C, and LSMYing (2021)ChinaCross-sectionalFull text13C-UBTUS141225436851025NANAPing (2021)ChinaCross-sectionalFull text13C-UBTUS2302992344221.38 (1.09, 1.75)Age, carotid plaque status, ALT, AST, UA, FPG, TC, TG, SBP, DBP, LDL-C, and BMIWang (2021)ChinaCross-sectionalFull text13C-UBTUS199306490903NANARahman (2020)BangladeshCross-sectionalFull textSerologyUS62793562701.50 (0.94, 2.39)Age, gender, religion, BMI, DM, marital status, smoking, occupation, monthly income, MS, and educationAmer (2020)EgyptCross-sectionalFull textSATUS442829626NANAAlvarez (2020)GuatemalaCross-sectionalFull textSerologyFLI > 60 and HSI > 362222914528NANADoulberis (2020)SwitzerlandCase-controlFull textRUTLiver biopsy154009NANAXu (2020)ChinaCross-sectionalFull textSerologyUS25162309528778591.66 (1.55, 1.79)Age, gender, underlying diseases, and MSTian (2019)ChinaCross-sectionalFull text13C-UBTUS1022842111511021.27 (1.07, 1.50)Age, gender, education level, smoking, HTN, diabetes, dyslipidemia, BMI, ALT, AST, AKP, TBIL, UA, and ureaYu (2019)ChinaCross-sectionalFull textRUTUS583851379589NANAMahyar (2019)IranCross-sectionalFull textSerology and SATUS22431550NANAAbdel-Razik (2018)EgyptCohortFull textSATUS, HSI > 36 and NAFLD-LFS > −0.6402301481981.08 (1.02, 1.25)Age, gender, BMI, smoking, crowding index, education level, regular exercise, CRP, IL-6, TNF-α, HOMA-IR, FPG, TC, HDL-C, LDL-C, TG, and UAYu (2018)ChinaCross-sectionalFull text14C-UBTUS3132446047168081NANAFan (2018)ChinaCross-sectionalFull text14C-UBTUS390557686943115541.00 (0.70, 1.30)Age, gender, BMI, SBP, DBP, FPG, HbA1c, TG, TC, LDL-C, HDL-C, UA, and ScrLu (2018)ChinaCross-sectionalFull text13C-UBTUS199397390881NANAKang (2018)USACross-sectionalFull textSerologyUS6581065111525661.17 (0.95, 1.43)Age, gender, race ethnicity, income, diabetes, HTN, smoking, waist circumference, alcohol and caffeine consumption, TC, HDL-C, and transferrin saturationCai (2018)ChinaCross-sectionalFull text13C-UBTUS14528850011180.94 (0.70, 1.27)Gender, BMI, TG, HDL-C, and FPGKim (2017)South KoreaCohortFull textSerologyUS20801301783858091.16 (1.05, 1.30)Age, gender, BMI, year of screening exam, smoking status, alcohol intake, regular exercise, and education level, HS-CRP, HOMA-IR, SBP, FPG, TG, LDL-C, HDL-C, AST, ALT, and GGTChen (2017)ChinaCross-sectionalFull text13C-UBTUS3132907239371.39 (1.05, 1.73)Age, gender, UA, AST, ALT, GGT, TG, BMI, waist circumference, and HbA1CKumar (2017)IndiaCross-sectionalAbstractRUTUS11162073NANAAlbert (2016)SpainCross-sectionalFull textRUTLiver biopsy2641102517NANABaeg (2016)South KoreaCross-sectionalFull text13C-UBTHSI > 36505440113115871.13 (0.97, 1.31)Age, gender, smoking, and HS-CRPTang (2016)USACross-sectionalAbstractRUT, serology or SATUS or liver biopsy4973401081.18 (1.00, 2.96)Age, gender, and statin useZhang (2016)ChinaCase-controlFull text14C-UBTLiver biopsy3003001444563.17 (1.91, 5.74)Gender and geriatric diseasesOkushin (2015)JapanCross-sectionalFull textSerologyUS52312799262561NANASumida (2015)JapanCross-sectionalFull textSerologyLiver biopsyNANANANA2.92 (1.11, 7.64)Age, gender, BMI, dyslipidemia, HTN, and diabetesPolyzos (2013)GreeceCase-controlFull textSerologyLiver biopsy2351411NANAShen (2013)ChinaCross-sectionalAbstractSerologyUS566130718045414NANAAKP: alkaline phosphatase, ALT: alanine aminotransferase, AST: aspartate aminotransferase, BMI: basal metabolic index, BP: blood pressure, BUN: blood urea nitrogen, CRP: C-reactive protein, CI: confidence intervals, DBP: diastolic blood pressure, DM: diabetes mellitus, FBG/FPG: fasting plasma glucose, FLI: fatty liver index, GGT: gamma-glutamyl transpeptidase, HbA1c: glycosylated hemoglobin, HDL-C: high-densitylipoprotein-cholesterol, HP:Helicobacter pylori, HS-CRP: high-sensitivityC-reactive protein, HOMA-IR: homeostatic model assessment-insulin resistance, HR: hazard ratio, HSI: hepatic steatosis index, HTN: hypertension, IL-6: interleukin-6, LDL: low-density lipoprotein, LDL-C: low-densitylipoprotein-cholesterol, LSM: liver stiffness measurements, MS: metabolic syndrome, NA: not available, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, OR: odds ratio, PG: pepsinogen, RUT: rapid urease test, SAT: stool antigen test, Scr: serum creatinine, SBP: systolic blood pressure, TBIL: total bilirubin, TC: total cholesterol, TG: triglycerides, TNF-α: tumor necrosis factor-alpha, UA: uric acid, UBT: urea breath test, US: ultrasonography, and USA: the United States of America.


### 3.2. Study Quality

Among the cohort and case-control studies, 6 and 1 were of high and moderate quality, respectively (Supplementary Table1). Among the cross-sectional studies, 16 and 11 were of high and moderate quality, respectively (Supplementary Table 2).


### 3.3.H. pylori Infection and Presence of NAFLD

Based on the unadjusted data from 26 cross-sectional studies and 3 case-control studies, the meta-analysis showed thatH. pylori infection was significantly associated with the presence of NAFLD (OR = 1.26, 95% CI = 1.17–1.36, and P<0.001) (Figure 2). Heterogeneity was statistically significant (I2 = 88.7% nd P<0.001). Such a statistically significant association between them disappeared in the subgroup analyses of studies using the rapid urease test and fecal antigen test to detect H. pylori infection, but remained in others. The interaction between subgroups was statistically significant in the subgroup analyses according to the study design (P<0.001) and diagnostic methods of NAFLD (P<0.001), but not in others. Subgroup analyses did not identify any source of heterogeneity (Table 2). Meta-regression analyses showed that the study design (P<0.001) and diagnostic methods of NAFLD (P<0.001) might be the sources of heterogeneity (Supplementary Table 3). Sensitivity analyses did not identify any source of heterogeneity (Supplementary Table 4). Egger test did not show any significant publication bias (P=0.294).Figure 2
Forest plots for unadjusted data from cross-sectional studies and case-control studies.Table 2
Meta-analysis regarding the association ofH. pylori infection with NAFLD in studies unadjusted for confounders.
GroupsNo. studiesOR (95% CI)HeterogeneityPinteractionI2 (%)P valueStudy design<0.001Cross-sectional261.21 (1.13–1.30;P<0.001)86.40<0.001Case-control33.20 (2.52–4.07;P<0.001)0.000.839Region0.17Asia211.23 (1.14–1.34;P<0.001)91.30<0.001Non-Asia81.40 (1.18–1.66;P<0.001)45.700.075Study quality0.96Moderate-quality111.27 (1.06–1.52;P=0.01)77.40<0.001High-quality181.27 (1.17–1.39;P<0.001)91.20<0.001Diagnostic methods ofH. pylori0.75UBT121.27 (1.15–1.39;P<0.001)91.00<0.001RUT51.16 (0.98–1.39;P=0.088)35.600.184Serology91.21 (1.03–1.42;P=0.021)89.00<0.001SAT11.46 (0.89–2.39;P=0.133)0.00—Diagnostic methods of NAFLD<0.001US221.18 (1.10–1.27;P<0.001)87.30<0.001Liver biopsy42.75 (1.87–4.06;P<0.001)23.700.269Surrogate markers of NAFLD∗21.60 (1.39–1.85;P<0.001)0.000.772Sample size0.21>500091.20 (1.09–1.32;P=0.008)94.10<0.001<5000201.34 (1.16–1.55;P<0.001)82.20<0.001Publication form0.25Full text261.25 (1.15–1.35;P<0.001)89.60<0.001Abstract31.51 (1.10–2.09;P=0.011)41.500.181∗Surrogate markers of NAFLD include FLI > 60, HSI > 36, or NAFLD-LFS > −0.640. CI: confidence intervals, FLI: fatty liver index, HSI: hepatic steatosis index, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, OR: odds ratio, RUT: rapid urease test, SAT: stool antigen test, UBT: urea breath test, and US: ultrasonography.Based on the adjusted data from 15 cross-sectional studies and 1 case-control study, the meta-analysis showed thatH. pylori infection was significantly associated with the presence of NAFLD (aOR = 1.25, 95% CI = 1.08–1.44, and P<0.001). Heterogeneity was statistically significant (I2 = 90% and P<0.001) (Figure 3). Such a statistically significant association between them disappeared in the subgroup analyses of non-Asian studies, those using the rapid urease test to detect H. pylori infection, those using surrogate markers for diagnosis of NAFLD, those with a sample size of >5000, and those published as abstracts but remained in others. The interaction between subgroups was statistically significant in the subgroup analyses according to the study design (P<0.001), study quality (P<0.001), diagnostic methods of H. pylori (P=0.01), and diagnostic methods of NAFLD (P<0.001), but not in others. Subgroup analyses did not identify any source of heterogeneity (Table 3). Meta-regression analyses showed that the study design (P=0.015) and diagnostic methods of NAFLD (P=0.023) might be the sources of heterogeneity (Supplementary Table 5). Sensitivity analyses did not identify any source of heterogeneity (Supplementary Table 6). Egger test did not show any significant publication bias (P=0.591).Figure 3
Forest plots for adjusted data from cross-sectional studies and case-control studies.Table 3
Meta-analysis regarding the association ofH. pylori infection with NAFLD in studies adjusted for confounders.
GroupsNo. studiesaOR (95% CI)HeterogeneityPinterationI2 (%)P valueStudy design<0.001Cross-sectional151.20 (1.05–1.38;P=0.009)89.8%<0.001Case-control13.17 (1.83–5.50;P<0.001)——Region0.05Asia131.30 (1.10–1.53;P=0.002)91.5%<0.001Non-Asia31.05 (0.91–1.21;P=0.503)18.00%0.295Study quality<0.001Moderate-quality12.21 (1.18–4.12;P=0.029)——High-quality151.23 (1.07–1.42;P=0.004)90.4%<0.001Diagnostic methods ofH. pylori0.01UBT81.21 (1.05–1.40;P=0.009)78.1%<0.001Serology61.38 (1.06–1.73;P=0.015)84.7%<0.001RUT10.96 (0.82–1.13;P=0.618)——Diagnostic methods of NAFLD<0.001US121.19 (1.02–1.39;P=0.028)91.8%<0.001Liver biopsy23.11 (1.93–5.01;P<0.001)0.00%0.885Surrogate markers of NAFLD∗11.13 (0.97–1.31;P=0.111)——Sample size0.25>500051.15 (0.87–1.50;P=0.325)96.7%<0.001<5000111.29 (1.12–1.48;P<0.001)63.4%0.002Confounders adjusted0.05Full adjusted∗101.16 (1.04–1.29;P=0.006)61.5%0.005No full adjusted61.46 (1.08–1.98;P=0.013)91.6%<0.001Publication form0.35Full text151.25 (1.08–1.45;P=0.002)90.6%<0.001Abstract11.18 (0.69–2.03;P=0.550)——∗Surrogate markers of NAFLD include FLI > 60, HSI > 36, or NAFLD-LFS > −0.640. ∗Full adjusted: at least age, gender, BMI, and/or smoking, as well as three additional risk factors were adjusted. BMI: basal metabolic index, CI: confidence intervals, FLI: fatty liver index, HSI: hepatic steatosis index, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, aOR: adjusted odds ratio, RUT: rapid urease test, UBT: urea breath test, and US: ultrasonography.


### 3.4.H. pylori Infection and Severity of NAFLD

The association betweenH. pylori infection and severity of NAFLD was explored in 4 studies (Table 4).Table 4
H. pylori infection and NAFLD severity.
First author (year)NAFLDNon-NAFLDMildModerateSevereHP+/HP−HP+/HP−HP+/HP−HP+/HP−Wang (2022)6711/115491852/304454/8516128/32210Wang (2021)119/18768/10612/13490/903Amer (2020)80/49202/10160/2396/26Xu (2020)1901/1825407/323208/1615287/7859HP:Helicobacter pylori and NAFLD: nonalcoholic fatty liver disease.The meta-analysis showed no statistically significant difference in the proportion ofH. pylori infection between patients with mild NAFLD and those without NAFLD (OR = 1.14, 95% CI = 0.9–1.45, and P=0.286). Heterogeneity was statistically significant (I2 = 95.1% and P<0.001) (Supplementary Figure 1). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.The meta-analysis showed that the proportion ofH. pylori infection was significantly higher in patients with moderate NAFLD than those without NAFLD (OR = 1.67, 95% CI = 1.17–2.39, and P=0.005). Heterogeneity was statistically significant (I2 = 92.7% and P<0.001) (Supplementary Figure 2). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.The meta-analysis showed that the proportion ofH. pylori infection was significantly higher in patients with severe NAFLD than those without NAFLD (OR = 1.71, 95% CI = 1.30–2.24, and P<0.001). Heterogeneity was not statistically significant (I2 = 43.7% and P=0.149) (Supplementary Figure 3).


### 3.5.H. pylori Infection and Occurrence of NAFLD

Based on the unadjusted data from 3 cohort studies, the meta-analysis showed thatH. pylori infection was not significantly associated with the occurrence of NAFLD (RR = 1.41, 95% CI = 0.80–2.48, and P=0.237). Heterogeneity was statistically significant (I2 = 98.3% and P<0.001) (Supplementary Figure 4). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.Based on the adjusted data from 3 cohort studies, the meta-analysis showed thatH. pylori infection was associated with the occurrence of NAFLD (aHR = 1.18, 95% CI = 1.05–1.34, and P=0.007). Heterogeneity was statistically significant (I2 = 70.8% and P=0.032) (Supplementary Figure 5). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.


## 3.1. Study Characteristics

We initially searched 2025 papers. Finally, 34 studies with 218573 participants were included (Figure1). Characteristics of included studies are shown in Table 1. Among them, 4 were cohort studies, 3 were case-control studies, and 27 were cross-sectional studies; 3 studies were published as abstracts and 31 as full texts; 25 studies were performed in Asia [17–41], 3 in North America [42–44], 2 in Africa [45, 46], and 4 in Europe [47–50]. The publication date ranged from 2013 to 2022.Figure 1
Flowchart of the study selection process.Table 1
Characteristics of studies regarding the association ofH. pylori infection with NAFLD.
First author (year)CountryStudy designPublication formDiagnostic methods ofH. pyloriDiagnostic methods of NAFLDNAFLDControlAdjusted OR/HR (95% CI)Adjusted confoundersHP+HP−HP+HP−Wernly (2022)AustriaCross-sectionalFull textRUTUS487194053223790.96 (0.82, 1.13)Age, gender, type 2 diabetes, and LDLWang (2022)ChinaCross-sectionalFull text13C-UBTUS86171467816128322101.02 (0.97, 1.08)Age, gender, BMI, SBP, DBP, FBG, HbA1C, LDL-C, HDL-C, TG, AST, ALT, GGT, Scr, and BUNZhao (2022)ChinaCohortFull text13C-UBTUS3773169396NANAKim (2022)South KoreaCohortFull textSerologyUSNANANANA1.36 (1.18, 1.56)SBP, FPG, TG, LDL-C, HDL-C, ALT, GGT, and HS-CRPChoi (2022)South KoreaCross-sectionalFull textSerologyUS6604457045481.36 (1.04, 1.78)BMI, HTN, diabetes, dyslipidemia, and smokingHan (2021)South KoreaCross-sectionalFull textSerologyUS3435283655480.96 (0.78, 1.19)Age, gender, HTN, diabetes, BMI, fasting glucose, TG, HDL-C, and LSMYing (2021)ChinaCross-sectionalFull text13C-UBTUS141225436851025NANAPing (2021)ChinaCross-sectionalFull text13C-UBTUS2302992344221.38 (1.09, 1.75)Age, carotid plaque status, ALT, AST, UA, FPG, TC, TG, SBP, DBP, LDL-C, and BMIWang (2021)ChinaCross-sectionalFull text13C-UBTUS199306490903NANARahman (2020)BangladeshCross-sectionalFull textSerologyUS62793562701.50 (0.94, 2.39)Age, gender, religion, BMI, DM, marital status, smoking, occupation, monthly income, MS, and educationAmer (2020)EgyptCross-sectionalFull textSATUS442829626NANAAlvarez (2020)GuatemalaCross-sectionalFull textSerologyFLI > 60 and HSI > 362222914528NANADoulberis (2020)SwitzerlandCase-controlFull textRUTLiver biopsy154009NANAXu (2020)ChinaCross-sectionalFull textSerologyUS25162309528778591.66 (1.55, 1.79)Age, gender, underlying diseases, and MSTian (2019)ChinaCross-sectionalFull text13C-UBTUS1022842111511021.27 (1.07, 1.50)Age, gender, education level, smoking, HTN, diabetes, dyslipidemia, BMI, ALT, AST, AKP, TBIL, UA, and ureaYu (2019)ChinaCross-sectionalFull textRUTUS583851379589NANAMahyar (2019)IranCross-sectionalFull textSerology and SATUS22431550NANAAbdel-Razik (2018)EgyptCohortFull textSATUS, HSI > 36 and NAFLD-LFS > −0.6402301481981.08 (1.02, 1.25)Age, gender, BMI, smoking, crowding index, education level, regular exercise, CRP, IL-6, TNF-α, HOMA-IR, FPG, TC, HDL-C, LDL-C, TG, and UAYu (2018)ChinaCross-sectionalFull text14C-UBTUS3132446047168081NANAFan (2018)ChinaCross-sectionalFull text14C-UBTUS390557686943115541.00 (0.70, 1.30)Age, gender, BMI, SBP, DBP, FPG, HbA1c, TG, TC, LDL-C, HDL-C, UA, and ScrLu (2018)ChinaCross-sectionalFull text13C-UBTUS199397390881NANAKang (2018)USACross-sectionalFull textSerologyUS6581065111525661.17 (0.95, 1.43)Age, gender, race ethnicity, income, diabetes, HTN, smoking, waist circumference, alcohol and caffeine consumption, TC, HDL-C, and transferrin saturationCai (2018)ChinaCross-sectionalFull text13C-UBTUS14528850011180.94 (0.70, 1.27)Gender, BMI, TG, HDL-C, and FPGKim (2017)South KoreaCohortFull textSerologyUS20801301783858091.16 (1.05, 1.30)Age, gender, BMI, year of screening exam, smoking status, alcohol intake, regular exercise, and education level, HS-CRP, HOMA-IR, SBP, FPG, TG, LDL-C, HDL-C, AST, ALT, and GGTChen (2017)ChinaCross-sectionalFull text13C-UBTUS3132907239371.39 (1.05, 1.73)Age, gender, UA, AST, ALT, GGT, TG, BMI, waist circumference, and HbA1CKumar (2017)IndiaCross-sectionalAbstractRUTUS11162073NANAAlbert (2016)SpainCross-sectionalFull textRUTLiver biopsy2641102517NANABaeg (2016)South KoreaCross-sectionalFull text13C-UBTHSI > 36505440113115871.13 (0.97, 1.31)Age, gender, smoking, and HS-CRPTang (2016)USACross-sectionalAbstractRUT, serology or SATUS or liver biopsy4973401081.18 (1.00, 2.96)Age, gender, and statin useZhang (2016)ChinaCase-controlFull text14C-UBTLiver biopsy3003001444563.17 (1.91, 5.74)Gender and geriatric diseasesOkushin (2015)JapanCross-sectionalFull textSerologyUS52312799262561NANASumida (2015)JapanCross-sectionalFull textSerologyLiver biopsyNANANANA2.92 (1.11, 7.64)Age, gender, BMI, dyslipidemia, HTN, and diabetesPolyzos (2013)GreeceCase-controlFull textSerologyLiver biopsy2351411NANAShen (2013)ChinaCross-sectionalAbstractSerologyUS566130718045414NANAAKP: alkaline phosphatase, ALT: alanine aminotransferase, AST: aspartate aminotransferase, BMI: basal metabolic index, BP: blood pressure, BUN: blood urea nitrogen, CRP: C-reactive protein, CI: confidence intervals, DBP: diastolic blood pressure, DM: diabetes mellitus, FBG/FPG: fasting plasma glucose, FLI: fatty liver index, GGT: gamma-glutamyl transpeptidase, HbA1c: glycosylated hemoglobin, HDL-C: high-densitylipoprotein-cholesterol, HP:Helicobacter pylori, HS-CRP: high-sensitivityC-reactive protein, HOMA-IR: homeostatic model assessment-insulin resistance, HR: hazard ratio, HSI: hepatic steatosis index, HTN: hypertension, IL-6: interleukin-6, LDL: low-density lipoprotein, LDL-C: low-densitylipoprotein-cholesterol, LSM: liver stiffness measurements, MS: metabolic syndrome, NA: not available, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, OR: odds ratio, PG: pepsinogen, RUT: rapid urease test, SAT: stool antigen test, Scr: serum creatinine, SBP: systolic blood pressure, TBIL: total bilirubin, TC: total cholesterol, TG: triglycerides, TNF-α: tumor necrosis factor-alpha, UA: uric acid, UBT: urea breath test, US: ultrasonography, and USA: the United States of America.


## 3.2. Study Quality

Among the cohort and case-control studies, 6 and 1 were of high and moderate quality, respectively (Supplementary Table1). Among the cross-sectional studies, 16 and 11 were of high and moderate quality, respectively (Supplementary Table 2).


## 3.3.H. pylori Infection and Presence of NAFLD

Based on the unadjusted data from 26 cross-sectional studies and 3 case-control studies, the meta-analysis showed thatH. pylori infection was significantly associated with the presence of NAFLD (OR = 1.26, 95% CI = 1.17–1.36, and P<0.001) (Figure 2). Heterogeneity was statistically significant (I2 = 88.7% nd P<0.001). Such a statistically significant association between them disappeared in the subgroup analyses of studies using the rapid urease test and fecal antigen test to detect H. pylori infection, but remained in others. The interaction between subgroups was statistically significant in the subgroup analyses according to the study design (P<0.001) and diagnostic methods of NAFLD (P<0.001), but not in others. Subgroup analyses did not identify any source of heterogeneity (Table 2). Meta-regression analyses showed that the study design (P<0.001) and diagnostic methods of NAFLD (P<0.001) might be the sources of heterogeneity (Supplementary Table 3). Sensitivity analyses did not identify any source of heterogeneity (Supplementary Table 4). Egger test did not show any significant publication bias (P=0.294).Figure 2
Forest plots for unadjusted data from cross-sectional studies and case-control studies.Table 2
Meta-analysis regarding the association ofH. pylori infection with NAFLD in studies unadjusted for confounders.
GroupsNo. studiesOR (95% CI)HeterogeneityPinteractionI2 (%)P valueStudy design<0.001Cross-sectional261.21 (1.13–1.30;P<0.001)86.40<0.001Case-control33.20 (2.52–4.07;P<0.001)0.000.839Region0.17Asia211.23 (1.14–1.34;P<0.001)91.30<0.001Non-Asia81.40 (1.18–1.66;P<0.001)45.700.075Study quality0.96Moderate-quality111.27 (1.06–1.52;P=0.01)77.40<0.001High-quality181.27 (1.17–1.39;P<0.001)91.20<0.001Diagnostic methods ofH. pylori0.75UBT121.27 (1.15–1.39;P<0.001)91.00<0.001RUT51.16 (0.98–1.39;P=0.088)35.600.184Serology91.21 (1.03–1.42;P=0.021)89.00<0.001SAT11.46 (0.89–2.39;P=0.133)0.00—Diagnostic methods of NAFLD<0.001US221.18 (1.10–1.27;P<0.001)87.30<0.001Liver biopsy42.75 (1.87–4.06;P<0.001)23.700.269Surrogate markers of NAFLD∗21.60 (1.39–1.85;P<0.001)0.000.772Sample size0.21>500091.20 (1.09–1.32;P=0.008)94.10<0.001<5000201.34 (1.16–1.55;P<0.001)82.20<0.001Publication form0.25Full text261.25 (1.15–1.35;P<0.001)89.60<0.001Abstract31.51 (1.10–2.09;P=0.011)41.500.181∗Surrogate markers of NAFLD include FLI > 60, HSI > 36, or NAFLD-LFS > −0.640. CI: confidence intervals, FLI: fatty liver index, HSI: hepatic steatosis index, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, OR: odds ratio, RUT: rapid urease test, SAT: stool antigen test, UBT: urea breath test, and US: ultrasonography.Based on the adjusted data from 15 cross-sectional studies and 1 case-control study, the meta-analysis showed thatH. pylori infection was significantly associated with the presence of NAFLD (aOR = 1.25, 95% CI = 1.08–1.44, and P<0.001). Heterogeneity was statistically significant (I2 = 90% and P<0.001) (Figure 3). Such a statistically significant association between them disappeared in the subgroup analyses of non-Asian studies, those using the rapid urease test to detect H. pylori infection, those using surrogate markers for diagnosis of NAFLD, those with a sample size of >5000, and those published as abstracts but remained in others. The interaction between subgroups was statistically significant in the subgroup analyses according to the study design (P<0.001), study quality (P<0.001), diagnostic methods of H. pylori (P=0.01), and diagnostic methods of NAFLD (P<0.001), but not in others. Subgroup analyses did not identify any source of heterogeneity (Table 3). Meta-regression analyses showed that the study design (P=0.015) and diagnostic methods of NAFLD (P=0.023) might be the sources of heterogeneity (Supplementary Table 5). Sensitivity analyses did not identify any source of heterogeneity (Supplementary Table 6). Egger test did not show any significant publication bias (P=0.591).Figure 3
Forest plots for adjusted data from cross-sectional studies and case-control studies.Table 3
Meta-analysis regarding the association ofH. pylori infection with NAFLD in studies adjusted for confounders.
GroupsNo. studiesaOR (95% CI)HeterogeneityPinterationI2 (%)P valueStudy design<0.001Cross-sectional151.20 (1.05–1.38;P=0.009)89.8%<0.001Case-control13.17 (1.83–5.50;P<0.001)——Region0.05Asia131.30 (1.10–1.53;P=0.002)91.5%<0.001Non-Asia31.05 (0.91–1.21;P=0.503)18.00%0.295Study quality<0.001Moderate-quality12.21 (1.18–4.12;P=0.029)——High-quality151.23 (1.07–1.42;P=0.004)90.4%<0.001Diagnostic methods ofH. pylori0.01UBT81.21 (1.05–1.40;P=0.009)78.1%<0.001Serology61.38 (1.06–1.73;P=0.015)84.7%<0.001RUT10.96 (0.82–1.13;P=0.618)——Diagnostic methods of NAFLD<0.001US121.19 (1.02–1.39;P=0.028)91.8%<0.001Liver biopsy23.11 (1.93–5.01;P<0.001)0.00%0.885Surrogate markers of NAFLD∗11.13 (0.97–1.31;P=0.111)——Sample size0.25>500051.15 (0.87–1.50;P=0.325)96.7%<0.001<5000111.29 (1.12–1.48;P<0.001)63.4%0.002Confounders adjusted0.05Full adjusted∗101.16 (1.04–1.29;P=0.006)61.5%0.005No full adjusted61.46 (1.08–1.98;P=0.013)91.6%<0.001Publication form0.35Full text151.25 (1.08–1.45;P=0.002)90.6%<0.001Abstract11.18 (0.69–2.03;P=0.550)——∗Surrogate markers of NAFLD include FLI > 60, HSI > 36, or NAFLD-LFS > −0.640. ∗Full adjusted: at least age, gender, BMI, and/or smoking, as well as three additional risk factors were adjusted. BMI: basal metabolic index, CI: confidence intervals, FLI: fatty liver index, HSI: hepatic steatosis index, NAFLD: nonalcoholic fatty liver disease, NAFLD-LFS: NAFLD-liver fat score, aOR: adjusted odds ratio, RUT: rapid urease test, UBT: urea breath test, and US: ultrasonography.


## 3.4.H. pylori Infection and Severity of NAFLD

The association betweenH. pylori infection and severity of NAFLD was explored in 4 studies (Table 4).Table 4
H. pylori infection and NAFLD severity.
First author (year)NAFLDNon-NAFLDMildModerateSevereHP+/HP−HP+/HP−HP+/HP−HP+/HP−Wang (2022)6711/115491852/304454/8516128/32210Wang (2021)119/18768/10612/13490/903Amer (2020)80/49202/10160/2396/26Xu (2020)1901/1825407/323208/1615287/7859HP:Helicobacter pylori and NAFLD: nonalcoholic fatty liver disease.The meta-analysis showed no statistically significant difference in the proportion ofH. pylori infection between patients with mild NAFLD and those without NAFLD (OR = 1.14, 95% CI = 0.9–1.45, and P=0.286). Heterogeneity was statistically significant (I2 = 95.1% and P<0.001) (Supplementary Figure 1). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.The meta-analysis showed that the proportion ofH. pylori infection was significantly higher in patients with moderate NAFLD than those without NAFLD (OR = 1.67, 95% CI = 1.17–2.39, and P=0.005). Heterogeneity was statistically significant (I2 = 92.7% and P<0.001) (Supplementary Figure 2). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.The meta-analysis showed that the proportion ofH. pylori infection was significantly higher in patients with severe NAFLD than those without NAFLD (OR = 1.71, 95% CI = 1.30–2.24, and P<0.001). Heterogeneity was not statistically significant (I2 = 43.7% and P=0.149) (Supplementary Figure 3).


## 3.5.H. pylori Infection and Occurrence of NAFLD

Based on the unadjusted data from 3 cohort studies, the meta-analysis showed thatH. pylori infection was not significantly associated with the occurrence of NAFLD (RR = 1.41, 95% CI = 0.80–2.48, and P=0.237). Heterogeneity was statistically significant (I2 = 98.3% and P<0.001) (Supplementary Figure 4). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.Based on the adjusted data from 3 cohort studies, the meta-analysis showed thatH. pylori infection was associated with the occurrence of NAFLD (aHR = 1.18, 95% CI = 1.05–1.34, and P=0.007). Heterogeneity was statistically significant (I2 = 70.8% and P=0.032) (Supplementary Figure 5). Because only a small number of studies was included, subgroup analyses were not performed to explore the sources of heterogeneity.


## 4. Discussion

Based on the data from cross-sectional studies and case-control studies,H. pylori infection was associated with the presence of NAFLD, especially moderate and severe NAFLD. Based on the data from cohort studies, H. pylori infection increased the risk of NAFLD occurrence after adjustment for confounders. Notably, seven previous meta-analyses [51–57] also concluded a significant association between H. pylori infection with NAFLD. Our current meta-analysis has several advantages compared to previous ones [51–57]. First, there was a more comprehensive collection of eligible studies by expanding the search strategy and updating the final search date. Thus, the number of studies included was larger in the current meta-analysis than in the previous ones. Second, some of the previous meta-analyses did not strictly follow the prespecified inclusion and exclusion criteria to collect all relevant studies. For example, in both meta-analyses by Zhou et al. [51] and Heydari et al. [56], a cross-sectional study by Sumida et al. [40] would have been included based on their eligibility criteria, but neither of them included this study. Third, some of the previous meta-analyses only calculated ORs to evaluate their association. By comparison, the current meta-analysis further pooled HRs to evaluate their cause-effect association. Fourth, the interaction between subgroups was tested to infer whether the impact of H. pylori infection on NAFLD was significantly influenced by some confounding factors, which have not been performed in previous meta-analyses yet. Last, the association of H. pylori infection with the severity of NAFLD was evaluated in the current meta-analysis, which has not been performed in previous meta-analyses  yet.Metabolic syndrome, including overweight/obesity, type 2 diabetes mellitus (T2DM), and metabolic disorders, is an important pathogenic factor of NAFLD/MAFLD [58, 59]. It is also closely associated with H. pylori infection [60]. Notably, insulin resistance (IR) is a key factor in the development of metabolic syndrome [61]. Thus, the pathophysiological interrelationship between H. pylori infection and MAFLD/NAFLD may be explained by IR [62–64] (Supplementary Figure 6). First, H. pylori infection can stimulate the release of proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α) [65–67]. TNF-α induces serine/threonine-mediated phosphorylation of insulin receptor substrate 1 (IRS-1), which attenuates IRS-1-mediated insulin signaling, leading to the occurrence of IR [68]. Second, H. pylori infection causes a decrease in adiponectin levels [48, 69]. Adiponectin can reduce gluconeogenesis and lipogenesis in the liver, and therefore has the effect of an insulin sensitizer to inhibit intrahepatic lipid accumulation [70]. Thus, decreased adiponectin levels would result in increased intrahepatic fat content and IR [71–73]. Third, there is an interaction of reciprocal inhibition between adiponectin and TNF-α in terms of their production and action, thereby enhancing IR [74]. Fourth, H. pylori infection leads to elevated fetuin-A levels [75]. Fetuin-A can stimulate adipocytes and macrophages to produce proinflammatory cytokines and then induce IR [76, 77].Besides, their association may be attributed to altered gut microbiota [13]. Chronic H. pylori infection causes significant changes in the gut microbiota composition [78]. Gut microbiota can release endotoxin composed of the outer wall of Gram-negative bacteria, which can introduce into the liver directly through the portal vein. Endotoxin can stimulate inflammatory response via Toll-like receptor 4 (TLR4), thereby exacerbating hepatic inflammation [79]. Indeed, some studies have shown that lipopolysaccharide, a surrogate marker of endotoxin, is elevated in patients with NAFLD [80, 81].Our previous study found a higher rate ofH. pylori infection in young military personnel than in civilians [82]. This phenomenon is probably explained by the fact that increased mental stress caused by high-intensity military training suppresses the body’s humoral and cellular immunity, thereby increasing the risk of H. pylori infection. On the other hand, high occupational and personal stress are independent predictors of NAFLD development [83]. Therefore, the association of H. pylori infection with the presence of NAFLD may be because both of them have a concomitant predisposing factor (i.e., stress).Another previous meta-analysis by our group also showed a significant association betweenH. pylori infection and irritable bowel syndrome (IBS) [84]. It should be noted that multiple etiological factors, including obesity, gut microbiota, dietary factors, and immune-mediated causes [85], overlap between IBS and NAFLD. Thus, such factors should not be neglected to explain our current findings about the association of H. pylori infection with NAFLD.Current consensus recommends that dietary modification, exercise, and weight loss as the major treatment option for NAFLD to reduce liver fat and improve IR [86, 87]. Besides, considering that silymarin has antioxidant, anti-inflammatory, immunomodulatory, antifibrotic, and hepatoprotective activities and stimulates protein synthesis and liver tissue regeneration [88], silymarin may be used for the treatment of NAFLD. Notably, it seems that silymarin can also inhibit H. pylori activity [89–92]. Therefore, it may be hypothesized that silymarin can be helpful for the treatment of NAFLD and H. pylori infection.Considering an association ofH. pylori infection with NAFLD, it appears that H. pylori eradication is beneficial in preventing NAFLD. However, this is still controversial. A randomized controlled study by Maharshi et al. showed that successful eradication of H. pylori in patients with NAFLD resulted in significant improvement in IR [93]. However, a study by Jamali et al. found that H. pylori eradication may not influence liver fat content, liver function tests, lipid profile, IR, and anthropometric measurements in patients with dyspeptic NAFLD [94]. Therefore, whether H. pylori eradication influences the occurrence or progression of NAFLD needs to be confirmed by more studies in the future.Consensus and practice guidelines recommend bismuth quadruple therapy, which consists of a proton pump inhibitor, a bismuth, and two antibiotics, for a duration of 10–14 days as the first-line treatment ofH. pylori. Commonly used antibiotics include amoxicillin, clarithromycin, and metronidazole [95–98]. However, considering an increased burden of multidrug resistant Gram-negative infections [99], some studies have suggested that a combination of tetracycline and tinidazole should achieve a higher rate of H. pylori eradication [100–102].Our meta-analysis has some limitations. First, a majority of these included studies provided only cross-sectional data, which can only establish a possible association betweenH. pylori infection and NAFLD, but not any cause-effect association. Second, only some of these included studies adjusted the confounders in multivariate regression analyses, and the confounders adjusted were inconsistent among them. Third, only a minority of these included studies evaluated the prevalence of H. pylori infection according to the severity of NAFLD. Finally, none of these included studies have evaluated the association of H. pylori infection with MAFLD.


## 5. Conclusion

There seems to be an association betweenH. pylori infection and NAFLD, but this association was weak. More prospective cohort studies are needed in the future to demonstrate the impact of H. pylori infection and its eradication on MAFLD, and experimental studies should also be necessary to elucidate their potential mechanisms. Undoubtedly, these studies may provide promising approaches for the management of MAFLD.



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*Source: 5521239-2023-01-25.xml*