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. 2022 Jun 28;12(1):10890.
doi: 10.1038/s41598-022-13222-0.

Environmental variables and genome-environment interactions predicting IBD diagnosis in large UK cohort

Alan Z Yang  1 , Luke Jostins-Dean  2
Affiliations

Environmental variables and genome-environment interactions predicting IBD diagnosis in large UK cohort

Alan Z Yang et al. Sci Rep. .

Abstract

A combination of genetic susceptibility and environmental exposure is thought to cause inflammatory bowel disease (IBD), but the non-genetic component remains poorly characterized. We therefore undertook a search for environmental variables and gene-environment interactions associated with future IBD diagnosis in a large UK cohort. Using self-report and electronic health records, we identified 1946 Crohn's disease (CD) and 3715 ulcerative colitis (UC) patients after quality control in the UK Biobank. Based on prior literature and biological plausibility , we tested 38 candidate environmental variables for association with CD, UC, and overall IBD using Cox proportional hazard regressions. We also tested whether these variables interacted with polygenic risk in predicting disease, following up significant (FDR < 0.05) results with tests for SNP-environment associations. We performed robustness analyses on all significant results. As in previous reports, appendectomy protected against UC, smoking (both current and previous) elevated risk for CD, current smoking protected against UC, and previous smoking imparted a risk for UC. Childhood antibiotic use associated with IBD, as did sun exposure during the winter. Socioeconomic deprivation was conferred a risk for IBD, CD, and UC. We uncovered negative interactions between polygenic risk and previous oral contraceptive use for IBD and UC. Polygenic risk also interacted negatively with previous smoking in predicting UC. There were no individually significant SNP-environment interactions. Thus, for a limited set of environmental variables, there was strong evidence of association with IBD diagnosis in the UK Biobank, and interaction with polygenic risk was minimal.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Number of IBD cases in the UK Biobank based on method of identification. ICD10-coded diagnoses were recorded in hospital episode statistics (HES) while self-reported conditions were gathered by survey at recruitment.
Figure 2
Figure 2
Lag time between self-reported IBD and HES-coded IBD for patients self-reporting IBD after 1997, measured by number of years from earliest recollection of first diagnosis to first instance of relevant ICD10-code in HES records.
Figure 3
Figure 3
Schematic of the primary analyses we carried out for each variable. All variables were measured at a single time point except 24-h diet recall, which represented the average value from 3 to 5 surveys taken at multiple time points. IBD = inflammatory bowel disease, IMD = Index of Multiple Deprivation (a measure of socioeconomic status), OCT = oral contraceptive therapy.
Figure 4
Figure 4
Forest plot of hazard ratios (dots) and 95% confidence intervals (lines) obtained from Cox regressions. Hazard ratios were adjusted for other covariates, including polygenic risk. Statistically significant results (FDR < 0.05) represented by filled circles. For continuous variables, hazard ratios are given per standard deviation of the variable. For binary variables, raw hazard ratios are given. Results for 24-h dietary variables are shown in Supplemental Fig. 3. Results for hormone replacement therapy not displayed because they did not meet the proportional hazards assumption.
Figure 5
Figure 5
Forest plot of hazard ratios (dots) and 95% confidence intervals (lines) for PRSxE interactions obtained from Cox regressions. Hazard ratios were adjusted for other covariates, including polygenic risk. Statistically significant results (FDR < 0.05) represented by filled circles. x-axis truncated at 2. For continuous variables, hazard ratios are given per standard deviation of the variable per standard deviation of PRS. For binary variables, hazard ratios are given per standard deviation of PRS. Results for 24-h dietary variables are shown in Supplemental Fig. 4. Results for hormone replacement therapy not displayed because they did not meet the proportional hazards assumption.
Figure 6
Figure 6
Kaplan–Meier curves for the statistically significant (FDR < 0.05 in Cox regressions conditional on polygenic risk and other covariates) environmental variables with IBD diagnosis as the event. Shading indicates 95% confidence interval. For IMD, hazard ratios are given per standard deviation of the variable. IMD = Index of Multiple Deprivation.
Figure 7
Figure 7
Kaplan–Meier curves for variables with statistically significant PRSxE interactions (FDR < 0.05 in Cox regressions conditional on polygenic risk and other covariates), with IBD diagnosis as the event. “Never” indicates participants never exposed to variable, “previous” refers to participants who started and subsequently stopped exposure, “lowPRS” refers to polygenic risk below median, “highPRS” refers to polygenic risk above median. Curves of current users are omitted to emphasize the interactive effect. OCT = oral contraceptive therapy.
See this image and copyright information in PMC

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