Antibiotic Use and New-Onset Inflammatory Bowel Disease in Olmsted County, Minnesota: A Population-Based Case-Control Study

Antibiotic Use and New-Onset Inflammatory Bowel Disease in Olmsted County, Minnesota: A... Abstract Background and Aims Several studies have suggested significant associations between environmental factors and the risk of developing inflammatory bowel disease [IBD]. However, data supporting the role of antibiotics are conflicting. The aim of this study was to evaluate the association between antibiotic use and new-onset IBD. Methods We conducted a population-based case-control study using the Rochester Epidemiology Project of Olmsted County, Minnesota. We identified 736 county residents diagnosed with IBD between 1980 and 2010 who were matched to 1472 controls, based on age, sex and date of IBD diagnosis. Data on antibiotic use between 3 months and 5 years before IBD diagnosis were collected. Logistic regression models were used to estimate associations between antibiotic use and IBD, and were expressed as adjusted odds ratio [AOR] with 95% confidence interval [CI]. Results Antibiotic use occurred in 455 IBD cases [61.8%] and 495 controls [33.6%] [p < 0.001]. In multivariate analysis, there were statistically significant associations between antibiotic use and new-onset IBD [AOR, 2.93; 95% CI, 2.40–3.58], Crohn’s disease [CD] [AOR, 3.01; 2.27–4.00] and ulcerative colitis [UC] [AOR, 2.94; 95% CI, 2.23–3.88]. A cumulative duration of antibiotic use ≥ 30 days had the strongest AOR [6.01; 95% CI, 4.34–8.45]. AOR for those receiving antibiotics under the age of 18 years was 4.27 [95% CI, 2.39–7.91], 2.97 for age 18–60 years [2.36–3.75] and 2.72 for age > 60 years [1.60–4.67]. Conclusions This population-based case-control study suggests a strong association between antibiotic use and the risk of both new-onset CD and new-onset UC. The risk was increased among all age-onset IBD. Antibiotics, epidemiology, inflammatory bowel disease 1. Introduction Inflammatory bowel diseases [IBD], consisting of Crohn’s disease [CD] and ulcerative colitis [UC], are chronic idiopathic inflammatory conditions. Despite advances in the management of IBD, the pathogenesis of IBD is incompletely understood. The key contributors to the pathogenesis of IBD are thought to be genetics, host immune response and environmental factors.1 The low concordance rate of IBD [approximately 35–50% for CD and 15% for UC] in identical twins suggests that genetic factors do not completely explain the disease aetiology.2–4 Likewise, the increasing incidence of IBD worldwide including North America, Europe, Australia and Asia over the past 50 years suggests the important role of host immune response and environmental factors.5–7 Emerging evidence suggests that disturbances in the gut microbiota and their interaction with the host immune system in a genetically susceptible individual may be the fundamental development of IBD.1,8 Changes in microbial diversity and loss of protective microbial species have been demonstrated in patients with IBD.1 In particular, some dietary changes and antibiotic exposure have been shown to alter gut microbiota.9 Several studies have suggested significant associations between environmental factors, including cigarette smoking10,11and westernized diet,12,13 and the risk of developing IBD. However, data for antibiotics are conflicting.14–20 A previous meta-analysis has shown a modest association between antibiotic use and IBD development (pooled odds ratio [OR], 1.57; 95% confidence interval [CI], 1.27–1.94).15 Antibiotic use increased the odds of CD development [OR, 1.74; 95% CI, 1.35–2.23] but not UC [OR, 1.08; 95% CI, 0.91–1.27]. The authors noted a great deal of heterogeneity across the studies. For example, a study from the UK using the General Practice Research Database20 and a study from Canada using the University of Manitoba IBD Database16 relied upon diagnostic and prescription codes to identify cases and exposures. As a result, misclassification bias may have occurred. Furthermore, some important predisposing factors to development of IBD, including cigarette smoking status and family history of IBD, could not be identified by administrative data. On the other hand, previous studies using a self-administered questionnaire14,17–19 to assess antibiotic exposure were limited due to recall bias. Moreover, using prevalent patients may overrepresent those who regularly follow up, and may not include patients who migrated, lived in rural regions or those with indolent disease who were followed by their primary physician.19 Hence, the implications of antibiotic use as a predisposing factor in IBD aetiology remain unresolved, hindering our understanding of the pathogenesis of IBD. Therefore, to address this gap in knowledge, the present study sought to evaluate the association between the antibiotic use and new-onset IBD [both CD and UC] in a population-based cohort from Olmsted County, Minnesota. 2. Material and Methods 2.1. Rochester Epidemiology Project The resources of the Rochester Epidemiology Project were used to identify permanent residents of Olmsted County, Minnesota, who were diagnosed with IBD and non-IBD between 1980 and 2010. The Rochester Epidemiology Project is a unique medical records linkage system developed in the 1950s and supported by the National Institutes of Health.21 It exploits the fact that virtually all the health care for residents of Olmsted County is provided by two organizations: the Mayo Medical Center, consisting of the Mayo Clinic and its two affiliated hospitals [Rochester Methodist and St. Mary’s], and Olmsted Medical Center, consisting of a smaller multispecialty clinic and its affiliated hospital [Olmsted Community Hospital]. In any 4-year period, over 95% of county residents are examined at either one of the two healthcare systems.22 Diagnoses generated from all outpatients visits, emergency room visits, hospitalizations, nursing home visits, laboratory, surgical procedures, medical treatment, autopsy examination and death certification are contained in a single comprehensive medical records linkage system. Thus, this population-based data resource ensures virtually complete case ascertainment and follow-up in a well-defined geographical region.23 2.2. Study population This population-based case-control study comprised 736 Olmsted County residents first diagnosed with IBD [339 CD and 397 UC] between 1980 and 2010 according to well-defined criteria as previously described.5,24–26 The index date was based on the first IBD diagnosis. For each case with IBD diagnosis, two controls were randomly selected from Olmsted County residents without an IBD diagnosis after matching for age, sex and index date of IBD diagnosis, which assigned the index date corresponding to the index date of IBD diagnosis. To determine whether IBD developed or not, individuals in the control cohort were followed through their medical history from the index date until death, emigration or the end of the study [June 30, 2016], whichever came first. We investigated the impact of age on the association between antibiotic use and IBD development. Age groups at index date were stratified as <18 years, 18–60 years and > 60 years. 2.3. Antibiotic use Data on the use of antibiotics with regard to class of antibiotic, indication, duration and number of antibiotic courses were collected for 5 years before the index date. Because antibiotics are commonly prescribed for symptoms of IBD prior to diagnosis, antibiotic use within 3 months prior to the index date was excluded. Classes of antibiotic were categorized as follows: penicillins, macrolides, cephalosporins, sulfonamides, quinolones, tetracyclines, metronidazole and others. Indications for antibiotic use were classified as: [i] ear, nose, throat and respiratory tract infection; [ii] gastrointestinal tract infection; [iii] genitourinary tract infection; [iv] skin, soft tissue and bone infection; or [v] other organ or systemic infection. The number of antibiotic courses was categorized as none, 1–3, 4–6, and ≥ 7. The start and stop dates of antibiotic use were recorded. The cumulative duration of antibiotic use for each individual was categorized as none, 1–14 days, 15–29 days and ≥ 30 days. 2.4. Statistical analysis Assuming the prevalence of antibiotic use in the control group ranged from 25 to 50%, a sample size of at least 330 cases and 660 controls in each CD and UC cohort was required to detect an OR of at least 2 with a power of 80% at a two-sided significance level of 0.05. Descriptive statistical analysis was used to determine baseline characteristics of IBD cases and controls. To compare categorical variables, a chi-square test or Fisher exact test was used. To compare continuous variables, Student’s t-test or Mann–Whitney U-test was used. Antibiotic use and potential confounding factors (i.e. first degree family history of IBD, current cigarette smoking, former cigarette smoking, obesity [body mass index ≥ 30 kg/m2], daily aspirin use and statins use) were tested by univariate analysis. Daily aspirin use was defined as taking aspirin at least once a day for at least 3 months before the index date. Statin use was defined as taking a statin [pravastatin, atorvastatin, simvastatin, rosuvastatin, fluvastatin, lovastatin] for at least 3 months before the index date. A multivariate analysis model was then analysed using unconditional logistic regression models to estimate the adjusted OR [AOR] and 95% CI. Age, sex and any variables with a p < 0.1 in the univariate analysis were included in the multivariate analysis. Separate analyses for IBD subtypes [CD and UC] and age groups at the index date were performed. To evaluate whether there was a dose-dependent relationship between antibiotic use and the risk of IBD development, the analysis was also stratified by the cumulative duration of antibiotic use and by the number of antibiotic courses. An alpha-level of 0.05 was considered as statistically significant. Statistical analyses were performed by using the JMP 10 statistical software package [SAS Institute Inc., Cary, NC, USA]. 3. Results A total of 736 incident IBD cases and 1472 controls were included in this study. This sample size was sufficient to achieve the a priori statistical power assumptions for the study. Of the IBD cases, 339 had CD [46%] and 397 had UC [54%]. IBD cases had data available with a median duration of 5 years before the index date [range, 0.5–5 years] and 14.8 years after the index date [range, 0.1–36.2 years]. Controls had data available with a median duration of 5 years before the index date [range, 0.4–5 years] and 10 years after the index date [range, 0.1–36.5 years]. Table 1 shows the demographic characteristics of the IBD cases and controls. By study design, there were no differences in the age and sex distribution between IBD cases and controls. In both IBD cases and controls, the median age at the index date was 34 years [range, 1.2–93 years], and 55% were male. Table 1. Demographic characteristics of IBD cases and controls at the index date from 1980 to 2010 Characteristic  IBD [n = 736]  Controls [n = 1472]  p-value  Median age at index date, years [range]  34 [1.2–93]  34 [1.2–93]  1.00  < 18 years, n [%]  87 [11.8%]  174 [11.8%]    18–60 years, n [%]  559 [76.0%]  1118 [76.0%]    > 60 years, n [%]  90 [12.2%]  180 [12.2%]    Male, n [%]  405 [55%]  810 [55%]  1.00  Race, n [%]        White  665 [90%]  1074 [73%]  <0.001  African American  12 [1.7%]  81 [5.5%]    Asian  10 [1.4%]  52 [3.5%]    Other  6 [0.8%]  31 [2%]    Unknown  43 [6.1%]  234 [16%]    Ethnicity, n [%]        Hispanic  6 [1%]  45 [3%]  <0.001  Non-Hispanic  626 [85%]  996 [68%]    Unknown  104 [14%]  431 [29%]    Median duration before index date, years [range]  5 [0.5–5]  5 [0.4–5]  <0.001  Median duration of follow-up after index date, years [range]  14.8 [0.1–36.2]  10.0 [0.1–36.5]  <0.001  Obesity [BMI ≥ 30 kg/m2], n [%]  221/730 [30.0%]  371/1467 [25.2%]  0.08  1st degree family history of IBD, n [%]  42 [5.7%]  3 [0.2%]  <0.001  Smoking, n [%]        Current smoking  123 [16.7%]  229 [15.6%]  0.48  Former smoking  150 [20.4%]  152 [10.3%]  <0.001  Any antibiotic use, n [%]  455 [61.8%]  495 [33.6%]  <0.001  1–3 courses of antibiotics  357 [48.5%]  436 [29.6%]    4–6 courses of antibiotics  77 [10.5%]  53 [3.6%]    ≥ 7 courses of antibiotics  21 [2.8%]  6 [0.4%]    Daily aspirin use, n [%]  33 [4.5%]  59 [4.0%]  0.60  Statin use, n [%]  20 [2.7%]  63 [4.3%]  0.08  Characteristic  IBD [n = 736]  Controls [n = 1472]  p-value  Median age at index date, years [range]  34 [1.2–93]  34 [1.2–93]  1.00  < 18 years, n [%]  87 [11.8%]  174 [11.8%]    18–60 years, n [%]  559 [76.0%]  1118 [76.0%]    > 60 years, n [%]  90 [12.2%]  180 [12.2%]    Male, n [%]  405 [55%]  810 [55%]  1.00  Race, n [%]        White  665 [90%]  1074 [73%]  <0.001  African American  12 [1.7%]  81 [5.5%]    Asian  10 [1.4%]  52 [3.5%]    Other  6 [0.8%]  31 [2%]    Unknown  43 [6.1%]  234 [16%]    Ethnicity, n [%]        Hispanic  6 [1%]  45 [3%]  <0.001  Non-Hispanic  626 [85%]  996 [68%]    Unknown  104 [14%]  431 [29%]    Median duration before index date, years [range]  5 [0.5–5]  5 [0.4–5]  <0.001  Median duration of follow-up after index date, years [range]  14.8 [0.1–36.2]  10.0 [0.1–36.5]  <0.001  Obesity [BMI ≥ 30 kg/m2], n [%]  221/730 [30.0%]  371/1467 [25.2%]  0.08  1st degree family history of IBD, n [%]  42 [5.7%]  3 [0.2%]  <0.001  Smoking, n [%]        Current smoking  123 [16.7%]  229 [15.6%]  0.48  Former smoking  150 [20.4%]  152 [10.3%]  <0.001  Any antibiotic use, n [%]  455 [61.8%]  495 [33.6%]  <0.001  1–3 courses of antibiotics  357 [48.5%]  436 [29.6%]    4–6 courses of antibiotics  77 [10.5%]  53 [3.6%]    ≥ 7 courses of antibiotics  21 [2.8%]  6 [0.4%]    Daily aspirin use, n [%]  33 [4.5%]  59 [4.0%]  0.60  Statin use, n [%]  20 [2.7%]  63 [4.3%]  0.08  IBD, inflammatory bowel disease; BMI, body mass index. View Large The use of antibiotics between 3 months and 5 years before the index date occurred in 455 IBD cases [61.8%] and 495 controls [33.6%] [p < 0.001]. Among the antibiotic users, IBD cases received a greater number of antibiotic courses [median, 2: range, 1–12] than controls [median, 1; range, 1–8] [p < 0.001]. There were no differences in indications for antibiotic use between IBD cases and controls with regard to: ear/nose/throat and respiratory tract infections [76% vs. 70%, p = 0.06]; skin/soft tissue and bone infections [25% vs. 24%, p = 0.29]; and genitourinary tract infections [23% vs. 23%, p = 0.96]. However, the indication of antibiotic use for gastrointestinal tract infections in IBD cases was higher than controls [6% vs. 3%, p = 0.01]. Regardless of antibiotic use, gastrointestinal tract infections occurred in 29 of 736 IBD cases [3.9%] and 14 of 1472 controls [1.0%] [p < 0.001]. Of these, Clostridium difficile infection occurred in two controls but none was found in IBD cases. Table 2 shows the univariate and multivariate analysis for the association between various factors and new-onset IBD. In multivariate logistic regression, after adjusting for age, sex, smoking status, family history of IBD, obesity and statin use, there were statistically significant associations between any antibiotic use and the onset of IBD [AOR, 2.93; 95% CI, 2.40–3.58], CD [AOR, 3.01; 2.27–4.00] and UC [AOR, 2.94; 2.23–3.88]. These significant associations were observed across different antibiotic classes, including penicillins, macrolides, cephalosporins, sulfonamides, quinolones and tetracyclines, with AORs ranging from 2.01 to 2.53 for IBD, 2.21 to 2.81 for CD and 1.90 to 2.66 for UC [Tables 3 and 4]. Metronidazole was significantly associated with the risk of developing CD [AOR, 2.80; 95% CI, 1.14–7.11] but not with IBD overall [AOR, 1.84; 0.99–3.38] or UC [AOR, 1.38; 0.58–3.12]. Notably, three CD patients were given metronidazole for perianal infection prior to a definite CD diagnosis. After we excluded those cases, no statistically significant association between metronidazole and the risk of developing CD was observed [AOR, 1.94; 95% CI, 0.73–5.14]. Table 2. Univariate and multivariate analysis of associations between various factors and new-onset IBD Variable  Inflammatory bowel disease  Unadjusted  Adjusted†  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  p-value  OR  95% CI  p-value  Age [per 1 year]      1.00  1.00–1.00  1.00  1.00  0.99–1.00  0.16  Male [n]  405  810  1.00  0.84–1.19  1.00  1.24  1.01–1.51  0.04  Current smoking [n]  123  229  0.92  0.72–1.17  0.48  –  –  –  Former smoking [n]  150  152  2.22  1.74–2.84  <0.001  2.18  1.64–2.90  <0.001  1st degree family history of IBD [n]  42  3  29.6  9.15–95.9  <0.001  27.3  9.58–115.2  <0.001  Obesity [n]  221  371  1.20  0.98–1.47  0.08  1.14  0.92–1.42  0.22  Statin use [n]  20  63  0.62  0.37–1.04  0.08  0.46  0.25–0.80  0.01  Aspirin use [n]  33  59  1.12  0.73–1.74  0.60  –  –  –  Antibiotic use [n]  455  495  3.20  2.66–3.84  <0.001  2.93  2.40–3.58  <0.001  Variable  Inflammatory bowel disease  Unadjusted  Adjusted†  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  p-value  OR  95% CI  p-value  Age [per 1 year]      1.00  1.00–1.00  1.00  1.00  0.99–1.00  0.16  Male [n]  405  810  1.00  0.84–1.19  1.00  1.24  1.01–1.51  0.04  Current smoking [n]  123  229  0.92  0.72–1.17  0.48  –  –  –  Former smoking [n]  150  152  2.22  1.74–2.84  <0.001  2.18  1.64–2.90  <0.001  1st degree family history of IBD [n]  42  3  29.6  9.15–95.9  <0.001  27.3  9.58–115.2  <0.001  Obesity [n]  221  371  1.20  0.98–1.47  0.08  1.14  0.92–1.42  0.22  Statin use [n]  20  63  0.62  0.37–1.04  0.08  0.46  0.25–0.80  0.01  Aspirin use [n]  33  59  1.12  0.73–1.74  0.60  –  –  –  Antibiotic use [n]  455  495  3.20  2.66–3.84  <0.001  2.93  2.40–3.58  <0.001  OR, odds ratio; CI, confidence interval. †Age, sex, and any variables with a p < 0.1 in the univariate analysis were included in the multivariate analysis. View Large Table 3. Unadjusted and adjusted odds ratio for IBD stratified by class of antibiotic Antibiotic use  Inflammatory bowel disease  Unadjusted  Adjusted  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  OR  95% CI  Any antibiotic [n]  455  495  3.20  2.66–3.84  2.93  2.40–3.58  Penicillin [n]  246  228  2.74  2.22–3.37  2.53  2.02–3.18  Macrolide [n]  178  179  2.30  1.83–2.90  2.01  1.57–2.57  Cephalosporin [n]  107  94  2.49  1.86–3.34  2.29  1.68–3.12  Sulfonamide [n]  84  80  2.24  1.63–3.09  2.20  1.56–3.11  Quinolone [n]  75  73  2.17  1.56–3.04  2.10  1.47–3.01  Tetracycline [n]  55  46  2.50  1.67–3.74  2.10  1.37–3.22  Metronidazole [n]  22  26  1.71  0.96–3.04  1.84  0.99–3.38  Antibiotic use  Inflammatory bowel disease  Unadjusted  Adjusted  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  OR  95% CI  Any antibiotic [n]  455  495  3.20  2.66–3.84  2.93  2.40–3.58  Penicillin [n]  246  228  2.74  2.22–3.37  2.53  2.02–3.18  Macrolide [n]  178  179  2.30  1.83–2.90  2.01  1.57–2.57  Cephalosporin [n]  107  94  2.49  1.86–3.34  2.29  1.68–3.12  Sulfonamide [n]  84  80  2.24  1.63–3.09  2.20  1.56–3.11  Quinolone [n]  75  73  2.17  1.56–3.04  2.10  1.47–3.01  Tetracycline [n]  55  46  2.50  1.67–3.74  2.10  1.37–3.22  Metronidazole [n]  22  26  1.71  0.96–3.04  1.84  0.99–3.38  IBD, inflammatory bowel disease; OR, odds ratio; CI, confidence interval. View Large Table 4. Unadjusted and adjusted odds ratio for Crohn’s disease and ulcerative colitis stratified by class of antibiotic Antibiotic use  Crohn’s disease  Unadjusted  Adjusted  Ulcerative colitis  Unadjusted  Adjusted  Cases [n = 339]  Controls [n = 678]  OR  95% CI  OR  95% CI  Cases [n = 397]  Controls [n = 794]  OR  95% CI  OR  95% CI  Any antibiotic [n]  212  241  3.03  2.31–3.97  3.01  2.27–4.00  243  254  3.35  2.61–4.31  2.94  2.23–3.88  Penicillin [n]  119  117  2.59  1.92–3.50  2.66  1.94–3.65  127  111  2.89  2.16–3.87  2.42  1.76–3.33  Macrolide [n]  87  88  2.31  1.66–3.22  2.28  1.61–3.21  91  91  2.30  1.67–3.16  1.90  1.33–2.71  Cephalosporin [n]  51  53  2.09  1.39–3.14  2.21  1.44–3.37  56  41  3.02  1.98–4.60  2.66  1.71–4.16  Sulfonamide [n]  40  30  2.89  1.77–4.73  2.81  1.67–4.75  44  50  1.85  1.21–2.84  2.00  1.26–3.17  Quinolone [n]  35  32  2.32  1.41–3.83  2.41  1.41–4.11  40  41  2.06  1.31–3.24  1.94  1.18–3.16  Tetracycline [n]  24  20  2.51  1.36–4.61  2.34  1.24–4.43  31  26  2.50  1.46–4.28  2.10  1.18–3.76  Metronidazole [n]  12  9  2.73  1.14–6.54  2.80  1.14–7.11  10  17  1.18  0.54–2.60  1.38  0.58–3.12  Antibiotic use  Crohn’s disease  Unadjusted  Adjusted  Ulcerative colitis  Unadjusted  Adjusted  Cases [n = 339]  Controls [n = 678]  OR  95% CI  OR  95% CI  Cases [n = 397]  Controls [n = 794]  OR  95% CI  OR  95% CI  Any antibiotic [n]  212  241  3.03  2.31–3.97  3.01  2.27–4.00  243  254  3.35  2.61–4.31  2.94  2.23–3.88  Penicillin [n]  119  117  2.59  1.92–3.50  2.66  1.94–3.65  127  111  2.89  2.16–3.87  2.42  1.76–3.33  Macrolide [n]  87  88  2.31  1.66–3.22  2.28  1.61–3.21  91  91  2.30  1.67–3.16  1.90  1.33–2.71  Cephalosporin [n]  51  53  2.09  1.39–3.14  2.21  1.44–3.37  56  41  3.02  1.98–4.60  2.66  1.71–4.16  Sulfonamide [n]  40  30  2.89  1.77–4.73  2.81  1.67–4.75  44  50  1.85  1.21–2.84  2.00  1.26–3.17  Quinolone [n]  35  32  2.32  1.41–3.83  2.41  1.41–4.11  40  41  2.06  1.31–3.24  1.94  1.18–3.16  Tetracycline [n]  24  20  2.51  1.36–4.61  2.34  1.24–4.43  31  26  2.50  1.46–4.28  2.10  1.18–3.76  Metronidazole [n]  12  9  2.73  1.14–6.54  2.80  1.14–7.11  10  17  1.18  0.54–2.60  1.38  0.58–3.12  OR, odds ratio; CI, confidence interval. View Large Subgroup analysis stratified by age group at IBD diagnosis showed that the association between any antibiotic use and the risk of IBD was statistically significant in all age groups. Those under 18 years receiving any antibiotic had an AOR for IBD of 4.27 [95% CI, 2.39–7.91], and the corresponding values at ages 18–60 years and > 60 years were 2.97 [2.36–3.75] and 2.72 [1.60–4.67], respectively. A dose-dependent relationship between antibiotic use and the risk of IBD development was observed. Stratifying the analysis by the number of courses of antibiotic use, the AOR for one to three courses was 2.61 [95% CI, 2.12–3.23], 4.61 for for to six courses [95% CI, 3.12–6.84] and 10.34 for seven or more courses [95% CI, 4.19–29.19]. When the analysis was stratified by the cumulative duration of antibiotic use, there were significant associations with the risk of IBD. The AOR for 1–14 days was 2.27 [95% CI, 1.78–2.89] and 2.64 for 15–29 days [95% CI, 1.94–3.61], while using antibiotics ≥ 30 days had the strongest AOR of 6.01 [95% CI, 4.34–8.45]. We also found evidence of dose-dependent relationships between antibiotic use and the risks of developing CD or UC, as shown in Table 5. Table 5. Adjusted odds ratio for IBD, CD and UC stratified by course, duration and year before diagnosis of antibiotic use Antibiotic use  IBD  CD  UC  Cases [n = 736]  Controls [n = 1472]  Adjusted OR  95% CI  Cases [n = 339]  Controls [n = 678]  Adjusted OR  95% CI  Cases [n = 397]  Controls [n = 794]  Adjusted OR  95% CI  No use [n]  281  977  Ref    127  437  Ref    154  540  Ref    Any use [n]  455  495  2.93  2.40–3.58  212  241  3.01  2.27–4.00  243  254  2.94  2.23–3.88  Courses  1–3 courses [n]  357  436  2.61  2.12–3.23  160  206  2.65  1.97–3.58  197  230  2.65  1.99–3.53  4–6 courses [n]  77  53  4.61  3.12–6.84  38  31  4.37  2.58–7.45  39  22  5.23  2.9 2–9.56  ≥ 7 courses [n]  21  6  10.34  4.19–29.19  14  4  11.89  4.00–43.6  7  2  10.88  2.49–75.17  Duration†  1–14 days [n]  208  293  2.27  1.78–2.89  91  136  2.17  1.53–3.07  117  157  2.30  1.66–3.18  15–29 days [n]  19  130  2.64  1.94–3.61  51  62  2.98  1.92–4.59  58  68  2.66  1.73–4.08  ≥ 30 days [n]  138  72  6.03  4.34–8.45  70  43  5.89  3.80–9.23  68  29  7.07  4.31–11.83  Year before diagnosis  1 year [n]  187  174  3.60  2.75–4.72  91  92  3.32  2.30–4.81  96  82  3.65  2.50–5.35  2 years [n]  101  140  2.44  1.79–3.30  50  67  2.74  1.78–4.21  51  73  2.37  1.54–3.63  3 years [n]  71  79  2.62  1.79–3.81  33  34  2.98  1.73–5.14  38  45  2.75  1.65–4.57  4 years [n]  53  51  3.00  1.94–4.63  20  26  2.69  1.41–5.06  33  25  3.26  1.80–5.94  > 4 years [n]  43  51  2.60  1.63–4.11  18  22  2.93  1.48–5.72  25  29  2.42  1.31–4.43  Antibiotic use  IBD  CD  UC  Cases [n = 736]  Controls [n = 1472]  Adjusted OR  95% CI  Cases [n = 339]  Controls [n = 678]  Adjusted OR  95% CI  Cases [n = 397]  Controls [n = 794]  Adjusted OR  95% CI  No use [n]  281  977  Ref    127  437  Ref    154  540  Ref    Any use [n]  455  495  2.93  2.40–3.58  212  241  3.01  2.27–4.00  243  254  2.94  2.23–3.88  Courses  1–3 courses [n]  357  436  2.61  2.12–3.23  160  206  2.65  1.97–3.58  197  230  2.65  1.99–3.53  4–6 courses [n]  77  53  4.61  3.12–6.84  38  31  4.37  2.58–7.45  39  22  5.23  2.9 2–9.56  ≥ 7 courses [n]  21  6  10.34  4.19–29.19  14  4  11.89  4.00–43.6  7  2  10.88  2.49–75.17  Duration†  1–14 days [n]  208  293  2.27  1.78–2.89  91  136  2.17  1.53–3.07  117  157  2.30  1.66–3.18  15–29 days [n]  19  130  2.64  1.94–3.61  51  62  2.98  1.92–4.59  58  68  2.66  1.73–4.08  ≥ 30 days [n]  138  72  6.03  4.34–8.45  70  43  5.89  3.80–9.23  68  29  7.07  4.31–11.83  Year before diagnosis  1 year [n]  187  174  3.60  2.75–4.72  91  92  3.32  2.30–4.81  96  82  3.65  2.50–5.35  2 years [n]  101  140  2.44  1.79–3.30  50  67  2.74  1.78–4.21  51  73  2.37  1.54–3.63  3 years [n]  71  79  2.62  1.79–3.81  33  34  2.98  1.73–5.14  38  45  2.75  1.65–4.57  4 years [n]  53  51  3.00  1.94–4.63  20  26  2.69  1.41–5.06  33  25  3.26  1.80–5.94  > 4 years [n]  43  51  2.60  1.63–4.11  18  22  2.93  1.48–5.72  25  29  2.42  1.31–4.43  IBD, inflammatory bowel disease; CD, Crohn’s disease; UC, ulcerative colitis; OR, odds ratio; CI, confidence interval; Ref, reference. †Cumulative duration of antibiotic use 3 months to 5 years before diagnosis. View Large The significant association between antibiotic use and the risk of IBD remained regardless of year of antibiotic use before IBD diagnosis. For each individual year, the associations were statistically significant with AORs of 3.60 [95% CI, 2.75–4.72] at 1 year, 2.44 [1.79–3.30] at 2 years, 2.62 [1.79–3.81] at 3 years, 3.00 [1.94–4.63] at 4 years and 2.60 [1.63–4.11] at > 4 years before IBD diagnosis. These associations were observed in both CD and UC as well [Table 5]. 4. Discussion The results of this population-based case-control study demonstrate a strong association between antibiotic use and the risk of either new-onset CD or new-onset UC. In addition, significant dose-dependent relationships between antibiotic use and the development of CD and UC were observed. This relationship existed irrespective of age groups and presented across 5 years before IBD diagnosis. The majority of antibiotic classes were associated with an increased risk of developing CD and UC. Our findings corroborate previous studies from the UK20 and Canada.16,27 Card et al. used the General Practice Research Database and illustrated the association between antibiotic use and the development of CD.20 They showed that antibiotic use increased the odds of CD by a factor of over 30% [AOR, 1.32; 95% CI, 1.05–1.65]. Shaw et al. performed a case-control study using the University of Manitoba IBD Epidemiology Database.16 The authors found that the odds of IBD for those receiving antibiotic dispensation at 2–5 years before diagnosis was 1.29 [95% CI, 1.18–1.40] for CD and 1.26 [1.16–1.36] for UC. A dose-dependent relationship was shown. At 2 years before diagnosis, those receiving one or more and two or more antibiotic dispensations had a 1.27- and 1.48-fold increase in the odds of CD, respectively. The AOR of UC was 1.62 for three of more antibiotic dispensations. When stratifying by age groups, significant associations between antibiotic use within 2–5 years and adult-onset IBD were identified [AOR 1.32 for age 19–65 years and 1.25 for > 65 years].16 In a Manitoba nested case-control study, antibiotic use in the first year of life was associated with a 2.9-fold increased risk of new-onset IBD in childhood [age < 16 years].27 Despite the use of large administrative databases, studies that rely on drug dispensation codes have limitations, in particular confounding factors. Important potential confounding factors such as family history of IBD and cigarette smoking status use were unavailable. Furthermore, the database could not verify that a dispensed antibiotic was actually consumed. A key strength of this study is that all incident IBD cases and controls were derived from a well-defined geographical region and mostly received the same healthcare provision. More than 95% of Olmsted County residents receive medical care from either Mayo Clinic Medical Center or Olmsted Medical Center. Because of the medical record linkage system [Rochester Epidemiology Project], which provides access to all local medical records, we were able to review data from all available patients from 5 years before the index date until the end of the study period. We were able to verify the data regarding antibiotic consumption and accounted for other confounding factors. However, this study also had some limitations. First, because of its retrospective nature, any data not recorded in the medical records would have been missed. Secondly, socioeconomic factors are associated with hygiene status and healthcare utilization. The socioeconomic status of Olmsted County residents is slightly higher than that of the Upper Midwest population and the entire US population.23 In 2000, 91% of Olmsted County residents were highschool graduates compared to 86% of the Upper Midwest population and 80% of the entire US population.23 Median household income among Olmsted County residents [$51316] was higher than that of the Upper Midwest population [$43200] and of the entire US population [$41994].23 The relatively high socioeconomic status in the region may limit the generalizability of these data to populations with different socioeconomic characteristics. Thirdly, Olmsted County had less ethnic diversity than the US population [90% vs. 75% white] between 1970 and 2000.23 However, this gap has narrowed: 85.7% of county residents were whites in 2010 compared with 74.8% of the US population.28 This change is similar to other Western countries. For example, according to the Office for National Statistics of the United Kingdom, the percentage of whites in England and Wales decreased from 94.1% in 1991 to 86% in 2011.29 Although there were changes in the ethnic diversity of the Olmsted County population, the generalization of these study results to populations with more heterogeneous ethnicity may not be applicable. It is unclear why antibiotic use was associated with new-onset IBD. Our findings may support a role of microbiome alterations in the pathogenesis of IBD. Previous studies in healthy humans have suggested that antibiotic exposure perturbs the gut microbiota composition,30,31 and repeated antibiotic exposure results in persistent dysbiosis.32,33 Many studies consistently reported changes in the total numbers and diversity of the gut microbiota in IBD patients.34 For instance, increases in the numbers of adherent-invasive Escherichia coli, Enterobacteriaceae and Fusobacteriaceae, and decreases in the numbers of Bacteroides, Bifidobacteriaceae and Clostridia have been reported in IBD patients.1 In addition to quantitative changes in the bacteria comprising the gut microbiota, antibiotics may alter the functional composition of the gut microbiota, as well.35 Recently, a longitudinal paediatric IBD cohort study has shown an increased gut microbial dysbiosis index in new-onset IBD patients when compared to healthy controls.36 In CD patients, the dysbiosis index at diagnosis was predictive of subsequent disease severity defined by the Pediatric CD Activity Index.36 Although we found a strong association and dose–response relationship between antibiotic use and the development of IBD, a possible triggering role of antibiotics in the onset of IBD should be interpreted with caution. First, not all classes of antibiotics have the same local effect on the gut microbiome. Metronidazole, which directly targets specific gut bacteria, showed the strongest association with the approximately three-fold increased risk for developing IBD in the Manitoba study.16 In contrast to the Manitoba study, an increased risk for IBD in metronidazole was not observed in our study. The relatively small number of metronidazole users in our study may have contributed to the lack of statistical significance. For other classes of antibiotics, an approximately two-fold increased risk of IBD among each class of antibiotic [i.e. penicillins, cephalosporins, quinolones, sulfonamides, macrolides and tetracyclines.] was observed. Our results are line with previous studies. The Manitoba study showed significant associations with all types of antibiotics except clindamycin, with AORs of 1.12–2.86.16 In a large retrospective cohort study from the UK, tetracycline use for acne therapy was associated with IBD development (hazard ratio [HR], 1.39; 95% CI, 1.02–1.90].37 Secondly, we examined antibiotic usage for 5 years prior to IBD diagnosis. We did not examine earlier periods or during the first year of life, which theoretically is the important period for development and adaptation of the gut microbiota.1 Thirdly, antibiotic use may act as a surrogate marker for infection, leading to IBD development. A previous study found that the HR of developing IBD was 2.4 [95% CI, 1.7–3.3] in patients with acute gastroenteritis.38 Hypothetically, either gastrointestinal tract infection or antibiotic use could be confounding factors. However, we found that only 3.9% of the IBD cases had a gastrointestinal tract infection. More than 70% of infections were ear, nose, throat and respiratory tract infections. These infection sites are concordant with a previous study of the association between antibiotic use in the first year of life and childhood-onset IBD.27 In conclusion, antibiotic use was an independent risk factor for the development of both CD and UC in this population-based nested case-control study. The risk was increased among all age-onset of IBD. Further longitudinal prospective studies investigating the infection and antibiotic use in parallel with gut microbiome analysis in IBD are warranted to unravel the pathogenesis of IBD. Funding This work was supported in part by the Mayo Foundation for Medical Education & Research, and the Rochester Epidemiology Project [grant number R01 AG034676 from the National Institute on Aging of the National Institutes of Health]. The contents of the publication are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health. Conflict of Interest None of the authors has any relevant conflicts of interests. Author Contributions SA: writing the proposal, submitting the proposal to Institutional Review Board, collecting the data, analysing the data, and drafting of the manuscript. WJT, LER and SVK: critical revision of the manuscript. EVL: conception and design of the study, critical revision of the manuscript. All authors provided final approval of the version to be submitted. Acknowledgments We are grateful to W. Scott Harmsen M.S. for identifying matched controls from the Olmsted County residents. References 1. Sheehan D , Shanahan F. The gut microbiota in inflammatory bowel disease. Gastroenterol Clin North Am  2017; 46: 143– 54. Google Scholar CrossRef Search ADS PubMed  2. Thompson NP , Driscoll R, Pounder RE, Wakefield AJ. 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A prospective study of cigarette smoking and the risk of inflammatory bowel disease in women. Am J Gastroenterol  2012; 107: 1399– 406. Google Scholar CrossRef Search ADS PubMed  11. Tobin MV , Logan RF, Langman MJ, McConnell RB, Gilmore IT. Cigarette smoking and inflammatory bowel disease. Gastroenterology  1987; 93: 316– 21. Google Scholar CrossRef Search ADS PubMed  12. Burisch J , Pedersen N, Cukovic-Cavka Set al.  ; EpiCom-group. Environmental factors in a population-based inception cohort of inflammatory bowel disease patients in Europe – an ECCO-EpiCom study. J Crohns Colitis  2014; 8: 607– 16. Google Scholar CrossRef Search ADS PubMed  13. Niewiadomski O , Studd C, Wilson Jet al.   Influence of food and lifestyle on the risk of developing inflammatory bowel disease. Intern Med J  2016; 46: 669– 76. Google Scholar CrossRef Search ADS PubMed  14. Ng SC , Tang W, Leong RWet al.  ; Asia-Pacific Crohn’s and Colitis Epidemiology Study ACCESS Group. Environmental risk factors in inflammatory bowel disease: a population-based case-control study in Asia-Pacific. Gut  2015; 64: 1063– 71. Google Scholar CrossRef Search ADS PubMed  15. Ungaro R , Bernstein CN, Gearry Ret al.   Antibiotics associated with increased risk of new-onset Crohn’s disease but not ulcerative colitis: a meta-analysis. Am J Gastroenterol  2014; 109: 1728– 38. Google Scholar CrossRef Search ADS PubMed  16. Shaw SY , Blanchard JF, Bernstein CN. Association between the use of antibiotics and new diagnoses of Crohn’s disease and ulcerative colitis. Am J Gastroenterol  2011; 106: 2133– 42. Google Scholar CrossRef Search ADS PubMed  17. Castiglione F , Diaferia M, Morace Fet al.   Risk factors for inflammatory bowel diseases according to the “hygiene hypothesis”: A case-control, multi-centre, prospective study in Southern Italy. J Crohns Colitis  2012; 6: 324– 9. Google Scholar CrossRef Search ADS PubMed  18. Han DY , Fraser AG, Dryland P, Ferguson LR. Environmental factors in the development of chronic inflammation: a case-control study on risk factors for Crohn’s disease within New Zealand. Mutat Res  2010; 690: 116– 22. Google Scholar CrossRef Search ADS PubMed  19. Gearry RB , Richardson AK, Frampton CM, Dodgshun AJ, Barclay ML. Population-based cases control study of inflammatory bowel disease risk factors. J Gastroenterol Hepatol  2010; 25: 325– 33. Google Scholar CrossRef Search ADS PubMed  20. Card T , Logan RF, Rodrigues LC, Wheeler JG. Antibiotic use and the development of Crohn’s disease. Gut  2004; 53: 246– 50. Google Scholar CrossRef Search ADS PubMed  21. Rocca WA , Yawn BP, St Sauver JL, Grossardt BR, Melton LJ3rd. History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population. Mayo Clin Proc  2012; 87: 1202– 13. Google Scholar CrossRef Search ADS PubMed  22. St Sauver JL , Grossardt BR, Yawn BP, Melton LJ3rd, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project. Am J Epidemiol  2011; 173: 1059– 68. Google Scholar CrossRef Search ADS PubMed  23. St Sauver JL , Grossardt BR, Leibson CL, Yawn BP, Melton LJ3rd, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc  2012; 87: 151– 60. Google Scholar CrossRef Search ADS PubMed  24. Loftus EV Jr , Silverstein MD, Sandborn WJ, Tremaine WJ, Harmsen WS, Zinsmeister AR. Ulcerative colitis in Olmsted County, Minnesota, 1940–1993: incidence, prevalence, and survival. Gut  2000; 46: 336– 43. Google Scholar CrossRef Search ADS PubMed  25. Loftus EV Jr , Silverstein MD, Sandborn WJ, Tremaine WJ, Harmsen WS, Zinsmeister AR. Crohn’s disease in Olmsted County, Minnesota, 1940–1993: incidence, prevalence, and survival. Gastroenterology  1998; 114: 1161– 8. Google Scholar CrossRef Search ADS PubMed  26. Loftus CG , Loftus EVJr, Harmsen WSet al.   Update on the incidence and prevalence of Crohn’s disease and ulcerative colitis in Olmsted County, Minnesota, 1940–2000. Inflamm Bowel Dis  2007; 13: 254– 61. Google Scholar CrossRef Search ADS PubMed  27. Shaw SY , Blanchard JF, Bernstein CN. Association between the use of antibiotics in the first year of life and pediatric inflammatory bowel disease. Am J Gastroenterol  2010; 105: 2687– 92. Google Scholar CrossRef Search ADS PubMed  28. United States Census Bureau. Census 2010 total population and discription in Olmsted County, Minnesota. https://factfinder.census.gov/faces/nav/jsf/pages/community_facts.xhtml?src=bkmk. Accessed September 5, 2017. 29. Office for National Statistics. Ethnicity and national identity in England and Wales 2011. http://webarchive.nationalarchives.gov.uk/20160107112033/http://www.ons.gov.uk/ons/dcp171776_290558.pdf. Accessed September 8, 2017. 30. Gibson MK , Wang B, Ahmadi Set al.   Developmental dynamics of the preterm infant gut microbiota and antibiotic resistome. Nat Microbiol  2016; 1: 16024. Google Scholar CrossRef Search ADS PubMed  31. Jakobsson HE , Jernberg C, Andersson AF, Sjölund-Karlsson M, Jansson JK, Engstrand L. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS One  2010; 5: e9836. Google Scholar CrossRef Search ADS PubMed  32. Fouhy F , Guinane CM, Hussey Set al.   High-throughput sequencing reveals the incomplete, short-term recovery of infant gut microbiota following parenteral antibiotic treatment with ampicillin and gentamicin. Antimicrob Agents Chemother  2012; 56: 5811– 20. Google Scholar CrossRef Search ADS PubMed  33. Dethlefsen L , Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A  2011; 108[ Suppl 1]: 4554– 61. Google Scholar CrossRef Search ADS PubMed  34. Kostic AD , Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology  2014; 146: 1489– 99. Google Scholar CrossRef Search ADS PubMed  35. Pérez-Cobas AE , Gosalbes MJ, Friedrichs Aet al.   Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut  2013; 62: 1591– 601. Google Scholar CrossRef Search ADS PubMed  36. Shaw KA , Bertha M, Hofmekler Tet al.   Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease. Genome Med  2016; 8: 75. Google Scholar CrossRef Search ADS PubMed  37. Margolis DJ , Fanelli M, Hoffstad O, Lewis JD. Potential association between the oral tetracycline class of antimicrobials used to treat acne and inflammatory bowel disease. Am J Gastroenterol  2010; 105: 2610– 6. Google Scholar CrossRef Search ADS PubMed  38. García Rodríguez LA , Ruigómez A, Panés J. Acute gastroenteritis is followed by an increased risk of inflammatory bowel disease. Gastroenterology  2006; 130: 1588– 94. Google Scholar CrossRef Search ADS PubMed  Copyright © 2017 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Crohn's and Colitis Oxford University Press

Antibiotic Use and New-Onset Inflammatory Bowel Disease in Olmsted County, Minnesota: A Population-Based Case-Control Study

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Copyright © 2017 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com
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10.1093/ecco-jcc/jjx135
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Abstract

Abstract Background and Aims Several studies have suggested significant associations between environmental factors and the risk of developing inflammatory bowel disease [IBD]. However, data supporting the role of antibiotics are conflicting. The aim of this study was to evaluate the association between antibiotic use and new-onset IBD. Methods We conducted a population-based case-control study using the Rochester Epidemiology Project of Olmsted County, Minnesota. We identified 736 county residents diagnosed with IBD between 1980 and 2010 who were matched to 1472 controls, based on age, sex and date of IBD diagnosis. Data on antibiotic use between 3 months and 5 years before IBD diagnosis were collected. Logistic regression models were used to estimate associations between antibiotic use and IBD, and were expressed as adjusted odds ratio [AOR] with 95% confidence interval [CI]. Results Antibiotic use occurred in 455 IBD cases [61.8%] and 495 controls [33.6%] [p < 0.001]. In multivariate analysis, there were statistically significant associations between antibiotic use and new-onset IBD [AOR, 2.93; 95% CI, 2.40–3.58], Crohn’s disease [CD] [AOR, 3.01; 2.27–4.00] and ulcerative colitis [UC] [AOR, 2.94; 95% CI, 2.23–3.88]. A cumulative duration of antibiotic use ≥ 30 days had the strongest AOR [6.01; 95% CI, 4.34–8.45]. AOR for those receiving antibiotics under the age of 18 years was 4.27 [95% CI, 2.39–7.91], 2.97 for age 18–60 years [2.36–3.75] and 2.72 for age > 60 years [1.60–4.67]. Conclusions This population-based case-control study suggests a strong association between antibiotic use and the risk of both new-onset CD and new-onset UC. The risk was increased among all age-onset IBD. Antibiotics, epidemiology, inflammatory bowel disease 1. Introduction Inflammatory bowel diseases [IBD], consisting of Crohn’s disease [CD] and ulcerative colitis [UC], are chronic idiopathic inflammatory conditions. Despite advances in the management of IBD, the pathogenesis of IBD is incompletely understood. The key contributors to the pathogenesis of IBD are thought to be genetics, host immune response and environmental factors.1 The low concordance rate of IBD [approximately 35–50% for CD and 15% for UC] in identical twins suggests that genetic factors do not completely explain the disease aetiology.2–4 Likewise, the increasing incidence of IBD worldwide including North America, Europe, Australia and Asia over the past 50 years suggests the important role of host immune response and environmental factors.5–7 Emerging evidence suggests that disturbances in the gut microbiota and their interaction with the host immune system in a genetically susceptible individual may be the fundamental development of IBD.1,8 Changes in microbial diversity and loss of protective microbial species have been demonstrated in patients with IBD.1 In particular, some dietary changes and antibiotic exposure have been shown to alter gut microbiota.9 Several studies have suggested significant associations between environmental factors, including cigarette smoking10,11and westernized diet,12,13 and the risk of developing IBD. However, data for antibiotics are conflicting.14–20 A previous meta-analysis has shown a modest association between antibiotic use and IBD development (pooled odds ratio [OR], 1.57; 95% confidence interval [CI], 1.27–1.94).15 Antibiotic use increased the odds of CD development [OR, 1.74; 95% CI, 1.35–2.23] but not UC [OR, 1.08; 95% CI, 0.91–1.27]. The authors noted a great deal of heterogeneity across the studies. For example, a study from the UK using the General Practice Research Database20 and a study from Canada using the University of Manitoba IBD Database16 relied upon diagnostic and prescription codes to identify cases and exposures. As a result, misclassification bias may have occurred. Furthermore, some important predisposing factors to development of IBD, including cigarette smoking status and family history of IBD, could not be identified by administrative data. On the other hand, previous studies using a self-administered questionnaire14,17–19 to assess antibiotic exposure were limited due to recall bias. Moreover, using prevalent patients may overrepresent those who regularly follow up, and may not include patients who migrated, lived in rural regions or those with indolent disease who were followed by their primary physician.19 Hence, the implications of antibiotic use as a predisposing factor in IBD aetiology remain unresolved, hindering our understanding of the pathogenesis of IBD. Therefore, to address this gap in knowledge, the present study sought to evaluate the association between the antibiotic use and new-onset IBD [both CD and UC] in a population-based cohort from Olmsted County, Minnesota. 2. Material and Methods 2.1. Rochester Epidemiology Project The resources of the Rochester Epidemiology Project were used to identify permanent residents of Olmsted County, Minnesota, who were diagnosed with IBD and non-IBD between 1980 and 2010. The Rochester Epidemiology Project is a unique medical records linkage system developed in the 1950s and supported by the National Institutes of Health.21 It exploits the fact that virtually all the health care for residents of Olmsted County is provided by two organizations: the Mayo Medical Center, consisting of the Mayo Clinic and its two affiliated hospitals [Rochester Methodist and St. Mary’s], and Olmsted Medical Center, consisting of a smaller multispecialty clinic and its affiliated hospital [Olmsted Community Hospital]. In any 4-year period, over 95% of county residents are examined at either one of the two healthcare systems.22 Diagnoses generated from all outpatients visits, emergency room visits, hospitalizations, nursing home visits, laboratory, surgical procedures, medical treatment, autopsy examination and death certification are contained in a single comprehensive medical records linkage system. Thus, this population-based data resource ensures virtually complete case ascertainment and follow-up in a well-defined geographical region.23 2.2. Study population This population-based case-control study comprised 736 Olmsted County residents first diagnosed with IBD [339 CD and 397 UC] between 1980 and 2010 according to well-defined criteria as previously described.5,24–26 The index date was based on the first IBD diagnosis. For each case with IBD diagnosis, two controls were randomly selected from Olmsted County residents without an IBD diagnosis after matching for age, sex and index date of IBD diagnosis, which assigned the index date corresponding to the index date of IBD diagnosis. To determine whether IBD developed or not, individuals in the control cohort were followed through their medical history from the index date until death, emigration or the end of the study [June 30, 2016], whichever came first. We investigated the impact of age on the association between antibiotic use and IBD development. Age groups at index date were stratified as <18 years, 18–60 years and > 60 years. 2.3. Antibiotic use Data on the use of antibiotics with regard to class of antibiotic, indication, duration and number of antibiotic courses were collected for 5 years before the index date. Because antibiotics are commonly prescribed for symptoms of IBD prior to diagnosis, antibiotic use within 3 months prior to the index date was excluded. Classes of antibiotic were categorized as follows: penicillins, macrolides, cephalosporins, sulfonamides, quinolones, tetracyclines, metronidazole and others. Indications for antibiotic use were classified as: [i] ear, nose, throat and respiratory tract infection; [ii] gastrointestinal tract infection; [iii] genitourinary tract infection; [iv] skin, soft tissue and bone infection; or [v] other organ or systemic infection. The number of antibiotic courses was categorized as none, 1–3, 4–6, and ≥ 7. The start and stop dates of antibiotic use were recorded. The cumulative duration of antibiotic use for each individual was categorized as none, 1–14 days, 15–29 days and ≥ 30 days. 2.4. Statistical analysis Assuming the prevalence of antibiotic use in the control group ranged from 25 to 50%, a sample size of at least 330 cases and 660 controls in each CD and UC cohort was required to detect an OR of at least 2 with a power of 80% at a two-sided significance level of 0.05. Descriptive statistical analysis was used to determine baseline characteristics of IBD cases and controls. To compare categorical variables, a chi-square test or Fisher exact test was used. To compare continuous variables, Student’s t-test or Mann–Whitney U-test was used. Antibiotic use and potential confounding factors (i.e. first degree family history of IBD, current cigarette smoking, former cigarette smoking, obesity [body mass index ≥ 30 kg/m2], daily aspirin use and statins use) were tested by univariate analysis. Daily aspirin use was defined as taking aspirin at least once a day for at least 3 months before the index date. Statin use was defined as taking a statin [pravastatin, atorvastatin, simvastatin, rosuvastatin, fluvastatin, lovastatin] for at least 3 months before the index date. A multivariate analysis model was then analysed using unconditional logistic regression models to estimate the adjusted OR [AOR] and 95% CI. Age, sex and any variables with a p < 0.1 in the univariate analysis were included in the multivariate analysis. Separate analyses for IBD subtypes [CD and UC] and age groups at the index date were performed. To evaluate whether there was a dose-dependent relationship between antibiotic use and the risk of IBD development, the analysis was also stratified by the cumulative duration of antibiotic use and by the number of antibiotic courses. An alpha-level of 0.05 was considered as statistically significant. Statistical analyses were performed by using the JMP 10 statistical software package [SAS Institute Inc., Cary, NC, USA]. 3. Results A total of 736 incident IBD cases and 1472 controls were included in this study. This sample size was sufficient to achieve the a priori statistical power assumptions for the study. Of the IBD cases, 339 had CD [46%] and 397 had UC [54%]. IBD cases had data available with a median duration of 5 years before the index date [range, 0.5–5 years] and 14.8 years after the index date [range, 0.1–36.2 years]. Controls had data available with a median duration of 5 years before the index date [range, 0.4–5 years] and 10 years after the index date [range, 0.1–36.5 years]. Table 1 shows the demographic characteristics of the IBD cases and controls. By study design, there were no differences in the age and sex distribution between IBD cases and controls. In both IBD cases and controls, the median age at the index date was 34 years [range, 1.2–93 years], and 55% were male. Table 1. Demographic characteristics of IBD cases and controls at the index date from 1980 to 2010 Characteristic  IBD [n = 736]  Controls [n = 1472]  p-value  Median age at index date, years [range]  34 [1.2–93]  34 [1.2–93]  1.00  < 18 years, n [%]  87 [11.8%]  174 [11.8%]    18–60 years, n [%]  559 [76.0%]  1118 [76.0%]    > 60 years, n [%]  90 [12.2%]  180 [12.2%]    Male, n [%]  405 [55%]  810 [55%]  1.00  Race, n [%]        White  665 [90%]  1074 [73%]  <0.001  African American  12 [1.7%]  81 [5.5%]    Asian  10 [1.4%]  52 [3.5%]    Other  6 [0.8%]  31 [2%]    Unknown  43 [6.1%]  234 [16%]    Ethnicity, n [%]        Hispanic  6 [1%]  45 [3%]  <0.001  Non-Hispanic  626 [85%]  996 [68%]    Unknown  104 [14%]  431 [29%]    Median duration before index date, years [range]  5 [0.5–5]  5 [0.4–5]  <0.001  Median duration of follow-up after index date, years [range]  14.8 [0.1–36.2]  10.0 [0.1–36.5]  <0.001  Obesity [BMI ≥ 30 kg/m2], n [%]  221/730 [30.0%]  371/1467 [25.2%]  0.08  1st degree family history of IBD, n [%]  42 [5.7%]  3 [0.2%]  <0.001  Smoking, n [%]        Current smoking  123 [16.7%]  229 [15.6%]  0.48  Former smoking  150 [20.4%]  152 [10.3%]  <0.001  Any antibiotic use, n [%]  455 [61.8%]  495 [33.6%]  <0.001  1–3 courses of antibiotics  357 [48.5%]  436 [29.6%]    4–6 courses of antibiotics  77 [10.5%]  53 [3.6%]    ≥ 7 courses of antibiotics  21 [2.8%]  6 [0.4%]    Daily aspirin use, n [%]  33 [4.5%]  59 [4.0%]  0.60  Statin use, n [%]  20 [2.7%]  63 [4.3%]  0.08  Characteristic  IBD [n = 736]  Controls [n = 1472]  p-value  Median age at index date, years [range]  34 [1.2–93]  34 [1.2–93]  1.00  < 18 years, n [%]  87 [11.8%]  174 [11.8%]    18–60 years, n [%]  559 [76.0%]  1118 [76.0%]    > 60 years, n [%]  90 [12.2%]  180 [12.2%]    Male, n [%]  405 [55%]  810 [55%]  1.00  Race, n [%]        White  665 [90%]  1074 [73%]  <0.001  African American  12 [1.7%]  81 [5.5%]    Asian  10 [1.4%]  52 [3.5%]    Other  6 [0.8%]  31 [2%]    Unknown  43 [6.1%]  234 [16%]    Ethnicity, n [%]        Hispanic  6 [1%]  45 [3%]  <0.001  Non-Hispanic  626 [85%]  996 [68%]    Unknown  104 [14%]  431 [29%]    Median duration before index date, years [range]  5 [0.5–5]  5 [0.4–5]  <0.001  Median duration of follow-up after index date, years [range]  14.8 [0.1–36.2]  10.0 [0.1–36.5]  <0.001  Obesity [BMI ≥ 30 kg/m2], n [%]  221/730 [30.0%]  371/1467 [25.2%]  0.08  1st degree family history of IBD, n [%]  42 [5.7%]  3 [0.2%]  <0.001  Smoking, n [%]        Current smoking  123 [16.7%]  229 [15.6%]  0.48  Former smoking  150 [20.4%]  152 [10.3%]  <0.001  Any antibiotic use, n [%]  455 [61.8%]  495 [33.6%]  <0.001  1–3 courses of antibiotics  357 [48.5%]  436 [29.6%]    4–6 courses of antibiotics  77 [10.5%]  53 [3.6%]    ≥ 7 courses of antibiotics  21 [2.8%]  6 [0.4%]    Daily aspirin use, n [%]  33 [4.5%]  59 [4.0%]  0.60  Statin use, n [%]  20 [2.7%]  63 [4.3%]  0.08  IBD, inflammatory bowel disease; BMI, body mass index. View Large The use of antibiotics between 3 months and 5 years before the index date occurred in 455 IBD cases [61.8%] and 495 controls [33.6%] [p < 0.001]. Among the antibiotic users, IBD cases received a greater number of antibiotic courses [median, 2: range, 1–12] than controls [median, 1; range, 1–8] [p < 0.001]. There were no differences in indications for antibiotic use between IBD cases and controls with regard to: ear/nose/throat and respiratory tract infections [76% vs. 70%, p = 0.06]; skin/soft tissue and bone infections [25% vs. 24%, p = 0.29]; and genitourinary tract infections [23% vs. 23%, p = 0.96]. However, the indication of antibiotic use for gastrointestinal tract infections in IBD cases was higher than controls [6% vs. 3%, p = 0.01]. Regardless of antibiotic use, gastrointestinal tract infections occurred in 29 of 736 IBD cases [3.9%] and 14 of 1472 controls [1.0%] [p < 0.001]. Of these, Clostridium difficile infection occurred in two controls but none was found in IBD cases. Table 2 shows the univariate and multivariate analysis for the association between various factors and new-onset IBD. In multivariate logistic regression, after adjusting for age, sex, smoking status, family history of IBD, obesity and statin use, there were statistically significant associations between any antibiotic use and the onset of IBD [AOR, 2.93; 95% CI, 2.40–3.58], CD [AOR, 3.01; 2.27–4.00] and UC [AOR, 2.94; 2.23–3.88]. These significant associations were observed across different antibiotic classes, including penicillins, macrolides, cephalosporins, sulfonamides, quinolones and tetracyclines, with AORs ranging from 2.01 to 2.53 for IBD, 2.21 to 2.81 for CD and 1.90 to 2.66 for UC [Tables 3 and 4]. Metronidazole was significantly associated with the risk of developing CD [AOR, 2.80; 95% CI, 1.14–7.11] but not with IBD overall [AOR, 1.84; 0.99–3.38] or UC [AOR, 1.38; 0.58–3.12]. Notably, three CD patients were given metronidazole for perianal infection prior to a definite CD diagnosis. After we excluded those cases, no statistically significant association between metronidazole and the risk of developing CD was observed [AOR, 1.94; 95% CI, 0.73–5.14]. Table 2. Univariate and multivariate analysis of associations between various factors and new-onset IBD Variable  Inflammatory bowel disease  Unadjusted  Adjusted†  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  p-value  OR  95% CI  p-value  Age [per 1 year]      1.00  1.00–1.00  1.00  1.00  0.99–1.00  0.16  Male [n]  405  810  1.00  0.84–1.19  1.00  1.24  1.01–1.51  0.04  Current smoking [n]  123  229  0.92  0.72–1.17  0.48  –  –  –  Former smoking [n]  150  152  2.22  1.74–2.84  <0.001  2.18  1.64–2.90  <0.001  1st degree family history of IBD [n]  42  3  29.6  9.15–95.9  <0.001  27.3  9.58–115.2  <0.001  Obesity [n]  221  371  1.20  0.98–1.47  0.08  1.14  0.92–1.42  0.22  Statin use [n]  20  63  0.62  0.37–1.04  0.08  0.46  0.25–0.80  0.01  Aspirin use [n]  33  59  1.12  0.73–1.74  0.60  –  –  –  Antibiotic use [n]  455  495  3.20  2.66–3.84  <0.001  2.93  2.40–3.58  <0.001  Variable  Inflammatory bowel disease  Unadjusted  Adjusted†  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  p-value  OR  95% CI  p-value  Age [per 1 year]      1.00  1.00–1.00  1.00  1.00  0.99–1.00  0.16  Male [n]  405  810  1.00  0.84–1.19  1.00  1.24  1.01–1.51  0.04  Current smoking [n]  123  229  0.92  0.72–1.17  0.48  –  –  –  Former smoking [n]  150  152  2.22  1.74–2.84  <0.001  2.18  1.64–2.90  <0.001  1st degree family history of IBD [n]  42  3  29.6  9.15–95.9  <0.001  27.3  9.58–115.2  <0.001  Obesity [n]  221  371  1.20  0.98–1.47  0.08  1.14  0.92–1.42  0.22  Statin use [n]  20  63  0.62  0.37–1.04  0.08  0.46  0.25–0.80  0.01  Aspirin use [n]  33  59  1.12  0.73–1.74  0.60  –  –  –  Antibiotic use [n]  455  495  3.20  2.66–3.84  <0.001  2.93  2.40–3.58  <0.001  OR, odds ratio; CI, confidence interval. †Age, sex, and any variables with a p < 0.1 in the univariate analysis were included in the multivariate analysis. View Large Table 3. Unadjusted and adjusted odds ratio for IBD stratified by class of antibiotic Antibiotic use  Inflammatory bowel disease  Unadjusted  Adjusted  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  OR  95% CI  Any antibiotic [n]  455  495  3.20  2.66–3.84  2.93  2.40–3.58  Penicillin [n]  246  228  2.74  2.22–3.37  2.53  2.02–3.18  Macrolide [n]  178  179  2.30  1.83–2.90  2.01  1.57–2.57  Cephalosporin [n]  107  94  2.49  1.86–3.34  2.29  1.68–3.12  Sulfonamide [n]  84  80  2.24  1.63–3.09  2.20  1.56–3.11  Quinolone [n]  75  73  2.17  1.56–3.04  2.10  1.47–3.01  Tetracycline [n]  55  46  2.50  1.67–3.74  2.10  1.37–3.22  Metronidazole [n]  22  26  1.71  0.96–3.04  1.84  0.99–3.38  Antibiotic use  Inflammatory bowel disease  Unadjusted  Adjusted  Cases [n = 736]  Controls [n = 1472]  OR  95% CI  OR  95% CI  Any antibiotic [n]  455  495  3.20  2.66–3.84  2.93  2.40–3.58  Penicillin [n]  246  228  2.74  2.22–3.37  2.53  2.02–3.18  Macrolide [n]  178  179  2.30  1.83–2.90  2.01  1.57–2.57  Cephalosporin [n]  107  94  2.49  1.86–3.34  2.29  1.68–3.12  Sulfonamide [n]  84  80  2.24  1.63–3.09  2.20  1.56–3.11  Quinolone [n]  75  73  2.17  1.56–3.04  2.10  1.47–3.01  Tetracycline [n]  55  46  2.50  1.67–3.74  2.10  1.37–3.22  Metronidazole [n]  22  26  1.71  0.96–3.04  1.84  0.99–3.38  IBD, inflammatory bowel disease; OR, odds ratio; CI, confidence interval. View Large Table 4. Unadjusted and adjusted odds ratio for Crohn’s disease and ulcerative colitis stratified by class of antibiotic Antibiotic use  Crohn’s disease  Unadjusted  Adjusted  Ulcerative colitis  Unadjusted  Adjusted  Cases [n = 339]  Controls [n = 678]  OR  95% CI  OR  95% CI  Cases [n = 397]  Controls [n = 794]  OR  95% CI  OR  95% CI  Any antibiotic [n]  212  241  3.03  2.31–3.97  3.01  2.27–4.00  243  254  3.35  2.61–4.31  2.94  2.23–3.88  Penicillin [n]  119  117  2.59  1.92–3.50  2.66  1.94–3.65  127  111  2.89  2.16–3.87  2.42  1.76–3.33  Macrolide [n]  87  88  2.31  1.66–3.22  2.28  1.61–3.21  91  91  2.30  1.67–3.16  1.90  1.33–2.71  Cephalosporin [n]  51  53  2.09  1.39–3.14  2.21  1.44–3.37  56  41  3.02  1.98–4.60  2.66  1.71–4.16  Sulfonamide [n]  40  30  2.89  1.77–4.73  2.81  1.67–4.75  44  50  1.85  1.21–2.84  2.00  1.26–3.17  Quinolone [n]  35  32  2.32  1.41–3.83  2.41  1.41–4.11  40  41  2.06  1.31–3.24  1.94  1.18–3.16  Tetracycline [n]  24  20  2.51  1.36–4.61  2.34  1.24–4.43  31  26  2.50  1.46–4.28  2.10  1.18–3.76  Metronidazole [n]  12  9  2.73  1.14–6.54  2.80  1.14–7.11  10  17  1.18  0.54–2.60  1.38  0.58–3.12  Antibiotic use  Crohn’s disease  Unadjusted  Adjusted  Ulcerative colitis  Unadjusted  Adjusted  Cases [n = 339]  Controls [n = 678]  OR  95% CI  OR  95% CI  Cases [n = 397]  Controls [n = 794]  OR  95% CI  OR  95% CI  Any antibiotic [n]  212  241  3.03  2.31–3.97  3.01  2.27–4.00  243  254  3.35  2.61–4.31  2.94  2.23–3.88  Penicillin [n]  119  117  2.59  1.92–3.50  2.66  1.94–3.65  127  111  2.89  2.16–3.87  2.42  1.76–3.33  Macrolide [n]  87  88  2.31  1.66–3.22  2.28  1.61–3.21  91  91  2.30  1.67–3.16  1.90  1.33–2.71  Cephalosporin [n]  51  53  2.09  1.39–3.14  2.21  1.44–3.37  56  41  3.02  1.98–4.60  2.66  1.71–4.16  Sulfonamide [n]  40  30  2.89  1.77–4.73  2.81  1.67–4.75  44  50  1.85  1.21–2.84  2.00  1.26–3.17  Quinolone [n]  35  32  2.32  1.41–3.83  2.41  1.41–4.11  40  41  2.06  1.31–3.24  1.94  1.18–3.16  Tetracycline [n]  24  20  2.51  1.36–4.61  2.34  1.24–4.43  31  26  2.50  1.46–4.28  2.10  1.18–3.76  Metronidazole [n]  12  9  2.73  1.14–6.54  2.80  1.14–7.11  10  17  1.18  0.54–2.60  1.38  0.58–3.12  OR, odds ratio; CI, confidence interval. View Large Subgroup analysis stratified by age group at IBD diagnosis showed that the association between any antibiotic use and the risk of IBD was statistically significant in all age groups. Those under 18 years receiving any antibiotic had an AOR for IBD of 4.27 [95% CI, 2.39–7.91], and the corresponding values at ages 18–60 years and > 60 years were 2.97 [2.36–3.75] and 2.72 [1.60–4.67], respectively. A dose-dependent relationship between antibiotic use and the risk of IBD development was observed. Stratifying the analysis by the number of courses of antibiotic use, the AOR for one to three courses was 2.61 [95% CI, 2.12–3.23], 4.61 for for to six courses [95% CI, 3.12–6.84] and 10.34 for seven or more courses [95% CI, 4.19–29.19]. When the analysis was stratified by the cumulative duration of antibiotic use, there were significant associations with the risk of IBD. The AOR for 1–14 days was 2.27 [95% CI, 1.78–2.89] and 2.64 for 15–29 days [95% CI, 1.94–3.61], while using antibiotics ≥ 30 days had the strongest AOR of 6.01 [95% CI, 4.34–8.45]. We also found evidence of dose-dependent relationships between antibiotic use and the risks of developing CD or UC, as shown in Table 5. Table 5. Adjusted odds ratio for IBD, CD and UC stratified by course, duration and year before diagnosis of antibiotic use Antibiotic use  IBD  CD  UC  Cases [n = 736]  Controls [n = 1472]  Adjusted OR  95% CI  Cases [n = 339]  Controls [n = 678]  Adjusted OR  95% CI  Cases [n = 397]  Controls [n = 794]  Adjusted OR  95% CI  No use [n]  281  977  Ref    127  437  Ref    154  540  Ref    Any use [n]  455  495  2.93  2.40–3.58  212  241  3.01  2.27–4.00  243  254  2.94  2.23–3.88  Courses  1–3 courses [n]  357  436  2.61  2.12–3.23  160  206  2.65  1.97–3.58  197  230  2.65  1.99–3.53  4–6 courses [n]  77  53  4.61  3.12–6.84  38  31  4.37  2.58–7.45  39  22  5.23  2.9 2–9.56  ≥ 7 courses [n]  21  6  10.34  4.19–29.19  14  4  11.89  4.00–43.6  7  2  10.88  2.49–75.17  Duration†  1–14 days [n]  208  293  2.27  1.78–2.89  91  136  2.17  1.53–3.07  117  157  2.30  1.66–3.18  15–29 days [n]  19  130  2.64  1.94–3.61  51  62  2.98  1.92–4.59  58  68  2.66  1.73–4.08  ≥ 30 days [n]  138  72  6.03  4.34–8.45  70  43  5.89  3.80–9.23  68  29  7.07  4.31–11.83  Year before diagnosis  1 year [n]  187  174  3.60  2.75–4.72  91  92  3.32  2.30–4.81  96  82  3.65  2.50–5.35  2 years [n]  101  140  2.44  1.79–3.30  50  67  2.74  1.78–4.21  51  73  2.37  1.54–3.63  3 years [n]  71  79  2.62  1.79–3.81  33  34  2.98  1.73–5.14  38  45  2.75  1.65–4.57  4 years [n]  53  51  3.00  1.94–4.63  20  26  2.69  1.41–5.06  33  25  3.26  1.80–5.94  > 4 years [n]  43  51  2.60  1.63–4.11  18  22  2.93  1.48–5.72  25  29  2.42  1.31–4.43  Antibiotic use  IBD  CD  UC  Cases [n = 736]  Controls [n = 1472]  Adjusted OR  95% CI  Cases [n = 339]  Controls [n = 678]  Adjusted OR  95% CI  Cases [n = 397]  Controls [n = 794]  Adjusted OR  95% CI  No use [n]  281  977  Ref    127  437  Ref    154  540  Ref    Any use [n]  455  495  2.93  2.40–3.58  212  241  3.01  2.27–4.00  243  254  2.94  2.23–3.88  Courses  1–3 courses [n]  357  436  2.61  2.12–3.23  160  206  2.65  1.97–3.58  197  230  2.65  1.99–3.53  4–6 courses [n]  77  53  4.61  3.12–6.84  38  31  4.37  2.58–7.45  39  22  5.23  2.9 2–9.56  ≥ 7 courses [n]  21  6  10.34  4.19–29.19  14  4  11.89  4.00–43.6  7  2  10.88  2.49–75.17  Duration†  1–14 days [n]  208  293  2.27  1.78–2.89  91  136  2.17  1.53–3.07  117  157  2.30  1.66–3.18  15–29 days [n]  19  130  2.64  1.94–3.61  51  62  2.98  1.92–4.59  58  68  2.66  1.73–4.08  ≥ 30 days [n]  138  72  6.03  4.34–8.45  70  43  5.89  3.80–9.23  68  29  7.07  4.31–11.83  Year before diagnosis  1 year [n]  187  174  3.60  2.75–4.72  91  92  3.32  2.30–4.81  96  82  3.65  2.50–5.35  2 years [n]  101  140  2.44  1.79–3.30  50  67  2.74  1.78–4.21  51  73  2.37  1.54–3.63  3 years [n]  71  79  2.62  1.79–3.81  33  34  2.98  1.73–5.14  38  45  2.75  1.65–4.57  4 years [n]  53  51  3.00  1.94–4.63  20  26  2.69  1.41–5.06  33  25  3.26  1.80–5.94  > 4 years [n]  43  51  2.60  1.63–4.11  18  22  2.93  1.48–5.72  25  29  2.42  1.31–4.43  IBD, inflammatory bowel disease; CD, Crohn’s disease; UC, ulcerative colitis; OR, odds ratio; CI, confidence interval; Ref, reference. †Cumulative duration of antibiotic use 3 months to 5 years before diagnosis. View Large The significant association between antibiotic use and the risk of IBD remained regardless of year of antibiotic use before IBD diagnosis. For each individual year, the associations were statistically significant with AORs of 3.60 [95% CI, 2.75–4.72] at 1 year, 2.44 [1.79–3.30] at 2 years, 2.62 [1.79–3.81] at 3 years, 3.00 [1.94–4.63] at 4 years and 2.60 [1.63–4.11] at > 4 years before IBD diagnosis. These associations were observed in both CD and UC as well [Table 5]. 4. Discussion The results of this population-based case-control study demonstrate a strong association between antibiotic use and the risk of either new-onset CD or new-onset UC. In addition, significant dose-dependent relationships between antibiotic use and the development of CD and UC were observed. This relationship existed irrespective of age groups and presented across 5 years before IBD diagnosis. The majority of antibiotic classes were associated with an increased risk of developing CD and UC. Our findings corroborate previous studies from the UK20 and Canada.16,27 Card et al. used the General Practice Research Database and illustrated the association between antibiotic use and the development of CD.20 They showed that antibiotic use increased the odds of CD by a factor of over 30% [AOR, 1.32; 95% CI, 1.05–1.65]. Shaw et al. performed a case-control study using the University of Manitoba IBD Epidemiology Database.16 The authors found that the odds of IBD for those receiving antibiotic dispensation at 2–5 years before diagnosis was 1.29 [95% CI, 1.18–1.40] for CD and 1.26 [1.16–1.36] for UC. A dose-dependent relationship was shown. At 2 years before diagnosis, those receiving one or more and two or more antibiotic dispensations had a 1.27- and 1.48-fold increase in the odds of CD, respectively. The AOR of UC was 1.62 for three of more antibiotic dispensations. When stratifying by age groups, significant associations between antibiotic use within 2–5 years and adult-onset IBD were identified [AOR 1.32 for age 19–65 years and 1.25 for > 65 years].16 In a Manitoba nested case-control study, antibiotic use in the first year of life was associated with a 2.9-fold increased risk of new-onset IBD in childhood [age < 16 years].27 Despite the use of large administrative databases, studies that rely on drug dispensation codes have limitations, in particular confounding factors. Important potential confounding factors such as family history of IBD and cigarette smoking status use were unavailable. Furthermore, the database could not verify that a dispensed antibiotic was actually consumed. A key strength of this study is that all incident IBD cases and controls were derived from a well-defined geographical region and mostly received the same healthcare provision. More than 95% of Olmsted County residents receive medical care from either Mayo Clinic Medical Center or Olmsted Medical Center. Because of the medical record linkage system [Rochester Epidemiology Project], which provides access to all local medical records, we were able to review data from all available patients from 5 years before the index date until the end of the study period. We were able to verify the data regarding antibiotic consumption and accounted for other confounding factors. However, this study also had some limitations. First, because of its retrospective nature, any data not recorded in the medical records would have been missed. Secondly, socioeconomic factors are associated with hygiene status and healthcare utilization. The socioeconomic status of Olmsted County residents is slightly higher than that of the Upper Midwest population and the entire US population.23 In 2000, 91% of Olmsted County residents were highschool graduates compared to 86% of the Upper Midwest population and 80% of the entire US population.23 Median household income among Olmsted County residents [$51316] was higher than that of the Upper Midwest population [$43200] and of the entire US population [$41994].23 The relatively high socioeconomic status in the region may limit the generalizability of these data to populations with different socioeconomic characteristics. Thirdly, Olmsted County had less ethnic diversity than the US population [90% vs. 75% white] between 1970 and 2000.23 However, this gap has narrowed: 85.7% of county residents were whites in 2010 compared with 74.8% of the US population.28 This change is similar to other Western countries. For example, according to the Office for National Statistics of the United Kingdom, the percentage of whites in England and Wales decreased from 94.1% in 1991 to 86% in 2011.29 Although there were changes in the ethnic diversity of the Olmsted County population, the generalization of these study results to populations with more heterogeneous ethnicity may not be applicable. It is unclear why antibiotic use was associated with new-onset IBD. Our findings may support a role of microbiome alterations in the pathogenesis of IBD. Previous studies in healthy humans have suggested that antibiotic exposure perturbs the gut microbiota composition,30,31 and repeated antibiotic exposure results in persistent dysbiosis.32,33 Many studies consistently reported changes in the total numbers and diversity of the gut microbiota in IBD patients.34 For instance, increases in the numbers of adherent-invasive Escherichia coli, Enterobacteriaceae and Fusobacteriaceae, and decreases in the numbers of Bacteroides, Bifidobacteriaceae and Clostridia have been reported in IBD patients.1 In addition to quantitative changes in the bacteria comprising the gut microbiota, antibiotics may alter the functional composition of the gut microbiota, as well.35 Recently, a longitudinal paediatric IBD cohort study has shown an increased gut microbial dysbiosis index in new-onset IBD patients when compared to healthy controls.36 In CD patients, the dysbiosis index at diagnosis was predictive of subsequent disease severity defined by the Pediatric CD Activity Index.36 Although we found a strong association and dose–response relationship between antibiotic use and the development of IBD, a possible triggering role of antibiotics in the onset of IBD should be interpreted with caution. First, not all classes of antibiotics have the same local effect on the gut microbiome. Metronidazole, which directly targets specific gut bacteria, showed the strongest association with the approximately three-fold increased risk for developing IBD in the Manitoba study.16 In contrast to the Manitoba study, an increased risk for IBD in metronidazole was not observed in our study. The relatively small number of metronidazole users in our study may have contributed to the lack of statistical significance. For other classes of antibiotics, an approximately two-fold increased risk of IBD among each class of antibiotic [i.e. penicillins, cephalosporins, quinolones, sulfonamides, macrolides and tetracyclines.] was observed. Our results are line with previous studies. The Manitoba study showed significant associations with all types of antibiotics except clindamycin, with AORs of 1.12–2.86.16 In a large retrospective cohort study from the UK, tetracycline use for acne therapy was associated with IBD development (hazard ratio [HR], 1.39; 95% CI, 1.02–1.90].37 Secondly, we examined antibiotic usage for 5 years prior to IBD diagnosis. We did not examine earlier periods or during the first year of life, which theoretically is the important period for development and adaptation of the gut microbiota.1 Thirdly, antibiotic use may act as a surrogate marker for infection, leading to IBD development. A previous study found that the HR of developing IBD was 2.4 [95% CI, 1.7–3.3] in patients with acute gastroenteritis.38 Hypothetically, either gastrointestinal tract infection or antibiotic use could be confounding factors. However, we found that only 3.9% of the IBD cases had a gastrointestinal tract infection. More than 70% of infections were ear, nose, throat and respiratory tract infections. These infection sites are concordant with a previous study of the association between antibiotic use in the first year of life and childhood-onset IBD.27 In conclusion, antibiotic use was an independent risk factor for the development of both CD and UC in this population-based nested case-control study. The risk was increased among all age-onset of IBD. Further longitudinal prospective studies investigating the infection and antibiotic use in parallel with gut microbiome analysis in IBD are warranted to unravel the pathogenesis of IBD. Funding This work was supported in part by the Mayo Foundation for Medical Education & Research, and the Rochester Epidemiology Project [grant number R01 AG034676 from the National Institute on Aging of the National Institutes of Health]. The contents of the publication are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health. Conflict of Interest None of the authors has any relevant conflicts of interests. Author Contributions SA: writing the proposal, submitting the proposal to Institutional Review Board, collecting the data, analysing the data, and drafting of the manuscript. WJT, LER and SVK: critical revision of the manuscript. EVL: conception and design of the study, critical revision of the manuscript. All authors provided final approval of the version to be submitted. Acknowledgments We are grateful to W. Scott Harmsen M.S. for identifying matched controls from the Olmsted County residents. References 1. Sheehan D , Shanahan F. The gut microbiota in inflammatory bowel disease. Gastroenterol Clin North Am  2017; 46: 143– 54. Google Scholar CrossRef Search ADS PubMed  2. Thompson NP , Driscoll R, Pounder RE, Wakefield AJ. 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Journal of Crohn's and ColitisOxford University Press

Published: Feb 1, 2018

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