Causal Associations Between Ulcerative Colitis and COVID-19: A Bidirectional Mendelian Randomization Study

doi:10.21203/rs.3.rs-2626839/v1

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Causal Associations Between Ulcerative Colitis and COVID-19: A Bidirectional Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-2626839/v1

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Background

Corona Virus Disease 2019(COVID-19) has put human health and medical resources under strain since 2019. However, it’s still equivocal whether ulcerative colitis (UC) and COVID-19 have a bidirectional causal relationship, so we attempted to clarify this issue using two-sample mendelian randomization (two-sample MR) analysis.

Methods

To explore the relationship between ulcerative colitis and COVID-19, we obtained summary statistics from the GWAS database and used single nucleotide polymorphisms (SNPs) as a genetic tool. The inverse-variance weighted (IVW) method was chosen as the primary analytical method for two-sample Mendelian randomization analysis, complemented by a combination of MR-egger. Further validation using sensitivity analysis such as Q-test, MR-PRESSO, MR-Egger intercepts method.

Result

All forward MR analyses demonstrated that UC has a significant susceptibility and severity to COVID-19, 1. COVID-19 vs population: IVW (OR = 1.053; CI, 1.012–1.096; P = 0.009) and MR Egger (OR = 1.015; CI, 0.946–1.089; P = 0.672). 2.hospitalized vs population: IVW (OR = 1.080; CI, 1.004–1.162; P = 0.037) and MR Egger (OR = 1.009; CI, 0.882–1.153; P = 0.898). 3. very severe respiratory confirmed vs population: IVW (OR = 1.187; CI, 1.018–1.384; P = 0.027) and MR Egger (OR = 1.104; CI, 0.801–1.521; P = 0.554). In the bidirectional MR study, the P values (P > 0.05) obtained by several methods were not statistically significant.

Conclusion

There is significant evidence that UC is both a risk factor for COVID-19 and may increase the risk of COVID-19. However, there is no reverse causal relationship between the two diseases.

ulcerative colitis

COVID-19

SARS-CoV-2

mendelian randomization

GWAS

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is a highly transmissible and deadly coronavirus that emerged in 2019. The disease was later named "coronavirus disease 2019" (COVID-19), which is threatening human health and putting a significant strain on existing medical resources.[1–3]. Notably, the main clinical manifestation of COVID-19 include tiredness, dyspnea, smell and taste dysfunction, malaise, impairments in memory and attention, and varieties of neuropsychiatric disorders[4–6]. According to several recent studies, obesity, diabetes, and hypertension are high-risk factors for the development of COVID-19 and can increase the infection and mortality rates of the disease[7–9]. Some studies clearly concluded that UC is a chronic inflammatory bowel disease (IBD) with an unknown cause that usually presents as bloody diarrhea and can be diagnosed by colonoscopy or histological findings[10]. Many factors contribute to its pathogenesis, including genetic background, environment, microbiota, lifestyle factors, and mucosal immune dysregulation[11]. The current treatment focuses primarily on the administration of drugs to alleviate the associated symptoms, such as aminosalicylates, corticosteroids, thiopurines, anti-tumor necrosis factor (TNF) drugs, and ustekinumab (anti-IL12/23)[12]. However, no studies have yet been conducted to investigate the causal relationship between ulcerative colitis and COVID-19.A meta-analysis study showed that patients with UC were associated with an increased risk of COVID-19[13]. It has also been demonstrated that novel coronavirus has the potential to trigger UC with angiotensin I-converting enzyme 2 (ACE2), a host receptor for SARS-CoV-2 infection of human cells. The gastrointestinal system may be affected by COVID-19 through angiotensin-converting enzyme-2 (ACE2) expression[14, 15]. Therefore, whether there is a complex bidirectional causal relationship between UC and COVID-19 needs to be further investigated.

Mendelian randomization analysis is now widely used in epidemiological studies to determine whether there is a causal relationship between exposure and outcome.[16, 17]. Taking advantage of the fact that genetic variation is random among children from the same parents, the relationship between exposure and outcome is not susceptible to confounding factors, so the results of MR are highly reliable compared to conventional observational studies [18]. As a result, we used a two-sample bidirectional MR analyses to investigate the relationship between UC and COVID-19, hoping to widen the horizon and provide new ideas for the clinical treatment and prevention of diseases.

The Mendelian randomized design needs to satisfy the following three assumptions[19]: firstly, genetic variation is strongly correlated with exposure factors; secondly, genetic variation is not associated with outcome; thirdly, instrumental variables are unaffected by unmeasured confounders. Our MR design framework is explicitly mentioned in Fig. 1.

2.1 Data source from GWAS

The data for this study were derived from the Genome-wide association studies (GWAS), which identifies associations between genotype and phenotype by testing genetic variation in the genomes of a good many individuals (https://gwas.mrcieu.ac.uk). Over the years, it has provided many convincing associations for complex human traits and diseases[20]. In our study, the GWAS data on UC included 4320 UC patients and 210300 controls. Without making a distinction between genders, all data sources came from Europe ancestors.

The COVID Host Genetics Initiative, which was launched in 2020, provides us with a COVID-19 genetic datasetsto aid in the study of the association between COVID-19 and the susceptibility and severity of other disease[21]. Thus, three cohort studies of COVID-19 were selected for this study,1.Data for covid-19 susceptibility included 32,494 individuals and 1,316,207 controls (COVID-19 vs population). 2. General inpatient data included 9,986 cases and 1,877,672 controls (hospitalized vs population). 3. Data for the third cohort represented very severe respiratory confirmed patients, and included 4,792 individuals and 1,054,664 controls (very severe respiratory confirmed vs population). The first cohort was used to assess susceptibility, while the second and third cohorts were used to assess severity phenotype. The cases of patients with COVID-19 are all from the European ancestors population, details are shown in Table 1.

Table 1

Sources of data from GWAS database about UC and COVID-19
Trait	GWAS catalog accession number	Sample size	Year	Cases	Controls	population
Ulcerative colitis	finn-b-K11_ULCER	214,600	2021	4,320	210,300	European
COVID-19 susceptibility (COVID-19 vs. population)	ebi-a-GCST011074	1,348,701	2020	32,494	1,316,207	European
COVID-19 severity (hospitalized vs. population)	ebi-a-GCST011081	1,887,658	2020	9,986	1,877,672	European
COVID-19 severity (very severe respiratory confirmed vs. population)	ebi-a-GCST011078	1,054,664	2020	4,792	1,059,456	European
Abbreviations: COVID-19, coronavirus disease 2019.

2.2 Instrument variables for UC and COVID-19

All data analysis of MR is performed by R software (R version 4.2.2). The genetic variants associated with UC were used as instrumental variables for exposure factors, and the three phenotypes regarding COVID-19 were used as outcomes. First, we set P < 5e-08 as a statistically significant criterion in order to select valid instrumental variables. Furthermore, linkage disequilibrium (LD) of the SNPs associated with UC were be remained in the analysis (clumping r² cut-off = 0.001, clumping window = 5000 kb). Subsequently, the data on exposure and outcomes were harmonized separately, including information on the effect allele, SNP, other allele, standard error, effect size, and P-values. Eligible SNPs were selected to assess the impact of UC on the newly crowned susceptible and severe populations. We calculated the F-statistic to quantify the strength of genetic variation, and if the F-statistic was greater than 10, then the probability of deviation if the instrumental variable was small[22]. In addition, to investigate the effect of COVID-19 on UC, we needed to perform a reverse analysis using genetic variants associated with COVID-19 as instrumental variables.

2.3 MR analysis

We choose inverse variance-weighted as the main analysis method, supplemented by MR-egger, weighted median, and mode-based estimate method. Inverse variance weighted estimates of causal effects combine ratio estimates for each variant in the fixed effects meta-analysis model to provide consistent estimates of the causal effects of exposure and outcome[23]. Notably, the presence of the intercept term is typically not taken into account by IVW. In the case of weak IV, the IVW estimator may be significantly biased[24]. In contrast, the greatest feature of MR-egger is the presence of a consideration of the intercept term. It is possible to determine whether genetic variation has pleiotropic effects on outcomes using the MR-Egger method, which can also offer reliable estimates of causal effects when fewer assumptions are made[25]. Sensitivity analyses were also performed in this study. Firstly, MR-PRESSO was used to test for outliers, it requires at least 50% of the variants to be valid instruments and is primarily used to remove SNPs that cause bias[26]. When horizontal pleiotropy tests in MR-Egger were carried out (P > 0.05), the outcomes were regarded as reliable. Additionally, the Cochran's Q test and Leave-one-out analysis were used to further examine the results.

Causal effect between UC and COVID-19

The results of all data are summarized in Table 2. Forward MR analyses concluded that there was statistically significant evidence that patients with UC were more likely to be infected with COVID-19. In the first phenotype(COVID-19 vs population), the MR results showed that there was an association between UC and the susceptibility phenotype of COVID-19. We obtained 12 SNPs associated with genetic variation, IVW (OR = 1.053; CI, 1.012–1.096; P = 0.009) and MR-Egger (OR = 1.015; CI, 0.946–1.089; P = 0.672). Even though other method P value were not statistically significant, they all displayed the same direction OR > 1; Beta > 0), which provides us robust evidence on the causal association between COVID-19 and UC. The MR-Egger intercept test showed no directional horizontal pleiotropy (egger-intercept = 0.017, P = 0.262), meanwhile, examination of the risk relationship between UC and COVID-19 according to Cochran’s Q-test(P > 0.05) and MR-PRESSO method showed no heterogeneity or horizontal pleiotropy. Additionally, the association between UC and COVID-19 hospitalization and severe respiratory phenotype risk was discovered. The second phenotype (hospitalized vs population) using IVW (OR = 1.080; CI, 1.004–1.162; P = 0.037) and MR Egger (OR = 1.009; CI, 0.882–1.153; P = 0.898). According to the third phenotype (very severe respiratory confirmed vs population) using IVW (OR = 1.187; CI, 1.018–1.384; P = 0.027) and MR Egger (OR = 1.104; CI, 0.801–1.521; P = 0.554).

Table 2

MR results on the susceptibility and severity of UC and COVID-19
Exposure /Outcome	nSNP	OR (95%CI)	Beta	SE	P
UC/COVID-19 susceptibility (COVID-19 vs. population)
IVW	12	1.053(1.012–1.096)	0.052	0.020	0.009
MR- Egger	12	1.015 (0.946–1.089)	0.156	0.035	0.672
Weighted median Simple mode Weighted mode	12 12 12	1.036(0.982–1.093) 1.096(1.002-1.200) 1.023(0.961–1.089)	0.035 0.092 0.023	0.027 0.045 0.032	0.196 0.068 0.485
UC/COVID-19 severity (hospitalized vs population)
IVW	10	1.080(1.004–1.162)	0.077	0.037	0.037
MR-Egger	10	1.009(0.882–1.153)	0.008	0.068	0.898
Weighted median Simple mode Weighted mode	10 10 10	1.055(0.956–1.164) 1.047(0.902–1.215) 1.031(0.924–1.151)	0.053 0.046 0.031	0.050 0.076 0.056	0.286 0.559 0.591
UC/COVID-19 severity (very severe respiratory confirmed vs population)
IVW	13	1.187(1.018–1.384)	0.172	0.078	0.027
MR- Egger	13	1.104(0.809–1.521)	0.099	0.163	0.554
Weighted median Simple mode Weighted mode	13 13 13	1.150(0.955–1.386) 1.121(0.809–1.553) 1.084(0.824–1.427)	0.140 0.114 0.081	0.095 0.166 0.140	0.139 0.504 0.572
Exposure, Ulcerative colitis (UC); Outcome, include three phenotype of COVID-19. (1) COVID-19 susceptibility (COVID-19 vs. population); (2) COVID-19 severity (hospitalized vs population); (3) COVID-19 severity(very severe respiratory confirmed vs population). nSNP, the number of instrumental variables; OR, odds ratio; Beta is the estimated effect size; SE, standard error; P < 0.05 statistically significant.

Sensitivity analyses demonstrated the consistent direction of causal effect estimates by egger-intercept test, the second phenotype (egger-intercept = 0.009, P = 0.244)and the third phenotype (egger-intercept = 0.017, P = 0.620). Moreover, based on Cochran’s Q, the MR-PRESSO test further validated the risk association between UC and the two severe phenotypes of COVID-19 without significant heterogeneity, horizontal pleiotropy, or outliers. Consequently, all results suggest that UC is a risk factor for COVID-19 susceptibility and severity. Taken together, the leave out sensitivity analysis outcome can be seen in Fig. 2 and Fig. 3. On the contrary, using each of the three phenotypes of COVID-19 as an exposure factor, the P (P > 0.05) obtained by all methods were not statistically significant, so the conclusion of the MR analysis concluded that COVID-19 had no causal risk effect on UC. (The outcomes can be seen in Table 3)

Table 3

The reverse MR results on the susceptibility and severity of UC and COVID-19
Exposure /Outcome	nSNP	OR (95%CI)	Beta	SE	P
COVID-19 susceptibility (COVID-19 vs. population) / UC
IVW	5	0.912(0.620–1.342)	-0.091	0.196	0.641
MR- Egger	5	1.409(0.453–4.379)	0.343	0.5789	0.594
Weighted median	5	0.978(0.705–1.357)	-0.021	0.167	0.895
Simple mode Weighted mode	5 5	0.967(0.618–1.513) 0.954(0.644–1.413)	-0.032 -0.446	0.228 0.200	0.893 0.826
COVID-19 severity (hospitalized vs population) / UC
IVW	6	0.955(0.810–1.125)	-0.045	0.083	0.583
MR-Egger	6	0.993(0.679–1.452)	-0.006	0.193	0.974
Weighted median Simple mode Weighted mode	6 6 6	0.962(0.841-1.100) 0.952(0.796–1.139) 0.963(0.827–1.121)	-0.038 -0.048 -0.037	0.069 0.091 0.077	0.574 0.616 0.651
COVID-19 severity (very severe respiratory confirmed vs population) /UC
IVW	10	0.926(0.866–0.991)	-0.075	0.034	0.028
MR- Egger	10	0.947(0.790–1.135)	-0.053	0.092	0.574
Weighted median Simple mode Weighted mode	10 10 10	0.962(0.882–1.050) 0.961(0.844–1.095) 0.963(0.865–1.072)	-0.037 -0.039 -0.037	0.044 0.066 0.054	0.396 0.570 0.514
Exposure, include three phenotype of COVID-19. (1) COVID-19 susceptibility (COVID-19 vs. population); (2) COVID-19 severity (hospitalized vs population); (3) COVID-19 severity (very severe respiratory confirmed vs population). Outcome, Ulcerative colitis (UC); nSNP, the number of instrumental variables; OR, odds ratio; Beta is the estimated effect size; SE, standard error; P < 0.05 statistically significant.

This is the first study to use the two-sample MR analysis to examine the causal relationship between UC and COVID-19. According to our study, UC is a risk factor for COVID-19. However, there was no anti-causal relationship between these two diseases. A recent meta-analysis has shown patients with UC had a higher risk of infecting COVID-19 than patients with Crohn's disease, which were consistent with the findings of a previous study.[13]. Several clinical studies had reported that a large proportion of COVID-19 patients exhibit gastrointestinal symptoms, including diarrhea, nausea or vomiting, and bloody stools[27, 28]. Jin et al. reported that 11.4% of 651 confirmed COVID-19 cases included in Zhejiang, China, presented with at least one gastrointestinal symptom (nausea, vomiting or diarrhea)[29]. It was clear that in patients with COVID-19, gastrointestinal symptoms were significantly linked to an increased risk of hospitalization and/or a more severe disease. Importantly, some scholars believed that in order for the virus to enter human host cells, a spike protein (S protein) component of the virus interacted with cell surface ACE-2 receptors[30]. It has been previously demonstrated that ACE-2 expression is increased in the terminal ileum and colon tissues of UC patients, which may be an important mechanism for SARS-CoV-2 infection and result in adverse outcomes[31]. Moreover, the results of a reverse study suggest that a large number of COVID-19 patients may experience organ damage to the brain, gastrointestinal tract, liver, and testes as sequelae [32–36]. Some studies have shown that SARS-CoV-2 may be transmitted via the fecal-oral route[37]. Another study also reported that stool samples from COVID-19 patients were positive for SARS-CoV-2 viral RNA, thus the study concluded that SARS-CoV-2 can infect the intestinal tract[38]. These studies have consistently concluded that COVID-19 had an inverse causal effect on UC, which differs from our findings. This may be related to confounding factors such as environmental factors, the increasing number of elderly UC cases, and the aging of the patient population. The most important point is that few clinical studies and mechanistic studies on the sequelae of COVID-19 and UC have been conducted, so we were unable to obtain a sufficient sample size for our study.

The primary strength of this study is that it is the first study to analyze the causal relationship between UC and COVID-19 using a two-sample MR study, providing solid evidence to support previous studies. In addition, the study chose the most recent data for both types of diseases with a large sample size and effectively avoided the influence of potential confounders and reverse causal associations. Our study also has some limitations, although both ulcerative colitis and Crohn's disease are inflammatory bowel diseases, ulcerative colitis were more typical and had a large sample size with data newly published in GWAS 2021.Therefore, further studies on Crohn's disease can be conducted in the future. Even though the MR sensitivity analysis method yielded results without heterogeneity and horizontal pleiotropy, the influence of other factors cannot be completely excluded. Additionally, the study's sample was from a European background and did not differentiate between sexes or ages, so the findings are not generalizable and cannot be confirmed in other populations.

In summary, our study reveals that UC is a risk factor for COVID-19 and that patients with UC may be at increased risk for COVID-19 infection and healing, while there were no reverse causal relationship between the two diseases. The above research results will help to further explore the possible biological mechanisms of UC-induced COVID-19. In the future, we will advocate for effective treatment measures for this patient group, taking into account the effects of age, gender, population, and environment. A combination of drugs is used to improve gastrointestinal protection in COVID-19 patients, effectively reducing the disease burden.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

Publicly available datasets were analyzed in this study. This data can be found here: UC (https://gwas.mrcieu.ac.uk/datasets/finn-b-K11_ULCER/),COVID-19 susceptibility and severity (https://gwas.mrcieu.ac.uk./datasets/?trait__icontains=covid-19).

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

None

Authors' contributions

Yu Gong and Kezhen Yang were responsible for experimental design and writing the manuscript, Xudong Zhang and Qingguo Liu supervised the experiment, Xin Lan, Taotao Lv and Qi Xie revised the manuscript, Yinyin Li and Jipeng Liu analyzed the data, Rui Li and Bingnan Yue were responsible for statistical data. All authors contributed to the article and approved the submitted version.

Acknowledgements

Thanks to all researchers and participants of the COVID-19 Host Genetics Initiative and DIAGRAM consortium for sharing these data.

Availability of data and material

All data analyzed in this study are included in the manuscript.

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No competing interests reported.

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Causal Associations Between Ulcerative Colitis and COVID-19: A Bidirectional Mendelian Randomization Study

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Abstract

Background

Methods

Result

Conclusion

Figures

1. Introduction

2. Methods

2.1 Data source from GWAS

2.2 Instrument variables for UC and COVID-19

2.3 MR analysis

3. Results

4. Discussion

Declarations

References

Additional Declarations

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