Causal Relationship between Serum Metabolites, Plasma Lipids, and Hemorrhoidal Disease: A Mendelian Randomization Study

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Causal Relationship between Serum Metabolites, Plasma Lipids, and Hemorrhoidal Disease: A Mendelian Randomization Study

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Hemorrhoidal Disease (HD) is a common anorectal disorder that significantly impacts patients' quality of life over the long term and imposes a substantial economic burden. However, the potential link between HD and serum metabolites and lipids has been scarcely studied, and its pathogenesis remains unclear. In this study, the causal relationship between serum metabolite traits, plasma lipids, and HD was evaluated using Mendelian Randomization (MR). Three methods were employed to estimate the causal effect of exposure on outcomes. The study identified 27 serum metabolic traits and 4 plasma lipids with significant causal effects on HD. Among these, 11 serum metabolic traits were identified as risk factors, and the top three ones exhibiting the most significant causal effects on the outcomes are Cortolone glucuronide (1) levels(OR=1.544, 95%CI=1.124-2.123,p=0.007), 3-methyl-2-oxobutyrate levels(OR=2.079, 95%CI=1.189-3.635,p=0.010), and Glycocholate levels(OR=1.405, 95%CI=1.063-1.857, p=0.017). Additionally, 16 serum metabolic traits were found to be protective factors, with lipids or lipoids and their metabolites comprising the largest proportion. In the Mendelian randomization analysis of plasma lipids and HD,Phosphatidylcholine (18:2_20:4) (OR=1.588,95%CI=1.097-2.299,p=0.014) and Phosphatidylcholine (O-16:1_16:0) (OR=1.676，95%CI=1.109-2.533,p=0.014) were identified as risk factors for HD.

Health sciences/Gastroenterology/Gastrointestinal diseases

Health sciences/Medical research

Hemorrhoidal Disease

Mendelian Randomization

Serum Metabolites

Plasma Lipids

Hemorrhoidal Disease (HD) is one of the most common types of anorectal disorders. Epidemiological studies indicate that over 50% of individuals experience at least one episode of HD before the age of 50¹. Furthermore,studies have suggested that HD may be associated with an increased risk of colorectal cancer⁴. The pathogenesis of HD is complex and involves multiple factors, including dietary habits, lifestyle, local inflammation, and abnormalities in local anatomical structures^2,3. HD is prevalent worldwide and has a long-term and significant negative impact on patients' quality of daily life. However, the scientific community has yet to fully understand the specific pathophysiological processes of HD and its association with risk factors.

Numerous studies have indicated significant correlations between serum metabolites and HD triggers, such as constipation, diarrhea, and varicose veins. For example, C4 and fibroblast growth factor 19 (FGF19) in serum are considered biomarkers for bile acid diarrhoea⁵, highlighting the importance of serum metabolites in the pathogenesis of diarrhea, which itself may trigger HD. Notably, many studies have discovered complex interactions between serum metabolites and the gut microbiota^6–8.Evidence from Mendelian Randomization studies supports a causal correlation between the gut microbiota and the occurrence of HD⁹.Although existing evidence suggests a potential link between serum metabolites and HD, the direct causal relationship between the two requires further research to be clarified.

Lipids are important components of cell membranes and are involved in many physiological functions, including energy storage, signal transduction, and immune responses^10–12. Studies indicate that plasma lipids may be associated with various inflammatory diseases¹³,and HD, as an inflammatory anorectal disease, may be related to vascular inflammation and tissue damage. For instance, oxidized low-density lipoprotein (ox-LDL) can activate endothelial cells and smooth muscle cells, promoting the release of inflammatory factors^14,15,which may exacerbate the local inflammatory state in HD patients. Additionally, dyslipidemia is related to an imbalance in the gut microbiota¹⁶,which has been proven to be associated with various intestinal diseases, including HD^9,17.Therefore, regulating plasma lipid levels and improving dyslipidemia may have positive significance in the prevention and treatment of HD. However, further in-depth research is needed to clarify the specific mechanisms between dyslipidemia and HD.

Mendelian Randomization (MR), as an innovative method for causal inference, has been widely applied in epidemiological and genetic research in recent years. MR utilizes single nucleotide polymorphisms (SNPs) as naturally occurring instrumental variables (IVs). By analyzing the association between genetic variants and diseases, MR infers the potential causal relationship between exposure factors and outcomes. Since genetic variations are determined before an individual's birth and are not directly influenced by environmental factors, MR analysis can simulate the conditions of randomized controlled trials, effectively reducing confounding factors and reverse causation, thereby providing more reliable causal inference evidence.

This study integrates large-scale genetic association data, metabolomics data, and plasma lipidomics data, offering new perspectives and insights for understanding the pathogenesis of Hemorrhoidal Disease (HD), risk prediction, and precision treatment.

Instrumental Variables

This process resulted in the inclusion of 2093 SNPs for Mendelian randomization (MR) analysis (see Supplementary Table S1). Additionally, the same criteria were applied to 179 plasma lipids, leading to the inclusion of 17712 SNPs for analysis (see Supplementary Table S2).

Causal effects of serum metabolites on HEM

IVW analysis identified 54 serum metabolitc traits with causal relationships to HD (p < 0.05). To mitigate the impact of instrumental variables on MR results, sensitivity analyses were conducted. For positive results, neither MR Egger nor IVW detected heterogeneity (Q_pval > 0.05). Additionally, MR-Egger revealed no evidence of horizontal pleiotropy (p-values for egger_intercept > 0.05). However, for the result of Pseudouridine levels, the MR-Presso method excluded this finding due to an insufficient number of remaining SNPs after removing outliers, deeming it unreliable and thus excluded (Supplementary Table S3). Moreover, the leave-one-out analysis indicated SNP interference with MR results (Supplementary Fig. S1), and funnel plots showed apparent bias among SNPs for some results (Supplementary Fig. S2). Considering these sensitivity analysis results, reliable outcomes were derived after excluding those failing sensitivity tests (Fig. 1). The OR and beta values obtained from different methods are shown in Fig. 2(turn to Supplementary Table S4 for detailed information).

Among all risk factors, the top three serum metabolic traits with the most significant causal effects on outcomes are Cortolone glucuronide (1) levels(OR = 1.544, 95%CI = 1.124–2.123,p = 0.007), 3-methyl-2-oxobutyrate levels(OR = 2.079, 95%CI = 1.189–3.635,p = 0.010), and Glycocholate levels(OR = 1.405, 95%CI = 1.063–1.857, p = 0.017). Among protective factors, the top three serum metabolic traits with the most significant causal effects on outcomes are 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (p-16:0/18:1) levels(OR = 0.566, 95%CI = 0.413–0.776, p = 0.0004), X-11299 levels(OR = 0.439, 95%CI = 0.257–0.747, p = 0.002),5alpha-pregnan-3beta,20alpha-diol disulfate levels(OR = 0.667, 95%CI = 0.511–0.871, p = 0.003)(turn to Supplementary Table S4 for the remaining results). Notably, in protective factors, lipids or lipoids and their metabolites, account for the largest proportion, including eight identified protective metabolites.

Causal effects of plasma lipids on HD

IVW analysis identified 10 plasma lipids with significant causal effects on HD (p < 0.05). In the sensitivity analysis, MR Egger and IVW methods found heterogeneity in the results for Sterol ester (27:1/16:1) levels (Q_pval < 0.05). Using MR-Egger also provided evidence of horizontal pleiotropy for Sterol ester (27:1/16:1) levels (p-values for egger_intercept < 0.05). The MR-Presso method detected the results for Sterol ester (27:1/16:1) levels to be unreliable (Supplementary Table S5). Besides, funnel plots and leave-one-out analyses were conducted(Supplementary Fig. S3,4). After excluding all results deemed unreliable by sensitivity analysis, the positive results are shown as indicated (Fig. 3, 4. Turn to Supplementary Table S6 for detailed information). The risk factors for HD are Phosphatidylcholine (18:2_20:4) levels (OR = 1.588, 95%CI = 1.097–2.299, p = 0.014), Phosphatidylcholine (O-16:1_16:0) levels (OR = 1.676, 95%CI = 1.109–2.533, p = 0.014). The protective factors are Phosphatidylcholine (18:2_20:4) levels (OR = 0.462, 95%CI = 0.293–0.727, p = 0.001), Phosphatidylethanolamine (O-18:1_18:2) levels (OR = 0.569, 95%CI = 0.384–0.842, p = 0.005).

Pathway enrichment analysis of serum metabolites

After excluding serum metabolites from the risk factor and protective factor groups that could not be retrieved from the HMDB, PubChem, and KEGG databases(Supplementary Fig. S7), the number of metabolites remaining for enrichment analysis was too small to yield statistically significant results after applying the Holm-Bonferroni method(Supplementary Fig. S8).

In the development of HD, the dilation and congestion of hemorrhoidal veins are the main pathological features. Inflammation and damage of local vascular endothelium play a crucial role in the occurrence of HD².However, it remains unclear whether local inflammation in hemorrhoid patients causes changes in blood composition or if abnormal blood components trigger local inflammation, leading to the development of HD. In this study, metabolomics, lipidomics, and genomics were combined for the first time to explore the causal relationships between serum metabolites, plasma lipids, and HD. It was found that 27 serum metabolic tarits and 4 plasma lipids have causal relationships with the occurrence of HD, and 11 risk serum metabolic traits were determined.

Three metabolites were identified in our study with the most significant causal effects on HD, Cortolone glucuronide (1) levels(p = 0.007), 3-methyl-2-oxobutyrate levels(p = 0.010), and Glycocholate levels(p = 0.017), may potentially trigger HEM through mechanisms such as inflammation. Cortolone glucuronide (1), a metabolite of cortisol, is an important marker of glucocorticoid metabolism, with glucocorticoids possessing anti-inflammatory and immunosuppressive functions¹⁸.The interaction of glucocorticoids or glucocorticoid receptor complexes with certain transcription factors, such as Activator Protein 1 (AP1), Nuclear Factor-kappa B (NF-kB), or Interferon Regulatory Factor 3 (IRF-3)¹⁹, is involved in the regulation of pro-inflammatory gene expression. In the case of HD, both local and systemic inflammation may lead to upregulation of glucocorticoid metabolism, reflected by elevated levels of Cortolone glucuronide (1), which may be part of a feedback mechanism controlling inflammation and tissue damage in the anorectal area. 3-Methyl-2-oxobutyrate, an intermediate product in the metabolism of branched-chain amino acids (especially valine)^20,21.Amino acid metabolism abnormalities are associated with various inflammations and metabolic disorders^22,23.Elevated levels of 3-methyl-2-oxobutyrate may indicate increased valine turnover²⁴,potentially linked to increased protein breakdown and muscle degradation, which are common in chronic inflammatory states. In the context of HD, such metabolic changes may contribute to a systemic inflammatory environment, exacerbating local tissue damage and repair mechanisms. Glycocholate, a bile acid conjugate involved in dietary fat emulsification and absorption, has been less studied. Based on the findings, Cortolone glucuronide (1) levels, 3-methyl-2-oxobutyrate levels, and Glycocholate levels as risk factors for HD show potential for clinical application.

It was observed that many lipids or lipoids and their metabolites are included among the protective factors for HD. Lipoids such as ceramides can maintain the integrity of the intestinal barrier by enhancing tight junctions in intestinal epithelial cells²⁵,thereby preventing harmful substances and bacteria from entering the bloodstream²⁶. Additionally, fatty acids, especially unsaturated fatty acids, play an important role in maintaining vascular health. Polyunsaturated fatty acids (PUFAs) such as eicosapentaenoic acid (EPA) can reduce the risk of hemorrhoids by inhibiting platelet aggregation and decreasing inflammation and damage in the vascular endothelium²⁷.

Mendelian randomization analysis of plasma lipidomics identified Phosphatidylcholine (18:2_20:4) and Phosphatidylcholine (O-16:1_16:0) as risk factors for HD, both of which are types of phosphatidylcholine. Previous research has shown that phosphatidylcholines play a key role in the formation of varicose veins²⁸, which, if occurring in the anal and rectal areas, may develop into HD. Phosphatidylcholine (18:2_20:4) contains two unsaturated fatty acid chains, one of which is arachidonic acid, while Phosphatidylcholine (O-16:1_16:0) has one unsaturated palmitoleic acid and one saturated palmitic acid. Palmitoleic acid can inhibit the release of inflammatory factors such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6)²⁹. It is speculated that, during HD development, Phosphatidylcholine (O-16:1_16:0) might participate in the sequestration of free palmitoleic acid, thereby reducing its anti-inflammatory effects. However, a study on tumor-induced fatty liver pointed out that palmitic acid can induce the secretion of TNF, creating a pro-inflammatory microenvironment³⁰.It is noteworthy that esterified arachidonic acid can be hydrolyzed into its free form by phospholipase A2 (PLA2) and further metabolized into bioactive mediators such as prostaglandins, leukotrienes (LTs), and epoxyeicosatrienoic acids (EETs) by cyclooxygenases (COXs), lipoxygenases (LOXs), and cytochrome P450 (CYP) enzymes³¹.This may play an important role in the occurrence of HD.

Interestingly, arachidonic acid is both a metabolite of linoleate (18:2n6), a protective factor for HD, and a component of the risk factor Phosphatidylcholine (18:2_20:4). This seemingly contradictory phenomenon may be due to the dual effects of arachidonic acid in promoting and inhibiting inflammation³².On one hand, as previously mentioned, arachidonic acid metabolism produces various pro-inflammatory substances. On the other hand, some metabolites of arachidonic acid, such as certain EETs and lipoxins, have been found to possess anti-inflammatory properties and can inhibit inflammatory processes³¹.Additionally,a 2024 study reported that elevated arachidonic acid levels could inhibit inflammasome activity and reduce the production of the inflammatory factor IL-1β, thus exerting anti-inflammatory effects³³.It should also be noted that the relationship between linoleic acid and arachidonic acid is not a simple linear one^34–36.The metabolic pathway from linoleic acid to arachidonic acid is regulated by various factors,including enzyme activity,substrate availability,and competing metabolic pathways^35,37,38.The relationship between their concentrations warrants further investigation to elucidate the underlying mechanisms.

Furthermore, after the serum metabolites involved in the study were divided into risk and protective factors groups, enrichment analysis was also performed. However, after applying the Holm-Bonferroni method, no metabolite set achieved significance (p < 0.05). The results of the enrichment analysis are shown in Fig. 5.

This study reports for the first time the causal relationship between serum metabolites, plasma lipids, and the occurrence of HD, identifying risk factors for HD and providing new insights into the mechanisms underlying HD, as well as new perspectives for clinical diagnosis and treatment. Unfortunately, although potential risk factors associated with HD were identified through Mendelian randomization analysis, the exact connections between these metabolites and lipids and HD at multi-omics levels remain unclear and require further research to elucidate. Additionally, to better understand the role of serum metabolites in HD, larger sample sizes and more detailed metabolite-wide association studies (GWAS) data are needed.

Study design

The three assumptions underlying the causal interpretation of MR analysis and the design of this study are displayed in Fig. 6. The IVs used in the analysis need to satisfy three assumptions: (1) IVs must be strongly associated with the exposure variable; (2) IVs must follow the principle of random assignment and not be influenced by confounding factors; (3) IVs should not be directly related to the outcome but should influence the outcome only through the potential causal relationship between the exposure and the outcome³².First, MR analysis was conducted using three methods to evaluate the causal relationship between serum metabolic traits, plasma lipids, and HD, treating each as an exposure factor. Sensitivity analyses were performed on the positive results to ensure their reliability. Then, enrichment analysis was conducted on the serum metabolites involved in the serum metabolic traits.

GWAS data for serum metabolites and HEM

Data on 1,091 metabolites and 309 metabolite ratios were sourced from the genome-wide association study by Chen et al³⁹. The sample included 8,299 unrelated individuals from the Canadian Longitudinal Study on Aging (CLSA) who underwent genome-wide genotyping and circulating plasma metabolite measurement. All individuals were of European ancestry, and 203 individuals with first and second-degree relatives were excluded³⁹.

The plasma lipidomics data were derived from a study involving whole-genome sequencing of plasma lipids in 7,174 participants⁴⁰, including 179 plasma lipids. The samples were entirely from the Finnish population.

The GWAS data for HD were obtained from the IEU OpenGWAS database, using summary-level data from the UK Biobank (https://gwas.mrcieu.ac.uk/). This study included 441 case samples and 6,017 control samples, totaling 15,526,533 SNPs.

Selection of instrumental variables

IVs were extracted from 1,400 metabolite traits using a threshold of p < 1e-05. The 1000 Genomes Project was used as the reference panel, with the linkage disequilibrium (LD) threshold set to r²≤ 0.001 and a physical distance greater than 10,000 kb. The F-statistic for each metabolite was calculated to assess the strength of the instrumental variables, and SNPs with an F-statistic less than 10 were discarded.

Mendelian randomization analysis

MR analysis was performed using the "TwoSampleMR" package (version 0.6.4) in R (version 4.3.1).Statistical significance was defined as a P-value < 0.05. The causal relationship between exposure and HD was estimated using three methods: Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median. IVW was the primary analysis method, as it employs weighted linear regression to assess the association between IVs and the outcome. When the genetic variants satisfy the three assumptions of instrumental variables and are not affected by pleiotropy, IVW provides a consistent estimate of the causal effect between exposure and outcome. MR-Egger accounts for potential heterogeneity in IVs and can detect and correct for bias, thus being used to evaluate horizontal pleiotropy⁴¹. When the results of these three methods were inconsistent, the IVW results were used as the primary evaluation, while the “forest plot” packages were employed for graphical representations.

Sensitivity analysis

Sensitivity analyses included heterogeneity testing, horizontal pleiotropy testing, and leave-one-out testing. Heterogeneity was assessed using MR-Egger and IVW methods, quantified by Cochran’s Q statistic. A P-value > 0.05 indicated that heterogeneity was not statistically significant. Horizontal pleiotropy was evaluated using the MR-Egger regression intercept and MR-PRESSO. For the MR-Egger regression intercept, a P-value > 0.05 suggested that horizontal pleiotropy could be ignored⁴². For MR-PRESSO, a P-value > 0.05 after the Global Test indicated that horizontal pleiotropy could be ignored. The stability of the results was assessed using the leave-one-out method; if the remaining results showed no significant changes after removing a single SNP, the results were considered stable. MR-PRESSO was performed using the MR-PRESSO package (version 1.0) in R (version 4.3.1), while the“forest plot”packages were employed for graphical representations.

Metabolic pathway analysis

The correlation between serum metabolites and metabolic pathways was analyzed using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/). Serum metabolite phenotypes with causal relationships to HD were classified into risk factor groups and protective factor groups according to the following rules: based on the sign of the beta value for serum metabolite levels, they were classified as either risk factors or protective factors. For serum metabolite ratios, if the beta value was positive, the numerator component was classified as a risk factor, and the denominator component was classified as a protective factor; if the beta value was negative, the classification was reversed. Enrichment analysis was conducted separately for the two groups to determine their correlation with metabolic pathways. The data were sourced from The Small Molecule Pathway Database (SMPDB). A significance threshold of p < 0.05 was set for this study.

Data availability

Serum metabolic traits and plasma lipids data were respectively obtained from the genome-wide association studies by Chen et al.³⁹ and Ottensmann, L. et al.⁴⁰ The GWAS data of HD were sourced from the publicly available GWAS dataset ukb-e-455_AFR (GWAS, https://gwas.mrcieu.ac.uk/).

Acknowledgements

This study is funded by Key project fund of Jiangsu Provincial Health Commission (grant number ZD2023016).

Author information

Hongkai Yu^1,+, Yingqi Yang^1,+,Chang You²,Jiayu Zhao³,Luyao Wang¹,Haohan Sun¹,Yutong Li²,Xiaomeng Jiang^4,*,Yu Fan^5,*

¹The Second School of Clinical Medicine, Nanjing Medical University, Nanjing, 211166, China

²School of Stomatology, Nanjing Medical University, Nanjing, 211166, China

³School of Nursing, Nanjing Medical University, Nanjing, 211166, China

⁴Second Affiliated Hospital, Nanjing Medical University, Nanjing, 210003, China

⁵cancer institute, The affiliated people’s hospital, Jiangsu university, Zhenjiang, Jiangsu, 212300, China

^*corresponding author

⁺these authors contributed equally to this work

Contributions

Hongkai Yu and Yingqi Yang made equal contributions to this manuscript.

Hongkai Yu: Writing - original draft, Investigation, Data curation, Project administration. Yingqi Yang: Formal analysis, Methodology, Supervision, Writing - review & editing. Chang You: Writing - review & editing. Jiayu Zhao: Writing - review & editing. Haohan Sun: Writing - review & editing. Yutong Li: Visualization. Xiaomeng Jiang: Conceptualization. Yu Fan: Conceptualization.

Competing interests

The authors declare no competing interests

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No competing interests reported.

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Causal Relationship between Serum Metabolites, Plasma Lipids, and Hemorrhoidal Disease: A Mendelian Randomization Study

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Abstract

Figures

Introduction

Results

Instrumental Variables

Causal effects of serum metabolites on HEM

Causal effects of plasma lipids on HD

Pathway enrichment analysis of serum metabolites

Discussion

Materials and methods

Study design

GWAS data for serum metabolites and HEM

Selection of instrumental variables

Mendelian randomization analysis

Sensitivity analysis

Metabolic pathway analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1