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World Journal of Emergency Medicine logoLink to World Journal of Emergency Medicine
. 2026 Sep 1;17(5):470–477. doi: 10.5847/wjem.j.1920-8642.2026.090

Multilevel genome-wide analysis reveals shared genetic architecture between acute pancreatitis and body fat distribution

Zheng Tan 1, Zhirui Rong 2, Xiaotong Han 1,✉, Tong Wang 3,✉, Huibao Long 3, Xiang Li 1, Fang Yu 1, Wei Zhou 1
PMCID: PMC13616883  PMID: 42807309

Abstract

BACKGROUND Acute pancreatitis (AP) is a heterogeneous inflammatory disease, and its risk and severity may be shaped more by body fat distribution than by overall obesity. However, the shared genetic basis between AP and depot-specific fat accumulation remains unclear.

METHODS We meta-analyzed AP genome-wide association study (GWAS) summary statistics from FinnGen, the Million Veteran Program, and the UK Biobank, including 18,045 cases and 1,285,319 controls. Four magnetic resonance imaging (MRI)-derived fat distribution traits, namely intrapancreatic fat deposition (IPFD), liver fat (LF), visceral adipose tissue (VAT), and abdominal subcutaneous adipose tissue (ASAT), were examined. We estimated genetic correlations using linkage disequilibrium score regression (LDSC) and high definition likelihood (HDL); identified pleiotropic loci using cross-phenotype association analysis (CPASSOC) and functional mapping and annotation (FUMA); tested colocalization; and assessed gene-level, pathway, and cell-type signals using multi-marker analysis of genomic annotation (MAGMA), Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), and web-based cell-type-specific enrichment analysis (WebCSEA). Two-sample and multivariable Mendelian randomization (MR) analyses were conducted to evaluate causal associations.

RESULTS The AP meta-analysis identified nine loci of genome-wide significance, including four previously unreported regions. AP showed positive genetic correlations with all four fat traits, particularly with VAT and ASAT. CPASSOC identified 50 pleiotropic risk loci across 26 independent regions, with recurrent signals at 2p21, 5q32, 7q34, and 16q23.1. MAGMA prioritized 63 unique pleiotropic genes, including ABCG5/ABCG8, SPINK1, PRSS1, CTRB1, TM4SF4, and ZFYVE21. Enrichment analyses highlighted digestion; lipid metabolism; xenobiotic processing; inflammation; and pancreatic, intestinal, and hepatic cell contexts. MR supported independent effects of IPFD and VAT on AP risk.

CONCLUSION This multilevel genome-wide analysis provides evidence of the shared genetic architecture between AP and depot-specific fat distribution and highlights the pancreas-intestine-liver regulatory axis as a relevant biological context.

Keywords: Acute pancreatitis, Fat distribution, Genome-wide association study, Pleiotropy, Genetic correlation, Cross-phenotype association analysis

INTRODUCTION

Acute pancreatitis (AP) is a common gastrointestinal emergency that ranges from mild self-limiting disease to organ failure and death.[1,2] In addition to gallstones and alcohol consumption, obesity and abnormal fat distribution are increasingly recognized as determinants of AP susceptibility and prognosis. Unlike body mass index (BMI), imaging-derived depots such as abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT), liver fat (LF), and intrapancreatic fat deposition (IPFD) capture biologically relevant adiposity patterns. Clinical studies have linked visceral adiposity, abdominal fat, hepatic steatosis, and pancreatic fat to severe AP, organ failure, necrosis, prolonged hospitalization, and mortality.[3-5]

Body fat distribution is heritable; depot specific; and closely related to insulin resistance, dyslipidemia, and cardiovascular disease.[6] Magnetic resonance imaging (MRI)-based genome-wide association study (GWAS) of UK Biobank data has enabled the precise quantification of LF, IPFD, VAT, and ASAT and revealed distinct genetic architectures across depots.[7] While analyses of shared genetic architecture can identify genetic overlap between fat distribution and AP, causal inference methods provide complementary evidence regarding whether genetically predicted adiposity may influence AP susceptibility. In this context, a MR study suggests that abdominal obesity may be more relevant to AP than general obesity is.[8] However, the shared loci, genes, and pathways linking MRI-derived fat depots with AP remain poorly defined.

We integrated large-scale European-ancestry GWAS summary statistics for AP and four fat distribution phenotypes—IPFD, LF, VAT, and ASAT. Using linkage disequilibrium score regression (LDSC)/high-definition likelihood (HDL), cross-phenotype association analysis (CPASSOC), functional mapping and annotation (FUMA)/multi-marker analysis of genomic annotation (MAGMA), Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), web-based cell-type-specific enrichment analysis (WebCSEA), and Mendelian randomization (MR),[9-13] we characterized genome-wide correlations, pleiotropic loci, genes, pathways, cell contexts, and potential causal associations underlying the relationship between AP and fat distribution (Figure 1).

Figure 1. Analytical workflow for identifying the genetic architecture shared between acute pancreatitis and body fat distribution phenotypes. LDSC: linkage disequilibrium score regression; CPASSOC: cross-phenotype association analysis; FUMA: functional mapping and annotation; MAGMA: multi-marker analysis of genomic annotation; HDL: high-definition likelihood; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; WebCSEA: web-based cell-type-specific enrichment analysis; Coloc.abf: coloc-approximate bayes factor; TSMR: two-sample mendelian randomization; MVP: Million Veteran Program; MVMR: multivariable Mendelian randomization.

Figure 1.

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METHODS

Sources of genome-wide association study summary statistics

AP GWAS summary statistics were obtained from FinnGen, the Million Veteran Program (MVP), and the UK Biobank.[14-16] In all three cohorts, AP cases were identified based on the International Classification of Diseases, 10th Revision (ICD-10) code K85. The fixed-effects meta-analysis combined 18,045 cases and 1,285,319 controls (FinnGen: 8,446/437,418; MVP: 7,613/439,644; UK Biobank: 1,986/408,257), increasing its power to discover AP risk loci.

Fat distribution GWAS summary statistics were obtained from an abdominal MRI study by Liu et al[17] in ~30,000 European-ancestry UK Biobank participants. Deep-learning models quantified VAT and ASAT as adipose-tissue volumes (L), whereas LF and IPFD represented MRI-derived fat content (%). The results of the GWAS analyses were adjusted for age, sex, BMI, imaging center, and technical covariates. Details regarding quality control for the GWAS are provided in supplementary file 1.

Genome-wide genetic correlation analysis of AP and body-fat distribution traits

Genome-wide genetic correlations between AP and each fat distribution trait were estimated using LDSC restricted to HapMap3 single-nucleotide polymorphisms (SNPs)and European LD scores.[9] LDSC regresses SNP-level χ2 statistics on LD scores to estimate genetic correlation, while the intercept assesses potential confounding from sample overlap or population stratification. HDL was additionally applied as a cross-method consistency and sensitivity analysis because it uses a distinct likelihood-based framework and a high-dimensional LD covariance matrix to estimate genetic correlation.[10] The Benjamini-Hochberg false discovery rate (FDR) procedure was used to correct for multiple comparisons, with an FDR<0.05 considered to indicate statistical significance.

Cross-trait CPASSOC analysis to identify pleiotropic risk loci

Cross-trait pleiotropic variants shared between AP and each fat distribution phenotype were identified using CPASSOC.[11] Because IPFD, LF, VAT, and ASAT represent biologically related but anatomically distinct fat depots, pairwise analyses were performed to identify depot-specific pleiotropic signals shared with AP. Given the biological heterogeneity between AP and fat traits, the heterogeneity-robust S_Het statistic was used as the primary test, with genome-wide significance defined as P_SHet < 5.0×10-8. Significant SNPs were annotated and mapped to pleiotropic loci using FUMA and the 1000 Genomes Phase 3 European reference panel.[12] A more detailed methodological description is provided in supplementary file 1.

Bayesian colocalization analysis

Bayesian colocalization analysis was performed to test whether the CPASSOC-identified loci shared between AP and fat traits were driven by the same causal variant.[18,19] The analyses used ±250 kb windows with priors p1=1×10-4, p2=1×10-4, and p12=1×10-5. PP.H4≥0.80 was considered strong evidence of a shared causal signal. A more detailed methodological description is provided in supplementary file 1.

MAGMA-based identification of cross-trait pleiotropic genes and their stratified enrichment across biological pathways and cell types

Gene-level MAGMA was performed using 1000 Genomes Phase 3 European LD and GENCODE GRCh37/hg19 annotations with ±10 kb gene windows.[13] Gene statistics were calculated by integrating SNP-level association signals while accounting for gene size, SNP density, and local LD. Significant genes from each AP-fat distribution pair were subsequently examined by GO and KEGG enrichment analysis using all MAGMA-annotated genes as the background set. Tissue and cell-type specificity was assessed using WebCSEA, which integrates human single-cell transcriptomic references and the deTS framework.[20] The Benjamini-Hochberg FDR procedure was used to correct for multiple comparisons, with an FDR<0.05 considered to indicate statistical significance.

Two-sample and multivariable Mendelian randomization analyses

Two-sample MR was performed with IPFD, LF, VAT, and ASAT as exposures and AP as the outcome.[21] Multivariable MR was used to estimate the direct effects of fat depots after accounting for smoking, alcohol consumption, type 2 diabetes, and LDL cholesterol.[22] Instrument selection, harmonization, sensitivity analyses, and the three core assumptions of MR are described in supplementary file 1.

RESULTS

Genome-wide meta-analysis of AP reveals novel genetic susceptibility loci

We meta-analyzed AP GWAS summary statistics from FinnGen, MVP, and the UK Biobank, including 18,045 cases and 1,285,319 controls (Figure 2). Genomic inflation was modest (λGC=1.099, χ2=1.13). Nine independent loci of genome-wide significance were identified (P< 5×10-8), including four previously unreported regions: 1p36.11 (rs121909293), 3q25.33 (rs11448800), 7q35 (rs4987704), and 9q34.3 (rs10901252). The concordant directions of their effects across cohorts supported their relevance for downstream analyses.

Figure 2. Fixed-effects meta-analysis of acute pancreatitis. Manhattan plot showing genome-wide association results for AP across FinnGen, MVP, and the UK Biobank, including 18,045 cases. The red dashed line indicates genome-wide significance (P=5×10-8), and significant lead variants are labeled by rsID. Insets show cohort-specific case numbers, and the QQ plot with λGC=1.099 indicates limited genomic inflation. AP: acute pancreatitis; GWAS: genome-wide association study; MVP: Million Veteran Program; QQ: quantile-quantile; rsID: reference SNP identifier.

Figure 2.

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Genome-wide genetic correlations between AP and fat distribution phenotypes

LDSC and HDL yielded consistent positive genetic correlation estimates between AP and all four fat distribution traits (supplementary file 1: figure S1). After FDR correction, all four AP-fat distribution associations remained significant, with the strongest LDSC signals observed for VAT (rg=0.50, P=1.12×10-12), ASAT (rg=0.43, P=3.02×10-10), and LF (rg=0.46, P=8.37×10-8), whereas IPFD showed a weaker but still significant association (rg=0.22, P=2.69×10-2). LDSC intercepts were close to 1.00 (1.01-1.04), suggesting limited inflation from sample overlap or population stratification. The HDL sensitivity analysis showed consistent results, with comparable or slightly stronger genetic correlation estimates for IPFD (rg=0.27, P=5.21×10-3), LF (rg=0.47, P=1.14×10-6), ASAT (rg=0.50, P=1.15×10-12), and VAT (rg=0.55, P=2.89×10-16).

Cross-trait pleiotropic architecture of acute pancreatitis and body fat distribution revealed by CPASSOC and bayesian colocalization analysis

Using CPASSOC S_Het with P_SHet < 5.0×10-8, we identified 441-1,200 pleiotropic SNVs across the four AP-fat distribution pairs (supplementary file 2: Table S1). ANNOVAR revealed that most variants were intronic or intergenic, with enrichment in exonic, upstream regulatory, and ncRNA exonic regions.

FUMA annotation and LD clumping defined 50 pleiotropic risk loci across 26 independent regions (Figure 3 and supplementary file 2: Table S2). The 2p21, 2q37.3, 5q32, 9q34.2, and 16q23.1 regions recurred across all four pairs, indicating core shared genetic hubs (supplementary file 2: Table S3). These recurrent loci included both established pancreatic protease regions and lipid/metabolic loci, supporting convergence between exocrine pancreatic biology and adiposity-related pathways. Bayesian colocalization analysis identified three loci with strong shared causal evidence (PP.H4 ≥ 0.80).

Figure 3. Cross-trait pleiotropic architecture between acute pancreatitis and fat distribution phenotypes. Radial dendrogram showing pleiotropic genes and genomic loci shared across the four AP-fat distribution trait pairs. Colocalized loci are marked in orange, and MAGMA-prioritized genes are shown in blue. IPFD: intrapancreatic fat deposition; LF: liver fat; VAT: visceral adipose tissue; ASAT: abdominal subcutaneous adipose tissue; AP: acute pancreatitis.

Figure 3.

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Shared pleiotropic genes linking AP and fat distribution identified by cross-trait MAGMA

MAGMA identified 25-33 significant pleiotropic genes (FDR<0.05) for the AP-ASAT, AP-LF, AP-IPFD, and AP-VAT pairs. Overall, the analysis identified 115 gene associations representing 63 unique genes (supplementary file 2: Table S4). CTRB1, DPYD, PPP1R13B, PRSS1, SPINK1, TM4SF4, TRAF3, and ZFYVE21 were detected across all four pairs, which suggests that they are core pleiotropic hub genes linking AP susceptibility with fat distribution.

Cross-trait enrichment analyses

The MAGMA enrichment study revealed consistent biological patterns across the four AP-fat distribution pairs, involving digestion, lipid metabolism, biotransformation, and inflammation (FDR<0.05; supplementary file 2: Table S5). Trait-specific signals included digestion, IL-17 signaling, and bacterial epithelial invasion for AP-VAT; glucuronidation, retinoic acid metabolism, and detoxification for AP-ASAT; triglyceride/lipid homeostasis and steroid metabolism for AP-LF; and xenobiotic, porphyrin, and cytochrome P450 metabolism for AP-IPFD.

WebCSEA further identified enrichment in digestive-system cell types (supplementary file 1: Figure S2). Shared genes were preferentially represented in pancreatic acinar and ductal cells, intestinal epithelial or absorptive cells, and hepatocytes, highlighting pancreas-intestine-liver cellular contexts potentially relevant to the observed genetic overlap. These enrichment signals provide transcriptomic context for the pleiotropic loci but do not establish direct cellular mechanisms.

Potential causal associations of intrapancreatic and visceral fat on the risk of AP

After instrument filtering, 56 IVs with F-statistics > 10 were retained (supplementary file 2: Table S6). On univariable MR, IPFD was positively associated with AP risk (β=0.181, P=0.011), and VAT showed a stronger association than IPFD (β=0.195, P=1.32×10-4). ASAT and LF were not significantly associated with AP risk (supplementary file 2: Table S7). MR-Egger intercepts and heterogeneity tests supported the robustness of the IPFD and VAT findings (supplementary file 2: Table S8).

In multivariable MR adjusted for smoking, alcohol consumption, T2DM, and LDL, IPFD (β=0.109, SE=0.021, P=0.042) and VAT (β=0.151, SE=0.047, P=0.001) remained associated with increased AP risk (supplementary file 2: Table S9). Smoking was also consistently associated with AP. These findings suggest that genetically predicted intrapancreatic fat and visceral adiposity are associated with increased AP risk after accounteding for potential confounders (supplementary file 2: Table S9).

DISCUSSION

This study systematically characterized the shared genetic architecture between AP and four fat distribution phenotypes at the variant, gene, pathway, cell-type, and causal-inference levels. AP showed positive but heterogeneous genetic correlations with these traits, with strongest correlations for central and visceral fat. Cross-trait analyses identified convergent pleiotropic loci, including 2p21, 5q32, 7q34, and 16q23.1, that linked AP susceptibility with fat distribution patterns and systemic metabolic status. Two-sample MR supported the directional effects of IPFD and VAT on AP, while multivariable MR indicated that these signals were not fully explained by smoking, alcohol consumption, type-2 diabetes mellitus (T2DM), or low-density lipoprotein (LDL). Thus, the integrated results support a model in which depot-specific fat-related genetic variation is associated with AP susceptibility through pathways involving local pancreatic effects and systemic metabolic or inflammatory processes.

These findings extend previous AP studies focused on BMI or single adiposity indicators.[23] By integrating anatomically distinct IPFD, LF, VAT, and ASAT, the present study suggests that AP risk reflects depot-specific fat accumulation and redistribution rather than overall adiposity alone. The positive genetic correlation and MR evidence for IPFD are consistent with the Pandora hypothesis, which links pancreatic fat deposition with pancreatitis, diabetes, and pancreatic cancer.[24] Stronger genome-wide correlations for VAT, ASAT, and LF further place AP vulnerability within a broader adiposity network involving visceral expansion, hepatic lipid accumulation, insulin resistance, and systemic metabolic remodeling, which is consistent with clinical evidence that adverse fat distribution increases AP risk even at a modest BMI.[25]

Several shared loci mapped to plausible AP-related pathways. The 2p21 ABCG5/ABCG8 region encodes sterol transporters involved in cholesterol and plant sterol efflux.[26-27] Variation at this locus has been associated with lipid profiles, gallstone disease, and cholesterol-related liver disease,[28] suggesting that this region may capture a biliary-related component of AP susceptibility within a broader lipid-metabolic context. The 5q32 SPINK1 and 7q34 PRSS1 loci define a key exocrine-pancreatic axis: dysregulated PRSS1 promotes intrapancreatic trypsin activation,[29] whereas SPINK1 limits premature intra-acinar trypsin activation and is strongly associated with recurrent and progressive pancreatitis.[30] Although these loci were also detected in the cross-trait analyses with fat distribution phenotypes, the present findings do not establish PRSS1 or SPINK1 as a direct regulator of adipose distribution. Rather, their recurrence may reflect overlap between pancreatic susceptibility pathways and the broader genetic background captured by fat-related traits. These genes should therefore be distinguished from loci with more direct roles in lipid metabolism or adipose distribution.[31-32] The 16q23.1 CTRB1/CTRB2 locus regulates trypsinogen and trypsin degradation and has been implicated in alcoholic and nonalcoholic pancreatitis.[33-34] Proteomic MR evidence linking CTRB2 to obesity-related traits and pancreatic disease further supports a possible role for protease regulation in the relationship between adiposity burden and inherited AP risk.[35] Collectively, these loci should be interpreted not as reflecting a single etiological mechanism but rather as evidence for genetic convergence across biliary, pancreatic enzyme-regulatory, and metabolic-inflammatory pathways in AP susceptibility.

Beyond protease loci, MAGMA identified TM4SF4 and ZFYVE21 as recurrent pleiotropic genes. TM4SF4 is involved in cell adhesion, migration, pancreatic endocrine differentiation, and tissue repair, whereas ZFYVE21 regulates focal adhesion turnover, endosomal signaling, cell motility, and NF-κB/NLRP3-related inflammatory pathways.[36-37] These functions align with extracellular matrix remodeling, immune cell infiltration, and macrophage-driven inflammation during adipose expansion,[38] suggesting that the association between AP and fat may also involve tissue remodeling and inflammatory microenvironment regulation, although direct functional validation remains limited.

WebCSEA cell-type enrichment supported a pancreas-intestine-liver axis, with pleiotropic genes enriched in pancreatic acinar and ductal cells, intestinal epithelial cells, and hepatocytes. These signals correspond to enzyme synthesis and secretion involving PRSS1, SPINK1, and CTRB1;[39] lipid absorption and bile acid handling involving ABCG5/ABCG8 and DPYD;[40] and hepatic lipid metabolism, bile synthesis, and inflammatory regulation. Shared genetic effects may therefore act through acinar injury susceptibility, intestinal-hepatic lipid and bile metabolism, and epithelial barrier homeostasis.

Clinically, these findings provide a framework for future AP risk stratification rather than an immediately applicable prediction tool. Automated CT/MRI-derived fat measures could be integrated with genetic and clinical factors, although prospective studies are needed to determine whether such models improve prediction beyond established tools such as Bedside Index of Severity in Acute Pancreatitis (BISAP), Acute Physiology and Chronic Health Evaluation II (APACHE II), or Computed Tomography Severity Index (CTSI).

Several limitations should be acknowledged. First, the GWAS datasets were from populations with predominantly European ancestry, limiting their generalizability to other demographics. Second, AP could not be stratified into biliary, alcoholic, hypertriglyceridemic, idiopathic, severe, or necrotizing subtypes. Third, imaging-derived fat traits were largely measured in older populations, raising the possibility of survivor bias and limiting inferences about dynamic fat redistribution during acute disease. Fourth, the summary-level data precluded individual-level adjustment for alcohol, smoking, medication use, and comorbidities, as well as temporal inference; therefore, the findings should be interpreted as shared genetic architecture rather than definitive causality. Finally, prioritization with FUMA, MAGMA, and WebCSEA depends on reference resources; MAGMA ±10 kb mapping may assign variants to multiple neighboring genes, introducing redundancy. Functional validation using expression quantitative trait locus (eQTL), single-cell, spatial transcriptomic, Clustered regularly interspaced short palindromic repeat (CRISPR), organoid, or animal-model methods is therefore warranted.

CONCLUSION

In this multilevel genome-wide analysis, we identified a shared genetic architecture between AP and MRI-derived fat distribution traits. These findings support shared biological pathways involving pancreatic exocrine function, lipid and bile metabolism, epithelial homeostasis, and inflammatory regulation, with IPFD and VAT showing the strongest potential relevance to AP susceptibility.

Funding: The study was supported by Class C Medical Discipline Construction Project of Hunan Province (Department of Emergency Medicine), 2025 (20260106-1006) and Sanming Project of Medicine in Shenzhen (SZSM202402020).

Ethical approval: Not needed.

Conflicts of interest: All authors declare no potential conflicts of interest.

Contributors: All authors contributed to the study conception and design. ZT and ZRR contributed to data generation and/or collection. ZT and HBL contributed to data analysis. ZT contributed to drafting of manuscript. All authors contributed to editing and approval of manuscript.

All the supplementary files in this paper are available at http://wjem.com.cn.

Contributor Information

Xiaotong Han, Email: 744953695@qq.com.

Tong Wang, Email: tongwang316@163.com.

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