association of processed food intake with risk of

Confidential: For Review OnlyAssociation of Processed Food Intake with Risk of

Inflammatory Bowel Disease: Results from the Prospective Urban Rural Epidemiology (PURE) study

Journal: BMJ

Manuscript ID BMJ-2020-063175

Article Type: Research

BMJ Journal: BMJ

Date Submitted by the Author: 16-Nov-2020

Complete List of Authors: Narula, Neeraj; McMaster University Faculty of Health SciencesWong, Emily; McMaster University Faculty of Health SciencesDehghan, Mahshid; Population Health Research Institute, Hamilton Health Sciences/McMaster UniversityMente, Andrew; Population Health Research Institute Hamilton General Hospital Hamilton CanadaRangarajan, Sumathy; Population Health Research Institute, Hamilton Health Sciences/McMaster UniversityLanas, Fernando; Universidad de La Frontera, Internal MedicineLópez-Jaramillo, Patricio; Universidad de Santander (UDES), Fundacion Oftalmologica de Santander (FOSCAL) and Medical SchoolRohatgi, Priyanka; Apollo Hospitals Bangalore, Department of Nutrition and DieteticsPinnaka, Lakshmi; Post Graduate Institute of Medical Education and Research (PGIMER) School of Public HealthVarma, Raviprasad; Sree Chitra Tirunal Institute for Medical Sciences and TechnologyOrlandini, Andres; ECLA FoundationAvezum, Alvaro; International Research Centre, Hospital Alemao Oswaldo Cruz & Universidade Santo Amaro (UNISA), , CardiologyWielgosz, Andreas; University of OttawaPoirier, Paul; Institut universitaire de cardiologie et de pneumologie de Quebec, Centre de rechercheAlmadi, Majid; King Saud University, Department of MedicineAltuntas, Yuksel; SB Pediatric Endocrinology and Metabolism, Training and Research HospitalNg, Kien; National Defence University of MalaysiaChifamba, Jephat; University of Zimbabwe College of Health Sciences, Physiology DepartmentYeates, Karen; Queen's University, MedicinePuoane, Thandi; University of Western Cape, School of Public HealthKhatib, Rasha; Northwestern UniversityYusuf, Rita; Independent University Boström, Kristina; University of GothenburgZatonska, Katarzyna; Medical University of Wrocław, Department of Social Medicine

https://mc.manuscriptcentral.com/bmj

BMJ

Confidential: For Review OnlyIqbal, Romaina; Aga Khan University, Departments of Community Health Sciences and MedicineLiu, Weida; Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease，FuwaiHospital,National Center for Cardiovascular DiseasesZhu, Yibing; Chinese Academy of Medical Sciences and Peking Union Medical CollegeSidong, Li; Chinese Academy of Medical Sciences and Peking Union Medical College Dans, Antonio; Lyceum of the Philippines UniversityYusufali, Afzalhussein; Dubai Health AuthorityMohammadifard, Noushin; Isfahan Cardiovascular Research CenterMarshall, John; McMaster University Faculty of Health SciencesMoayyedi, Paul; McMaster University, MedicineReinisch, Walter; Medical University ViennaYusuf, Salim; Population Health Research Institute, Hamilton Health Sciences/McMaster University

Keywords: processed foods, Crohn's disease, ulcerative colitis, processed meat, Salty snacks, Sweets, Sugar

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Confidential: For Review OnlyAssociation of Processed Food Intake with Risk of

Inflammatory Bowel Disease: Results from the Prospective Urban Rural Epidemiology (PURE) study

Neeraj Narula1,2

Emily C.L. Wong1

Mahshid Dehghan2

Andrew Mente2

Sumathy Rangarajan2

Fernando Lanas3

Patricio Lopez-Jaramillo4

Priyanka Rohatgi5P V M Lakshmi6

Ravi Prasad Varma7

Andres Orlandini8Alvaro Avezum9

Andreas Wielgosz10

Paul Poirier11

Majid A. Almadi12

Yuksel Altuntas13

Kien Keat Ng14

Jephat Chifamba15

Karen Yeates16

Thandi Puoane17

Rasha Khatib18

Rita Yusuf19

Kristina Bengtsson Boström20

Katarzyna Zatonska21

Romaina Iqbal22

Liu Weida23

Zhu Yibing23

Li Sidong23

Antonio Dans24

Afzalhussein Yusufali25

Noushin Mohammadifard26

John K. Marshall1

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Confidential: For Review OnlyPaul Moayyedi1,2

Walter Reinisch27

Salim Yusuf2

1 Department of Medicine (Division of Gastroenterology) and Farncombe Family Digestive Health Research Institute; McMaster University, Hamilton ON, Canada2 Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada3 Universidad de La Frontera, Temuco, Chile4 Masira Research Institute, Universidad de Santander (UDES) Fundación Oftalmológica de Santander-FOSCAL - Bucaramanga, Colombia5 Chief Clinical Dietician, HOD, Dept. of Nutrition and Dietetics, Apollo Hospitals, 154/11,opp.IIM, Bannerghatta Road, Bangalore -766 Professor of Epidemiology School of Public Health Post Graduate Institute of Medical Education & Research Chandigarh - 160012, India7 Achutha Menon Centre for Health Science Studies, SCTIMST and Health Action by People, Thiruvananthapuram, India, Postcode – 6950118 Estudios Clinicos Latinoamerica ECLA Rosario, Santa Fe Argentina9 Hospital Alemao Oswaldo Cruz & UNISA Sao Paulo, SP Brazil10 University of Ottawa Department of Medicine 501 Smyth Rd Ottawa, ON, Canada K1H 8L611 Faculté de pharmacie, Université Laval Institut universitaire de cardiologie et de pneumologie de Québec 2725 Chemin Sainte-Foy Québec, Québec, Canada, G1V 4G512 Division of Gastroenterology, Department of Medicine, College of Medicine, King Saud University. P.O. Box 2925(59) Riyadh 11472, Saudi Arabia13 University of Health Sciences, Faculty of Medicine, Istanbul Sisli Hamidiye Etfal Health Training and Research Hospital, Clinic of Endocrinology and Metabolism Sisli/Istanbul-TURKEY14 Faculty of Medicine and Defence Health, National Defence University of Malaysia (UPNM), Sungai Besi Camp, 57000 Kuala Lumpur, Malaysia15 University of Zimbabwe College of Health Sciences Physiology Department Harare, Zimbabwe16 Department of Medicine Queen's University, Kingston, ON, Canada17 School of Public Health, University of the Western Cape, Private Bag X17, Bellville. South Africa18 Institute for Community and Public Health, Birzeit University, Birzeit, Palestine. Advocate Research Institute, Advocate Health Care, Illinois, USA19 Independent University, Bangladesh Bashundhara, Dhaka Bangladesh20 Närhälsan R&D Centre Skaraborg Primary Care Skövde and Department of Public Health and Community Medicine, Primary Health Care, the Sahlgrenska Academy, University of Gothenburg, Sweden

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Confidential: For Review Only21 Wroclaw Medical University Department of Social Medicine Bujwida 44 St, 50-345 Wroclaw, Poland, EU22 Department of Community Health Sciences, The Aga Khan University, P.O. Box 3500 Stadium Road, Karachi 74800, Pakistan23 Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences Mentougou District, Beijing, China24 University of Philippines, Section of Adult Medicine & Medical Research Unit, Manila, Philippines25 Dubai Medical University, Hatta Hospital, Dubai Health Authority, Dubai, United Arab Emirates26 Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran27 Department of Internal Medicine III, Division of Gastroenterology and Hepatology, Medical University of Vienna, WähringerGürtel 18-20, Vienna, Austria

Version: November 2020

Guarantor of the article: Neeraj Narula

Corresponding author: Neeraj NarulaEmail: [email protected]: McMaster University Medical Centre, 1280 Main St West, Unit 3V67, Hamilton, ON, L8S 4K1

Specific author contributions: Neeraj Narula - study concept and design; compilation of data; statistical analysis; data interpretation; drafting of the manuscript; Emily Wong– compilation of data; statistical analysis; drafting of the manuscript; Mahshid Dehghan - study design; drafting of the manuscriptAndrew Mente - study design; drafting of the manuscriptSumathy Rangarajan - acquisition of data, drafting of the manuscriptFernando Lanas - contribution of data and review of manuscriptPatricio Lopez-Jaramillo - contribution of data and review of manuscriptPriyanka Rohatgi - contribution of data and review of manuscriptP V M Lakshmi - contribution of data and review of manuscriptRavi Prasad Varma - contribution of data and review of manuscriptAndres Orlandini - contribution of data and review of manuscriptAlvaro Avezum - contribution of data and review of manuscriptAndreas Wielgosz - contribution of data and review of manuscriptPaul Poirier - contribution of data and review of manuscriptMajid A Almadi - contribution of data and review of manuscriptYuksel Altuntas - contribution of data and review of manuscriptKien Keat Ng - contribution of data and review of manuscriptJephat Chifamba - contribution of data and review of manuscriptKaren Yeates - contribution of data and review of manuscript

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mailto:[email protected]

Confidential: For Review OnlyThandi Puoane - contribution of data and review of manuscriptRasha Khatib - contribution of data and review of manuscriptRita Yusuf - contribution of data and review of manuscriptKristina Bengtsson Boström - contribution of data and review of manuscriptKatarzyna Zatonska - contribution of data and review of manuscriptRomaina Iqbal - contribution of data and review of manuscriptLiu Weida - contribution of data and review of manuscriptZhu Yibing - contribution of data and review of manuscriptLi Sidong - contribution of data and review of manuscriptAntonio Dans - contribution of data and review of manuscriptAfzalhussein Yusufali - contribution of data and review of manuscriptNoushin Mohammadifard - contribution of data and review of manuscriptJohn Marshall – study design; drafting of the manuscriptPaul Moayyedi - study design; drafting of the manuscriptWalter Reinisch - study concept and design; drafting of the manuscript;Salim Yusuf - study concept and design; acquisition of data, funding, drafting of the manuscript

Conflicts of interest:Neeraj Narula holds a McMaster University Department of Medicine Internal Career Award. Neeraj Narula has received honoraria from Janssen, Abbvie, Takeda, Pfizer, Merck, and Ferring.

John K. Marshall has received honoraria from Janssen, AbbVie, Allergan, Bristol-Meyer-Squibb, Ferring, Janssen, Lilly, Lupin, Merck, Pfizer, Pharmascience, Roche, Shire, Takeda and Teva.

Walter Reinisch has received support for the following:Speaker for Abbott Laboratories, Abbvie, Aesca, Aptalis, Astellas, Centocor, Celltrion, Danone Austria, Elan, Falk Pharma GmbH, Ferring, Immundiagnostik, Mitsubishi Tanabe Pharma Corporation, MSD, Otsuka, PDL, Pharmacosmos, PLS Education, Schering-Plough, Shire, Takeda, Therakos, Vifor, Yakult, Consultant for Abbott Laboratories, Abbvie, Aesca, Algernon, Amgen, AM Pharma, AMT, AOP Orphan, Arena Pharmaceuticals, Astellas, Astra Zeneca, Avaxia, Roland Berger GmBH, Bioclinica, Biogen IDEC, Boehringer-Ingelheim, Bristol-Myers Squibb, Cellerix, Chemocentryx, Celgene, Centocor, Celltrion, Covance, Danone Austria, DSM, Elan, Eli Lilly, Ernest & Young, Falk Pharma GmbH, Ferring, Galapagos, Genentech, Gilead, Grünenthal, ICON, Index Pharma, Inova, Janssen, Johnson & Johnson, Kyowa Hakko Kirin Pharma, Lipid Therapeutics, LivaNova, Mallinckrodt, Medahead, MedImmune, Millenium, Mitsubishi Tanabe Pharma Corporation, MSD, Nash Pharmaceuticals, Nestle, Nippon Kayaku, Novartis, Ocera, OMass, Otsuka, Parexel, PDL, Periconsulting, Pharmacosmos, Philip Morris Institute, Pfizer, Procter & Gamble, Prometheus, Protagonist, Provention, Robarts Clinical Trial, Sandoz, Schering-Plough, Second Genome, Seres Therapeutics, Setpointmedical, Sigmoid, Sublimity, Takeda, Therakos, Theravance, Tigenix, UCB, Vifor, Zealand, Zyngenia, and 4SC, Advisory board member for Abbott Laboratories, Abbvie, Aesca, Amgen, AM Pharma, Astellas, Astra Zeneca, Avaxia, Biogen IDEC, Boehringer-Ingelheim, Bristol-Myers Squibb, Cellerix, Chemocentryx, Celgene, Centocor, Celltrion, Danone Austria, DSM, Elan, Ferring, Galapagos, Genentech, Grünenthal, Inova, Janssen, Johnson & Johnson, Kyowa Hakko Kirin Pharma, Lipid Therapeutics, MedImmune, Millenium, Mitsubishi Tanabe Pharma Corporation, MSD, Nestle, Novartis, Ocera, Otsuka, PDL, Pharmacosmos, Pfizer, Procter & Gamble, Prometheus, Sandoz, Schering-Plough, Second Genome, Setpointmedical, Takeda, Therakos, Tigenix, UCB, Zealand, Zyngenia, and 4SC

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Confidential: For Review OnlyNo other authors have any relevant conflicts of interest.

No authors have received support for the submitted manuscript.

All authors approved the final version of the manuscript.

Summary box:

What is already known on this topic:- Inflammatory bowel disease (IBD) is more common in industrialized nations, and it is

hypothesized that environmental factors such as diet may influence the risk of developing IBD- Many dietary risk factors have been investigated for their association with IBD, but limited data

exists regarding the association of processed food intake (food containing additives and preservatives) and the risk of IBD

What this study adds:- We observed that higher processed food intake was associated with higher risk of IBD - As individual food categories (meats, dairy, starches, fruits/vegetables) were not found to be

associated with development of IBD, this indicates that it may not be food itself, but rather the way it is processed or ultra processed that confers this risk.

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Confidential: For Review OnlyAbstractObjectives: Dietary factors may influence the risk of developing inflammatory bowel disease (IBD), but

evidence from large, prospective studies is scarce. This study aimed to evaluate the relationship

between processed food intake and the risk of developing IBD in the prospective PURE cohort study.

Design and Setting: This was a prospective cohort study (Prospective Urban Rural Epidemiology [PURE])

of 116,087 individuals between the ages of 35-70 from 21 countries across seven geographic regions.

Country-specific validated food frequency questionnaires were used to document baseline dietary intake.

Participants were followed prospectively at least every 3 years.

Outcomes: The main clinical outcome for this study was development of IBD, including Crohn’s disease

(CD) or ulcerative colitis (UC).

Statistical Methods: Cox proportional hazard multivariate models were used to assess associations

between processed food intake and risk of IBD. Results are presented as hazard ratios (HR) with 95%

confidence intervals (CI).

Results: Participants were enrolled in the study between 2003 and 2016. During the median follow-up of

9·7 years (IQR 8.9–11.2), we recorded 467 incident cases of IBD (90 with CD and 377 with UC). Higher

processed food intake was associated with a higher risk of incident IBD (HR 1.82, 95% CI 1.22 to 2.72 for

>5 servings/day and HR 1.67, 95% CI 1.18 to 2.37, for 1-4 servings/day as compared to <1 serving/day,

ptrend=0.006), after adjustment for potential confounding factors. Different subgroups of processed food

including soft drinks, sweets, salty snacks, and processed meat each were associated with higher HR for

IBD. Results were consistent in UC and CD with low heterogeneity. White meat, red meat, dairy, starch,

fruits, vegetables, and legumes intake were not associated with incident IBD.

Conclusions: Higher processed food consumption was positively associated with development of IBD.

Further studies are needed to identify the potential culprits within processed foods.

Funding: Full funding sources listed at end of paper (see acknowledgements)

KEYWORDS: Crohn’s Disease; Processed foods; ulcerative colitis; soft drinks; processed meat; salty snacks; sweets; sugar

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Confidential: For Review OnlyTABLES AND FIGURES LEGEND:

Table 1 – Characteristics of the study population at time of enrollment by region

Table 2- Results from Univariate and Multivariate Analyses

Table 3- Association Between Total Processed Food Intake (USDA servings/day) and Development of IBD

Table 4- Association Between Urinary Sodium (grams/day) and Development of IBD

Table 5- Summary of Associations Between Various Food Types and Development of IBD

Figure 1 - Association Between Total Processed Meat Intake (servings/day) and Development of IBD

Figure 2 - Association Between Total Soft Drink Intake (servings/day) and Development of IBD

Figure 3 - Association Between Sweets Intake (grams/day) and Development of IBD

Figure 4 - Association Between Salty Food and Snacks Intake (grams/day) and Development of IBD

Supplementary Appendix 1 - PURE Study Participant Selection Methodology as Excerpted from Teo et al

Supplementary Appendix 2 – Validation exercise for 20% of IBD participants

Supplementary Table 1 – PURE food frequency questionnaire validation studies

Supplementary Table 2 – Association Between Total Processed Food Intake (grams/day) and Development of IBD

Supplementary Table 3 - Association Between Total Processed Meat Intake (servings/day) and Development of IBD

Supplementary Table 4 - Association Between Total Soft Drink Intake (servings/day) and Development of IBD

Supplementary Table 5 - Association Between Sweets Intake (grams/day) and Development of IBD

Supplementary Table 6 - Association Between Salty Food and Snacks Intake (grams/day) and Development of IBD

Supplementary Table 7 - Association of Urinary Sodium (grams/day) and Development of IBD (sensitivity analyses)

Supplementary Table 8 - Association of White Meat Intake and Development of IBD

Supplementary Table 9 - Association of Red Meat (Unprocessed) Intake and Development of IBD

Supplementary Table 10 - Association of Dairy Intake and Development of IBD

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Confidential: For Review OnlySupplementary Table 11 - Association of Starch Intake and Development of IBD

Supplementary Table 12 - Association of Fruit, Vegetable & Legume Intake and Development of IBD

Supplementary Table 13 - Association of Fruit Intake and Development of IBD

Supplementary Table 14 - Association of Vegetable Intake and Development of IBD

Supplementary Table 15 - Association of Legume Intake and Development of IBD

Supplementary Table 16 - Association of Fried Foods Intake and Development of IBD

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Confidential: For Review OnlyIntroduction

Inflammatory bowel disease (IBD), comprised of Crohn’s disease (CD) and ulcerative colitis (UC), are

chronic inflammatory disorders of the gastrointestinal tract. Its pathophysiology is thought to be related

to activation of the intestinal mucosal immune system in response to dysbiosis of the gastrointestinal

tract in genetically susceptible individuals [1]. Diet alters the microbiome [2], modifies the intestinal

immune response and so could play a role in the pathogenesis of IBD [3]. The incidence of IBD has

increased in several countries where these diseases were previously uncommon [4]. This increase has

paralleled westernization of diet in these countries. In particular, dietary changes in such countries,

including increased intake of dietary fats such as n-6 polyunsaturated fatty acids, refined sugars, and

decreases in fibre intake, have all been suggested as potential risk factors for development of IBD [5-7].

Specific data linking dietary factors with IBD in human populations have been limited and conflicting [8-

10]. Most prior studies of diet and IBD have been retrospective and case-control in design [9, 10]. The

few prospective studies which have examined dietary risk factors have been limited by a small number

of cases, lack of adjustment for potential confounders, or use of homogenous populations confined to

individual countries or specific regions of countries [6, 7, 11-15].

A recent systematic review synthesized all studies which have evaluated dietary intake and risk of IBD

[10]. From this, it is evident that many associations have been examined repeatedly, including the

association of different dietary fats, carbohydrates, proteins, fruits, vegetables, fibre, and dairy [10].

Meats have been assessed at as a whole, with many studies suggesting an increased odds of

development of IBD with higher meat intake [10, 16]. This systematic review also found high intake of

total fats, poly-unsaturated fatty acids, and omega-6 fatty acids to be associated with increased risk of

IBD. High fibre and fruit intake may decrease the risk of CD, and high vegetable intake was associated

with decreased UC risk [17, 18].

Recent attention has been focused on non-nutritional components of the diet and their potential risks.

Processed foods often include many non-natural ingredients and additives such as artificial flavours,

sugars, stabilizers, emulsifiers, and preservatives. Detergents and emulsifiers that are added to foods

may have a detrimental impact on the gut barrier. Carboxymethylcellulose has been shown to increase

bacterial adherence to intestinal epithelium and may lead to bacterial overgrowth and infiltration of

bacteria into the spaces in between intestinal villi [19]. Polysorbate 80 is an emulsifier commonly used

in processed foods that increases translocation of bacteria like E.coli across M cells and Peyer’s patches

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Confidential: For Review Onlyin patients with CD [20]. Associations have been reported between diets high in processed foods and

development of diseases including malignancy and cardiovascular disease [21, 22]. One recent study

investigated whether ultra processed foods were associated with risk of IBD and did not find any

association, but was limited by few IBD cases (75 among 105,832 participants)[15].

This report aims to describe the association between dietary processed food intake and the risk of

developing IBD using the Prospective Urban Rural Epidemiology (PURE) cohort (24-26).

Methods

The design and methods of the PURE study have been described and published previously [23-25]. The

first and second phases of the PURE study took place between January 1, 2003 and December 31, 2016

and included 136,384 individuals between the ages of 35-70 who had dietary information assessed.

Participants were enrolled from over 21 countries including Argentina, Bangladesh, Brazil, Canada, Chile,

China, Colombia, India, Iran, Malaysia, Palestine, Pakistan, Philippines, Poland, South Africa, Saudi

Arabia, Sweden, Tanzania, Turkey, United Arab Emirates, and Zimbabwe. The sampling and recruitment

strategy from PURE has been previously published and is described in supplementary appendix 1 for

reference [26]. Data were collected at the community, household, and individual level using

standardized questionnaires. Standard case-report forms were used to record new diagnoses of CD and

UC during the follow-up period. For the current analysis, the data includes all outcome events up to July

5, 2019. The study is coordinated by the Population Health Research Institute and McMaster University

and Hamilton Health Sciences in Hamilton, ON, Canada. The PURE study is registered on

ClinicalTrials.gov, identifier: NCT03225586. The ethical conduct of this study has been approved through

protocol 03-206 by the Hamilton Integrated Research Ethics Board.

Procedures

For each participant in PURE, habitual food intake was assessed at baseline using country-specific

validated food frequency questionnaires (FFQ). Validated FFQs were developed for countries where a

validated FFQ was not previously available (supplementary table 1). A list of food items was provided to

participants, who were asked to input their frequency of intake of each item in the past year. To

compute daily food and nutrient intakes, participant answers were converted to daily intake and

multiplied by serving size. Food was converted into nutrients using country-specific nutrient databases,

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Confidential: For Review Onlywhich are primarily based on the US Department of Agriculture (USDA) food composition database

(release 18 and 21), which was modified to account for local food composition tables and supplemented

with recipes of local commonly eaten dishes. Processed food included all types of packaged and

formulated foods and beverages which contain food additives, artificial flavorings, colors, or other

chemical ingredients. Processed meat, cold breakfast cereal, various types of sauce, soft drinks, candies,

chips, ice-cream, commercially prepared pastries, biscuits, and fruit drinks were included in this group.

To make the unit of consumption consistent between countries, we used daily serving intake.

Outcomes

The primary outcome was the development of IBD after completion of the baseline questionnaire.

Other outcomes of interest were development of CD and UC individually for dietary risk factors that had

statistically significant association with development of IBD overall. Although data collection for PURE

started in 2003, an amendment was implemented in 2014 to record diagnoses of CD and UC.

Participants were asked at every follow-up questionnaire whether they had a diagnosis of CD or UC.

Participants were asked when their diagnosis occurred and those with prevalent diagnoses of IBD

(diagnosis before baseline questionnaire) were excluded. Cases where baseline FFQ was completed less

than one year from time of reported IBD diagnosis were excluded. Diagnosis was per self-report by a

participant. A validation exercise was conducted to validate 20% of the diagnoses using a sample of the

population where medical records could be provided for review by the authors.

Statistical analysis

Continuous variables were expressed as means with standard deviation (SD) and categorical variables as

percentages. Education was categorized as College/University/Trade (>11 years of age), Secondary/High

School (7-11 years of age), or None/Primary (first 6 years of age). Alcohol was categorized as <1 serving

per month, 1 per month – 1 per week, or > 1 per week. Geographic region was treated as binary

variable of North America/Europe or rest of the world, due to prevalence of IBD typically being highest

in North America and Europe, and lower in other parts of the world. Other binary variables included

smoking status (current vs. not) and location (rural vs. urban). Other categorical variables included body

mass index (BMI) classified as obese, overweight, or normal, and physical activity classified as low (<600

metabolic equivalent of task (MET) per min per week), moderate (600-3000 MET min per week), or high

(>3000 MET min per week). Continuous variables included age, daily total energy in kcal and waist to

hip ratio.

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Confidential: For Review OnlyFor the overall analysis of total processed food, participants were grouped into less than one

serving/day, one to four servings per day, or five or more servings per day. The lowest intake group was

always used as a reference group. As a sensitivity analysis, we also grouped participants based on gram

intake of processed food into less than 50 grams/day, 50-99 grams/day, and 100 or more grams/day.

We also assessed the association using categories of processed foods individually. Processed meat was

categorized into less than one serving per week, one to six servings per week, or seven or more servings

per week. Soft drink intake was categorized into less than 0.5 serving per week, 0.5 to <3 servings per

week, or 1 or more serving per week. Sweets included foods that are high in refined sugars (i.e. cake,

cookies, etc.), and was categorized into none, 1-99 grams/day, or 100 or more grams/day. Salty foods

and snacks were categorized into less than 50 grams/day, 50-99 grams/day, and 100 or more

grams/day. An additional analysis of urinary sodium was conducted, as a surrogate of dietary sodium, to

determine whether dietary sodium could potentially be implicated should processed foods be

associated with risk of IBD. The Kawasaki formula was used to estimate urinary sodium excretion [27].

Based on a previous paper assessing urinary sodium, categories chosen were less than 2.5 grams/day,

2.5-3.4 grams/day, and 3.5 or more grams/day [27]. A sensitivity analysis was planned to exclude China

from this analysis, due to significantly higher sodium intake observed in China compared to the rest of

the world and relatively low event rates for IBD [27].

Exploratory analyses were planned with other dietary variables to confirm or refute risk factors which

have been previously reported in the literature. Participants were categorized by white meat intake into

less than one serving per week, one to two servings per week, or three or more servings per week. Red

meat (unprocessed) was categorized into less than three servings per week, three to six servings per

week, or seven or more servings per week. Dairy intake was categorized into less than 1 serving per day,

1 to less than 2 servings per day, or 2 or more servings per day. Starch intake was categorized into less

than 200 grams per day, 200-399 grams per day, and 400 or more grams per day. Fruits, vegetables, and

legumes were analyzed all together and classified as less than 2 servings per day, 2-5 servings per day,

and 6 or more servings per day, but were also analyzed separately. Lastly, fried foods were categorized

into none, one serving per week to less than one serving per day, or one or more serving per day.

We calculated hazard ratios (HRs) using multivariate Cox proportional hazard models. Estimates of HRs

and 95% CIs are presented for each dietary risk factor of interest. Results are presented as unadjusted

HRs, minimally adjusted HRs for age, sex, and geographical region, and fully adjusted HRs. Covariates to

include in the fully adjusted model were those found to have an association on univariate analyses (p

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Confidential: For Review Onlyvalue <0.15). Covariates for consideration in the fully adjusted multivariate model included age, gender,

geographical region, education, alcohol intake, smoking status, physical activity, energy intake, BMI, waist

to hip ratio, and urban vs. rural location. The Kolmogorov-type Supremum test was used to test the

proportional hazards assumptions [28]. Chi square test of linear trend was used to compare across

categories of food intake. Statistical significance was chosen to be at a two-sided p-value <0.05. For

subgroup analyses of CD and UC conducted when a dietary risk factor had statistically significant

association with development of IBD, low power to achieve statistical significance within a subgroup was

anticipated. For comparison of subgroup effects, we assessed heterogeneity by calculating the Chi² and

I² statistics. For the Chi² test, we considered a p value <0.10 to be statistically significant. I² values of

greater than 50% were considered to indicate substantial heterogeneity. Heterogeneity of the subgroup

analyses was used to determine whether the association was predominantly due to effect from one

subgroup. Data were analyzed using Stata/IC 15.

Role of the funding source

The external funders of the PURE study had no input on study design, data collection and analysis,

interpretation, and preparation of the manuscript. The corresponding author (NN), coauthors (EW, AM,

MD, SR), and senior author (SY) had full access to the data used for the study and made the final decision

on submission for publication.

Patient and public involvement

No patients were involved in setting the research question or outcome measures, or in the design and

implementation of this study.

Results

Between January 1, 2003 and December 31, 2016, a total of 153,220 participants completed the FFQ.

126,662 participants had plausible energy intake (500–5000 kcal per day) and had at least one cycle of

follow-up. We excluded 10,625 (8.3%) who did not have complete FFQ data, leaving a total of 116,037

participants included in this study.

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Confidential: For Review OnlyDuring the median follow-up of 9·7 years (IQR 8.9–11.2), we recorded 467 individuals with incident IBD

during follow up. This included 90 subjects with CD and 377 with UC. Results of the validation exercise

are provided in supplementary appendix 2.

Processed food intake was higher in North America, Europe, and South America, than in other regions

(table 1), both in servings and grams of intake per day. Similarly, processed meat and soft drink intakes

were highest in these three regions. Sweets consumption was highest in South America, followed by the

Middle East and Southeast Asia. Salty and snack food intake was highest in North America and

Southeast Asia. Sodium intake was highest in China.

Table 2 summarizes the results of the univariate associations. Those dietary variables which were

nominally significant were included within the multivariate Cox proportional hazard regression models.

In addition to age, gender, and geographic region used in the minimally adjusted model, education,

alcohol intake, smoking status, BMI, total energy intake and location were all found to be significantly

associated with the development of IBD and were included within the multivariate model. None of the

variables in the model violated the proportional hazards assumption when tested using the Supremum

test.

Processed food intake and risk of IBD

Table 3 shows the graded association between intake of total processed foods and risk of development

of IBD. Higher processed food intake was associated with a higher risk of incident IBD (HR 1.82, 95% CI

1.22-2.72 for >5 servings/day and HR 1.67 (1.18-2.37) for 1-4 servings/day as compared to <1

serving/day, ptrend=0.006). Similar increased risks were observed for both CD and UC (tests of

heterogeneity: Chi² p-value=0.595; I² = 0%). On sensitivity analysis (supplementary table 2), participants

with processed food intake as measured in grams/day intake also demonstrated significantly increased

risk of IBD development. Higher grams intake of processed food was associated with higher risk of

incident IBD (HR 1.73, 95% CI 1.23-2.45 for >100 grams/day and HR 1.33 (0.88-2.01) for 50-100

grams/day as compared to <50 grams/day, ptrend =0.0065).

Associations of categories of processed foods and risk of IBD are presented in figures 1-4 (with further

details available in supplementary tables 3-6). Figure 1 demonstrates that higher processed meat intake

is associated with higher risk of IBD (HR 2.07, 95% CI 1.14-3.76 for > 1 serving/day and HR 1.92 (1.24-

2.98) for 1 serving/week-<1 serving/day as compared to <1 serving/week, ptrend =0.0126). Increased risk

was observed for both CD and UC (tests of heterogeneity: Chi² p-value=0.929; I² = 0%). Figure 2 also

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Confidential: For Review Onlydemonstrates a graded risk for soft drink intake and risk of IBD. Highest soft drink intake (> 3

servings/week) versus < 0.5 serving/week was associated with a higher risk of IBD development (HR

1.94, 95% CI 1.42-2.66, ptrend =0.0001), with similar patterns for CD and UC (Chi² p-value=0.722; I² = 0%).

Consumption of sweets was associated with higher risk of IBD (Figure 3). Participants with ≥100g/day of

sweets intake had the highest risk of IBD development (HR 2.58, 95% CI 1.44-4.62, ptrend =0.0029)

compared to those with zero intake. Results were consistent for CD and UC (tests of heterogeneity: Chi²

p-value=0.165; I² = 41%). A similar pattern was observed for salty foods and snacks, where participants

with the highest consumption (≥100g/day) had increased risk of IBD development as compared to those

with <50g/day (HR 2.06, 95% CI 1.41-3.00, ptrend =0.0009)(Figure 4). There again was consistency

observed in the risk of CD and UC (tests of heterogeneity: Chi² p-value=0.245; I² = 27.8%)

Urinary sodium and risk of IBD

Urinary sodium was used as a surrogate of dietary sodium, and participants with higher levels of urinary

sodium (≥3.5g/day) did not have higher risk of IBD than those patients with the lowest levels of urinary

sodium (<2.5g/day)(ptrend =0.6058)(Table 4). As there was a large number of IBD cases among the

highest urinary sodium cohort, a sensitivity analysis was performed to further subdivide this cohort of

patients (supplementary table 7), but again no difference in risk of IBD was seen in patients with the

highest levels of urinary sodium (>5g/day) compared to those with lower levels of urinary sodium (ptrend

=0.4469). Although we confirmed that urinary sodium was highest among participants from China (table

1) with only 19 incident cases of IBD, exclusion of participants from China also did not affect the results

(ptrend =0.6807).

Other food categories and risk of IBD

Several other food categories were evaluated in exploratory analyses. The results are summarized in

Table 5, with detailed analyses available in supplementary tables 8-16. White meat, unprocessed red

meat, dairy, starchy foods, fruits, vegetables, and legumes intake were not found to be associated with

risk of IBD development (supplementary tables 8-15). Consumption of fried foods was associated with

higher risk of IBD (supplementary table 16). Those with one or more serving per day of fried food had

the highest risk of IBD development (HR 3.02, 95% CI 1.51-6.03, ptrend =0.0060) compared to those with

zero intake. No significant difference in risk was observed between CD and UC (tests of heterogeneity:

Chi² p-value=0.186; I² = 37.6%).

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Confidential: For Review OnlyDiscussion

In this large, multinational, prospective cohort study involving 116,087 participants from 21 low, middle-

and high-income countries, we found that higher intake of processed foods was associated with

increased incidence of IBD. This was seen for all processed foods (including ultra and minimally

processed foods), as well as individual types of ultra processed foods including processed meats, soft

drinks, sweets, and salty foods and snacks. No significant heterogeneity was observed when comparing

results for CD and UC within each of these types of processed foods. Our findings support the

hypothesis that consumption of processed foods may be an environmental factor which increases the

risk for IBD.

Processed foods are a diverse food group that include meats, dairy, starchy foods, fruits and vegetables.

None of these categories on its own was found to be implicated as a risk factor for IBD in our study.

Recent attention has been focused on the diet as an environmental factor which may be implicated in

the development of IBD, and studies have suggested that ‘western’ diets which are typically high in

protein, fat, salt, and sugar, but low in fruits, vegetables, and fibre, are associated with increased risk of

IBD [29-31]. A recent meta-analysis suggested western diets were associated with a relative risk for IBD

of 1.92 (95% CI 1.37-2.68)[32]. However, food consumed in westernized nations also contain higher

levels of additives and preservatives, which may explain why ‘western’ diets are associated with higher

risk of IBD. We hypothesized that increased sodium intake could be implicated, as various animal

models have demonstrated increased dietary sodium to be associated with exacerbation of

autoimmune conditions including collagen-induced arthritis model for rheumatoid arthritis [33] and

TNBS-induced colitis for IBD [34]. We however found no relationship between urinary sodium, a

surrogate for dietary sodium intake, and subsequent development of IBD. This suggests components

other than sodium in processed foods may be responsible for the higher risk of IBD.

Further studies are now required to identify potential culprits within processed foods. Pre-clinical

studies have demonstrated that emulsifiers, which are widely prevalent in westernized diets and include

carboxymethylcellulose and polysorbate 80, can induce thinning of the mucosal layer and induce

dysbiosis, and promote development of colitis and colitis-associated colon cancer in mice models [35-

37]. Other additives that have been implicated in pre-clinical studies include maltodextrin [38, 39] and

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Confidential: For Review Onlytitanium dioxide [40]. Consumption of foods containing these types of additives could be a plausible

pathway for disruption of gut microbiota and propagation of the subsequent immune activation that

occurs in IBD.

Recent studies have assessed the association of processed food intake and IBD. One study from

Romania and Belgium reported IBD patients were more likely to report higher intake of sweets,

processed and high fat meats, fried food, salt, ice cream, mayonnaise, margarine, and chips/other

snacks, compared to healthy controls. An American study using data from the National Health Interview

Study in 2015 reported higher intake of cheese, cookies, French fries, sports drinks, and soda, among

participants with IBD compared to those without. These studies however both had design limitations

including case control and retrospective design which is vulnerable to recall bias. A nested matched

case-control study conducted using the large EPIC prospective database found that diets with high sugar

and soft drink consumption with low vegetable intake was associated with increased risk for UC [5]. A

prospective study using data from the Nurses Health Study II cohort revealed participants with a

‘prudent’ dietary pattern in high school (higher intake of vegetables, fibre, and fish) had lower risk of

subsequent CD [41]. A French study using the NutriNet-Sante prospective cohort did not find higher

intake of ultra processed foods among participants who would subsequently be diagnosed with IBD

compared to those without IBD, after adjustment for confounders, but their study may have been

underpowered due to few IBD cases (n=75)[15].

We also found higher intake of fried food to be associated with higher risk of IBD. This association may

exist since many fried foods are processed as well, i.e. chicken nuggets, French fries, etc. It could be that

the process of frying and processing in oil leads to modification of nutrients within the food [42, 43].

The type and quality of oil used may also be relevant [44].

White meat, unprocessed red meat, dairy, starch, fruits, vegetables, and legumes intake were not found

to be associated with risk of IBD development in our study. Recent meta-analyses which included high

quality studies have not found associations between pre-illness intake of carbohydrates, sugar, protein,

or fat for either CD or UC [45-47]. Although some studies have suggested increased meat intake could

increase the risk of IBD [10, 16], and higher fruit and vegetable intake may be protective [17, 18], these

have not been found consistently in all studies that have examined this question [10, 48, 49]. This may

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Confidential: For Review Onlybe partly due to study design limitations, particularly in case control studies where the selection of

controls may have an impact on associations being evaluated.

For positive associations demonstrated in our study, similar directional effects were observed for CD and

UC. Overall, a lower number of CD cases were observed in our study (n=90) compared to 390 UC cases.

Reasons for the large incidence of UC cases compared to CD likely reflects that this an international

study with several cases reported in developing nations, where UC is known to be more prevalent [50].

Further, the age of participants in the PURE study was 35-70, and the risk of CD is lower at older ages,

whereas UC has a bimodal peak with many patients receiving diagnoses in their fifth and sixth decades

of life [1, 51].

One of the strengths of our multinational study is that it is one of the largest prospective cohort studies

to date examining dietary risk factors for IBD. This overcomes the limitations of many of the existing

studies in the field, which are retrospective, case control in design, and limited to homogenous

populations with limited external validity [10]. Its longitudinal design allowed us to focus on incident

IBD cases, a sample of which were later validated by medical record review and central adjudication.

Dietary assessments were collected at baseline before diagnosis to minimize risk of bias due to reverse

causation or recall bias. Cases that were diagnosed within one year of the baseline FFQ assessment

were excluded to account for the possibility of dietary changes participants may make when

experiencing gastrointestinal symptoms that would later lead to a diagnosis of IBD. Validated,

standardized, and country-specific questionnaires were used for collection of dietary information. The

validity of our study findings is increased based on the findings of other baseline covariates on univariate

analysis that were found to be associated with risk of IBD including smoking status and location [52, 53].

We also identified other factors on univariate analysis such as alcohol intake and BMI which were

significant, but likely correlated with other confounding factors and when put into a multivariate model,

were no longer associated with risk of IBD (analyses not shown).

Nonetheless, our study also has some potential limitations. Due to the age of participants in the PURE

study (ages 35-70), there were a relatively small number of incident CD cases observed, which may have

caused our study to be underpowered for risk factors evaluating risk of CD. Only a sample of IBD cases

were selected for validation, which creates the risk of misclassification for cases which did not undergo

verification processes. Dietary changes over time were not accounted for, but dietary intake as

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Confidential: For Review Onlymeasured by the FFQ has previously been demonstrated to be relatively stable over time [54].

However, during the period of follow up, some of the included countries may have undergone various

degrees of ‘Westernization’ of the diet, which could lead to some inaccuracy in capturing of the degree

of processed foods and their influence in these participants. FFQs are not an ideal instrument for

measurement of absolute intake, however may still be useful for relative intake, so our results should be

taken in the context of higher versus lower processed food intake [55]. Multiple comparisons were

performed and there remains a possibility of positive results being found due to chance alone. Lastly,

although we were able to adjust for many variables through our multivariate models, there remains the

possibility of residual bias from unmeasured (i.e. early life antibiotic use) or unknown confounders due

to the observational nature of our study.

In conclusion, in the largest prospective cohort study to date, we observed that higher processed food

intake was associated with higher risk of IBD. As white meat, unprocessed red meat, dairy, starch, fruits,

vegetables, and legumes were not found to be associated with development of IBD, this study suggests

that it may not be food itself, but rather the way it is processed or ultra processed that confers this risk.

Further studies are needed to identify the potential culprits within processed foods.

ACKNOWLEDGEMENTS: The authors would like to acknowledge Mr. Shofiqul Islam for his statistical advice and Ms. Dawn Agapay for her assistance with the validation exercise.Funding: SY is supported by the Heart & Stroke Foundation/Marion W. Burke Chair in Cardiovascular Disease. The PURE Study is an investigator-initiated study funded by the Population Health Research Institute, the Canadian Institutes of Health Research (CIHR), Heart and Stroke Foundation of Ontario, support from CIHR’s Strategy for Patient Oriented Research (SPOR) through the Ontario SPOR Support Unit, as well as the Ontario Ministry of Health and Long-Term Care and through unrestricted grants from several pharmaceutical companies, with major contributions from AstraZeneca (Canada), Sanofi-Aventis (France and Canada), Boehringer Ingelheim (Germany and Canada), Servier, and GlaxoSmithkline, and additional contributions from Novartis and King Pharma and from various national or local organisations in participating countries; these include: Argentina: Fundacion ECLA; Bangladesh: Independent University, Bangladesh and Mitra and Associates; Brazil: Unilever Health Institute, Brazil; Canada: Public Health Agency of Canada and Champlain Cardiovascular Disease Prevention Network; Chile: Universidad de la Frontera; China: National Center for Cardiovascular Diseases; Colombia: Colciencias, grant number 6566-04-18062; India: Indian Council of Medical Research; Malaysia: Ministry of Science, Technology and Innovation of Malaysia, grant numbers 100 -IRDC/BIOTEK 16/6/21 (13/2007) and 07-05-IFN-BPH 010, Ministry of Higher Education of Malaysia grant number 600 -RMI/LRGS/5/3 (2/2011), Universiti Teknologi MARA, Universiti Kebangsaan Malaysia (UKM-Hejim-Komuniti-15-2010); occupied Palestinian

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Confidential: For Review Onlyterritory: the UN Relief and Works Agency for Palestine Refugees in the Near East, occupied Palestinian territory; International Development Research Centre, Canada; Philippines: Philippine Council for Health Research & Development; Poland: Polish Ministry of Science and Higher Education grant number 290/W-PURE/2008/0, Wroclaw Medical University; Saudi Arabia: the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia (research group number RG -1436-013); South Africa: the North-West University, SANPAD (SA and Netherlands Programme for Alternative Development), National Research Foundation, Medical Research Council of SA, The SA Sugar Association (SASA), Faculty of Community and Health Sciences (UWC); Sweden: grants from the Swedish state under the Agreement concerning research and education of doctors; the Swedish Heart and Lung Foundation; the Swedish Research Council; the Swedish Council for Health, Working Life and Welfare, King Gustaf V’s and Queen Victoria Freemasons Foundation, AFA Insurance, Swedish Council for Working Life and Social Research, Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, grant from the Swedish State under the Läkar Utbildnings Avtalet agreement, and grant from the Västra Götaland Region; Turkey: Metabolic Syndrome Society, AstraZeneca, Turkey, Sanofi Aventis, Turkey; United Arab Emirates (UAE): Sheikh Hamdan Bin Rashid Al Maktoum Award For Medical Sciences and Dubai Health Authority, Dubai UAE.

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Confidential: For Review Only46. Wang F, Feng J, Gao Q, Ma M, Lin X, Liu J, et al. Carbohydrate and protein intake and risk of ulcerative colitis: Systematic review and dose-response meta-analysis of epidemiological studies. Clinical nutrition (Edinburgh, Scotland). 2017;36(5):1259-65.47. Wang F, Lin X, Zhao Q, Li J. Fat intake and risk of ulcerative colitis: Systematic review and dose-response meta-analysis of epidemiological studies. Journal of gastroenterology and hepatology. 2017;32(1):19-27.48. Reif S, Klein I, Lubin F, Farbstein M, Hallak A, Gilat T. Pre-illness dietary factors in inflammatory bowel disease. Gut. 1997;40(6):754-60.49. Halfvarson J, Jess T, Magnuson A, Montgomery SM, Orholm M, Tysk C, et al. Environmental factors in inflammatory bowel disease: a co-twin control study of a Swedish-Danish twin population. Inflammatory bowel diseases. 2006;12(10):925-33.50. Ng WK, Wong SH, Ng SC. Changing epidemiological trends of inflammatory bowel disease in Asia. Intest Res. 2016;14(2):111-9.51. Cosnes J, Gower-Rousseau C, Seksik P, Cortot A. Epidemiology and natural history of inflammatory bowel diseases. Gastroenterology. 2011;140(6):1785-94.52. Mahid SS, Minor KS, Soto RE, Hornung CA, Galandiuk S. Smoking and inflammatory bowel disease: a meta-analysis. Mayo Clinic proceedings. 2006;81(11):1462-71.53. Benchimol EI, Kaplan GG, Otley AR, Nguyen GC, Underwood FE, Guttmann A, et al. Rural and Urban Residence During Early Life is Associated with Risk of Inflammatory Bowel Disease: A Population-Based Inception and Birth Cohort Study. The American journal of gastroenterology. 2017;112(9):1412-22.54. Nagel G, Zoller D, Ruf T, Rohrmann S, Linseisen J. Long-term reproducibility of a food-frequency questionnaire and dietary changes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Heidelberg cohort. The British journal of nutrition. 2007;98(1):194-200.55. Steinemann N, Grize L, Ziesemer K, Kauf P, Probst-Hensch N, Brombach C. Relative validation of a food frequency questionnaire to estimate food intake in an adult population. Food & nutrition research. 2017;61(1):1305193.

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Confidential: For Review Only

Table 1 – Characteristics of the study population at time of enrollment by region

Overall (n=116,037)

Europe and North America (n=17,232)

South America (n=18,611)

Africa (n=3,871)

Middle East (n=9,399)

South Asia (n=21,834)

Southeast Asia (n=8,998)

China (n=36,092)

Participants who developed IBD (n=467)

Participants who did not develop IBD (n=115,570)

Cases of IBD Crohn’s diseaseUlcerative colitis

467 (0.4%)90 (0.1%)377 (0.3%)

192 (1.1%)51 (0.3%)141 (0.8%)

33 (0.2%)5 (0%)28 (0.2%)

25 (0.6%)4 (0.1%)21 (0.5%)

103 (1.1%)5 (0.1%)98 (1.0%)

94 (0.4%)24 (0.1%)70 (0.3%)

1 (0.01%)01 (0%)

19 (0.1%)1 (0%)18 (0.1%)

Age (years), SD 50.2 (9.7) 52.7 (9.2) 51.0 (9.5) 50.0 (10.1)

47.8 (9.2) 47.3 (9.8) 51.4 (9.7) 50.8 (9.7) 50.5 (9.3) 50.2 (9.7)

Female 68,732 (59.2%)

9,766 (56.7%)

11,655 (62.6%)

2,897 (74.9%)

4,951 (52.7%)

12,879 (59.0%)

5,503 (61.2%)

21,081 (58.4%)

273 (58.5%) 68,459 (59.2%)

Urban 60,412 (52.1%)

11,925 (69.2%)

10,489 (56.4%)

1,772 (45.8%)

5,298 (56.4%)

9,110 (41.7%)

4,472 (49.7%)

17,346 (48.1%)

276 (59.1%) 60,136 (52.0%)

Current smoker 22,563 (19.6%)

2,820 (16.4%)

3,673 (19.8%)

807 (21.4%)

1,323 (14.1%)

4,847 (22.3%)

1,320 (14.8%)

7,773 (21.9%)

75 (16.1%) 22,488 (19.6%)

Energy intake (kcal), SD

2161 (812) 2277 (832) 2198 (794) 2084 (965)

2322 (827) 2115 (829)

2552 (987)

1984 (664)

2105 (775) 2161 (812)

Processed food servings per day (SD)

1.8 (3.9) 6.0 (7.3) 2.9 (3.7) 0.7 (1.0) 1.2 (1.4) 0.3 (1.2) 2.0 (2.1) 0.3 (1.0) 4.0 (6.8) 1.8 (3.9)

Processed food grams per day (SD)

94.2 (228.3) 220.9 (255.1)

238.4 (416.8)

83.4 (184.3)

88.7 (159.3)

14.5 (56.1)

73.7 (95.7)

15.0 (58.4)

145.4 (223.2)

94.0 (228.3)

Processed meat servings per week (SD)

1.2 (2.8) 2.8 (3.7) 2.2 (4.0) 1.8 (2.5) 0.6 (1.3) 0.01 (0.1) 0.3 (0.5) 0.2 (0.6) 2.2 (3.3) 1.2 (2.8)

Soft drink servings per week (SD)

1.2 (4.0) 2.0 (4.3) 3.8 (7.8) 1.7 (3.8) 0.8 (1.6) 0.2 (0.9) 1.5 (3.2) 0.1 (0.3) 1.5 (3.4) 1.2 (4.0)

Grams of sweets per day (SD)

92.0 (216.3) 99.9 (138.1)

306.1 (418.2)

61.6 (139.7)

103.0 (180.1)

25.8 (44.3)

100.5 (122.5)

17.2 (34.1)

100.4 (180.6)

92.0 (216.4)

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Grams of salty foods/snacks per day (SD)

28.4 (37.6) 57.9 (50.0) 35.7 (35.5) 38.5 (52.0)

29.8 (29.2) 6.5 (16.4) 55.6 (46.7)

16.0 (20.3)

39.9 (47.2) 28.4 (37.6)

Grams of sodium per day (SD)*

4.1 (1.6) 3.6 (1.6) 4.7 (1.4) 4.2 (1.5) 4.6 (1.7) 3.4 (1.7) 3.8 (1.2) 5.7 (1.9) 4.2 (1.5) 4.1 (1.6)

*Urinary sodium was used as a surrogate for sodium intake

Table 2 – Results from Univariate and Multivariate Analyses

Univariate Multivariate*Predictor HR (95% CI) P-value HR (95% CI) P-value Age 1.00 (0.99-1.01) 0.5132 1.01 (0.99-1.02) 0.1600Sex (Male) 1.03 (0.86-1.24) 0.7329 0.93 (0.72-1.20) 0.5901North America/Europe 4.04 (3.36-4.86) <0.0001 5.57 (3.97-7.81) <0.0001Education None Secondary/High/Higher secondary Trade or College/University

1.00 (reference)1.26 (1.01-1.58)2.24 (1.78-2.81)

<0.00011.00 (reference)1.60 (1.13-2.26)1.57 (1.07-2.26)

0.0226

Alcohol <1 serving per month 1 serving per month to <1 serving per week ≥1 serving per week

1.00 (reference)3.52 (2.46-5.04)2.93 (2.28-3.77)

<0.00011.00 (reference)1.21 (0.82-1.80)0.92 (0.67-1.26)

0.3435

Current smoking 0.78 (0.61-1.00) 0.0469 0.73 (0.51-1.06) 0.1024Physical activity (MET score) Low (MET score <600) Moderate (MET score 600 to <3000) High (MET score ≥ 3000)

1.00 (reference)1.28 (0.95-1.71)1.30 (0.98-1.74)

0.1580

Energy (kcal) 1.00 (0.99-1.00) 0.1315 1.00 (0.99-1.00) 0.082BMI Normal (<25) Overweight (25 to <30) Obese (≥30)

1.00 (reference)1.09 (0.88-1.34)1.38 (1.09-1.75)

0.03451.00 (reference)0.91 (0.69-1.19)0.96 (0.70-1.32)

0.7778

Waist to hip ratio 1.80 (0.61-5.28) 0.2849

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Location Urban Rural

1.00 (reference)0.75 (0.62-0.90)

0.00221.00 (reference)0.99 (0.76-1.28)

0.9133

*Processed food intake in servings per day was used as the main predictor

Table 3- Association Between Total Processed Food Intake (USDA servings/day) and Development of IBD

IBD<1 serving/day 1 to <5 servings/day ≥ 5 servings/day P trend

No. of participants 76,415 25,453 11,742No. of events (%) 199 (0.26) 134 (0.53) 95 (0.81)Unadjusted HR (95% CI) 1 (reference) 2.20 (1.77-2.74) 3.18 (2.49-4.07) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.41 (1.11-1.79) 1.42 (1.07-1.90) 0.0105Fully adjusted HR (95% CI) 1 (reference) 1.67 (1.18-2.37) 1.82 (1.22-2.72) 0.0063

Crohn’s diseaseNo. of participants 76,415 25,453 11,742No. of events (%) 34 (0.04) 23 (0.09) 30 (0.26)Unadjusted HR (95% CI) 1 (reference) 2.19 (1.29-3.72) 5.84 (3.57-9.54) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.15 (0.64-2.06) 1.92 (1.05-3.49) 0.0699Fully adjusted HR (95% CI) 1 (reference) 2.72 (1.06-6.97) 4.50 (1.67-12.13) 0.0103

Ulcerative colitisNo. of participants 76,415 25,453 11,742No. of events (%) 165 (0.22) 111 (0.44) 65 (0.55)Unadjusted HR (95% CI) 1 (reference) 2.20 (1.73-2.80) 2.63 (1.97-3.51) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.48 (1.13-1.93) 1.27 (0.91-1.77) 0.0151Fully adjusted HR (95% CI) 1 (reference) 1.55 (1.06-2.28) 1.46 (0.93-2.28) 0.0761Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.595; I² = 0%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

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Table 4 - Association Between Urinary Sodium (grams/day) and Development of IBD

<2.5g/day 2.5 to <3.5g/day ≥3.5g/day P trendNo. of participants 8,444 11,555 61,033No. of events (%) 42 (0.50) 73 (0.62) 269 (0.44)Unadjusted HR (95% CI) 1 (reference) 1.33 (0.91-1.95) 0.89 (0.64-1.23) 0.0081Min. adjusted HR (95% CI) 1 (reference) 1.88 (1.28-2.76) 1.66 (1.18-2.34) 0.0039Fully adjusted HR (95% CI) 1 (reference) 1.26 (0.80-1.97) 1.17 (0.79-1.74) 0.6058Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Table 5- Summary of Associations Between Various Food Types and Development of IBD

White Meat <1 serving/week

1 to <3 servings/week

≥3 servings/week

P trend

Min. adjusted HR (95% CI)

1 (reference) 1.16 (0.92-1.45) 1.06 (0.82-1.36) 0.4568

Fully adjusted HR (95% CI)

1 (reference) 1.31 (0.99-1.74) 1.38 (0.97-1.95) 0.1008

Red Meat <3 servings/week

3 to <7 servings/week

≥7 servings/week

P trend


1 (reference) 0.83 (0.65-1.05) 1.03 (0.82-1.30) 0.1764


1 (reference) 1.00 (0.74-1.35) 1.12 (0.81-1.53) 0.7183

Dairy <1 serving/day 1 to <2 servings/day

≥2 servings/day P trend


1 (reference) 1.06 (0.80-1.41) 1.50 (1.15-1.96) 0.0056


1 (reference) 1.14 (0.78-1.68) 1.26 (0.86-1.83) 0.4928

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Starch <200g/day 200 to <400g/day

≥400g/day P trend


1 (reference) 1.22 (0.89-1.67) 1.23 (0.89-1.71) 0.4277


1 (reference) 1.41 (0.98-2.04) 1.50 (0.95-2.38) 0.1597

Fruits, vegetables, & legumes

<2 servings/day

2 to <6 servings/day



1 (reference) 1.02 (0.72-1.43) 1.23 (0.86-1.77) 0.2083


1 (reference) 0.97 (0.59-1.60) 1.31 (0.77-2.22) 0.1110

Fried foods 0 servings <1 serving/day ≥1 serving/day P trendMin. adjusted HR (95% CI)

1 (reference) 1.00 (0.72-1.41) 1.26 (0.88-1.79) 0.1237


1 (reference) 2.35 (1.21-4.55) 3.02 (1.51-6.03) 0.0060

Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

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Figure 1 – Association Between Total Processed Meat Intake (servings/day) and Development of IBD

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Figure 2 - Association Between Total Soft Drink Intake (servings/day) and Development of IBD

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Figure 3 – Association Between Sweets Intake (grams/day) and Development of IBD

Supplementary Figure 4 – Association between sweets intake and outcome events

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Figure 4 - Association Between Salty Food and Snacks Intake (grams/day) and Development of IBD

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Confidential: For Review OnlySupplementary Appendix 1 - PURE Study Participant Selection Methodology as Excerpted from Teo et al (27).

Selection of CountriesThe choice and number of countries selected in PURE reflects a balance between involving a large number of communities in countries at different economic levels, with substantial heterogeneity in social and economic circumstances and policies, and the feasibility of centers to successfully achieve long-term follow-up. Thus, PURE included sites in which investigators are committed to collecting good-quality data for a low-budget study over the planned 10-year follow-up period and did not aim for a strict proportionate sampling of the entire world.Selection of CommunitiesWithin each country, urban and rural communities were selected based on broad guidelines. A common definition for “community” that is applicable globally is difficult to establish. In PURE, a community was defined as a group of people who have common characteristics and reside in a defined geographic area. A city or large town was not usually considered to be a single community, rather communities from low-, middle-, and high-income areas were selected from sections of the city and the community area defined according to a geographical measure (e.g., a set of contiguous postal code areas or a group of streets or a village). The primary sampling unit for rural areas in many countries was the village. The reason for inclusion of both urban and rural communities is that for many countries, urban and rural9 environments exhibit distinct characteristics in social and physical environment, and hence, by sampling both, we ensured considerable variation in societal factors across PURE communities. The number of communities selected in each country varied, with the aim to recruit communities with substantial heterogeneity in social and economic circumstances balanced against the capacity of local investigators to maintain follow-up. In some countries (e.g., India, China, Canada, and Colombia), communities from several states/provinces were included to capture regional diversity, in policy, socioeconomic status, culture, and physical environment. In other countries (e.g., Iran, Poland, Sweden, and Zimbabwe), fewer communities were selected.Selections of Households and IndividualsWithin each community, sampling was designed to achieve a broadly representative sample of that community of adults aged between 35 and 70 years. The choice of sampling frame within each center was based on both “representativeness” and feasibility of long-term follow-up, following broad study guidelines. Once a community was identified, where possible, common and standardized approaches were applied to the enumeration of households, identification of individuals, recruitment procedures, and data collection. The method of approaching households differed between regions. For example, in rural areas of India and China, a community announcement was made to the village through contact of a community leader, followed by in-person door-to-door visits of all households. In contrast in Canada, initial contact was by mail followed by telephone inviting members of the households to a centralclinic. Households were eligible if at least 1 member of the household was between the 10 ages of 35 and 70 years and the household members intended to continue living in their current home for a further 4 years. For each approach, at least 3 attempts at contact were made. All individuals within these households between 35 and 70 years providing written informed consent were enrolled. When an eligible household or eligible individual in a household refused to participate, demographics and self-reported data about CVD risk factors, education, and history of CVD, cancers and deaths in the households within the two previous years were recorded. To ensure standardization and high data quality, we used a comprehensive operations manual, training workshops, DVDs, regular communication with study personnel and standardized report forms. We entered all data in a customized database programmed with range and consistency

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Confidential: For Review Onlychecks which was transmitted electronically to the Population Health Research Institute in Hamilton (Ontario, Canada) where further quality checks were implemented.

Supplementary Appendix 2 – Validation exercise for 20% of IBD participants

Of 467 cases of IBD, we aimed to validate 20% of cases (93 participants). Medical records for these 93 participants (where available in English) were requested from participating centres selected at random, upon which centres were able to provide medical records (such as consultation, imaging, and endoscopy reports) for 88 participants. Manual review of these records was able to confirm these 83 cases included 26 diagnoses of Crohn’s disease and 57 diagnoses of ulcerative colitis. 83/93 (89.2%) cases could be verified. For the 5 cases that records were not provided, this was due to lack of access to medical records at that site.

Supplementary Table 1 – PURE food frequency questionnaire validation studies

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Confidential: For Review OnlySupplementary Table 2 - Association Between Total Processed Food Intake (grams/day) and Development of IBD

IBD<50g/day 50 to <100g/day ≥100g/day P trend


Crohn’s diseaseNo. of participants 77,318 12,820 24,928No. of events (%) 34 (0.04) 12 (0.09) 41 (0.16)Unadjusted HR (95% CI) 1 (reference) 2.31 (1.20-4.47) 4.00 (2.54-6.30) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.22 (0.61-2.45) 1.50 (0.87-2.59) 0.3356Fully adjusted HR (95% CI) 1 (reference) 2.42 (0.91-6.42) 2.83 (1.17-6.83) 0.0686

Ulcerative colitisNo. of participants 77,318 12,820 24,928No. of events (%) 184 (0.24) 54 (0.42) 139 (0.56)Unadjusted HR (95% CI) 1 (reference) 1.93 (1.43-2.62) 2.51 (2.02-3.13) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.47 (1.07-2.02) 1.60 (1.24-2.08) 0.0010Fully adjusted HR (95% CI) 1 (reference) 1.16 (0.73-1.87) 1.57 (1.07-2.30) 0.0518Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.595; I² = 0%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 3 - Association Between Total Processed Meat Intake (servings/day) and Development of IBD

IBD<1 serving/week 1 serving/week to

<1 serving/day≥1 serving/day P trend

No. of participants 33,827 50,274 3,045No. of events (%) 43 (0.13) 263 (0.52) 24 (0.79)Unadjusted HR (95% CI) 1 (reference) 4.20 (3.05-5.80) 6.30 (3.82-10.38) <0.0001Min. adjusted HR (95% CI) 1 (reference) 2.39 (1.69-3.37) 2.36 (1.39-4.01) <0.0001Fully adjusted HR (95% CI) 1 (reference) 1.92 (1.24-2.98) 2.07 (1.14-3.76) 0.0126

Crohn’s diseaseNo. of participants 33,827 50,274 3,045No. of events (%) 2 (0.01) 56 (0.11) 4 (0.13)Unadjusted HR (95% CI) 1 (reference) 19.23 (4.69-

78.79)22.67 (4.15-123.75)

Min. adjusted HR (95% CI) 1 (reference) 6.13 (1.43-26.36) 3.80 (0.66-22.04)Fully adjusted HR (95% CI) 1 (reference) 3.99 (0.83-19.22) 2.50 (0.39-16.03)

Ulcerative colitisNo. of participants 33,827 50,274 3,045

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Confidential: For Review OnlyNo. of events (%) 41 (0.12) 207 (0.41) 20 (0.66)Unadjusted HR (95% CI) 1 (reference) 3.47 (2.48-4.85) 5.50 (3.22-9.39)Min. adjusted HR (95% CI) 1 (reference) 2.19 (1.53-3.13) 2.43 (1.38-4.29)Fully adjusted HR (95% CI) 1 (reference) 1.77 (1.11-2.80) 2.19 (1.16-4.16)Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.929; I² = 0%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 4- Association Between Total Soft Drink Intake (servings/day) and Development of IBD

IBD<0.5 serving/week

0.5 to <3 servings/week

≥3 servings/week P trend


Crohn’s diseaseNo. of participants 83,067 17,814 12,106No. of events (%) 49 (0.06) 24 (0.13) 17 (0.14)Unadjusted HR (95% CI) 1 (reference) 2.41 (1.48-3.93) 2.62 (1.51-4.54)Min. adjusted HR (95% CI) 1 (reference) 1.51 (0.91-2.49) 1.54 (0.87-2.72)Fully adjusted HR (95% CI) 1 (reference) 2.07 (1.12-3.83) 2.36 (1.23-4.55)

Ulcerative colitisNo. of participants 83,067 17,814 12,106No. of events (%) 212 (0.26) 71 (0.40) 49 (0.40)Unadjusted HR (95% CI) 1 (reference) 1.66 (1.26-2.17) 1.75 (1.28-2.39)Min. adjusted HR (95% CI) 1 (reference) 1.22 (0.92-1.61) 1.24 (0.90-1.71)Fully adjusted HR (95% CI) 1 (reference) 1.35 (0.95-1.93) 1.84 (1.29-2.64)Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.722; I² = 0%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 5- Association Between Sweets Intake (grams/day) and Development of IBD

IBD0 g/day <100g/day ≥100g/day P trend

No. of participants 20,812 68,360 23,109No. of events (%) 60 (0.29) 283 (0.41) 123 (0.53)Unadjusted HR (95% CI) 1 (reference) 1.43 (1.08-1.89) 1.91 (1.40-2.60) 0.0002Min. adjusted HR (95% CI) 1 (reference) 1.00 (0.75-1.34) 1.22 (0.88-1.68) 0.1822Fully adjusted HR (95% CI) 1 (reference) 1.86 (1.08-3.23) 2.58 (1.44-4.62) 0.0029

Crohn’s diseaseNo. of participants 20,812 68,360 23,109

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Confidential: For Review OnlyNo. of events (%) 16 (0.08) 51 (0.07) 23 (0.10)Unadjusted HR (95% CI) 1 (reference) 0.97 (0.55-1.70) 1.34 (0.71-2.53)Min. adjusted HR (95% CI) 1 (reference) 0.47 (0.26-0.87) 0.54 (0.27-1.08)Fully adjusted HR (95% CI) 1 (reference) 0.74 (0.24-2.30) 1.17 (0.35-3.84)

Ulcerative colitisNo. of participants 20,812 68,360 23,109No. of events (%) 44 (0.21) 232 (0.34) 100 (0.43)Unadjusted HR (95% CI) 1 (reference) 1.60 (1.16-2.20) 2.11 (1.48-3.01)Min. adjusted HR (95% CI) 1 (reference) 1.20 (0.86-1.67) 1.48 (1.02-2.13)Fully adjusted HR (95% CI) 1 (reference) 2.31 (1.22-4.36) 3.08 (1.57-6.05)Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.165; I² = 41%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 6- Association Between Salty Food and Snacks Intake (grams/day) and Development of IBD

IBD<50g/day 50 to <100g/day ≥100g/day P trend


Crohn’s diseaseNo. of participants 91,756 15,611 5,849No. of events (%) 60 (0.07) 14 (0.09) 13 (0.22)Unadjusted HR (95% CI) 1 (reference) 1.42 (0.80-2.55) 3.70 (2.03-6.74)Min. adjusted HR (95% CI) 1 (reference) 0.72 (0.40-1.32) 1.32 (0.70-2.49)Fully adjusted HR (95% CI) 1 (reference) 0.97 (0.50-1.89) 1.73 (0.80-3.73)

Ulcerative colitisNo. of participants 91,756 15,611 5,849No. of events (%) 260 (0.28) 71 (0.45) 46 (0.79)Unadjusted HR (95% CI) 1 (reference) 1.67 (1.29-2.18) 3.02 (2.20-4.13)Min. adjusted HR (95% CI) 1 (reference) 1.23 (0.94-1.62) 1.84 (1.32-2.58)Fully adjusted HR (95% CI) 1 (reference) 1.30 (0.91-1.85) 2.16 (1.40-3.33)Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.245; I² = 27.8%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

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Confidential: For Review OnlySupplementary Table 7 - Association of Urinary Sodium (grams/day) and Development of IBD (sensitivity analyses)

Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 8 - Association of White Meat Intake and Development of IBD

<1 serving/week 1 to <3 servings/week


No. of participants 60,837 26,518 25,320No. of events (%) 191 (0.31) 152 (0.57) 87 (0.34)Unadjusted HR (95% CI) 1 (reference) 1.88 (1.52-2.32) 1.17 (0.91-1.51) <0.0001Min. adjusted HR (95% CI) 1 (reference) 1.16 (0.92-1.45) 1.06 (0.82-1.36) 0.4568Fully adjusted HR (95% CI) 1 (reference) 1.31 (0.99-1.74) 1.38 (0.97-1.95) 0.1008Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 9 - Association of Red Meat (Unprocessed) Intake and Development of IBD

<3 servings/week 3 to <7 servings/week


No. of participants 50,694 30,897 27,009

<2.5g/day 2.5 to <3.5g/day

3.5 to <5g/day

≥5g/day P trend

No. of participants 8,014 9,262 17,618 12,485No. of events (%) 42 (0.52) 72 (0.78) 151 (0.86) 100 (0.80)Unadjusted HR (95% CI) 1 (reference) 1.59 (1.09-

2.32)1.73 (1.23-2.43)

1.60 (1.11-2.29)

0.0194


1 (reference) 1.82 (1.24-2.67)

2.06 (1.46-2.92)

2.06 (1.42-2.99)

0.0004


1 (reference) 1.18 (0.75-1.84)

1.29 (0.86-1.93)

0.99 (0.62-1.59)

0.4469

Excluding China:<2.5g/day 2.5 to

<3.5g/day≥3.5g/day P trend

No. of participants 8,014 9,262 30,103No. of events (%) 42 (0.52) 72 (0.78) 251 (0.83)Unadjusted HR (95% CI) 1 (reference) 1.59 (1.09-

2.32)1.67 (1.21-2.32)

0.0086


1 (reference) 1.82 (1.24-2.67)

2.06 (1.48-2.88)

0.0001


1 (reference) 1.17 (0.75-1.82)

1.19 (0.81-1.75)

0.6807

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Confidential: For Review OnlyNo. of events (%) 179 (0.35) 115 (0.37) 133 (0.49)Unadjusted HR (95% CI) 1 (reference) 1.08 (0.85-1.36) 1.42 (1.14-1.78) 0.0068Min. adjusted HR (95% CI) 1 (reference) 0.83 (0.65-1.05) 1.03 (0.82-1.30) 0.1764Fully adjusted HR (95% CI) 1 (reference) 1.00 (0.74-1.35) 1.12 (0.81-1.53) 0.7183Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 10 - Association of Dairy Intake and Development of IBD

<1 serving/day 1 to <2 servings/day



Supplementary Table 11 - Association of Starch Intake and Development of IBD

<200g/day 200 to <400g/day

≥400g/day P trend

No. of participants 9,770 42,878 63,389No. of events (%) 48 (0.49) 198 (0.46) 221 (0.35)Unadjusted HR (95% CI) 1 (reference) 0.93 (0.68-1.27) 0.70 (0.51-0.95) 0.0045Min. adjusted HR (95% CI) 1 (reference) 1.22 (0.89-1.67) 1.23 (0.89-1.71) 0.4277Fully adjusted HR (95% CI) 1 (reference) 1.41 (0.98-2.04) 1.50 (0.95-2.38) 0.1597Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 12 - Association of Fruit, Vegetable & Legume Intake and Development of IBD

<2 servings/day 2 to <6 servings/day



Supplementary Table 13 - Association of Fruit Intake and Development of IBD

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Confidential: For Review Only<1 serving/day 1 to <4

servings/day≥4 servings/day P trend


Supplementary Table 14 - Association of Vegetable Intake and Development of IBD

<5 servings/day 5 to <7 servings/day



Supplementary Table 15 - Association of Legume Intake and Development of IBD

<1 serving/week 1 to <3 servings/week


No. of participants 25,015 33,463 54,197No. of events (%) 105 (0.42) 156 (0.47) 169 (0.31)Unadjusted HR (95% CI) 1 (reference) 1.11 (0.87-1.42) 0.73 (0.57-0.93) 0.0004Min. adjusted HR (95% CI) 1 (reference) 0.97 (0.75-1.24) 0.72 (0.57-0.92) 0.0082Fully adjusted HR (95% CI) 1 (reference) 0.97 (0.69-1.36) 1.20 (0.87-1.66) 0.2508Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

Supplementary Table 16 - Association of Fried Foods Intake and Development of IBD

IBD0 servings <1 serving/day ≥1 serving/day P trend


Crohn’s diseaseNo. of participants 18,290 56,606 39,685

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Confidential: For Review OnlyNo. of events (%) 12 (0.07) 28 (0.05) 50 (0.13)Unadjusted HR (95% CI) 1 (reference) 0.76 (0.39-1.50) 2.04 (1.09-3.83)Min. adjusted HR (95% CI) 1 (reference) 0.54 (0.27-1.08) 0.65 (0.31-1.36)Fully adjusted HR (95% CI) 1 (reference) 0.68 (0.18-2.56) 1.19 (0.30-4.66)

Ulcerative colitisNo. of participants 18,290 56,606 39,685No. of events (%) 32 (0.17) 135 (0.24) 174 (0.44)Unadjusted HR (95% CI) 1 (reference) 1.37 (0.93-2.02) 2.67 (1.83-3.89)Min. adjusted HR (95% CI) 1 (reference) 1.18 (0.80-1.74) 1.49 (0.99-2.23)Fully adjusted HR (95% CI) 1 (reference) 3.13 (1.43-6.82) 3.76 (1.67-8.51)Heterogeneity of results from Crohn’s disease and ulcerative colitis - Chi² p-value=0.186; I² = 37.6%.Minimal adjustments are for age, gender, and geographic region. Fully adjusted model includes age, gender, geographic region, education, alcohol intake, smoking status, BMI, total energy intake and location.

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association of processed food intake with risk of

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