Genome-wide mendelian randomization reveals causal effects of modifiable risk factors on inflammatory bowel disease

Genome-wide mendelian randomization reveals causal effects of modifiable risk factors on inflammatory bowel disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide mendelian randomization reveals causal effects of modifiable risk factors on inflammatory bowel disease Weixiong Zhu, Chuanlei Fan, Zengxi Yang, Wence Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The timely recognition of risk factors assumes paramount importance in the prevention of IBD. Our objective is to elucidate the relationship between risk factors and IBD risk. Methods: To investigate the associations between 24 modifiable risk factors and IBD, a combination of univariate and multivariate MR analysis methods was employed. The final outcomes were assessed through a comprehensive analysis of three large independent GWAS. To mitigate confounding biases, we conducted univariate MR analysis for each individual factor. Multivariate MR analysis was performed within each group to account for the influence of multiple factors simultaneously. Results: RA, asthma, the intake of cheese spread, carotene, and college or university degree were negatively associated with IBD risk. MS, PSC, AS, alcohol consumption, gut microbiota abundance, smoking, and sweet intake exhibited positive correlation with IBD risk. Type 2 diabetes, omega-3 fatty acids were correlated with reduced IBD risk. Total testosterone levels and albumin exhibited associations with IBD risk. Primary hypertension, body fat percentage, and whole-body fat mass suggested increased IBD risk. Three large-scale GWAS independently confirmed that gut microbiota abundance, primary hypertension, MS, PSC, AS, whole-body fat mass, and body fat percentage exhibited stronger associations with IBD risk. Conversely, omega-3 fatty acids, RA, asthma, type 2 diabetes, and attainment of a college or university degree were related to decreased IBD risk. Conclusions: Such robust evidence has the potential to inform preventive measures for IBD and, notably, illuminate pathways for future research endeavors. Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease/Crohns disease Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease/Ulcerative colitis inflammatory bowel diseases mendelian randomization risk factors lifestyle factors metabolic risk factors extraintestinal manifestations Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION IBD, encompassing CD and UC[ 1 ], is characterized by chronic inflammation capable of inducing grave and potentially fatal complications, notably colorectal cancer[ 1 , 2 ]. Current clinical interventions for IBD focus on managing symptoms rather than addressing the underlying causes. However, these interventions often come with long-term adverse effects such as nausea, headache, edema, and nasopharyngitis[ 3 – 6 ]. Given the unfavorable prognosis of IBD, it is crucial to identify the risk factors associated with IBD. The prevalence of IBD poses a significant challenge worldwide. Epidemiological research has identified several lifestyle factors associated with IBD, including smoking, alcohol consumption, sweet intake, physical activity, gut dysbiosis, and antibiotic exposure[ 7 , 8 ]. However, evaluating potential causal association between modifiable factors and IBD is challenging. The presence of residual confounders and the possibility of reverse causation may have led to confusion regarding the relationships observed in previous studies. A pivotal imperative emerges to determine whether these modifiable factors play a causal role in IBD risk or if they are merely outcomes of shared risk factors. MR analysis has emerged as a valuable approach to investigate potential associations between various risk factors and IBD[ 9 – 11 ]. By utilizing instrumental genetic variants, MR analysis aims to determine the underlying impacts and establish causality. This approach is advantageous as heritable genetic variations are randomly distributed during fetal development, which helps reduce confounding and minimize the bias caused by reverse causation. Randomized trials, although considered the gold standard, can be expensive, time-consuming, and challenging to conduct. However, MR analysis avoids these drawbacks and provides insights into causal factors and helps inform preventive measures. In our study, we employed a two-sample MR framework to investigate the causal relationships between 24 potential dominant factors and IBD. We utilized data from three comprehensive GWASs to assess the combined results. By leveraging the large-scale genetic data available in these GWASs, we aimed to provide a robust analysis of the causal relationships between these factors and IBD. METHODS Study design MR analysis relies on three key assumptions. Firstly, genetic variants used as instrumental variables should have strong association with interested exposure. Secondly, genetic variants should not be associated with any confounding factors that could influence both exposure and outcome. Lastly, genetic variants should affect outcome solely through risk factor being investigated, and not through any alternative pathways (as depicted in Fig. 1 ). Our methods adhered to the guidelines outlined in the STROBE-MR guidelines, which provide recommendations for conducting and reporting MR studies[ 12 ]. We first investigated the associations between modifiable risk variables and IBD using the FinnGen dataset. Subsequently, we performed replication analyses in two separate GWAS datasets (as illustrated in Fig. 1 ) and combined the discovery and replication datasets to strengthen the robustness of findings. Genetic instrument selection In our study, we investigated the relationship between potentially modifiable risk factors and IBD using instrumental variables. These risk factors were categorized into four groups: 1. Lifestyle factors: alcohol intake, tobacco smoking, sweet intake, physical activity, cheese intake, carotene intake, gut microbiota abundance, processed meat intake, and college or university degree. 2.Serum parameters: total testosterone levels, albumin, omega-3 fatty acids, vitamin D levels, and C-reactive protein. 3.Metabolic comorbidities: type 2 diabetes, primary hypertension, body fat percentage, whole body fat mass, and obesity. 4.Extraintestinal manifestations: RA, asthma, MS, PSC, and AS. To gather instrumental variables for these traits, we utilized various sources (as listed in Table 1 ): Neale Lab ( http://www.nealelab.is/uk-biobank ), GWAS Catalog ( https://www.ebi.ac.uk/gwas/home ), MRC-IEU ( https://gwas.mrcieu.ac.uk/ )[ 13 ], GABRIEL ( https://ginasthma.org/ ), IMSGC ( https://imsgc.net/ ), IPSCSG ( http://www.ipscsg.org/ ), and Within the Family GWAS Consortium ( https://www.withinfamilyconsortium.com/ ). All the statistics used in our study are publicly available and de-identified. To minimize potential bias due to population stratification, we exclusively evaluated genetic variants from individuals of European ancestry. Table 1. Characteristics of the GWAS summary data. Exposure Ethnicity Consortium Total population IBD Replication 1 Replication 2 Lifestyle factors SNPS F SNPS F SNPS F Alcohol intake European Neale Lab 172,454 13 35.91 15 35.54 14 35.74 Tobacco smoking European Neale Lab 91353 35 21.70 39 21.58 33 21.61 Sweet intake European MRC-IEU 64949 20 22.27 21 22.15 14 22.41 Physical activity European NA 377,234 18 34.08 19 34.39 18 34.08 Cheese spread intake European MRC-IEU 64949 36 21.91 35 21.88 29 22.00 Carotene intake European MRC-IEU 64979 31 21.48 31 21.48 28 21.41 Gut microbiota abundance European NA 14306 18 21.50 21 21.41 21 21.41 Processed meat intake European MRC-IEU 461981 23 38.54 23 38.33 20 38.14 College or University degree European MRC-IEU 458079 357 42.69 363 43.20 310 43.53 Serum parameters Total testosterone levels European NA 194453 213 85.41 215 85.09 179 85.18 Albumin European NA 115060 33 85.46 35 82.57 30 61.80 Omega-3 fatty acids European NA 114999 74 167.36 72 170.24 67 164.94 Vitamin D levels NA 496,946 165 99.81 173 97.19 146 99.76 C-reactive protein European Within family GWAS consortium 61308 36 71.73 38 67.49 38 67.63 Metabolic comorbidities Type 2 diabetes European NA 655666 131 67.26 134 66.53 123 66.67 primary hypertension European MRC-IEU 463010 74 47.55 80 46.86 61 46.63 Body fat percentage European MRC-IEU 454633 625 50.49 648 50.05 527 50.41 Whole body fat mass European MRC-IEU 454137 686 53.43 717 52.61 593 53.01 Obesity European NA 13848 5 42.57 5 42.57 5 42.57 Extraintestinal manifestations Rheumatoid arthritis European NA 58,284 75 157.20 58 107.77 69 117.25 Asthma European GABRIEL 26475 8 43.10 7 43.84 7 43.84 Multiple sclerosis European IMSGC 38589 55 78.34 49 51.56 47 78.51 Primary sclerosing cholangitis European IPSCSG 14890 24 131.02 17 81.67 25 111.61 Ankylosing spondylitis European NA 22647 29 124.70 19 70.02 32 144.91 SNP, single nucleotide polymorphisms. We identified genetic variants that exhibited a strong association (p < 5 × 10 − 8 ) with each trait of interest using GWASs. However, for traits such as tobacco smoking, gut microbiota abundance, sweet intake, cheese intake, and carotene, where only a few SNPs were significantly associated with IBD at the p < 5 × 10 − 8 level, we adjusted the threshold and set it at p < 5 × 10 − 6 . We then removed any SNPs that showed potential linkage disequilibrium, keeping only those with a physical distance greater than 5,000 kb and a low likelihood of linkage disequilibrium (R 2 < 0.01)[ 14 ]. Finally, we assessed 24 risk factors using selected genetic variants. GWAS summary statistics of IBD To minimize any potential overlap, we excluded IBD GWAS data when selecting exposure factors, as most of the exposure factors were derived from the UK Biobank. Therefore, we focused on three IBD GWAS datasets (Supplementary Table S1 ) for our analysis: Discovery stage: FinnGen dataset, which included 8,704 IBD cases and 300,450 controls. More information can be found at https://r4.finngen.fi/ [ 15 ]. Replication 1: IIBDGC. This consortium is dedicated to studying the genetic basis of IBD. Further details can be accessed at https://www.ibdgenetics.org/ . Replication 2: We considered association analyses that identified 38 susceptibility loci for IBD and highlighted shared genetic risk across populations[ 16 ]. Statistical analysis We used F statistic parameters to assess the strength of the identified genetic instruments used in the MR analysis[ 17 ]. F statistics ranged from 21.41 to 170.24, indicating that the instruments had sufficient strength to detect potential causal effects (as shown in Table 1 ). We used mRnd tool to evaluate the statistical power of our analysis ( https://shiny.cnsgenomics.com/mRnd/ )[ 18 ], which can assess the power of study design and identified genetic instruments in detecting causal effects between risk factors and IBD. The IVW method was used to assess whether all genetic instruments were valid and balanced pleiotropy[ 19 ]. Second, MR Egger regression and WM were conducted[ 20 ]. According to MR Egger, pleiotropic effects can occur in a directional fashion when some SNPs are acting on the outcome via a different pathway than the exposure of interest. However, the statistical power is sacrificed in this case[ 21 ]. The WM method allows up to 50% of the selected genetic instruments to be invalid[ 22 ]. Third, to test for general horizontal pleiotropy and outliers, the MR-PRESSO method uses global and SNP-specific observed residual sums of squares. Furthermore, it provides a distortion test that compares estimates before and after the removal of outliers[ 23 ]. Fourth, Multivariable MR estimates the impact of multiple exposures on a single outcome based on genetic variants associated with multiple, potentially related exposures. This approach is possible to retain the benefits associated with genetic instruments for causal inference, including Bias of confusion[ 24 ]. Finally, leave-one-out sensitivity analysis was conducted to assess the study's robustness. Heterogeneity of these analyses was examined with Cochran Q test. In addition, we tested pleiotropy by collecting the intercepts from the MR Egger regression. We applied Bonferroni-corrected significance level of P < 2.08×10 − 3 (0.05/24 risk factors) [ 14 ]. Analyses were conducted using R 4.1.3. The packages 'TwoSampleMR'[ 25 ] and 'MRPRESSO'[ 23 ] were used. RESULTS Baseline characteristics We used 24 potentially modifiable risk factors to determine causal relationships with IBD and classified them into 4 categories: lifestyle factors, serum parameters, metabolic risk factors, and extraintestinal manifestations (Table 1 ). Lifestyle factors included six traits associated with diet, physical activity, gut microbiota abundance, and college or university degree. Serum parameters included omega-3 fatty acids, total testosterone levels, vitamin D levels, C-reactive protein, and albumin. Metabolic comorbidities included obesity, and two related to obesity traits, type 2 diabetes, and primary hypertension. Extraintestinal manifestations included RA, asthma, MS, PSC, and AS. The number of SNPs ranged from 5 to 593 (Table 1 ). In all the considered traits, the F statistics were greater than 10, which suggested no potential weak instrument bias. Discovery results of IBD Figure 1 sums up the discovery results of IBD in the FinnGen consortium. Given the FinnGen outcome, the statistical power was 100%. The specific information on SNPs, as instrumental variables, is shown in Supplementary Table S2 . Lifestyle factors for IBD risk We found that a suggestive association existed between alcohol usually taken with meals and increased IBD risk. The OR (95% CI) of the IVW method was 4.20 (1.29–13.72) for a one-SD increase (Table 2 ). Smoking (OR = 8.90, P = 0.024), sweet intake (OR = 1.23, P = 0.034), and gut microbiota abundance (OR = 1.29, P = 0.005) had suggestive increased IBD risk. The ORs (95% CI) of the WM estimator for smoking was 207.46 (5.21-8267.87) ( P = 0.005) for a 1-SD increase. Cheese intake and college or university degree had low association with IBD. The ORs (95% CI) of the WM estimator were 0.29 (0.10–0.80) ( P = 0.017) and 0.77 (0.53–1.10) ( P = 0.149) for 1-SD increase, respectively. The heterogeneity of college or university degree was ( P heterogeneity =0.012). There was no significant causal association between processed meat intake and IBD (Fig. 2 ). Table 2 Modifiable risk factors for inflammatory bowel disease included in GWAS of FinnGen. exposure snps IVW MR-egger WM MR-presso pleiotropy heterogeneity Lifestyle factors OR P OR P OR P snps OR P P P Alcohol intake 13 4.20( 1.29–13.72) 0.017 0.16(1.34e-06-20225.84) 0.769 3.08(0.69–13.69) 0.139 13 4.20( 1.29–13.72) 0.002 0.597 0.968 Tobacco smoking 35 18.90(1.47-242.72) 0.024 458.29(1.21-174253.48) 0.051 207.46(5.21-8267.87) 0.005 35 18.90(1.47-242.72) 0.040 0.252 0.623 Sweet intake 20 1.23(1.02–1.50) 0.034 1.31(0.79–2.15) 0.308 1.23(0.94–1.59) 0.127 20 1.23(1.02–1.50) 0.056 0.811 0.893 Physical activity 18 1.05(0.52–2.11) 0.884 0.13(0.00-6.61) 0.322 0.92(0.38–2.19) 0.845 18 1.05(0.52–2.11) 0.847 0.303 0.940 Cheese spread intake 36 0.29(0.10–0.80) 0.017 0.17(0.02–1.45) 0.115 0.23(0.05–1.02) 0.052 36 0.29(0.10–0.80) 0.016 0.595 0.662 Carotene intake 31 0.71(0.53–0.94) 0.018 0.97(0.48–1.97) 0.940 0.76(0.50–1.13) 0.175 31 0.71(0.53–0.94) 0.024 0.338 0.344 Gut microbiota abundance 18 1.29(1.08–1.54) 0.005 0.90(0.52–1.57) 0.726 1.26(0.98–1.63) 0.069 18 1.29(1.08–1.54) 0.002 0.206 0.915 Processed meat intake 23 1.17(0.60–2.25) 0.647 0.27(0.01–7.59) 0.453 0.97(0.39–2.40) 0.943 23 1.17(0.60–2.25) 0.651 0.393 0.371 College or University degree 357 0.77(0.53–1.10) 0.149 2.93(0.71–12.03) 0.136 0.78(0.47–1.29) 0.331 356 0.67(0.47–0.95) 0.015 0.055 0.012 Serum parameters Total testosterone levels 213 1.12(1.01–1.24) 0.037 1.14(0.95–1.36) 0.164 1.17(0.98–1.40) 0.091 212 1.13(1.02–1.25) 0.054 0.823 0.033 Albumin 33 1.27(1.02–1.57) 0.030 1.37(0.97–1.94) 0.084 1.49(1.10–2.03) 0.011 33 1.27(1.02–1.57) 0.031 0.571 0.189 Omega-3 fatty acids 74 0.89(0.82–0.97) 0.009 0.83(0.73–0.94) 0.004 0.80(0.71–0.91) < 0.001 74 0.89(0.82–0.97) 0.010 0.123 0.129 Vitamin D levels 165 0.90(0.75–1.07) 0.234 1.02(0.76–1.39) 0.878 0.88(0.68–1.15) 0.353 163 0.90(0.76–1.06) 0.216 0.301 0.001 C-reactive protein 36 1.04(0.90–1.20) 0.587 0.93(0.68–1.29) 0.681 1.00(0.82–1.22) 0.999 36 1.04(0.90–1.20) 0.598 0.474 0.957 Metabolic comorbidities Type 2 diabetes 131 0.91(0.85–0.98) 0.009 0.90(0.76–1.06) 0.220 0.95(0.86–1.05) 0.280 130 0.91(0.85–0.97) 0.002 0.819 0.013 primary hypertension 74 4.73(1.53–14.63) 0.007 16.14(0.28-945.81) 0.185 7.35(1.59–33.91) 0.011 72 4.61(1.71–12.45) 0.002 0.540 0.031 Body fat percentage 625 1.22(1.04–1.43) 0.016 0.99(0.58–1.69) 0.981 1.24(0.98–1.58) 0.074 623 1.19(1.02–1.40) 0.016 0.434 < 0.001 Whole body fat mass 686 1.12(1.00-1.25) 0.049 1.14(0.81–1.59) 0.456 1.05(0.88–1.25) 0.599 681 1.11(1.00-1.24) 0.057 0.922 0.010 Obesity 5 1.01(0.91–1.11) 0.920 0.92(0.43–1.97) 0.846 1.00(0.88–1.12) 0.943 5 1.01(0.91–1.11) 0.510 0.835 0.978 Extraintestinal manifestations Rheumatoid arthritis 75 0.95(0.92–0.99) 0.015 0.89(0.84–0.93) < 0.001 0.94(0.90–0.98) 0.003 73 0.94(0.91–0.98) 0.009 < 0.001 < 0.001 Asthma 8 0.75(0.60–0.93) 0.008 0.42(0.09–1.85) 0.295 0.71(0.61–0.84) < 0.001 6 0.75(0.64–0.88) 0.018 0.470 < 0.001 Multiple sclerosis 55 1.07(1.01–1.14) 0.019 0.99(0.88–1.11) 0.833 1.04(0.97–1.10) 0.268 53 1.10(1.04–1.16) 0.002 0.122 < 0.001 Primary sclerosing cholangitis 24 1.09(1.03–1.14) 0.001 1.01(0.94–1.18) 0.782 1.05(1.01–1.10) 0.013 22 1.17(1.03–1.12) 0.005 0.014 < 0.001 Ankylosing spondylitis 29 1.52(1.15–2.03) 0.004 1.31(0.82–2.10) 0.270 1.45(1.14–1.83) 0.002 26 1.35(1.07–1.69) 0.022 0.439 < 0.001 SNP, single nucleotide polymorphisms; IVW, inverse variance weighted; MR-egger, Mendelian randomization-Egger; WM, weighted median; MR-presso, MR-pleiotropy residual sum and outlier; OR, odds ratio; P, p-value. Serum parameters for IBD risk Total testosterone levels and albumin were associated with increased IBD risk (Fig. 2 ). The ORs (95% CI) of IVW method were 1.12 (1.01–1.24) ( P = 0.037) and 1.27 (1.02–1.57) ( P = 0.03) for 1-SD increases, individually. The heterogeneity of total testosterone levels was ( P heterogeneity =0.033). Omega-3 fatty acids had low association with IBD risk (IVW method: OR = 0.89, P = 0.009) for 1-SD increase. No significant causal association was found between vitamin D levels or C-reactive protein and IBD risk. Metabolic comorbidities for IBD risk Body fat percentage and whole-body fat mass had significance IBD risk in which the ORs (95% CI) in IVW method were 1.22(1.04 to 1.43) ( P = 0.016) and 1.12(1.00 to 1.25) ( P = 0.049) for 1-SD increase, respectively. However, obesity had no significant causal association with the increased IBD risk (OR = 1.01, P = 0.92>0.05). Possible reasons are provided in the discussion section. Primary hypertension and type 2 diabetes increased IBD risk. The ORs (95% CI) of IVW method were 4.73 (1.53 to 14.63; P = 0.007) for 1-SD increase. The ORs (95% CI) of WM estimator were 7.35 (1.59–33.91) ( P = 0.011) for 1-SD increase. We found possible heterogeneity for primary hypertension ( P heterogeneity =0.031). Type 2 diabetes had low association with the increased IBD risk (IVW method: OR = 0.91, P = 0.009). Extraintestinal manifestations for IBD risk MS (OR = 1.07; P = 0.019), PSC (OR = 1.09; P = 0.001), and AS (OR = 1.52; P = 0.004), increased IBD risk in IVW method, and heterogeneity was low(all P heterogeneity <0.001). The ORs (95% CI) of WM method for PSC and AS were 1.05(1.01 to 1.10; P = 0.013) and 1.45(1.14 to 1.83; P = 0.002) for 1-SD increase, respectively. Possible pleiotropy for PSC ( P pleiotropy =0.014) while it remained stable in MR-PRESSO-corrected results ( P = 0.005). Through the direction and magnitude of the homogeneity, we found that the consequences were dependable even with potential biases that may violate exclusion restrictions (Fig. 2 ). RA and asthma had low association with increased IBD risk (IVW method: OR = 0.95; P = 0.015; OR = 0.75; P = 0.008). Multivariable MR analysis of IBD Given that positive association between alcohol consumption and CD, we performed multivariable MR analysis for omega-3 fatty acids, c-reactive protein, albumin, obesity, asthma, RA, MS, PSC, AS, type 2 diabetes, and primary hypertension after adjusting for alcohol intake (Fig. 3 ). In multivariable MR analyses, albumin, PSC, AS, and primary hypertension had positive associations with increased IBD risk. It confirmed the robustness of the results. However, no significant association was found between omega-3 fatty acids, asthma, RA, MS, type 2 diabetes and IBD risk, which suggested that these association may be impacted by alcohol intake. Validation results of IBD To verify the reliability of the consequences, we replicated MR analysis in two independent GWAS datasets (replication 1 and 2). In replication 1, only similar MR consequences of MS, PSC, and AS increased IBD risk (Supplementary Table S3 ). The ORs (95% CI) in IVW method were 1.12(1.00 to 1.26) ( P = 0.047), 1.10(1.02 to 1.18) ( P = 0.01), and 3.78(1.16 to 12.29) for a 1-SD increase, respectively. The OR (95% CI) of WM method for MS was 1.08 (1.01 to 1.16; P = 0.03). In replication 2, PSC (OR = 1.08; P = 0.037), AS (OR = 2.24; P = 0.019), and body fat percentage (OR = 1.21; P = 0.009) had significance association with IBD risk using IVW method for a 1-SD (Supplementary Table S4 ). Possible pleiotropy for PSC ( P pleiotropy =0.014) while it remained stable in MR-PRESSO-corrected results ( P <0.001). Combined results of IBD from the meta-analysis To increase statistical power, we made integration for three databases and combined modifiable risk factors on the results. The meta-analysis results are illustrated in Fig. 4 . The combined results in discovery and validation confirmed that gut microbiota abundance, MS, PSC, AS, body fat percentage, whole body fat mass, and primary hypertension could increase IBD risk, while omega-3 fatty acids, asthma, and type 2 diabetes could lower the IBD risk (Fig. 4 ). Furthermore, leave-one-out sensitivity to test above risk factors. MR results for the rest of SNPs were calculated after we eliminated SNPs of each risk factor one by one, which demonstrated the stability of the results (Supplementary Figures S1 - 12 ). DISCUSSION In light of the current circumstances, this MR study meticulously accounted for the full array of modifiable risk factors for IBD. Gut microbiota abundance serves as a robust indicator of IBD risk. Furthermore, MS, PSC, and AS were significantly linked to elevated IBD risk. Notably, potential associations between body fat percentage, whole body fat mass, primary hypertension, and increased IBD risk. Conversely, the comprehensive analysis indicated that omega-3 fatty acids, asthma, type 2 diabetes, and college or university degree were tentatively associated with reduced IBD risk. Upon adjusting for alcohol intake, no significant association between omega-3 fatty acids, asthma, RA, MS, type 2 diabetes, and IBD risk, hinting that these associations may be influenced by alcohol intake. These illuminating findings provide a clearer understanding of the prevention and management of IBD. In recent years, several MR studies investigating the risk factors of IBD [ 26 – 29 ], yet they all possess limitations. For instance, Yang et al. found causal association between MS and IBD risk. However, the causal relationship remains uncertain, as false-positive inferences due to commonly correlated pleiotropy are a prevalent concern[ 28 ]. Additionally, Xie Y et al. observed a heightened association between PSC and an increased IBD risk[ 29 ]. Notably, their inclusion of a multi-ethnic population may have introduced the potential risk of population stratification, consequently leading to spurious associations between exposure and variation. Another MR study highlighted the imbalance of gut microbiota as IBD risk factor, based on a single inverse variance, potentially biasing the interpretation[ 30 ]. Furthermore, a one-sample MR study revealed causal association between asthma onset in childhood and reduced IBD risk in adults, which have less statistical power compared to the two-sample approach[ 31 ]. Similarly, investigations into food intakes encountered limitations stemming from sample size issues[ 32 ]. In our study, we endeavored to mitigate the aforementioned limitations through specific approaches. Firstly, we exclusively analyzed genetic variants from individuals of European ancestry as data sources to minimize potential bias. Additionally, to bolster conclusiveness, we combined the results from three databases. A MR study revealed positive association between alcohol intake and CD risk. However, sensitivity analyses did not confirm this association, emphasizing the need to address horizontal pleiotropy and heterogeneity. Furthermore, a prospective cohort study uncovered that moderate beer consumption was linked to reduced CD risk, while liquor consumption was associated with increased UC risk[ 33 ]. While our study found suggestive association of alcohol consumption with IBD risk in discovery data, no significant association was observed in combined results. Nevertheless, the increase in sample size enhanced statistical power, rendering our integrated results potentially more representative. One study found no causal association between gut microbiota and IBD risk[ 34 ], while another study confirmed the opposite[ 30 ]. This discrepancy prompts further investigation. In our research, we sought to reconcile these conflicting findings by integrating results from three databases. The results (FinnGen dataset) revealed causal relationship between gut microbiota and IBD risk, also been found in combined results. This underscores further investigation into the relationship between gut microbiota and IBD risk, delving into the types of intestinal flora, their distribution within the intestine, and the causal relationship between intestinal flora and specific subtypes of IBD such as CD or UC. We were able to ascertain causal association between MS and IBD risk, a finding reinforced by the FinnGen dataset and our combined dataset. Similar outcomes were observed for PSC and AS, with our results aligning with prior study[ 29 ]. However, another MR analysis found no causal association of AS with IBD risk, may stem from database updates and large sample sizes[ 35 ]. Additionally, a causal role of RA in IBD risk was found[ 36 ], whereas our research indicated association of RA with reduced IBD risk, a trend confirmed in the combined results. Consequently, we firmly believe that the robustness of our findings. Moreover, another study demonstrated that protective effect of asthma in reduced IBD risk[ 31 ], align with our findings. Prior study found that serum omega-6 containing metabolites have causal association with CD risk[ 9 ]. Subsequently, MR investigation that delved into causal metabolites linked to multiple autoimmune diseases but did not focus on omega-3 fatty acids[ 37 ]. Therefore, our study incorporates omega-3 fatty acids into the analysis. Diminished association between omega-3 fatty acids and IBD risk that was subsequently corroborated in combined results. Omega-3 fatty acids play a multifaceted role in human physiology. Firstly, they promote the excretion of cholesterol from the stool and inhibit the synthesis of lipids and lipoproteins in the liver. Secondly, they are involved in arachidonic acid metabolism. These actions culminate in increased excretion and decreased synthesis of lipoproteins, leading to reduction in adiposity measures. This aligns with our outcomes, which evidenced strong association between body fat percentage and whole-body fat mass and increased IBD risk. However, our study did not find a significant association between obesity and IBD risk. This discrepancy can be attributed to the fact that body fat percentage and whole-body fat mass are correlated with obesity, determined by the degree of obesity or body mass index. Furthermore, obesity can be characterized by abdominal obesity and peripheral obesity. Importantly, our study did not account for the impact of fat distribution, which may have influenced the result. Our study represents a pioneering effort in illustrating the causal association between primary hypertension, type 2 diabetes, and IBD risk using MR and GWAS. We found that higher causal association between primary hypertension and increased IBD risk. We also found that lower association between type 2 diabetes and decreased IBD risk. The heterogeneous causes of secondary diabetes, the presence of multiple complications, and variations in the duration of onset emphasize the need for more precise prospective studies to thoroughly examine the impact of type 2 diabetes on IBD risk. In conclusion, our study has made valuable contributions to the understanding of intricate relationships between primary hypertension, type 2 diabetes, and IBD risk. Our study possesses several strengths. Firstly, the comprehensive approach to MR encompassing the discovery, validation, and meta-analysis stages represents a significant strength. This comprehensive methodology enhances credibility and reliability of the results, providing valuable insights into causal relationships between risk factors and IBD. Furthermore, the incorporation of multiple supplementary and sensitivity analyses to validate the instrumental variable assumptions for traits is another strength. By addressing potential issues such as pleiotropy, outliers, and sample overlap, our study demonstrates a high level of statistical rigor and robustness, bolstering the credibility of causal associations identified. The focus on a population of European ancestry also represents a strength, as it helps mitigate potential population stratification biases, thereby enhancing the internal validity of the findings. Additionally, to minimize sample overlap between exposure and outcome data sources stands out as a distinctive strength. By maintaining a low-down rate of sample overlap, our study effectively mitigates the risk of weak instrument bias, thereby enhancing the robustness and reliability of the causal associations identified. These strengths underscore the significance and reliability of our findings. Our study possesses several limitations. The limitation related to the sample size being restricted to European populations is an important consideration. While this focus enhances the internal validity of the findings within this demographic, it also underscores the need for future research to evaluate modifiable risks of IBD in other racial and ethnic groups. Additionally, the recognition of the need for future studies to identify SNPs associated with IBD severity and analyze the relationship of modifiable risk factors with IBD severity is an important research direction. While the absence of studies on the association of specific SNPs with IBD severity precluded correlation analysis in our study, this limitation highlights an area for potential future research to delve deeper into the nuanced impact of modifiable risk factors on IBD severity. By openly acknowledging these limitations, our study demonstrates a commitment to transparency and rigor, while also providing valuable insight into areas for further investigation. Conclusion Our comprehensive MR analysis has yielded valuable insights into the potential risk factors for the occurrence and development of IBD. The identification of primary hypertension as a strong indicator of increased IBD risk, along with the supported causal role of gut microbiota abundance, MS, PSC, AS, body fat percentage, and whole-body fat mass, represents a significant advancement in our understanding of the multifactorial nature of IBD. This insight has the potential to inform future research endeavors, clinical approaches to risk assessment, and the development of targeted interventions aimed at mitigating the impact of these risk factors on IBD occurrence and progression. Abbreviations IBD inflammatory bowel disease MR Mendelian randomization GWAS genome-wide association studies CD Crohn’s disease UC ulcerative colitis RA rheumatoid arthritis MA multiple sclerosis PSC primary sclerosing cholangitis AS ankylosing spondylitis STROBE-MR Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization SNPs single nucleotide polymorphisms IIBDGC International Inflammatory Bowel Disease Genetics Consortium IVW inverse variance weighted WM weighted median SD standard deviation OR odds ratio Declarations Ethical Statement According to the latest governmental legal ethical regulation titled “ Ethical Review Measures for Life Science and Medical Research Involving Human Beings”, issued and approved by the National Science and Technology Ethics Committee and State Council of P.R. China on the Feb 18th, 2023, a study utilizing public database data is exempt from ethical review. Consent for publication Not applicable. Availability of data and materials All data used for this study are publicly available. Neale Lab (http://www.neale lab.is/uk-biobank ); GWAS Catalog (https://www.ebi.ac.uk/gwas/home); MRC-IEU (https://gwas.mrcieu.ac.uk/); GABRIEL(https://ginasthma.org/); IMSGC (https://imsgc.net/); IPSCSG (http://www.ipscsg.org/); Within the family GWAS consortium (https://www.withinfamilyconsortium.com/); FinnGen (https://r4.finngen.fi/); The IIBDGC ( https://www.ibdgenetics.org/). Competing interests The authors declare that they have no conflicts of interest. Funding This work was financially supported by the following funding: (1) National Natural Science Foundation of China [grant number 82260555]; (2) Medical Innovation and Development Project of Lanzhou University [grant number lzuyxcx-2022-177]; (3) Major Science and Technology Projects of Gansu Province [grant number 22ZD6FA021-4]; (4) Science and Technology Program of Gansu Province [grant number 23JRRA0996]. Authors' contributions WXZ: Conceptualization, Writing – original draft, Writing – review & editing. CLF: Software, Writing – original draft. ZXY: Conceptualization, Funding acquisition, Project administration. WCZ: Conceptualization, Funding acquisition, Writing – review & editing. Acknowledgement: The authors want to acknowledge the participants and investigators of the GWAS datasets analyzed in this study, for sharing them publicly for research. Thanks to Dr. Edward C. Mignot, Shandong University, for linguistic advice. References Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, Panaccione R, Ghosh S, Wu JCY, Chan FKL et al : Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet (London, England) 2017, 390(10114):2769–2778. 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Frontiers in immunology 2022, 13:911631. Casey K, Lopes EW, Niccum B, Burke K, Ananthakrishnan AN, Lochhead P, Richter JM, Chan AT, Khalili H: Alcohol consumption and risk of inflammatory bowel disease among three prospective US cohorts. Alimentary pharmacology & therapeutics 2022, 55(2):225–233. Xu Q, Ni JJ, Han BX, Yan SS, Wei XT, Feng GJ, Zhang H, Zhang L, Li B, Pei YF: Causal Relationship Between Gut Microbiota and Autoimmune Diseases: A Two-Sample Mendelian Randomization Study. Frontiers in immunology 2021, 12:746998. Cui Z, Hou G, Meng X, Feng H, He B, Tian Y: Bidirectional Causal Associations Between Inflammatory Bowel Disease and Ankylosing Spondylitis: A Two-Sample Mendelian Randomization Analysis. Frontiers in genetics 2020, 11:587876. Meisinger C, Freuer D: Rheumatoid arthritis and inflammatory bowel disease: A bidirectional two-sample Mendelian randomization study. Seminars in arthritis and rheumatism 2022, 55:151992. Yu XH, Cao RR, Yang YQ, Lei SF: Identification of causal metabolites related to multiple autoimmune diseases. Human molecular genetics 2022, 31(4):604–613. Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf FigureS2.pdf FigureS3.pdf FigureS4.pdf FigureS5.pdf Figures6.pdf Figures7.pdf FigureS8.pdf Figures9.pdf Figures10.pdf Figures11.pdf Figures12.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4117254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":284785727,"identity":"a4a51386-e86e-4718-9c82-07d442a0de7e","order_by":0,"name":"Weixiong Zhu","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weixiong","middleName":"","lastName":"Zhu","suffix":""},{"id":284785728,"identity":"d446de25-17a9-4728-b3c6-ef4b6147067e","order_by":1,"name":"Chuanlei Fan","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuanlei","middleName":"","lastName":"Fan","suffix":""},{"id":284785729,"identity":"6884dfd9-3c16-482b-8bf8-e8d74cd0e8a5","order_by":2,"name":"Zengxi Yang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zengxi","middleName":"","lastName":"Yang","suffix":""},{"id":284785730,"identity":"65c68b95-74f3-4ae0-87ca-badc6dbbf89a","order_by":3,"name":"Wence Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACCQgpx8/e2PjwAwlaLIwlew43G0uQoKUiccON9DYBHmJ0yM9uPibNUyPBuOHmwzagfjs53QYCWhjnHEuT5jkmwSx5O7HtQQFDsrHZAQJamCVyzKRzGyTY+G4nthtIMBxI3EZICxtUCw/DzYNtQJIILTxQLRICNxiJ1CIhkZZs/eeYhIFkTyIwkA2I8Iv8jOSDN2fU1NX3sx9/+PBDhZ0cQS1owIA05aNgFIyCUTAKcAAAo8I86dkdjDUAAAAASUVORK5CYII=","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wence","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-03-17 13:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53958600,"identity":"40ed5d33-821d-436c-bd1f-e696ad74aaf8","added_by":"auto","created_at":"2024-04-02 17:46:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341437,"visible":true,"origin":"","legend":"\u003cp\u003eDirected acyclic graph of the MR framework investigating the causal relationship between modifiable risk factors and inflammatory bowel disease. A genome-wide association study was conducted in three independent studies.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117254/v1/69c7dbf902c77310f7f3799f.jpg"},{"id":53958598,"identity":"215b2eb0-8562-48be-ab29-5d793d8f80ee","added_by":"auto","created_at":"2024-04-02 17:46:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5231283,"visible":true,"origin":"","legend":"\u003cp\u003eUsing FinnGen datasets to analyze modifiable risk factors and inflammatory bowel disease.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117254/v1/0cc03d45a3edfb54bd2fa657.jpg"},{"id":53958599,"identity":"4c322177-5406-411e-b7ed-75a820f28da1","added_by":"auto","created_at":"2024-04-02 17:46:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265094,"visible":true,"origin":"","legend":"\u003cp\u003eA multivariable Mendelian randomization study examined the association between alcohol-adjusted mediators and IBD.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117254/v1/a9f0ab385d5efe699f366bf7.jpg"},{"id":53958604,"identity":"8912cab8-5d86-4d4c-b231-ca824f18e6e9","added_by":"auto","created_at":"2024-04-02 17:46:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":506734,"visible":true,"origin":"","legend":"\u003cp\u003eThree datasets from a meta-analysis are plotted in a forest plot.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117254/v1/deee080c8791cea84e1a1dbd.jpg"},{"id":101880534,"identity":"9e871d25-a385-41e5-b385-15a06e9c8deb","added_by":"auto","created_at":"2026-02-04 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Current clinical interventions for IBD focus on managing symptoms rather than addressing the underlying causes. However, these interventions often come with long-term adverse effects such as nausea, headache, edema, and nasopharyngitis[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given the unfavorable prognosis of IBD, it is crucial to identify the risk factors associated with IBD.\u003c/p\u003e \u003cp\u003eThe prevalence of IBD poses a significant challenge worldwide. Epidemiological research has identified several lifestyle factors associated with IBD, including smoking, alcohol consumption, sweet intake, physical activity, gut dysbiosis, and antibiotic exposure[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, evaluating potential causal association between modifiable factors and IBD is challenging. The presence of residual confounders and the possibility of reverse causation may have led to confusion regarding the relationships observed in previous studies. A pivotal imperative emerges to determine whether these modifiable factors play a causal role in IBD risk or if they are merely outcomes of shared risk factors.\u003c/p\u003e \u003cp\u003eMR analysis has emerged as a valuable approach to investigate potential associations between various risk factors and IBD[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By utilizing instrumental genetic variants, MR analysis aims to determine the underlying impacts and establish causality. This approach is advantageous as heritable genetic variations are randomly distributed during fetal development, which helps reduce confounding and minimize the bias caused by reverse causation. Randomized trials, although considered the gold standard, can be expensive, time-consuming, and challenging to conduct. However, MR analysis avoids these drawbacks and provides insights into causal factors and helps inform preventive measures.\u003c/p\u003e \u003cp\u003eIn our study, we employed a two-sample MR framework to investigate the causal relationships between 24 potential dominant factors and IBD. We utilized data from three comprehensive GWASs to assess the combined results. By leveraging the large-scale genetic data available in these GWASs, we aimed to provide a robust analysis of the causal relationships between these factors and IBD.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eMR analysis relies on three key assumptions. Firstly, genetic variants used as instrumental variables should have strong association with interested exposure. Secondly, genetic variants should not be associated with any confounding factors that could influence both exposure and outcome. Lastly, genetic variants should affect outcome solely through risk factor being investigated, and not through any alternative pathways (as depicted in Fig. \u003cspan\u003e1\u003c/span\u003e). Our methods adhered to the guidelines outlined in the STROBE-MR guidelines, which provide recommendations for conducting and reporting MR studies[\u003cspan\u003e12\u003c/span\u003e]. We first investigated the associations between modifiable risk variables and IBD using the FinnGen dataset. Subsequently, we performed replication analyses in two separate GWAS datasets (as illustrated in Fig. \u003cspan\u003e1\u003c/span\u003e) and combined the discovery and replication datasets to strengthen the robustness of findings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eGenetic instrument selection\u003c/h2\u003e\n \u003cp\u003eIn our study, we investigated the relationship between potentially modifiable risk factors and IBD using instrumental variables. These risk factors were categorized into four groups: 1. Lifestyle factors: alcohol intake, tobacco smoking, sweet intake, physical activity, cheese intake, carotene intake, gut microbiota abundance, processed meat intake, and college or university degree. 2.Serum parameters: total testosterone levels, albumin, omega-3 fatty acids, vitamin D levels, and C-reactive protein. 3.Metabolic comorbidities: type 2 diabetes, primary hypertension, body fat percentage, whole body fat mass, and obesity. 4.Extraintestinal manifestations: RA, asthma, MS, PSC, and AS. To gather instrumental variables for these traits, we utilized various sources (as listed in Table \u003cspan\u003e1\u003c/span\u003e): Neale Lab (\u003cspan\u003e\u003cspan\u003ehttp://www.nealelab.is/uk-biobank\u003c/span\u003e\u003c/span\u003e), GWAS Catalog (\u003cspan\u003e\u003cspan\u003ehttps://www.ebi.ac.uk/gwas/home\u003c/span\u003e\u003c/span\u003e), MRC-IEU (\u003cspan\u003e\u003cspan\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e)[\u003cspan\u003e13\u003c/span\u003e], GABRIEL (\u003cspan\u003e\u003cspan\u003ehttps://ginasthma.org/\u003c/span\u003e\u003c/span\u003e), IMSGC (\u003cspan\u003e\u003cspan\u003ehttps://imsgc.net/\u003c/span\u003e\u003c/span\u003e), IPSCSG (\u003cspan\u003e\u003cspan\u003ehttp://www.ipscsg.org/\u003c/span\u003e\u003c/span\u003e), and Within the Family GWAS Consortium (\u003cspan\u003e\u003cspan\u003ehttps://www.withinfamilyconsortium.com/\u003c/span\u003e\u003c/span\u003e). All the statistics used in our study are publicly available and de-identified. To minimize potential bias due to population stratification, we exclusively evaluated genetic variants from individuals of European ancestry.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the GWAS summary data.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsortium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReplication 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReplication 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifestyle factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPS \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPS \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPS \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNeale Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e172,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e35.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e35.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e35.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eTobacco smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNeale Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e91353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eSweet intake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e64949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e22.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e22.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e22.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e377,234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e34.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e34.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e34.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eCheese spread intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e64949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e22.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eCarotene intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e64979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eGut microbiota abundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e14306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e21.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eProcessed meat intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e461981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e38.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e38.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e38.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eCollege or University degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e458079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e42.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e43.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e43.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eTotal testosterone levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e194453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e85.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e85.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e85.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e115060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e85.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e82.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e61.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eOmega-3 fatty acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e114999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e167.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e170.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e164.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin\u0026nbsp;D\u0026nbsp;levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e496,946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e99.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e97.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e99.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eWithin family GWAS consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e61308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e71.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e67.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e67.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e655666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e67.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e66.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e66.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eprimary hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e463010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e47.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e46.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e46.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eBody fat percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e454633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e50.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e50.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e50.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eWhole body fat mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e454137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e53.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e52.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e53.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e13848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e42.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e42.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e42.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtraintestinal manifestations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e58,284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e157.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e107.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e117.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eGABRIEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e26475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e43.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e43.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e43.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eIMSGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e38589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e78.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e51.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e78.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary sclerosing cholangitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eIPSCSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e14890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e131.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e81.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e111.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.696914700544465%\" valign=\"top\"\u003e\n \u003cp\u003eAnkylosing spondylitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.070780399274048%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.885662431941924%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978221415607985%\" valign=\"top\"\u003e\n \u003cp\u003e22647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e124.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e70.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6225045372050815%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.166969147005444%\" valign=\"top\"\u003e\n \u003cp\u003e144.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eSNP, single nucleotide polymorphisms.\u003c/p\u003e\n \u003cp\u003eWe identified genetic variants that exhibited a strong association (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) with each trait of interest using GWASs. However, for traits such as tobacco smoking, gut microbiota abundance, sweet intake, cheese intake, and carotene, where only a few SNPs were significantly associated with IBD at the p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e level, we adjusted the threshold and set it at p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. We then removed any SNPs that showed potential linkage disequilibrium, keeping only those with a physical distance greater than 5,000 kb and a low likelihood of linkage disequilibrium (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)[\u003cspan\u003e14\u003c/span\u003e]. Finally, we assessed 24 risk factors using selected genetic variants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eGWAS summary statistics of IBD\u003c/h2\u003e\n \u003cp\u003eTo minimize any potential overlap, we excluded IBD GWAS data when selecting exposure factors, as most of the exposure factors were derived from the UK Biobank. Therefore, we focused on three IBD GWAS datasets (Supplementary Table \u003cspan\u003eS1\u003c/span\u003e) for our analysis: Discovery stage: FinnGen dataset, which included 8,704 IBD cases and 300,450 controls. More information can be found at \u003cspan\u003e\u003cspan\u003ehttps://r4.finngen.fi/\u003c/span\u003e\u003c/span\u003e [\u003cspan\u003e15\u003c/span\u003e]. Replication 1: IIBDGC. This consortium is dedicated to studying the genetic basis of IBD. Further details can be accessed at \u003cspan\u003e\u003cspan\u003ehttps://www.ibdgenetics.org/\u003c/span\u003e\u003c/span\u003e. Replication 2: We considered association analyses that identified 38 susceptibility loci for IBD and highlighted shared genetic risk across populations[\u003cspan\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe used F statistic parameters to assess the strength of the identified genetic instruments used in the MR analysis[\u003cspan\u003e17\u003c/span\u003e]. F statistics ranged from 21.41 to 170.24, indicating that the instruments had sufficient strength to detect potential causal effects (as shown in Table \u003cspan\u003e1\u003c/span\u003e). We used mRnd tool to evaluate the statistical power of our analysis (\u003cspan\u003e\u003cspan\u003ehttps://shiny.cnsgenomics.com/mRnd/\u003c/span\u003e\u003c/span\u003e)[\u003cspan\u003e18\u003c/span\u003e], which can assess the power of study design and identified genetic instruments in detecting causal effects between risk factors and IBD.\u003c/p\u003e\n \u003cp\u003eThe IVW method was used to assess whether all genetic instruments were valid and balanced pleiotropy[\u003cspan\u003e19\u003c/span\u003e]. Second, MR Egger regression and WM were conducted[\u003cspan\u003e20\u003c/span\u003e]. According to MR Egger, pleiotropic effects can occur in a directional fashion when some SNPs are acting on the outcome via a different pathway than the exposure of interest. However, the statistical power is sacrificed in this case[\u003cspan\u003e21\u003c/span\u003e]. The WM method allows up to 50% of the selected genetic instruments to be invalid[\u003cspan\u003e22\u003c/span\u003e]. Third, to test for general horizontal pleiotropy and outliers, the MR-PRESSO method uses global and SNP-specific observed residual sums of squares. Furthermore, it provides a distortion test that compares estimates before and after the removal of outliers[\u003cspan\u003e23\u003c/span\u003e]. Fourth, Multivariable MR estimates the impact of multiple exposures on a single outcome based on genetic variants associated with multiple, potentially related exposures. This approach is possible to retain the benefits associated with genetic instruments for causal inference, including Bias of confusion[\u003cspan\u003e24\u003c/span\u003e]. Finally, leave-one-out sensitivity analysis was conducted to assess the study\u0026apos;s robustness. Heterogeneity of these analyses was examined with Cochran Q test. In addition, we tested pleiotropy by collecting the intercepts from the MR Egger regression. We applied Bonferroni-corrected significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;2.08\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (0.05/24 risk factors) [\u003cspan\u003e14\u003c/span\u003e]. Analyses were conducted using R 4.1.3. The packages \u0026apos;TwoSampleMR\u0026apos;[\u003cspan\u003e25\u003c/span\u003e] and \u0026apos;MRPRESSO\u0026apos;[\u003cspan\u003e23\u003c/span\u003e] were used.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eWe used 24 potentially modifiable risk factors to determine causal relationships with IBD and classified them into 4 categories: lifestyle factors, serum parameters, metabolic risk factors, and extraintestinal manifestations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Lifestyle factors included six traits associated with diet, physical activity, gut microbiota abundance, and college or university degree. Serum parameters included omega-3 fatty acids, total testosterone levels, vitamin D levels, C-reactive protein, and albumin. Metabolic comorbidities included obesity, and two related to obesity traits, type 2 diabetes, and primary hypertension. Extraintestinal manifestations included RA, asthma, MS, PSC, and AS. The number of SNPs ranged from 5 to 593 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In all the considered traits, the F statistics were greater than 10, which suggested no potential weak instrument bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery results of IBD\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e sums up the discovery results of IBD in the FinnGen consortium. Given the FinnGen outcome, the statistical power was 100%. The specific information on SNPs, as instrumental variables, is shown in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle factors for IBD risk\u003c/h2\u003e \u003cp\u003eWe found that a suggestive association existed between alcohol usually taken with meals and increased IBD risk. The OR (95% CI) of the IVW method was 4.20 (1.29\u0026ndash;13.72) for a one-SD increase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Smoking (OR\u0026thinsp;=\u0026thinsp;8.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), sweet intake (OR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and gut microbiota abundance (OR\u0026thinsp;=\u0026thinsp;1.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) had suggestive increased IBD risk. The ORs (95% CI) of the WM estimator for smoking was 207.46 (5.21-8267.87) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) for a 1-SD increase. Cheese intake and college or university degree had low association with IBD. The ORs (95% CI) of the WM estimator were 0.29 (0.10\u0026ndash;0.80) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and 0.77 (0.53\u0026ndash;1.10) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.149) for 1-SD increase, respectively. The heterogeneity of college or university degree was (\u003cem\u003eP\u003c/em\u003e \u003csub\u003eheterogeneity\u003c/sub\u003e=0.012). There was no significant causal association between processed meat intake and IBD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModifiable risk factors for inflammatory bowel disease included in GWAS of FinnGen.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esnps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMR-egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eWM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eMR-presso\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003epleiotropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eheterogeneity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003esnps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.20( 1.29\u0026ndash;13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16(1.34e-06-20225.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.08(0.69\u0026ndash;13.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.20( 1.29\u0026ndash;13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.90(1.47-242.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e458.29(1.21-174253.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e207.46(5.21-8267.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.90(1.47-242.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweet intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23(1.02\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31(0.79\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.23(0.94\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.23(1.02\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05(0.52\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13(0.00-6.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92(0.38\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05(0.52\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCheese spread intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29(0.10\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17(0.02\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23(0.05\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29(0.10\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotene intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71(0.53\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97(0.48\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76(0.50\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71(0.53\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut microbiota abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29(1.08\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90(0.52\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26(0.98\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.29(1.08\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed meat intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17(0.60\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27(0.01\u0026ndash;7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97(0.39\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.17(0.60\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or University degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77(0.53\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.93(0.71\u0026ndash;12.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78(0.47\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67(0.47\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal testosterone levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12(1.01\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.95\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17(0.98\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.13(1.02\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(1.02\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37(0.97\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49(1.10\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.27(1.02\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega-3 fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.82\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83(0.73\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80(0.71\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.89(0.82\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin\u0026nbsp;D\u0026nbsp;levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.75\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(0.76\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88(0.68\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.90(0.76\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04(0.90\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.68\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(0.82\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04(0.90\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.85\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90(0.76\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95(0.86\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91(0.85\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprimary hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.73(1.53\u0026ndash;14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.14(0.28-945.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.35(1.59\u0026ndash;33.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.61(1.71\u0026ndash;12.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(1.04\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.58\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24(0.98\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.19(1.02\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole body fat mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12(1.00-1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.81\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05(0.88\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.11(1.00-1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01(0.91\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92(0.43\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(0.88\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01(0.91\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraintestinal manifestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.92\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89(0.84\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94(0.90\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94(0.91\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.60\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42(0.09\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71(0.61\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75(0.64\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07(1.01\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.88\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04(0.97\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10(1.04\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary sclerosing cholangitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(1.03\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01(0.94\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05(1.01\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.17(1.03\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkylosing spondylitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52(1.15\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31(0.82\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.45(1.14\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35(1.07\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eSNP, single nucleotide polymorphisms; IVW, inverse variance weighted; MR-egger, Mendelian randomization-Egger; WM, weighted median; MR-presso, MR-pleiotropy residual sum and outlier; OR, odds ratio; P, p-value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSerum parameters for IBD risk\u003c/h2\u003e \u003cp\u003eTotal testosterone levels and albumin were associated with increased IBD risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ORs (95% CI) of IVW method were 1.12 (1.01\u0026ndash;1.24) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) and 1.27 (1.02\u0026ndash;1.57) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) for 1-SD increases, individually. The heterogeneity of total testosterone levels was (\u003cem\u003eP\u003c/em\u003e \u003csub\u003eheterogeneity\u003c/sub\u003e=0.033). Omega-3 fatty acids had low association with IBD risk (IVW method: OR\u0026thinsp;=\u0026thinsp;0.89, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.009) for 1-SD increase. No significant causal association was found between vitamin D levels or C-reactive protein and IBD risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic comorbidities for IBD risk\u003c/h2\u003e \u003cp\u003eBody fat percentage and whole-body fat mass had significance IBD risk in which the ORs (95% CI) in IVW method were 1.22(1.04 to 1.43) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and 1.12(1.00 to 1.25) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) for 1-SD increase, respectively. However, obesity had no significant causal association with the increased IBD risk (OR\u0026thinsp;=\u0026thinsp;1.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92\u0026gt;0.05). Possible reasons are provided in the \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003ediscussion\u003c/span\u003e section.\u003c/p\u003e \u003cp\u003ePrimary hypertension and type 2 diabetes increased IBD risk. The ORs (95% CI) of IVW method were 4.73 (1.53 to 14.63; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) for 1-SD increase. The ORs (95% CI) of WM estimator were 7.35 (1.59\u0026ndash;33.91) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) for 1-SD increase. We found possible heterogeneity for primary hypertension (\u003cem\u003eP\u003c/em\u003e \u003csub\u003eheterogeneity\u003c/sub\u003e=0.031). Type 2 diabetes had low association with the increased IBD risk (IVW method: OR\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExtraintestinal manifestations for IBD risk\u003c/h2\u003e \u003cp\u003eMS (OR\u0026thinsp;=\u0026thinsp;1.07; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), PSC (OR\u0026thinsp;=\u0026thinsp;1.09; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and AS (OR\u0026thinsp;=\u0026thinsp;1.52; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), increased IBD risk in IVW method, and heterogeneity was low(all \u003cem\u003eP\u003c/em\u003e \u003csub\u003eheterogeneity\u003c/sub\u003e\u0026lt;0.001). The ORs (95% CI) of WM method for PSC and AS were 1.05(1.01 to 1.10; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and 1.45(1.14 to 1.83; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) for 1-SD increase, respectively. Possible pleiotropy for PSC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003epleiotropy\u003c/sub\u003e =0.014) while it remained stable in MR-PRESSO-corrected results (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Through the direction and magnitude of the homogeneity, we found that the consequences were dependable even with potential biases that may violate exclusion restrictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRA and asthma had low association with increased IBD risk (IVW method: OR\u0026thinsp;=\u0026thinsp;0.95; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015; OR\u0026thinsp;=\u0026thinsp;0.75; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable MR analysis of IBD\u003c/h2\u003e \u003cp\u003eGiven that positive association between alcohol consumption and CD, we performed multivariable MR analysis for omega-3 fatty acids, c-reactive protein, albumin, obesity, asthma, RA, MS, PSC, AS, type 2 diabetes, and primary hypertension after adjusting for alcohol intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In multivariable MR analyses, albumin, PSC, AS, and primary hypertension had positive associations with increased IBD risk. It confirmed the robustness of the results. However, no significant association was found between omega-3 fatty acids, asthma, RA, MS, type 2 diabetes and IBD risk, which suggested that these association may be impacted by alcohol intake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation results of IBD\u003c/h2\u003e \u003cp\u003eTo verify the reliability of the consequences, we replicated MR analysis in two independent GWAS datasets (replication 1 and 2).\u003c/p\u003e \u003cp\u003eIn replication 1, only similar MR consequences of MS, PSC, and AS increased IBD risk (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The ORs (95% CI) in IVW method were 1.12(1.00 to 1.26) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), 1.10(1.02 to 1.18) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and 3.78(1.16 to 12.29) for a 1-SD increase, respectively. The OR (95% CI) of WM method for MS was 1.08 (1.01 to 1.16; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eIn replication 2, PSC (OR\u0026thinsp;=\u0026thinsp;1.08; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), AS (OR\u0026thinsp;=\u0026thinsp;2.24; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), and body fat percentage (OR\u0026thinsp;=\u0026thinsp;1.21; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) had significance association with IBD risk using IVW method for a 1-SD (Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Possible pleiotropy for PSC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003epleiotropy\u003c/sub\u003e =0.014) while it remained stable in MR-PRESSO-corrected results (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001).\u003c/p\u003e \u003cp\u003eCombined results of IBD from the meta-analysis\u003c/p\u003e \u003cp\u003eTo increase statistical power, we made integration for three databases and combined modifiable risk factors on the results. The meta-analysis results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The combined results in discovery and validation confirmed that gut microbiota abundance, MS, PSC, AS, body fat percentage, whole body fat mass, and primary hypertension could increase IBD risk, while omega-3 fatty acids, asthma, and type 2 diabetes could lower the IBD risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, leave-one-out sensitivity to test above risk factors. MR results for the rest of SNPs were calculated after we eliminated SNPs of each risk factor one by one, which demonstrated the stability of the results (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn light of the current circumstances, this MR study meticulously accounted for the full array of modifiable risk factors for IBD. Gut microbiota abundance serves as a robust indicator of IBD risk. Furthermore, MS, PSC, and AS were significantly linked to elevated IBD risk. Notably, potential associations between body fat percentage, whole body fat mass, primary hypertension, and increased IBD risk. Conversely, the comprehensive analysis indicated that omega-3 fatty acids, asthma, type 2 diabetes, and college or university degree were tentatively associated with reduced IBD risk. Upon adjusting for alcohol intake, no significant association between omega-3 fatty acids, asthma, RA, MS, type 2 diabetes, and IBD risk, hinting that these associations may be influenced by alcohol intake. These illuminating findings provide a clearer understanding of the prevention and management of IBD.\u003c/p\u003e \u003cp\u003eIn recent years, several MR studies investigating the risk factors of IBD [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], yet they all possess limitations. For instance, Yang et al. found causal association between MS and IBD risk. However, the causal relationship remains uncertain, as false-positive inferences due to commonly correlated pleiotropy are a prevalent concern[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, Xie Y et al. observed a heightened association between PSC and an increased IBD risk[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, their inclusion of a multi-ethnic population may have introduced the potential risk of population stratification, consequently leading to spurious associations between exposure and variation. Another MR study highlighted the imbalance of gut microbiota as IBD risk factor, based on a single inverse variance, potentially biasing the interpretation[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, a one-sample MR study revealed causal association between asthma onset in childhood and reduced IBD risk in adults, which have less statistical power compared to the two-sample approach[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, investigations into food intakes encountered limitations stemming from sample size issues[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our study, we endeavored to mitigate the aforementioned limitations through specific approaches. Firstly, we exclusively analyzed genetic variants from individuals of European ancestry as data sources to minimize potential bias. Additionally, to bolster conclusiveness, we combined the results from three databases. A MR study revealed positive association between alcohol intake and CD risk. However, sensitivity analyses did not confirm this association, emphasizing the need to address horizontal pleiotropy and heterogeneity. Furthermore, a prospective cohort study uncovered that moderate beer consumption was linked to reduced CD risk, while liquor consumption was associated with increased UC risk[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. While our study found suggestive association of alcohol consumption with IBD risk in discovery data, no significant association was observed in combined results. Nevertheless, the increase in sample size enhanced statistical power, rendering our integrated results potentially more representative.\u003c/p\u003e \u003cp\u003eOne study found no causal association between gut microbiota and IBD risk[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while another study confirmed the opposite[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This discrepancy prompts further investigation. In our research, we sought to reconcile these conflicting findings by integrating results from three databases. The results (FinnGen dataset) revealed causal relationship between gut microbiota and IBD risk, also been found in combined results. This underscores further investigation into the relationship between gut microbiota and IBD risk, delving into the types of intestinal flora, their distribution within the intestine, and the causal relationship between intestinal flora and specific subtypes of IBD such as CD or UC.\u003c/p\u003e \u003cp\u003eWe were able to ascertain causal association between MS and IBD risk, a finding reinforced by the FinnGen dataset and our combined dataset. Similar outcomes were observed for PSC and AS, with our results aligning with prior study[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, another MR analysis found no causal association of AS with IBD risk, may stem from database updates and large sample sizes[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, a causal role of RA in IBD risk was found[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], whereas our research indicated association of RA with reduced IBD risk, a trend confirmed in the combined results. Consequently, we firmly believe that the robustness of our findings. Moreover, another study demonstrated that protective effect of asthma in reduced IBD risk[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], align with our findings.\u003c/p\u003e \u003cp\u003ePrior study found that serum omega-6 containing metabolites have causal association with CD risk[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Subsequently, MR investigation that delved into causal metabolites linked to multiple autoimmune diseases but did not focus on omega-3 fatty acids[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, our study incorporates omega-3 fatty acids into the analysis. Diminished association between omega-3 fatty acids and IBD risk that was subsequently corroborated in combined results. Omega-3 fatty acids play a multifaceted role in human physiology. Firstly, they promote the excretion of cholesterol from the stool and inhibit the synthesis of lipids and lipoproteins in the liver. Secondly, they are involved in arachidonic acid metabolism. These actions culminate in increased excretion and decreased synthesis of lipoproteins, leading to reduction in adiposity measures. This aligns with our outcomes, which evidenced strong association between body fat percentage and whole-body fat mass and increased IBD risk. However, our study did not find a significant association between obesity and IBD risk. This discrepancy can be attributed to the fact that body fat percentage and whole-body fat mass are correlated with obesity, determined by the degree of obesity or body mass index. Furthermore, obesity can be characterized by abdominal obesity and peripheral obesity. Importantly, our study did not account for the impact of fat distribution, which may have influenced the result.\u003c/p\u003e \u003cp\u003eOur study represents a pioneering effort in illustrating the causal association between primary hypertension, type 2 diabetes, and IBD risk using MR and GWAS. We found that higher causal association between primary hypertension and increased IBD risk. We also found that lower association between type 2 diabetes and decreased IBD risk. The heterogeneous causes of secondary diabetes, the presence of multiple complications, and variations in the duration of onset emphasize the need for more precise prospective studies to thoroughly examine the impact of type 2 diabetes on IBD risk. In conclusion, our study has made valuable contributions to the understanding of intricate relationships between primary hypertension, type 2 diabetes, and IBD risk.\u003c/p\u003e \u003cp\u003eOur study possesses several strengths. Firstly, the comprehensive approach to MR encompassing the discovery, validation, and meta-analysis stages represents a significant strength. This comprehensive methodology enhances credibility and reliability of the results, providing valuable insights into causal relationships between risk factors and IBD. Furthermore, the incorporation of multiple supplementary and sensitivity analyses to validate the instrumental variable assumptions for traits is another strength. By addressing potential issues such as pleiotropy, outliers, and sample overlap, our study demonstrates a high level of statistical rigor and robustness, bolstering the credibility of causal associations identified. The focus on a population of European ancestry also represents a strength, as it helps mitigate potential population stratification biases, thereby enhancing the internal validity of the findings. Additionally, to minimize sample overlap between exposure and outcome data sources stands out as a distinctive strength. By maintaining a low-down rate of sample overlap, our study effectively mitigates the risk of weak instrument bias, thereby enhancing the robustness and reliability of the causal associations identified. These strengths underscore the significance and reliability of our findings.\u003c/p\u003e \u003cp\u003eOur study possesses several limitations. The limitation related to the sample size being restricted to European populations is an important consideration. While this focus enhances the internal validity of the findings within this demographic, it also underscores the need for future research to evaluate modifiable risks of IBD in other racial and ethnic groups. Additionally, the recognition of the need for future studies to identify SNPs associated with IBD severity and analyze the relationship of modifiable risk factors with IBD severity is an important research direction. While the absence of studies on the association of specific SNPs with IBD severity precluded correlation analysis in our study, this limitation highlights an area for potential future research to delve deeper into the nuanced impact of modifiable risk factors on IBD severity. By openly acknowledging these limitations, our study demonstrates a commitment to transparency and rigor, while also providing valuable insight into areas for further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur comprehensive MR analysis has yielded valuable insights into the potential risk factors for the occurrence and development of IBD. The identification of primary hypertension as a strong indicator of increased IBD risk, along with the supported causal role of gut microbiota abundance, MS, PSC, AS, body fat percentage, and whole-body fat mass, represents a significant advancement in our understanding of the multifactorial nature of IBD. This insight has the potential to inform future research endeavors, clinical approaches to risk assessment, and the development of targeted interventions aimed at mitigating the impact of these risk factors on IBD occurrence and progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIBD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003einflammatory bowel disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMendelian randomization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGWAS\u003c/em\u003e\u003c/strong\u003e genome-wide association studies\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCD\u003c/em\u003e\u003c/strong\u003e Crohn\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUC\u003c/em\u003e\u003c/strong\u003e ulcerative colitis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRA\u003c/em\u003e\u003c/strong\u003e rheumatoid arthritis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMA\u003c/em\u003e\u003c/strong\u003e multiple sclerosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePSC\u003c/em\u003e\u003c/strong\u003e primary sclerosing cholangitis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAS\u003c/em\u003e\u003c/strong\u003e ankylosing spondylitis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSTROBE-MR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eStrengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNPs\u003c/em\u003e\u003c/strong\u003e single nucleotide polymorphisms\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIIBDGC\u003c/em\u003e\u003c/strong\u003e International Inflammatory Bowel Disease Genetics Consortium\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIVW\u003c/em\u003e\u003c/strong\u003e inverse variance weighted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWM\u003c/em\u003e\u003c/strong\u003e weighted median\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e odds ratio\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the latest governmental legal ethical regulation titled \u0026ldquo; Ethical Review Measures for Life Science and Medical Research Involving Human Beings\u0026rdquo;, \u0026nbsp; issued and approved by the National Science and Technology Ethics Committee and State Council of P.R. China on the Feb 18th, 2023, a study utilizing public database data is exempt from ethical review. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used for this study are publicly available. Neale Lab (http://www.neale lab.is/uk-biobank ); GWAS Catalog (https://www.ebi.ac.uk/gwas/home); MRC-IEU (https://gwas.mrcieu.ac.uk/); GABRIEL(https://ginasthma.org/); IMSGC (https://imsgc.net/); IPSCSG (http://www.ipscsg.org/); Within the family GWAS consortium (https://www.withinfamilyconsortium.com/); FinnGen (https://r4.finngen.fi/); The IIBDGC ( https://www.ibdgenetics.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the following funding:\u003c/p\u003e\n\u003cp\u003e(1) National Natural Science Foundation of China [grant number 82260555];\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) Medical Innovation and Development Project of Lanzhou University [grant number lzuyxcx-2022-177];\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) Major Science and Technology Projects of Gansu Province [grant number 22ZD6FA021-4];\u003c/p\u003e\n\u003cp\u003e(4)\u0026nbsp;Science and Technology Program of Gansu Province\u0026nbsp;[grant number 23JRRA0996].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWXZ:\u0026nbsp;Conceptualization,\u0026nbsp;Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. CLF: Software, Writing \u0026ndash; original draft. ZXY: Conceptualization,\u0026nbsp;Funding acquisition, Project administration. WCZ: Conceptualization, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to acknowledge the participants and investigators of the GWAS datasets analyzed in this study, for sharing them publicly for research. Thanks to Dr. Edward C. Mignot, Shandong University, for linguistic advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, Panaccione R, Ghosh S, Wu JCY, Chan FKL \u003cem\u003eet al\u003c/em\u003e: Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. 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Frontiers in genetics 2020, 11:587876.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeisinger C, Freuer D: Rheumatoid arthritis and inflammatory bowel disease: A bidirectional two-sample Mendelian randomization study. Seminars in arthritis and rheumatism 2022, 55:151992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu XH, Cao RR, Yang YQ, Lei SF: Identification of causal metabolites related to multiple autoimmune diseases. Human molecular genetics 2022, 31(4):604\u0026ndash;613.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"inflammatory bowel diseases, mendelian randomization, risk factors, lifestyle factors, metabolic risk factors, extraintestinal manifestations","lastPublishedDoi":"10.21203/rs.3.rs-4117254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe timely recognition of risk factors assumes paramount importance in the prevention of IBD. Our objective is to elucidate the relationship between risk factors and IBD risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the associations between 24 modifiable risk factors and IBD, a combination of univariate and multivariate MR analysis methods was employed. The final outcomes were assessed through a comprehensive analysis of three large independent GWAS.\u003c/p\u003e\n\u003cp\u003eTo mitigate confounding biases, we conducted univariate MR analysis for each individual factor. Multivariate MR analysis was performed within each group to account for the influence of multiple factors simultaneously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRA, asthma, the intake of cheese spread, carotene, and college or university degree were negatively associated with IBD risk. MS, PSC, AS, alcohol consumption, gut microbiota abundance, smoking, and sweet intake exhibited positive correlation with IBD risk. Type 2 diabetes, omega-3 fatty acids were correlated with reduced IBD risk. Total testosterone levels and albumin exhibited associations with IBD risk. Primary hypertension, body fat percentage, and whole-body fat mass suggested increased IBD risk. Three large-scale GWAS independently confirmed that gut microbiota abundance, primary hypertension, MS, PSC, AS, whole-body fat mass, and body fat percentage exhibited stronger associations with IBD risk. Conversely, omega-3 fatty acids, RA, asthma, type 2 diabetes, and attainment of a college or university degree were related to decreased IBD risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuch robust evidence has the potential to inform preventive measures for IBD and, notably, illuminate pathways for future research endeavors.\u003c/p\u003e","manuscriptTitle":"Genome-wide mendelian randomization reveals causal effects of modifiable risk factors on inflammatory bowel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 17:46:32","doi":"10.21203/rs.3.rs-4117254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d93848c9-e75c-472b-899c-d685cb3409eb","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29979943,"name":"Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease"},{"id":29979944,"name":"Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease/Crohns disease"},{"id":29979945,"name":"Health sciences/Diseases/Gastrointestinal diseases/Inflammatory bowel disease/Ulcerative colitis"}],"tags":[],"updatedAt":"2026-02-02T02:09:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-02 17:46:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4117254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117254","identity":"rs-4117254","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}