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Analysis of causal effects on metabolic syndrome and inflammatory bowel disease: a Mendelian randomization study

Diabetology & Metabolic Syndrome volume 17, Article number: 143 (2025) Cite this article

Abstract

Background

Metabolic syndrome (MetS) is a conglomerate of metabolic abnormalities including hypertension, obesity, hyperglycemia, hypertriglyceridemia, and low levels of high-density lipoprotein cholesterol (HDL-C). The relationship between MetS and Inflammatory Bowel Disease (IBD) has received a lot of attention lately. Epidemiological investigation has yet to determine if the two illnesses are causally related. To investigate the causal link between IBD and MetS levels, we screened publically available genome-wide association study (GWAS) data using Mendelian randomization (MR) analysis. The study aimed to comprehensively analyze the causal association of each component of MetS, including fasting blood glucose(FBG), HDL-C, triglyceride(TG), waist circumference(WC), and hypertension, on the risk of IBD and its subtypes via univariate, two-way, and multivariate MR (MVMR) methods.

Methods

We selected independent genetic variants of MetS and IBD as instrumental variables (IVs) from published data from the IEU OpenGWAS project and IIBDGC (International Inflammatory Bowel Disease Genetic Consortium), used MR to infer potential causal effects between them, and used a variety of methods (random effect inverse variance weighting (IVW), weighted median, MR-Egger regression, etc.) to ensure the robustness of causal effects.

Results

Univariate two-sample MR (TSMR) revealed that WC was significantly linked to the risk of Crohn’s disease (CD) (OR = 1.659; 95% CI: 1.144–2.405; p = 0.008) and IBD (OR = 1.383; 95% CI: 1.050–1.822; p = 0.021). However, MVMR did not support this finding. In MVMR analysis, hypertension was predicted to be positively associated with the risk of IBD (OR = 2.322516, 95% CI: 1.097713–4.91392, p = 0.0275365), whereas FBG was confirmed to reduce the risk of CD in MVMR studies (OR = 0.4346427, 95% CI: 0.2685399–0.7034868, p = 0.0006948939). Other elements of the MetS did not significantly correlate with IBD.

Conclusion

Although confounding factors cannot be completely ruled out, certain metabolic components, such as WC, may impact the risk of IBD. In addition to highlighting the need for more research to understand the underlying mechanisms and potential indirect effects between MetS components and IBD, this research offers insight into therapeutic treatment decisions for patients with IBD and MetS.

Background

MetS is a collection of metabolic risk factors such as obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low HDL-C. Together, these variables contribute to an increase in the prevalence of different chronic illnesses and cancers [1]. MetS now affects roughly one-quarter of adults globally as a result of lifestyle and dietary habit changes [2]. Not only has it been confirmed to be a risk factor for cardiovascular diseases and cancer, but epidemiological studies have suggested that the incidence of IBD and MetS has shown a similar upward trend in recent decades, indicating that there may be a common pathophysiological mechanism between these two diseases. They were discovered to have similar clinically significant properties, such as an increased risk of cardiovascular disease, nonalcoholic cirrhosis, and obesity [3]. However, the connection between MetS and IBD is still poorly understood and warrants further exploration. Most previous investigations have been primarily observational [4], which renders the results susceptible to confounding factors interfering with the link between them. Because observational studies cannot determine the causative relationship of illness occurrence, more robust and accurate methodologies are needed to characterize the underlying mechanisms.

IBD is a collection of illnesses, primarily ulcerative colitis (UC) and CD, that are typified by persistent, nonspecific intestinal inflammation. Epidemiological statistics indicate that the incidence rate is approximately 0.3% worldwide [5]. Inflammation can develop in several intestinal regions, and its onset is concealed. It is a prevalent autoimmune condition. The illness was discovered in Western nations in the 18th century, and its prevalence is rising globally every year [6]. Although the specific cause of IBD is still unknown, it is generally accepted that genetics, environmental factors, aberrant immune responses, and other variables are involved [7]. As with other chronic conditions, patients experience a number of aftereffects or problems during long-term management, including cancer, rheumatic diseases, immunological disorders, malnutrition, etc [8]. which will impact the course and treatment of the illness. As a result, early detection of risk factors and illness associations can enhance patient quality of life and disease prognosis. With the advent of the notion of comorbidities, people have begun to pay more attention to the coexistence of various comorbidities, such as cardiovascular diseases, cognitive disorders, and MetS [9], and they have come to the realization that managing these conditions can help control IBD better.

MR is an epidemiological method for inferring causality that uses genetic diversity to identify the causal impact of risk factors on research outcomes [10]. Using genetic variation that is unchangeable and randomly assigned at the birth of individuals to avoid common confounding or reverse causality problems in observational studies [11], similar to clinical randomized controlled trials (RCTs), is the gold standard for causality inference, but it is not feasible to conduct RCTs with ethical issues. Therefore, we apply MR methods to reduce the possibility of reverse causality, exclude environmental factors and other interferences, and clarify the causal effects between them. MR uses Single Nucleotide Polymorphisms (SNPs) as IVs to assess the causal relationship between exposure factors and outcome events, univariate TSMR is used to assess the impact of a single exposure on the outcome, and MVMR allows simultaneous assessment of the impact of multiple exposures on the outcome, thus providing a comprehensive understanding of the independent impact of each factor on the disease outcome [12,13,14]. In this study, we used genetic data from large-scale genome-wide association studies to clarify the causal effects of each MetS component on IBD and its subtypes via the TSMR and MVMR methods, followed by reverse MR analysis to test the possibility of reverse causality.

Methods

Study design

We employed the TSMR and MVMR methods to determine the potential causality of MetS and IBD and performed reverse MR analysis to assess the possibility of reverse causality. Three hypotheses support the current investigation following the justification and fundamental presumptions of MR: (1) genetic IVs must be closely tied to exposure; (2) SNPs are not associated with any confounders of risk-outcome associations; and (3) the SNPs don’t affect the outcome through any pathway other than the exposure of interest. The research framework is shown in Fig. 1.

Fig. 1
figure 1

The basic principles of Mendelian analysis

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Source of data

We used GWAS summary data mainly from the IEU OpenGWAS database(IEU OpenGWAS project (mrcieu.ac.uk)), the European Bioinformatics Institute database (EMBL-EBI homepage| EMBL-EBI), the FinnGen Consortium (FinnGen: an expedition into genomics and medicine| FinnGen) and International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) (IBD Genetics Consortium (ibdgc.org), and univariate, multivariate, and bidirectional MR was performed. All original studies involved in this study received ethical approval, and SNPs related to exposure and outcome are displayed in Table 1.

Table 1 Description of GWAS data sources and details
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SNPs associated with IBD, UC, or CD were identified from several previously published GWASs by the IIBDGC. The IBD dataset included 12,882 cases and 21,770 controls for a total of 12,716,084 SNPs. The genetic association data included 27,432 UC participants (N = 6968 cases, 20,464 controls), respectively, and 20,883 CD participants (N = 5956 cases, 14,927 controls) covering 12,255,197 SNPs in UC patients and 12,276,506 SNPs in CD patients. Screening of IBD was diagnosed by recognized radiological, endoscopic, and histopathological assessments, and all included patients met the clinical diagnostic criteria for the disease. The datasets for MetS include WC (sample size 245,746), hypertension (129,909 cases and 354,689 controls), HDL-C (sample size 315,133), FBG (sample size 58,074), TG (sample size 441,016), and metabolic disorders (21,533 cases and 197,259 controls). For the purpose of this study, the MetS criteria were defined on the basis of the criteria of the International Diabetes Federation (IDF) [15]. All GWAS are based in Europe to guarantee the homogeneity of the population.

SNPs associated with IBD, UC, or CD were identified from several previously published GWASs by the IIBDGC. The IBD dataset included 12,882 cases and 21,770 controls for a total of 12,716,084 SNPs. The genetic association data included 27,432 UC participants (N = 6968 cases, 20,464 controls), respectively, and 20,883 CD participants (N = 5956 cases, 14,927 controls) covering 12,255,197 SNPs in UC patients and 12,276,506 SNPs in CD patients. Screening of IBD was diagnosed by recognized radiological, endoscopic, and histopathological assessments, and all included patients met the clinical diagnostic criteria for the disease. The datasets for MetS include WC (sample size 245,746), hypertension (129,909 cases and 354,689 controls), HDL-C (sample size 315,133), FBG (sample size 58,074), TG (sample size 441,016), and metabolic disorders (21,533 cases and 197,259 controls). For the purpose of this study, the MetS criteria were defined on the basis of the criteria of the International Diabetes Federation (IDF) [15]. All GWAS are based in Europe to guarantee the homogeneity of the population.

IVs selection

SNPs that attained genome-wide significance (P < 5 × 10 − 8) and independence (linkage disequilibrium r2 < 0.001, clustering window = 10,000 kb) were selected as IVs in all datasets. Approximate F statistics were used to evaluate the instrumental intensity of the SNPs in MR. IVs with F-statistics significantly greater than 10 were deemed free of instrumental variable bias [16].

Statistical analysis

We initially chose IVW as the main analysis approach for univariate TSMR to evaluate the genetic correlation betwee overall metabolic disorders and each component of MetS with IBD. Additionally, to guarantee robustness and confirm the consistency of the results, MR-Egger, the maximum likelihood approach, and the weighted median method were added. Lastly, we created a forest plot based on the results. Sensitivity analysis was performed using Cochran’s Q heterogeneity test (P < 0.05), and potential pleiotropy was evaluated using the MR-Egger intercept test (P < 0.05). Additionally, we further construct other scatter plots, funnel plots, and leave-one-out plots to show the impact of each SNP on the results. Finally, reverse MR analysis was also performed to determine the direction of causality.

In the second step, the causal effects of MetS and its five components (WC, FBG, HDL-C, TG, and hypertension) on IBD, including its subtypes, were assessed using MVMR analysis. We used the same IVs as the univariate MR analysis; similarly, clustering genetic variables to establish independence (linkage 7 disequilibrium r² < 0.001 within a 10,000 kb window). The software packages R (version 4.4. 1), TwoSampleMR (version 0.6. 8), MendelianRandomization (version 0.10. 0), and forestploter (version 1.1. 2) were used for all analyses in this study. The specific process design is shown in Fig. 2.

Fig. 2
figure 2

Workflow of the MR study design

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Results

Two-sample Mendelian randomization (TSMR)

In the TSMR analysis, we identified 14 metabolic disorder SNPs, 65 WC SNPs, 22 FBG SNPs, 277 hypertension SNPs, 313 TG SNPs, and 278 HDL-C SNPs. In total, 18 separate TSMR analyses were performed covering five MetS components (WC, FBG, HDL-C, TG, hypertension) [15, 17], overall metabolic disorders and three outcomes (IBD and the UC and CD subtypes).

In terms of IBD, the IVW results of TSMR analysis revealed that only WC had a significant causal relationship with IBD (OR = 1.383; 95% CI: 1.050–1.822; p = 0.021), and the other MetS components consistently produced nonsignificant results: metabolic disorders (OR = 1.034; 95% CI: 0.912–1.173; p = 0.605), FBG (OR = 0.919; 95% CI: 0.661–1.277; p = 0.614), hypertension (OR = 1.526; 95% CI: 0.985–2.367; p = 0.059), TG (OR = 1.013; 95% CI: 0.902–1.134; p = 0.824) and HDL-C (OR = 0.945; 95% CI: 0.837–1.068; p = 0.366).

For UC, the IVW results of TSMR analysis indicated that there was no significant association between MetS components and UC, only MR analysis of hypertension and UC showed the strongest nonsignificant relationship (OR = 1.592; 95% CI: 0.970–2.613; p = 0.066). Other abnormal metabolic components suggest that there may be no direct causal effect on the risk of UC. The specific results are as follows: metabolic disorders (OR = 1.064; 95% CI: 0.907–1.248; p = 0.448), FBG (OR = 0.998; 95% CI: 0.685–1.455; p = 0.993), WC (OR = 1.291; 95% CI: 0.909–1.832; p = 0.153), TG (OR = 1.000; 95% CI: 0.877–1.140; p = 0.998) and HDL-C (OR = 0.958; 95% CI: 0.845–1.087; p = 0.508).

As for CD, the TSMR analysis IVW for CD revealed the same findings as for IBD, with WC being directly proportional to the risk of CD (OR = 1.659; 95% CI: 1.144–2.405; p = 0.008). The remaining MetS components didn’t reveal consistent significant results: metabolic disorders (OR = 0.983; 95% CI: 0.829–1.165; p = 0.841), FBG (OR = 0.715; 95% CI: 0.483–1.060; p = 0.095), hypertension (OR = 1.326; 95% CI: 0.741–2.373; p = 0.342), TG (OR = 1.083; 95% CI: 0.923–1.271; p = 0.327) and HDL-C (OR = 0.940; 95% CI: 0.806–1.096; p = 0.430). All the above two-sample MR analysis results are shown in Fig. 3.

Fig. 3
figure 3

The TSMR forest plots of causal links of IBD, UC, or CD with MetS. (A)The TSMR forest plots of causal links of IBD with MetS; (B)The TSMR forest plots of causal links of UC with MetS; (C)The TSMR forest plots of causal links of CD with MetS

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Sensitivity analysis and visualization

To corroborate the conservatism of the main findings, we verified the accuracy of the results using MR-Egger and weighted median estimation methods in addition to the main IVW analysis method, with MR-Egger regression indicating no potential pleiotropy and all P > 0.05. The Cochran-Q statistic was used to assess the heterogeneity test. The test results indicate that some MR analyses are heterogeneous. The particular results are shown in Fig. 3. However, we used the IVW random effects model as the main outcome when performing MR analysis. Therefore, even if there is some degree of heterogeneity among the original studies, this method can obtain accurate and stable results by giving each study the right weight. Moreover, a leave-one-out plot was used to assess the impact of a single SNP on the final MR results for MetS components that were found to have a causal relationship with IBD and its subtypes. The analysis results expressed that no single SNP was crucial to the final results, demonstrating the robustness, stability, and reliability of the MR study. The visualization results are shown in Supplementary Fig. 1.

Reverse MR analyses

Following the abovementioned MR investigation of MetS components and IBD, certain findings with noteworthy correlations were discovered. To further verify the causal relationship between exposure factors and outcomes, we used reverse MR analysis. Finally, there was no evidence of reverse causality. This could suggest that MetS disease is not directly caused by IBD disease, or that there is more to the association between MetS and IBD diseases than can be explained by a single causal chain. The symptoms of MetS are a collection of disorders that together impact the body’s metabolic functions. Genetic and lifestyle variables are typically strongly associated with its occurrence. IBD may not be the main cause of this complex health state, but rather merely an indirect one. We acknowledge that there are limitations in the research process, including insufficient sample size, incomplete data, and lack of more sophisticated measurement tools, which may also make it difficult to identify possible weak associations or causality, but hope that this experiment will serve as a reference for further research in the future. Figure 4 displays specific data.

Fig. 4
figure 4

Outcome of reverse causality of the TSMR

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Multivariable Mendelian randomization (MVMR)

MVMR can more comprehensively control the influence of potential confounding factors and improve the reliability of causal inference. Thus, we used MVMR analysis to explore the relationships between the composition of multiple MetS components and the outcomes of IBD, CD and UC simultaneously after performing the univariate two-sample MR analysis. We obtained 285 SNPs as genetic tools for all and components of MetS (WC, FBG, hypertension, TG, HDL-C) when linkage disequilibrium was removed. In the MVMR analysis of IBD, we found that hypertension was significantly associated with IBD (OR = 2.322516, 95% CI: 1.097713–4.91392, p = 0.0275365), whereas other components were not statistically significant for the time being; Similarly, only hypertension was found to have a meaningful causal relationship with UC (OR = 2.567268, 95% CI: 0.744939–1.10019, p = 0.0141063), but despite this statistical significance, because the confidence interval of the OR value contains 1, the effect of its true outcome in practice may be small OR unstable; Also, FBG was found to be associated with a reduced risk of CD (OR = 0.4346427, 95% CI: 0.2685399–0.7034868, p = 0.0006948939). The analysis results are shown in Fig. 5.

Fig. 5
figure 5

The MVMR forest plots of causal links of IBD, UC, or CD with MetS

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Discussion

The study analyzed the causal relationships between MetS, including FBG, HDL-C, TG, WC and hypertension, and IBD by univariate TSMR and MVMR methods. Notably, our study demonstrated that increasing WC is a risk factor for increased CD risk, but there was no significant association between MetS and UC, and directional tests verified the accuracy of the causal direction. The total analysis of these results reveals that the association between MetS and IBD is complicated and diverse, demonstrating causative differences in metabolic levels between UC patients and CD patients. These findings can serve as a guide for preventing metabolic abnormalities in IBD patients.

In the TSMR analysis, we discovered that every standard deviation increase in WC increased the risk of IBD by 38.3%, and for CD, it raised the risk by 65.9%. However, this association was not confirmed in MVMR analysis. It is possible that other metabolic factors could have confused the effects observed in the TSMR analysis. The TSMR provides valuable preliminary judgment, while MVMR provides more reliable evaluation results when multiple metabolic factors are considered simultaneously. The inconsistencies between the TSMR and MVMR results emphasize the importance of explaining the interactions between different metabolic components.

Increased WC is linked to IBD, underscoring the possible role of central obesity in the development of this disease. Insulin resistance and systemic low-grade inflammation are caused by the release of proinflammatory factors and the suppression of the production of the anti-inflammatory factor adiponectin [18]. Adipose tissue represents metabolically and hormonally active organs that can produce proinflammatory adipokines with deleterious effects on disease activity, affecting metabolic disorders and gut microbial dysbiosis, and ultimately contributing to the development of IBD through mechanisms such as chronic inflammation and oxidative stress [19]. The incidence of IBD has consistently demonstrated a rising tendency due to genetics, dietary changes, environmental changes, and variations in lifestyle, especially in developing countries [5, 20]. Our results are in line with some clinical investigations that demonstrated that WC is independently connected to an elevated risk of CD but not UC [21, 22]. The pathogenesis of CD may therefore be significantly influenced by visceral obesity. Visceral adipose tissue prediction may be a risk factor for the onset of this disease since adipose tissue is an active, multifunctional metabolic organ that functions in lipid storage, immunological, and endocrine functions [23]. Cytokines related to CD (IL-4, IL-6, IL-8, IL-10, TNF-α, etc.) are derived mainly from mesenteric adipose tissue, and the degree of expression of inflammatory cytokines is related to the number of adipocytes in the test results [24]. On the other hand, the reaction of visceral adipose tissue with gut microbes contributes to the development of CD illness. Adipocytes are the main reservoir of bacteria in the mesentery, and altered bacterial composition in turn leads to altered intestinal barrier function [25]. During inflammation, gut bacteria affect inflammatory mediator release by invading adipose tissue, and visceral adipose tissue in CD patients is more susceptible to inflammation and colonization by commensal bacteria in the gut than in UC patients, resulting in adipocyte proliferation [26, 27]. In contrast, there is a lack of evidence for an association between visceral obesity and UC in human studies. In summary, the increase in WC, especially the increase of visceral adipose tissue and mesenteric adipose tissue base, may be a major component of the disease and development of CD.

Because WC lacks a valuable association in MVMR analysis, the direct causal effect of WC may be attenuated or confounded by these factors when other MetS components are considered. This finding emphasizes the importance of considering the interaction of different metabolic components when understanding the collective impact of abnormalities in different metabolic components on IBD.

Surprisingly, FBG had an inverse relationship with CD risk in the MVMR study. Because the incretin axis is crucial in the pathophysiology of IBD, glucagon-like peptides (GLP) such as GLP-1 and GLP-2 are released by endocrine cells in the intestinal mucosa during nutritional absorption [28]. They not only lower blood glucose and body weight, but also have immunomodulatory properties. They have been found to inhibit macrophage infiltration and inflammatory cytokine production in adipose tissue [29]. As an enteric insulinotropic hormone, GLP-1 could increase insulin sensitivity and hepatic metabolism, while GLP-2 significantly improves intestinal barrier function. GLP-1 Ras reduces intestinal inflammation by improving insulin sensitivity, decreasing oxidative stress, and modulating inflammatory pathways. The majority of people with CD have poor eating habits, which can upset the gut microbiota, causing ecological dysregulation and raising the risk of CD. In contrast, Dietary fiber is produced by a healthy diet and affects the makeup and function of the gut microbiota. This process supports the production of beneficial metabolites by the gut microbiota, such as short-chain fatty acids (SCFAs) and bile acids (BAs), which can enter the liver or influence intestinal permeability, stimulating the release of GLP-1 and GLP-2 and preventing the development of CD disease [30]. Furthermore, one possible explanation for this finding is the influence of hypoglycemic medications. A preclinical investigation found that glucose dysregulation worsens the severity of colitis and that treating with hyperglycemia reverses it [31]. Metformin, a hypoglycemic drug, has been found to have anti-inflammatory and antioxidant effects in both in vitro and in vivo studies [32, 33], prevents various proinflammatory cytokine signaling pathways, enhances the integrity of the intestinal barrier in diabetic patients and restores the intestinal microbiota, thereby improving intestinal inflammation [34]. More mechanisms by which impaired fasting glucose may affect the incidence of CD remain unknown and warrant further investigation in the future. Because we did not reach the same conclusion in the TSMR analysis, and clinical trials have reported no changes in FBG levels in CD patients [35, 36], the relationship between them needs further analysis.

Conversely, hypertension and IBD are positively correlated. Hypertension and UC according to the MVMR analysis have a statistically significant relationship (P < 0.05). However, the confidence interval of its OR contains 1, which indicates that the results are contradictory and unstable, suggesting no statistical significance, even if the p-value is < 0.05. The causal relationship between them may be strongly affected by confounding factors, so we carefully consider their significant association. Compared with the general population, UC may be associated with a greater risk of hypertensive morbidity, according to a UK biobank cohort research [37]. Furthermore, no clinical research has found a connection between UC and hypertension; instead, the underlying mechanisms may involve systemic inflammation, vascular endothelial dysfunction, intestinal microbiota distribution, and immune dysfunction [38]. Hypertension is often accompanied by vascular endothelial dysfunction, which disrupts intestinal barrier function, causes damage to the intestinal mucosal structure, increases intestinal permeability, and allows bacteria and toxic products to enter the intestinal wall, triggering an inflammatory reaction [39]. Significant gut barrier dysfunction and intestinal microbiota abnormalities have been reported in hypertension patients in a number of investigations [40]. In 2017, Li et al. [41]. found that the richness, diversity, and gene number of the gut microbiota were lower in hypertensive patients than in healthy people. This resulted in a decline in gut barrier function, immunological function, resistance to gut colonization, and an increase in gut inflammation [42]. We are unable to conduct additional research due to the lack of effective genetic tools, which could cause discrepancies between the results and clinical investigations. Given the close relationship between hypertension and IBD, the genetic association will be explored in depth in the future. More potent genetic tools are needed.

We investigated the causal link between various MetS components and IBD via a number of Mendelian techniques. In that study, we found a positive correlation between WC and CD disease, which has been confirmed in clinical studies and is consistent with the reliability and accuracy of our findings. The result that fasting glucose may reduce the risk of CD disease needs to be interpreted with caution, but the importance of gut-liver-metabolic interactions in IBD-related metabolic dysfunction has been highlighted in the latest study and this hypothesis may be confirmed in future research. The influences of unmeasured or unknown confounding factors and reverse causality were avoided in MR analysis compared with those in clinical observational studies. The robustness of the results and the consistency of causal estimations are ensured by the outcomes of sensitivity and pleiotropy analysis. Lastly, MVMR research provides additional detail about causality, and the results provide potential implications for clinical work. We anticipate that these results will support clinical research on IBD and help in the development of patient-beneficial strategies.

Ecological dysbiosis, which is caused by a change in the gut microbiota’s composition, has been associated with a number of illnesses, such as diabetes mellitus, IBD, and atherosclerosis. Tomas et al. reported that a high-fat diet caused changes in the intestinal microbiome, including a marked rise in the frequency of the phylum Aspergillus and Thickwellia [43]. Changes in the microbial composition, the emergence of distinct taxa, and their biochemical functions and outcomes may all be linked to obesity [44]. Additionally, the gut microbiota is a significant environmental factor that regulates the host’s body’s storage of fat, which ultimately influences the prevalence of obesity. Some physiologically helpful microbiota, such butyrate-producing bacteria, are fewer in T2DM patients, whereas pathogenic bacteria are more prevalent [45]. Ecological disruption of the gut microbiota can worsen lipid metabolism problems, and dyslipidemia has been shown to cause a disturbance in the gut microbiota in both in vitro and animal tests. Some studies have also revealed a lack of variety in the gut microbiota in a small percentage of hypertensive patients. Li et al. discovered that microbial abundance and diversity were significantly lower in prehypertensive and hypertensive populations compared to healthy controls [46]. MetS is a condition that results from the interaction of extrinsic (such as diet and lifestyle) and intrinsic (such as genetics and gut microbiome) host factors. It is frequently associated with a disorder in the gut microbiota, which triggers a low-grade inflammatory response by rupturing the intestinal barrier and causes insulin resistance through metabolites that impact host metabolism and hormone release. This vicious cycle exacerbates the MetS disease, especially when combined with IBD disease. According to gut microbiological research, IBD patients’ gut microbiota composition differs significantly from that of healthy controls, with reduced abundance and diversity and high inter-individual heterogeneity in patient populations [46]. There is a decrease in the abundance of Roseburia and Phascolarctobacterium and a rise in Clostridium, which lowers the anti-inflammatory effects and exacerbates symptoms in IBD patients [47]. Thus, gut flora may be a suitable target for clinical treatment and a possible moderator of the causative link between MetS components and IBD in patients with MetS and IBD. Several studies have shown that the primary pathophysiological basis of both IBD and MetS is chronic low-grade inflammation, whereby bacteria or their components, like endotoxins, enter the bloodstream and cause low-level inflammation due to intestinal barrier disruption and intestinal microbiota discord [48]. IBD has traditionally been treated with immunomodulatory and anti-inflammatory measures. However, it has been discovered that gut microbial therapies, including probiotics, prebiotics, and synbiotics, can also alleviate or maintain IBD remission, lower the disease activity index, and boost the quantity of beneficial bacteria, particularly bifidobacteria, in IBD patients’ guts [49]. By adjusting the immune response, colonic epithelial integrity, microbial composition, and related metabolites, this treatment mostly avoids or lessens the severity of IBD [50]. The findings provide more evidence in favor of using microecological agents to treat and control the disease, particularly UC. Additionally, gut microbiota-targeted therapy has been shown to significantly improve metabolic indicators like serum ALT, AST, and GGT enzyme levels [51], and there is a significant correlation with diastolic blood pressure, which may have an impact on blood pressure levels in T2DM patients [52]. Intake of microbial preparations promotes the development of beneficial bacteria in the gut, which generate compounds linked to decreased inflammation and enhanced insulin sensitivity [53]. In conclusion, metabolic illnesses may benefit from its use as a therapeutic target. Although there is presently debate regarding the findings of certain research on the therapeutic potential of microbes, it is undeniable that future probiotic personalized treatment strategies for patients with Mets and IBD patients.

The gut microbiota is influenced by many factors including diet, pharmacological interventions, socioeconomic conditions, smoking and alcohol consumption. Dietary influences have been confirmed in many studies as increased intake of high-fat and high-sugar diets has triggered many metabolic disorders such as obesity, diabetes, and MetS, as well as immune-related diseases such as IBD [54]. Gastrointestinal symptoms in approximately two-thirds of IBD patients are thought to be caused by irregular eating habits [55]. When metabolically healthy (e.g., in individuals eating a high-fibre diet), gut microbes can regulate the integrity of the gut through various mechanisms of action [56]. Ecological diseases brought on by diet have an impact on tissue function, systemic inflammation, and metabolism. A diet high in salt has a detrimental effect on the gut immune system, altering the gut microbiota in addition to the immunological components [57]. Exercise influences the composition of the gut microbiota, which improves metabolic function and lowers the risk of obesity and insulin resistance. Through exercise can independently change the composition and function of the gut microbiota, promoting the growth of beneficial flora and enhancing the function of the gut barrier [58]. IBD and MetS levels may potentially be impacted by drug use. Antibiotic abuse, for instance, can lead to dysbiosis of the intestinal flora, compromised intestinal barrier function, elevated intestinal permeability, and weakened immune systems. Additionally, the etiology of immunological or metabolic diseases is linked to the disrupted microbiota. Differences in disease risk can be explained by variations in antibiotic kind, dosage, and duration [59]. Antibiotic use has been linked in many studies to the development of CD and UC later on; in CD patients, the correlation is stronger [60]. According to cohort studies, the use of antibiotics may raise the incidence of type 2 diabetes and obesity, two conditions that are closely related to changes in the makeup and activity of human microbes [61]. Non-steroidal anti-inflammatory medications are frequently used in medical settings and have the potential to cause ecological dysbiosis and bacterial overgrowth in the small intestine. The development of MetS and IBD disorders may be influenced by such changes in intestinal flora in two ways: firstly, by leading to dysfunction of tight junctions, which play a key role in the increase of intestinal permeability; and secondly, by causing weight gain, insulin resistance, adipogenesis, fibrogenesis, and hepatic oxidative stress [62]. Both MetS components and gut microbiota can be impacted by lifestyle choices like diet, physical activity, and medication use (such as antibiotics and non-steroidal anti-inflammatory medicines). This has been identified in a growing number of observational clinical and epidemiological studies with potential effects on MetS and IBD diseases, which need to be further confirmed and investigated.

Naturally, we recognize that this study is not without limitations. Firstly, as the study population was exclusively European, the results may not be applicable to other groups. There is no clear causal relationship between IBD and MetS diseases, as it has been observed that in some individuals the OR between IBD and MetS approximates 1. In the GWAS larger sample study, even if the effect sizes are extremely small, statistical tests may give very small p-values (i.e. reach statistical significance P < 0.05), but this statistical significance does not necessarily mean that the results are clinically or practically significant. Despite the implementation of adjustments for confounders, there is a possibility that unmeasured or inadequately adjusted variables remain, which could potentially influence the outcomes. The presence of measurement bias, selection bias, and other such factors represents a significant challenge that must be completely eliminated in large-scale datasets. This represents a substantial limitation of the present study and underscores the necessity for meticulous interpretation of data and the undertaking of well-designed studies to substantiate our observations. Additionally, the absence of IVs and the inconclusive outcomes of analyses of various metabolic component abnormalities and IBD hindered a comprehensive investigation into the relationship between IBD and metabolic abnormalities. It is anticipated that this may be addressed in future studies through further advancements in genetic approaches.

Conclusion

In conclusion, this work used pooled GWAS data to demonstrate the causative association between MetS and IBD via MR, which revealed a potential causal link between WC, FBG, hypertension, and IBD. Additionally, the unexpected protective effect of FBG on IBD warrants more research, suggesting a potential influence of metabolic effects in the inflammatory process. The findings of this study offer several potential clinical implications. Each element of MetS has a different effect on IBD, and individualized evaluations are required to determine the risk of developing IBD. Our findings underline the value of long-term monitoring of IBD patients who also have concomitant MetS, including early identification of those who may be at risk to consider whether interventions can be made through lifestyle improvements. However, it is crucial to carefully evaluate the findings and take further clinical evidence into account. We hope that these findings will stimulate further clinical studies to develop more targeted strategies for the management of MetS. These results deepen our knowledge of the intricate connection between IBD and metabolic health, highlighting the need for more research to clarify the underlying mechanisms and possible indirect effects. It is believed that this could help medical professionals in considering the significance of metabolic trait interactions in metabolic dysfunction associated with IBD in more practical clinical interventions.

Data availability

The online version contains supplementary material available at https://figshare.com/s/e11ccfe10122487002d3https://figshare.com/s/e39c5efb9a71031d80f7.

Abbreviations

IBD:

Inflammatory Bowel Disease

MetS:

Metabolic Syndrome

GWAS:

Genome-Wide association study

MR:

Mendelian Randomization

FBG:

Fasting Blood Glucose

HDL-C:

HDL Cholesterol

TG:

Triglycerides

WC:

Waist Circumference

MVMR:

Multivariate Mendelian Randomization

IVs:

Instrumental Variables

IVW:

Inverse Variance Weighting

TSMR:

Two-Sample Mendelian Randomization

IIBDGC:

International Inflammatory Bowel Disease Genetic Consortium

CD:

Crohn’s Disease

UC:

Ulcerative Colitis

RCTs:

Randomized controlled trials

SNPs:

Single Nucleotide Polymorphisms

GLP:

glucagon-like peptides

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Acknowledgements

We thank all the participants in this study.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157 of Xiwu Road, Xi’an, Shaanxi, 710004, China

    Danyang Zhang, Haitao Shi, Chongcao Wei, Fenrong Chen, Pan Zhang, Xin Gao & Yan Wang

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  1. Danyang Zhang
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  2. Haitao Shi
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  3. Chongcao Wei
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  4. Fenrong Chen
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  5. Pan Zhang
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  6. Xin Gao
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  7. Yan Wang
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Contributions

DZ: Data curation, Formal analysis, Investigation, Methodology, Writing– original draft; HS: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing– review & editing; CW: Data curation, Formal analysis, Investigation, Visualization, Writing original draft; FC: Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing– review & editing; PZ: Data curation, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing– review & editing; XG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing– original draft; YW: Writing– review & editing, Writing– original draft, Supervision, Visualization, Project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yan Wang.

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This study utilized publicly available data and summary statistics from previously published genome-wide association studies (GWAS). No new data were collected specifically for this study, and no additional ethics approval or consent to participate was required for this MR study.

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Zhang, D., Shi, H., Wei, C. et al. Analysis of causal effects on metabolic syndrome and inflammatory bowel disease: a Mendelian randomization study. Diabetol Metab Syndr 17, 143 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01704-w

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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