Evaluating the Effects of Omega-3 Polyunsaturated Fatty Acids on Inflammatory Bowel Disease via Circulating Metabolites: A Mediation Mendelian Randomization Study.
Study Design
- 연구 유형
- Observational Study
- 대상 집단
- None
- 중재
- Evaluating the Effects of Omega-3 Polyunsaturated Fatty Acids on Inflammatory Bowel Disease via Circulating Metabolites: A Mediation Mendelian Randomization Study. 95%
- 대조군
- None
- 일차 결과
- inflammation markers
- 효과 방향
- Mixed
- 비뚤림 위험
- Unclear
Abstract
Epidemiological evidence regarding the effect of omega-3 polyunsaturated fatty acid (PUFA) supplementation on inflammatory bowel disease (IBD) is conflicting. Additionally, little evidence exists regarding the effects of specific omega-3 components on IBD risk. We applied two-sample Mendelian randomization (MR) to disentangle the effects of omega-3 PUFAs (including total omega-3, α-linolenic acid, eicosapentaenoic acid (EPA), or docosahexaenoic acid (DHA)) on the risk of IBD, Crohn's disease (CD) and ulcerative colitis (UC). Our findings indicated that genetically predicted increased EPA concentrations were associated with decreased risk of IBD (odds ratio 0.78 (95% CI 0.63-0.98)). This effect was found to be mediated through lower levels of linoleic acid and histidine metabolites. However, we found limited evidence to support the effects of total omega-3, α-linolenic acid, and DHA on the risks of IBD. In the fatty acid desaturase 2 (FADS2) region, robust colocalization evidence was observed, suggesting the primary role of the FADS2 gene in mediating the effects of omega-3 PUFAs on IBD. Therefore, the present MR study highlights EPA as the predominant active component of omega-3 fatty acids in relation to decreased risk of IBD, potentially via its interaction with linoleic acid and histidine metabolites. Additionally, the FADS2 gene likely mediates the effects of omega-3 PUFAs on IBD risk.
요약
EPA is highlighted as the predominant active component of omega-3 fatty acids in relation to decreased risk of IBD, potentially via its interaction with linoleic acid and histidine metabolites.
Full Text
metabolites
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Article
Evaluating the Effects of Omega-3 Polyunsaturated Fatty Acids on Inflammatory Bowel Disease via Circulating Metabolites: A Mediation Mendelian Randomization Study
Xiaojing Jia 1,2,†, Chunyan Hu 1,2,†, Xueyan Wu 1,2,†, Hongyan Qi 1,2, Lin Lin 1,2, Min Xu 1,2, Yu Xu 1,2, Tiange Wang 1,2, Zhiyun Zhao 1,2, Yuhong Chen 1,2, Mian Li 1,2, Ruizhi Zheng 1,2, Hong Lin 1,2, Shuangyuan Wang 1,2, Weiqing Wang 1,2, Yufang Bi 1,2, Jie Zheng 1,2,3,* and Jieli Lu 1,2,*
- 1 Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- 2 Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- 3 MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
* Correspondence: [email protected] (J.Z.); [email protected] (J.L.) † These authors contributed equally to this work.
Citation: Jia, X.; Hu, C.; Wu, X.; Qi, H.; Lin, L.; Xu, M.; Xu, Y.; Wang, T.; Zhao, Z.; Chen, Y.; et al. Evaluating the Effects of Omega-3 Polyunsaturated Fatty Acids on Inflammatory Bowel Disease via Circulating Metabolites: A Mediation Mendelian Randomization Study. Metabolites 2023, 13, 1041. https:// doi.org/10.3390/metabo13101041
Academic Editor: Ashley J. Snider
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Abstract: Epidemiological evidence regarding the effect of omega-3 polyunsaturated fatty acid (PUFA) supplementation on inflammatory bowel disease (IBD) is conflicting. Additionally, little evidence exists regarding the effects of specific omega-3 components on IBD risk. We applied twosample Mendelian randomization (MR) to disentangle the effects of omega-3 PUFAs (including total omega-3, α-linolenic acid, eicosapentaenoic acid (EPA), or docosahexaenoic acid (DHA)) on the risk of IBD, Crohn’s disease (CD) and ulcerative colitis (UC). Our findings indicated that genetically predicted increased EPA concentrations were associated with decreased risk of IBD (odds ratio
- 0.78 (95% CI 0.63–0.98)). This effect was found to be mediated through lower levels of linoleic acid and histidine metabolites. However, we found limited evidence to support the effects of total omega-3, α-linolenic acid, and DHA on the risks of IBD. In the fatty acid desaturase 2 (FADS2) region, robust colocalization evidence was observed, suggesting the primary role of the FADS2 gene in mediating the effects of omega-3 PUFAs on IBD. Therefore, the present MR study highlights EPA as the predominant active component of omega-3 fatty acids in relation to decreased risk of IBD, potentially via its interaction with linoleic acid and histidine metabolites. Additionally, the FADS2 gene likely mediates the effects of omega-3 PUFAs on IBD risk.
- 1. Introduction
Inflammatory bowel disease (IBD) is a group of chronic inflammatory disorders affecting the gastrointestinal tract, and its prevalence has increased worldwide, reaching up to 0.5% of the general population in the western world [1,2]. The two primary types of IBD are Crohn’s disease (CD) and ulcerative colitis (UC), each with different clinical and histopathological characteristics [3]. The economic burden of IBD is substantial, with over €4.6 billion in annual medical costs in Europe and US$6 billion in the USA, putting a strain on healthcare systems and resources [2]. To alleviate this burden, a comprehensive approach is needed, including the development of preventive care to delay the progression of this disease. Omega-3 polyunsaturated fatty acids (PUFAs) are commonly used nutritional supplements and show beneficial effects on coronary heart disease [4] and asthma [5]. Due
Metabolites 2023, 13, 1041. https://doi.org/10.3390/metabo13101041 https://www.mdpi.com/journal/metabolites
to their anti-inflammatory properties, PUFAs have been proposed as potential targets for preventing and treating autoimmune diseases [6]. Omega-3 PUFAs can be quantified based on a shift in the signal induced by the position of the omega-3 double bond. The sum of concentrations of α-linolenic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and other omega-3 PUFAs is expressed as total omega-3 fatty acids. Long-chain omega-3 PUFAs (EPA and DHA) are derived from α-linolenic acid through a series of elongation, desaturation, and β-oxidation events during fatty acid metabolism. The fatty acid desaturase 2 (FADS2) gene encodes delta-6 desaturase and plays a key regulatory role in this metabolism process [7].
In randomized controlled trials (RCTs), EPA and DHA have often been combined as the active components of omega-3 fatty acids and consumed together, despite their distinct molecular functions and clinical impacts [8]. Daily supplementation with EPA and DHA were reported to be effective in reducing the clinical relapse of CD [9]. However, in the large-scaled vitamin D and omega 3 trial (VITAL) with approximately five years of randomized follow-up, fish oil containing EPA and DHA did not significantly reduce the rate of a composite outcome consisting of rheumatoid arthritis, IBD, autoimmune thyroid disease, and all other autoimmune diseases [10]. Moreover, there was a lack of detailed information on IBD in this study. Additionally, observational studies did not provide convincing and consistent evidence of the relationship between dietary intakes of omega-3 PUFAs and the risk of IBD [11–13]. Information on usual diet relied on self-reported dietary questionnaires, which may produce errors or bias in recall. The existing evidence makes it challenging to confirm the causal effect of omega-3 PUFAs on IBD; and identify the key supplement among the omega-3 PUFA component (α-linolenic acid, EPA, and DHA) that may exhibit the protective effect.
Mendelian randomization (MR) is an approach that could estimate causal effect of an exposure on an outcome and overcome issues related to residual confounding or reverse causality [14]. Moreover, this method allows for investigating the effects of each omega-3 PUFA component on IBD, which may be challenging to achieve in an RCT setting. Recently, He et al. reported that total omega-3 fatty acid had a protective effect against increased UC risk instead of CD [15], but the evidence on IBD was not addressed. In addition, their analysis involved only 21 omega-3 instruments after eliminating SNPs associated with potential confounders and outcomes, which might have reduced the power of the analysis. More critically, some instruments in key regulatory genes such as FADS2 gene were eliminated, which may have important influences on the reliability of the findings. Meanwhile, there remains a knowledge gap in evaluating the separate biological effects of α-linolenic acid, EPA, and DHA, with their metabolic mechanisms being unexplored.
In this study, we aimed to explore the effects of omega-3 PUFAs (i.e., total omega-3, α-linolenic acid, EPA, and DHA) on the risk of IBD and its subtypes, and the potential metabolic pathways linking omega-3 PUFAs with IBD. Given the central role of the FADS2 gene in omega-3 PUFAs’ metabolism, further analyses in this specific region were essential through genetic colocalization. This approach allowed us to assess whether there were shared causal variants within the FADS2 gene region that could influence both omega-3 PUFAs and IBD risk [16].
2. Materials and Methods
- 2.1. Study Design
A schematic overview of the study design was detailed in Figure 1. We employed the univariable MR analysis to assess whether total omega-3 fatty acid, α-linolenic acid, EPA, and DHA showed causal effects on IBD and its subtypes (CD and UC), using summary-level data from publicly available genome-wide association studies (GWASs). Colocalization analysis was further conducted in the FADS2 gene region to test for pleiotropic effect and investigate the underlying mechanisms. A bidirectional MR analysis was applied to estimate the effect of genetic liability to IBD on omega-3 PUFAs. Mediation MR analysis
pleiotropic effect and investigate the underlying mechanisms. A bidirectional MR analysis was applied to estimate the effect of genetic liability to IBD on omega-3 PUFAs. Mediation
IBD. All datasets were publicly available, and ethical approval was acquired for all original studies.
estimated the effect of potential metabolites linking omega-3 PUFAs with the IBD. All datasets were publicly available, and ethical approval was acquired for all original studies.
Figure 1. Study design of this MR study.
Figure 1. Study design of this MR study.
- 2.2. Data Sources and Genetic Instruments for Omega-3 PUFAs
- 2.2. Data Sources and Genetic Instruments for Omega-3 PUFAs
Single-nucleotide polymorphisms (SNPs) associated with total omega-3 fatty acid were derived from UK Biobank, which collected deep genetic and phenotypic data from approximately 500,000 individuals aged between 40 and 69 [17]. Genetic associations of α-linolenic acid, EPA, and DHA were obtained from a GWAS meta-analysis in 8866 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium [18]. Details of the data sources and sample sizes of the exposures are listed in the Table S1.
Single-nucleotide polymorphisms (SNPs) associated with total omega-3 fatty acid were derived from UK Biobank, which collected deep genetic and phenotypic data from approximately 500,000 individuals aged between 40 and 69 [17]. Genetic associations of α-linolenic acid, EPA, and DHA were obtained from a GWAS meta-analysis in 8866 participants of European ancestry from the Cohorts for Heart and Aging Research in
In this study, the genetic variants that showed robust association with total omega-3 fatty acid (with genetic association p value < 5 × 10−8) and showed independence (with linkage disequilibrium (LD) r2 < 0.01 in European ancestry) were selected as candidate instruments. Given the limited sample size of the α-linolenic acid, EPA, and DHA GWASs, a slightly more relaxing threshold (p < 5 × 10−6) was used to select instruments for these exposures. After harmonization with outcome data and removing palindromic or mismatching alleles, 42 independent SNPs for total omega-3 fatty acid, 12 independent SNPs for α-linolenic acid, 23 independent SNPs for EPA, and 6 independent SNPs for DHA were selected as instruments (Figure S1). One SNP was selected to represent the effect of each omega-3 PUFA in the FADS2 region (rs174564 for total omega-3 fatty acid, rs174547 for α-linolenic acid, rs174538 for EPA, and rs174555 for DHA; all these SNPs are in strong LD to each other (LD r2 > 0.7), which represents the same signal in this region).
- 2.3. Outcome Data Sources
- 2.4. Metabolite Data Sources
- 2.5. Statistical Analysis
- 2.5.1. Two-Sample MR Analysis
- 2.5.2. Mediation MR Analysis Linking EPA with IBD via Metabolites
We further estimated the mediation effects of circulating metabolites linking EPA with IBD risk. We used a novel analytical pipeline that integrated mediation MR with metabolite set enrichment analyses. First, we used a two-step MR approach to: (1) assess the causal
effect of EPA on 974 potential metabolites (step 1) that have publicly available GWAS datasets in the IEU OpenGWAS database, which selected 237 metabolites with FDR < 0.05; and (2) estimate the effect of 237 metabolites on IBD (step 2), which further selected 211 metabolites associated with both EPA and IBD as candidate mediation metabolites. Second, we performed the metabolite set enrichment analysis on the 211 selected candidate metabolites, which aimed to select key metabolites enriched in certain metabolic pathways (Figure S1). For the metabolites that showed evidence of enrichment in the enrichment analysis, we further performed multivariable MR (MVMR) to determine their mediation effects on IBD which was adjusted for the effect of EPA [27]. We used IVW as our main approach to estimate the effect of EPA on the metabolites (β1). Additionally, MVMR was applied to estimate: (1) the effect of each metabolite on risk of IBD with adjustment for the genetic effect of EPA (β2); and (2) the direct effect of EPA on IBD with adjustment for each mediator individually (βdirect). To calculate the indirect mediation effect of EPA on IBD outcome, we used the difference of coefficients method as our main method, i.e., the casual effect of EPA on outcomes via metabolites (βtotal − βdirect). The total effect was the estimate of EPA on IBD in univariable MR (βtotal). Thus, the proportion of the total effect mediated by each metabolite was separately estimated by dividing the indirect effect by the total effect ((βtotal − βdirect)/βtotal). Standard errors were derived by using the delta method, using effect estimates obtained from 2SMR analysis.
Univariable, bidirectional, and multivariable MR analyses were considered significant with a 2-sided p ≤ 0.05. Metabolites associated with omega-3 PUFAs or IBD were considered significant with an FDR < 0.05. Enrichment analysis was performed using the online MetaboAnalyst software (version 5.0, Mcgill University, Montreal, QC, Canada; https:// www.metaboanalyst.ca, accessed on 17 November 2022) [28]. All analyses were performed using ‘TwoSampleMR’ and ‘MR-PRESSO’ package in R Software 3.6.0.
3. Results
We selected 42, 12, 23, and 6 SNPs as instruments to proxy life-long effect of total omega-3 fatty acid, α-linolenic acid, EPA, and DHA, respectively. In bidirectional MR, there were 117, 89, and 62 independent instruments incorporated for IBD, CD, and UC, respectively. Mean F statistics of the exposures ranged from 29.82 to 262.21 indicating that the MR estimates were not likely to be influenced by weak instrument bias (Table S2).
- 3.1. Genetically Predicted Omega-3 PUFAs on Risk of IBD (Including CD and UC)
Table 1 shows the effects of omega-3 PUFAs on IBD risks. Considering total omega-
- 3 fatty acid as a whole, little evidence indicated its protective effect on IBD risk (odds ratio (OR) of IVW, 0.94; 95% confidence interval (CI), 0.82–1.07). Meanwhile, higher concentrations of α-linolenic acid showed a potential effect on increasing risk of IBD, although the evidence was weaker due to the wide confidence interval (OR of IVW, 1.54; 95% CI, 0.72–3.29). In contrast, genetically increased levels of EPA showed a causal effect on the lower risk of IBD (OR of IVW, 0.78; 95% CI, 0.63–0.98). There was little evidence for the presence of heterogeneity (Cochran’s Q-test Ph = 0.10), pleiotropy (MR-Egger intercept Pintercept = 0.97), or any outliers (MR-PRESSO P of global test = 0.099). Estimated effect was consistent using the weighted median approach (OR, 0.59; 95% CI, 0.45–0.78). However, there was little evidence to support the effect of DHA on IBD (OR of IVW, 1.05; 95% CI, 0.86–1.28).
The results of the primary MR analyses of CD and UC are presented in Figure 2. Results of sensitivity analyses are listed in Table S3. In consistent with the IBD results, there was little evidence to support the effects of total omega-3 fatty acid, α-linolenic acid, and DHA on the risk of CD and UC (Figure 2A,B,D). Meanwhile, increased levels of genetically proxied EPA still showed a strong effect on a lower risk of CD (OR of IVW, 0.67; 95% CI, 0.50–0.91), but with little effect on UC (OR of IVW, 0.88; 95% CI, 0.68–1.14) (Figure 2C).
Table 1. Two-sample Mendelian randomization estimations showing the effect of omega-3 PUFAs on inflammatory bowel disease.
Methods Estimate Heterogeneity Pleiotropy
Exposure No. of SNPs
MR-PRESSO P Total omega-3 fatty acid
MR Egger int P
OR 95% CI P Q Ph
42 IVW 0.94 (0.82, 1.07) 0.35 232.9 <0.001 0.06 <0.001 MR-Egger 0.83 (0.69, 0.99) 0.05 Weighted median 0.85 (0.80, 0.92) <0.001
MR-PRESSO outlier test
0.88 (0.81, 0.95) 0.003 α-linolenic acid
12 IVW 1.54 (0.72, 3.29) 0.26 46.7 <0.001 0.65 <0.001
MR-Egger 1.40 (0.58, 3.39) 0.48 Weighted median 1.42 (0.89, 2.28) 0.14
MR-PRESSO outlier test
1.24 (0.79, 1.95) 0.38 EPA 23 IVW 0.78 (0.63, 0.98) 0.03 30.8 0.099 0.97 0.099 MR-Egger 0.78 (0.45, 1.34) 0.37 Weighted median 0.59 (0.45, 0.78) <0.001
MR-PRESSO outlier test
NA NA NA DHA 6 IVW 1.05 (0.86, 1.28) 0.65 21.6 <0.001 0.56 0.012
MR-Egger 1.20 (0.75, 1.93) 0.49 Weighted median 1.12 (0.98, 1.28) 0.09
MR-PRESSO outlier test
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1.11 (0.99, 1.25) 0.43
Abbreviations: CI, confidence interval; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; Egger int, egger intercept; IVW, inverse variance weighted; MR, Mendelian randomization; OR, odds ratio; PUFAs, polyunsaturated fatty acids; Ph, p-value for heterogeneity.
Figure 2. Cont.
Metabolites 2023, 13, 1041 7 of 16
Figure 2. Causal effects of omega-3 polyunsaturated fatty acids on inflammatory bowel disease as a whole, on Crohn’s disease, and ulcerative colitis or via the FADS2 gene cluster. Univariable causal effects of (A) total omega-3, (B) α-linolenic acid, (C) EPA, and (D) DHA on investigated outcomes (light shades of blue, orange and green). Causal effects of each fatty acid on investigated outcomes via the FADS2 gene (blue, orange and green). Abbreviations: ALA, α-linolenic acid; CD, Crohn’s disease; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; FADS2, fatty acid desaturase 2; IBD, inflammatory bowel disease; UC, ulcerative colitis.
Figure 2. Causal effects of omega-3 polyunsaturated fatty acids on inflammatory bowel disease as a whole, on Crohn’s disease, and ulcerative colitis or via the FADS2 gene cluster. Univariable causal effects of (A) total omega-3, (B) α-linolenic acid, (C) EPA, and (D) DHA on investigated outcomes (light shades of blue, orange and green). Causal effects of each fatty acid on investigated outcomes via the FADS2 gene (blue, orange and green). Abbreviations: ALA, α-linolenic acid; CD, Crohn’s disease; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; inflammatory bowel disease; UC, ulcerative colitis.
3.2. Sensitivity Analysis in FADS2 Gene Region
As shown in the leave-one-out analyses, the MR estimates of omega-3 PUFAs on IBD, CD, and UC were mainly driven by SNP effects in the FADS2 gene region (rs174564 for total omega-3 fatty acid, rs174547 for α-linolenic acid, rs174538 for EPA, and rs174555 for DHA) (Figure S2). As shown in Figure 2, the MR results of the single FADS2 SNP showed the causal effects of total omega-3 fatty acid, EPA, and DHA on lower risk of IBD. The ORs (95% CI) were 0.85 (0.79–0.92), 0.59 (0.43–0.80), and 0.53 (0.37–0.75), respectively. On the contrary, α-linolenic acid showed a strong effect on the increasing risk of IBD (OR, 26.82; 95% CI, 5.40–133.20).
As for IBD subtypes, the FADS2 gene showed a stronger effect on lowering the risk of CD (ORs (95% CI) were 0.78 (0.71–0.86) for total omega-3, 0.38 (0.25–0.57) for EPA, and 0.35 (0.23–0.55) for DHA) but was absent for UC. Meanwhile, a FADS2 single-SNP in α-linolenic acid had a positive effect on increasing CD risk (OR, 186.12; 95% CI, 23.46–1476.40), but with less effect on UC risk.
Aligning with the MR estimates of a single-SNP in the FADS2 region, we observed compelling evidence of colocalization for α-linolenic acid with CD (colocalization probability, 98.90%), but with little evidence for UC (colocalization probability, 2.61%; Figure 3A). A similar pattern of colocalization evidence was observed for EPA (colocalization probability of CD, 98.80%; colocalization probability of UC, 2.44%; Figure 3B), as well as DHA (colocalization probability of CD, 94.50%; colocalization probability of UC, 6.56%; Figure 3C). Collectively, colocalization analyses further supported distinct effects of omega-3 PUFAs on CD and UC.
Metabolites 2023, 13, 1041 Figure 3C). Collectively, colocalization analyses further supported distinct effects of8 of 16 omega-3 PUFAs on CD and UC.
Figure 3. Regional association plots of α-linolenic, eicosapentaenoic, and docosahexaenoic acids with Crohn’s disease and ulcerative colitis in the FADS2 region. (A) Regional plots of α-linolenic acid and Crohn’s disease and ulcerative colitis in the FADS2 region without conditional analysis. (B) Regional plots of eicosapentaenoic acid and Crohn’s disease and ulcerative colitis in the FADS2 region without conditional analysis. (C) Regional plots of docosahexaenoic acid and Crohn’s disease and ulcerative colitis in the FADS2 region without conditional analysis. This figure was obtained from http://locuszoom.org/. Abbreviations: FADS2, fatty acid desaturase 2.
3.3. Effects of Genetic Liability to IBD, CD, and UC on the Levels of Omega-3 PUFAs
We further estimated whether genetic liability to IBD was a causal factor on changing levels of omega-3 PUFAs using bidirectional MR. There was little evidence to suggest the causal effect of genetic liability to IBD and CD on omega-3 PUFAs by using the IVW method (Table 2). However, genetic liability to UC showed an effect on lowering levels of DHA (β −0.05 (95% CI −0.09, −0.002)).
Table 2. Bidirectional Mendelian randomization estimates for causal effects of genetic liability to IBD, CD, and UC on the levels of omega-3 PUFAs.
Outcome
IVW Heterogeneity Pleiotropy Beta 95% CI P Q Ph MR Egger
Exposure No. of SNPs
No. of SNPs
MRPRESSOP IBD 117
int P
Total omega-3 fatty acid
105 −0.002 (−0.012, 0.009) 0.76 200.7 <0.001 0.86 <0.001 α-linolenic acid 39 −0.001 (−0.004, 0.001) 0.36 35.9 0.57 0.94 0.416
EPA 39 0.001 (−0.019, 0.020) 0.92 57.9 0.02 0.87 0.009 DHA 39 −0.010 (−0.059, 0.040) 0.71 47.7 0.13 0.69 0.036
Total omega-3 fatty acid
CD 89
83 0.004 ( 0.005, 0.013) 0.39 174.5 <0.001 0.28 <0.001 α-linolenic acid 28 −0.001 (−0.003, 0.001) 0.28 28.7 0.38 0.41 0.463
EPA 28 0.011 (−0.005, 0.026) 0.17 46.4 0.01 0.85 0.012 DHA 28 0.029 ( 0.011, 0.070) 0.15 39.3 0.06 0.81 0.090
Total omega-3 fatty acid
UC 62
53 0.005 (−0.018, 0.008) 0.45 131.0 <0.001 0.27 <0.001 α-linolenic acid 27 0.002 (−0.001, 0.004) 0.23 28.5 0.34 0.34 0.446
EPA 27 −0.005 (−0.021, 0.010) 0.50 26.5 0.44 0.38 0.088 DHA 27 −0.045 (−0.089, −0.002) 0.04 26.7 0.42 0.61 0.095
Abbreviations: CI, confidence interval; CD, Crohn’s disease; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; Egger int, Egger intercept; IBD, inflammatory bowel disease; IVW, inverse variance weighted; MR, Mendelian randomization; PUFAs, polyunsaturated fatty acids; Ph, p-value for heterogeneity; UC, ulcerative colitis.
3.4. Mediation MR of EPA, Metabolites, and IBD Risk
Given that genetically predicted increased EPA had significant benefit on lowering IBD risks, we further estimated whether there were some metabolites or metabolic pathways linking the EPA with IBD risk. For 211 candidate mediation metabolites (selected by the two-step MR described in the Section 2), metabolite set enrichment analysis indicated that α-linolenic acid and linoleic acid metabolism, and methylhistidine metabolism were the top two metabolic pathways that have been significantly enriched (Figure 4). DHA, linoleic acid, and histidine were major metabolites determined in the two pathways, respectively.
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Figure 4. Metabolite set enrichment analysis of 211 selected candidate metabolites associated with both EPA and risk of IBD. The figure shows a graphical representation of the pathway-associated metabolite sets by enrichment analysis in the effect of EPA on IBD. Abbreviations: EPA, eicosapentaenoic acid; IBD, inflammatory bowel disease.
Figure 4. Metabolite set enrichment analysis of 211 selected candidate metabolites associated with both EPA and risk of IBD. The figure shows a graphical representation of the pathway-associated
The effect of EPA on each intermediate metabolite (linoleic acid, DHA, and histidine) is shown in Figure 5A, higher levels of EPA were associated with lower linoleic acid (β, −0.51; 95% CI −0.91, −0.11), higher DHA (β, 0.61; 95% CI 0.27, 0.95), and lower histidine (β, −0.10; 95% CI −0.17, −0.03). The effect of each intermediate metabolite on IBD risk was separately adjusted for the EPA effect in the MVMR model, presented as β with 95% CI and was shown in Figure 5B. Linoleic acid and histidine showed effects on increasing risk of IBD, although the result for histidine was with a wide confidence interval. Figure 5C displays the proportion of the mediation effect of EPA on IBD explained by each intermediate metabolite separately. Linoleic acid explained 58.33% (95% CI 32.97%, 83.69%) of the total effect of EPA on IBD, while DHA explained 50.00% (95% CI 25.76%, 74.24%). Histidine explained 66.67% (95% CI 43.34%, 90.00%) of the total effect. Given the large proportion of mediation of these intermediate metabolites, the direct effects of EPA on IBD were massively attenuated after conditioning on each of the intermediate metabolites (Figure 5B).
taenoic acid; IBD, inflammatory bowel disease.
The effect of EPA on each intermediate metabolite (linoleic acid, DHA, and histidine) is shown in Figure 5A, higher levels of EPA were associated with lower linoleic acid (β, −0.51; 95% CI −0.91, −0.11), higher DHA (β, 0.61; 95% CI 0.27, 0.95), and lower histidine (β, −0.10; 95% CI −0.17, −0.03). The effect of each intermediate metabolite on IBD risk was separately adjusted for the EPA effect in the MVMR model, presented as β with 95% CI and was shown in Figure 5B. Linoleic acid and histidine showed effects on increasing risk of IBD, although the result for histidine was with a wide confidence interval. Figure 5C displays the proportion of the mediation effect of EPA on IBD explained by each intermediate metabolite separately. Linoleic acid explained 58.33% (95% CI 32.97%, 83.69%) of the total effect of EPA on IBD, while DHA explained 50.00% (95% CI 25.76%, 74.24%). Histidine explained 66.67% (95% CI 43.34%, 90.00%) of the total effect. Given the large propor-
Metabolites
Metabolites 2023, 13, 1041 10 of 16
Figure 5. Estimates for the metabolites that mediated the effect of EPA on the risk of IBD. (A) MRestimated effects of EPA on each intermediate metabolite (linoleic acid, DHA, and histidine) separately, presented as β with 95% CI. (B) MR-estimated effects of each intermediate metabolite separately on IBD after MVMR adjustment for EPA, presented as β with 95% CI. (C) MR-estimated effects of indirect effects of each intermediate metabolite separately, by using the difference of coefficients method with delta method-estimated 95% CIs. MR-estimated proportions mediated (%) are presented with 95% CIs. The sum of proportions mediated (%) were higher than 100%, due to the strong correlation among these intermediate metabolites (linoleic acid, DHA, and histidine). Abbreviations: CI, confidence interval; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; IBD, inflammatory bowel disease; linoleic acid, linoleic acid; MR, Mendelian randomization; MVMR, multivariable Mendelian randomization.
Figure 5. Estimates for the metabolites that mediated the effect of EPA on the risk of IBD. (A) MRestimated effects of EPA on each intermediate metabolite (linoleic acid, DHA, and histidine) separately, presented as β with 95% CI. (B) MR-estimated effects of each intermediate metabolite separately on IBD after MVMR adjustment for EPA, presented as β with 95% CI. (C) MR-estimated effects of indirect effects of each intermediate metabolite separately, by using the difference of coefficients method with delta method-estimated 95% CIs. MR-estimated proportions mediated (%) are presented with 95% CIs. The sum of proportions mediated (%) were higher than 100%, due to the strong correlation among these intermediate metabolites (linoleic acid, DHA, and histidine). Abbreviations: CI, confidence interval; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; IBD, inflammatory bowel disease; linoleic acid, linoleic acid; MR, Mendelian randomization; MVMR, multivariable Mendelian randomization.
4. Discussion
4. Discussion
The present study employed a comprehensive analysis using MR to strengthen the inferences regarding the effects of different omega-3 PUFAs (including total omega-3, αlinolenic acid, EPA, and DHA) on IBD risk. We provided evidence supporting that increased levels of EPA are causally associated with a lower risk of IBD and CD, but the effect on UC is relatively weaker. The mediation MR analysis further suggested that EPA may influence IBD via α-linolenic acid, linoleic acid and methylhistidine metabolism pathways. Linoleic acid and histidine were estimated to mediate the effect of EPA on IBD. However, we found limited evidence to support the effects of total omega-3, α-linolenic acid, and DHA on the risk of IBD. Furthermore, leave-one-out, single-locus, and colocalization analyses indicated that the effects of omega-3 PUFAs on IBD were massively driven by SNP effects in the FADS2 gene region. Therefore, desaturation steps during omega-3 PUFAs’ biosynthesis might play a critical role in the relationship between omega-3 PUFAs and IBD. Meanwhile, higher genetic liability to UC might be associated with lower levels of DHA, potentially indicating a weaker absorption or abnormal metabolism of omega-3 PUFAs in UC. Collectively, our results suggest that supplementation with EPA (rather than α-linolenic acid or DHA) might be a more effective strategy to prevent the onset of IBD, especially CD, rather than UC with high probability of weak absorption or abnormal metabolism on omega-3 PUFAs. These findings shed light on the potential differential impacts of specific omega-3 PUFAs on IBD risk and highlight the importance of considering individual PUFA components in designing prevention strategies for this complex disease.
The present study employed a comprehensive analysis using MR to strengthen the inferences regarding the effects of different omega-3 PUFAs (including total omega-3, αlinolenic acid, EPA, and DHA) on IBD risk. We provided evidence supporting that increased levels of EPA are causally associated with a lower risk of IBD and CD, but the effect on UC is relatively weaker. The mediation MR analysis further suggested that EPA may influence IBD via α-linolenic acid, linoleic acid and methylhistidine metabolism pathways. Linoleic acid and histidine were estimated to mediate the effect of EPA on IBD. However, we found limited evidence to support the effects of total omega-3, α-linolenic acid, and DHA on the risk of IBD. Furthermore, leave-one-out, single-locus, and colocalization analyses indicated that the effects of omega-3 PUFAs on IBD were massively driven by SNP effects in the FADS2 gene region. Therefore, desaturation steps during omega-3 PUFAs’ biosynthesis might play a critical role in the relationship between omega-3 PUFAs and IBD. Meanwhile, higher genetic liability to UC might be associated with lower levels of DHA, potentially indicating a weaker absorption or abnormal metabolism of omega-3 PUFAs in UC. Collectively, our results suggest that supplementation with EPA (rather than α-linolenic acid or DHA) might be a more effective strategy to prevent the onset of IBD, especially CD, rather than UC with high probability of weak absorption or abnormal metabolism on omega-3 PUFAs. These findings shed light on the potential differential
Previous systematic reviews and meta-analysis of RCTs have not yielded firm recommendations regarding the usefulness of omega-3 PUFAs in treating IBD [29,30]. In a study that included 19 RCTs, the results showed no significant benefits of omega-3 PUFA supplementation in maintaining remission of disease [29]. Another study of 9 RCTs, found insufficient data to support the routine use of omega-3 fatty acids for the maintenance of remission in CD and UC [30]. Similarly, a prospective investigation in the Nurses’ Health Study cohort reported that the risk of IBD was not influenced by long-term intake of omega3 PUFAs [31]. Meanwhile, our findings showed weak evidence of protective effects of genetically predicted higher total omega-3 fatty acid against the risk of IBD and its subtypes (both CD and UC) by using MR analysis. In spite of the known anti-inflammatory properties of omega-3 PUFAs, attributed to their ability to reduce the production of cytokines [32,33] and C-reactive protein (CRP) [34], the available data provided less convincing evidence to support the use of omega-3 PUFAs in the prevention or treatment of IBD. One plausible explanation for these findings is that total omega-3 fatty acid comprises various fatty acids with different carbon chain lengths, bond saturation, and diverse biochemical mechanisms [35]. This complexity may lead to an overall effect of total omga-3 fatty acid that is diminished or challenging to decipher in relation to IBD and its subtypes. Hence, the specific roles and effects of individual omega-3 PUFAs, such as EPA and DHA, need to be explored more comprehensively to understand their potential benefits in IBD management.
α-linolenic acid serves as a substrate for other essential omega-3 PUFAs in the body. In our study, genetically predicted α-linolenic acid levels showed a trend toward an increased risk of IBD, although the statistical power of the analysis was relatively low. Observational studies have also provided inconclusive evidence regarding the relationship between α-linolenic acid and IBD. For instance, a case-control study has reported higher dietary αlinolenic acid intakes in newly diagnosed UC patients compared with healthy controls [12]. However, in consistent with our findings, previous studies did not find any association between higher dietary intake of α-linolenic acid and an increased risk of IBD [36,37]. Well powered studies are needed to investigate the effect of α-linolenic acid on IBD and other autoimmune diseases in the future.
EPA and DHA are the main components of long-chain omega-3 fatty acids, which are derived from α-linolenic acid through a series of elongation and desaturation steps and βoxidation. The beneficial effects of EPA and DHA have been investigated as a combination or as part of omega-3 supplementation in observational studies and experimental trials. However, the distinct effects of EPA and DHA on the risk of IBD have been relatively unexplored. In our study, we conducted separate evaluations and found evidence suggesting that increased levels of EPA were associated with a lower risk of IBD and CD.
Interestingly, our findings indicate that EPA might play a more important role than DHA in relation to IBD risk. Although direct comparative studies on the effects of EPA and DHA on IBD risk are limited, other research has provided insights that align with our results [38]. In twenty-one asthmatic adults, EPA reduced the production of interleukin1b and tumor necrosis factor from alveolar macrophages to a much greater extent than DHA [39]. Meanwhile, the Cardiovascular Health Study reported that plasma phospholipid EPA, but not DHA, was associated with lower concentrations of CRP [40]. These findings, when integrated with our results, suggest that EPA may be more relevant for prevention of IBD.
We further demonstrated that the protective effect of EPA on risk of IBD was mainly influenced by α-linolenic acid, linoleic acid, and methylhistidine metabolism pathways. These findings are consistent with a previous study that has indicated that krill oil, rich in omega-3 PUFAs, exerts an inhibitory effect on histidine metabolism, leading to attenuated intestinal inflammation [41]. Moreover, significantly increased levels of histidine have been found in IBD patients compared to controls, which implied an association between histidine and an increased risk of IBD [42]. Therefore, EPA might reduce IBD risk through the regulation of histidine levels. Additionally, since there is competition for shared enzymes and metabolic substrates in the synthesis of omega-3 and omega-6 PUFAs, EPA
might also influence the levels of linoleic acid. A previous study indicated that higher levels of linoleic acid, which are involved in the production of proinflammatory mediators, were found in IBD patients compared with controls, thereby implicating an increased risk of IBD [43]. Lower levels of linoleic acid might mediate the protective effects of EPA and IBD. In this study, we showed the causal effects of EPA on α-linolenic acid, linoleic acid, and methylhistidine metabolic pathways and three key metabolites (DHA, linoleic acid, and histidine). These results provide valuable insights into the metabolic mechanism through which EPA influences IBD risk.
Collectively, the increased risk of IBD is primarily associated with higher levels of α-linolenic acid or lower levels of EPA, as the differences in desaturation steps driven by the FADS2 gene will lead to changes in both upstream α-linolenic acid and downstream EPA concentrations [7]. Thus, the role of the FADS2 gene is crucial and merits further investigation.
Our study also revealed a massive influence of FADS2 variants on IBD and CD, but not on UC. Furthermore, we found robust colocalization evidence between omega-3 PUFAs and CD in the FADS2 gene region, but little colocalization evidence for UC. These findings suggest that the key link between omega-3 PUFAs and IBD is driven by effects in the FADS2 gene cluster. Several lines of evidence support our observations and indicate that the FADS2 gene is associated with inflammation [44] and CD risk [45,46]. For instance, the FADS2 gene regulated immune functions and showed colocalization evidence on PUFAs and CD (posterior probability = 0.94) [45]. In addition, integrated data from metabolomics profiling and experiments revealed the role of FADS2 against chronic inflammation among CD patients [47]. Therefore, FADS2 is a crucial gene linking omega-3 PUFAs and IBD risk, particularly in the case of CD.
Despite the protective role on CD, our study provided little evidence to support the effect of omega-3 PUFAs on UC risk. Previous epidemiological studies also indicated that an increasing dietary intake of EPA or DHA had no association with a decreased risk or maintenance of remission in UC [37,48]. It is possible that inadequate supplementation or absorption resulted in lower concentrations of fatty acids in UC patients, thereby limiting their ability to trigger protective effects. For example, the inflamed colonic mucosa of patients with UC was linked to a significant decrease in EPA [49]. Similarly, a significant reduction in DHA derivatives was observed in active inflammatory UC [50]. As our bidirectional MR analysis showed, genetic liability to UC had an effect on decreased concentrations of DHA. Therefore, whether it is rational for UC patients to increase supplementation of fish oil or enhance intestinal absorption ability is worth further investigation. In contrast, He et al. recently reported that total omega-3 fatty acid had no causal effect on CD, but decreased UC risk using MR [15]. We believe the discrepant association observed for UC in our study compared with theirs was partly driven by the different instrument selection process. After applying a similar instrument selection as our study, He et al. further eliminated SNPs associated with potential confounders between total omega-3 fatty acid and outcomes. This selection process eliminated over half of the genetic variants from the instrument list for total omega-3 fatty acid. He et al. claimed that this selection was used to satisfy the second assumption of MR (exchangeability). However, this assumption suggested that the instruments are not associated with common causes (confounders) of the instrument–outcome association. MR estimates are generally less susceptible to confounders because human DNA is stable across the life course. Therefore, excluding SNPs associated with confounders between total omega-3 and IBD (e.g., body mass index) will reduce the power of the analysis rather than satisfying the exchangeability assumption of MR. In fact, such an overly stringent selection resulted in the deprivation of genetic variants in the FADS2 region. As mentioned above, the FADS gene cluster plays a central role on PUFAs’ metabolism, where genetic effects in the FADS2 region massively influenced the MR estimates of omega-3 PUFAs on IBD and its subtypes. The effects of total omega-3 fatty acid were found to potentially increase IBD risks after removing the FADS2 instrument (Figure S2). In summary, the previously reported effect of total omega-3 fatty acid on a
lower risk of UC was methodologically arguable and did not align with the evidence from our MR study and other observational studies.
There were several strengths of the present study. First, our study comprehensively explored the causal effects of the different components of omega-3 PUFAs on IBD risk by using a robust MR setting, which reduced bias from residual confounding and excluded reverse causality. Current data contributed to produce informed recommendations based on the relative importance of EPA in preventing IBD. Second, our investigation of the metabolic pathways involving linoleic acid and histidine metabolites provided valuable insights into the mechanisms underlying the effect of EPA on IBD risk, which may have implications for future clinical practice. Third, our findings suggest that supplementation policies should consider the different subtypes of IBD, as EPA demonstrated a significant effect on reducing the risk of CD but not UC, and genetic liability to UC was associated with lower concentrations of DHA. Additionally, we used colocalization methods to thoroughly explore the possibility of a single shared effect signal in the FADS2 gene region, thus validating the underlying mechanism linking omega-3 PUFAs with CD.
However, there were some limitations that should be considered when interpreting our findings. First, we used different data sources for the exposure variables. The genetic instruments for total omega-3 were obtained from the UK Biobank study, while instruments for α-linolenic acid, EPA, and DHA were derived from the CHARGE Consortium. Although both datasets involved participants with European ancestry, there could still be potential biases introduced by using different sources. Second, we assumed that the relationships between omega-3 fatty acids and IBD risk were linear. Non-linear relationships were not taken into consideration and further investigation is needed to explore potential non-linear effects. Finally, although we used univariable MR analyses to estimate the effect of each fatty acid, we were unable to directly estimate the effect of EPA-to-DHA ratio. The EPA-toDHA ratio is considered important in the clinical application of fish oil, and its potential impact on IBD risk merits further exploration.
5. Conclusions
In conclusion, our comprehensive MR analyses identified that EPA was the key component among the omega-3 PUFAs that may exhibit a protective effect on IBD and CD, but not on UC. There was little evidence to support the effect of total omega-3, α-linolenic acid, or DHA on IBD risks. We also provided novel insights into the underlying mechanisms of EPA, which may influence IBD via α-linolenic acid, linoleic acid and methylhistidine metabolic pathways. Furthermore, the FADS2 gene is likely to be a core gene that mediates the effects of omega-3 PUFAs on IBD risk. Based on these findings, our study recommended the supplementation or dietary intake of EPA, rather than α-linolenic acid or DHA, might be beneficial for preventing the onset of IBD. The proposed mediators have provided novel insights into the underlying mechanisms of EPA. More well powered epidemiological studies and clinical trials are needed to explore the potential benefits of high EPA concentration or EPA/DHA in IBD and its subtypes. Moreover, further research is needed to investigate the role of histidine metabolites in the context of IBD.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/metabo13101041/s1, Figure S1: Selection process for the data included in the study; Figure S2: Leave one out plots of the causal effects of omega-3 polyunsaturated fatty acids on inflammatory bowel disease, Crohn’s disease, and ulcerative colitis showing inverse variance weighted estimates after omitting each SNP; Table S1: Data sources of genome-wide association studies included in the Mendelian randomization analysis; Table S2: Statistics used to assess instrument strength; Table S3: Sensitivity analyses used to assess causal effects of omega-3 polyunsaturated fatty acids on the risk of Crohn’s disease, and ulcerative colitis.
Author Contributions: Conceptualization, J.L., Y.B. and J.Z.; formal analysis, X.J., C.H. and X.W.; writing—original draft preparation, X.J.; writing—review and editing, H.Q., L.L., M.X., Y.X., T.W., Z.Z., Y.C., M.L., R.Z., H.L., S.W., W.W., J.Z. and J.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China (grant numbers 81930021, 81970728, 81970691, 82170819, 82370810, and 21904084); Shanghai Outstanding Academic Leaders Plan (grant number 20XD1422800); Shanghai Medical and Health Development Foundation (grant number DMRFP_I_01); Clinical Research Plan of SHDC (grant numbers SHDC2020CR3064B and SHDC2020CR1001A); Science and Technology Committee of Shanghai (grant numbers 20Y11905100 and 19411964200); Clinical Research Project of Shanghai Municipal Health Commission (grant number 20214Y0002); Ministry of Science and Technology of China (grant number 2022YFC2505202); and Innovative research team of high-level local universities in Shanghai. J.Z. was funded by the Academy of Medical Sciences (AMS) Springboard Award; the Wellcome Trust; the Government Department of Business; Energy and Industrial Strategy (BEIS); the British Heart Foundation and Diabetes UK (grant number SBF006\1117); and the Vice-Chancellor Fellowship from the University of Bristol.
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: The summary statistics of total omega-3 fatty acid were obtained from UK Biobank study at https://doi.org/10.1186/s12916-022-02399-w, accessed on 6 November 2022, and instruments for α-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid were derived from Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium at https://www. chargeconsortium.com/main/results, accessed on 6 November 2022. Genetic association estimates for inflammatory bowel disease were obtained from the study by the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) at https://doi.org/10.1038/ng.3760, accessed on 6 November 2022. The full summary statistics of the circulating metabolites were derived from the IEU OpenGWAS database at https://gwas.mrcieu.ac.uk/, accessed on 6 November 2022.
Acknowledgments: The authors thank all investigators for the publicly available summary data. Conflicts of Interest: The authors declare no conflict of interest.
Figures
Figure 1
Study design overview for a Mendelian randomization analysis evaluating the causal effects of omega-3 polyunsaturated fatty acids on inflammatory bowel disease through circulating metabolite mediators.
diagramFigure 2
Directed acyclic graph or causal framework illustrating the mediation Mendelian randomization approach used to assess omega-3 PUFA effects on IBD via circulating metabolites.
diagramFigure 3
Primary Mendelian randomization results showing the estimated causal effect of omega-3 PUFA supplementation on inflammatory bowel disease risk, addressing conflicting epidemiological evidence.
chartFigure 4
Mediation analysis results identifying circulating metabolites that may mediate the relationship between omega-3 polyunsaturated fatty acids and inflammatory bowel disease outcomes.
chartFigure 5
Publication metadata for the omega-3 PUFA and IBD Mendelian randomization study, received August 2023 and published September 2023 in Metabolites.
Figure 6
Supplementary Mendelian randomization analysis (Figure 6) examining specific omega-3 fatty acid subtypes or individual metabolite pathways in relation to inflammatory bowel disease susceptibility.
chartFigure 7
Supplementary Mendelian randomization analysis (Figure 7) examining specific omega-3 fatty acid subtypes or individual metabolite pathways in relation to inflammatory bowel disease susceptibility.
chartFigure 8
Supplementary Mendelian randomization analysis (Figure 8) examining specific omega-3 fatty acid subtypes or individual metabolite pathways in relation to inflammatory bowel disease susceptibility.
chartFigure 9
Supplementary Mendelian randomization analysis (Figure 9) examining specific omega-3 fatty acid subtypes or individual metabolite pathways in relation to inflammatory bowel disease susceptibility.
chartFigure 10
Supplementary Mendelian randomization analysis (Figure 10) examining specific omega-3 fatty acid subtypes or individual metabolite pathways in relation to inflammatory bowel disease susceptibility.
chartFigure 11
Sensitivity analysis plot (Figure 11) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 12
Sensitivity analysis plot (Figure 12) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 13
Sensitivity analysis plot (Figure 13) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 14
Sensitivity analysis plot (Figure 14) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 15
Sensitivity analysis plot (Figure 15) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 16
Sensitivity analysis plot (Figure 16) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 17
Sensitivity analysis plot (Figure 17) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 18
Sensitivity analysis plot (Figure 18) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 19
Sensitivity analysis plot (Figure 19) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 20
Sensitivity analysis plot (Figure 20) from the omega-3 PUFA and IBD Mendelian randomization study, testing the robustness of causal estimates under different analytical assumptions.
chartFigure 21
Forest plot or scatter plot (Figure 21) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 22
Forest plot or scatter plot (Figure 22) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 23
Forest plot or scatter plot (Figure 23) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 24
Forest plot or scatter plot (Figure 24) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 25
Forest plot or scatter plot (Figure 25) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 26
Forest plot or scatter plot (Figure 26) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 27
Forest plot or scatter plot (Figure 27) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 28
Forest plot or scatter plot (Figure 28) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 29
Forest plot or scatter plot (Figure 29) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 30
Forest plot or scatter plot (Figure 30) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 31
Forest plot or scatter plot (Figure 31) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 32
Forest plot or scatter plot (Figure 32) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 33
Forest plot or scatter plot (Figure 33) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 34
Forest plot or scatter plot (Figure 34) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 35
Forest plot or scatter plot (Figure 35) presenting individual SNP-level estimates for the association between genetically predicted omega-3 PUFA levels and inflammatory bowel disease risk.
forest_plotFigure 36
Leave-one-out or funnel plot analysis (Figure 36) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 37
Leave-one-out or funnel plot analysis (Figure 37) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 38
Leave-one-out or funnel plot analysis (Figure 38) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 39
Leave-one-out or funnel plot analysis (Figure 39) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 40
Leave-one-out or funnel plot analysis (Figure 40) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 41
Leave-one-out or funnel plot analysis (Figure 41) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 42
Leave-one-out or funnel plot analysis (Figure 42) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 43
Leave-one-out or funnel plot analysis (Figure 43) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 44
Leave-one-out or funnel plot analysis (Figure 44) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 45
Leave-one-out or funnel plot analysis (Figure 45) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 46
Leave-one-out or funnel plot analysis (Figure 46) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 47
Leave-one-out or funnel plot analysis (Figure 47) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 48
Leave-one-out or funnel plot analysis (Figure 48) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 49
Leave-one-out or funnel plot analysis (Figure 49) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 50
Leave-one-out or funnel plot analysis (Figure 50) assessing potential pleiotropy and outlier influence in the omega-3 PUFA and IBD Mendelian randomization framework.
chartFigure 51
Metabolite-specific mediation result (Figure 51) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 52
Metabolite-specific mediation result (Figure 52) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 53
Metabolite-specific mediation result (Figure 53) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 54
Metabolite-specific mediation result (Figure 54) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 55
Metabolite-specific mediation result (Figure 55) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 56
Metabolite-specific mediation result (Figure 56) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 57
Metabolite-specific mediation result (Figure 57) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 58
Metabolite-specific mediation result (Figure 58) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 59
Metabolite-specific mediation result (Figure 59) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 60
Metabolite-specific mediation result (Figure 60) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 61
Metabolite-specific mediation result (Figure 61) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 62
Metabolite-specific mediation result (Figure 62) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 63
Metabolite-specific mediation result (Figure 63) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 64
Metabolite-specific mediation result (Figure 64) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 65
Metabolite-specific mediation result (Figure 65) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 66
Metabolite-specific mediation result (Figure 66) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 67
Metabolite-specific mediation result (Figure 67) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 68
Metabolite-specific mediation result (Figure 68) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 69
Metabolite-specific mediation result (Figure 69) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 70
Metabolite-specific mediation result (Figure 70) quantifying the proportion of the omega-3 PUFA effect on IBD that operates through a particular circulating metabolite pathway.
chartFigure 71
Subgroup or stratified analysis (Figure 71) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 72
Subgroup or stratified analysis (Figure 72) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 73
Subgroup or stratified analysis (Figure 73) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 74
Subgroup or stratified analysis (Figure 74) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 75
Subgroup or stratified analysis (Figure 75) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 76
Subgroup or stratified analysis (Figure 76) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 77
Subgroup or stratified analysis (Figure 77) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 78
Subgroup or stratified analysis (Figure 78) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 79
Subgroup or stratified analysis (Figure 79) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 80
Subgroup or stratified analysis (Figure 80) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 81
Subgroup or stratified analysis (Figure 81) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 82
Subgroup or stratified analysis (Figure 82) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 83
Subgroup or stratified analysis (Figure 83) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 84
Subgroup or stratified analysis (Figure 84) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 85
Subgroup or stratified analysis (Figure 85) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 86
Subgroup or stratified analysis (Figure 86) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 87
Subgroup or stratified analysis (Figure 87) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 88
Subgroup or stratified analysis (Figure 88) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 89
Subgroup or stratified analysis (Figure 89) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 90
Subgroup or stratified analysis (Figure 90) from the Mendelian randomization study, examining omega-3 PUFA effects separately for Crohn's disease and ulcerative colitis.
chartFigure 91
Supplementary statistical plot (Figure 91) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 92
Supplementary statistical plot (Figure 92) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 93
Supplementary statistical plot (Figure 93) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 94
Supplementary statistical plot (Figure 94) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 95
Supplementary statistical plot (Figure 95) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 96
Supplementary statistical plot (Figure 96) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 97
Supplementary statistical plot (Figure 97) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 98
Supplementary statistical plot (Figure 98) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 99
Supplementary statistical plot (Figure 99) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 100
Supplementary statistical plot (Figure 100) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 101
Supplementary statistical plot (Figure 101) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 102
Supplementary statistical plot (Figure 102) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 103
Supplementary statistical plot (Figure 103) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 104
Supplementary statistical plot (Figure 104) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 105
Supplementary statistical plot (Figure 105) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 106
Supplementary statistical plot (Figure 106) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 107
Supplementary statistical plot (Figure 107) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 108
Supplementary statistical plot (Figure 108) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 109
Supplementary statistical plot (Figure 109) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 110
Supplementary statistical plot (Figure 110) providing additional evidence on the causal pathway between omega-3 polyunsaturated fatty acids and inflammatory bowel disease through circulating metabolites.
chartFigure 111
Extended analysis figure (Figure 111) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 112
Extended analysis figure (Figure 112) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 113
Extended analysis figure (Figure 113) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 114
Extended analysis figure (Figure 114) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 115
Extended analysis figure (Figure 115) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 116
Extended analysis figure (Figure 116) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 117
Extended analysis figure (Figure 117) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 118
Extended analysis figure (Figure 118) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 119
Extended analysis figure (Figure 119) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 120
Extended analysis figure (Figure 120) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 121
Extended analysis figure (Figure 121) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 122
Extended analysis figure (Figure 122) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 123
Extended analysis figure (Figure 123) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 124
Extended analysis figure (Figure 124) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 125
Extended analysis figure (Figure 125) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 126
Extended analysis figure (Figure 126) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 127
Extended analysis figure (Figure 127) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 128
Extended analysis figure (Figure 128) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 129
Extended analysis figure (Figure 129) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 130
Extended analysis figure (Figure 130) from the omega-3 PUFA and IBD study, presenting results for additional metabolite mediators or alternative instrument variable selections.
chartFigure 131
Robustness check (Figure 131) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 132
Robustness check (Figure 132) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 133
Robustness check (Figure 133) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 134
Robustness check (Figure 134) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 135
Robustness check (Figure 135) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 136
Robustness check (Figure 136) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 137
Robustness check (Figure 137) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 138
Robustness check (Figure 138) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 139
Robustness check (Figure 139) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 140
Robustness check (Figure 140) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 141
Robustness check (Figure 141) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 142
Robustness check (Figure 142) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 143
Robustness check (Figure 143) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 144
Robustness check (Figure 144) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 145
Robustness check (Figure 145) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 146
Robustness check (Figure 146) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 147
Robustness check (Figure 147) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 148
Robustness check (Figure 148) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 149
Robustness check (Figure 149) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 150
Robustness check (Figure 150) from the mediation Mendelian randomization analysis, validating the omega-3 PUFA and IBD causal pathway using weighted median or MR-Egger methods.
chartFigure 151
Metabolomic pathway visualization (Figure 151) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 152
Metabolomic pathway visualization (Figure 152) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 153
Metabolomic pathway visualization (Figure 153) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 154
Metabolomic pathway visualization (Figure 154) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 155
Metabolomic pathway visualization (Figure 155) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 156
Metabolomic pathway visualization (Figure 156) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 157
Metabolomic pathway visualization (Figure 157) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 158
Metabolomic pathway visualization (Figure 158) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 159
Metabolomic pathway visualization (Figure 159) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 160
Metabolomic pathway visualization (Figure 160) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 161
Metabolomic pathway visualization (Figure 161) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 162
Metabolomic pathway visualization (Figure 162) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 163
Metabolomic pathway visualization (Figure 163) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 164
Metabolomic pathway visualization (Figure 164) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 165
Metabolomic pathway visualization (Figure 165) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 166
Metabolomic pathway visualization (Figure 166) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 167
Metabolomic pathway visualization (Figure 167) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 168
Metabolomic pathway visualization (Figure 168) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 169
Metabolomic pathway visualization (Figure 169) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 170
Metabolomic pathway visualization (Figure 170) mapping the intermediary role of specific circulating metabolites in the omega-3 PUFA and inflammatory bowel disease relationship.
chartFigure 171
Additional MR analysis (Figure 171) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 172
Additional MR analysis (Figure 172) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 173
Additional MR analysis (Figure 173) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 174
Additional MR analysis (Figure 174) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 175
Additional MR analysis (Figure 175) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 176
Additional MR analysis (Figure 176) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 177
Additional MR analysis (Figure 177) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 178
Additional MR analysis (Figure 178) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 179
Additional MR analysis (Figure 179) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 180
Additional MR analysis (Figure 180) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 181
Additional MR analysis (Figure 181) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 182
Additional MR analysis (Figure 182) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 183
Additional MR analysis (Figure 183) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 184
Additional MR analysis (Figure 184) exploring dose-response relationships or nonlinear effects of omega-3 PUFA levels on IBD risk.
chartFigure 185
Regional association plot (Figure 185) showing genetic loci associated with omega-3 fatty acid levels, including alpha-linolenic, eicosapentaenoic, and docosahexaenoic acids in relation to Crohn's disease and ulcerative colitis.
chartFigure 186
Supplementary figure (Figure 186) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 187
Supplementary figure (Figure 187) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 188
Supplementary figure (Figure 188) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 189
Supplementary figure (Figure 189) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 190
Supplementary figure (Figure 190) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 191
Supplementary figure (Figure 191) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 192
Supplementary figure (Figure 192) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 193
Supplementary figure (Figure 193) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 194
Supplementary figure (Figure 194) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 195
Supplementary figure (Figure 195) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 196
Supplementary figure (Figure 196) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 197
Supplementary figure (Figure 197) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 198
Supplementary figure (Figure 198) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 199
Supplementary figure (Figure 199) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 200
Supplementary figure (Figure 200) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 201
Supplementary figure (Figure 201) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 202
Supplementary figure (Figure 202) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 203
Supplementary figure (Figure 203) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 204
Supplementary figure (Figure 204) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 205
Supplementary figure (Figure 205) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 206
Supplementary figure (Figure 206) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 207
Supplementary figure (Figure 207) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 208
Supplementary figure (Figure 208) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 209
Supplementary figure (Figure 209) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 210
Supplementary figure (Figure 210) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 211
Supplementary figure (Figure 211) from a comprehensive Mendelian randomization study with over 200 analyses examining omega-3 polyunsaturated fatty acid effects on inflammatory bowel disease via circulating metabolite mediators.
chartFigure 212
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 213
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 214
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 215
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 216
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 217
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 218
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 219
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 220
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 221
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 222
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 223
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 224
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 225
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 226
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 227
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 228
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 229
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 230
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 231
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 232
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 233
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 234
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 235
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 236
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 237
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 238
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 239
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 240
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 241
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 242
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 243
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 244
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 245
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 246
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 247
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 248
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 249
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 250
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 251
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 252
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 253
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 254
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 255
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 256
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 257
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 258
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 259
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 260
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 261
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 262
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 263
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 264
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 265
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 266
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 267
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 268
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 269
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 270
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 271
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 272
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 273
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 274
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 275
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 276
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 277
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 278
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 279
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 280
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 281
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 282
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 283
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 284
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 285
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 286
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 287
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 288
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 289
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 290
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 291
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 292
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 293
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 294
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 295
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 296
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 297
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 298
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 299
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 300
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 301
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 302
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 303
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 304
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 305
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 306
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 307
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 308
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 309
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 310
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 311
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 312
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 313
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 314
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 315
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 316
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 317
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 318
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 319
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 320
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 321
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 322
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 323
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 324
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 325
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 326
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 327
Scatter plot depicting the causal effect estimates of individual genetic instruments for alpha-linolenic acid (ALA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 328
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of alpha-linolenic acid (ALA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 329
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of alpha-linolenic acid (ALA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 330
Leave-one-out sensitivity analysis for the causal relationship between alpha-linolenic acid (ALA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 331
Mediation analysis diagram illustrating the potential pathway through which alpha-linolenic acid (ALA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 332
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 333
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 334
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 335
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 336
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 337
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 338
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 339
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 340
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 341
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 342
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 343
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 344
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 345
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 346
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 347
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 348
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 349
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 350
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 351
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 352
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 353
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 354
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 355
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 356
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 357
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 358
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 359
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 360
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 361
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 362
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 363
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 364
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 365
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 366
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 367
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 368
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 369
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 370
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 371
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 372
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 373
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 374
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 375
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 376
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 377
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 378
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 379
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 380
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 381
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 382
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 383
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 384
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 385
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 386
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 387
Scatter plot depicting the causal effect estimates of individual genetic instruments for eicosapentaenoic acid (EPA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 388
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of eicosapentaenoic acid (EPA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 389
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 390
Leave-one-out sensitivity analysis for the causal relationship between eicosapentaenoic acid (EPA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 391
Mediation analysis diagram illustrating the potential pathway through which eicosapentaenoic acid (EPA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 392
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 393
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 394
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 395
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 396
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 397
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 398
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 399
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 400
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 401
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 402
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 403
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 404
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 405
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 406
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 407
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 408
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on inflammatory bowel disease (IBD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 409
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 410
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and inflammatory bowel disease (IBD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 411
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence inflammatory bowel disease (IBD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 412
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 413
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 414
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 415
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 416
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 417
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 418
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 419
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 420
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 421
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 422
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 423
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 424
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 425
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 426
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 427
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on Crohn's disease (CD), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 428
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on Crohn's disease (CD). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 429
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and Crohn's disease (CD). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 430
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and Crohn's disease (CD). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 431
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence Crohn's disease (CD) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 432
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 433
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 434
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 435
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 436
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 437
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 438
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 439
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 440
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 441
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 442
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 443
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 444
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 445
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 446
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 447
Scatter plot depicting the causal effect estimates of individual genetic instruments for docosahexaenoic acid (DHA) on ulcerative colitis (UC), assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 448
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of docosahexaenoic acid (DHA) on ulcerative colitis (UC). Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 449
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of docosahexaenoic acid (DHA) and ulcerative colitis (UC). Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 450
Leave-one-out sensitivity analysis for the causal relationship between docosahexaenoic acid (DHA) and ulcerative colitis (UC). Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 451
Mediation analysis diagram illustrating the potential pathway through which docosahexaenoic acid (DHA) may influence ulcerative colitis (UC) risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 452
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on IBD and its subtypes, assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 453
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on IBD and its subtypes. Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 454
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and IBD and its subtypes. Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartFigure 455
Leave-one-out sensitivity analysis for the causal relationship between total omega-3 fatty acids and IBD and its subtypes. Sequentially removing each genetic instrument and recalculating the pooled estimate indicates the robustness of the overall finding to any single influential variant.
chartFigure 456
Mediation analysis diagram illustrating the potential pathway through which total omega-3 fatty acids may influence IBD and its subtypes risk via circulating metabolites. Direct and indirect effects are quantified using two-step Mendelian randomization, highlighting metabolites that may partially mediate the observed association.
diagramFigure 457
Scatter plot depicting the causal effect estimates of individual genetic instruments for total omega-3 fatty acids on IBD and its subtypes, assessed using Mendelian randomization. Each point represents a single SNP, with the slope reflecting the overall causal estimate.
chartFigure 458
Forest plot summarizing the individual and pooled Mendelian randomization estimates for the effect of total omega-3 fatty acids on IBD and its subtypes. Each horizontal line represents the causal estimate from a single genetic variant, with the diamond indicating the combined effect across multiple MR methods.
forest_plotFigure 459
Funnel plot evaluating potential directional pleiotropy in the Mendelian randomization analysis of total omega-3 fatty acids and IBD and its subtypes. Asymmetry in the distribution of individual SNP estimates around the overall effect size may indicate the presence of pleiotropic bias.
chartUsed In Evidence Reviews
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