The use of two-sample methods for Mendelian randomization analyses on single large datasets

AbstractBackgroundWith genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.MethodsWith simulations mimicking a typical study in UK Biobank we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing for: presence/absence of a true causal effect; amount of confounding; presence and type of pleiotropy (none, balanced or directional).ResultsEven in the presence of substantial correlation due to confounding, all methods performed well when used in one-sample MR except for MR-Egger, which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrumental strength across variants (I2GX of 97%).ConclusionsTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high.Key MessagesCurrent availability of phenotypic and genetic data from large biobanks, such as UK Biobank, has led to increasing use of one-sample Mendelian randomization (MR) to investigate causal relationships in epidemiological researchRobust MR methods have been developed to address pleiotropy, but they assume independence between the gene-exposure and gene-outcome association estimates; this holds in two-sample MR but not in one-sample MRWe illustrate the practical implications, in terms of bias and precision of the MR causal effect estimate, of using robust two-sample methods in one-sample MR studies performed within large biobanksTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger regressionMR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high

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Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

Journal of Thrombosis and Thrombolysis ◽

10.1007/s11239-021-02494-4 ◽

2021 ◽

Author(s):

Shuai Yuan ◽

Maria Bruzelius ◽

Susanna C. Larsson

Keyword(s):

Renal Function ◽

Venous Thromboembolism ◽

Mendelian Randomization ◽

Causal Effect ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genome Wide ◽

Increased Risk ◽

Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

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Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.695480 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zixian Wang ◽

Shiyu Chen ◽

Qian Zhu ◽

Yonglin Wu ◽

Guifeng Xu ◽

...

Keyword(s):

Heart Failure ◽

Human Blood ◽

Large Scale ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Causal Effect ◽

Meta Analysis ◽

Sensitivity Analyses ◽

Blood Metabolites ◽

Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

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Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study

PLoS Genetics ◽

10.1371/journal.pgen.1009525 ◽

2021 ◽

Vol 17 (4) ◽

pp. e1009525

Author(s):

Mark Gormley ◽

James Yarmolinsky ◽

Tom Dudding ◽

Kimberley Burrows ◽

Richard M. Martin ◽

...

Keyword(s):

Cancer Risk ◽

Genetic Variants ◽

Mendelian Randomization ◽

Causal Effect ◽

Meta Analysis ◽

Uk Biobank ◽

Limited Evidence ◽

Cholesterol Lowering ◽

Increased Risk ◽

Secondary Analyses

Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk using two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p = 9.31 x10-05), with good concordance between GAME-ON and UK Biobank (I2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p = 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects; however, this was not supported uniformly across all sensitivity analyses and further replication of this finding is required.

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Plasma Proteomics of Renal Function: A Trans-ethnic Meta-analysis and Mendelian Randomization Study

Journal of the American Society of Nephrology ◽

10.1681/asn.2020071070 ◽

2021 ◽

pp. ASN.2020071070

Author(s):

Pamela Matías-García ◽

Rory Wilson ◽

Qi Guo ◽

Shaza Zaghlool ◽

James Eales ◽

...

Keyword(s):

Renal Function ◽

Plasma Proteins ◽

Mendelian Randomization ◽

Causal Effect ◽

Meta Analysis ◽

Kidney Tissue ◽

Renal Tissue ◽

Cross Sectional Study ◽

Causal Relationships ◽

Cross Sectional

Background: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. Methods: A cross-sectional study of 993 plasma proteins among 2,882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified trans-ethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR . Results: Fifty-seven plasma proteins were associated with eGFR, including one novel protein. Twenty-three of these were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. Conclusions: In a discovery-replication setting, we identified 57 proteins trans-ethnically associated with eGFR. The revealed causal relationships are an important stepping-stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.

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Cardiometabolic traits, sepsis and severe covid-19: a Mendelian randomization investigation

10.1101/2020.06.18.20134676 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mark J Ponsford ◽

Apostolos Gkatzionis ◽

Venexia M Walker ◽

Andrew J Grant ◽

Robyn E Wootton ◽

...

Keyword(s):

Respiratory Failure ◽

Mendelian Randomization ◽

Causal Effect ◽

Health Interventions ◽

List Type ◽

Uk Biobank ◽

Standard Deviation Increase ◽

Increased Risk ◽

Health Related

AbstractObjectivesTo investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.DesignMendelian randomisation analysis.SettingUK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.Participants12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).ExposureGenetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.Main outcome measuresRisk of sepsis and severe covid-19 with respiratory failure.ResultsHigher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64). Higher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75). There was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19. Similar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.ConclusionsOur findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19. Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.Summary boxesWhat is already known on this topicSepsis and severe covid-19 are major contributors to global morbidity and mortality.Cardiometabolic risk factors have been associated with risk of sepsis and severe covid-19, but it is unclear if they are having causal effects.What this study addsUsing Mendelian randomization analyses, this study provides evidence to support that higher body mass index and lifetime smoking score both increase risk of sepsis and severe covid-19 with respiratory failure.Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.

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Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

International Journal of Epidemiology ◽

10.1093/ije/dyaa266 ◽

2021 ◽

Author(s):

Fernando Pires Hartwig ◽

Kate Tilling ◽

George Davey Smith ◽

Deborah A Lawlor ◽

Maria Carolina Borges

Keyword(s):

Waist Circumference ◽

Genetic Variants ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Real Data ◽

Sensitivity Analyses ◽

Effect Estimate ◽

Genome Wide Association Studies ◽

Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

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Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

10.1101/865956 ◽

2019 ◽

Cited By ~ 1

Author(s):

Simon Haworth ◽

Pik Fang Kho ◽

Pernilla Lif Holgerson ◽

Liang-Dar Hwang ◽

Nicholas J. Timpson ◽

...

Keyword(s):

Dental Caries ◽

Causal Inference ◽

Mendelian Randomization ◽

Association Studies ◽

List Type ◽

Genome Wide Association Studies ◽

Causal Relationships ◽

Link Type ◽

Alternative Method ◽

Causal Variable

AbstractBackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments.MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference.ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health.ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome.Key messagesThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met.We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

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Smoking, DNA methylation and lung function: a Mendelian randomization analysis to investigate causal relationships

10.1101/19003335 ◽

2019 ◽

Author(s):

Emily Jamieson ◽

Roxanna Korologou-Linden ◽

Robyn E. Wootton ◽

Anna L. Guyatt ◽

Thomas Battram ◽

...

Keyword(s):

Dna Methylation ◽

Lung Function ◽

Mendelian Randomization ◽

Causal Effect ◽

Forced Expiratory Volume ◽

Multiple Trait ◽

Genome Wide ◽

Causal Variants ◽

Association Data ◽

Randomization Analysis

AbstractWhether smoking-associated DNA methylation has a causal effect on lung function has not been thoroughly evaluated. We investigated the causal effects of 474 smoking-associated CpGs on forced expiratory volume in one second (FEV1) in two-sample Mendelian randomization (MR) using methylation quantitative trait loci and genome-wide association data for FEV1. We found evidence of a possible causal effect for DNA methylation on FEV1 at 18 CpGs (p<1.2×10−4). Replication analysis supported a causal effect at three CpGs (cg21201401 (ZGPAT), cg19758448 (PGAP3) and cg12616487 (AHNAK) (p<0.0028). DNA methylation did not clearly mediate the effect of smoking on FEV1, although DNA methylation at some sites may influence lung function via effects on smoking. Using multiple-trait colocalization, we found evidence of shared causal variants between lung function, gene expression and DNA methylation. Findings highlight potential therapeutic targets for improving lung function and possibly smoking cessation, although large, tissue-specific datasets are required to confirm these results.

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Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

Frontiers in Genetics ◽

10.3389/fgene.2021.772343 ◽

2022 ◽

Vol 12 ◽

Author(s):

Chenglin Duan ◽

Jingjing Shi ◽

Guozhen Yuan ◽

Xintian Shou ◽

Ting Chen ◽

...

Keyword(s):

Heart Failure ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Sensitivity Analysis ◽

Causal Relationship ◽

Observational Studies ◽

Mendelian Randomization ◽

Causal Effect ◽

Causal Relationships ◽

Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

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Causal Association Between Atopic Dermatitis and Inflammatory Bowel Disease: A 2-Sample Bidirectional Mendelian Randomization Study

Inflammatory Bowel Diseases ◽

10.1093/ibd/izab329 ◽

2021 ◽

Author(s):

Christa Meisinger ◽

Dennis Freuer

Keyword(s):

Inflammatory Bowel Disease ◽

Atopic Dermatitis ◽

Bowel Disease ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Causal Effect ◽

The Other ◽

Uk Biobank ◽

Inflammatory Bowel ◽

The Uk

Abstract Background Observational studies postulated an association between atopic dermatitis (AD) and inflammatory bowel disease (IBD). However, it remains unclear whether this relationship is causal. Methods To determine whether AD is causally related to IBD and vice versa, a 2-sample Mendelian randomization study was conducted. Independent genetic instruments from the largest available genome-wide association study for AD (EAGLE eczema consortium without the 23andMe study including 10,788 cases and 30,047 controls) were used to investigate the association with IBD in the UK Biobank study (7045 cases, 456,327 controls) and a second European IBD sample (12,882 cases, 21,770 controls). Results Atopic dermatitis was strongly associated with higher risk of IBD as a whole (odds ratio [OR], 1.107; 95% confidence interval [CI], 1.035; 1.183; P = .003) in the UK Biobank study. The positive association was not significant in the other IBD study (OR, 1.114; 95% CI, 0.956; 1.298), but in meta-analyses of results from the 2 studies, the strong association could be confirmed (OR, 1.11; 95% CI, 1.04; 1.18). When evaluating the causal relationship in the other direction, IBD as a whole did not show an association with AD. Subtype analyses revealed that AD was suggestively associated with ulcerative colitis (UC; OR, 1.149; 95% CI, 1.018; 1.297) but not Crohn’s disease (CD). However, there was a suggestive association between CD and AD (OR, 1.034; 95% CI, 1.004; 1.064) but not UC and AD. Conclusions This study supports a causal effect between AD and IBD—but not between IBD and AD. There seems to be considerable differences between UC and CD regarding their specific associations with AD. These findings have implications for the management of IBD and AD in clinical practice.

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