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. 2025 Aug 21;300(1):78. doi: 10.1007/s00438-025-02284-0

Genome-wide lipidomic profiling reveals causal lipid species as targets for inguinal hernia risk

Xiaoling Xu 1, Zemingshan Luo 2, Xiaojun Xie 1,✉
PMCID: PMC12370862  PMID: 40839010

Abstract

Inguinal hernia (IH) is a common condition with a substantial health burden and emerging evidence suggests that lipid metabolism-related indicators may contribute to its risk. However, the exact role of specific lipid types in causing IH is still unclear. This study aims to investigate whether any of 179 distinct lipid species have a causal impact on IH risk using causal inference. We applied a two-sample Mendelian randomization (MR) framework, integrating lipidomic genome-wide association studies (GWAS) data from 7,174 Finnish individuals with IH summary statistics from the UK Biobank (16,749 cases and 439,599 controls). Linkage disequilibrium pruning and genome-wide significance (P < 5E-8) were used to choose genetic instruments. Primary causal estimates were derived with inverse-variance weighted (IVW) method, and further supported by weighted median (WM) and robust adjusted profile score (RAPS). We employed sensitivity tests, like Cochran’s Q for heterogeneity, MR-Egger for directional pleiotropy, Radial MR for outlier detection, and leave-one-out analysis to measure the impact of individual variants. Among 179 lipid species, 162 had valid IVs, and 94 met the criteria for causal inference. IVW analysis identified 25 lipid species with nominal significance, 24 of which were supported by WM and RAPS. Sensitivity analyses consistently provided robust evidence supporting a causal relationship between four lipid species and increased IH risk: diacylglycerol (18:1_18:1) (OR = 1.16, P = 0.005), diacylglycerol (18:1_18:2) (OR = 1.12, P = 0.006), phosphatidylinositol (18:0_20:4) (OR = 1.10, P = 1.47E-04), and triacylglycerol (54:6) (OR = 1.21, P = 0.001). Our findings provides genetic molecular evidence that four lipid species are causally linked to an increased IH susceptibility, offering novel insights into lipid-centered interventions for disease prevention and highlighting the importance of metabolic health in hernia pathogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00438-025-02284-0.

Keywords: Inguinal hernia, Lipidomes, Causality, Mendelian randomization, Diacylglycerol

Introduction

Inguinal hernia (IH) is a prevalent gastrointestinal surgical condition with significant clinical and economic implications, often requiring surgical intervention (International 2018). Globally, approximately 20 million IH repair procedures are performed annually (Estridge et al. 2019). It arises from the protrusion of abdominal contents through a weakened inguinal canal and is commonly attributed to structural defects, aging, and male sex (Pilkington et al. 2021). Yet, emerging evidence suggests that metabolic factors, particularly lipid dysregulation, may play a contributory role in its pathogenesis (Burcharth et al. 2013; Howard et al. 2022). Lipids are fundamental components of cell membranes and extracellular matrix; thus, dysregulated lipid homeostasis might impair connective tissue integrity and healing, increasing susceptibility to herniation (Ke et al. 2022; Dietz et al. 2021). Observational studies have linked obesity—a key metabolic disorder—to an increased risk of surgical site infections and postoperative IH development (Ke et al. 2022; Dietz et al. 2021; Robinson et al. 2020). Notably, lipid-related indices appear to serve as more precise diagnostic markers of excessive adiposity (Robinson et al. 2020). Previous studies examining metabolic influences on IH have encountered limitations. Observational analyses of obesity and body composition have yielded inconsistent results, in part because broad measures like body mass index (BMI) do not distinguish fat distribution or metabolic health (Hemberg et al. 2022). For example, visceral adiposity rather than overall BMI appears more closely associated with IH risk (Hemberg et al. 2022). These limitations underscore the need for more precise metabolic markers. Lipid-related indices have been proposed as better measures of adiposity and metabolic risk, but the specific lipid species contributing to IH remain unknown. Mendelian randomization (MR) studies increasingly suggest that traits linked to lipid metabolism like BMI, body fat percentage, and fat distribution play a causal role in IH risk (Li et al. 2024, 2023; Shi et al. 2024; Xv et al. 2024). However, a comprehensive lipidomic analysis to pinpoint individual lipid species involved in IH pathogenesis has not been previously reportedAdvancements in genomic and lipidomic research have significantly improved our understanding of IH pathophysiology. Genome-wide association studies (GWAS) have uncovered genetic loci linked to IH risk, suggesting a possible role for lipid metabolism in its development (Jiang et al. 2021). Furthermore, beyond conventional lipid indicators such as low-density and high-density lipoprotein cholesterol, the latest release of comprehensive lipidomic GWAS datasets provides a unique opportunity to investigate specific lipid species that may be implicated in IH pathogenesis (Ottensmann et al. 2023). By employing genetic variants as instrumental variables (IVs), MR enables more reliable inference of causality between lipid metabolism and IH risk, effectively addressing limitations such as confounding and reverse causation inherent to observational designs (Zheng and Tan 2024; Chen et al. 2024).

To deepen understanding of how lipid metabolism relates to IH, this study applies MR analysis to investigate the causal impact of 179 lipidomic variants on IH development. By integrating large-scale, recently released lipidomic and IH GWAS datasets, we seek to identify specific lipid species that may causally modify IH risk. These findings will enhance our understanding of IH pathophysiology and may inform targeted preventive and therapeutic strategies by modulating lipid metabolism for improved clinical outcomes.

Methods

Study design

An overview of the methodological framework for the two-sample MR analysis is presented in Fig. 1. This study integrates recently published lipidomic GWAS data (179 lipid species) and IH GWAS summary statistics to investigate potential causal relationships and identify lipid species that may modulate IH risk. First, genetic variants serving as IVs were identified from 179 lipidomic GWAS datasets. Following harmonization with IH GWAS data, the selected genetic variants were used in MR analysis under three key assumptions: relevance (strong association with lipid traits), independence (no relation to confounders), and exclusion restriction (no direct effect on IH except through lipids).

Fig. 1.

Fig. 1

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Study design and key findings. A Research flowchart overview and key findings. Initially, 179 lipid species were examined, of which 162 had valid genetic instruments meeting genome-wide significance, linkage disequilibrium pruning, and instrument strength. Lipid traits with fewer than three independent instrumental variables (IVs) were excluded, resulting in 94 lipids eligible for Mendelian randomization (MR) analysis. Using the inverse-variance weighted (IVW) method as the primary estimator, 25 lipid species showed nominal significance, of which 24 were consistently supported by both the weighted median (WM) and robust adjusted profile score (RAPS) methods. These 24 lipid traits were further categorized based on directionality of association with IH (17 with OR > 1 indicating increased risk, and 7 with OR < 1 indicating protective effects). Sensitivity analyses, including Cochran’s Q test for heterogeneity and MR-Egger intercept tests for directional pleiotropy, confirmed the robustness of associations for all 24 lipid traits. A leave-one-out analysis identified 4 lipid species whose causal estimates remained stable upon sequential SNP exclusion. Finally, a Radial MR framework was employed to detect and adjust for potential pleiotropic outliers, and these analyses further supported the robustness of four lipid traits: diacylglycerol (18:1_18:1), diacylglycerol (18:1_18:2), phosphatidylinositol (18:0_20:4), and triacylglycerol (54:6), as having consistent, significant causal effects on IH risk. B Three core assumptions of MR study.(a) Single nucleotide polymorphisms (SNPs) severe as IVs only affect IH via lipidomes; (b) SNPs were independent of known confounders; (c)SNPs were strongly associated with IH. C GWAS data sources for IH and 179 lipidomic profiles. Lipidomic GWAS summary statistics for 179 lipid species were obtained from a cohort of 7,174 Finnish individuals and are publicly available through the GWAS Catalog (accessions GCST90277238 to GCST90277416). Summary-level GWAS data for IH were derived from the UK Biobank, comprising 16,749 IH cases and 439,599 controls of European ancestry (accession GCST90044147)

Data source

Lipidomic data were obtained from a cohort of 7,174 individuals from Finland, which includes 179 lipid species measured through comprehensive lipidomic profiling. The full summary statistics of these lipid traits are cataloged in the HGRI-EBI GWAS Catalog database (Lipidomic GWAS: GCST90277238 to GCST90277416 available at https://www.ebi.ac.uk/gwas/publications/37907536)15. Detailed characteristics of these 179 lipid species are provided in Table S1. To ensure sample independence in the two-sample MR design, the IH GWAS dataset was derived from the UK Biobank, comprising 456,348 individuals of European ancestry, including 16,749 IH cases and 439,599 controls (Jiang et al. 2021). The IH GWAS summary statistics were also downloaded from the GWAS Catalog (GCST90044147 available athttps://www.ebi.ac.uk/gwas/studies/GCST90044147). The datasets analyzed in this work originate from ethically approved, previously conducted studies. Given that only de-identified, publicly available summary statistics were reanalyzed, this study did not require additional ethical oversight.

IVs selection

To infer causality between lipid traits and IH, genetic IVs were selected from the 179 lipidomic GWAS datasets based on the following criteria: genome-wide significance (P < 5E-8) to ensure strong genetic association; linkage disequilibrium (LD) pruning (r^2 < 0.001 within a 10,000 kb window) to retain independent variants; F-statistic (beta^2/SE^2) > 10 to exclude weak IVs and minimize weak instrument bias; Further LD filtering using LDlink (r= 0.1 within ± 500,000 bp) to eliminate SNPs with potential confounding effects (Zheng et al. 2024). The exclusion restriction assumption requires that causal estimates remain robust in the absence of horizontal pleiotropy, which was thoroughly assessed in our subsequent sensitivity analyses. Under these stringent criteria, lipid traits that did not meet the strong MR criteria as IVs, or had fewer than three independent IVs, were excluded from further analysis to ensure the consistence of MR inferences.

MR analyses

Each lipid trait was analyzed via MR using TwoSampleMR (v0.5.8) in R (v4.3.1), employing the inverse-variance weighted (IVW) approach as the principal method for causal inference. The IVW aggregates Wald estimates across genetic instruments to obtain a meta-effect estimate of lipid traits on IH risk. To enhance the robustness of causal inference and mitigate potential pleiotropic bias, additional analyses were conducted using the Weighted Median (WM) and Robust Adjusted Profile Score (RAPS) methods. A lipid trait was considered to have a significant causal effect if the IVW method produced a statistically significant P-value (P < 0.05) and the result was consistently supported by both WM and RAPS analyses, qualifying it for further sensitivity assessments. In this study, we applied a P-value threshold of < 0.05 for statistical significance, deliberately opting not to implement a Bonferroni correction for multiple comparisons. This decision was made to maintain the exploratory nature of our research and to avoid overlooking potential causal relationships between lipid traits and IH risk (Zheng and Tan 2024). Given the binary nature of IH as an outcome, odds ratios (ORs) with 95% confidence intervals (CIs) were reported to quantify the effect size of lipid traits on IH risk.

Sensitivity analysis

To assess the stability of the MR estimates, a series of sensitivity analyses were conducted. Heterogeneity among instrument-specific effects was evaluated with Cochran’s Q test, where P< 0.05 indicates possible heterogeneous effects (suggesting that a random-effects IVW model may be more appropriate). The MR-Egger intercept test examined directional pleiotropy; a significant intercept would suggest bias from non-lipid pathways (Bowden et al. 2015). A nonzero intercept (P< 0.05) would indicate potential pleiotropic bias. Additionally, a leave-one-out analysis assessed the influence of individual variants on the aggregate estimate by sequentially omitting each SNP. Radial MR-Egger (intercept) analysis was further employed to reassess MR estimates and detect potential pleiotropic outliers, providing adjusted causal estimates after outlier removal. This approach allowed for a more rigorous evaluation of whether pleiotropic variants influenced the observed causal effects (Bowden et al. 2018). Any outliers detected were removed, and the MR analyses were rerun to ensure that the causal estimates were not driven by those variants. Only lipid traits that met the significance threshold across all three MR methods (IVW, WM, and RAPS) and remained robust in sensitivity analyses were considered to have a significant causal relationship with IH. All analyses were conducted using R software (version 4.3.2,www.r-project.org), with the following R packages: TwoSampleMR (version 0.5.11), mr.raps (version 0.4.1), and RadialMR (version 1.1).

Results

The causal role of lipids in IH

To estimate the potential causal effects of 179 lipid species on IH, we first identified genetic IVs for these lipid traits. Based on stringent criteria ensuring strong association and independence, 162 lipid species were successfully assigned valid IVs, with F-statistics ranging from 29.79 to 1946.15, conforming no weak instrument bias (Table S2). For statistical rigor, 68 lipids with fewer than three IVs were excluded (29 lipids had fewer than one IV, and 39 had fewer than two IVs), leaving 94 lipid traits for causal inference (Fig. 1). The IVW method initially identified 25 lipid species with nominal significance (P < 0.05), suggesting potential causal relationships with IH (Fig. 2, Table S3). Robust sensitivity analyses using the WM and RAPS methods further supported these findings, identifying 51 and 29 significant lipid traits, respectively. Importantly, these methods collectively supported 24 lipid species, reinforcing their robust causal effects on IH (Fig. 2, Tables S4–S5). These 24 lipid traits included 3 sterol esters, 2 diacylglycerols, 14 phosphatidylcholines, 2 phosphatidylinositols, and 3 triacylglycerols (Fig. 3). Among them, 17 lipid species were identified as risk factors for IH (OR = 1.04–1.21), including 2 triacylglycerols, 2 phosphatidylinositols, 1 sterol ester, 8 phosphatidylcholines, and 2 diacylglycerols. Conversely, 7 lipid species, including 6 phosphatidylcholines and 1 sterol ester, exhibited protective effects against IH (OR = 0.78–0.94) (Fig. 3). Notably, Triacylglycerol (54:6) emerged as the strongest IH risk factor, with an OR of 1.21 (95% CI = 1.09–1.35, P = 0.001), highlighting its potential role in IH pathophysiology (Fig. 3).

Fig. 2.

Fig. 2

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Volcano plot representing causality estimates of multiple Mendelian randomization model estimates for lipid groups (94 species) and inguinal hernia

Fig. 3.

Fig. 3

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Results of Mendelian randomization estimation of 24 lipids with a significant causal relationship with inguinal hernia (IH). The left panel bubble plots indicate that 7 lipids have a causal protective effect on IH (OR < 1) whereas 17 lipids have a causal risk effect on IH (OR > 1), and the right panel forest plots show the risk estimates and their corresponding 95% confidence intervals (95% CI). nsnp, number of single nucleotide polymorphisms

Sensitive analysis

To ensure the reliability of our causal estimates, we examined heterogeneity and pleiotropy across the 24 lipid traits showing significant associations. Cochran’s Q test revealed no significant heterogeneity (P > 0.05), suggesting stable and consistent SNP-specific effects. Similarly, MR-Egger intercept analysis showed no indication of directional pleiotropy, with all P-values exceeding 0.05, thereby supporting the validity of the MR findings (Table 1). Additionally, we also conducted a leave-one-out sensitivity analysis to evaluate whether any individual SNP unduly influenced the global causal estimates. For 20 lipid traits, at least one SNP significantly influenced the causal estimate (Supplementary Fig. 1). However, four lipid traits remained unaffected by single IV removal, demonstrating robust causal associations with IH, namely diacylglycerol (18:1_18:1) (OR = 1.16, 95% CI = 1.05–1.29, P = 0.005), diacylglycerol (18:1_18:2) (OR = 1.12, 95% CI = 1.03–1.22, P = 0.006), phosphatidylinositol (18:0_20:4) (OR = 1.10, 95% CI = 1.05–1.15, P = 1.47E-04), and triacylglycerol (54:6) (OR = 1.21, 95% CI = 1.09–1.35, P = 0.001) (Fig. 4).

Table 1.

Results of heterogeneity analysis of causality Estimation and Pleiotropy by MR Egger regression for the Estimation of the causal relationship between lipid groups and IH

ID.exposure Lipid traits Heterogeneity Q_pval Pleiotropy
Inverse variance weighted MR Egger Egger_intercept SE P value
GCST90277241 Sterol ester (27:1/16:1) levels 0.904 0.743 0.031 0.103 0.812
GCST90277250 Sterol ester (27:1/20:4) levels 0.284 0.219 −0.004 0.012 0.728
GCST90277251 Sterol ester (27:1/20:5) levels 0.583 0.704 −0.018 0.018 0.511
GCST90277261 Diacylglycerol (18:1_18:1) levels 0.184 0.102 0.001 0.028 0.984
GCST90277262 Diacylglycerol (18:1_18:2) levels 0.264 0.177 0.006 0.021 0.780
GCST90277267 Phosphatidylcholine (18:2_0:0) levels 0.346 0.145 0.005 0.116 0.975
GCST90277268 Phosphatidylcholine (20:4_0:0) levels 0.661 0.494 −0.005 0.013 0.712
GCST90277287 Phosphatidylcholine (16:0_20:4) levels 0.407 0.282 0.010 0.019 0.644
GCST90277298 Phosphatidylcholine (17:0_20:4) levels 0.422 0.316 0.007 0.016 0.685
GCST90277304 Phosphatidylcholine (18:0_20:4) levels 0.434 0.375 −0.007 0.010 0.502
GCST90277308 Phosphatidylcholine (18:1_18:1) levels 0.403 0.656 0.030 0.021 0.285
GCST90277309 Phosphatidylcholine (18:1_18:2) levels 0.347 0.451 0.014 0.011 0.269
GCST90277311 Phosphatidylcholine (18:1_20:2) levels 0.222 0.105 0.012 0.030 0.769
GCST90277312 Phosphatidylcholine (18:1_20:3) levels 0.582 0.836 0.052 0.051 0.494
GCST90277313 Phosphatidylcholine (18:1_20:4) levels 0.404 0.332 0.008 0.014 0.568
GCST90277317 Phosphatidylcholine (18:2_20:4) levels 0.765 0.581 −0.007 0.029 0.825
GCST90277322 Phosphatidylcholine (O-16:0_20:3) levels 0.274 0.164 0.028 0.048 0.666
GCST90277323 Phosphatidylcholine (O-16:0_20:4) levels 0.872 0.699 −0.005 0.013 0.784
GCST90277330 Phosphatidylcholine (O-16:1_20:4) levels 0.391 0.665 −0.018 0.014 0.417
GCST90277360 Phosphatidylinositol (16:0_20:4) levels 0.256 0.227 −0.075 0.080 0.522
GCST90277364 Phosphatidylinositol (18:0_20:4) levels 0.555 0.514 0.015 0.018 0.453
GCST90277400 Triacylglycerol (52:6) levels 0.149 0.193 0.050 0.045 0.381
GCST90277407 Triacylglycerol (54:6) levels 0.372 0.446 0.026 0.022 0.447
GCST90277408 Triacylglycerol (54:7) levels 0.199 0.175 0.037 0.045 0.500
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SE standard error

Fig. 4.

Fig. 4

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Four lipids robustly and significantly causally associated with IH sensitivity filtered by leave-one-out method. A MR for diacylglycerol (18:1_18:1) levels on IH; B MR for diacylglycerol (18:1_18:2) levels on IH; C, MR for phosphatidylinositol (18:0_20:4) levels on IH; D, MR for triacylglycerol (54:6) levels on IH. The upper panels show scatter plots of SNP-exposure versus SNP-outcome associations using different MR methods. The lower panels present leave-one-out sensitivity analysis, where each SNP is removed in turn to assess its influence on the overall causal estimate. The red line represents the overall MR estimate, while black dots indicate individual SNP contributions. IVW (MRE) inverse variance weighted (multiplicative random effects), RAPS robust adjusted profile score

To further refine these estimates and reduce potential pleiotropic bias, Radial MR analysis was employed, incorporating MR-Egger regression slopes to detect pleiotropic outliers. All Radial MR models, including IVW effect (1st), IVW Exact (FE), and IVW Exact (RE), consistently supported the significant causal relationship (Table 2). Among the four robust lipid traits, three showed no outliers, whereas diacylglycerol (18:1_18:2) exhibited a potential pleiotropic outlier (rs79624003) (Fig. 5). However, excluding rs79624003 in a reanalyzed Radial MR model did not alter the original significant estimate (Table 2), reinforcing the robustness of the findings. Furthermore, Radial MR-Egger intercept tests provided additional support for the validity of the results, ruling out pleiotropic bias.

Table 2.

Causal estimates and Pleiotropy (intercept) generated through radial MR work frame

Analysis Method Estimate SE P value
Diacylglycerol (18:1_18:1) levels on IH Radial IVW effect (1st) 0.148 0.053 5.00E-03
Radial IVW exact (FE) 0.151 0.043 5.00E-04
Radial IVW exact (RE) 0.151 0.045 0.028
Radial MR-egger (intercept) 0.589 2.213 0.807
Diacylglycerol (18:1_18:2) levels on IH Radial IVW effect (1st) 0.116 0.042 0.006
Radial IVW exact (FE) 0.118 0.037 0.001
Radial IVW exact (RE) 0.117 0.038 0.028
Radial MR-egger (intercept) −0.696 1.679 0.699

Diacylglycerol (18:1_18:2) levels on IH

Corrected by rs79624003

 Radial IVW effect (1st) 0.085 0.031 0.005
Radial IVW exact (FE) 0.086 0.041 0.034
Radial IVW exact (RE) 0.085 0.029 0.045
Radial MR-egger (intercept) −1.215 0.984 0.304
Phosphatidylinositol (18:0_20:4) levels on IH Radial IVW effect (1st) 0.092 0.022 2.71E-05
Radial IVW exact (FE) 0.092 0.024 1.51E-04
Radial IVW exact (RE) 0.092 0.023 6.94E-03
Radial MR-egger (intercept) 1.293 0.747 0.144
Triacylglycerol (54:6) levels on IH Radial MR-egger (intercept) 3.887 1.644 0.254
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SE standard error of effect size, Radial IVW effect (1st) radial inverse-variance weighted effect (First-Order Weights), Radial IVW Exact (FE) radial inverse-variance weighted exact (Fixed Effects Model), Radial IVW Exact (RE) radial inverse-variance weighted exact (Random Effects Model)

Fig. 5.

Fig. 5

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Radial MR plots display ratio estimates for each genetic variant alongside the overall MR estimate from the inverse variance weighted (IVW) method with first-order weights, incorporating MR-Egger regression slopes to identify potential pleiotropic outliers. A Radial MR for diacylglycerol (18:1_18:1) levels on IH; B The left panel represents the Radial MR for diacylglycerol (18:1_18:2) levels on IH, while the right panel shows the plot results after correcting for the outlier rs79624003; C Radial MR for phosphatidylinositol (18:0_20:4) levels on IH; D Radial MR for triacylglycerol (54:6) levels on IH

Overall, our MR analysis provided significant and consistent evidence supporting a causal relationship between four lipid species and increased IH risk. These findings were highly robust across multiple sensitivity analyses, confirming their reliability. No additional lipid traits demonstrated significant causal associations with IH.

Discussion

Given the complex relationship between metabolic markers and hernias, this study soughts to identify specific lipid species that may play a direct role in IH pathogenesis. By integrating large-scale lipidomic and IH GWAS datasets, we identified four lipid species—diacylglycerol (18:1_18:1), diacylglycerol (18:1_18:2), phosphatidylinositol (18:0_20:4), and triacylglycerol (54:6)—that causally increase the risk of developing IH. This represents the first genetic molecular evidence implicating individual lipid molecules in hernia susceptibility, moving beyond traditional risk markers and suggesting a metabolic dysfunction contribution to the integrity of the abdominal wall. While previous studies have primarily focused on general metabolic disorders such as obesity, fat distribution, and dyslipidemia with IH risk, the specific role of lipidomic profiles has remained largely unexplored. BMI, a widely used obesity marker, has shown inconsistent associations with IH, likely due to its inability to distinguish between lean and fat mass. Aquina et al. reported that visceral obesity, rather than BMI, was significantly associated with IH, highlighting the need for better metabolic markers (Aquina et al. 2015). Our results provide novel insights into the metabolic mechanisms underlying IH pathogenesis. Lipids play a crucial role in maintaining extracellular matrix integrity and cellular architecture. Disruptions in lipid homeostasis may impair collagen synthesis, fibroblast function, and tissue remodeling, leading to a weakened abdominal wall and increased hernia susceptibility (Besch et al. 2018; Forghani et al. 2024). IH has been linked to metabolic abnormalities in connective tissue, where lipid metabolism may contribute to structural integrity via membrane composition and signaling pathways (Besch et al. 2018; Forghani et al. 2024). Diacylglycerol (DAG), triacylglycerol (TAG), and phosphatidylinositol (PI) are key lipid species involved in these processes. Below, we discuss possible mechanisms by which the identified lipid species could contribute to IH pathophysiology, as well as potential clinical implications.

DAG and cellular signaling

DAG molecules are major intermediates in lipid metabolism and intracellular signaling. They can contain long-chain polyunsaturated fatty acids, such as arachidonic acid (C20:4), or be composed primarily of saturated fatty acids (Li et al. 2023). DAG is a product of PI hydrolysis by phospholipase C (PLC) and functions as a secondary messenger in protein kinase C (PKC) activation (Ahyayauch et al. 2015). Excessive PKC activation can affect cellular functions in wound healing and inflammation. If DAG levels are chronically elevated (as suggested by genetically higher DAG 18:1_18:1 and 18:1_18:2), this may lead to aberrant PKC signaling, disrupting normal repair of the abdominal wall. In support of this idea, DAG accumulation has been associated with impaired connective tissue healing in other contexts, which could translate into a propensity for hernia due to a weaker or slower healing abdominal fascia (Yoshida et al. 1986). Moreover, DAG serves as a precursor for TAG biosynthesis, which could influence lipid storage and energy metabolism (Igal et al. 2001; Xu et al. 2018; Hardman et al. 2018).

TAG and inflammation

TAG is primarily composed of fatty acids such as linoleic acid (18:2), oleic acid (18:1), and palmitic acid (Gili and Alonso 2004). Under ischemic conditions, TAG composition shifts, with decreased palmitic acid and increased arachidonic acid, which may be linked to inflammation and altered signaling pathways (Yoshida et al. 1986). TAG accumulation has been associated with increased intra-abdominal pressure, which may contribute to IH formation (Yoshida et al. 1986). Furthermore, TAG hydrolysis releases free fatty acids (FFAs), which can exacerbate chronic inflammation and weaken connective tissue structures, further predisposing individuals to IH (Gili and Alonso 2004). During metabolic stress, TAG accumulation coincides with reduced membrane lipid content, suggesting a shift in lipid metabolism toward storage rather than structural maintenance (Meï 2016).

PI and membrane stability

PI are phospholipids integral to cell membranes and precursors to critical signaling molecules like PIP2 and PIP3. The PI species identified (18:0_20:4) contains stearic and arachidonic acid, indicating a role in inflammatory signaling (arachidonic acid is a precursor to eicosanoids). Adequate PI levels are necessary for maintaining membrane structure and for membrane signaling domains that recruit cytoskeletal proteins (Lejeune et al. 2021). PI-derived signaling molecules, such as phosphatidylinositol 4,5-bisphosphate (PIP2), modulate cytoskeletal reorganization and cellular adhesion (Ahyayauch et al. 2015). PI depletion has been observed in ischemic tissues and may lead to impaired cell-matrix interactions, weakening the mechanical stability of the abdominal wall.

Clinical relevance

Our results highlight potential lipid biomarkers for IH risk. If validated in further studies, individuals with higher levels of the identified risk lipids (e.g., DAG 18:1_18:1, DAG 18:1_18:2, TAG 54:6, PI 18:0_20:4) could be recognized as having an elevated hernia risk, especially in contexts like pre-surgical evaluations or occupational health screenings. This could prompt more proactive measures such as weight management, targeted core-strengthening exercises, or closer monitoring for hernia development in these individuals. From a drug development standpoint, pathways involving DAG-PKC signaling or PI metabolism could be explored for potential targets to improve connective tissue resilience. Although these applications are speculative at this stage, they open interesting directions for translational research connecting metabolic health and surgical outcomes in IH.

Strengths and limitations

One of the main strengths of our study is the use of MR analysis, a method less prone to bias than traditional observational designs (Wu 2021). This genetic approach enabled the identification of lipid species that may directly contribute to IH pathogenesis. Furthermore, sensitivity analyses showed no evidence of horizontal pleiotropy, reinforcing the robustness of our findings. These results provide a foundation for future studies exploring lipid-modifying interventions to mitigate IH risk.

However, our study has several limitations. First, despite using rigorous MR methodology, potential biases such as weak instrument bias and residual pleiotropy remain concerns. Although we addressed these issues through stringent IV selection and sensitivity analyses, they cannot be eliminated (Hu et al. 2022). Second, our analysis was confined to individuals of European ancestry; genetic effects may differ in other populations, and the lipid profiles associated with IH might vary by ethnicity. Future studies should examine these relationships in more diverse cohorts to ensure generalizability (Zheng and Tan 2024). Future studies should incorporate diverse cohorts to identify population-specific lipidomic risk factors. Third, our lipidomic data were derived from plasma measurements, which may not fully capture tissue-specific lipid metabolism relevant to IH. Functional studies using cellular and animal models are needed to validate the biological role of these lipid species. Finally, due to data constraints, we could not stratify IH cases by subtype (direct vs. indirect), sex, or age. It remains possible that lipid effects differ in specific subgroups or hernia subtypes, which warrants further investigation. Despite these limitations, our study provides novel evidence of a metabolic component in IH pathogenesis.

Conclusion

This study presents novel causal evidence linking lipid metabolism to IH susceptibility. We identified four lipid species, diacylglycerol (18:1_18:1), phosphatidylinositol (18:0_20:4), triacylglycerol (54:6), and diacylglycerol (18:1_18:2), as potential contributors to IH risk. These findings highlight a previously underappreciated metabolic dimension in IH pathogenesis and open new avenues for therapeutic development. Future research should prioritize experimental validation and explore lipid-targeted strategies for IH prevention and clinical management.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (2.7MB, docx)
Supplementary Material 2 (119KB, xlsx)

Acknowledgements

We thank the researchers and consortia who generated and shared the publicly available data that enabled this study.

Authors’ contributions

XLX: Writing-original draft, Resources, Conceptualization, Methodology. ZMSL: Investigation, Supervision, Validation, Data curation. XJX: Writing-review, Funding acquisition, Project administration, Resources.

Funding

This work was supported by the Guangdong Provincial Science and Technology Fund for high-level hospital construction (Grant No. STKJ2021119), 2021 Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (2021-88-53) and 2022 Guangdong Province Science and Technology Special Fund (2022-124-6).

Data availability

All data supporting the findings of this study are available within the article and its supplementary materials.

Declarations.

Declarations

Conflict of interest

None.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (2.7MB, docx)
Supplementary Material 2 (119KB, xlsx)

Data Availability Statement

All data supporting the findings of this study are available within the article and its supplementary materials.

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