Genetically proxied risk and protective factors for pancreatic cancer: a systematic review and meta-analysis of Mendelian randomization studies

Introduction

Over the past few decades, both the incidence and mortality of pancreatic cancer have significantly increased, establishing it as a prominent cause of cancer-related death (1). Pancreatic ductal adenocarcinoma (PDAC), frequently diagnosed at advanced stages, primarily relies on surgical intervention for curative treatment, though most patients are not surgical candidates at diagnosis. Despite extensive investigation to improve outcomes, the 5-year survival rate remains poor, ranging between a mere 3% and 15% (2). To address this critical issue, there is an urgent need for the development of screening programs aimed at earlier detection and more effective preventive strategies. A comprehensive understanding of the potential risk and protective factors associated with PDAC is crucial (1-3).

Multiple prospective studies have consistently demonstrated that various factors can either elevate or diminish the risk of pancreatic cancer. These include lifestyle factors such as maintaining a healthy body mass index (BMI) and waist circumference (WC); engaging in physical activity, consuming fruits, vegetables, and whole grains; reducing alcohol intake frequency; and avoiding smoking. Other factors shown to have an influence on pancreatic cancer risk include chronic inflammation markers such as C-reactive protein (CRP), interleukin-6, and tumor necrosis factor; a plant-based diet; specific oral microbial profiles; a non-O blood type; and high level of glycosylated hemoglobin level (4-8). However, the causal relationship between these factors and pancreatic cancer remains uncertain and requires further validation.

In recent years, Mendelian randomization (MR) has gained widespread acceptance as a powerful tool for addressing biases, confounding, and reverse causations often encountered in observational studies. MR uses modifiable exposures or risk factors as instrumental variables (IVs) to assess the causality of observed associations with clinically relevant outcomes. Additionally, as a relatively cost-effective and time-efficient method, its popularity continues to grow (9,10). The combined use of genome-wide association studies (GWAS) and MR to elucidate the associations between pancreatic cancer risk and various influencing factors is becoming increasingly common. Nevertheless, individual MR studies face limitations in statistical power and generalizability due to small sample sizes. Furthermore, there is a notable lack of systematic reviews and meta-analyses that specifically examine the relationship between pancreatic cancer and MR studies. This systematic review and meta-analysis of MR studies aims to reconcile discordant findings between observational and causal inference paradigms, and establish evidence hierarchies for targetable prevention factors. To comprehensively characterize and synthesize the evidence regarding pancreatic cancer and its various influencing factors, we conducted a systematic review of 82 published MR studies related to pancreatic cancer and performed a meta-analysis. We present this article in accordance with the PRISMA reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-305/rc).

Methods

Search strategy

This systematic review was duly registered in the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD42024565491). We conducted a comprehensive search for studies published from database inception through June 7, 2024, across five platforms: PubMed, Embase, Web of Science, Scopus, and Ovid. The search terms included “pancreatic neoplasms”, “Mendelian randomization analysis”, and “genome-wide association study”, along with their synonyms and related keywords. No filters were applied to limit the search results, and we manually included any missing documents. The results obtained from the five databases were meticulously combined and organized using Zotero software (The Corporation for Digital Scholarship, Vienna, VA, USA) on a repeated basis. The search strategies for each database, including any filters used, are detailed in table available at https://cdn.amegroups.cn/static/public/jgo-2025-305-1.xlsx.

The inclusion criteria for the literature were as follows: (I) studies that employed MR to investigate pancreatic cancer and causal relationships between various phenotypes and associated influences; (II) genome-wide association studies (GWAS) or prospective studies that incorporated MR as part of their analysis; (III) studies that included multiple outcome variables, with pancreatic cancer being one of them; and (IV) studies with no restrictions on age, gender, cohort, or race. Meanwhile, the exclusion criteria were as follows: (I) studies involving nonhuman subjects or that included nonhuman participants; (II) studies that used MR but were unable to provide the necessary complete data or obtain a full manuscript; and (III) reviews, case reports, letters, conference abstracts, evaluations, and comments that lacked original data.

MR

MR is a genetic epidemiological method that uses genetic variants as instrumental variables to infer causal relationships between modifiable exposures and health outcomes. By leveraging the natural randomization of genetic alleles at conception, MR minimizes confounding bias and reverse causation. The method requires three core assumptions: strong association between the genetic variant and exposure, absence of confounding factors, and exclusive influence of the variant on the outcome through the exposure pathway. While MR reduces confounding, it provides evidence consistent with—but not definitive proof of—causality, contingent on instrumental variable assumptions.

Data extraction and quality assessment

Data extraction was conducted from 82 included studies, as detailed in tables available at https://cdn.amegroups.cn/static/public/jgo-2025-305-1.xlsx, https://cdn.amegroups.cn/static/public/jgo-2025-305-2.xlsx. Furthermore, we employed a quality assessment scale derived from previous research and stratified the quality assessment scores into <75%, 75–85%, and >85%, which corresponded to high, medium, and low risk of bias, respectively, as elaborated in table available at https://cdn.amegroups.cn/static/public/jgo-2025-305-3.xlsx (11). Researchers (F.L. and Y.Z.) independently applied the quality assessment scale to the 82 studies, adhering to the established guidelines. In cases of disagreement, a third researcher, L.Z., was available to provide a definitive opinion and resolve any discrepancies.

The extracted data encompassed the first author’s name, publication date, data source, type of MR study, ethnicity, exposure factors, number of cases, noncases, and total sample size. Additionally, we recorded the study findings, including relative risk estimates [odds ratio (OR)] associated with sensitivity analyses using the inverse variance weighting (IVW) method as the primary approach, the weighted median (WME) method, and the MR Egger (MRE) method, along with their corresponding 95% confidence intervals (CIs). If a study used MR of multivariate residuals and outliers (ME-PRESSO), we also collected the P values obtained from this analysis. Three researchers (W.M., F.L., and Y.Z.) independently managed the data extraction process. To ensure the accuracy and integrity of the data, another researcher, L.Z., rigorously conducted quality control measures and cross-validated the data.

Among the factors investigated, body size was assessed through comprehensive anthropometric indices including BMI, fat mass index (FMI), fat-free mass index (FFMI), and height, with study-defined thresholds applied to classify obesity status. Fasting insulin levels were measured in plasma/serum samples following ≥8-hour fasting periods using validated immunoassays (e.g., ELISA, CLIA), with results reported in both pmol/L and mmol/L to accommodate international variability in laboratory reporting standards. Microbiota in included studies were classified using 16S rRNA gene sequencing or shotgun metagenomics, with taxonomic assignments (phylum/genus/species) annotated via reference databases (Greengenes, SILVA, HMP). Dysbiosis was defined by shifts in microbial diversity or specific taxa abundance, exemplified by reduced Lactobacillus or elevated Proteobacteria, and sometimes stratified into enterotypes through multivariate analyses (e.g., PCA). Serum cathepsin B levels were quantified and no study employed binary categorization of cathepsin B presence/absence.

Statistical analysis

We defined the primary outcome as the OR with the accompanying 95% CI, obtained through the application of the IVW method. This method allowed us to establish correlations between various influencing factors and the incidence of pancreatic cancer. Inclusion of an influencing factor in the meta-analysis required that its causal relationship with pancreatic cancer was evaluated by at least two independent studies.

For the conduct of our meta-analysis, we used the “Meta” software package within the R programming environment (version 4.3.3; The R Foundation for Statistical Computing), complemented by R Studio (version 2024.04.2+764). In determining the appropriate statistical model, we relied on the I² statistic. Specifically, we employed a fixed-effects model when the I² value was 50% or below, which indicated low-to-moderate heterogeneity. Conversely, for situations characterized by higher heterogeneity, we opted for a random-effects model.

Results

Literature search, quality assessment, and study characteristics

The flow diagram related to MR studies on pancreatic cancer is provided in Figure 1. We conducted an extensive literature search across five databases, retrieving a total of 3,684 documents. These documents were then imported into Zotero software for meticulous duplicate identification and merging, ultimately yielding 2,366 unique records. Following a thorough review of titles and abstracts, we applied our stringent inclusion and exclusion criteria, reducing the pool to 107 studies. Upon completing an in-depth examination of the full texts of these 107 studies, we selected 82 for inclusion in our analysis. Among these 82 studies, the majority (n=68) were determined to have a low risk of bias, 12 were deemed to have a medium risk, and only 2 carried a high risk of bias, confirming the high overall quality of the papers included (Figure 2; table available at https://cdn.amegroups.cn/static/public/jgo-2025-305-3.xlsx).

Figure 1 PRISMA flow diagram of pancreatic cancer-related Mendelian randomization.

Figure 2 Distribution of bias risk across included studies.

From these 82 studies, we extracted a comprehensive range of data, encompassing both causal and noncausal findings, resulting in 292 independent outcomes. Notably, 82 studies contributed data on exposure factors sourced from UK Biobank (housing genetic and phenotypic data from over 500,000 participants), FinnGen (focusing on genetic mechanisms of diseases in Finnish populations), and Biobank Japan (BBJ, specialized in disease-association analyses within East Asian cohorts). The pancreatic cancer datasets were primarily generated through two landmark international consortia: the Pancreatic Cancer Cohort Consortium (PanScan), which identified multiple susceptibility loci via genome-wide association studies, and PanC4, which integrates multicenter cohorts to advance etiological research on pancreatic neoplasia. European participants were the primary focus, although a minority of trials also involved Asian individuals (12-19).

Upon consolidating all the data, we were able to designate 12 distinct categories: biochemical markers and human metabolites (12,14,15,17,20-27), diseases (excluding pancreatic cancer itself) (12,13,15,16,19,25,28-46), medications (47-52), enzymes and inflammatory markers (53-56), genetic material and amino acids (12,29,57-59), hormone levels (25,26,28,60-64), tobacco and alcohol intake (12,25), lifestyle factors (12,65-67), microorganisms (18,68-73), nutritional intake (12,73-77), BMI (12,21,25,26,28,29,61,78-80), and vitamins (25,81-89).

Meta-analysis of risk factors for pancreatic cancer

In our meta-analysis, we evaluated the causal relationship between various risk factors and pancreatic cancer, ensuring that at least two studies were included for each assessment. A rigorous screening process led to the consideration of 56 studies, which explored a wide array of potential risk factors. These studies examined health indicators and lifestyle factors, including adiponectin (17,25,60), alcohol consumption (12,25), asthma (25,35), bilirubin levels (23,25), blood metabolites (22), BMI (12,21,25,28,29,61,78-80,90), body size (evaluated through anthropometric indices such as BMI, FMI, FFMI, and height) (61,91), cholesterol levels (12,25,26,28), triglycerides (12,20,25,28), fasting blood glucose (15,25,28), fasting insulin (25,28,61), high-density lipoprotein (HDL) (12,20,25,28), low-density lipoprotein (LDL) (12,20,25,28), gut microbiota (68-72), polyunsaturated fatty acid (PUFA) levels (14,77), nonalcoholic fatty liver disease (NAFLD) (43), leisure sedentary behaviors (LSB) (66,92), physical activity (92), lycopene intake (24,82), mitochondrial DNA copy number (MDCN) (59), rheumatoid arthritis (19), smoking (25), diabetes (12,15,16,25,28,30,31), ulcerative colitis (13,34), urate levels (24), vitamin A (24,82), vitamin B (25,88), vitamin C (24,82-84), vitamin D (25,85-87,89), vitamin E (24,81,82), WC (12,26,61,90), waist-to-hip ratio (12,28,61), hip circumference (29,61), and multiple cathepsins including B, E, F, G, H, L2, O, S, and Z (54,56). Additionally, studies investigated height (28,79), coffee consumption (12,75), green tea intake (74), inflammatory bowel disease (IBD) (13), depression (36,39), glutathione peroxidase (GPx) activity (53), malondialdehyde (MDA) levels (53), morning chronotype (65), psoriasis (45), schizophrenia (46,50), sleep duration (67), superoxide dismutase (SOD) activity (53), and total testosterone (TT) levels (62).

The number of studies included for each impact factor ranged from a minimum of 2 to a maximum of 34. Despite a few studies sharing a common authorship, their analyses were deemed meaningful for inclusion in the meta-analysis due to their focus on different single nucleotide polymorphisms (SNPs), ethnicities, cohort directions, and other variables. The relationship between these 56 influencing factors and pancreatic cancer are visually represented in Figures 3,4 and Figures S1-S9. These figures offer a comprehensive overview of the associations and trends observed in our meta-analysis.

Figure 3 Forest plots assessing the causal relationship between risk factors and pancreatic cancer using all OR values obtained via the IVW MR method. The results for BMI (A), diabetes (B), gut microbiota (C), body size (D), fasting insulin (E), hip circumference (F), IBD (G) and lycopene (H). BMI, body mass index; CI, confidence interval; IVW, inverse variance weighting; MR, Mendelian randomization; OR, odds ratio; BMI, body mass index; IBD, inflammatory bowel disease.

Figure 4 Forest plots assessing the causal relationship between cathepsin family and pancreatic cancer using all OR values obtained via the IVW MR method. The results of cathepsin E (A), cathepsin B (B), cathepsin F (C), cathepsin G (D), cathepsin H (E), cathepsin L2 (F), cathepsin O (G), cathepsin S (H), and cathepsin Z (I). CI, confidence interval; IVW, inverse variance weighting; MR, Mendelian randomization; OR, odds ratio.

Influencing factors associated with pancreatic cancer risk

We evaluated risk factors for pancreatic cancer using the IVW method and confirmed the results using WME and MR Egger methods. The risk factors positively correlated with pancreatic cancer included BMI, body size, fasting insulin, gut microbiota, diabetes, hip circumference, and IBD. Under the random effects model, significant associations were found for BMI (OR: 1.23, 95% CI: 1.10–1.36; I²=73%), diabetes (OR: 1.07, 95% CI: 1.02–1.13; I²=70%), and gut microbiota (OR: 1.25, 95% CI: 1.05–1.49; I²=81%) (Figure 3A-3C). Under the fixed-effects model, significant associations were observed for body size (OR: 1.72, 95% CI: 1.48–2.00; I²=0%), fasting insulin (OR: 2.23, 95% CI: 1.61–3.09; I²=38%), hip circumference (OR: 1.34, 95% CI: 1.11–1.61; I²=0%) and IBD (OR: 1.18, 95% CI: 1.04–1.34; I²=0%) (Figure 3D-3G). The results for BMI, body size, and fasting insulin were well confirmed by WME and MR Egger methods (Figures S1,S2). However, for gut microbiota, diabetes, and IBD, there was possible heterogeneity and pleiotropy, as indicated by the WME and MR Egger results (Figures S1,S2).

The meta-analysis with IVW indicated a negative association between lycopene and pancreatic cancer risk (OR: 0.87, 95% CI: 0.77–0.99; I²=0%) (Figure 3H). Additional meta-analyses with the WME and MRE methods for lycopene yielded consistent but slightly different results, with WME producing an OR of 0.87 (95% CI: 0.73–1.03) and MRE producing an OR of 0.70 (95% CI: 0.56–0.89), both with an I² of 0% (Figures S1G,S2F).

A meta-analysis investigating the association between multiple serum cathepsins (B, E, F, G, H, L2, O, S, and Z) and pancreatic cancer risk produced varied results. Specifically, a negative association was observed for cathepsin E (OR: 0.96, 95% CI: 0.94–0.99; I²=0%) (Figure 4A). The results indicated no significant causal link between the other cathepsins and pancreatic cancer risk (Figure 4B-4I).

In addition, the analysis indicated there to be no significant causal link between certain factors listed and pancreatic cancer risk, with ORs and heterogeneity (I²) values varying across the studied factors. Among metabolic factors, adiponectin, fasting blood glucose, cholesterol, HDL, LDL, triglycerides, bilirubin, MDCN, NAFLD, and PUFA yielded ORs close to 1, with heterogeneity values indicating varying levels of consistency across studies (Figure S3A-S3J). Blood metabolites exhibited high heterogeneity (I²=95%) and a nonsignificant association (Figure S3K). Lifestyle factors such as alcohol consumption, LSB, physical activity, WC, and waist-to-hip ratio exhibited no significant correlation (Figure S4A-S4E). Nutritional factors, including vitamins A, B, C, and E, yielded ORs close to 1, with vitamin D producing a slightly higher OR but within a nonsignificant range (Figure S5A-S5G). For behavioral and anthropometric factors, including coffee consumption, green tea intake, morning chronotype, sleep duration, depression, schizophrenia, psoriasis and height, the ORs and I² values were varied and inconsistent (Figure S6A-S6H). Health conditions such as asthma, rheumatoid arthritis, and ulcerative colitis also had ORs near 1 (Figure S7A-S7C). Biochemical factors including GPx activity, MDA levels, SOD activity, and TT similarly had varied ORs and I² values (Figure S8A-S8D). Smoking and urate levels were associated with nonsignificant increases in pancreatic cancer risk (Figure S9A,S9B).

Discussion

Our study involved a comprehensive meta-analysis of 82 published MR studies, with the primary objective of identifying the risk and protective factors associated with pancreatic cancer. Among the 56 factors examined, 8 were positively correlated with increased risk of pancreatic cancer, including BMI, body size, fasting insulin levels, gut microbiota composition, diabetes, hip circumference, and IBD. Conversely, lycopene and cathepsin E emerged as factors negatively associated with pancreatic cancer risk, suggesting their potential protective effect.

The modifiable risk factors of BMI, hip circumference, and body size have been associated with an elevated risk of pancreatic cancer risk across studies. Our research has further confirmed this correlation, although the details of the causal mechanisms remain unclear. Several reasons may account for these associations. Obesity triggers metabolic disruptions in adipose tissue, influencing the secretion of various substances such as adipokines, hormones, growth factors, inflammatory cytokines, and free fatty acids, all of which are implicated as risk factors for cancer incidence and mortality (93,94). Furthermore, the interaction between adipocytes and cancer cells alters the function of adipose tissue, resulting in signaling changes that may foster tumor cell proliferation, invasion, and metastasis (94). In addition, CRP may mediate the relationship between BMI and pancreatic cancer, as indicated by one study in which the causal link disappeared after CRP was controlled for (21). CRP also might function as a confounder, yet it has also been implicated as a correlate of metabolic syndrome-associated inflammation in obesity.

One meta-analysis produced strong evidence indicating that diabetes is both an early manifestation and an etiologic factor of increased pancreatic cancer risk (95). Research in this area has primarily focused on the link between type 2 diabetes mellitus (T2DM) and pancreatic cancer risk, although the degree of correlation observed has varied across different studies. Metabolic disturbances in individuals with T2DM, particularly hyperinsulinemia and dyslipidemia, have been implicated as causal factors linking diabetes to cancer (94). Hyperinsulinemia, characterized by elevated insulin levels, plays a direct role in initiating pancreatic cancer by acting on the insulin receptor in acinar cells, resulting in increased digestive enzyme production and pancreatic inflammation (96). Hyperinsulinemia also activates the insulin/IGF signaling pathway, leading to the activation of PI3K/Akt/mTOR and MAPK signaling cascades, which promote cancer cell growth, survival, motility, and drug resistance (94,97). Additionally, hyperlipidemia results in increased cholesterol and nonesterified fatty acids (NEFAs), which activate oncogenic signaling pathways, facilitate membrane synthesis, and influence adenosine triphosphate (ATP) production, further contributing to the development of cancer (98). Meanwhile, insulin resistance in T2DM leads to increased fasting insulin levels, which has been linked to pancreatic cancer (95). However, further research is needed to clarify the relationship between diabetes and pancreatic cancer.

The gut microbiota, an evolving and integral part of human growth and development, has been associated with multiple diseases, including pancreatic cancer (99,100). In our comprehensive meta-analysis, we treated the gut flora as a synergistic entity and concluded that it is associated with an increased risk of pancreatic cancer. In healthy individuals, the gut microbiome is dominated by Firmicutes and Bacteroidetes, with Actinobacteria, Proteobacteria, and Verrucomicrobia also present (101). However, in patients with pancreatic cancer, there is a decrease in gut microbial diversity and Firmicutes abundance, along with an increase in pathogenic bacteria like Klebsiella pneumoniae and Enterobacteriaceae (101). These findings are consistent with those in malignant pancreatic cancer murine models and indicate that the gut microbiome can be targeted to slow tumor progression (101-103).

Cathepsin B expression in pancreatic tumor specimens has been reported to be associated with adverse clinicopathological features and surgical outcomes (104). Furthermore, silencing cathepsin B impairs autophagic flux and abolishes cathepsin B-mediated glycolysis and cell proliferation in pancreatic cancer (105). Our findings suggest that serum cathepsin B might not be a risk factor for pancreatic cancer. The nonsignificant association in our meta-analysis may be due to various factors, including low-quality or unsuitable studies, unaccounted for heterogeneity, publication/selective bias, or inherent study limitations such as a small sample size. Consequently, additional research is required to clarify the relationship between cathepsin B and the risk of pancreatic cancer. In addition, our study revealed that serum cathepsin E acts as a protective factor against pancreatic cancer. However, the biological roles of cathepsin E expression and activity in pancreatic cancer remain unclear and warrant further investigation (106).

The meta-analysis of cohort studies revealed that patients with IBD face a substantially elevated risk of pancreatic cancer, with an approximate 80% increase in risk (107). In our study, we also found that IBD was associated with an increased risk of pancreatic cancer. IL-6 and IL-18 may play pivotal roles in the pathogenesis of both IBD and PDAC through a shared pathway (108,109). IL-18 is involved in T helper type 1 (Th1)/Th2 responses and immune cell activation, whereas IL-6 plays a role in IBD and pancreatic cancer via the cross-signaling of soluble IL-6R.

Our analysis suggests smoking may not significantly impact pancreatic cancer risk, contradicting prior reports, potentially due to several factors: systematic study design biases (e.g., imprecise smoking measurements, incomplete confounder adjustment, inconsistent diagnostic criteria) suppressing effect estimates; a genuinely weak or nonexistent association, especially if smoking risks are context-dependent or overshadowed by stronger risk factors; limited sample sizes reducing statistical power to detect modest effects; and overly simplified smoking definitions (e.g., binary categorization) or pancreatic cancer subtyping masking subgroup-specific risks.

A previous study has proposed antioxidants as a potential explanation for reduced cancer rates, suggesting that they may prevent cancer by neutralizing free radicals and mitigating oxidative DNA damage (110). Our study included lycopene as an antioxidant, but the findings contradicted the conclusions of several previous studies [both those included in our systematic review (111-113) and others we examined (24,82)], which found lycopene to be ineffective or only mildly effective against cancer. Given the limited number of studies included, these results should be more closely scrutinized, and further research is crucial to validate these findings.

We conducted a systematic evaluation and meta-analysis of MR related to pancreatic cancer, given the increasing number of such studies. However, our study involved certain limitations which should be addressed. The use of UK Biobank, FinnGen, PanScan, and PanC4 databases led to population overlap. Most included studies focused on European populations, limiting generalizability to other ethnicities. Moreover, some studies could have included outdated data that are no longer considered the most comprehensive datasets available. Additionally, detailed data for subgroup analyses were unavailable, and some influencing factors were included with low frequency. Furthermore, the meta-analyses showed significant heterogeneity, and results may be different if derived from larger sample sizes. Differences in judgments between datasets and researchers might have affected final results and causality. For example, the substantial heterogeneity observed in pooled associations likely arises from methodological differences in BMI measurement (self-reported vs. clinical), diabetes diagnostics (HbA1c vs. fasting glucose), microbiota sequencing approaches (16S rRNA vs. metagenomics), and reference database inconsistencies. Residual pleiotropy remains a critical concern, particularly for obesity-related SNPs with documented inflammatory pathways operating independently of BMI. These factors collectively limit the interpretability of pooled estimates and highlight the need for standardized protocols in future studies. Additionally, partial reliance on self-reported data introduces recall bias risk, potentially exaggerating or underestimating exposure effects. Notably, our study focused on MR-compatible risk factors, which inherently excludes critical determinants of pancreatic cancer such as family history, ethnicity, and occupational exposures due to insufficient GWAS data for robust genetic instruments. While chronic pancreatitis represents a modifiable risk factor and BRCA1/2 variants are established hereditary markers, their causal roles remain less examined in MR framework. These collective limitations underscore the need for standardized protocols in future studies, including harmonized phenotypic definitions, ancestrally diverse cohorts, and expanded genetic studies to develop valid proxies for comprehensive risk assessment in pancreatic cancer.

Conclusions

Our study conducted meta-analysis of MR studies to systematically evaluate 12 pancreatic cancer risk factors (selected from 56 candidate exposures). Seven modifiable risk factors were identified: BMI, body size, fasting insulin levels, gut microbiota composition, diabetes mellitus, hip circumference, and IBD. Given the multifactorial complexity and dynamic interplay among these variables, conventional analytical frameworks remain insufficient to definitively establish causal directions. Future investigations utilizing expanded genetic datasets, refined measurement techniques, and advanced analytical methods are warranted to elucidate these complex relationships.

Acknowledgments

We thank Dr. Roland Andersson (Lund University and Skåne University Hospital, Sweden) for the critical comments and valuable advice on this study.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-305/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-305/prf

Funding: This study was supported by funding from the Yunnan Digestive Endoscopy Clinical Medical Center Foundation for Health Commission of Yunnan Province (Nos. 2022LCZXKF-XH02 and ZX2019-01-02), the Yunnan Fundamental Research Projects (No. 202401AT070053), and the Yunnan Province Science and Technology Talents and Platform Plan-Workstation of Professor Liu Liang (No. 202305AF150148).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-305/coif). S.G.K. is the PI of investigator-initiated studies from Mauna Kea Technologies, Paris, France, and TaeWoong Medical, USA, and serves as a consultant for Boston Scientific. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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