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. Author manuscript; available in PMC: 2026 Jul 2.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2026 Aug 3;35(8):1421–1427. doi: 10.1158/1055-9965.EPI-26-0038

Helicobacter pylori infection and metabolic dysfunction-associated steatotic liver disease: a prospective study and meta-analysis

Longgang Zhao 1, Yun Chen 1, Xinyuan Zhang 2, Catherine Mezzacappa 3,4, Xiaomei Ma 5, Bubu Banini 3, Jung Eun Lee 6, Chen Liu 7, Xuchen Zhang 7, Ann W Hsing 8, Mindie H Nguyen 9, Chul S Hyun 3, M Constanza Camargo 10, Katherine A McGlynn 10, Lifang Hou 11, Tamar H Taddei 3,4, Xuehong Zhang 1,5,12,#
PMCID: PMC13320657  NIHMSID: NIHMS2185909  PMID: 42233960

Abstract

Background:

Helicobacter pylori (H. pylori) infection and metabolic dysfunction-associated steatotic liver disease (MASLD) are significant global health concerns, but their association remains unclear, with evidence mainly from cross-sectional studies and Asian populations.

Methods:

We analyzed UK Biobank (UKB) participants aged 40-69 years with objectively measured H. pylori serostatus data (N=4,246). Cox models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for demographic, lifestyle, and cardiometabolic factors. We meta-analyzed seven prospective cohorts (all from non-European populations) and the UKB (8 cohorts in total with 36,145 participants and 6,979 cases) to evaluate the association between H. pylori seropositivity and MASLD risk.

Results:

About 35.1% of the participants were seropositive for H. pylori in the UKB. With median follow-up of 11.1 years, we identified 32 MASLD cases. H. pylori seropositivity was marginally associated with higher MASLD risk (HR, 1.88; 95% CI, 0.92–3.82), whereas CagA seropositivity was significantly associated with higher risk (2.40; 1.17–4.92). Participants seropositive for both H. pylori and CagA had a higher MASLD risk than participants seronegative for both markers (2.58; 1.19–5.61). The meta-analysis showed that H. pylori was associated with a 24% higher MASLD risk (Risk Ratio, 1.24; 95% CI, 1.17–1.31).

Conclusions:

H. pylori infection, especially CagA seropositivity, was associated with higher MASLD risk in prospective studies.

Impact:

Our findings support a potential role of H. pylori virulence serology (CagA) in MASLD development. If confirmed, these findings could have implications for risk stratification and inform targeted eradication or prevention strategies.

Keywords: H. pylori, CagA protein, metabolic dysfunction-associated steatotic liver disease, prospective study, meta-analysis

Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects one in three adults worldwide (1). In the US, the prevalence of MASLD is projected to rise to 41.4% by 2050 (2) and the more advanced stage, metabolic dysfunction-associated steatohepatitis (MASH) is projected to be the leading indication for liver transplant (3). Understanding the potential causes for MASLD is critical to reduce its incidence and progression (4). Well-known risk factors of MASLD include overweight or obesity, prediabetes or diabetes, hypertension, hyperlipidemia, and family history of metabolic syndrome (5). Previous studies have also evaluated the role of lifestyle and dietary factors (6,7), but potential associations with infections have been less studied.

The prevalence of Helicobacter pylori (H. pylori) infection worldwide ranged from 32.8% (Americas region) to 56.1% (Eastern Mediterranean region) between 2011-2022 (8). This bacterial infection is a well-known risk factor for gastritis, peptic ulcer, and gastric cancer (9). Recent studies have shown that H. pylori infection is associated with a variety of extra gastric conditions, including hypertriglyceridemia, chronic cholecystitis, cholelithiasis, gestational diabetes mellitus, and systemic sclerosis (10). Helicobacter species also colonize human liver tissue and could potentially affect the risk of liver diseases. The specific strains of Helicobacter species induce hepatic inflammation in murine models and could result in human liver damage and cause liver diseases via different mechanisms, such as cellular vacuolation, DNA damage, growth stimulation, and angiogenesis (11). The resultant hepatocyte damage is part of the pathogenesis of liver steatosis and fibrosis (12,13). Additionally, H. pylori infection contributes to microbial dysbiosis in gastric and intestinal microbiomes, which may be involved in steatotic liver disease pathogenesis, mainly through its effects on the innate immune system, gut permeability, intestinal production of short-chain fatty acids, and fermentation of indigestible carbohydrates (14). Epidemiological studies examined the associations between H. pylori infection and MASLD, but most were cross-sectional, limiting the ability to establish temporality (15). More interestingly, cytotoxin-associated gene A (CagA) protein, one of the most studied virulence factors of H. pylori has been strongly implicated in gastric carcinogenesis (16). However, it is still largely unclear whether H. pylori infection and the CagA genotype are associated with MASLD, especially in Western populations with a higher alcohol intake but a lower prevalence of chronic hepatitis virus infection (17).

In the current study, we aimed to comprehensively evaluate the potential association between H. pylori infection and MASLD using data from a prospective cohort in the UK. We further conducted a meta-analysis of prospective cohort studies to summarize the evidence on the association between H. pylori infection and MASLD risk.

Materials and Methods

Cohort analysis

Study population

We included participants from the UK Biobank (UKB) who had objectively measured H. pylori serology, assessed in a randomly selected subset of the cohort with archived blood samples. The UKB is a community-based cohort study conducted at 22 participating centers in the UK (18). Baseline examinations were conducted from 2006 to 2010, and 502,389 volunteers aged 40–69 years were recruited. All participants provided informed consent for data linkage to their medical records. We restricted the analysis to participants (n = 8,448) who had H. pylori antibodies and essential covariates measured at baseline. We further excluded participants with invalid CagA antigen results and missing value in follow up time. Finally, 4,246 participants were included (Supplementary Figure S1). Baseline characteristics were generally comparable between the sample analyzed and participants with serology data (Supplementary Table S1). This study was conducted under the UKB application number 87303.

Ascertainment of H. pylori infection

A random subsample of the full cohort had seropositivity to 20 pathogens measured in a pilot study using multiplex serology methodology, including six antigens related to H. pylori infection (19). Specifically, the bound serum antibodies were quantified using a Luminex flow cytometer (Luminex Corp.) with biotin-labeled secondary antibodies against human IgG, followed by detection with a Streptavidin–R-Phycoerythrin conjugate (20). Although the assay detects these three isotypes, the seropositivity cutoffs for H. pylori antigens were calibrated and validated against gold-standard assays for IgG-specific responses, with pathogen-specific assays achieving sensitivity and specificity ≥85% (median 97.0% and 93.7% across the panel) (19). We followed the UKB algorithm to define H. pylori positivity as having two or more IgG-based positive antibodies against the following six antigens (with the following cutoff values): CagA >400, vacuolating cytotoxin A (VacA) >100, outer membrane protein (OMP) >170, GroEL (HSP60) >80, Catalase (CatA) >180, and urease subunit alpha (UreA) >130 (21,22). Due to a documented laboratory handling error, CagA was not available for a subset of samples. We restricted our analyses to participants with valid CagA measurements and applied a seropositivity definition that included CagA. Participants were classified as three groups based on H. pylori and CagA status: H. pylori (−) and CagA (−), H. pylori (+) and CagA (−), H. pylori (+) and CagA (+) (23). We excluded those participants with H. pylori (−) and CagA (+) in the joint analyses because this category was rare and biologically implausible, and likely reflects assay variability rather than true infection, resulting in unstable estimates, leaving 31 MASLD cases among 4,111 participants in the joint analyses.

Identification of MASLD

We identified clinically recognized MASLD cases using the International Classification of Diseases, Tenth Revision (ICD-10) codes K75.8 and K76.0, obtained through linkage to hospital inpatient records until February 29, 2020. Because MASLD has a prolonged subclinical phase, this endpoint is likely to reflect first clinical detection rather than true disease onset. In the sensitivity analysis, we further restricted the analysis to participants with liver imaging data available during follow-up (n = 390, imaging subsample). In this subsample, we defined hepatic steatosis as magnetic resonance imaging (MRI) proton density fat fraction (PDFF) ≥ 5% (n = 97 imaging-defined steatosis cases).

Assessments covariates

Information on age, sex, ethnicity group, Townsend deprivation index, smoking status, alcohol drinking, and physical activity was collected from baseline interviews. Height, weight, and waist circumference were measured at the Assessment Center. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m2). Medical history of diabetes was self-reported at baseline.

Statistical analysis

Baseline characteristics were summarized as mean (standard deviation) or proportion by H. pylori and CagA serostatus (Table 1 and Supplementary Table S2). We used Cox proportional hazards models with follow up time as time scale to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between H. pylori infection or CagA seropositivity and MASLD risk. We fit stepwise multivariable models: Model 1 adjusted for age at baseline (years), sex (female or male), ethnicity (White or non-White), Townsend deprivation index (a census-based continuous measure of material deprivation in a population), smoking status (never, former, and current smokers), and physical activity (<500 metabolic equivalent tasks [METs], 500–<1000 METs, ≥1000 METs, or missing category); Model 2 additionally adjusted for alcohol intake (g/day); and Model 3 further adjusted for key metabolic determinants (BMI [kg/m2] and history of diabetes [yes or no]). In sensitivity analysis, we additionally adjusted for waist circumference (cm). We evaluated the joint association between H. pylori and CagA seropositivity and MASLD risk. The interaction was assessed by including a multiplicative product term between H. pylori and CagA in the regression model. We further evaluated all other antigens for MASLD risk. We did not test interactions with prespecified factors (e.g., sex and obesity) because the limited number of incident cases would yield underpowered and unstable estimates.

Table 1.

Baseline characteristics of study population according to H. pylori infection serostatus in the UK Biobank (2006-2010)

Characteristics Total n=4,246 H. pylori (−) n=2,754 H. pylori (+) n=1,492 P value
Age at baseline, years, mean (SD) 56.0 (8.1) 55.6 (8.1) 56.9 (8.1) < 0.001
Male, % 43.1 41.1 46.8 < 0.001
Non-White, % 5.2 3.1 9.2 < 0.001
Townsend deprivation index, mean (SD) −1.4 (3.1) −1.7 (2.9) −0.9 (3.3) < 0.001
Current smoker, % 9.8 8.7 11.7 < 0.001
Almost daily alcohol use, % 20.9 23.4 21.3 < 0.001
Alcohol intake, g/d, mean (SD) 21.0 (25.2) 21.4 (25.0) 20.2 (25.6) 0.16
Diagnosed diabetes, % 4.9 4.0 6.4 < 0.001
MVPA≥1000 MET minutes/week, % 38.0 38.5 37.1 0.01
Waist circumference, cm, mean (SD) 89.6 (13.6) 89.1 (13.5) 90.6 (13.7) < 0.001
Body mass index, kg/m2, mean (SD) 27.3 (4.8) 27.1 (4.8) 27.6 (4.8) < 0.001
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Abbreviations: MET, metabolic equivalent–minutes; MVPA, moderate-to-vigorous physical activity; SD, standard deviation.

We conducted several sensitivity analyses to assess robustness. First, to assess robustness to exposure definition, we varied the antigen-positivity threshold used to define H. pylori seropositivity (≥ 3 and ≥ 4 of 6 antigens) or any two antigens seropositivity excluding CagA. Second, in an imaging-based sensitivity analysis restricted to participants with liver MRI data, we evaluated MRI-PDFF–defined hepatic steatosis (PDFF ≥ 5%) as a secondary endpoint using modified Poisson model. Third, to reduce potential reverse causation and diagnostic intensity near baseline, we repeated analyses after excluding MASLD events occurring within the first 2 or 3 years of follow-up and conducted a 5-year landmark analysis. Fourth, to probe detection bias and residual confounding, we evaluated incident osteoarthritis as a negative-control outcome and cytomegalovirus (CMV) seropositivity (measured on the same platform) as a negative-control exposure. Fifth, to evaluate potential selection related to inclusion in the analytic serology cohort, we performed inverse probability weighted analyses. Sixth, to account for competing events, we conducted competing-risk analyses treating death as a competing event. Seventh, to address potential sparse-data bias, we fit a Firth penalized Cox model. Finally, we fit a modified Poisson model with robust (sandwich) standard errors to estimate the risk ratio (RR) and 95% CIs for the association between H. pylori infection or CagA seropositivity and MASLD risk. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA), with statistical significance defined as a two-sided P < 0.05.

Ethics statement

Our UKB research was conducted under Application Number 87303 with ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 11/NW/0382); all participants provided written informed consent.

Meta-analysis

The meta-analysis followed the PRISMA 2020 guidelines for systematic reviews and meta-analyses (24). To summarize all available prospective evidence on the association between H. pylori and MASLD, we conducted a meta-analysis (PROSPERO: CRD420251158268). We searched PubMed through October 1, 2025, for prospective studies that reported the risk estimates of H. pylori infection and MASLD risk without language or year restrictions (Supplementary Table S3). Reference lists of eligible articles were also screened. Two researchers (LZ and YC) independently performed the literature search, study selection, and data extraction. Eligible studies were prospective in design and reported RRs or HRs with 95% CIs for MASLD according to H. pylori serostatus. We used a priori abstract table to obtain information including the last name of the first author, publication year, country, name of the study, study period or follow-up period, cohort size, number of MASLD cases, RRs/HRs, and 95% CIs. Study quality was assessed using the Newcastle–Ottawa Scale (NOS) for cohort studies, focusing on selection, comparability, and outcome domains (25). We pooled the estimates using the random-effects model by DerSimonian and Laird, which considers variation both within and between studies (26). We evaluated heterogeneity by estimating the variance between studies using Cochran’s Q test and the I-squared (I2) statistic (27). Publication bias was evaluated using Egger’s test (28), and sensitivity analyses were performed by omitting one study at a time (29). Analyses were conducted in R (version 4.0; R Foundation for Statistical Computing), and a two-sided P < 0.05 was considered statistically significant.

Data availability

For the UKB, this research was conducted under Application Number 87303. The UKB data are available to bona fide researchers upon application to the UKB (www.ukbiobank.ac.uk), subject to registration, approval, and payment of an access fee. The authors are not permitted to publicly share the raw data used in this study. The code used to generate the results of this article is available from the corresponding author upon request.

Results

The prevalence of H. pylori seropositive infection was 35.1% among the 4,246 participants. Participants seropositive for H. pylori infection were more likely to be older, male, race other than White, current smokers, and had with a higher Townsend Deprivation Index (Table 1).

In UKB, with a median follow-up of 11.1 years (interquartile: 10.4-11.8), we identified 32 incident MASLD cases. After adjustment for potential confounders, H. pylori seropositivity was borderline associated with a higher risk of MASLD (HR, 1.88; 95% CI, 0.92–3.82). Further adjustment for waist circumference yielded similar results (HR, 1.95; 95% CI, 0.96–3.82). CagA seropositivity was associated with a higher risk of MASLD (HR, 2.40; 95% CI, 1.17–4.92) (Table 2). Joint analysis further confirmed participants with both positive H. pylori and CagA showed higher risk of MASLD compared those with double-negative (HR, 2.58; 95% CI, 1.19–5.61). No interaction was detected between H. pylori and CagA on MASLD risk (P interaction = 0.66). Several exposure categories, particularly in the joint H. pylori/CagA analyses, included few incident MASLD events, resulting in wide confidence intervals and imprecise estimates. We found no significant associations between the other antigens and MASLD risk (Supplementary Table S4). Associations were generally similar when defining H. pylori seropositivity using more stringent antigen-positivity thresholds (Supplementary Table S5). Results were similar in direction and magnitude defining H. pylori seropositivity using only the 5 non-CagA antigens (HR, 1.74; 95% CI, 0.85–3.54). In the MRI subset, associations with MRI-PDFF–defined hepatic steatosis (PDFF ≥ 5%) were attenuated and not statistically significant (RR, 0.93; 95% CI, 0.65–1.32) (Supplementary Table S6). Results were similar after excluding events within the first 2 or 3 of follow-up, or 5-year landmark analysis (Supplementary Table S7). H. pylori seropositivity showed a near-null association with incident osteoarthritis (HR, 1.10; 95% CI, 0.97–1.24) (Supplementary Table S8). Similarly, as a negative-control exposure, CMV seropositivity was not statistically associated with incident MASLD (HR, 1.59; 95% CI, 0.92–2.75). Inverse probability weighted, competing-risk, Firth-penalized Cox, and modified Poisson analyses yielded estimates similar to the primary results (Supplementary Table S8 and S9).

Table 2.

Associations between H. pylori infection and metabolic dysfunction–associated steatotic liver disease in the UK Biobank

Hazard Ratio (95% confidence intervals)
Cases/N Model 1 a Model 2 b Model 3 c
H. pylori infection
 H. pylori (−) 15/2754 1 (reference) 1 (reference) 1 (reference)
 H. pylori (+) d 17/1492 1.90 (0.94-3.85) 1.85 (0.91-3.75) 1.88 (0.92-3.82)
CagA positive
 CagA (−) 18/3272 1 (reference) 1 (reference) 1 (reference)
 CagA (+) 14/974 2.38 (1.16-4.85) 2.33 (1.14-4.76) 2.40 (1.17-4.92)
Joint analyses e
 H. pylori (−) CagA (−) 14/2619 1 (reference) 1 (reference) 1 (reference)
 H. pylori (+) CagA (−) 4/653 1.09 (0.36-3.32) 1.07 (0.35-3.26) 1.06 (0.35-3.27)
 H. pylori (+) CagA (+) 13/839 2.59 (1.19-5.61) 2.53 (1.16-5.50) 2.58 (1.19-5.61)
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a

Model 1 adjusted for age, sex, ethnic groups, Townsend deprivation index, smoking status, and physical activity.

b

Model 2 additionally adjusted for alcohol drinking.

c

Model 3 additionally adjusted for metabolic factors including history of diabetes and body mass index.

d

H. pylori seropositivity was defined as any two of CagA, VacA, OMP, GroEL, CatA, and UreA.

e

Primary analysis included 32 MASLD cases among 4,246 participants. Joint analyses excluded those participants with H. pylori (−) and CagA (+), leaving 31 MASLD cases among 4,111 participants.

In our meta-analysis, we identified seven prospective cohort studies that reported associations between H. pylori infection and MASLD risk (Supplementary Table S10) (30–36). Two studies assessed H. pylori using serology, three used the urea breath test, one relied on clinical diagnosis, and one used a rapid urease test. When we pooled our results from the UKB with other seven prospective cohorts including 36,145 participants with 6,979 MASLD cases, H. pylori infection was associated with a higher risk of MASLD (RR, 1.24; 95% CI, 1.17–1.31) with no heterogeneity observed between studies (I2 = 0%) (Figure 1). When analyses were stratified by H. pylori test method, the association was slightly stronger in studies using non-serologic methods (RR, 1.30; 95% CI, 1.19–1.41) than in those using serologic assays (RR, 1.20; 95% CI, 1.11–1.29). Egger’s test (P = 0.23) suggested no significant publication bias. Sensitivity analyses excluding one study at a time yielded similar pooled estimates, indicating that the results were robust (Supplementary Figure S2).

Figure 1.

Figure 1.

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Helicobacter pylori infection and metabolic dysfunction–associated steatotic liver disease risk. Forest plot of study-specific and pooled risk estimates for incident metabolic dysfunction–associated steatotic liver disease comparing participants with vs without H. pylori infection. Zhao (2026) denotes the current UK Biobank analysis. The pooled estimate excluding Zhao (2026) includes previously published studies only; the overall pooled estimate includes all studies, including the current analysis.

Discussion

Based on a prospective cohort in the UK and a meta-analysis of prospective studies, we found that H. pylori infection was positively associated with risk of MASLD. No obvious heterogeneity was observed in the meta-analysis. Additionally, CagA seropositivity was associated with a higher risk of MASLD.

In general, our findings are comparable with previous studies. A recently published meta-analysis of 24 cross-sectional and 4 longitudinal studies (also included here) reported that H. pylori infection was significantly associated with increased risk of MASLD (cross-sectional studies: odds ratio, 1.11; 95% CI, 1.05–1.18; longitudinal studies: RR, 1.20; 95% CI, 1.08–1.33) (37). However, these studies involved predominantly individuals of Asian ethnicity. To our knowledge, our study is among the first to evaluate the association between H. pylori seropositivity and MASLD in a Western population. This is supported by evidence from animal models showing H. pylori may promote liver steatosis through transforming growth factor-beta1 (38). Additionally, a previous study indicated that H. pylori infection was significantly related to systemic insulin resistance and liver dysfunction (39), which may contribute to the steatosis process. Interestingly, the slightly stronger association observed for CagA positive H. pylori infection supports the notion that CagA seropositivity may serve as a marker of infection with more virulent H. pylori strains, which could plausibly relate to MASLD through inflammatory and metabolic dysregulation pathways. CagA-positive H. pylori infections are significantly more likely to cause severe gastric diseases, including gastric adenocarcinoma, compared to CagA-negative (40). However, evidence for liver disease is rare. The CagA protein could be either a proxy for strain virulence or a key virulence factor that promotes MASLD risk primarily by driving systemic, low-grade inflammation throughout the body (41). This inflammation contributes to insulin resistance and directly disrupts the liver’s ability to regulate lipid metabolism, leading to excessive fat accumulation and liver injury. Our finding contrasts with a NHANES study that reported a paradoxical association, in which the CagA-negative H. pylori strain showed a significantly higher odds ratio for MASLD in a cross-sectional analysis (42). Because H. pylori status and MASLD were measured at the same time in the NHANES study, the possibility of reverse causation cannot be ruled out. Therefore, future studies in other larger prospective studies are needed to verify our findings.

H. pylori infection may promote the development of MASLD, possibly through the following mechanisms. First, H. pylori infection accelerates hepatic inflammation and fibrosis by increasing proinflammatory cytokines such as C-reactive protein, interleukin 6 and tumor necrosis factor-α (43). For example, H. pylori CagA positive infection can markedly aggravate hepatic steatosis and elevate serum inflammatory factors in mouse models (44). Second, H. pylori infection may directly or indirectly play a role in substrate overload lipotoxicity and further increase the oxidative stress (45), one of the steps contributing to the pathogenesis of MASLD. Third, H. pylori is a risk factor for insulin resistance (46), which has been involved in the development of metabolic syndrome and MASLD. Converging evidence indicates that H. pylori perturbs hepatic insulin signaling, amplifies N-terminal kinase-dependent inflammation, induces mitochondrial reactive oxygen species injury, creating a metabolic milieu favoring MASLD (47). Fourth, H. pylori may contribute to gut dysfunction by disrupting the gut microbiome and impairing the integrity of the intestinal barrier, further leading to the translocation of toxins and bacteria from the gut to the liver (48).

Our study combined evidence from a well-characterized prospective cohort and a pre-registered meta-analysis of prospective studies. We evaluated joint effect of H. pylori and CagA to indicate biological possibility. Our meta-analysis synthesized large, independent cohorts, enabling assessment of consistency and precision and reinforcing the cohort findings with broader external validity. However, several limitations should be noted. The UKB has a limited sample for incident MASLD. The possibility of chance findings cannot be ruled out. To address this, we present both minimally adjusted and fully adjusted models that show the robustness of the associations. Additionally, the results from the meta-analysis of prospective studies confirm our findings from the UKB cohort. Moreover, H. pylori infection was measured once using IgG serology, limiting our ability to distinguish current from past infection or infer infection timing or account for eradication during follow-up; CagA serves only as a proxy for strain virulence rather than directly the antigen. In addition, detailed eradication history (e.g., treatment records or reliable antibiotic-use proxies) was not available in this dataset. Measurement methods for H. pylori varied across included cohorts, although our meta-analysis of seven additional prospective studies showed consistent results with little heterogeneity. H. pylori seropositivity (IgG) may capture broader microbial exposures and could act as a marker for correlated microorganisms or co-infections. We cannot exclude the possibility that other Helicobacter species or related microbes (including those reported in hepatobiliary tissues) contribute to the observed associations, and species-level microbial assessment will be needed to clarify causal mechanisms. Furthermore, MASLD ascertainment from hospital records likely captures more advanced diseases and may miss mild or asymptomatic cases, introducing nondifferential misclassification. Thus, ICD-based “incident MASLD” may reflect first clinical detection rather than true disease onset. Because our analyses were restricted to participants with available serology and, for the imaging analysis, those with MRI-PDFF data, findings may not be fully representative of the entire UKB. The MRI-PDFF analysis evaluates hepatic steatosis in a smaller, selected subset and likely captures a subclinical phenotype compared with clinically recognized MASLD. Accordingly, differences in event rates and attenuated or nonsignificant associations in the MRI subset may reflect selection, limited sample size, and phenotype differences rather than inconsistency in the underlying association. Although exposure preceded outcome, reverse causation cannot be entirely excluded, as subclinical MASLD could potentially influence susceptibility to H. pylori infection or antibody response (49). The prospective design and temporal sequence of serology measured prior to MASLD diagnosis help mitigate but do not eliminate this concern. Residual confounding remains possible despite adjustment, e.g., from diet, alcohol, BMI change, and medications such as antibiotics and statins. Finally, the volunteer study population of UKB participants (ages 40–69, predominantly European ancestry) and the subsample with H. pylori data may limit its generalizability.

Our findings provide epidemiologic evidence that H. pylori infection, particularly CagA-positive infection, may contribute to MASLD. Given the high global prevalence of H. pylori infection and the growing burden of MASLD, even modest associations could translate into substantial population-level impact. If confirmed, these results suggest that identifying and treating H. pylori infection, long recognized for its role in gastric diseases, might also offer novel benefits for liver disease prevention. Integrating infection control into metabolic health strategies could therefore yield broad public health benefits.

Conclusion

In conclusion, we observed that H. pylori seropositivity was positively associated with MASLD in a Western cohort and in a meta-analysis of eight prospective studies. However, given the limited number of incident MASLD cases in the cohort, these findings should be interpreted cautiously. Further studies in larger and more diverse populations are warranted to elucidate underlying mechanisms and clarify the role of H. pylori in MASLD development.

Supplementary Material

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Acknowledgements:

UK Biobank Resource: This research has been conducted using the UK Biobank Resource under Application Number 87303. NHS England: Copyright© (2023), NHS England. Re-used with the permission of the NHS England and UK Biobank. All rights reserved. “This work uses data provided by patients and collected by the NHS as part of their care and support.” Public Health Scotland: This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation.

Funding:

Xuehong Zhang is supported by NIH/NCI R37 CA262299, American Cancer Society Research Scholar Grant (RSG NEC-130476) and American Cancer Society Interdisciplinary Team Award (PASD-22-1003396).

Disclaimer:

This research was supported [in part] by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

Abbreviations:

BMI

body mass index

CagA

cytotoxin-associated gene A; 95% CI, confidence interval

H. pylori

Helicobacter pylori

HR

hazard ratio

MASLD

metabolic dysfunction-associated steatotic liver disease

RR

risk ratio

UKB

UK biobank

Footnotes

Conflicting interests: The authors declare no potential conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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NIHMS2185909-supplement-1.docx (104.3KB, docx)
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NIHMS2185909-supplement-2.docx (83.2KB, docx)
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NIHMS2185909-supplement-3.docx (25.9KB, docx)
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NIHMS2185909-supplement-6.docx (26.5KB, docx)
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NIHMS2185909-supplement-9.docx (27.5KB, docx)
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NIHMS2185909-supplement-11.docx (26.5KB, docx)
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NIHMS2185909-supplement-12.docx (29.6KB, docx)

Data Availability Statement

For the UKB, this research was conducted under Application Number 87303. The UKB data are available to bona fide researchers upon application to the UKB (www.ukbiobank.ac.uk), subject to registration, approval, and payment of an access fee. The authors are not permitted to publicly share the raw data used in this study. The code used to generate the results of this article is available from the corresponding author upon request.

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