Dear Editor,
Endometriosis (EMS), a chronic gynecological disorder characterized by the presence of endometrial‐like tissue outside the uterus, is intricately understood. The presence of an inflammatory environment outside the pelvic region has prompted researchers to reconsider EMS as an immunity‐associated systemic disorder. 1 The term inflammatory bowel disease (IBD) refers to inflammation in the gastrointestinal tract, traditionally categorized into ulcerative colitis (UC) and Crohn's disease (CD). The coexistence of IBD and EMS has been documented. 2 Craninx et al. reported histological features of EMS in CD patients undergoing surgical resection without a preoperative EMS diagnosis. 3 Deep infiltrating EMS and posterior adenomyosis were significantly more frequent in patients with IBD. Additionally, Jess et al. reported that women with EMS were 50% more likely to suffer from IBD than women in general. 4 However, in observational studies, potential biases from residual confounding and reverse causality affect the inference of cause‐effect relationships. The question of whether IBD induces or promotes EMS, or vice versa, remains unclear.
Mendelian randomization (MR) is a methodological approach in epidemiological research that assesses the potential causality of a risk factor or modifiable exposure and clinical outcome by utilizing genetic instruments. Genetic variants, unalterable and randomly assigned during human germ cell formation, enable MR to impartially evaluate exposure effects, avoiding common confounding or reverse causality issues in observational studies (Figure 1A). 5 The current understanding of the relationship between EMS and IBD is confined to observational studies, incapable of establishing a direct causal relationship. Additionally, conducting randomized controlled trials is not feasible. Therefore, we applied a two‐sample MR approach to elucidate the causality between the two.
FIGURE 1.
(A) The basic principles of mendelian analysis (MR). Instrumental variables (IVs) have to fulfill three principal assumptions: 1. they are closely related to the risk factor of interest; 2, they are not correlated with any potential confounders influencing the outcomes; 3, they affect the outcomes only via the risk factor; (B) workflow of the MR study design. Single nucleotide polymorphisms (SNPs) were extracted as IVs if reaching the genome‐wide association studies (GWAS) P < 5E‐8 and were further clumped to ensure the independency of instruments (clumping criteria: linkage disequilibrium [LD] r 2 = 0.001 and window size = 10,000 kb). Also, SNPs with P < 1E‐5 in the PhenoScanner database would be deleted in the following analysis. (C) The results of MR analysis taking endometriosis (EMS) as exposure to estimate their causal effect on inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn's disease (CD). (D) The results of MR analysis of EMS (Sapkota Y) on IBD, UC and CD. ASRM, American Society for Reproductive Medicine; MR‐PRESSO, mendelian Randomization Pleiotropy RESidual Sum and Outlier; OR, odds ratio; CI, confidence interval.
EMS and IBD large‐scale genome‐wide association study (GWAS) summary results are presented in Tables 1A and 1B, with no sample overlap between them. 6 , 7 Figure 1B displays the selection process for instrumental variables (IVs) of EMS. Detailed information about the finally selected IVs is listed in Table S1.
TABLE 1A.
Description of the outcome IBD.
| Sample size | ||||
|---|---|---|---|---|
| Outcome (total and subgroups) | case | control | GWAS Catalog accession number | PMID |
| IBD | 12 882 | 21,770 | ieu‐a‐31 | 26192919 |
| UC | 5956 | 14,927 | ieu‐a‐32 | 26192919 |
| CD | 6968 | 20,464 | ieu‐a‐30 | 26192919 |
Abbreviations: CD, Crohn's disease; GWAS, genome‐wide association studies; IBD, inflammatory bowel disease; UC, ulcerative colitis.
TABLE 1B.
Description of the exposure EMS.
| Sample size | |||
|---|---|---|---|
| Exposure (total and subgroups) | Case | Control | GWAS catalog accession number |
| Total EMS | 12 056 | 94 394 | finn‐b‐N14_ENDOMETRIOSIS |
| ASRM I/II | 4554 | 169 192 | finn‐b‐N14_ENDOMETRIOSIS_ASRM_STAGE1_2 |
| ASRM III/IV | 6032 | 167 714 | finn‐b‐N14_ENDOMETRIOSIS_ASRM_STAGE3_4 |
| Ovary | 4648 | 94 394 | finn‐b‐N14_ENDOMETRIOSIS_OVARY |
| Pelvic peritoneum | 1979 | 94 394 | finn‐b‐N14_ENDOMETRIOSIS_PELVICPERITONEUM |
| Uterus | 3433 | 94 394 | finn‐b‐N14_ENDOMETRIOSIS_UTERUS |
| Rectovaginal septum and vagina | 1979 | 94 394 | finn‐b‐N14_ENDOMETRIOSIS_RECTPVAGSEPT_ VAGINA |
| Occurring infertility | 2423 | 166 260 | finn‐b‐N14_ENDOMET_INFERT |
| Total EMS (Sapkota Y) | 14 926 | 189 715 | GCST004549 |
Abbreviations: ASRM, American Society for Reproductive Medicine; EMS, endometriosis; GWAS, genome‐wide association studies.
We employed five different methods for the MR effect estimates. We used inverse‐variance weighted (IVW) linear regression as our primary method. A statistically significant causal association is defined as an adjusted p < 0.017, indicating a considerable relationship between the exposure and outcome phenotype. We used a series of sensitivity analyses to address variants heterogeneity and the pleiotropy effect, the flow of all MR analyses can be available in Figure 1B. 8 , 9 Another EMS GWAS dataset provided by Sapkota Y was also used to validate our findings. An R language version of 4.2.0 was used for all analyses. R‐based packages ‘TwoSampleMR’ and ‘MRPRESSO’ were applied to conduct MR analyses. 10 The data visualization was conducted using the ‘TwoSampleMR’ and ‘forestplot’.
In a cohort of people with different subtypes of EMS, MR analysis was carried out to estimate the risk of IBD following EMS. As shown in Table 1C, the genetically predicted EMS increased IBD risk (OR = 1.128, PIVW = 0.003). Subsequent analyses focusing on the main subtypes of IBD, UC, and CD, suggested a causal association between EMS and CD (PIVW = 0.040). However, the statistical significance was lost after applying the Bonferroni adjustment to account for multiple comparisons. When performing MR analysis with different subtypes of EMS as the exposures and IBD as the outcome, a significantly higher risk of IBD was observed only for ovarian EMS (OR = 1.093, PIVW = 0.005). Furthermore, we performed MR analyses of different EMS stages as exposure (as determined by the American Society for Reproductive Medicine [ASRM]). The causality between EMS ASRM III/IV and IBD as a whole is notably significant (OR = 1.098, PIVW = 0.014). However, null significant causal associations are observed between different EMS stages and UC or CD. Additionally, our reverse MR analysis revealed no causal relationship between IBD (exposure) and EMS (outcome) (see Table S2). Another MR analysis, utilizing the GWAS data provided by Sapkota, provides additional support for these findings (Table 1D). Genetically predicted EMS increased the risk of IBD (OR = 1.240, PIVW = 0.001). Although this analysis further suggested the causality of EMS on UC (OR = 1.323, PIVW = 0.005), this conclusion was not consistent with the original analysis. No obvious horizontal pleiotropy or outliers were detected in all MR analyses (p > 0.05). Analyses of sensitivity are shown in the Supplementary Figure.
TABLE 1C.
Causal estimation for the effect of EMS on IBD, UC as well as CD.
| Exposure | Outcome | MR methods | Number of SNPs | OR (95% CI) | SE |
MR p‐value |
MR‐Steiger |
p‐Value heterogeneity |
p‐Value pleiotropy |
|---|---|---|---|---|---|---|---|---|---|
| EMS_total | IBD | MR Egger | 14 | 1.130 (.877–1.456) | .129 | .362 | True | .840 | .985 |
| IVW | 14 | 1.128 (1.042–1.221) | .040 | .003 | .888 | ||||
| Weighted median | 14 | 1.120 (1.011–1.240) | .054 | .041 | |||||
| EMS_total | UC | MR Egger | 13 | 1.052 (.764–1.450) | .163 | .760 | True | .797 | .802 |
| IVW | 13 | 1.095 (.985–1.217) | .054 | .092 | .852 | ||||
| Weighted median | 13 | 1.137 (.994–1.301) | .074 | .062 | |||||
| EMS_total | CD | MR Egger | 14 | 1.249 (.842–1.853) | .201 | .291 | TRUE | .212 | .622 |
| IVW | 14 | 1.134 (1.006–1.278) | .061 | .040 | .255 | ||||
| Weighted median | 14 | 1.099 (.943–1.281) | .081 | .227 | |||||
| EMS (ASRM I/II) | IBD | MR Egger | 8 | .828 (.381–1.800) | .650 | .650 | True | .743 | .488 |
| IVW | 8 | 1.108 (1.015–1.209) | .045 | .023 | .773 | ||||
| Weighted median | 8 | 1.079 (.961–1.211) | .059 | .200 | |||||
| EMS (ASRM I/II) | UC | MR Egger | 8 | .557 (.209–1.488) | .501 | .288 | True | .616 | .196 |
| IVW | 8 | 1.149 (1.029–1.284) | .057 | .014 | .476 | ||||
| Weighted median | 8 | 1.174 (1.006–1.369) | .079 | .042 | |||||
| EMS (ASRM I/II) | CD | MR Egger | 8 | 1.375 (.480–3.938) | .537 | .575 | True | .922 | .608 |
| IVW | 8 | 1.030 (.914–1.160) | .061 | .628 | .943 | ||||
| Weighted median | 8 | .969 (.836–1.123) | .075 | .678 | |||||
| EMS (ASRM III/IV) | IBD | MR Egger | 19 | 1.270 (1.019–1.584) | .112 | .048 | True | .023 | .776 |
| IVW | 19 | 1.098 (1.019–1.184) | .038 | .014 | .047 | ||||
| Weighted median | 19 | 1.118 (1.026–1.218) | .044 | .014 | |||||
| EMS (ASRM III/IV) | UC | MR Egger | 19 | 1.162 (.863–1.564) | .152 | .337 | True | .023 | .776 |
| IVW | 19 | 1.115 (1.012–1.227) | .049 | .027 | .032 | ||||
| EMS (ASRM III/IV) | UC | Weighted median | 19 | 1.144 (1.029–1.272) | .0542 | .016 | |||
| EMS(ASRM III/IV) | CD | MR Egger | 19 | 1.352 (1.024–1.785) | .142 | .049 | True | .179 | .121 |
| IVW | 19 | 1.086 (.987–1.195) | .049 | .090 | .108 | ||||
| Weighted median | 19 | 1.063 (.945–1.196) | .060 | .310 | |||||
| EMS_ovary | IBD | MR Egger | 17 | 1.190 (1.001–1.415) | .088 | .068 | True | .155 | .321 |
| IVW | 17 | 1.093 (1.028–1.163) | .031 | .005 | .146 | ||||
| Weighted median | 17 | 1.103 (1.023–1.190) | .039 | .011 | |||||
| EMS_ovary | UC | MR Egger | 9 | 1.100 (.801–1.512) | .162 | .575 | True | .729 | .955 |
| IVW | 9 | 1.090 (.994–1.196) | .047 | .068 | .816 | ||||
| Weighted median | 9 | 1.101 (.977–1.240) | .061 | .114 | |||||
| EMS_ovary | CD | MR Egger | 18 | 1.283 (1.008–1.635) | .123 | .060 | True | .092 | .139 |
| IVW | 18 | 1.072 (.981–1.173) | .046 | .126 | .051 | ||||
| Weighted median | 18 | 1.073 (.969–1.189) | .052 | .177 | |||||
| EMS_pelvic peritoneum | IBD | MR Egger | 9 | 1.160 (.731–1.843) | .236 | .549 | NA | .644 | .733 |
| IVW | 9 | 1.068 (.990–1.153) | .039 | .087 | .730 | ||||
| Weighted median | 9 | 1.062 (.960–1.175) | .052 | .245 | |||||
| EMS_pelvic peritoneum | UC | MR Egger | 9 | 1.060 (.558–2.014) | .327 | .863 | NA | .299 | .915 |
| IVW | 9 | 1.099 (.996–1.212) | .050 | .060 | .394 | ||||
| Weighted median | 9 | 1.086 (.959–1.230) | .063 | .191 | |||||
| EMS_pelvic peritoneum | CD | MR Egger | 9 | 1.311 (.701–2.454) | .320 | .424 | NA | .980 | .432 |
| IVW | 9 | 1.008 (.909–1.118) | .053 | .875 | .972 | ||||
| Weighted median | 9 | .973 (.854–1.109) | 0.066 | .684 |
| Exposure | Outcome | MR methods | Number of SNPs | OR (95% CI) | SE |
MR p‐Value |
MR‐Steiger |
p‐Value heterogeneity |
p‐Value pleiotropy |
|---|---|---|---|---|---|---|---|---|---|
| EMS_rectovaginal septum and vagina | IBD | MR Egger | 8 | .729 (.467–1.139) | .227 | .214 | NA | .970 | .128 |
| IVW | 8 | 1.085 (1.018–1.157) | .033 | .012 | .726 | ||||
| Weighted median | 8 | 1.079 (.990–1.175) | .044 | .083 | |||||
| EMS_rectovaginal septum and vagina | UC | MR Egger | 8 | .565 (.322–.991) | .286 | .093 | NA | .946 | .051 |
| IVW | 8 | 1.127 (1.036–1.226) | .043 | .005 | .368 | ||||
| Weighted median | 8 | 1.107 (.995–1.231) | .054 | .061 | |||||
| EMS_rectovaginal septum and vagina | CD | MR Egger | 8 | 1.139 (.621–2.091) | .310 | .688 | NA | .900 | .725 |
| IVW | 8 | 1.018 (.932–1.111) | .045 | .697 | .939 | ||||
| Weighted median | 8 | .987 (.887–1.099) | .054 | .816 | |||||
| EMS_occurring infertility | IBD | MR Egger | 6 | 1.256 (.765–2.062) | .253 | .418 | NA | .766 | .544 |
| IVW | 6 | 1.065 (.970–1.170) | .048 | .186 | .810 | ||||
| Weighted median | 6 | 0.990 (.846–1.158) | .080 | .900 | |||||
| EMS_occurring infertility | UC | MR Egger | 6 | 1.538 (.825–2.864) | .317 | .247 | NA | .745 | .288 |
| IVW | 6 | 1.049 (.933–1.181) | .060 | .423 | .631 | ||||
| Weighted median | 6 | .990 (.846–1.158) | .080 | .900 | |||||
| EMS_occurring infertility | CD | MR Egger | 6 | 1.150 (.543–2.435) | .383 | .734 | NA | .306 | .922 |
| IVW | 6 | 1.105 (.972–1.257) | .066 | .127 | .436 | ||||
| Weighted median | 6 | 1.078 (.917–1.266) | .082 | .363 |
Abbreviations: ASRM, American Society for Reproductive Medicine; CD, Crohn's disease; CI, confidence interval; EMS, endometriosis; IBD, inflammatory bowel disease; IVW, inverse‐variance weighted; MR, Mendelian randomization; OR, odds ratio; SNP, single nucleotide polymorphism; UC, ulcerative colitis.
TABLE 1D.
Causal estimation for the effect of EMS (Sapkota Y) on IBD, UC as well as CD.
| Exposure | Outcome | MR methods | Number of SNPs | OR (95% CI) | SE |
MR p‐value |
MR‐Steiger |
p‐Value heterogeneity |
p‐Value pleiotropy |
|---|---|---|---|---|---|---|---|---|---|
| EMS_total | IBD | MR Egger | 9 | 1.286 (.661–2.501) | .339 | .483 | True | .425 | .917 |
| IVW | 9 | 1.240 (1.087–1.415) | .067 | .001 | .532 | ||||
| Weighted median | 9 | 1.245 (1.046–1.483) | .089 | .014 | |||||
| EMS_total | UC | MR Egger | 9 | 1.429 (.505–4.045) | .531 | .523 | True | .144 | .917 |
| IVW | 9 | 1.323 (1.090–1.607) | .099 | .005 | .207 | ||||
| Weighted median | 9 | 1.421 (1.125–1.796) | .119 | .003 | |||||
| EMS_total | CD | MR Egger | 11 | 1.192 (.362–3.930) | .609 | .780 | NA | .039 | .863 |
| IVW | 11 | 1.072 (.863–1.330) | .110 | .531 | .059 | ||||
| Weighted median | 11 | .997 (.797–1.247) | .114 | .979 |
Abbreviations: CD, Crohn's disease; CI, confidence interval; EMS, endometriosis; IBD, inflammatory bowel disease; IVW, inverse‐variance weighted; MR, Mendelian randomization; OR, odds ratio; SNP, single nucleotide polymorphism; UC, ulcerative colitis.
It is the first MR study that analyzes the largest GWAS data of EMS patients available, providing robust evidence for detecting the causal estimates of IBD and EMS. Our findings suggest increased IBD risks following EMS, corroborating published research. Notably, our research contributes significant new insights to the field by unveiling the distinct effects of different EMS subtypes and stages on the risk of IBD. Owing to the multi‐system effects of EMS, the diagnoses are often delayed or misdiagnosed. We utilized genetic variants to identify genetic EMS, providing an alternative solution to this challenge.
The potential pathophysiological overlap between EMS and IBD may be a driving factor for the occurrence of IBD in EMS patients. Izumi et al. documented peritoneal immune features in EMS, noting abnormalities in neutrophils, macrophages, natural killer cells, and T/B lymphocytes. 11 These immune dysregulations, associated with autoimmune diseases, have also been observed in IBD. Additionally, EMS involves excess production of prostaglandins, metalloproteinases (MMP), chemokines, and tumour necrosis factor‐alpha (TNF‐α), similar to indicators seen in IBD. Elevated MMP levels were observed in inflamed intestinal mucosa. 12 Anti‐TNF therapy, an effective treatment for IBD, is also being explored for EMS treatment. Dysbiosis is a major risk factor for IBD and also closely correlates with EMS. 13 These shared biological characteristics may provide some insight into the potential link between EMS and IBD, albeit with uncertain underlying mechanisms.
Although further validation is needed, this study contributes to optimizing the clinical management of EMS. First‐line therapy for EMS involves combining non‐steroidal anti‐inflammatory drugs (NSAIDs) with contraceptives or progestins. 14 However, NSAIDs are involved in the development of IBD through various mechanisms, increasing the risk of UC (multivariate HR = 1.87) and CD (multivariate HR = 1.59). The use of contraceptives is also associated with a higher incidence of IBD (CD: RR = 1.46–1.51; UC: RR = 1.28–1.53). 15 Alternative treatment options should be considered for EMS patients exposed to additional risks of IBD whenever possible. Gastrointestinal involvement can manifest in 5%−15% of EMS patients, showing symptoms like constipation, diarrhea, dysmenorrhea and dyspareunia, a colonoscopy is recommended to distinguish between IBD or EMS‐related intestinal manifestations. Tailored treatment is essential for EMS patients with coexisting IBD. For those without gastrointestinal symptoms, regular surveillance colonoscopies are beneficial.
Our research has certain limitations. While the outlier assessment and MR‐Egger test did not indicate any influence of pleiotropy on the results, it is possible that in certain unknown scenarios, complete correction of horizontal pleiotropy may not be achieved. On the other hand, the sample size of exposure and outcome are relatively small, we could not further distinguish the effect of EMS on two IBD subgroups.
In conclusion, our study confirmed the increased risk of IBD following EMS, but not vice versa.
AUTHOR CONTRIBUTIONS
Study concept and design: Shutian Zhang. Data acquisition and analysis: Shutian Zhang and Yan Dang. Drafting of the manuscript: Yan Dang.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
FUNDING INFORMATION
This research received no specific grant from any funding agency in the public, commercial or not‐for‐profit sectors.
ETHICAL STATEMENT
Not Applicable.
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ACKNOWLEDGEMENTS
The authors want to acknowledge the participants and investigators of the FinnGen study‘’ Kurki M.I., et al. FinnGen: Unique genetic insights from combining isolated population and national health register data, medRxiv 2022.03.03.22271360; DOI: https://doi.org/10.1101/2022.03.03.22271360. They express their gratitude to the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) (https://www.ibdgenetics.org) for generously providing publicly accessible data. They are also thankful to Sapkota Y for supplying the EMS GWAS data. For further details, please see https://doi.org/10.1038/ncomms15539.
DATA AVAILABILITY STATEMENT
All data involved in the current study are publicly available data from individual referenced papers.
REFERENCES
- 1. Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. The Lancet, 2021;397(10276): 839‐852. doi: 10.1016/s0140-6736(21)00389-5 [DOI] [PubMed] [Google Scholar]
- 2. Shigesi N, Kvaskoff M, Kirtley S, et al. The association between endometriosis and autoimmune diseases: a systematic review and meta‐analysis. Human Reproduction Update, 2019;25(4):486‐503. doi: 10.1093/humupd/dmz014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Craninx M, DʼHaens G, Cokelaere K, et al. Crohnʼs disease and intestinal endometriosis. European Journal of Gastroenterology & Hepatology, 2000;12(2):217‐221. doi: 10.1097/00042737-200012020-00014 [DOI] [PubMed] [Google Scholar]
- 4. Jess T, Frisch M, Jørgensen KT, Pedersen BV, Nielsen NM. Increased risk of inflammatory bowel disease in women with endometriosis: a nationwide Danish cohort study. Gut, 2011;61(9):1279‐1283. doi: 10.1136/gutjnl-2011-301095 [DOI] [PubMed] [Google Scholar]
- 5. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ, k601. 2018. doi: 10.1136/bmj.k601 [DOI] [PMC free article] [PubMed]
- 6. Liu JZ, van Sommeren S, Huang H, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature Genetics, 2015;47(9):979‐986. doi: 10.1038/ng.3359 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Garitazelaia A, Rueda‐Martínez A, Arauzo R, et al. A Systematic Two‐Sample Mendelian Randomization Analysis Identifies Shared Genetic Origin of Endometriosis and Associated Phenotypes. Life, 2021;11(1):24. doi: 10.3390/life11010024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLOS Genetics, 2017;13(11):e1007081. doi: 10.1371/journal.pgen.1007081 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Verbanck M, Chen C‐Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics, 2018;50(5);693‐698. doi: 10.1038/s41588-018-0099-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hemani G, Zheng J, Elsworth B, et al. The MR‐Base platform supports systematic causal inference across the human phenome. ELife, 7. CLOCKSS. 2018. doi: 10.7554/elife.34408 [DOI] [PMC free article] [PubMed]
- 11. Izumi G, Koga K, Takamura M, et al. Involvement of immune cells in the pathogenesis of endometriosis. Journal of Obstetrics and Gynaecology Research, 2018;44(2):191‐198. Portico. doi: 10.1111/jog.13559 [DOI] [PubMed] [Google Scholar]
- 12. von Lampe B. Differential expression of matrix metalloproteinases and their tissue inhibitors in colon mucosa of patients with inflammatory bowel disease. Gut, 2000;47(1):63‐73. doi: 10.1136/gut.47.1.63 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Salliss ME, Farland LV, Mahnert ND, Herbst‐Kralovetz MM. The role of gut and genital microbiota and the estrobolome in endometriosis, infertility and chronic pelvic pain. Human Reproduction Update, 2021;28(1):92‐131. doi: 10.1093/humupd/dmab035 [DOI] [PubMed] [Google Scholar]
- 14. Felder JB, Korelitz BI, Rajapakse R, Schwarz S, Horatagis AP, Gleim G. Effects of nonsteroidal antiinflammatory drugs on inflammatory bowel disease: a case‐control study. The American Journal of Gastroenterology, 2000;95(8):1949‐1954. doi: 10.1111/j.1572-0241.2000.02262.x [DOI] [PubMed] [Google Scholar]
- 15. Cornish JA, Tan E, Simillis C, Clark SK, Teare J, Tekkis PP. The Risk of Oral Contraceptives in the Etiology of Inflammatory Bowel Disease: A Meta‐Analysis. The American Journal of Gastroenterology, 2008;103(9);2394‐2400. doi: 10.1111/j.1572-0241.2008.02064.x [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
All data involved in the current study are publicly available data from individual referenced papers.