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Obesity is Associated With Increased Risk of Crohn’s disease, but not Ulcerative Colitis: A Pooled Analysis of Five Prospective Cohort Studies

Open AccessPublished:July 06, 2021DOI:https://doi.org/10.1016/j.cgh.2021.06.049

      Background and Aims

      It is unclear whether obesity is associated with the development of inflammatory bowel disease despite compelling data from basic science studies. We therefore examined the association between obesity and risk of Crohn’s disease (CD) and ulcerative colitis (UC).

      Methods

      We conducted pooled analyses of 5 prospective cohorts with validated anthropometric measurements for body mass index (BMI) and waist-hip ratio and other lifestyle factors. Diagnoses of CD and UC were confirmed through medical records or ascertained using validated definitions. We used Cox proportional hazards modeling to calculate pooled multivariable-adjusted HRs (aHRs) and 95% confidence intervals (CIs).

      Results

      Among 601,009 participants (age range, 18-98 years) with 10,110,018 person-years of follow-up, we confirmed 563 incident cases of CD and 1047 incident cases of UC. Obesity (baseline BMI ≥30 kg/m2) was associated with an increased risk of CD (pooled aHR, 1.34; 95% CI, 1.05-1.71, I2 = 0%) compared with normal BMI (18.5 to <25 kg/m2). Each 5 kg/m2 increment in baseline BMI was associated with a 16% increase in risk of CD (pooled aHR, 1.16; 95% CI, 1.05-1.22; I2 = 0%). Similarly, with each 5 kg/m2 increment in early adulthood BMI (age, 18-20 years), there was a 22% increase in risk of CD (pooled aHR, 1.22; 95% CI, 1.05-1.40; I2 = 13.6%). An increase in waist-hip ratio was associated with an increased risk of CD that did not reach statistical significance (pooled aHR across quartiles, 1.08; 95% CI, 0.97-1.19; I2 = 0%). No associations were observed between measures of obesity and risk of UC.

      Conclusions

      In an adult population, obesity as measured by BMI was associated with an increased risk of older-onset CD but not UC.

      Keywords

      Abbreviations used in this paper:

      BMI (body mass index), CI (confidence interval), CD (Crohn’s disease), HR (hazard ratios), IBD (inflammatory bowel disease), MV (multivariable), TNF (tumor necrosis factor), UC (ulcerative colitis), WHR (waist-hip ratio), WHO (World Health Organization)

       Background

      Plausible biological mechanisms imply a role for obesity in the pathogenesis of inflammatory bowel disease, but the relationship between obesity and risk of Crohn’s disease (CD) and ulcerative colitis (UC) is unclear.

       Findings

      In a pooled analysis of 5 prospective cohorts, obesity as defined by body mass index is associated with a significant risk of incident CD but not UC.

       Implications for patient care

      The growing burden of obesity may be an important contributor to the increasing incidence of CD worldwide with the effects of obesity potentially playing a stronger role in the etiopathogenesis of CD compared with UC.
      The global incidence of the inflammatory bowel diseases (IBDs), Crohn’s disease (CD) and ulcerative colitis (UC), has increased especially in developing countries that have witnessed a dramatic Westernization of lifestyle.
      • Ng S.C.
      • Shi H.Y.
      • Hamidi N.
      • et al.
      Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies.
      Over a similar time frame, obesity is becoming increasingly prevalent worldwide,
      NCD Risk Factor Collaboration (NCD-RisC)
      Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants.
      and is one of the strongest risk factors for chronic disease morbidity and mortality.
      • Berrington de Gonzalez A.
      • Hartge P.
      • Cerhan J.R.
      • et al.
      Body-mass index and mortality among 1.46 million white adults.
      As a major environmental factor in the development of autoimmune diseases,
      • Versini M.
      • Jeandel P.Y.
      • Rosenthal E.
      • et al.
      Obesity in autoimmune diseases: not a passive bystander.
      obesity may contribute substantially to the etiopathogenesis of IBD, particularly in those with older-onset IBD, where, relative to those with younger disease onset, the overall contribution of the environment is significantly greater.
      • Ruel J.
      • Ruane D.
      • Mehandru S.
      • et al.
      IBD across the age spectrum: is it the same disease?.
      Obesity is often linked to Westernized lifestyles such as physical inactivity and excess calorie consumption from meals high in refined grains and unhealthy fats. Adipocyte hypertrophy resulting from obesity generates a proinflammatory state through the secretion of inflammatory mediators including tumor necrosis factor (TNF)-α
      • Hotamisligil G.S.
      • Arner P.
      • Caro J.F.
      • et al.
      Increased adipose tissue expression of tumor necrosis factor-alpha in human obesity and insulin resistance.
      and C-reactive protein.
      • Greenfield J.R.
      • Samaras K.
      • Jenkins A.B.
      • et al.
      Obesity is an important determinant of baseline serum C-reactive protein concentration in monozygotic twins, independent of genetic influences.
      These are also elevated in patients with IBD, especially TNF-α, whose pathogenic role is antagonized by anti-TNF therapies. Obesity is further associated with increased markers of bowel inflammation based on fecal calprotectin measurements
      • Poullis A.
      • Foster R.
      • Shetty A.
      • et al.
      Bowel inflammation as measured by fecal calprotectin: a link between lifestyle factors and colorectal cancer risk.
      and intestinal permeability,
      • Moreno-Navarrete J.M.
      • Sabater M.
      • Ortega F.
      • et al.
      Circulating zonulin, a marker of intestinal permeability, is increased in association with obesity-associated insulin resistance.
      biological hallmarks of IBD. Yet despite these compelling data, epidemiologic studies have failed to identify a consistent link between obesity and the risk of CD and UC.
      • Chan S.S.
      • 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).
      ,
      • Khalili H.
      • Ananthakrishnan A.N.
      • Konijeti G.G.
      • et al.
      Measures of obesity and risk of Crohn’s disease and ulcerative colitis.
      This is likely to be due to the significant limitations of previous studies that include retrospective design, sample size, and inability to control for confounding from other important lifestyle exposures and lack of detailed measures of obesity.
      We therefore investigated the relationship between measures of obesity and risk of CD and UC while addressing the methodological limitations of prior studies through a pooled analysis of several well-established prospective cohorts. These robust population-based cohorts had detailed and validated data on measures of obesity and lifestyle data, offering us a unique opportunity to comprehensively examine the relationship between obesity and risk of older-onset IBD.

      Methods

       Study Populations

      A pooled analysis of the primary data from 5 large prospective cohorts was conducted within the Dietary and Environmental Factors IN-IBD (DEFINe-IBD) study. DEFINe-IBD is an international consortium of cohort studies aimed at identifying environmental and lifestyle risk factors for IBD by analyzing harmonized individual-level data from multiple prospective cohorts using standardized criteria. The consortium includes the European Prospective Investigation into Cancer and Nutrition (EPIC), the Nurses’ Health Study (NHS) and NHS II, the Cohort of Swedish Men (COSM), and the Swedish Mammography Cohort (SMC). An overview of the study populations, the design of each cohort and predefined inclusion criteria are given in the Supplementary Methods. All cohorts provided their primary individual level data for analyses related to this study. At baseline, we excluded individuals with missing baseline anthropometric measurements needed to determine body mass index (BMI), those with BMI <18.5 kg/m2 (due to the possibility of underlying disease), and participants diagnosed with IBD prior to start of follow-up in each cohort. We excluded incident cases of CD and UC diagnosed within the first 2 years of follow-up to minimize the possibility of reverse causation.
      Each cohort’s ethics committee approved participation in this study.

       Exposure Assessment

      Anthropometric measurements were obtained from baseline questionnaires to calculate BMI and waist-hip ratio (WHR) as measures of obesity. Where information on key variables including covariates (see below) were unavailable at baseline but were collected later, on subsequent questionnaires, the baseline was redefined as the later date. Therefore, baseline questionnaires were collected from EPIC participants from 1991 to 1997; NHS and NHS II participants in 1986 and 1993, respectively; and COSM and SMC participants in 1997. Self-reported weight in early adulthood (18–20 years) recorded in baseline questionnaires was used to calculate early adulthood BMI, assuming that participants’ height in early adulthood would be the same as that recorded at baseline.
      • Mouratidou N.
      • Malmborg P.
      • Sachs M.C.
      • et al.
      Adult height in patients with childhood-onset inflammatory bowel disease: a nationwide population-based cohort study.
      For additional details, see Supplementary Figure 1 and Supplementary Table 1.

       Assessment of Covariates

      Total physical activity, total energy intake, dietary fiber and fat intake were calculated from baseline questionnaires for each cohort as summarized in the Supplementary Methods. Self-reported smoking status at baseline was recorded as current, past, or never.

       Outcome Ascertainment

      Case ascertainment for CD and UC have been described in detail for EPIC,
      • Chan S.S.
      • 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).
      NHS and NHS II,
      • Ananthakrishnan A.N.
      • Higuchi L.M.
      • Huang E.S.
      • et al.
      Aspirin, nonsteroidal anti-inflammatory drug use, and risk for Crohn disease and ulcerative colitis: a cohort study.
      COSM, and SMC,
      • Jakobsson G.L.
      • Sternegard E.
      • Olen O.
      • et al.
      Validating inflammatory bowel disease (IBD) in the Swedish National Patient Register and the Swedish Quality Register for IBD (SWIBREG).
      ,
      • Khalili H.
      • Hakansson N.
      • Chan S.S.
      • et al.
      No association between consumption of sweetened beverages and risk of later-onset Crohn’s disease or ulcerative colitis.
      and a summary of IBD case ascertainment for each cohort is given in the Supplementary Methods.

       Statistical Analysis

      Measures of obesity were modeled categorically and continuously. For the categorical analysis, BMI was modeled using cut points proposed by the World Health Organization (WHO): 18.5 to <25 kg/m2 (normal), 25 to <30 kg/m2 (overweight), and ≥30 kg/m2 (obese).
      World Health Organization
      Physical status: the use of and interpretation of anthropometry, report of a WHO expert committee.
      We used BMI of 18.5 to <25 kg/m2 as the referent category. Categorical analysis of WHR was modeled using the WHO definition of abdominal obesity (female >0.85, male >0.90)

      World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization; 2011.

      and as quartiles. Continuous analyses modeled per 5 kg/m2 increase in BMI and for WHR per quartile and per unit increase.
      We calculated person-time for each participant from the date of the baseline questionnaire until the date of CD or UC diagnosis, death from another cause, loss to follow-up, or end of follow-up, whichever came first. Minimally adjusted (age and sex) and multi-variable (MV)-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models for each cohort. Covariables were formatted to be consistently classified across all cohorts, and multiple imputations with chain equations were used to carry out 50 imputations for missing data (physical activity, n = 38,418 [6.4%]; smoking status, n = 6428 [1.0%]; total energy intake, n = 25,397 [4.2%]; total fiber intake, n = 25,397 [4.2%]; total fat intake, n = 25,397 [4.2%]). Where the outcome was CD, MV-adjusted HRs were adjusted for sex (male/female), smoking status (nonsmoker/ex-smoker/current smoker), total energy intake (kcal/day), physical activity (metabolic equivalent task hours/week modeled as quartiles according to cohort distribution), and dietary fiber (g/day). For UC, MV-adjusted HRs models were similar but were adjusted for dietary fat (g/day) instead of dietary fiber. These variables were selected based on their known associations with risk of CD or UC.
      • Ananthakrishnan A.N.
      • Khalili H.
      • Konijeti G.G.
      • et al.
      Long-term intake of dietary fat and risk of ulcerative colitis and Crohn’s disease.
      ,
      • Ananthakrishnan A.N.
      • Khalili H.
      • Konijeti G.G.
      • et al.
      A prospective study of long-term intake of dietary fiber and risk of Crohn's disease and ulcerative colitis.
      For models in which baseline BMI was not the main exposure (ie, WHR or BMI in early adulthood), BMI was included as a covariable in the model to examine the independent associations of our secondary exposures. Lastly, models that examined the relationship between change in weight from early to middle adulthood and risk of CD and UC were additionally adjusted for height and early adulthood weight but not baseline BMI.
      Cohort-specific Cox proportional HRs were pooled using a fixed effect meta-analysis.
      • Smith-Warner S.A.
      • Spiegelman D.
      • Ritz J.
      • et al.
      Methods for pooling results of epidemiologic studies: the Pooling Project of Prospective Studies of Diet and Cancer.
      Heterogeneity between studies was evaluated using the Q and I2 statistic, I2 > 50% indicated substantial heterogeneity. In addition, we conducted sensitivity analyses to evaluate whether associations between BMI and risk of CD and UC were modified by age at start of follow-up (<50 vs ≥50 years), sex (female vs male), and smoking status (never vs ever). Lastly, we also examined the association between BMI and risk of CD according to age of diagnosis (< 40 years, 40 to <60 years, ≥ 60 years). All P-values were 2-sided, and P < .05 was considered statistically significant.
      We calculated the population attributable risk conferred by obesity as defined by BMI (≥30 kg/m2) to estimate the percentage of IBD cases that may have been preventable if participants had maintained a normal BMI, assuming a causal relationship between BMI and IBD.
      Stata 16.1/MP (StataCorp LLC, College Station, TX) and SAS 9.4 (SAS Institute Inc, Cary, NC) were used for analyses of individual cohorts and pooling of estimates.

      Results

      Following exclusions, our pooled cohort included 601,009 participants from 9 countries, and 71% were female. Over 10,110,018 person-years of follow-up with a mean follow-up of 16 years, we identified 563 incident cases of CD (incidence rate, 6 cases/100,000 person-years) and 1047 incident cases of UC (incidence rate, 10 cases/100,000 person-years). Table 1 reports the baseline characteristics of participants categorized by BMI. Compared with participants in the lowest category of BMI, those in the highest category had higher WHR, were less physically active, and more likely to be smokers.
      Table 1Baseline Characteristics of Participants in Each Cohort According to Extreme Categories of BMI at Baseline
      EPICNHSNHS IICOSMSMC
      BMI, kg/m2
      18.5 to <25 (n = 154,465)≥30 (n = 47,401)18.5 to <25 (n = 44,847)≥30 (n = 11,995)18.5 to <25 (n = 59,481)≥30 (n = 17,784)18.5 to <25 (n = 18,820)≥30 (n = 4351)18.5 to <25 (n = 20,459)≥30 (n = 4087)
      Age at recruitment, years50.0 (11.2)54.1 (8.7)52.6 (7.2)53.4 (7.0)38.4 (4.6)39.6 (4.5)60.2 (10.0)59.7 (9.0)61.3 (9.4)62.3 (8.5)
      Female sex, %736210010010010000100100
      BMI, kg/m222.5 (1.6)33.3 (3.3)22.3 (1.6)34.3 (4.2)22.0 (1.7)35.5 (5.1)23.1 (1.4)32.6 (2.7)22.5 (1.6)33.0 (3.2)
      Waist to hip ratio0.80 (0.08)0.90 (0.10)0.77 (0.17)0.83 (0.11)0.77 (0.07)0.83 (0.09)0.92 (0.06)1.00 (0.09)0.80 (0.09)0.86 (0.10)
      Smoking, %
       Never49484348666539295156
       Former27323437232434462225
       Current24202415111126252518
      Physical activity, MET-hr/wk35.1 (26.9)29.9 (26.0)16.0 (22.8)9.8 (15.8)22.9 (28.6)15.2 (20.8)41.8 (4.8)40.7 (5.2)42.6 (4.7)41.6 (5.1)
      Energy intake, kcal/day2109 (656)2111 (733)1754 (518)1806 (549)1776 (537)1842 (571)2699 (281)2611 (1030)1756 (555)1713 (632)
      Total fat intake, g81 (31)82 (34)63 (23)68 (24)61 (22)68 (24)94 (36)90 (42)63 (24)60 (26)
      Total fiber intake, g24 (8)23 (8)20 (8)20 (8)19 (8)18 (8)32 (13)30 (15)23 (9)22 (10)
      Note: All nonpercentage values presented as mean with standard deviation.
      BMI, Body mass index; CoSM, Cohort of Swedish Men; EPIC, European Prospective Cohort Investigation into Cancer and Nutrition; MET, metabolic equivalent task; NHS, Nurses’ Health Study; SMC, Swedish Mammography Cohort.
      For baseline BMI, we observed an increased risk of CD in those who were obese (BMI ≥30 kg/m2) in our minimally adjusted model (HR, 1.27; 95% CI, 0.97–1.68; I2 = 0%) when compared with participants in the lowest category of BMI (BMI 18.5 to <25 kg/m2) (Table 2), and for each 5 kg/m2 increase in baseline BMI, a statistically significant 10% increase in risk of CD (HR, 1.10; 95% CI, 1.00–1.22; I2 = 0%). These associations were not altered after adjusting for known and potential risk factors for CD including smoking, physical activity, and dietary fiber intake. Compared with participants in the lowest category of BMI, the MV-adjusted HR of CD for participants with BMI ≥30 kg/m2 was 1.34 (95% CI, 1.05–1.71; I2 = 0%). Similarly, for every 5 kg/m2 increase in baseline BMI, we observed a 16% increase in risk of CD (MV-adjusted HR, 1.16; 95% CI, 1.05–1.22). We observed no heterogeneity in the association between the highest category of BMI (≥30 kg/m2) or per 5 kg/m2 increment in BMI and risk of CD across the 5 cohorts (Qstatistic = 2.11; I2 = 0%; P = .71 and Qstatistic = 0.85; I2 = 0%; P = .93, respectively). We used data on worldwide prevalence of BMI ≥30 kg/m2 from the WHO Global Health Observatory to estimate the population attributable risk for older-onset CD. In Western Europe and the United States, the prevalence of obesity increased from 15% to 29% at the mid-point of the study (∼2005) to 20% to 36% towards the end of the study period (∼2016), conferring an adjusted population attributable risk ranging from 5% to 11%, assuming a causal relationship between BMI and CD.
      Table 2Risk of Crohn’s Disease According to BMI at Baseline
      BMI at Baseline, kg/m2HR (95% CI) per 5 kg/m2 increase in BMI
      18.5 to <2525 to <30≥30
      EPIC
       Person-years of follow-up2,229,2201,701,427642,078
       No. of cases574622
       Age- and sex-adjusted HR (95% CI)1.001.10 (0.74–1.63)1.37 (0.84–2.23)1.10 (0.90–1.34)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.001.12 (0.75–1.66)1.43 (0.89–2.31)1.16 (0.95–1.40)
      NHS
       Person-years of follow-up1,137,959572,120282,393
       No. of cases824822
       Age- and sex-adjusted HR (95% CI)1.001.14 (0.80–1.63)1.06 (0.66–1.70)1.10 (0.90–1.28)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.001.16 (0.81–1.67)1.09 (0.67–1.75)1.10 (0.95–1.28)
      NHS II
       Person-years of follow-up1,302,853517,841375,603
       No. of cases823131
       Age- and sex-adjusted HR (95% CI)1.001.22 (0.70–2.14)1.04 (0.50–2.13)1.10 (0.86–1.34)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.97 (0.64–1.47)1.35 (0.89–2.06)1.16 (0.95–1.34)
      COSM
       Person-years of follow-up310,259330,22169,958
       No. of cases403412
       Age- and sex-adjusted HR (95% CI)1.000.80 (0.51–1.26)1.33 (0.70–2.53)1.05 (0.77–1.47)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.79 (0.50–1.26)1.27 (0.66–2.44)1.05 (0.77–1.40)
      SMC
       Person-years of follow-up351,461217,60969,016
       No. of cases291611
       Age- and sex-adjusted HR (95% CI)1.000.93 (0.51–1.72)2.01 (1.00–4.03)1.22 (0.90–1.61)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.94 (0.51–1.73)2.00 (0.99–4.03)1.22 (0.90–1.61)
      Pooled age- and sex-adjusted HR (95% CI)1.001.04 (0.85–1.28)1.27 (0.97–1.68)1.10 (1.00–1.22)
      Pooled multivariable HR (95% CI)1.001.01 (0.83–1.23)1.34 (1.05–1.71)1.16 (1.05–1.22)
      BMI, Body mass index; CI, confidence interval; CoSM, Cohort of Swedish Men; EPIC, European Prospective Cohort Investigation into Cancer and Nutrition; HR, hazard ratio; NHS, Nurses’ Health Study; SMC, Swedish Mammography Cohort.
      a Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      In contrast to CD, we did not observe an association between those who were overweight (BMI 25 to <30 kg/m2) or obese (BMI ≥30 kg/m2) and risk of UC in our minimally adjusted or MV-adjusted models (Table 3). For every 5 kg/m2 increase in BMI, the minimally adjusted HR for UC was 1.00 (95% CI, 0.95–1.10), and the MV-adjusted HR was 1.00 (95% CI, 0.90–1.05). We observed no heterogeneity across cohorts in the association between the highest category of BMI (≥30 kg/m2) or per 5 kg/m2 increment in BMI and risk of UC across the 5 cohorts (Qstatistic = 3.29; I2 = 0%; P = .51 and Qstatistic = 1.25; I2 = 0%; P = .87, respectively).
      Table 3Risk of Ulcerative Colitis According to BMI at Baseline
      BMI at baseline, kg/m2HR (95% CI) per 5 kg/m2 increase in BMI
      18.5 to <2525 to <30≥ 30
      EPIC
       Person-years of follow-up2,229,2201,701,427642,078
       No. of cases14813553
       Age- and sex-adjusted HR (95% CI)1.001.01 (0.80–1.29)1.10 (0.81–1.43)1.10 (0.90–1.34)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.001.02 (0.81–1.30)1.13 (0.82–1.55)1.05 (0.95–1.22)
      NHS
       Person-years of follow-up1 137 959572 120282 393
       No. of cases984320
       Age- and sex-adjusted HR (95% CI)1.000.88 (0.62–1.26)0.85 (0.52–1.37)0.95 (0.82–1.16)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.87 (0.61–1.25)0.83 (0.51–1.35)0.95 (0.82–1.16)
      NHS II
       Person-years of follow-up1 302 853517 841375 603
       No. of cases1204730
       Age- and sex-adjusted HR (95% CI)1.001.20 (0.75–1.90)0.91 (0.49–1.69)1.10 (0.90–1.28)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.95 (0.68–1.33)0.81 (0.54–1.23)0.95 (0.86–1.10)
      COSM
       Person-years of follow-up310 259330 22169 958
       No. of cases8612614
       Age- and sex-adjusted HR (95% CI)1.001.38 (1.05–1.82)0.72 (0.41–1.26)1.00 (0.83–1.22)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.001.34 (1.02–1.76)0.68 (0.39–1.20)0.95 (0.77–1.03)
      SMC
       Person-years of follow-up351 461217 60969 016
       No. of cases773911
       Age- and sex-adjusted HR (95% CI)1.000.89 (0.60–1.31)0.79 (0.42–1.48)0.95 (0.77–1.22)
       Multivariable-adjusted HR (95% CI)
      Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      1.000.90 (0.61–1.33)0.76 (0.40–1.44)0.95 (0.77–1.22)
      Pooled age- and sex-adjusted HR (95% CI)1.001.07 (0.93–1.24)0.92 (0.74–1.14)1.00 (0.95–1.10)
      Pooled multivariable HR (95% CI)1.001.03 (0.91–1.19)0.88 (0.72–1.08)1.00 (0.90–1.05)
      BMI, Body mass index; CI, confidence interval; CoSM, Cohort of Swedish Men; EPIC, European Prospective Cohort Investigation into Cancer and Nutrition; HR, hazard ratio; NHS, Nurses’ Health Study; SMC, Swedish Mammography Cohort.
      a Adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fat (continuous). I2 values for highest category of BMI and per 5 kg/m2 increment in BMI for pooled analyses = 0%.
      We explored whether the association between baseline BMI and risk of CD and UC was consistent across several subgroups defined by sex, age, and smoking (Figure 1) and observed no evidence of effect modification (all Pinteraction ≥ .44). We considered the possibility that the association between obesity and risk of CD may differ according to age of diagnosis, and therefore evaluated the associations according to different categories of age of diagnosis (18 to <40, 40 to <60, and ≥60 years). Compared with participants in the lowest category of BMI, participants with BMI ≥ 30 kg/m2 had MV-adjusted HRs of 1.35 (95% CI, 0.92-1.99; Qstatistic = 6.68; I2 = 40%; P = .12) and 1.66 (95% CI, 1.12-2.30; Qstatistic = 4.57; I2 = 12%; P = .33) for diagnosis of CD during middle age (40 to <60 years) and older age (≥ 60 years), respectively. We could not examine the association with younger-onset CD (18 to <40 years), given limited numbers of cases in all the cohorts. Conducting an analysis limiting our cohort to participants with above average physical activity level in each cohort, we observed a 22% increase in risk of CD with every 5 kg/m2 increase in BMI (MV-adjusted HR, 1.22; 95% CI, 1.10–1.40) and no increase in risk of UC (MV-adjusted HR, 1.00; 95% CI, 0.90–1.16).
      Figure thumbnail gr1
      Figure 1Association between baseline BMI and risk of CD (left) and UC (right) according to selected strata. HR, hazard ratio; MV, multivariable.
      Information on BMI in early adulthood (age 18–20 years) was available in just over two-thirds of participants, and we further explored the association between early adulthood BMI and risk of CD and UC. In those with an early adulthood BMI ≥25 kg/m2 (ie, overweight), the minimally adjusted HR of developing CD later in life was 1.52 (95% CI, 1.15–1.99; I2 = 0%) when compared with participants with an early adulthood BMI <25 kg/m2. This estimate was not materially altered in MV-adjusted analysis (HR, 1.48; 95% CI, 1.12–1.95; I2 = 0%). Similarly, we observed a 22% increase in risk of CD with every 5 kg/m2 increase in early adulthood BMI (MV-adjusted HR, 1.22; 95% CI, 1.05–1.40; I2 = 0%). No associations were found between early adulthood BMI and risk of UC in minimally and MV-adjusted HRs of developing UC later in life. For every 5 kg/m2 increase in early adulthood BMI, the MV-adjusted HR for UC was 1.05 (95% CI, 0.90–1.22; I2 = 0%). We also examined the association between weight change from early adulthood (age 18–20 years) and mid-life (baseline in each cohort) and observed no significant associations with risk of CD or UC (Supplementary Table 2). However, in joint analysis of early adulthood BMI and weight change from early adulthood to midlife, the highest risk of CD was observed in participants with early adult BMI ≥25 kg/m2 and the largest category of weight gain (≥10 kg) (Figure 2). Compared with participants with BMI <25 kg/m2 and no changes in weight (<2 kg), the MV-adjusted HR of CD among those with BMI ≥25 kg/m2 and ≥10 kg change in weight was 2.14 (95% CI, 1.43–3.22; I2 = 38%).
      Figure thumbnail gr2
      Figure 2Multivariable-adjusted risk of incident CD (A) and UC (B) according to early adult BMI and weight change since early adulthood. Each column shows the multivariable-adjusted hazard ratio for the joint association of BMI and weight gain and risk of CD and UC. Models were adjusted for variables listed in and . Pooled estimates were derived using fixed-effect meta-analyses.
      Finally, we evaluated the association between abdominal obesity and risk of CD and UC (Table 4). Compared with participants in the lowest quartile of WHR, the MV-adjusted HR for the second, third, and highest quartile of WHR were associated with a non-statistically significant increased risk of CD (continuous HR per WHR quartile, 1.08; 95% CI, 0.98–1.19) and were unaffected by additional adjustments for baseline BMI (continuous HR per WHR quartile, 1.08; 95% CI, 0.97–1.19). Similarly, assessing WHR as a continuous variable or based on the WHO WHR classification of abdominal obesity showed a non-statistically significant increased risk of CD in both these models (HR, 1.02; 95% CI, 0.97–1.07 and HR, 1.09; 95% CI, 0.85–1.40, respectively). No associations between WHR and risk of UC were observed in any of our models.
      Table 4Risk of Crohn’s Disease and Ulcerative Colitis Based on WHR (Quartiles) and on WHO Classification of WHR at Baseline
      WHR at baseline
      Quartile 1Quartile 2Quartile 3Quartile 4Per quartile increaseNon-obese
      Non-obese WHR = female ≤ 0.85, male ≤ 0.90; Obese WHR = female > 0.85, male > 0.90.
      Obese
      Non-obese WHR = female ≤ 0.85, male ≤ 0.90; Obese WHR = female > 0.85, male > 0.90.
      Per unit increase
      Crohn’s disease
       Pooled age- and sex-adjusted HR (95% CI)1.001.20 (0.89–1.63)1.30 (0.95–1.77)1.38 (1.00–1.90)1.10 (1.00–1.22)1.001.19 (0.93–1.53)1.27 (0.68–2.40)
       Pooled MV-adjusted HR (95% CI)
      Models adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.
      1.001.13 (0.84–1.51)1.22 (0.90–1.66)1.28 (0.93–1.77)1.08 (0.98–1.19)1.001.14 (0.89–1.47)1.33 (0.56–3.16)
       Pooled MV-adjusted HR (95% CI)
      Models adjusted for age at baseline (continuous), BMI (3 categories 18.5 to <25, 25 to <30, ≥30 kg/m2), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.
      1.001.12 (0.84–1.50)1.19 (0.88–1.62)1.23 (0.88–1.71)1.08 (0.97–1.19)1.001.09 (0.85–1.40)1.02 (0.97–1.07)
      Ulcerative colitis
       Pooled age- and sex-adjusted HR (95% CI)1.001.09 (0.90–1.33)0.93 (0.74–1.14)1.04 (0.82–1.32)0.99 (0.92–1.07)1.001.09 (0.92–1.30)1.21 (0.85–1.74)
       Pooled MV-adjusted HR (95% CI)
      Models adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.
      1.001.12 (0.91–1.36)0.93 (0.75–1.15)0.98 (0.77–1.24)0.97 (0.91–1.05)1.001.05 (0.89–1.25)1.21 (0.83–1.76)
       Pooled MV-adjusted HR (95% CI)
      Models adjusted for age at baseline (continuous), BMI (3 categories 18.5 to <25, 25 to <30, ≥30 kg/m2), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.
      1.001.10 (0.87–1.30)0.91 (0.73–1.12)0.98 (0.77–1.25)0.97 (0.90–1.04)1.001.04 (0.87–1.25)1.01 (0.97–1.06)
      Note: I2 values for highest category of WHR and as continuous variable for pooled analyses = 0%.
      CD, Crohn’s disease; CI, confidence interval; HR, hazard ratio; UC, ulcerative colitis; WHO, World Health Organization; WHR, waist to hip ratio.
      a Non-obese WHR = female ≤ 0.85, male ≤ 0.90; Obese WHR = female > 0.85, male > 0.90.
      b Models adjusted for age at baseline (continuous), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.
      c Models adjusted for age at baseline (continuous), BMI (3 categories 18.5 to <25, 25 to <30, ≥30 kg/m2), sex, smoking status (never/former/current), physical activity (quartiles), energy intake (continuous), dietary fiber (continuous) for CD only, dietary fat (continuous) for UC only.

      Discussion

      In a pooled analysis of over 600,000 participants in 5 prospective cohorts of predominantly middle-aged men and women from 9 Western countries, we showed that obesity, as measured by BMI, is associated with an increased risk of CD. Similarly, we found that BMI in early adulthood is associated with an increased risk of subsequently developing CD. These findings were consistent across multiple sensitivity and subgroup analyses, after accounting for unhealthy lifestyles associated with and after adjusting for dietary risk factors for IBD. Abdominal obesity, as measured by WHR, was associated with a non-statistically significant increased risk of CD. No associations were seen between any of these anthropometric measures and UC.
      Several biologically plausible mechanisms support our findings for an association between obesity and risk of CD. First, obesity causes adipocyte hypertrophy and dysfunction, leading to the secretion of many adipokines and pro-inflammatory mediators that are elevated in those with active CD.
      • Zulian A.
      • Cancello R.
      • Micheletto G.
      • et al.
      Visceral adipocytes: old actors in obesity and new protagonists in Crohn’s disease?.
      Second, levels of intestinal inflammation and permeability are associated with obesity
      • Poullis A.
      • Foster R.
      • Shetty A.
      • et al.
      Bowel inflammation as measured by fecal calprotectin: a link between lifestyle factors and colorectal cancer risk.
      ,
      • Moreno-Navarrete J.M.
      • Sabater M.
      • Ortega F.
      • et al.
      Circulating zonulin, a marker of intestinal permeability, is increased in association with obesity-associated insulin resistance.
      ,
      • Gummesson A.
      • Carlsson L.M.
      • Storlien L.H.
      • et al.
      Intestinal permeability is associated with visceral adiposity in healthy women.
      and may facilitate bacterial translocation and loss of immune tolerance in the gastrointestinal tract. Third, CD is associated with ‘creeping’ of mesenteric fat, which is associated with fibrosis and structuring.
      • Buning C.
      • von Kraft C.
      • Hermsdorf M.
      • et al.
      Visceral adipose tissue in patients with Crohn’s disease correlates with disease activity, inflammatory markers, and outcome.
      Lastly, obesity has been linked to alterations in the gut microbiome.
      • Ley R.E.
      • Backhed F.
      • Turnbaugh P.
      • et al.
      Obesity alters gut microbial ecology.
      We speculate these mechanisms are likely to have a greater role in the etiopathogenesis of CD compared with UC.
      To our knowledge, this is the largest prospective adult cohort study to examine the relationship between different measures of obesity and risk of CD with the opportunity to adjust for multiple lifestyle and dietary exposures. Few studies have previously investigated the associations between obesity and IBD, and both null
      • Chan S.S.
      • 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).
      ,
      • Melinder C.
      • Hiyoshi A.
      • Hussein O.
      • et al.
      Physical fitness in adolescence and subsequent inflammatory bowel disease risk.
      and positive associations
      • Khalili H.
      • Ananthakrishnan A.N.
      • Konijeti G.G.
      • et al.
      Measures of obesity and risk of Crohn’s disease and ulcerative colitis.
      ,
      • Harpsoe M.C.
      • Basit S.
      • Andersson M.
      • et al.
      Body mass index and risk of autoimmune diseases: a study within the Danish National Birth Cohort.
      • Mendall M.
      • Harpsoe M.C.
      • Kumar D.
      • et al.
      Relation of body mass index to risk of developing inflammatory bowel disease amongst women in the Danish National Birth Cohort.
      • Mendall M.A.
      • Gunasekera A.V.
      • John B.J.
      • et al.
      Is obesity a risk factor for Crohn’s disease?.
      have been reported. Reasons for the inconsistences in these associations may be due to differences in study design, small sample size (≤100 cases), inability to control for risk factors such as smoking, limited number of cases for higher categories of obesity, and different definitions of obesity.
      Our findings are supported by prior work from the NHS II cohort,
      • Khalili H.
      • Ananthakrishnan A.N.
      • Konijeti G.G.
      • et al.
      Measures of obesity and risk of Crohn’s disease and ulcerative colitis.
      part of this pooled analysis, which previously reported that obesity defined by BMI at baseline was associated with increased risk of CD. Interestingly, in this study, obesity defined by BMI at baseline in EPIC-IBD showed a positive association with CD, albeit not statistically significant. This is in contrast to previous data from EPIC-IBD, which found no associations between BMI and CD
      • Chan S.S.
      • 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).
      and may be due to the analysis of EPIC-IBD as a full cohort, rather than the previous nested case-control design and additional follow-up time, affording greater statistical power. In addition, our observation that BMI in early adulthood is associated with increased risk of later-onset CD is supported by a study that reported obesity in childhood (age 8–13 years) is associated with CD before the age of 30 years.
      • Jensen C.B.
      • Angquist L.H.
      • Mendall M.A.
      • et al.
      Childhood body mass index and risk of inflammatory bowel disease in adulthood: a population-based cohort study.
      Because BMI in middle-aged and older populations may not sufficiently represent the changes in fat distribution that are associated with age, we used WHR to assess the role of abdominal obesity and observed a non-statistically significant increased risk for CD. To date, only a single study has found an association with WHR and IBD,
      • Khalili H.
      • Ananthakrishnan A.N.
      • Konijeti G.G.
      • et al.
      Measures of obesity and risk of Crohn’s disease and ulcerative colitis.
      and based on the findings from our study with a significantly larger sample size, any associations between WHR and the risk of CD or UC are likely to be very modest.
      There are several strengths to our study. First, the prospective design avoids the potential selection and recall biases of retrospective case-control studies. Second, accurate and validated measures for weight, height, and waist and hip circumference ensured that recorded and self-reported anthropometric measures were valid and reproducible. Third, the diversity of included studies, large sample size, and long follow-up period ensured that our estimates are generalizable to similar populations. Fourth, the availability of detailed and validated information on smoking, physical activity, and dietary fiber and fat intakes allowed us to control for several risk factors that may have impacted our associations. Fifth, all diagnoses of CD and UC were either confirmed through medical records or ascertained using validated definitions, minimizing the risk of outcome misclassification. Finally, by pooling individual-level data, we were able to harmonize the exposures, covariates, outcomes, and analytic modeling, leading to little heterogeneity and decreased variability in effect estimates that can often be found in standard meta-analyses of the published literature.
      We acknowledge several limitations. First, our study participants were predominantly Western middle-aged men and women, and so our findings may not be generalizable to a non-Western or younger population at risk of IBD. That said, environmental exposures may play a greater role in IBD development of older-onset disease.
      • Ruel J.
      • Ruane D.
      • Mehandru S.
      • et al.
      IBD across the age spectrum: is it the same disease?.
      Second, our study was reliant mainly on anthropometric measures recorded at a single point in middle age. The lack of repeated anthropometric measures in all cohorts over follow-up time meant that we were unable to assess the time point during the life-course at which obesity becomes a critical factor for risk of developing CD. However, we did have data on early adulthood BMI in the majority of our participants and found similar associations to those with BMI at baseline. Finally, although we were able to account for several confounding factors, we further acknowledge that our observed associations are unable to demonstrate causality and may be related to residual confounding for factors such as early life exposures, which we are unable to adjust for. However, our results remained consistent after adjusting for known confounders and were consistent in several subgroups.

      Conclusions

      Our data implies that the growing burden of obesity is likely contributing to the increasing incidence of CD worldwide. Together with previous observations that obesity modulates immune responses,
      • Zulian A.
      • Cancello R.
      • Micheletto G.
      • et al.
      Visceral adipocytes: old actors in obesity and new protagonists in Crohn’s disease?.
      intestinal permeability,
      • Moreno-Navarrete J.M.
      • Sabater M.
      • Ortega F.
      • et al.
      Circulating zonulin, a marker of intestinal permeability, is increased in association with obesity-associated insulin resistance.
      ,
      • Gummesson A.
      • Carlsson L.M.
      • Storlien L.H.
      • et al.
      Intestinal permeability is associated with visceral adiposity in healthy women.
      and alterations in the gut microbiota,
      • Ley R.E.
      • Backhed F.
      • Turnbaugh P.
      • et al.
      Obesity alters gut microbial ecology.
      such pathways may play a more important role in the etiopathogenesis of CD compared with UC. Future work should consider examining the precise mechanisms through which obesity may influence the etiopathogenesis of CD.

      Acknowledgments

      The authors thank all EPIC, NHS, NHSII, COSM, and SMC participants for their contribution to the study. EPIC-Norfolk are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The authors acknowledge the Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts as the home of the Nurses’ Health Studies.
      The DEFINe-IBD study data cannot be deposited publicly as these collaborative data originate from multiple research institutions across 8 European countries and the United States with different legal frameworks. Information on submitting applications to access the data from the cohorts used in this study are as follows: COSM and SMC (https://www.simpler4health.se/researchers and https://www.simpler4health.se/researchers/cohorts), EPIC (https://epic.iarc.fr/access/index.php), NHS and NHSII (https://www.nurseshealthstudy.org/researchers).

      CRediT Authorship Contributions

      Simon S. M. Chan, MB BChir, PhD (Conceptualization: Equal; Data curation: Equal; Formal analysis: Supporting; Investigation: Equal; Methodology: Equal; Writing – original draft: Lead; Writing – review & editing: Equal)
      Ye Chen, MS (Formal analysis: Lead; Writing – review & editing: Supporting)
      Kevin Casey, MS (Formal analysis: Supporting; Writing – review & editing: Supporting)
      Ola Olen, MD, PhD (Writing – review & editing: Supporting)
      Jonas F. Ludvigsson, MD, PhD (Data curation: Equal; Writing – review & editing: Supporting)
      Franck Carbonnel, MD, PhD (Writing – review & editing: Supporting)
      Bas Oldenburg, MD, PhD (Writing – review & editing: Supporting)
      Marc J. Gunter, PhD (Data curation: Equal; Writing – review & editing: Supporting)
      Anne Tjønneland, PhD (Data curation: Equal; Writing – review & editing: Supporting)
      Olof Grip, MD, PhD (Writing – review & editing: Supporting)
      Paul Lochhead, MB ChB, PhD (Writing – review & editing: Supporting)
      Andrew T. Chan, MD, MPH (Writing – review & editing: Supporting)
      Alicia Wolk, DMSc (Data curation: Equal; Writing – review & editing: Supporting)
      Hamed Khalili, MD, MPH (Conceptualization: Equal; Data curation: Equal; Investigation: Equal; Methodology: Equal; Writing – original draft: Supporting; Writing – review & editing: Equal)

      Supplementary Material

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