Associations between participation in, intensity of, and time spent on leisure time physical activity and risk of inflammatory bowel disease among older adults (PA-IBD): A prospective cohort study

Abstract

Background

Physical activity

Over the past two decades, there has been a consensus about the preventative and health promoting effect of physical activity (PA). The World Health Organization (WHO) states(1), PA contributes to various health benefits, such as reducing the risk of several noncommunicable diseases (NCDs) – diseases, which are a result of a combination of genetic, physiological, environmental and behavioural factors(2). Chronic inflammatory bowel diseases (IBD) are an example of important NCDs that have emerged as a worldwide public health challenge, and therefore PA is highly relevant in the discussion of the prevention of these diseases.

Chronic inflammatory bowel diseases

Crohn’s disease (CD) and ulcerative colitis (UC) are the two major forms of the IBDs. They are intestinal disorders resulting from an inappropriate inflammatory response to intestinal microbes(3, 4). Production of pro-inflammatory cytokines by antigen-presenting cells may lead to a secretion of large amounts of tumour necrosis factor alpha (TNF-α). This further stimulates the activation of other pro-inflammatory responses, leading to a more permeable mucosal barrier which can result in microbial antigens from the intestinal lumen gaining access to the mucosal epithelium(5-8).

The diseases represent a public health problem because of their large impact on the patients and their families’ quality of life, on the health care system due to costly treatments, and on the society due to absence from work. Furthermore, the diseases have become a worldwide challenge because of their high prevalence and incidence(9-11). In general, research on IBD focus on the occurrence of these diseases in early life (20-40 years of age), but the IBDs are also prevalent as late onset (40+ years of age) diseases(12-14). Reported age-adjusted (45 to 69 years of age) incidences of the IBDs in Denmark are 6-10 for CD(13, 15) and 18-23 for UC(13, 15) per 100 000 person-years. Worldwide incidences up to 23 per 100,000 for CD and 57 per 100,000 for UC have been reported(10).

The detailed aetiology of the IBDs remains unknown, but experimental and observational studies such as meta-analyses and cohort- and case-control studies suggest that CD and UC have both genetic and environmental predisposing factors(16-20). Use of nonsteroidal anti-inflammatory drugs (NSAIDs)(21), use of hormone replacement therapy (HRT)(22), obesity(23, 24) and dietary factors such as red meat(25) and alcohol(26, 27) have been suggested as risk factors for intestinal inflammation and IBD onset, while a preventative role of intake of dietary fibre(16, 28) and fermented dairy products(29, 30) has been suggested. Smoking, however, has been associated with a higher risk of CD, while being protective for UC(31, 32).

The preventative role of PA on the risk of IBD

Studies have also suggested a preventative effect of PA on the risk of IBD. Early studies have investigated the impact of occupational PA and found that occupations characterized by more physical work appeared to be protective compared with those occupations characterized as sedentary(33, 34). A study by Persson et al. investigating the impact of leisure time PA revealed that the relative risk (RR) of CD, was inversely related to weekly regular exercise (RR= 0.6 [0.4; 0.9])(35).

Recent studies have found contradictive results. The Nurses’ Health Study I and II(36) observed that women aged between 25 to 55 years who engaged in PA of more than 27 metabolic equivalents of task (MET) hours per week, had a 44% lower risk of developing CD compared with women who were inactive(36). Chan et al. found that there was no association between PA and onset of IBD in “the European Prospective Investigation into Cancer and Nutrition” (EPIC) cohort which included people aged between 45 to 80 years old(37). Furthermore, a meta-analysis including, among others, the mentioned studies, presented that higher levels of PA were associated with a reduced risk of CD compared to those with low PA, but no association was found for UC(38).

The biological mechanisms supporting the hypothesis that being physically inactive compared to being active is associated with risk of IBD are based on suggested mechanisms of contracting muscles, adipose tissue, and intestinal inflammation(5). Furthermore, there is a growing body of evidence pointing at the role of physical activity in the modulation of gut microbiota towards a more diverse composition of the microbiome, which is associated with higher immunity(39). The hypothesis is illustrated in figure 1.

Through these mechanisms of contracting muscles, adipose tissue, diversity of the gut microbiome, and intestinal inflammation, PA may have a protective role in the development of IBD.

Aim

Based on the inconclusive previous research, the aim of the present study was as follows: To investigate the association between PA and risk of IBD onset among older adults using leisure time PA as a proxy for PA, by:

• operationalising leisure time PA as 1) intensity measured in MET hours, and 2) time spent on six different types of activities, to examine if a potential association may be primarily ascribed to specific activities.
• analysing intensity and time spent as A) being physically active compared to being inactive, and as B) a dose-response-association.

Furthermore, the study aimed to investigate whether the effect of leisure time PA was modified by the level of occupational PA, age, BMI and smoking habits.

Methods

Design and setting

A prospective cohort study design was used to investigate the described hypothesis. The study was based on data from the Danish “Diet, Cancer and Health” (DCH) cohort and from Danish registries. Participants in the DCH cohort were recruited between December 1993 and May 1997, and included a total of 57 053 participants (27 178 men and 29 875 women) aged between 50 and 64 years of age, with residence in the Copenhagen area or Aarhus area, born in Denmark and with no previous cancer diagnosis in the Danish Cancer Registry. At baseline, the participants completed a lifestyle questionnaire and a food frequency questionnaire (FFQ). Both questionnaires were interviewer-checked and validated regarding PA and diet(42, 43). A detailed description of the DCH cohort has been described elsewhere(44).

Participants, eligibility criteria and follow-up

Inclusion criteria: All men and women included in the DCH cohort with no diagnosis of CD or UC (diagnostic codes described in next section) before entry to the cohort and with information on PA were included in the analyses.

Follow-up: The participants were followed from the date of their first visit at the DCH study clinic until the date of diagnosis of CD or UC, date of death or emigration, or December 31, 2018, whichever came first.

Materials, data sources, and methods

Outcome diagnostic criteria: The outcome late onset IBD was defined by the criteria: 1) having a main diagnosis (A-diagnosis) registered in the Danish National Patient Registry (DNPR)(45) with the International Classification of Diseases (ICD) 8 and 10 codes for CD (563.00–563.09, 563.91 and K50 (including all sub-codes)) and UC (563.19, 563.99, 569.04, and K51 (including all sub-codes)) in years 1977-2018 from a department with a relevant area of specialization (Surgical Gastroenterology, Medical Gastroenterology, Internal medicine) and 2) the diagnosis was followed by at least one additional registration in the DNPR (inpatient or outpatient visit) related to the first diagnosis within 180 days. The date and year of the diagnosis were defined as the date and year of the first diagnosis registered in the DNPR.

Registry data: The Danish health registries included the DNPR and the Danish Civil Registration System (DCRS)(46). The DNPR was used to identify patients with IBD during follow-up. ICD-8 and ICD-10 codes from the DNPR was used to identify cases diagnosed before and after entry to the DCH cohort, and to calculate comorbidity in the cohort using the updated Charlson’s comorbidity index(47). Comorbidity was categorized as a binary variable (comorbidity=no/yes) to ensure enough power of the group with comorbidity. High completeness of IBD registration in the DNPR has previously been reported(94%), with an estimated positive predictive value of 97% for CD and 90% for UC(48). The DCRS was used to extract follow-up information on death and immigration. Data were linked by the unique identification number assigned to all residents in Denmark at birth or first immigration.

Exposure: The exposure was defined as 1) a binary indicator of exposure: being active/inactive, both for total intensity of PA and separated on the six different types of leisure time PA: walking, housework, gardening, do-it-yourself work, cycling and sports, and as 2) levels of intensity of total PA and time spend on the six types of activities. The intensity was measured as MET hours/week. The time spent was measured as hours/week.

The MET system is based on the understanding that all activities are assigned an intensity unit based on their rate of energy expenditure. One MET is defined as the energy expenditure at rest (the resting metabolic rate), which for the average adult is approximately 3.5 ml of O₂/kg body weight/min. The intensities of different activities are calculated as the ratio between the associated metabolic rate for the specific activity and the resting metabolic rate(49).

Being active was defined as having an intensity level of ≥3 MET hours/week or as spending >0 hours/week on each type of activity. The cut point for the binary variable of MET hours/week was chosen to correspond to inactivity equivalent of <1 hour of walking at an average pace per week, consistent with prior studies(36). The levels of intensity and time spent were categorised in quartiles. The pre-defined variable for MET hours/week from the DCH dataset was used. The variable is further described below.

Questionnaire data: Information on leisure time PA was based on six questions covering the average number of hours per week spent the past year on the six types of leisure time PA during summer and winter, respectively. The MET hours/week variable was calculated by multiplying the MET value of each specific activity by duration and frequency of the activities. The following MET-values were used according to Ainsworth’s Compendium of Physical Activities(49, 50): walking 3.0, housework 3.0, gardening 4.0, do-it-yourself work 4.5, cycling 6.0, and sports 6.0.

Covariates and potential confounders: Based on the known and putative IBD risk factors and preventative factors, the analyses were adjusted for occupational PA, smoking, intake of fibre, fermented dairy products, red and processed meat, alcohol, HRT (only for women), NSAID, comorbidity, and also the demographic factors age and gender. These factors were expected to be possible confounders of the association between leisure time PA and risk of IBD.

Information on occupational PA was obtained from a question with five categories (sitting, standing, light manual work, heavy manual work, no occupation). Light and heavy manual work were combined in one category. Total energy intake was measured in mega joule (MJ) per day, alcohol consumption and intake of fibre, fermented dairy products and meat (red and processed meat) were measured in grams per day – all retrieved from the FFQ. A detailed description of the calculation of the dietary variables in the DCH study is described elsewhere(51). Smoking habits within the past year was defined as current, never or former. The questionnaire also gave information on the use of a pain-relieving drug, which was defined by the variable NSAID and assessed as >1 pill per month during the last year before baseline (yes/no). HRT was divided into the following categories: never, current, and former user.

Statistical analyses

To investigate the risk of and time to IBD event the Cox proportional hazards model with age as the underlying time scale was applied. Death, emigration and loss to follow-up were not considered as competing risks, and thus, were handled as censoring. The assumption of proportional hazards was checked by evaluating parallel curves of the cumulative hazard function on the log-scale. Furthermore, sensitivity analyses modelling time-varying effect of covariates that did not fulfil the proportional hazard assumption, were performed. 

Hazard-ratios (HRs) and the corresponding 95% confidence intervals (95% CI) and p-values for IBD onset associated with participation in and levels of leisure time PA were estimated. All analyses were carried out according to the principle of complete-case-analysis(52) to ensure an equal number of participants in all analyses. The analyses were carried out in both a crude model and a model adjusted for baseline values of preventative factors and risk factors for IBD. In the dose-response analyses, inactive individuals were included by assigning indicator variables of being active/inactive, as the IBD risk among inactive individuals may deviate from the risk among active individuals. Since the lowest quartile group of gardening, do-it-yourself-work, cycling, and sports only included inactive people (0 hours/week), the second quartile group was used as a reference for these variables.

Furthermore, the analyses were stratified according to strata of age groups (50-59 and 60-64 years), BMI (<25 kg/m² and ≥25 kg/m²⁾, smoking (‘never smoker’ and ‘current/former smoker’), occupational PA (‘not active at work’, including sitting and not working, and ‘active at work’, including standing and manual work) and work status (not working/working), as these variables were assumed to interact with the effect of leisure time PA.

All analyses were carried out using Stata version 15 (53). For all tests, a P-value below 0.05 was considered statistically significant.

The study was not submitted to the Southern Denmark Ethics Committee, which is the local ethics committee. The study does not need approval from the Ethics committee or Institutional Review Board by Danish law: “Questionnaire studies and health science registry research projects must be reported to the scientific ethics committee system only if the project includes human biological material”. (the Act on Research Ethics Review of Health Research Projects (Danish: Lov om videnskabsetisk behandling af sundhedsvidenskabelige forskningsprojekter, Lov nr. 593 af 14. juni 2011, § 14, stk. 2)).(54)

Results

In total, 1 823 individuals were excluded from the study cohort; 59 participants were excluded because they had a CD or UC diagnosis before entry to the DCH cohort; three individuals where excluded because of no link to the DCRS; 1 209 were excluded because of missing values in one or more of the PA variables, and 14 were excluded due to implausible long times spent on leisure time PA (>105 hours/week), which was in accordance with previous studies using the DCH cohort(55, 56). The remaining 538 individuals were excluded because of missing information on other variables. A total of 28 526 women and 26 119 men were included in the analyses. During a mean follow-up of 25 years, 529 IBD cases (106 CD cases and 423 UC) were observed (figure 2).

The median MET hours in each quartile group were as follows: 1^st quartile: 27 hours/week; 2^nd quartile: 47 hours/week; 3^rd quartile: 69 hours/week, and 4^th quartile: 112 hours/week (table 1). Table 1 also presents the medians and 25^th and 75^th percentiles of hours spent on the six types of activity in each quartile group.

Table 1. Median and percentiles of MET hours and hours per week in quartile groups

Abbreviations: MET: metabolic equivalent of task, p1; p3: 25^th and 75^th percentiles. 

Baseline characteristics of the cohort grouped in quartiles of MET hours of PA per week are presented in table 2. Due to asymmetric distributions, continuous variables were presented as medians with interquartile ranges. The groups differ on several parameters. A higher proportion of the youngest age group (50-54 years) was represented in each MET quartile group, but the proportion was higher in the lowest quartile group compared to the highest quartile group. A higher proportion of women, were represented in all MET quartile groups except the lowest quartile group. A markedly higher proportion of people not working and a lower proportion of people with sedentary work were represented in the highest MET quartile group compared to the distribution in the lowest MET group.

Table 2. Baseline characteristics of participants in quartile groups of MET hours/week

^a Median and 25^th and 75^th percentiles (p1; p3) are presented for continuous variables. Number, N, and percent (%) are presented for categorical variables. All values after exclusion of missings. Abbreviations: MET: metabolic equivalent of task; g/d: gram per day; BMI: body mass index; CCI: Charlson’s comorbidity index; HRT: hormone replacement therapy; NSAID: nonsteroidal anti-inflammatory drugs; Active/inactive

An intensity of ≥3 MET hours/week as an indicator of being active was not associated with any significantly lower risk of IBD (0.89 [0.13; 6.27]) after adjustment for potential confounders. Engaging in any of the six types of leisure time activities neither was associated with a lower risk (table 3).

Table 3. Risk of inflammatory bowel disease according to being physically active

^aAdjusted for age, gender, occupational physical activity, smoking, energy intake, intake of meat, fibre, fermented dairy products and alcohol, nonsteroidal anti-inflammatory drugs, hormone replacement therapy, comorbidity. The six types of activity were mutually adjusted. ^bInactive is the reference for all estimates. Abbreviations: MET: metabolic equivalent of task; HR: hazard ratio; CI: confidence interval

Dose-response

Table 4. Risk of inflammatory bowel disease according to quartiles of physical activity

^aAdjusted for age, gender, occupational physical activity, smoking, energy intake, intake of meat, fibre, fermented dairy products and alcohol, nonsteroidal anti-inflammatory drugs, hormone replacement therapy, comorbidity. The six types of activity were mutually adjusted. ^b1st quartile is the reference for MET hours, walking and housework. 2nd quartile is reference for gardening, do-it-yourself, cycling and sports. Abbreviations: MET: metabolic equivalent of task; HR: hazard ratio; CI: confidence interval

Compared with participants in the lowest quartile of MET hours/week, there were no statistically significantly higher or lower risk of IBD with increasing MET hours/week. Furthermore, no statistically significantly associations were found for quartiles of walking, housework, gardening, cycling and sports. For do-it-yourself-work, the unadjusted HRs indicated that the lowest and the third quartiles was associated with a higher risk of IBD compared to the second quartile of hours/week (HR=1.27 [1.01 ; 1.59] and HR=1.43 [1.09 ; 1.86]). Furthermore, in the adjusted analyses the association remained significant for the third quartile (HR=1.44 [1.10 ; 1.90]. 

Stratification

The results of the stratified analyses indicated that the dose-response estimates were not modified by age (50-59 and 60-64), occupational PA (sedentary/not working and standing/manual work), BMI (<25.0 and ≥25.0), smoking (never and former/current) or work status (not working and working) (all P for z-tests>0.05).

Discussion

This cohort study of Danish middle-aged men and women did not find any support for the hypothesis that being physically active compared to inactive measured by MET hours/week and as participation in six types of activities lowered the risk of IBD. Generally, estimates had wide confidence intervals.

Comparison with other studies

The findings of this study may to some extent be compared with those of the EPIC cohort, which did not observe an association between PA and risk of CD and UC(37). Compared with the present study, the EPIC study (which included participants from the DCH cohort) only included 75 CD cases and 177 UC cases and was also unable to account for long-term changes in PA.

A combined study of the Nurses’ Health studies I and II used detailed and updated information on physical activity and known risk factors for CD and UC and included 284 CD cases and 363 UC cases. They reported a HR of 0.56 (0.37; 0.84) for the risk of developing CD when comparing active women with at least 27 MET hours/week of PA with sedentary women with <3 MET hours/week. No association was found for UC. Age, smoking, and BMI also did not significantly modify the association between PA and risk of UC or CD. Furthermore, the findings from the Nurse’s Health studies are consistent with findings from two prior case-control studies(35, 57). Persson et al. used a mailed questionnaire to obtain information on PA from cases and controls and reported an inverse association between weekly regular PA and risk of CD but not UC(35). Klein et al. found that IBD patients had lower levels of PA during their pre-illness period than clinic controls(57).

A meta-analysis by Wang et al. (38) argues that the variations in definitions of PA across studies make it difficult to compare them. The Nurse’s health study also used MET hours/week and therefore was more comparable with the present study compared to the EPIC study, where they used a PA index combining occupational and recreational PA. However, the median of MET hours/week in the highest quartile group in this study was 112 hours/week compared to only 45 hours/week in the Nurse’s Health study. Thus, the study population in the present study generally reported very high numbers of hours spent on leisure time PA, complicating the comparability. The Nurse’s Health study found an effect at 27 MET hours/week when compared to the lowest category of PA (<3 MET hours/week). If the effect of PA has a threshold value the effect is probably not shown in this study, as this study uses a higher PA level as reference group (27 MET hours/week) and not the < 3 MET hours/week as in the Nurse’s Health study.

Strengths and limitations

The present study has several strengths. A major strength is its prospective design, which reduced selection bias. As the participants were followed using registries, the loss to follow-up was minimal. Information on PA and covariates were measured several years before the onset of disease, and hence, were unrelated to IBD status later during follow-up. Another major strength is the linkage to the DNPR, which is considered of high validity and completeness, and the restrictive diagnostic criteria by including only A-diagnoses and cases with at least two diagnoses during follow-up. This approach ensured that a high proportion of the identified cases really had IBD, hence increasing the specificity but lowering the sensitivity, as some ‘real’ IBD cases might not have been identified. Moreover, the diagnostic criteria were in accordance with the criteria used in a Danish nationwide cohort study of IBD using data from the DNPR(13).

The study had three notable limitations. Firstly, the available data did not allow examining the association between timing in life of PA and risk of IBD. It could be argued that a potential protective effect of PA is due to the accumulated PA exposure during life. Whether the PA level reported at baseline is representative of the PA level during whole life can be questioned.

Second, there were only 106 CD and 423 UC cases. These numbers are higher than in the EPIC study, but still limit the opportunity to look at the diseases separately. The low number of cases can be explained by the restriction to the age group 50–64 years, which is not the ages of typical IBD onset (20-30 years)(13). On the other hand, 50-64 years old is an age group with increasing incidence (12-14).

Thirdly, although the DCH questionnaire is demonstrated to be a reliable and valid tool(43), the limitations according to self-reported information should be considered. PA is in general difficult to measure accurately in observational studies as there is a risk of measurement error and misclassification. The participants had generally reported very high levels of PA per week equivalent of up to 15 hours/week, hence it is possible that physical activity may have been overreported. This must have been independent of the outcome and may have resulted in bias towards the null hypothesis in the analysis of the dichotomised exposure variables. In the analyses with the exposure in quartiles groups, the misclassification resulted in a risk of both over- and underestimation of the association. Overall, the results of the present study should be interpreted with caution.

Although the present study cannot explicitly conclude that individuals likely reduce their risk of IBD through participation in PA, there are plausible biological mechanisms for how PA may be involved in the aetiology of IBD. These mechanisms originate from the so far evidence from experimental animal and human studies suggesting that PA may exert its anti-inflammatory effect via a reduction in visceral fat mass and/or by induction of an anti-inflammatory intestinal environment(5, 6). Therefore, there is a rationale for future cohort studies investigating the association. Further research is needed to clarify the association which is not fully understood by the present study.

Conclusion

In conclusion, this study did not find any association between intensity of and time spent on physical activity and risk of IBD when comparing physically active with inactive participants. Neither did the results indicate any dose-response effect when comparing quartile groups of MET hours/week and hours/week spent on specific activities. Do-it-yourself work, however, seemed to be associated with a higher risk of IBD when comparing the third quartile with the second quartile of hours/week. The five other activities (walking, housework, cycling, gardening and sport) did not show any significant associations. The study has important public health implications in a society dominated by a sedentary lifestyle, and clinical relevance by its contribution to the explanatory field of the causes of IBD.  

Abbreviations

PA = physical activity

IBD = inflammatory bowel disease

CD = Crohn’s disease

UC = ulcerative colitis

DCH = Diet, Cancer and Health

EPIC = European Prospective Investigation into Cancer and Nutrition

MET = metabolic equivalent of task

NSAID = nonsteroidal anti-inflammatory drugs

HRT = hormone replacement therapy

HR = hazard ratio

95% CI = 95% confidence interval

p1 and p3 = 25^th and 75^th percentiles

Declarations

Ethics approval and consent to participate

The study is approved by the Danish Data Protection Agency (2012-58-0018). The study is an open register-based cohort study. The study does not need approval from the Ethics committee or Institutional Review Board by Danish law: “Questionnaire studies and health science registry research projects must be reported to the scientific ethics committee system only if the project includes human biological material”. (the Act on Research Ethics Review of Health Research Projects (Danish: Lov om videnskabsetisk behandling af sundhedsvidenskabelige forskningsprojekter, Lov nr. 593 af 14. juni 2011, § 14, stk. 2)).(54)

Consent for publication

Consent was obtained from the participants in the “Diet, Cancer and Health” study.

Availability of data and material

The data that support the findings of this study are available from Open Patient data Explorative Network (OPEN) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Danish Health Data Authority. Permissions obtained to access data before the initiation of this project were obtained from The Danish Data Protection Agency, and furthermore from the data owners: The Danish Cancer Society and the Danish Health Data Authority.

Competing interests

The author declares to have no competing interests.

Funding

This research was supported by the Region of Southern Jutland grant number [17/33849] (Vibeke Andersen) and by Knud and Edith Eriksen’s Mindefond. Data collection and the DCH cohort are funded by The Danish Cancer Society. The funders were not involved in the research. All aspects of the study including the research design and interpretation of findings is the responsibility of the author and not the funder.

Authors' contributions

The first author (NFR) designed the study, made the statistical analyses and interpretation of the data, and wrote the first draft of the manuscript. NFR, BHB, KHR and VA contributed to the manuscript and accepted the final version. BHB especially contributed with epidemiologic knowledge, KHR contributed with specialised knowledge within registry research and the individual Danish registries. VA together with NFR developed the project idea and VA contributed with specialised clinical knowledge within IBDs and know of the DCH cohort.

Acknowledgements

The authors would like to thank Inge Petersen (statistician at OPEN) for her role in data management support.

References

1. World Health Organization. Diet, Physical activity and Health [Available from: https://www.who.int/dietphysicalactivity/pa/en/. Assessed 25 May 2019.
2. World Health Organization. Noncommunicable diseases [Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Assessed 25 May 2019.
3. Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L. Crohn's disease. Lancet (London, England). 2017;389(10080):1741-55.
4. Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet (London, England). 2017;389(10080):1756-70.
5. Bilski J, Brzozowski B, Mazur-Bialy A, Sliwowski Z, Brzozowski T. The role of physical exercise in inflammatory bowel disease. Biomed Res Int. 2014;2014:429031.
6. Bilski J, Mazur-Bialy A, Brzozowski B, Magierowski M, Zahradnik-Bilska J, Wojcik D, et al. Can exercise affect the course of inflammatory bowel disease? Experimental and clinical evidence. Pharmacological reports : PR. 2016;68(4):827-36.
7. Bilski J, Mazur-Bialy A, Magierowski M, Kwiecien S, Wojcik D, Ptak-Belowska A, et al. Exploiting Significance of Physical Exercise in Prevention of Gastrointestinal Disorders. Current pharmaceutical design. 2018;24(18):1916-25.
8. Bilski J, Mazur-Bialy AI, Wierdak M, Brzozowski T. The impact of physical activity and nutrition on inflammatory bowel disease: the potential role of cross talk between adipose tissue and skeletal muscle. Journal of physiology and pharmacology : an official journal of the Polish Physiological Society. 2013;64(2):143-55.
9. Molodecky NA, Soon IS, Rabi DM, Ghali WA, Ferris M, Chernoff G, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology. 2012;142(1):46-54.e42; quiz e30.
10. Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet (London, England). 2018;390(10114):2769-78.
11. Burisch J, Jess T, Martinato M, Lakatos PL. The burden of inflammatory bowel disease in Europe. Journal of Crohn's and Colitis. 2013;7(4):322-37.
12. Taleban S, Colombel JF, Mohler MJ, Fain MJ. Inflammatory bowel disease and the elderly: a review. Journal of Crohn's & colitis. 2015;9(6):507-15.
13. Lophaven SN, Lynge E, Burisch J. The incidence of inflammatory bowel disease in Denmark 1980-2013: a nationwide cohort study. Alimentary pharmacology & therapeutics. 2017;45(7):961-72.
14. Moller FT, Andersen V, Wohlfahrt J, Jess T. Familial risk of inflammatory bowel disease: a population-based cohort study 1977-2011. Am J Gastroenterol. 2015;110(4):564-71.
15. Norgard BM, Nielsen J, Fonager K, Kjeldsen J, Jacobsen BA, Qvist N. The incidence of ulcerative colitis (1995-2011) and Crohn's disease (1995-2012) - based on nationwide Danish registry data. Journal of Crohn's & colitis. 2014;8(10):1274-80.
16. Ananthakrishnan AN. Environmental Risk Factors for Inflammatory Bowel Diseases: A Review. Digestive diseases and sciences. 2015;60(2):290-8.
17. Bernstein CN. Review article: changes in the epidemiology of inflammatory bowel disease-clues for aetiology. Alimentary pharmacology & therapeutics. 2017;46(10):911-9.
18. van der Sloot KWJ, Amini M, Peters V, Dijkstra G, Alizadeh BZ. Inflammatory Bowel Diseases: Review of Known Environmental Protective and Risk Factors Involved. Inflammatory bowel diseases. 2017;23(9):1499-509.
19. Abegunde AT, Muhammad BH, Bhatti O, Ali T. Environmental risk factors for inflammatory bowel diseases: Evidence based literature review. World journal of gastroenterology. 2016;22(27):6296-317.
20. Ananthakrishnan AN. Environmental Triggers for Inflammatory Bowel Disease. Current Gastroenterology Reports. 2013;15(1):1-7.
21. Ananthakrishnan AN, Higuchi LM, Huang ES, Khalili H, Richter JM, Fuchs CS, et al. Aspirin, nonsteroidal anti-inflammatory drug use, and risk for Crohn disease and ulcerative colitis: a cohort study. Ann Intern Med. 2012;156(5):350-9.
22. Khalili H, Higuchi LM, Ananthakrishnan AN, Manson JE, Feskanich D, Richter JM, et al. Hormone therapy increases risk of ulcerative colitis but not Crohn's disease. Gastroenterology. 2012;143(5):1199-206.
23. Mendall MA, Gunasekera AV, John BJ, Kumar D. Is obesity a risk factor for Crohn's disease? Digestive diseases and sciences. 2011;56(3):837-44.
24. Pedersen BK. The Physiology of Optimizing Health with a Focus on Exercise as Medicine. Annual review of physiology. 2019;81(1):607.
25. Christensen R, Heitmann BL, Andersen KW, Nielsen OH, Sorensen SB, Jawhara M, et al. Impact of red and processed meat and fibre intake on treatment outcomes among patients with chronic inflammatory diseases: protocol for a prospective cohort study of prognostic factors and personalised medicine. BMJ open. 2018;8(2):e018166.
26. Swanson GR, Sedghi S, Farhadi A, Keshavarzian A. Pattern of alcohol consumption and its effect on gastrointestinal symptoms in inflammatory bowel disease. Alcohol (Fayetteville, NY). 2010;44(3):223-8.
27. Bishehsari F, Magno E, Swanson G, Desai V, Voigt RM, Forsyth CB, et al. Alcohol and Gut-Derived Inflammation. Alcohol research : current reviews. 2017;38(2):163-71.
28. Ananthakrishnan A KH, Konijeti GG, Higuchi LM, Silva P, Korzenik JR, Fuchs CS, Willett WC, Richter JM, Chan AT. A Prospective Study of Long-term Intake of Dietary Fiber and Risk of Crohn’s Disease and Ulcerative Colitis. Gastroenterology. 2013;145:970-7.
29. Bordoni A, Danesi F, Dardevet D, Dupont D, Fernandez AS, Gille D, et al. Dairy products and inflammation: A review of the clinical evidence. Critical reviews in food science and nutrition. 2017;57(12):2497-525.
30. Saez-Lara MJ, Gomez-Llorente C, Plaza-Diaz J, Gil A. The role of probiotic lactic acid bacteria and bifidobacteria in the prevention and treatment of inflammatory bowel disease and other related diseases: a systematic review of randomized human clinical trials. Biomed Res Int. 2015;2015:505878.
31. Cosnes J. Smoking, physical activity, nutrition and lifestyle: environmental factors and their impact on IBD. Digestive diseases (Basel, Switzerland). 2010;28(3):411-7.
32. Salih A, Widbom L, Hultdin J, Karling P. Smoking is associated with risk for developing inflammatory bowel disease including late onset ulcerative colitis: a prospective study. Scandinavian journal of gastroenterology. 2018;53(2):173-8.
33. Sonnenberg A. Occupational distribution of inflammatory bowel disease among German employees. Gut. 1990;31(9):1037-40.
34. Boggild H, Tuchsen F, Orhede E. Occupation, employment status and chronic inflammatory bowel disease in Denmark. Int J Epidemiol. 1996;25(3):630-7.
35. Persson PG, Leijonmarck CE, Bernell O, Hellers G, Ahlbom A. Risk indicators for inflammatory bowel disease. Int J Epidemiol. 1993;22(2):268-72.
36. Khalili H, Ananthakrishnan AN, Konijeti GG, Liao X, Higuchi LM, Fuchs CS, et al. Physical activity and risk of inflammatory bowel disease: prospective study from the Nurses' Health Study cohorts. Bmj. 2013;347:f6633.
37. Chan SS, Luben R, Olsen A, Tjonneland A, Kaaks R, Teucher B, 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;108(4):575-82.
38. Wang Q, Xu KQ, Qin XR, Wen L, Yan L, Wang XY. Association between physical activity and inflammatory bowel disease risk: A meta-analysis. Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2016;48(12):1425-31.
39. Codella R, Luzi L, Terruzzi I. Exercise has the guts: How physical activity may positively modulate gut microbiota in chronic and immune-based diseases. Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2018;50(4):331-41.
40. Narula N, Fedorak RN. Exercise and inflammatory bowel disease. Can J Gastroenterol. 2008;22(5):497-504.
41. Febbraio MA, Pedersen BK. Muscle-derived interleukin-6: mechanisms for activation and possible biological roles. FASEB journal : official publication of the Federation of American Societies for Experimental Biology. 2002;16(11):1335-47.
42. Tjonneland A, Overvad K, Haraldsdottir J, Bang S, Ewertz M, Jensen OM. Validation of a semiquantitative food frequency questionnaire developed in Denmark. Int J Epidemiol. 1991;20(4):906-12.
43. Cust AE, Smith BJ, Chau J, van der Ploeg HP, Friedenreich CM, Armstrong BK, et al. Validity and repeatability of the EPIC physical activity questionnaire: a validation study using accelerometers as an objective measure. The international journal of behavioral nutrition and physical activity. 2008;5(1):33-.
44. Tjonneland A, Olsen A, Boll K, Stripp C, Christensen J, Engholm G, et al. Study design, exposure variables, and socioeconomic determinants of participation in Diet, Cancer and Health: a population-based prospective cohort study of 57,053 men and women in Denmark. Scand J Public Health. 2007;35(4):432-41.
45. Schmidt M, Schmidt SAJ, Sandegaard JL, Ehrenstein V, Pedersen L, Sørensen HT. The Danish National Patient Registry: a review of content, data quality, and research potential. Clinical epidemiology. 2015;7:449-90.
46. Schmidt M, Pedersen L, Sorensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014;29(8):541-9.
47. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676-82.
48. 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. Scandinavian journal of gastroenterology. 1996;31(2):154-9.
49. Ainsworth BE, Haskell WL, Leon AS, Jacobs JDR, Montoye HJ, Sallis JF, et al. Compendium of physical activities: classification of energy costs of human physical activities. Medicine and science in sports and exercise. 1993;25(1):71-80.
50. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Medicine and science in sports and exercise. 2000;32(9 Suppl):S498-504.
51. Kopp TI, Vogel U, Tjonneland A, Andersen V. Meat and fiber intake and interaction with pattern recognition receptors (TLR1, TLR2, TLR4, and TLR10) in relation to colorectal cancer in a Danish prospective, case-cohort study. The American journal of clinical nutrition. 2018;107(3):465-79.
52. Vandenbroucke JP, Elm Ev, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Medicine. 2007 Oktober 16:1628-94.
53. StataCorp. Stata Statistical Software 15 ed2017.
54. The Danish Parliament. The Act on Research Ethics Review of Health Research Projects. Law nr. 593 of June 14, 2011, § 14, stk 2. Available at: https://www.retsinformation.dk/forms/r0710.aspx?id=137674.
55. Johnsen NF, Ekblond A, Thomsen BL, Overvad K, Tjonneland A. Leisure time physical activity and mortality. Epidemiology (Cambridge, Mass). 2013;24(5):717-25.
56. Johnsen NF, Christensen J, Thomsen BL, Olsen A, Loft S, Overvad K, et al. Physical activity and risk of colon cancer in a cohort of Danish middle-aged men and women. Eur J Epidemiol. 2006;21(12):877-84.
57. Klein I, Reif S, Farbstein H, Halak A, Gilat T. Preillness non dietary factors and habits in inflammatory bowel disease. Italian journal of gastroenterology and hepatology. 1998;30(3):247-51.