Abstract
Introduction
Obesity is associated with intestinal-specific inflammation. Nonetheless, a specific role of obesity in the etiology of inflammatory bowel disease (IBD) is unclear.
Methods
We conducted a prospective cohort study of U.S. women enrolled in 1989 in the Nurses’ Health Study II. At baseline, we collected information on height, weight, waist and hip circumference, weight at age 18, and body shape at age 20. We used Cox proportional hazards models to calculate hazard ratios (HR) and 95% confidence intervals (CIs).
Results
Among 111,498 women (median age, 35 years), we documented 153 cases of CD and 229 cases of UC over 18 years of follow-up, encompassing 2,028,769 person-years. Compared to women with normal BMI, the multivariate-adjusted HRs of CD were2.33 (95% CI, 1.15–4.69) for obese women at age 18 and 1.58 (95% CI, 1.01–2.47) for obese women at baseline. Increasing weight gain between age 18 and baseline was associated with increased risk of CD (Ptrend=.04). Adolescent body habitus was also associated with risk of CD with a multivariate-adjusted HR of CD of 1.63 (95% CI, 1.07–2.50) for women with overweight/obese body shape compared to women with a thin/slender body shape. We did not observe a significant association between any of these anthropometric measures and risk of UC.
Conclusion
In a large prospective cohort of US women, measures of adiposity were associated with an increased risk of CD but not UC. Further studies are needed to elucidate the biological mechanisms by which excess adiposity may increase the risk of CD.
Keywords: Inflammatory Bowel Disease, Crohn’s Disease, Ulcerative Colitis, Obesity, Body Mass Index, Waist to Hip Ratio, Nurses’ Health Study II
Introduction
Despite the success of genome wide association studies in identifying large number of common variants associated with Crohn’s disease (CD) and ulcerative colitis (UC),1 the exact pathophysiology of the disease remains largely unknown. As the overall risk contribution of these common variants is only modest,1 identification of novel environmental risk factors with plausible biologic link to disease risk is vital to improve our understanding of disease pathophysiology.
Obesity as measured by body mass index (BMI) has been linked to elevated levels of proinflammatory markers such as tumor necrosis factor-alpha (TNF-α) and C-reactive protein (CRP).1,2 More specifically, obese individuals have been shown to have higher levels of inflammation in the gastrointestinal tract as measured by stool calprotectin.3 Despite the presence of these compelling data, epidemiologic studies have failed to identify a consistent link between obesity and risk of CD and UC. Previous studies have had significant limitations including retrospective design, inability to adjust for important life style factors, and lack of detailed and updated information on BMI and other measures of obesity.
We therefore sought to investigate the association between measures of obesity and risk of CD and UC in the Nurses Health Study (NHS) II. With more than 20 years of biennially updated and validated data on lifestyle, diet, and medical diagnoses, this cohort offered us the unique opportunity to examine the association between measures of obesity assessed at multiple time points over adulthood and subsequent risk of UC and CD in the context of other potential risk factors.
METHODS
Study Population
The NHSII is a prospective cohort that began in 1989 when 116,686 U.S. female registered nurses, ages 25 to 42 years, completed a mailed health questionnaire. Follow-up questionnaires are mailed every two years to update health information; follow-up has consistently exceeded 90%. A validated assessment of physical activity is administered every two to four years; a validated semi-quantitative food frequency questionnaire is administered every four years. The institutional review board at the Brigham and Women’s Hospital approved this study.
Assessment of measures of obesity
Information on weight and height and weight at the age of 18 was obtained from the baseline 1989 questionnaire. Participants then updated their current weight on each biennial questionnaire. BMI at age 18, baseline, and every 2 years since baseline was calculated using the corresponding weight and height at baseline. Updated BMI was defined as most recent BMI reported prior to each 2-year interval (time between two consecutive questionnaires). Weight change since the age of 18 was calculated by subtracting the baseline weight from the weight at age 18. Information on body habitus at multiple time points early in life (age 5, 10, and 20) was also reported in the baseline questionnaire using a nine-level pictogram depicting body shapes ranging from 1=most lean to 9=most overweight.
Waist and hip measurements were reported in the 1993 questionnaire. For waist circumference, participants were provided a tape measure and instructed to measure their waist circumference at the level of the navel, and for hip circumference they were instructed to measure the largest circumference around the hips (including the buttocks). 44,291 participants measured their hip and waist sizes. Waist to hip ratio was calculated by dividing waist circumference over hip circumference.
The accuracy of self-reported anthropometric measures was evaluated among 140 NHS participants by having trained technicians visit those participants twice.4 After adjustment for age and within-person variability, the Pearson correlation coefficient between self-report and the average of the 2 technician measurements was 0.97 for weight, 0.89 for waist circumference, and 0.84 for hip circumference. In addition, recalled weight at age 18 and self-reported height were compared with records obtained from physical examination conducted at college or nursing school entrance in a study of 118 participants in the NHSII.5 The correlation between recalled and measured past weight was 0.87 and between reported current height and measured past height was 0.94.
Assessment of Other Covariates
On each biennial questionnaire, women were also asked about pertinent lifestyle factors, including smoking status, and use of non-steroidal anti-inflammatory drugs (NSAIDs), hormone therapy, and oral contraceptive use. Information on physical activity was also collected every 2–4 years. Participants’ self-report of physical activity and use of NSAIDs and oral contraceptives have been previously validated.5,6 In 1991 in NHSII, women were also asked their latitude of residence at age 30, which we have previously shown to be associated with risk of UC and CD.7 Information about history of appendectomy was collected in 1995 in NHSII. Intakes of dietary fiber and vitamin D were assessed using validated, self-administered, semi quantitative food frequency questionnaires (FFQs) administered in 1991, 1995, 1999, 2003, and 2007 in NHSII. A previous validation study from NHS showed a correlation of 0.61 between dietary fiber intake measured by FFQ and weighted records.8
Outcome Ascertainment
We have previously detailed our methods for confirming self-reported cases of CD and UC.7,9 In brief, since 1989, participants have reported diagnoses of UC or CD through an open-ended response on biennial surveys. In addition, we have specifically queried participants about diagnoses of both UC and CD since 1993. When a diagnosis was reported on any biennial questionnaire, a supplementary questionnaire and related medical records were requested and reviewed by two gastroenterologists blinded to exposure information.
We excluded participants who subsequently denied the diagnosis on the supplementary questionnaire or permission to review their records. Data were extracted on diagnostic tests, histopathology, anatomic location of disease, and disease behavior. Using standardized criteria,10–13 UC diagnosis was based on a typical clinical presentation ≥ 4 weeks and endoscopic or surgical pathological specimen consistent with UC (e.g. evidence of chronicity). CD diagnosis was based on a typical clinical history for ≥ 4 weeks and endoscopy or radiologic evaluation demonstrating small bowel findings, or surgical findings consistent with CD combined with pathology suggesting transmural inflammation or granuloma. Disagreements were resolved through consensus. Among those women whom we received adequate medical records, the case confirmation rate for IBD was 78%.9 Women for whom we did not confirm CD or UC were included in the analyses as non-cases. After excluding all cases of CD and UC at the baseline questionnaire and among women with missing physical activity information, we included 153 incident cases of CD and 229 incident cases of UC. These incidence rates are largely consistent with those reported in other U.S. populations.14–16
Statistical Analysis
We examined the possibility that there is a non-linear association between obesity and risk of CD and UC using a previously reported non-parametic cubic spline method,17 which also permits controlling for covariates and stepwise selection among spline variables. This analysis provides p-values from the likelihood ratio tests for non-linearity, a linear relation, and any relation. Using this method, the likelihood ratio tests comparing models with linear terms with those with spline terms were not statistically significant (All Pcomparisons> 0.50), indicating that the relationship between obesity and risk of CD and UC is linear. As these analyses are particularly sensitive to outliers we performed further sensitivity analyses removing observations beyond 3 interquartile ranges and confirmed that the likelihood ratio tests comparing models with linear terms with those with spline terms were not statistically significant (All Pcomparisons> 0.40). Based on these findings, we used clinical categories of overweight and obesity for our main analyses. In analyses of waist to hip ratio, because of our limited sample size, we used quartile categories to ensure an even distribution of participants in each category.
Person-time for each participant was calculated from the date of return of their baseline questionnaires to the date of the diagnosis of UC or CD, date of last returned questionnaire, or June 1, 2009, whichever came first. At baseline, we excluded participants with missing anthropometric measurements, history of IBD or cancer (with the exception of non-melanoma skin). We used Cox proportional hazards modeling with time-varying covariates to adjust for other known or suspected risk factors including age, smoking, oral contraceptives use, geographic latitude of residence at age 30, physical activity (Met-hr/week), regular use of NSAID’s, menopausal hormone therapy, dietary fat, fiber, and vitamin D intake and appendectomy prior to each 2-year interval to calculate adjusted hazard ratios (HR) and 95% confidence interval (CIs). The multivariate-adjusted HR of CD and UC according to all covariates included in the models are reported in Supplementary Table 1. All P-values were 2-sided and < 0.05 was considered statistically significant.
RESULTS
Through 2009, we documented 153 incident cases of CD and 229 incident cases of UC among 111,498 women who contributed 2,028,769 person-years of follow up. The median time from enrollment to diagnosis were 9.7 years for CD and 9.0 years for UC cases At baseline, compared to women with BMI of 20–24.9 kg/m2, obese women (BMI ≥ 30 kg/m2) tended to be older, were more likely to have had an appendectomy, use NSAIDs, have a obese body shape at age 20, and have a higher WHR and BMI at age 18 (Table 1). As expected, obese women in average had a lower level of physical activity. There were no significant differences according to geographic latitude of residence at age 30 or use of menopausal hormones or oral contraceptives.
Table 1.
Baseline Characteristics of Study Participants According to Body Mass Index in 1989*
| Body mass index (kg/m2) | |||||
|---|---|---|---|---|---|
| < 20 N = 17,196 |
20–24.9 N = 60,685 |
25–29.9 N = 20,817 |
≥ 30 N = 12,800 |
||
| Age (years), means (SD) | 33.7 (4.6) | 34.7 (4.6) | 35.4 (4.6) | 35.9 (4.5) | |
| Race (non-white), % | 4.0 | 5.0 | 5.0 | 4.0 | |
| Geographic residence at age 30, % | |||||
| Southern latitude | 18 | 16 | 15 | 15 | |
| Smoking, % | |||||
| Never | 67 | 65 | 65 | 66 | |
| Past | 19 | 22 | 21 | 21 | |
| Current | 14 | 13 | 14 | 13 | |
| Body mass index at age 18 (kg/m2), means (SD) | 18.8 (1.7) | 20.6 (2.3) | 22.5 (3.1) | 25.7 (4.8) | |
| Waist to hip ratio, means (SD) | 0.76 (0.07) | 0.77 (0.07) | 0.81 (0.08) | 0.84 (0.09) | |
| Body shape at age 20, % | |||||
| Thin/slender | 98 | 93 | 86 | 60 | |
| Overweight/obese | 2 | 7 | 14 | 40 | |
| Physical activity (Mets-hr/week), means (SD) | 29.2(42.5) | 26.1(37.5) | 21.5(31.6) | 17.7(28.7) | |
| Ever use of oral contraceptives, % | 83 | 84 | 83 | 80 | |
| Regular use of NSAIDs, % | |||||
| ≥ 2 tablets/wk | 15 | 18 | 22 | 28 | |
| Premenopausal, % | 95 | 94 | 93 | 92 | |
| Menopausal hormone therapy§, % | |||||
| Never Use | 40 | 50 | 43 | 43 | |
| Past Use | 20 | 17 | 14 | 14 | |
| Current Use | 40 | 33 | 43 | 43 | |
| Appendectomy, % | 11 | 12 | 14 | 16 | |
Abbreviations: Standard Deviation (SD).
Values are expressed as means (standard deviation) or percentages.
Characteristics based upon data from baseline questionnaire in 1989 with the exception of race and waist to hip ratio, which were derived from the 1993 questionnaire.
Percentages among postmenopausal women.
Using BMI at age 18, compared to women with BMI of 20–24.9 kg/m2, the age-adjusted HR of developing CD subsequently in adulthood was 1.01 (95% CI, 0.54–1.91) for overweight women (BMI of 25–29.9 kg/m2) and 2.48 (95% CI, 1.24–4.98) for obese women (BMI ≥ 30 kg/m2) (Table 2). This risk estimate was not significantly altered after adjusting for known and potential risk factors for CD including physical activity, smoking, appendectomy, and use of oral contraceptives, NSAIDs, and menopausal hormone therapy (multivariate-adjusted HR = 2.33, 95% CI 1.15–4.69 for obese women). In analyses in which we examined either BMI at baseline in 1989 or the most recent updated BMI before each two-year follow-up interval, we observed similar findings. Using baseline BMI, compared to women with BMI of 20–24.9 kg/m2, the multivariate adjusted HR of CD was 0.88 (95% CI, 0.56–1.39) for overweight and 1.58 (95% CI, 1.01–2.47) for obese women. Similarly using updated BMI, compared to women with BMI of 20–24.9 kg/m2, the multivariate adjusted HR of CD was 1.04 (95% CI, 0.68–1.59) for overweight and1.48 (95% CI, 0.98–2.23) for obese women.
Table 2.
Body Mass Index and Risk of Crohn’s Disease (1989–2009)*
| Body mass index (kg/m2) | ||||
|---|---|---|---|---|
| < 20 | 20–24.9 | 25–29.9 | ≥ 30 | |
| Body mass index at age 18 | ||||
| Person-years of follow-up | 798,733 | 1,022,097 | 157,742 | 50,197 |
| No. of cases | 62 | 71 | 11 | 9 |
| Age-adjusted HR (95% CI) | 1.11 (0.79–1.56) | 1.00 | 1.01 (0.54–1.91) | 2.48 (1.24–4.98) |
| Multivariate-adjusted HR (95% CI)§ | 1.14 (0.81–1.60) | 1.00 | 0.98 (0.52–1.85) | 2.33 (1.15–4.69) |
| Body mass index at baseline in 1989 | ||||
| Person-years of follow-up | 320,551 | 1,107,322 | 373,867 | 227,028 |
| No. of cases | 19 | 82 | 25 | 27 |
| Age-adjusted HR (95% CI) | 0.78 (0.47–1.29) | 1.00 | 0.93 (0.59–1.46) | 1.68 (1.09–2.60) |
| Multivariate-adjusted HR (95% CI)§ | 0.79 (0.48–1.35) | 1.00 | 0.88 (0.56–1.39) | 1.58 (1.01–2.47) |
| Updated body mass index | ||||
| Person-years of follow-up | 181,993 | 919,915 | 507,739 | 419,121 |
| No. of cases | 16 | 59 | 35 | 43 |
| Age-adjusted HR (95% CI) | 1.35 (0.77–2.35) | 1.00 | 1.09 (0.72–1.67) | 1.60 (1.07–2.39) |
| Multivariate-adjusted HR (95% CI)§ | 1.37 (0.78–2.39) | 1.00 | 1.04 (0.68–1.59) | 1.48 (0.98–2.23) |
Abbreviations: Hazard Ratio (HR), Confidence Interval (CI)
Models adjusted for age (months), smoking (never, past, current), physical activity at baseline in quintile (MET-hours/week), oral contraceptive use (never, past, current), menopausal hormone therapy (never, past, current, premenopause), appendectomy (no, yes), geographic latitude of residence at age 30 (southern, middle, northern, missing/unknown), and NSAID’s use (< 2 tablets/week, ≥ 2 tablets/week).
In sensitivity analyses, we additionally adjusted our models for dietary intake of fat, fiber, and vitamin D which have been previously associated with risk of IBD in our cohorts.18–20 We limited our analyses to follow up after 1991 at which point dietary data were collected in the NHSII. Compared to women with BMI of 20–24.9 kg/m2 at baseline and age 18, the multivariate-adjusted HRs of CD were 1.57 (95% CI, 1.00–2.45) for obese women at baseline and 2.33 (95% CI, 1.15–4.69) for obese women at age 18 after additionally adjusting for total fiber and fat intakes.
In contrast, we did not observe an association between BMI at age 18, baseline BMI, and updated BMI and risk of UC (Table 3). Compared to women with BMI of 20–24.9 kg/m2 at age 18, the multivariate-adjusted HR of UC for obese women was 1.17 (95% CI, 0.54–2.52). Similarly, compared to women with BMI of 20–24.9 kg/m2, the multivariate-adjusted HRs of UC for obese women were 0.99 (95% CI, 0.64–1.53) for baseline BMI and 0.85 (95% CI, 0.58–1.23) for updated BMI.
Table 3.
Body Mass Index and Risk of Ulcerative Colitis (1989–2009)*
| Body mass index (kg/m2) | ||||
|---|---|---|---|---|
| < 20 | 20–24.9 | 25–29.9 | ≥ 30 | |
| Body mass index at age 18 | ||||
| Person-years of follow-up | 798,733 | 1,022,097 | 157,742 | 50,197 |
| No. of cases | 88 | 121 | 13 | 7 |
| Age-adjusted HR (95% CI) | 0.93 (0.71–1.23) | 1.00 | 0.69 (0.39–1.23) | 1.18 (0.55–2.52) |
| Multivariate-adjusted HR (95% CI)§ | 0.95 (0.72–1.25) | 1.00 | 0.69 (0.39–1.22) | 1.17 (0.54–2.52) |
| Body mass index at baseline in 1989 | ||||
| Person-years of follow-up | 320,551 | 1,107,322 | 373,867 | 227,028 |
| No. of cases | 40 | 130 | 34 | 25 |
| Age-adjusted HR (95% CI) | 1.06 (0.75–1.52) | 1.00 | 0.78 (0.54–1.14) | 0.96 (0.63–1.48) |
| Multivariate-adjusted HR (95% CI)§ | 1.09 (0.76–1.55) | 1.00 | 0.78 (0.54–1.15) | 0.99 (0.64–1.53) |
| Updated body mass index | ||||
| Person-years of follow-up | 181,993 | 919,915 | 507,739 | 419,121 |
| No. of cases | 21 | 108 | 59 | 41 |
| Age-adjusted HR (95% CI) | 0.98 (0.61–1.56) | 1.00 | 1.01 (0.73–1.39) | 0.85 (0.59–1.23) |
| Multivariate-adjusted HR (95% CI)§ | 1.00 (0.63–1.61) | 1.00 | 1.00 (0.73–1.38) | 0.85 (0.58–1.23) |
Abbreviations: Hazard Ratio (HR), Confidence Interval (CI)
Models adjusted for age (months), smoking (never, past, current), physical activity at baseline in quintile (MET-hours/week), oral contraceptive use (never, past, current), menopausal hormone therapy (never, past, current, premenopause), appendectomy (no, yes), geographic latitude of residence at age 30 (southern, middle, northern, missing/unknown), and NSAID’s use (< 2 tablets/week, ≥ 2 tablets/week).
We also explored the association between change in weight from age 18 to age at baseline enrollment and risk of UC and CD (Table 4). Higher weight gain was associated with increased risk of CD (Ptrend = 0.04). Specifically, compared to women who maintained their weight (within 5 lbs), women who gained more than 30 lbs had a multivariate-adjusted HR of 1.52 (95% CI, 0.87–2.65). In contrast, we did not observe an association between weight change (from age 18 to baseline) and risk of UC (Ptrend = 0.17). Compared to women who maintained their weight (within 5 lb) from age 18 to baseline in the cohort, the multivariate-adjusted HR of UC for women who gained more than 30 lbs was 0.92 (95% CI, 0.60–1.40).
Table 4.
Weight Change and Risk of Ulcerative Colitis and Crohn’s Disease (1989–2009)*
| Weight change (lbs) | ||||||
|---|---|---|---|---|---|---|
| < −5 | ≥ −5 and < 5 | ≥ 5 and < 15 | ≥ 15 and < 30 | ≥ 30 | Ptrend | |
| Person-years of follow-up | 200,825 | 311,881 | 559,090 | 500,798 | 449,068 | |
| Crohn’s Disease | ||||||
| No. of cases | 13 | 19 | 37 | 41 | 43 | |
| Age-adjusted HR (95% CI) | 1.07 (0.53–2.16) | 1.00 | 1.10 (0.63–1.91) | 1.39 (0.80–2.40) | 1.68 (0.97–2.90) | 0.03 |
| Multivariate-adjusted HR (95% CI)§ | 0.96 (0.46–2.00) | 1.00 | 1.08 (0.62–1.88) | 1.31 (0.76–2.27) | 1.52 (0.87–2.65) | 0.04 |
| Ulcerative Colitis | ||||||
| No. of cases | 31 | 41 | 57 | 49 | 51 | |
| Age-adjusted HR (95% CI) | 1.17 (0.73–1.86) | 1.00 | 0.79 (0.53–1.18) | 0.75 (0.49–1.14) | 0.88 (0.58–1.34) | 0.15 |
| Multivariate-adjusted HR (95% CI)§ | 1.24 (0.76–2.02) | 1.00 | 0.78 (0.52–1.17) | 0.75 (0.49–1.14) | 0.92 (0.60–1.40) | 0.17 |
Abbreviations: Hazard Ratio (HR), Confidence Interval (CI).
Models adjusted for age (months), smoking (never, past, current), oral contraceptive use (never, past/current), physical activity at baseline in quintile (MET-hours/week), BMI at age 18 (< 20, 20–24.9, 25–29.9, ≥ 30 kg/m2), menopausal hormone therapy (premenopause, never, current, past).
We evaluated the association between abdominal obesity and risk of CD and UC (Table 5 and Supplementary Table 2). Compared to women in the lowest quartile of WHR, the multivariate-adjusted HR of CD were1.15 (95% CI, 0.53–2.49) for women in the second quartile, 1.54 (95% CI, 0.79–3.01) for women in the third quartile, and 1.58 (0.78–3.21) for women in the highest quartile, although this trend did not reach statistical significance (Ptrend = 0.14). this trend did not reach statistical significance (Ptrend = 0.14). In contrast, there was no association of WHR with risk of UC. Compared to women in the lowest quartile of WHR, the multivariate-adjusted HR of UC for participants in the highest quintile was 1.44 (95% CI, 0.80–2.59, Ptrend= 0.37).
Table 5.
Measurement of Waist to Hip Ratio and Risk of Ulcerative Colitis and Crohn’s Disease (1993–2009)*
| ≤ 0.73 | 0.74–0.76 | 0.77–0.82 | ≥ 0.83 | ||
| Person-years of follow-up | 189,993 | 147,055 | 208,551 | 165,080 | |
| Crohn’s Disease | |||||
| No. of cases | 14 | 12 | 23 | 18 | |
| Age-adjusted HR (95% CI) | 1.00 | 1.13 (0.52–2.44) | 1.51 (0.78–2.94) | 1.51 (0.75–3.04) | 0.17 |
| Multivariate-adjusted HR (95% CI)§ | 1.00 | 1.15 (0.53–2.49) | 1.54 (0.79–3.01) | 1.58 (0.78–3.21) | 0.14 |
| Ulcerative Colitis | |||||
| No. of cases | 21 | 21 | 23 | 25 | |
| Age-adjusted HR (95% CI) | 1.00 | 1.34 (0.73–2.46) | 1.05 (0.58–1.90) | 1.39 (0.78–2.50) | 0.42 |
| Multivariate-adjusted HR (95% CI)§ | 1.00 | 1.37 (0.74–2.52) | 1.07 (0.59–1.95) | 1.44 (0.80–2.59) | 0.37 |
Abbreviations: Hazard Ratio (HR), Confidence Interval (CI).
Models adjusted for age (months), smoking (never, past, current), oral contraceptive use (never, past/current), physical activity at baseline in quintile (MET-hours/week), menopausal hormone therapy (premenopause, never, current, past).
Finally, we explored the association between adolescent body habitus and risk of CD and UC. Compared to women with a thin/slender body habitus at age 20, the multivariate-adjusted HR of CD for women with overweight/obese habitus was 1.63 (95% CI, 1.07–2.50). Conversely, we did not observe an association between body habitus at age 20 and risk of UC. Compared to women with a thin/slender body habitus at age 20, the multivariate-adjusted HR of UC for women with overweight/obese habitus was 0.90 (95% CI, 0.58–1.39).
DISCUSSION
In a large prospective cohort of US women, we found that obesity, as measured by BMI, is associated with increased risk of CD. Higher weight gain since age 18 and body habitus characteristic of overweight and obesity were also associated with increased risk of CD. Central obesity, as measured by WHR was also associated with non-statistically significant increased risk of CD. In contrast, we did not observe an association between any of these measures of adiposity and risk of UC.
Several lines of evidence suggest that an association between adiposity and CD maybe biologically plausible. First, adipocyte-derived mediators such as leptin, TNF-α, and IL-6 are known to be prominent pro-inflammatory mediators.21 Second, creeping fat or fat wrapping has long been recognized as marker of CD activity and is strongly correlated with muscular hypertrophy, fibrosis, and stricturing.22 Third, obesity is associated with increased levels of intestinal inflammation as measured by stool calprotectin.3 Fourth, obesity has been linked to alterations in the gut microbiome,23–27 which in turn likely plays a key role in pathogenesis of CD. Lastly, excess adiposity is associated with markers of increased intestinal permeability that may play a key role in development of CD.28 In addition, the specific finding of creeping fat in CD and the key role of adipose tissue in innate immunity may also explain the unique association between measures of obesity and risk of CD but not UC.29
Our findings are supported by previous studies. A previous case-control study showed that obesity was more common among CD patients from a gastroenterology clinic than community controls without IBD (OR = 3.22, 95% CI 1.59–6.52).30 In addition, numerous other studies have shown that obesity is associated with more complicated disease as reflected by earlier time to surgery and anoperineal complications among patients with established disease.31,32In contrast, a recent study33 using data from the European Prospective Investigation in Cancer (EPIC) cohort found no association between obesity as measured by BMI and risk of UC or CD. However, BMI was only assessed at baseline and the participants in this study were significantly older than our study population (median age at recruitment 50–53 years).A significant limitation of using BMI in such an older population is that it poorly reflects age-associated changes in fat distribution. Specifically, aging is associated with a redistribution of fat into visceral (omental and mesenteric), subcutaneous abdominal, intramuscular, and intrahepatic compartments, which are risk factors for insulin resistance and metabolic diseases associated with chronic inflammation.34 Therefore, BMI may not fully reflect the level of pro-inflammatory fat depots found in older adults.
Our study has several notable strengths. First, our prospective study design avoids the potential selection biases of retrospective, case-control studies that collect data on lifestyle after diagnosis of CD or UC. Second, we confirmed all cases of CD and UC through medical record review, a significant advantage over studies that rely on self-report or discharge codes, which may not accurately reflect true diagnoses. Third, the availability of detailed and validated information on physical activity, prior oral contraceptive use, and smoking allowed us to control for a number of potential risk factors that may have influenced our observed associations. Fourth, in our analysis, we collected information on BMI at age 18 and every two years staring at the time of enrollment and used time-varying exposures in our Cox models. Thus, we were able to account for changes in participants’ weight over much of their adulthood. In addition, we collected information on other anthropometrics including hip and waist measurement and body habitus allowing us to evaluate the link using other measures of obesity. Last, our validation studies4,5 demonstrated that reported and recalled weight, height, and waist and hip circumferences from our participants are relatively valid and reproducible.
We acknowledge several limitations. First, our cohort may not be representative of the overall US population. However, as we have previously reported,7 our age-specific incidence of CD and UC are largely similar to rates from other U.S. populations. In addition, previous studies have shown that the prevalence of risk factors, such as smoking and BMI, in our cohort are consistent with those of the broader population of U.S. women.35,36 Second, despite our high follow-up rate over 20 years and participants’ health literacy as nurses, it is possible that some cases of UC and CD were not reported. However, such misclassification of the outcome would tend to attenuate our risk estimates towards the null. Third, we acknowledge that since only a subset of participants had available information on waist and hip measurements, we had limited power to detect an association between central obesity and risk of UC or CD. In addition, the limited number of cases in the highest categories of BMI may have limited the robustness of our findings. Fourth, our study is observational and we cannot exclude the possibility of residual confounding. However, adjustment for the most important risk factors previously identified for UC and CD did not appreciably alter our results. Nevertheless, we acknowledge that in analyses in which we additionally adjusted for dietary factors, the magnitude of our effect estimates were consistent, albeit with lower precision as our confidence interval included one.
In conclusion, in a large prospective cohort of US women we found that obesity as measured by BMI and body habitus, particularly in early adulthood, as well as change in BMI since age 18, is associated with risk of CD but not UC. Future studies examining the exact mechanism by which obesity may influence the development of CD are warranted.
Supplementary Material
Acknowledgments
Grant Support: Funded by R01 CA137178, R01 CA050385, P01 CA87969, P30 DK043351, K23 DK099681, K08 DK064256, K24 098311, and K23 DK091742. Dr. Chan is supported by a senior investigator grant from the Crohn’s and Colitis Foundation of America (CCFA). Dr. Khalili is supported by a career development award from the American Gastroenterological Association (AGA) and by National Institute of Diabetes and Digestive and Kidney Diseases (K23 DK099681). Dr. Higuchi is supported by National Institute of Diabetes and Digestive and Kidney Diseases (K08 DK064256).
Dr. Ananthakrishnan is a member of the scientific advisory board for Prometheus Inc, and Janssen, Inc. Dr. Richter is a consultant for policy analysis. Dr. Chan has served as a consultant for Bayer Healthcare, Millennium Pharmaceuticals, Pfizer Inc., and Pozen Inc.
Footnotes
Financial Disclosures: Other authors have no financial disclosures.
Competing Interest: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
Ethical Approval: The institutional review board at the Brigham and Women’s Hospital approved this study.
Data sharing: Requests for access to data, statistical code, questionnaires, and technical processes may be made by contacting the corresponding author at hkhalilipartners.org.
Authors Contributions
HK - study concept and design; acquisition of data; statistical analysis; interpretation of data; drafting of the manuscript;
ANA- acquisition of data; critical revision of the manuscript.
GK - acquisition of data; critical revision of the manuscript.
LMH - acquisition of data; critical revision of the manuscript for important intellectual content.
JMR - study concept and design; acquisition of data; critical revision of the manuscript.
CSF- acquisition of data; critical revision of the manuscript.
ATC- study concept and design; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript.
REFERENCES
- 1.Greenfield JR, Samaras K, Jenkins AB, et al. Obesity is an important determinant of baseline serum C-reactive protein concentration in monozygotic twins, independent of genetic influences. Circulation. 2004 Jun 22;109(24):3022–3028. doi: 10.1161/01.CIR.0000130640.77501.79. [DOI] [PubMed] [Google Scholar]
- 2.Hotamisligil GS, Arner P, Caro JF, Atkinson RL, Spiegelman BM. Increased adipose tissue expression of tumor necrosis factor-alpha in human obesity and insulin resistance. J Clin Invest. 1995 May;95(5):2409–2415. doi: 10.1172/JCI117936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Poullis A, Foster R, Shetty A, Fagerhol MK, Mendall MA. Bowel inflammation as measured by fecal calprotectin: a link between lifestyle factors and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 2004 Feb;13(2):279–284. doi: 10.1158/1055-9965.epi-03-0160. [DOI] [PubMed] [Google Scholar]
- 4.Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC. Validity of self-reported waist and hip circumferences in men and women. Epidemiology. 1990 Nov;1(6):466–473. doi: 10.1097/00001648-199011000-00009. [DOI] [PubMed] [Google Scholar]
- 5.Troy LM, Hunter DJ, Manson JE, Colditz GA, Stampfer MJ, Willett WC. The validity of recalled weight among younger women. Int J Obes Relat Metab Disord. 1995 Aug;19(8):570–572. [PubMed] [Google Scholar]
- 6.Hunter DJ, Manson JE, Colditz GA, et al. Reproducibility of oral contraceptive histories and validity of hormone composition reported in a cohort of US women. Contraception. 1997 Dec;56(6):373–378. doi: 10.1016/s0010-7824(97)00172-8. [DOI] [PubMed] [Google Scholar]
- 7.Khalili H, Huang ES, Ananthakrishnan AN, et al. Geographical variation and incidence of inflammatory bowel disease among US women. Gut. 2012 Dec;61(12):1686–1692. doi: 10.1136/gutjnl-2011-301574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol. 1985 Jul;122(1):51–65. doi: 10.1093/oxfordjournals.aje.a114086. [DOI] [PubMed] [Google Scholar]
- 9.Khalili H, Higuchi LM, Ananthakrishnan AN, et al. Oral contraceptives, reproductive factors and risk of inflammatory bowel disease. Gut. 2012 Jun 19; doi: 10.1136/gutjnl-2012-302362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.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 Jun;114(6):1161–1168. doi: 10.1016/s0016-5085(98)70421-4. [DOI] [PubMed] [Google Scholar]
- 11.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 Mar;46(3):336–343. doi: 10.1136/gut.46.3.336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Fonager K, Sorensen HT, Rasmussen SN, Moller-Petersen J, Vyberg M. Assessment of the diagnoses of Crohn's disease and ulcerative colitis in a Danish hospital information system. Scand J Gastroenterol. 1996 Feb;31(2):154–159. doi: 10.3109/00365529609031980. [DOI] [PubMed] [Google Scholar]
- 13.Moum B, Vatn MH, Ekbom A, et al. Incidence of inflammatory bowel disease in southeastern Norway: evaluation of methods after 1 year of registration. Southeastern Norway IBD Study Group of Gastroenterologists. Digestion. 1995;56(5):377–381. doi: 10.1159/000201262. [DOI] [PubMed] [Google Scholar]
- 14.Herrinton LJ, Liu L, Lewis JD, Griffin PM, Allison J. Incidence and prevalence of inflammatory bowel disease in a Northern California managed care organization, 1996–2002. Am J Gastroenterol. 2008 Aug;103(8):1998–2006. doi: 10.1111/j.1572-0241.2008.01960.x. [DOI] [PubMed] [Google Scholar]
- 15.Loftus CG, Loftus EV, Jr, Harmsen WS, et al. Update on the incidence and prevalence of Crohn's disease and ulcerative colitis in Olmsted County, Minnesota, 1940–2000. Inflamm Bowel Dis. 2007 Mar;13(3):254–261. doi: 10.1002/ibd.20029. [DOI] [PubMed] [Google Scholar]
- 16.Khalili H, Huang ES, Ananthakrishnan AN, et al. Geographical variation and incidence of inflammatory bowel disease among US women. Gut. 2012 Jan 11; doi: 10.1136/gutjnl-2011-301574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med. 1989 May;8(5):551–561. doi: 10.1002/sim.4780080504. [DOI] [PubMed] [Google Scholar]
- 18.Ananthakrishnan AN, Khalili H, Konijeti GG, et al. A prospective study of long-term intake of dietary fiber and risk of Crohn's disease and ulcerative colitis. Gastroenterology. 2013 Nov;145(5):970–977. doi: 10.1053/j.gastro.2013.07.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ananthakrishnan AN, Khalili H, Konijeti GG, et al. Long-term intake of dietary fat and risk of ulcerative colitis and Crohn's disease. Gut. 2013 Jul 4; doi: 10.1136/gutjnl-2013-305304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ananthakrishnan AN, Khalili H, Higuchi LM, et al. Higher predicted vitamin D status is associated with reduced risk of Crohn's disease. Gastroenterology. 2012 Mar;142(3):482–489. doi: 10.1053/j.gastro.2011.11.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Trayhurn P, Wood IS. Adipokines: inflammation and the pleiotropic role of white adipose tissue. Br J Nutr. 2004 Sep;92(3):347–355. doi: 10.1079/bjn20041213. [DOI] [PubMed] [Google Scholar]
- 22.Schaffler A, Herfarth H. Creeping fat in Crohn's disease: travelling in a creeper lane of research? Gut. 2005 Jun;54(6):742–744. doi: 10.1136/gut.2004.061531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Backhed F, Ding H, Wang T, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 2004 Nov 2;101(44):15718–15723. doi: 10.1073/pnas.0407076101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):11070–11075. doi: 10.1073/pnas.0504978102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006 Dec 21;444(7122):1027–1031. doi: 10.1038/nature05414. [DOI] [PubMed] [Google Scholar]
- 26.Turnbaugh PJ, Gordon JI. The core gut microbiome, energy balance and obesity. J Physiol. 2009 Sep 1;587(Pt 17):4153–4158. doi: 10.1113/jphysiol.2009.174136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Turnbaugh PJ, Hamady M, Yatsunenko T, et al. A core gut microbiome in obese and lean twins. Nature. 2009 Jan 22;457(7228):480–484. doi: 10.1038/nature07540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Moreno-Navarrete JM, Sabater M, Ortega F, Ricart W, Fernandez-Real JM. Circulating zonulin, a marker of intestinal permeability, is increased in association with obesity-associated insulin resistance. PLoS One. 2012;7(5):e37160. doi: 10.1371/journal.pone.0037160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Fink C, Karagiannides I, Bakirtzi K, Pothoulakis C. Adipose tissue and inflammatory bowel disease pathogenesis. Inflamm Bowel Dis. 2012 Aug;18(8):1550–1557. doi: 10.1002/ibd.22893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mendall MA, Gunasekera AV, John BJ, Kumar D. Is obesity a risk factor for Crohn's disease? Dig Dis Sci. 2011 Mar;56(3):837–844. doi: 10.1007/s10620-010-1541-6. [DOI] [PubMed] [Google Scholar]
- 31.Hass DJ, Brensinger CM, Lewis JD, Lichtenstein GR. The impact of increased body mass index on the clinical course of Crohn's disease. Clin Gastroenterol Hepatol. 2006 Apr;4(4):482–488. doi: 10.1016/j.cgh.2005.12.015. [DOI] [PubMed] [Google Scholar]
- 32.Blain A, Cattan S, Beaugerie L, Carbonnel F, Gendre JP, Cosnes J. Crohn's disease clinical course and severity in obese patients. Clin Nutr. 2002 Feb;21(1):51–57. doi: 10.1054/clnu.2001.0503. [DOI] [PubMed] [Google Scholar]
- 33.Chan SS, Luben R, Olsen A, et al. Body mass index and the risk for Crohn's disease and ulcerative colitis: data from a European Prospective Cohort Study (The IBD in EPIC Study) Am J Gastroenterol. 2013 Apr;108(4):575–582. doi: 10.1038/ajg.2012.453. [DOI] [PubMed] [Google Scholar]
- 34.Kuk JL, Saunders TJ, Davidson LE, Ross R. Age-related changes in total and regional fat distribution. Ageing Res Rev. 2009 Oct;8(4):339–348. doi: 10.1016/j.arr.2009.06.001. [DOI] [PubMed] [Google Scholar]
- 35.Sarna L, Bialous SA, Jun HJ, Wewers ME, Cooley ME, Feskanich D. Smoking trends in the Nurses' Health Study (1976–2003) Nurs Res. 2008 Nov-Dec;57(6):374–382. doi: 10.1097/NNR.0b013e31818bf38b. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.van Dam RM, Li T, Spiegelman D, Franco OH, Hu FB. Combined impact of lifestyle factors on mortality: prospective cohort study in US women. Bmj. 2008;337:a1440. doi: 10.1136/bmj.a1440. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.