{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Campus":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Degree":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","DegreeGrantor":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","GraduationDate":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","Program":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Supervisor":"http:\/\/purl.org\/dc\/terms\/contributor","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Science, Irving K. Barber Faculty of (Okanagan)","@language":"en"},{"@value":"Biology, Department of (Okanagan)","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Campus":[{"@value":"UBCO","@language":"en"}],"Creator":[{"@value":"D'Aloisio, Leah","@language":"en"}],"DateAvailable":[{"@value":"2024-04-02T23:05:49Z","@language":"en"}],"DateIssued":[{"@value":"2024","@language":"en"}],"Degree":[{"@value":"Master of Science - MSc","@language":"en"}],"DegreeGrantor":[{"@value":"University of British Columbia","@language":"en"}],"Description":[{"@value":"The growing immigrant population in North America are experiencing westernization at a rapid rate. Amidst the backdrop of rising immigration, health disparities are emerging in immigrants, including several modern diseases linked with the westernized lifestyle. Young Indian immigrants and Indo-Canadians face a significantly higher risk of inflammatory bowel disease (IBD) in westernized countries. While the root causes of IBD are not entirely understood, a main characteristic is an imbalanced gut microbiome no longer in symbiosis with its host. However, Indian populations are underrepresented in microbiome studies, making it challenging to determine the influences of their gut microbiome in this increased IBD risk. To understand why Indians are more vulnerable to IBD in Canada, it is essential to first investigate their gut microbiome.\r\nThis thesis explores the gut microbiome transition in Indian migrants in Canada by characterizing their microbial composition, and the impact of their dietary patterns on the microbiome. Using 16S and shotgun sequencing data obtained from stool samples of healthy subjects, our study compares the microbiome of Indians residing in India, Indian immigrants, and Indo-Canadians, with Euro-Canadians and Euro-Immigrants as westernized controls.  Our findings reveal significant differences in microbiota composition among these groups, with Indian residents showing a distinctive gut microbiota rich in Prevotella spp., whereas Indo-Canadians resembled more of an industrialized gut, with higher Bacteroides spp. abundance. While Indo-Immigrants had a gut microbiota that was distinct from Indians and westernized cohorts, some subjects displayed moderate levels of Prevotella spp. abundances, which may be due to their mixed diet that included both traditional Indian and westernized foods. This study concludes that Indo-Canadians undergo a marked transition towards an industrialized microbiome within just one generation, both through functional changes and the loss of Prevotella spp. This microbiota transition was associated with a large change in dietary habits, in particular a decrease in a high carbohydrate, high fibre diet, and an increase in ultra-processed foods. Overall, the data shown offers insight into how migration and lifestyle changes affect both microbial composition and functions in the gut, and their implications for understanding immigrant-health outcomes.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/87669?expand=metadata","@language":"en"}],"FullText":[{"@value":"A JOURNEY FROM INDIA TO CANADA: THE WESTERNIZATION OF THE GUT MICROBIOME IS ASSOCIATED WITH DIETARY ACCULTURATION IN INDIAN MIGRANTS  by Leah D\u2019Aloisio  B.Sc., McMaster University, 2020  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) March 2024  \u00a9 Leah D\u2019Aloisio, 2024  ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: A JOURNEY FROM INDIA TO CANADA: THE WESTERNIZATION OF THE GUT MICROBIOME IS ASSOCIATED WITH DIETARY ACCULTURATION IN INDIAN MIGRANTS submitted by Leah D\u2019Aloisio in partial fulfillment of the requirements of the degree of Master of Science .   Dr. Deanna Gibson, Irving K. Barber Faculty of Science Supervisor Dr. Sepideh Pakpour, School of Engineering Supervisory Committee Member Dr. Kirk Bergstrom, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Genelle Lunken, University of British Columbia- Vancouver University Examiner           iii Abstract The growing immigrant population in North America are experiencing westernization at a rapid rate. Amidst the backdrop of rising immigration, health disparities are emerging in immigrants, including several modern diseases linked with the westernized lifestyle. Young Indian immigrants and Indo-Canadians face a significantly higher risk of inflammatory bowel disease (IBD) in westernized countries. While the root causes of IBD are not entirely understood, a main characteristic is an imbalanced gut microbiome no longer in symbiosis with its host. However, Indian populations are underrepresented in microbiome studies, making it challenging to determine the influences of their gut microbiome in this increased IBD risk. To understand why Indians are more vulnerable to IBD in Canada, it is essential to first investigate their gut microbiome. This thesis explores the gut microbiome transition in Indian migrants in Canada by characterizing their microbial composition, and the impact of their dietary patterns on the microbiome. Using 16S and shotgun sequencing data obtained from stool samples of healthy subjects, our study compares the microbiome of Indians residing in India, Indian immigrants, and Indo-Canadians, with Euro-Canadians and Euro-Immigrants as westernized controls.  Our findings reveal significant differences in microbiota composition among these groups, with Indian residents showing a distinctive gut microbiota rich in Prevotella spp., whereas Indo-Canadians resembled more of an industrialized gut, with higher Bacteroides spp. abundance. While Indo-Immigrants had a gut microbiota that was distinct from Indians and westernized cohorts, some subjects displayed moderate levels of Prevotella spp. abundances, which may be due to their mixed diet that included both traditional Indian and westernized foods. This study concludes that Indo-Canadians undergo a marked transition towards an industrialized microbiome within just one generation, both through functional changes and the loss of Prevotella spp. This microbiota transition was associated with a large change in dietary habits, in particular a decrease in a high iv carbohydrate, high fibre diet, and an increase in ultra-processed foods. Overall, the data shown offers insight into how migration and lifestyle changes affect both microbial composition and functions in the gut, and their implications for understanding immigrant-health outcomes. Lay Summary As people from India continue to move to westernized countries like Canada, a concerning trend in health outcomes emerge among these immigrants and their descendants, with a higher likelihood of developing inflammatory bowel disease (IBD). IBD is a chronic lifelong disease involving intestinal inflammation that can greatly impact one\u2019s quality of life. Also within our intestines are trillions of microbes, known as our \u201cgut microbiome\u201d, which plays an intricate role in IBD. However, the gut microbiome of Indian immigrants remains underexplored. In this work, we observe a transition in the Indian gut microbiome upon immigration to Canada, as it becomes more similar to westernized-European individuals. This change was identified to be partly due to altered dietary patterns. Overall, by studying this gut transition alongside westernization, we can continue to uncover why this demographic is more susceptible to diseases such as IBD in the hopes of establishing preventative interventions.    ii Preface  The contents in Chapter 1 of this work have been previously published in FEMS Microbiology Ecology journal (Copyright Clearance Center\u2019s RightsLink\u00ae, license number: 5710941108452): D\u2019Aloisio LD, Shetty V, Ballal M, Gibson DL. Following the Indian Immigrant: adoption of westernization results in a western gut microbiome and an increased risk of inflammatory bowel disease. FEMS Microbiology Ecology 2022. (https:\/\/doi.org\/10.1093\/femsec\/fiac133). While under the supervision of Dr. Deanna Gibson, I performed the literature search and wrote this Current Opinions paper, and Dr. Gibson provided deep review and editing of the manuscript.    Chapters 2 and 3 present the data obtained from our clinical study, also referred to as the \u201cIndian Microbiome Project\u201d, which was designed and led by Dr. Deanna Gibson. In 2017-2019, Dr. Deanna Gibson and Dr. Sanjoy Ghosh organized the clinical study out of two institutes in India, where stool sample collection, homogenization, and DNA was extracted. The first institute was in Kolkata at the National Institute of Cholera and Enteric Diseases, run by researchers Dr. Shanta Dutta, Dr. Hemanta Koley and Dr. Ushasi Bhaumik. The second location was in Manipal at Kasturba Medical College, led by Dr. Mamatha Ballal and Dr. Vignesh Shetty. In Canada, the clinical study continued during 2021 in Kelowna, British Columbia with approval by the University of British Columbia Research Ethics Board under certificate number H21-01555. Dr. Deanna Gibson provided resources and funding for the entirety of this project. Dr. Sanjoy Ghosh, Dr. Kirk Bergstrom and Dr. Sepideh Pakpour provided both resources and expertise for this study. In this thesis, I was responsible for obtaining ethics approval in Canada, recruiting participants in the community, screening for eligibility, collecting samples, homogenizing and extracting DNA from samples, and all data analysis. I also received help for sample collection and homogenization from Hephzibah Bomide, Carson McComb, Cortney Klassen, Ayva Lewis and Erik Kaila. Stool iii samples were examined and photographed under a microscope for parasites by Mekenna Smith and then I analyzed the images for suspected parasite ova. 16S sequencing was completed at the Gut4Health Microbiome Core Facility (BC Children\u2019s Hospital) and shotgun sequencing was completed at the Centre for Health Genomics and Informatics (University of Calgary).  Chapter 2 presents the initial findings from our clinical study including participant demographics and gut microbiota composition from 16S and shotgun sequence data. I completed all data analysis for this chapter, with guidance from Nijiati Abulizi (Data Science, University of British Columbia Okanagan). Chapter 3 presents dietary data obtained from our participants, in which I received extensive guidance from Dr. Natasha Haskey (RD). Dietary data was inputted into ESHA by Nadia Anvari and Chuyi Liu, with deep data cleaning completed by Chuyi Liu. Dietary data collected from Indians and Indo-Immigrants were also re-entered into the EpiNu nutritional software and re-analyzed by Sudha Vasudevan, Sruthi Chowdary, and Lakshmipriya Siva (Madras Diabetes Research Foundation, Chennai, India). I also completed the metagenomic analysis, with assistance from Nijiati Abulizi, and help with quality control and running MetaPhlAn\/HUMAnN commands from Ramin Karimianghadim (Applied Sciences, University of British Columbia Okanagan). Chapter 4 contains a concluding summary of our findings, along with the limitations of this study and future directions from this work. I wrote the original draft of the manuscript of the findings from this study, and Dr. Deanna Gibson provided review and editing. The manuscript has been submitted as a preprint in bioRxiv: Leah D. D\u2019Aloisio, Natasha Haskey, Nijiati Abulizi, Ramin Karimianghadim, Chuyi Liu, Sruthi Chowdary, Lakshipriya Siva, Sudha Vasudevan, Vignesh Shetty, Ushasi Bhaumik, Mamatha Ballal, Debaki Ranjan Howlader, Sepideh Pakpour, Sanjoy Ghosh, Jacqueline Barnett, Deanna L. Gibson. The transition from a non-westernized to westernized gut microbiome in Indian-Immigrants and Indo-Canadians is associated with dietary acculturation. bioRxiv 2024. (https:\/\/doi.org\/10.1101\/2024.03.04.582285). iv Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................. i Preface ....................................................................................................................................... ii Table of Contents ..................................................................................................................... iv List of Tables .......................................................................................................................... viii List of Figures .......................................................................................................................... ix List of Abbreviations ................................................................................................................ x Acknowledgements ................................................................................................................. xi Dedication .............................................................................................................................. xiii Chapter 1: Introduction .......................................................................................................... 14 1.1 Literature Review .....................................................................................................14 1.1.1 The Industrialized Microbiome and Increasing IBD Rates .....................................14 1.1.2 IBD in Indian Populations ...............................................................................16 1.1.3 Westernization in India ...................................................................................18 1.1.4 The Indian Gut Microbiome ............................................................................19 1.1.5 The Gut Microbiome of Indian Migrants ..........................................................23 1.2 Hypothesis and Objectives ......................................................................................25 Chapter 2: Characterizing the Gut Microbiota in Indian Populations .................................. 26 2.1 Overview .................................................................................................................26 2.2 Methods ...................................................................................................................26 2.2.1 Study Design..................................................................................................26 2.2.2 Participants ....................................................................................................26 2.2.3 Ethics Statement ............................................................................................27 2.2.4 Sample Collection & Homogenization ............................................................27 2.2.5 Microbial DNA Extraction & Library Preparation .............................................28 v 2.2.6 16S Microbiome Analysis ...............................................................................28 2.2.7 Shotgun Taxonomic Analysis .........................................................................29 2.2.8 Statistical Analyses ........................................................................................30 2.2.9 Power Analysis ..............................................................................................30 2.2.10 Data Availability .............................................................................................31 2.3 Results ....................................................................................................................31 2.3.1 Participant Recruitment ..................................................................................31 2.3.2 Baseline Characteristics .................................................................................32 2.3.3 Immigrant Characteristics ...............................................................................33 2.3.4 Indian Microbiota is Distinctive from Westernized Groups ..............................34 2.3.5 Indian Migrants Lose Prevotella spp. Abundance over Generations ...............39 2.3.6 Indian Gut Microbiome Predicted to be More Robust .....................................43 2.4 Discussion ...............................................................................................................45 2.4.1 Indian Gut Microbiota is Distinctive from Westernized Populations ................45 2.4.2 Indian Migrants Transition Within One Generation .........................................45 Chapter 3: The Impact of Westernization on the Gut Microbiome in Indian Migrants ....... 50 3.1 Overview .................................................................................................................50 3.2 Methods ...................................................................................................................51 3.2.1 Dietary Data Collection ..................................................................................51 3.2.2 Nutritional Analysis.........................................................................................51 3.2.3 Functional Profiling.........................................................................................52 3.2.4 Statistical Analyses ........................................................................................52 3.3 Results ....................................................................................................................53 3.3.1 Indo-Canadian Diet had Highest Levels of Ultra-Processed Food Intake .......53 3.3.2 Indian Cohort had the Highest Proportion of Non-Meat Eaters .......................54 3.3.3 Low Variety of Cooking Oils Were Used by Westernized Groups ...................54 vi 3.3.4 Macronutrient Composition Differs from Indians and Westernized Cohorts ....56 3.3.5 Functional Potential in the Indian Gut is Distinctive from Westernized Groups 61 3.3.6 Processed Food & Alcohol Consumption are Drivers in the Distinction between Westernized and Indian cohorts ....................................................................................65 3.4 Discussion ...............................................................................................................66 3.4.1 Differences in Indian Diet is Reflective in CAZyme Activity .............................66 3.4.2 India is Currently Industrializing......................................................................67 3.4.3 Acculturation of Westernized Diet Drives Functional Shifts in Microbiome .....70 Chapter 4: Conclusion ............................................................................................................ 73 4.1 Summary .................................................................................................................73 4.2 Limitations ...............................................................................................................75 4.3 Knowledge Translation ............................................................................................78 4.4 Future Work .............................................................................................................78 4.5 Concluding Remarks ...............................................................................................78 Bibliography ............................................................................................................................ 80 Appendices ............................................................................................................................. 94 Appendix A. Posters and Pamphlets Used for Recruitment in Canada ..............................94 Appendix B. Demographics Questionnaire Provided to Participants ..................................96 Appendix C. Lifestyle Survey Provided to Subjects (Indo-Immigrant-Specific) ................. 100 Appendix D. Stool Collection Instructions Provided to Participants .................................. 105 Appendix E. PERMANOVA Results from Beta Diversity Analysis .................................... 107 Appendix F. Beta-Diversity Results ................................................................................. 108 Appendix G. LEfSe Results from 16S Sequence Data .................................................... 109 Appendix H. Suspected Parasites Detected from Stool Microscope Examination ............ 111 Appendix I. Random Forest Results from Shotgun Data.................................................. 112 vii Appendix J. BugBase Results ......................................................................................... 113 Appendix K. Dietary Log Provided to Participants............................................................ 114 Appendix L. Pairwise Comparisons of Macronutrient Intake in Participants ..................... 118 Appendix M. Micronutrient Intake in Males and Females ................................................. 119 Appendix N. Differential Microbial Metabolic Pathways Across Cohorts .......................... 123 Appendix O. Differential Abundances of CAZyme Gene Families Across Cohorts ........... 125 Appendix P. Top Taxa Contributions to Antimicrobial Resistance-Related KEGG Orthologies Abundant in Indians and Indo-Immigrants .................................................... 127 Appendix Q. Participant Overview (ethnicity and\/or region born) ..................................... 128    viii List of Tables Table 1. Summary of Gut Microbiota Studies in Indian Populations ..........................................21 Table 2. Summary of Baseline Characteristics of Participants ...................................................32 Table 3. Summary of Immigrant-Related Characteristics ..........................................................34 Table 4. Differentially Abundant Bacteria Calculated with LEfSe ...............................................42 Table 5. Percentages of Respondents Who Reported Use of Each Cooking Oil .......................55 Table 6. Male Participant Absolute Macronutrient Intake ...........................................................58 Table 7. Female Participant Absolute Macronutrient Intake.......................................................59  ix List of Figures Figure 1 Schematic Overview of the Indian Gut Microbiota Transition from Westernization ......24 Figure 2. Recruitment Flow Chart..............................................................................................31 Figure 3. Indians Have a Significantly Lower Alpha Diversity Than Other Cohorts ....................35 Figure 4. Beta Diversity Reveals Distinctions of Indian and Indo-Immigrants ............................36 Figure 5. Cladogram of Phylogenetic Differences Across Cohorts ............................................37 Figure 6. Significantly Different Gut Bacterial Abundances Detected Across Cohorts ...............39 Figure 7. Pattern of Prevotella spp. Loss Observed in Indian Migrants .....................................41 Figure 8. BugBase Predicts Higher Pathogenic Potential and Stress-Tolerant Microbiome in Indians ......................................................................................................................................44 Figure 9. Indian Migrants Consume Significantly More Ultra-Processed Food than Indians ......53 Figure 10. Decreased Variability in Cooking Oils are Used by Indian Migrants..........................55 Figure 11. Dietary Fat Types are Significantly Different Between EpiNu vs. ESHA ...................60 Figure 12. Microbial Metabolic Pathways Show Distinction in Functional Potential of Indian Gut Microbiome ...............................................................................................................................63 Figure 13. Prevotella copri is the Top Taxa Contributing to CAZy Families Abundant in Indians .................................................................................................................................................64 Figure 14. Ultra-Processed Foods and Alcohol Consumption Drives Differences in Gut Microbiota .................................................................................................................................65 Figure 15. Adoption of Westernized Dietary Practices is Associated with a Transition Away from the Traditional Indian Gut Microbiome .......................................................................................74   x List of Abbreviations 1. ANOVA \u2013 Analysis of Variance 2. ASV \u2013 Amplicon Sequence Variant 3. BloSSUM \u2013 Bloom or Selected in Societies of Urbanization\/Modernization 4. CAZyme \u2013 Carbohydrate-Active EnZyme 5. CVD \u2013 Cardiovascular Disease  6. FDR \u2013 False Discovery Rate 7. GH \u2013 Glycoside Hydrolase 8. IBD \u2013 Inflammatory Bowel Disease 9. IQR \u2013 Interquartile Range 10. KEGG \u2013 Kyoto Encyclopedia of Genes and Genomes 11. LDA \u2013 Linear Discriminant Analysis 12. LEfSe \u2013 Linear Discriminant Analysis Effect Size 13. LPS \u2013 Lipopolysaccharides 14. MACs \u2013 Microbiota Accessible Carbohydrates 15. MS \u2013 Metabolic Syndrome 16. MUFA \u2013 Monounsaturated Fatty Acids 17. PERMANOVA \u2013 Permutational Multivariate Analysis of Variance 18. PUFA \u2013 Polyunsaturated Fatty Acids 19. SFA \u2013 Saturated Fatty Acids 20. UC \u2013 Ulcerative Colitis 21. UK \u2013 United Kingdom 22. UPFs \u2013 Ultra-Processed Foods  23. US \u2013 United States 24. VANISH \u2013 Volatile and\/or Associated Negatively with Industrialized Societies of Humans  xi Acknowledgements First, I would like to express my deepest gratitude to my supervisor, Dr. Deanna Gibson. Your remarkable work ethic and enthusiasm continued to motivate me throughout this project. Thank you for helping me to build confidence in myself and providing so many opportunities for me to gain skills that extend far beyond the lab. I\u2019m so grateful to witness your courage while you continue to push boundaries in this field, as this has deeply inspired both my personal and professional growth. Secondly, to my lab members, Dr. Natasha Haskey, Andrea Verdugo Meza, (Dr.) Jacqueline Barnett, and Jessica Josephson, your support (both physically and emotionally) has not only been essential in my learning, but also provided a sense of family while I was out in Kelowna. I will forever cherish the memories we made together. From the beginning of my journey at UBC, I was deeply touched by the kindness and support I received from each individual mentioned above, and I have never been prouder to be a part of such an incredible group of women. I want to thank my supervisory committee, Dr. Kirk Bergstrom and Dr. Sepideh Pakpour for your valuable feedback and guidance during this project. Thank you to our many collaborators in India, specifically Dr. Mamatha Ballal, Dr. Vignesh Shetty, Dr. Hemanta Koley, Dr. Ushasi Bhaumik, Sudha Vasudevan, Sruthi Chowdary, and Lakshmipriya Siva. Thank you to the Centre for Health Genomics and Informatics (University of Calgary) and Gut4Health Microbiome Core Facility (BC Children\u2019s Hospital) for your sequencing services.  A special thank you to Nijiati Abulizi, for providing your knowledge and keeping my spirits up through the more difficult times during my microbiome analysis. In addition, thank you to Ramin Karimianghadim for your contribution in assisting with metagenomic analysis, and Mehrbod Estaki, Stefano Mezzini and Dr. Michael Noonan for guidance in my analysis. I would like to thank Hephzibah Bomide, Carson McComb, Ayva Lewis, Cortney Klassen, Mekenna Smith and Erik xii Kaila for all of your help in kickstarting our recruitment, sample collection and stool homogenization. I would also like to extend my appreciation for Dr. Sanjoy Ghosh, Trisheeta Hasan, Lukman Sarker, and Geet Hans for helping me to connect with Indian communities in British Columbia.  And lastly, I would like to thank my loved ones. To my parents, Bruno & Rita D\u2019Aloisio, words cannot express the gratitude I have for your tireless efforts to ensure I had the best possible upbringing. I will never forget your willingness to support me through my academic journey and various hobbies over the years. Thank you for being my role models and for instilling independence in me, as this was vital to my success. To my partner, Josh, thank you for always lending a listening ear to my weekly struggles, for reminding me to trust in my hard work, and for motivating me to seek fulfillment in areas outside of my professional life. Thank you to my big sisters, Tara & Morgan, my most important critics in life. Thanks for pretending to show interest in my research and for readily providing feedback on my work when I would randomly ask for help. And to my best friends (\u201cShnees\u201d), I am so grateful for your continued love and loyalty. Thank you for your patience and understanding in me being so absent these past three years. Finally, I extend my appreciation to all of my loved ones who never missed an opportunity to make fun of me for collecting poop for a year. xiii Dedication For my grandmothers, Antonietta D\u2019Aloisio (Nonna) & Filomena Ventresca (Nana), whose strength and resilience in times much harder than mine have paved the way for the opportunities I have today.  14 Chapter 1: Introduction 1.1 Literature Review 1.1.1 The Industrialized Microbiome and Increasing IBD Rates Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the intestinal tract with diagnosis often at an early age, leading to a growing disease prevalence. Although the underlying causes of IBD remain uncertain, a strong relationship exists between industrialization and IBD, suggesting extrinsic factors are involved (Kaplan, 2015). Industrialization influenced the development of \u201cwesternized\u201d lifestyle practices, including increased sanitation, antibiotic use, caesarian delivery and dietary changes (Sonnenburg & Sonnenburg, 2019). When comparing IBD rates globally, countries that are most developed display the highest IBD prevalence, and now, newly industrialized countries are on the rise (Kaplan & Ng, 2017). Furthermore, alarming evidence reveals an increased IBD risk in second-generation immigrants from India to westernized countries (Benchimol et al., 2015; Carroll et al., 2016; Pinsk et al., 2007). A likely contributor to this outcome stems from changes in the gut microbiome as a response to a new environment and subsequent loss of symbiosis with the intestinal mucosae. Those who migrate from India can therefore provide important insight into the underlying causes and effects of the environment on IBD development.   While IBD prevalence and industrial growth is a correlational observation, coincidingly, in this period, the gut microbiota also changed significantly (Sonnenburg & Sonnenburg, 2019). IBD development is believed to be a combination of genetics, environment and the gut microbiome (Aldars-Garc\u00eda et al., 2021). Several studies show the gut microbiome in IBD patients to be distinct from healthy controls, displaying reduced diversity, less stability, and altered gut function (Aldars-Garc\u00eda et al., 2021). In IBD, the gut is in a state of dysbiosis, meaning microbial communities are unstable, potential pathobionts increase in abundance, the mucosal lining is compromised, and 15 chronic inflammation hinders epithelial barrier integrity (Shan et al., 2022). Several studies address the causal roles of the gut microbiome in IBD (Metwaly et al., 2022), revealing the key role that the microbiome plays in IBD etiology.  The gut microbiome is heavily influenced by lifestyle, and its plasticity has allowed for adaptations to changing environments throughout all of human history (Rizzello et al., 2019). As researchers continue to improve the representation of non-westernized groups in microbiome research, our appreciation for the variation that exists within the human gut microbiome continues to grow. A frequent observation is the distinction of microbial communities observed in those living in westernized populations. Specifically, the industrialized microbiome has been previously characterized by the decline of VANISH (volatile and\/or associated negatively with industrialized societies of humans) taxa, frequently found in populations adhering to traditional lifestyles, and the rise of BloSSUM (bloom or selected in societies of urbanization\/modernization) taxa, which appear in modern, industrialized populations (Jha et al., 2018; Sonnenburg & Sonnenburg, 2019). Many new practices from westernization have drastically influenced the gut microbiome, even from infancy. A recent paper examining the gut microbiomes in infants from industrialized, newly industrialized (i.e. \u201ctransition\u201d), and non-industrialized lifestyles revealed that changes could be seen in the infant gut even within the first six months of life (Olm et al., 2022). Infants born in industrialized nations harbour bacteria distinct from infants born in non-industrialized nations, while infants born in transition countries exhibit a gut microbiota \u201cin-between\u201d the spectrum of non-industrialization to industrialization (Olm et al., 2022). Eventually, these distinctions of the gut microbiota across lifestyles are even more prevalent into adulthood, as studies comparing the microbiomes of those living in tribal, rural and urban settings tend to harbour significantly different microbial communities (Das et al., 2018; Kaur et al., 2020; Ramadass et al., 2017). India is a newly industrialized country that is already adopting western practices, and these early-life influences on the gut microbiome may eventually be common in India in the coming decades. 16 Hence, to better understand the extent to which these practices affect gut microbiome establishment in the early years of life, Indian populations can provide important insight in this field as they transition toward a more westernized lifestyle.   Another major influence on the gut microbiome is diet, and the historical transition from an ancestral to a westernized diet dramatically influenced a shift in gut microbial composition and their functional attributes. Firstly, when society transitioned from a hunter-gatherer lifestyle to farming, our ancestors started to consume a less diverse diet with higher-farmed foods like wheat and rice. The Industrial Revolution followed, and the consumption of processed foods and sugar intake increased substantially (Rizzello et al., 2019). This transition in diet along with other lifestyle changes that occurred in westernized countries have resulted in the \u201cindustrialized microbiome\u201d, which refers to the loss of taxa, and their functional potential, from the traditional or \u201cancestral\u201d microbiome that was replaced with microbes more suited for an urbanized environment (Sonnenburg & Sonnenburg, 2019). As India is continually expanding their market for fast food and other westernized dietary products, this transition is yet another indication of their likelihood in adopting the industrialized microbiome in the future.   1.1.2 IBD in Indian Populations In the mid-1900s, when industrialization was on the rise in western countries, IBD began to present itself and has continually increased in incidence over the decades (Altajar & Moss, 2020). Now, newly industrialized countries such as India are following this same pattern. In fact, India is projected to have one of the highest IBD burdens worldwide (Kedia & Ahuja, 2017). While twin studies in westernized populations have revealed genetics play a role in disease development (Glassner et al., 2020), a lack of data are available for twin studies in India. However, pointing to recent environmental influences in India, familial prevalence appears to be lower than what is observed in westernized countries (Banerjee et al., 2019). Rapid urbanization followed by 17 the rising IBD incidence in India further suggests an impact of westernization on this disease. Furthermore, Indians also present an increased risk of IBD when living in westernized countries. Several studies conducted in the United Kingdom (UK), the United States (US) and Canada have shown either a higher incidence rate of IBD or one comparable to westernized individuals living in the same area. Recent evidence indicates this IBD risk in Indians does not appear to have changed and is instead a growing concern in countries such as Canada, where the Indian demographic continues to increase (Dhaliwal et al., 2022; Dhaliwal et al., 2021).  Research to date that has examined IBD in immigrants reveals two key insights. First, age at immigration matters, as those who migrate as a child experience a higher risk of IBD than adult immigrants (Benchimol et al., 2015). Secondly, the second generation (children of immigrants) are at a much higher risk than their parents, indicating early-life exposure predisposes them to disease (Benchimol et al., 2015). A study of a pediatric cohort, mainly consisting of those born in Canada with Indian ancestry (i.e., Indo-Canadians), had a higher average incidence rate for IBD than the general pediatric cohort (Pinsk et al., 2007). Indo-Canadians also tend to develop IBD at a younger age and present a more severe disease phenotype and extensive colonic disease (Carroll et al., 2016; Dhaliwal et al., 2022). These results are not only contingent in Canada, as Indian adults living in the UK and US also experience an increased prevalence of Ulcerative Colitis (UC) (Malhotra et al., 2015; Misra et al., 2019; Probert et al., 1993). Indians make up a substantial proportion of immigrants admitted as permanent residents in Canada, and many of these individuals move to industrialized settings, which raises concern about how the western lifestyle is inducing their likelihood of developing IBD. In the next section, we aim to capture what is currently known about how westernization affects the gut microbiome in Indian populations and what implications this may have for their risk for IBD.  18 1.1.3 Westernization in India To effectively compare the gut microbiome and health outcomes in Indians versus westernized populations, lifestyle factors including geographic location, industrialization, and socioeconomic conditions can play considerable roles in the differences or similarities observed (Carter et al., 2022). Previous studies have already noted health consequences following the assimilation into the westernized culture. For instance, Irish Travellers who transitioned into social housing and adopted modern-day practices experienced microbiome changes linked with chronic diseases (Parizadeh & Arrieta, 2023). Similarly, Hmong and Karen peoples who migrated from Thailand to the US saw a shift in their gut microbiome and an increased risk of developing obesity (Vangay et al., 2018). Quantitative evidence of the degree to which diet and other factors influence differences in the gut microbiome are yet to be clearly established. Yet, studies show some of the most prominent effects on the gut microbiome include age, lifestyle, antibiotic use and long-term dietary patterns (Debelius et al., 2016). In terms of dietary patterns, there are considerable differences between India and westernized countries that must be discussed.   While each region in India has their own traditional dishes, fibre consumption is notably higher than in westernized countries. Indians generally consume more plant-based foods such as vegetables, fruits, and legumes (Jain et al., 2018; Tandon et al., 2018). This diet differs from the typical western diet, which consists of lower fibre consumption and a higher intake of animal protein and ultra-processed foods (Rizzello et al., 2019). Indian dishes also contain spices, including turmeric\/curcumin, ginger, fenugreek and mustard seed, which have been recognized for their anti-inflammatory and antioxidant properties (Kannappan et al., 2011; Kulkarni & Dhir, 2010; Mandegary et al., 2012; Nicoll & Henein, 2009; Yang et al., 2013). Furthermore, since India is newly industrialized, fast-food restaurants have only been recently introduced. Given that fast food and ultra-processed food consumption is expected to continually rise in India, this may further contribute to the current incidence rates of IBD (Gogoi, 2020).  19 In tribal and rural communities in India, Indians consume traditional diets made of fruits\/vegetables, lentils, whole grains, and conventional oils like ghee, milk\/dairy products, and coconut\/palm oils (Jain et al., 2018; Tandon et al., 2018). While urban populations still consume traditional Indian foods, they also have adopted western products such as fast food and western white oils, including sunflower oils rich in n-6 polyunsaturated fatty acid (PUFA). Saturated fats from coconut oil, palm oil and ghee, were a primary fat source in a traditional Indian diet; however, the demonization of saturated fats in the west has influenced Indian dietary habits in socioeconomically elevated Indians where saturated fats have been replaced with n-6 PUFA in recent years (Mani & Kurpad, 2016). Overall, as westernization spreads throughout India, we might expect these individuals to eventually harbour gut microbiomes that look more like the industrialized gut.    1.1.4 The Indian Gut Microbiome Previous studies conducted across several regions in India provide a consensus that the average gut microbiota in Indians living in India is dominated by Bacteroidetes, and Prevotella is a core genus in this population (Table 1) (Bhute et al., 2016; Chaudhari et al., 2020; Chauhan et al., 2018; Das et al., 2018; Dhakan et al., 2019; Dwiyanto et al., 2021; Kao et al., 2015; A. S. Kulkarni et al., 2019; Pareek et al., 2019; Tandon et al., 2018). A large-scale study (n= 1004 samples) from multiple regions throughout India compared the microbial taxa to westernized populations and showed a higher relative abundance of Prevotella spp. and Faecalibacterium prausnitizii (Dubey et al., 2018). Specific species that dominated the Indian gut included Prevotella copri, Prevotella stercorea and Prevotella spp. (Dubey et al., 2018; Pareek et al., 2019). A key factor in this association is the host diet; Prevotella spp. is correlated with a high fibre diet, whereas Bacteroides spp. is associated with the westernized diet (Chen et al., 2017; Nakayama et al., 2017). Therefore, Prevotella spp. dominance in the Indian gut microbiome is likely driven by their dietary practices, which consists of high plant-based foods. 20 High Prevotella spp. abundance is not typical in the westernized gut microbiota but is commonly documented in central and eastern Asia (Wu et al., 2011), Africa, and the Middle East (Deschasaux et al., 2018; Gomez et al., 2016; Shankar et al., 2017; Yatsunenko et al., 2012), as well as tribal populations in different continents (Anwesh et al., 2016; Schnorr et al., 2014). In fact, an inverse correlation exists between Prevotella spp. and Bacteroides spp., and the genus that dominates is influenced by lifestyle (Ley, 2016; Precup & Vodnar, 2019). A frequent trend occurs with urbanization in which Prevotella spp. abundance decreases, and instead, Bacteroides spp. is the predominant bacteria (Lin et al., 2013; Wu et al., 2011). This pattern would help to explain why the most industrialized and westernized settings tend to observe higher Bacteroides spp. abundance in their populations than those living other lifestyles. Furthermore, the majority of research has been conducted on westernized and mainly Caucasian people, which has likely led to an inaccurate depiction of what a typical \u201chealthy\u201d gut microbiome looks like in humans (Abdill et al., 2022). A recent meta-search on the populations represented in microbiome research revealed that while India makes up a significant proportion of the global population, Indian cohorts are a tiny fraction studied in the microbiome field (Abdill et al., 2022). As more research from non-westernized groups continue to make it into literature, it becomes more apparent that the average gut microbiome in populations around the world is reliant on a combination of several factors, including overall lifestyle, geographic location, genetics, and cultural\/social behaviours (Brooks et al., 2018; Yatsunenko et al., 2012). 21 Table 1. Summary of Gut Microbiota Studies in Indian Populations Study  Sample Size Geographic Region Sequencing Method Results  Bhute et al. 2016  34 Delhi; Pune  16S rRNA, Delhi: Illumina HiSeq; Pune: Ion Proton PGM \uf0e1    Actinobacteria, Bacteroidetes, Proteobacteria vs. Americans \uf0e2    Firmicutes   \uf0e1    Lachnospiraceae, Ruminococcaceae, Veillonellaceae  \uf0e1    Prevotella, Megasphaera, Lactobacillus, Lachnospira, Roseburia   Kao et al. 2016 30  Bangalore, India; Kingston, Jamaica; Houston, USA (female only)  16S rRNA (V3V5) \uf0e1    Prevotella vs. Jamaican & American  Bamola et al. 2017  32 New Delhi 16S rDNA \uf0e1    Clostridia, Negativicutes, Ruminococcaceae (healthy subjects)  Kumbhare et al. 2017 47 India; 52 Finland  India; Finland (children only) PCR-DGGE, 16S rRNA (V4) Illumina \uf0e1    Prevotella & Megasphaera vs. Finnish  Chauhan et al. 2018 135 Rural- Pune  16S rRNA (V2-V6) Roche GS FLX+ \uf0e1    Bacteroidetes, Firmicutes   Das et al. 2018 84 Rural- Ballabhgarh; Ladakh  Urban- Ballabhgarh  16S rRNA (V1-V5), WGS \uf0e1    Bacteroidetes, Actinobacteria, Proteobacteria   Rural:  \uf0e1  Prevotella  Urban: \uf0e2  Prevotella \uf0e1  Lactobacillus   Dubey et al. 2018 1004 Multiple regions in India 16S rRNA (V3-V4) Ion S5 System \uf0e1    Prevotella copri  \uf0e1    Faecalibacterium prausnitzii  Jain et al. 2018 16 11 Indian; 5 Chinese 16S rRNA (V4) Illumina  \uf0e1    Bacteroidetes, Cyanobacteria vs. Chinese  \uf0e1 Prevotella, Megasphaera, Catenibacterium, Lactobacillus,   Mitsuokella, Carnobacterium,   Lachnospira vs. Chinese  Tandon et al. 2018   80  Urban- Ahmedabad 16S rRNA (V3-V4) Illumina  \uf0e1   Bacteroidetes \uf0e1   Prevotella, Faecalibacterium, Alloprevotella, Roseburia,   Bacteroides   Dhakan et al. 2019 (Arumugam et al 2011)  110 (infants included) Bhopal; Kerala 16S rRNA (V3) Illumina, Shotgun, GC-MS\/MS \uf0e1  Prevotella, Megasphaera; (P. copri, Prevotella stercorea) (group 1) \uf0e1  Ruminococcus, Faecalibacterium (F. prausnitzii, R. bromii) (group 2)  Kulkarni et al. 2019 43 Urban\/Semi-urban- Pune; Kolhapur 16S rRNA  \uf0e1  Prevotella, Bacteroides, Roseburia, Megasphaera, Faecalibacterium, Dialister \uf0e1  P. copri, Megasphaera elsdenii\/Megasphaera indica, Eubacterium rectale, Bacteroides coprocola,  Phocaeicola vulgatus (formerly Bacteroides vulgatus)   Pareek et al. 2019 50 India; 47 Japan Delhi, India; Osaka, Japan Bacteria: 16S (V1-V2) Illumina Fungi:  ITS1 SMRT \uf0e1  Prevotella vs. Japanese \uf0e1  P. copri, P. stercorea & Prevotella sp.  \uf0e1  Candida \uf0e1  Candida albicans, Candida tropicalis & Candida glabrata  22    Distinctions between the rural and urban gut microbiota also reveal a significant change in microbial abundance patterns in India. Rural communities in India show higher dominance of Prevotella spp. than urban cohorts, along with other genera such as Parabacteroides spp. and Vibrio spp. (Bhute et al., 2016; Das et al., 2018). Urban groups from west India in Pune and Kolhapur continue to show the dominance of genera traditional to the Indian gut, including Prevotella spp., Roseburia spp. and Megasphaera spp. Yet, the second most dominant genus was Bacteroides, with two core species being Phocaeicola vulgatus (formerly Bacteroides vulgatus) and Bacteroides coprocola, both associated with IBD (Bloom et al., 2011; Gryaznova et al., 2021; Schirmer et al., 2018). A study of an urban population in western India found two distinct clusters. The first cluster was a mix of taxa typical to the industrialized microbiota, including Study  Sample Size Geographic Region Sequencing Method Results  Chaudhari et al. 2020 54 Urban- Pune   16S rRNA (V3-V4) Illumina \uf0e1   Prevotella, Dialister, Bacteroides, Megamonas, Succinivibrio.   Dwiyanto et al. 2021 214 (49 Indian) Segamat, Malaysia 16S rRNA (V3-V4) Illumina \uf0e1   Prevotella:Bacteroides ratio   Dehingia et al. 2015* 193 Assam; Manipur; Sikkim; Telangana  PCR-DGGE, 16S rDNA (V3-V4) Illumina \uf0e1   Prevotella \uf0e1   Faecalibacterium, Eubacterium, Clostridium, Blautia, Ruminococcus, Roseburia Ramadass et al. 2017* 20 Tamil Nadu   16S rRNA (V3-V4) pyroseq \uf0e1   Firmicutes, Proteobacteria, Actinobacteria  Tribal: \uf0e1  Clostridium  Rural: \uf0e1 Streptococcus   Kaur et al. 2020* 31 Ladakh; Jaisalmer; Khargone  Shotgun Illumina \uf0e1   Prevotella, Bifidobacterium  \uf0e1   Lactobacillus, Prevotella, Bifidobacterium  Marathe et al. 2012\u2020 6 Pune Culture-based, qPCR-DGGE  \uf0e1   Firmicutes with age  \uf0e2   Bacteroidetes with age   Kabeerdoss et al. 2012\u2020 56 Tamil, Nadu (female only) Culture-based: RT-PCR (SYBR Green)  \uf0e1   Clostridium cluster XIVa (Roseburia- E. rectale) in omnivorous vs. vegetarian   Rani et al. 2017\u2021 243 Urban\/Tribal- Southern India (infants included) 16S rRNA  \uf0e1   Methanobrevibacter smithii  Abbreviations: * tribal studies; \u2020 culture-based;  \u2021  archaea analysis    23 Bacteroides spp., Bifidobacterium spp., along with microbes generally found in Indians such as Faecalibacterium spp., Blautia spp. and Dorea spp. (Tandon et al., 2018). The second cluster consisted of Prevotella spp. and Faecalibacterium spp., which is more typical of the traditional Indian gut microbiota (Tandon et al., 2018). Currently, there appears to be a mixture of bacteria specific to Indian ancestry, along with taxa in the industrialized microbiota. Based on these data, we speculate that as urbanization continues to develop in India, the typical Indian gut microbiota will eventually transition into one that resembles more of the industrialized microbiota, as seen in westernized populations, and will experience a loss of taxa that are considered common in those with Indian ancestry.  1.1.5 The Gut Microbiome of Indian Migrants By comparing the gut microbiomes of various groups throughout India, it could be hypothesized that there is slow transition away from what is considered a more \u201ctraditional\u201d gut to an industrialized microbiota within similar genetic backgrounds. Now the question remains: what does the gut microbiome look like in Indians who are living in an already westernized country? To date, there is currently only one study that has focused on classifying the gut microbiome in Indian immigrants. First-generation immigrants living in Ontario, Canada, showed higher abundances of Dialister succinatiphilus, P. copri and P. stercorea than Indo-Canadians (Copeland et al., 2021). By the second generation, their microbiota was more similar to the industrialized gut, with significantly higher abundances of Bacteroides spp. and Dialister invisus (Copeland et al., 2021). A shared trend of decreased abundances of Veillonellaceae spp. and Prevotella spp. was observed in both Indo-Canadians and Americans, further demonstrating the shift to a westernized microbiome. This trend is not only specific to Indian immigrants, as second-generation Thai immigrants in the US also experienced a displacement of Prevotella spp. for Bacteroides spp. (Vangay et al., 2018). Based on these findings, it could be predicted that the longer Indians reside in a westernized country, the more their gut microbial taxa will transition into an industrialized gut, 24 with these effects being most noticeable by the second generation (Figure 1). However, with only one study published on Indian immigrants, more data are required for better insight into the microbial changes occurring and how this transition may be affecting overall functions in the gut ecosystem. While the microbial taxonomy of the gut provides valuable information, future studies should also aim to analyze the functional characteristics of the gut microbiome in Indians as they migrate and adopt westernized practices. Figure 1 Schematic Overview of the Indian Gut Microbiota Transition from Westernization Migration to westernized countries correlates with a loss of ancestral taxa in Indian populations. Indian tribal populations harbour microbes typical to the ancestral gut. Indian urban begin to harbour species from the industrialized microbiota. Indian Immigrant represents the first-generation population. Indo-Canadian experience a more drastic shift in gut composition towards an industrialized microbiota. The transition from the Indian immigrant gut microbiota to the western gut microbiota is correlated with their increased risk for IBD and chronic inflammation.  Figure adapted from (D'Aloisio et al., 2022).  25 1.2 Hypothesis and Objectives The central hypothesis of this thesis is that Indian migrants (Indo-Immigrants and Indo-Canadians) in Canada will display a transition towards an industrialized microbiome and loss of Prevotella spp. abundance overtime. This transition will be observed alongside the adoption of westernized lifestyle practices, particularly through dietary acculturation in Canada.  The objectives of this thesis are:  1) Characterize the gut microbiota of Indians, Indo-Immigrants and Indo-Canadians in comparison to the European westernized microbiota 2) Examine the effect of dietary patterns on the gut microbiomes of Indians and Indian migrants  26 Chapter 2: Characterizing the Gut Microbiota in Indian Populations  2.1 Overview The primary purpose of this chapter was to investigate the differences in taxonomic composition in Indians who are residing in India versus Indians living in Canada. Using 16S and shotgun sequence data from stool samples collected from healthy adults, we compared alpha and beta diversity metrics, determined differentially abundant bacteria, and predicted microbiome phenotypes amongst groups.    2.2 Methods 2.2.1 Study Design A prospective case-control cohort study was conducted that included three Indian cohorts: (1) Indians residing in India (\u201cIndians\u201d, also called \u201cIndian residents\u201d), (2) Indians who migrated to Canada (\u201cIndo-Immigrants\u201d) and (3) Indians born in Canada (\u201cIndo-Canadians\u201d). In this thesis, \u201cIndian migrants\u201d refers to both Indo-Immigrants and Indo-Canadians. Individuals born in Canada with European ancestry (Euro-Canadian) were a control, and immigrants with European ancestry from an already westernized country (Euro-Immigrants) as a westernized immigrant control.     2.2.2 Participants Healthy participants between the ages of 17-55 years were recruited at three sites: (1) The National Institute of Cholera and Enteric Diseases, Kolkata, Indian (2) Kasturba Medical College, Manipal, India and (3) The University of British Columbia Okanagan, Kelowna, Canada. In Canada, participants were also recruited from religious temples in Kelowna and Vancouver, and in classrooms on UBCO campus. Posters and pamphlets were handed out to the community, with recruitment material tailored for both Punjabi and Indian\/Bengali communities (Appendix A). 27 Participants were excluded if they were pregnant or had a diagnosis of any chronic inflammatory condition. Samples also were not collected if participants had taken antibiotics or travelled to India less than three months prior. A demographic questionnaire was completed by each participant to assess baseline characteristics such as smoking status, alcohol consumption, physical activity, supplement use and socioeconomic status (Appendix B). In Canada, a lifestyle survey was also completed to assess changes in lifestyle and immigration-related stress (Appendix C).   2.2.3 Ethics Statement This study was approved by the University of British Columbia Clinical Research Ethics Board (UBC CREB) (H21-01555). Approval for sample collection in India was approved under IEC No 411\/2018 and by UBC CREB (H17-01324). All participants signed a consent form prior to participation. A Southeast Asian (SEA) Microbiome Biobank was also created from this study, and a separate consent form was signed by all subjects who contributed to the SEA Microbiome Biobank.  2.2.4 Sample Collection & Homogenization In Canada, each participant was given an at-home stool collection kit with a large plastic container (Medline, 320-DYND36500), a stool collection hat (Medline, DYND36600), a vial containing sodium acetate fixative (SAF) (Fisher Scientific, R21921), gloves, and a facemask. Participants were instructed to follow the Stool Collection Guide (Appendix D). Stool collection was completed into the stool collection hat, then for participants in Canada, they were instructed to scoop some of their stool into a SAF container to preserve for parasite analysis. Upon completion of collection, stool was placed in an at-home freezer for no longer than 3 days, and the SAF collection was kept at room temperature. Samples were transported on dry ice to the lab, then stored in a -80\uf0b0C freezer until homogenization. In a biosafety cabinet (BSC), stool was homogenized in liquid nitrogen using a sterilized mortar and pestle until the frozen stool was 28 ground to a fine powder. Homogenized stool was then aliquoted into 1.5mL tubes and placed back into -80\uf0b0C freezer for storage. Microscope examination of SAF-preserved stool was conducted using the fecal smear technique, by mixing saline with stool on a glass slide to be placed directly under microscope (EVOS M5000). If suspected ova were detected, the participants were labelled positive for parasites.  2.2.5 Microbial DNA Extraction & Library Preparation DNA was extracted from stool samples using the QIAamp PowerFecal Pro DNA Kit (Qiagen, Cat. No. 51804) following the manufacturer\u2019s instructions. For samples collected in Canada, an additional wash step (C5) was included to improve DNA purity. Samples were tested for nucleic acid concentrations using the Thermo Scientific NanoDrop 2000c. DNA samples were sent to Gut4Health Microbiome Core Facility (BC Children\u2019s Hospital Research Institute, Vancouver, British Columbia) for 16S sequencing on the Illumina MiSeq platform (V4-V4 region amplified with 515f and 806r primers ~75,800 reads per sample). For shotgun sequencing, DNA concentration was normalized to 30uL in nuclease free water, then sent to the Center for Health Genomics and Informatics (University of Calgary, British Columbia) for shotgun sequencing on the Illumina NovaSeq platform (~9.9M reads per sample).   2.2.6 16S Microbiome Analysis Paired-end demultiplexed reads were imported into QIIME 2 (Version 2022.2) (Bolyen et al., 2019). Quality control was completed with DADA2, which included filtering, chimera removal, dereplication, denoising and merging paired-end reads (Callahan et al., 2016). For taxonomic classification, the q2-feature-classifer (Bokulich et al., 2018) was trained using the GreenGenes2 database (10.28.22)(McDonald et al., 2022). Amplicon sequence variants (ASVs) were filtered for unclassified ASVs, sequence counts below 1000, and mitochondrial\/chloroplast DNA. Using q2-alignment, ASVs were aligned with mafft (Katoh et al., 2002) to construct a phylogeny with fastree 29 via q2-phylogeny (Price et al., 2010). Alpha diversity was calculated with Shannon (Shannon, 1948) and Pielou\u2019s Evenness (Pielou, 1966). Beta diversity metrics were calculated using rarefied data with Bray Curtis (Sorenson, 1948) and Weighted UniFrac (Lozupone et al., 2007). To determine if there were differentially abundant bacteria across cohorts, the Linear Discriminate Analysis (LDA) Effect Size (LEfSe) algorithm was used via the Huttenhower Lab Galaxy Hub (Segata et al., 2011) and command line interface, using a one-against-all strategy with a threshold of 3.5 on the logarithmic LDA score and an alpha of 0.05 for Kruskal-Wallis test among classes. BugBase was used to predict microbiome phenotypes such as Gram-negative, potentially pathogenic and stress-tolerant bacteria (Chen et al., 2016; Kanehisa et al., 2012; Langille et al., 2013; Snyder et al., 2007; Ward et al., 2017).  2.2.7 Shotgun Taxonomic Analysis Paired-end demultiplexed raw sequence reads were first assessed for quality using FastQC and all samples were compiled into a MultiQC report to determine parameters for trimming, which can be found here. Quality control was performed using kneaddata, with reads trimmed at 10 base pairs at a fixed maximum length of 120 base pairs. Taxonomic profiling was conducted using MetaPhlAn4, then each sample was normalized to relative abundances. The normalized relative abundance output table was then imported into R statistical software for downstream analysis including alpha diversity metrics Shannon (Shannon, 1948) and Pielou\u2019s Evenness (Pielou, 1966), as well as beta diversity metrics Bray Curtis (Sorenson, 1948) and Weighted UniFrac (Lozupone et al., 2007). Differentially abundant bacteria were determined using LEfSE with a 3.5 threshold and alpha 0.05 (Segata et al., 2011). In this study, the MicrobiomeAnalyst 2.0 platform was used to apply a Random Forest algorithm to the MetaPhlAn4 output table of relative abundances, ranked according to their Mean Decrease Accuracy score, indicating the extent to which each taxon\u2019s presence or absence influences the overall accuracy of classifying samples into their respective cohort. These taxa were then visualized in a heatmap 30 in MicrobiomeAnalyst 2.0 (Lu et al., 2023); a low count filter was applied to remove features less than 4 in fewer than 20% of samples, and a low variance filter was set to remove the lowest 10% of features with minimal variability, as determined by an inter-quantile range threshold.  2.2.8 Statistical Analyses Statistical analyses for baseline and immigrant characteristics were performed using both R statistical software and GraphPad Prism (Version 10.0.3) for macOS (GraphPad Software, Boston, Massachusetts USA, www.graphpad.com). We applied the Kruskal-Wallis test coupled with Dunn\u2019s post hoc analysis for nonparametric data. For data conforming to normal distribution, an ordinary one-way Analysis of Variance (ANOVA) was used. Categorical data were analyzed using either a Chi-Square, Fisher\u2019s Exact or Fisher\u2019s Exact with Monte Carlo simulation, depending on the data structure. Results were reported as median values and Interquartile Range (IQR), unless otherwise specified.   2.2.9 Power Analysis To estimate the required sample size per group, beta diversity distance matrix scores were used (Jiang et al., 2022). Due to the nonparametric nature of our data, median and IQR values were used instead of mean and standard deviation. The required sample size was determined using the following formula: \ud835\udc5b = 2 (\u0396\ud835\udefc2  + \u03961\u2212\ud835\udefd)2  \u03942                          \u2206 =\ud835\udc401 \u2212 \ud835\udc402\ud835\udc3c\ud835\udc44\ud835\udc45 Median Bray Curtis distances of 0.8273044 and 0.608826 for the Indian and Indo-Immigrant cohorts were used, respectively, and the larger IQR (0.1767818) was chosen. Therefore, with a 5% alpha error and a 20% beta error, the total estimated sample size required per group was 10 participants.  31 2.2.10 Data Availability Both 16S and shotgun data has been made publicly available on NCBI under accession number PRJNA1082632.  2.3 Results 2.3.1 Participant Recruitment While the Principal Investigator (Dr. Deanna Gibson) was in India in 2017\u20132019, samples were collected, homogenized, and DNA was extracted prior to transportation to Canada in dry ice. The DNA samples were then stored in a -80\u00b0C freezer at the UBCO campus. In 2021, recruitment commenced in Canada, with a total of 205 people screened for eligibility, of which 113 participated in the study. The total sample size for the study was broken down into each cohort as follows: Indians (n = 61), Indo-Immigrants (n = 32), Indo-Canadians (n= 17), Euro-Canadians (n = 41) and Euro-Immigrants (n = 23) (Figure 2).    Figure 2. Recruitment Flow Chart 32 2.3.2 Baseline Characteristics Baseline demographics were compared across groups (Table 2). Cohorts did not differ significantly regarding sex (p = 0.10), mode of birth (p = 0.23), BMI (p = 0.08), smoking status (p = 0.29), socioeconomic class (p = 0.70), lifestyle born\/raised (p = 0.10) or physical activity (walking, p = 0.56; exercise, p = 0.18). To assess differences in age, age groups were classified into each decade. In R, a Fisher\u2019s Exact test using the Monte Carlo simulation was performed and pairwise comparisons were generated with Bonferroni correction. Median age in Indians was significantly higher than in Indo-Immigrants (pBONF = 0.006) and Euro-Canadians (pBONF = 0.03). Using Chi-Square analysis, significant differences were also detected in alcohol consumption (pBONF = 3.29e-09), as Indians reported to consume less alcohol than all other cohorts. For each cohort, the following percentages are those who reported to consume alcohol: Indian 24% (n = 15\/61); Indo-Immigrants 47% (n = 15\/32); Indo-Canadians 82% (n = 14\/17); Euro-Canadians 80% (n = 33\/41); Euro-Immigrants 78% (n= 18\/23).  Table 2. Summary of Baseline Characteristics of Participants Baseline characteristic Indian n = 61 Indo-Immigr n = 32 Indo-Can n = 17 Euro-Can n = 41 Euro-Immigr n = 23 P value  Sex       Female 37 (61) 18 (60) 6 (35) 16 (39) 10 (43) 0.10 a Male 24 (39) 12 (40) 11 (65) 25 (61) 13 (57)  Age, (years)       Median (IQR) 31 (12) 25 (8) 23 (7) 24 (5) 29 (15)  Min-max 17-52 18-53 18-38 19-50 19-53  Age classification, n (%)       17-29 years 26 (43) 27 (84) 14 (82) 32 (78) 12 (52) 0.002 *e 30-39 years 21 (34) 4 (13) 3 (18) 4 (10) 7 (30)  40-49 years 10 (16) 0 (0) 0 (0) 4 (10) 2 (9)  50-55 years 4 (7) 1 (3) 0 (0) 1 (2) 2 (9)  Mode of birth, n (%)       Vaginal 44 (75) 19 (63) 14 (88) 34 (83) 19 (83) 0.23 a Caesarian 15 (25) 11 (37) 2 (12) 7 (17) 4 (17)  BMI       Mean (SD) 24 (3) 24 (4) 22 (3) 23 (3) 22 (2)  Min-max 17-32 17-34 17-30 17-31 18-25  33 Baseline characteristic Indian n = 61 Indo-Immigr n = 32 Indo-Can n = 17 Euro-Can n = 41 Euro-Immigr n = 23 P value  BMI classification, n (%)       Underweight (< 18.5) 3 (5.0) 1 (4.2) 2 (12.5) 3 (8.0) 1 (5.0) 0.08 b Normal weight (18.5 - < 25) 40 (66.7) 14 (58.3) 12 (75) 24 (63.0) 21 (95.0)  Overweight (25 - < 30) 13 (21.7) 8 (33.3) 2 (12.5) 10 (26.0) 0 (0)  Obese (> 30) 4 (6.7) 1 (4.2) 0 (0) 1 (3.0) 0 (0)  Smokers, n (%)       Yes 6 (10) 0 (0) 0 (0) 2 (5) 2 (9) 0.2908 c No 56 (90) 32 (100) 17 (100) 39 (95) 21 (91)  Alcohol, n (%)       Yes 15 (24) 15 (47) 14 (82) 33 (80) 18 (78) <0.0001\u2020a No 47 (76) 17 (53) 3 (18) 8 (20) 5 (22)  Physical activity- walking (mins)       Median (IQR) NA 90 (128) 90 (120) 60 (75) 60 (90) 0.56 d Min-max NA 0-330 30-300 12-480 12-420  Physical activity- exercise (days)       Median (IQR) NA 3 (2) 3 (3) 4 (3) 4.5 (3) 0.18 e Min-max NA 0-7 0-7 0.5-6.5 1-6  Socioeconomic class, n (%)       Low 15 (25) 8 (25) 5 (33) 10 (25) 4 (17) 0.70 e Middle 40 (67) 21 (66) 7 (47) 26 (65) 14 (61)  Upper 5 (8) 3 (9) 3 (20) 4 (10) 5 (22)  Lifestyle born\/raised, n (%)       Rural NA 2 (6) 5 (31) 5 (12) 5 (22) 0.10 c Urban NA 30 (94) 11 (69) 36 (88) 18 (78)       2.3.3 Immigrant Characteristics Differences in immigration-related lifestyle factors were assessed between immigrant groups (Table 3). Age at immigration (p = 0.19) and years since immigration (p = 0.65) did not differ significantly between Indo-Immigrants and Euro-Immigrants. As expected, a more significant change in lifestyle towards westernization was reported by Indo-Immigrants (p = 0.002), with over 50% of Indo-Immigrants reporting a moderate increased westernization, whereas almost 60% of Euro-Immigrants reported little change towards a westernized lifestyle. Immigration-related stress was also significantly higher in Indo-Immigrants (p = 0.04), with 25% Abbreviations: SD, standard deviation; IQR, interquartile range; BMI, body mass index; aChi-Square, bANOVA, cFisher\u2019s Exact, dKruskal-Wallis, eFisher\u2019s Exact (Monte Carlo); * Bonferroni correction revealed significant differences between Indian vs. Indo-Immigr (pBONF = 0.006), Indian vs. Euro-Can (pBONF = 0.03); \u2020 Bonferroni correct revealed significant differences (pBONF = 3.29e-09).   34 reporting moderate stress and 6% high stress, whereas only 9% of Euro-Immigrants reported moderate stress and 0% high stress. Overall, Indo-Immigrants reported a more significant change from their previous lifestyle and a higher level of immigration-related stress than Euro-Immigrants.   Table 3. Summary of Immigrant-Related Characteristics  Characteristic Indo-Immigr n = 32 Euro-Immigr n = 23 P value \u25b3 westernization, n (%)        No westernization 0 (0) 4 (18) 0.002 a  Little westernization 12 (38) 13 (59)   Moderate westernization 17 (53) 4 (18)   High westernization 3 (9) 1 (4)  Immigration-related stress, n (%)     Low stress 22 (69) 21 (91) 0.04 a  Moderate stress 8 (25) 2 (9)   High stress 2 (6) 0 (0)  Age at immigration, n (%)     0-3 years 0 (0) 1 (4) 0.19 b  4-17 years 9 (28) 3 (13)   18-49 years 23 (72) 19 (83)  Years since immigration     Median (IQR) 4 (6) 3 (9) 0.65 c  Min-max 0-22 0-48    2.3.4 Indian Microbiota is Distinctive from Westernized Groups Alpha diversity analysis on 16S sequence data was assessed using Pielou\u2019s Evenness and Shannon Diversity (Figure 3). Pielou\u2019s Evenness scores indicated Indians had the lowest evenness, whereas Euro-Canadians showed the highest. Indo-Immigrants also scored significantly lower in evenness when compared to Euro-Canadians (H = 41.43, q = 6.09e-10) and Euro-Immigrants (H = 4.996, q = 0.0314), but also significantly different from Indians (H = 6.908, q = 0.0123). Indo-Immigrants and Indo-Canadians did not differ significantly in evenness. Shannon Diversity scores also showed a trend of consecutive increasing richness from Indians to Indo-Immigrants and Indo-Canadians, with all 3 Indian cohorts significantly lower than the Abbreviations: IQR, interquartile range; aChi-Square for Trend, bFisher\u2019s Exact, cMann-Whitney    Abbreviations: IQR, interquartile range; aChi-Square for Trend, bFisher\u2019s Exact, cMann-Whitney   35 European groups. Indians scored significantly lower than both Indo-Immigrants (H = 17.61, q < 0.0001) and Indo-Canadians (H = 14.30, q = 0.0003), but Indo-Immigrants and Indo-Canadians were not significantly different.  Beta diversity analysis on shotgun sequence data using Bray-Curtis dissimilarity revealed significant differences in microbiomes across groups, with 45.3% of variation explained by the first 2 axes (Figure 4A). Pairwise Permutational Multivariate Analysis of Variance (PERMANOVA) showed that the microbiomes in Indians and Indo-Immigrant are distinct from westernized cohorts (Appendix E). Additionally, Indians and Indo-Immigrants revealed a significant difference (pseudo-F = 9.132, pBONF = 0.01). Weighted UniFrac also demonstrated distinctions in the Indian and Indo-Immigrant microbiomes (pseudo-F = 25.98, pBONF = 0.01), with 61.8% of the variation explained on the first 2 axes (Figure 4B). Significant differences between samples collected in   Figure 3. Indians Have a Significantly Lower Alpha Diversity Than Other Cohorts  Using 16S amplicon sequences, demultiplexed forward and reverse reads were truncated at 251 bases. Boxes represent the interquartile range (IQR) between the first and third quartiles, the horizontal line indicates the median, and whiskers are the upper and lower values within 1.5 times the IQR. Alpha diversity was compared across groups using the Kruskal-Wallis test. (A) Pielou\u2019s Evenness showed significant differences (H = 85.7, p = 1.07e-17). Pairwise comparisons reveal Indians had significantly lower evenness than Euro-Canadians (H = 56.2, q = 6.51e-13), Euro-Immigrants (H = 17.0, q = 7.32e-05) and Indo-Canadians (H = 10.8, q =1.69e-03). Euro-Canadian controls also score significantly higher evenness than Indo-Immigrants (H = 41.4, q = 6.09e-10) and Indo-Canadians (H = 21.7, q = 8.10e-06). (B) Shannon\u2019s Diversity showed significant differences (H = 79.8, p = 1.89e-16). Pairwise comparisons reveal Indians had significantly lower diversity than Euro-Canadians (H = 56.7, q = 3.91e-12), Euro-Immigrants (H = 32.9, q = 4.94e-08), Indo-Canadians (H = 14.3, q = 2.59e-04) and Indo-Immigrants (H = 17.6, q = 6.76e-05). Euro-Canadian controls also scored significantly higher evenness than Indo-Immigrants (H = 20.0, q = 2.62e-05) and Indo-Canadians (H = 14.4, q = 2.59e-04). 36 Figure 4. Beta Diversity Reveals Distinctions of Indian and Indo-Immigrants Using shotgun sequences, demultiplexed forward and reverse reads were trimmed at 10 base pairs at a fixed maximum length of 120 base pairs. Beta diversity was explored with Bray Curtis dissimilarity and Weighted UniFrac, using Pairwise Permutational Multivariate Analysis of Variance (PERMANOVA) to test differences between groups. (A) Bray Curtis principle coordinate analysis (PCoA) plot shows 45.3% of variation was captured on the first two axes. Pairwise comparisons with Bonferroni correction revealed significant differences between most groups, with distinct clustering of Indians and Indo-Immigrants from westernized cohorts. (B) Weighted UniFrac PCoA plot shows 61.8% of the variation was captured on the first two axes, with significant differences also detected. Pairwise comparisons results can be found in Appendix E.  Manipal vs. Kolkata in India were found for both Bray-Curtis (pseudo-F = 2.919, pBONF = 0.03) and Weighted UniFrac (pseudo-F = 9.425, pBONF = 0.015), however, comparisons between both regions in India were still significantly dissimilar when compared to other cohorts (Appendix F). Consistent findings were also measured in beta diversity on 16S amplicon sequence data, which can also be found in Appendix F. Overall, these results indicate that Indians have a lower alpha diversity than westernized groups, and beta diversity analysis reveals a clear distinction from the Indian gut microbiota when compared to westernized Indians and Europeans.              LEfSe analysis revealed Bacteroidetes and Proteobacteria were dominant in the Indian gut, whereas Euro-Canadians had the highest abundance of Firmicutes (Figure 5). Indo-Immigrants displayed high abundances of Eubacterium and Erysipelotrichaceae, previously categorized as VANISH taxa (Sonnenburg & Sonnenburg, 2019). As expected, genera common in the industrialized microbiome were enriched in the westernized European cohorts such as Alistipes, Bacteroides, and Clostridia, which are bacteria categorized as BloSSUM taxa 37 Figure 5. Cladogram of Phylogenetic Differences Across Cohorts LEfSe (Linear discriminate analysis Effect Size) cladogram results, depicting differentially abundant bacteria across cohorts, with genus set as lowest taxonomic rank. Indian gut displayed a dominance of Bacteroidetes and Proteobacteria, Euro-Canadians showed high abundance of Firmicutes. Indo-Immigrants had high abundances of VANISH (Volatile and\/or Associated Negatively with Industrialized Societies of Humans) taxa including Eubacterium and Erysipelotrichaceae. Indo-Canadians harboured high abundances of both VANISH taxa like Collinsella, and BloSSUM (Bloom or Selected in Societies of Urbanization\/Modernization) taxa such as Anaerostipes and Blautia. (Sonnenburg & Sonnenburg, 2019). Indo-Canadians represented a transition gut microbiota, harbouring both VANISH taxa such as Collinsella, and BloSSUM taxa including Anaerostipes and Blautia (Jha et al., 2018). These findings demonstrate that Indians living in India harbour microbial taxa characteristic of pre-industrialized societies, while westernized Europeans possess microbes typical of industrialized populations. Notably, Indo-Canadians exhibit a gut microbiota that       embodies a blend of both lifestyles, as evidenced by the presence of both VANISH and BloSSUM taxa (Figure 6). Similar results were also found in 16S sequence data (Appendix G). Shotgun sequencing depth was not sufficient enough to resolve parasite communities, and though the fecal smear method is prone to both false positives and false negatives, there were two notable Indo-Immigrant samples identified under microscope examination that displayed distinct morphologies of Taenia spp. and Entamoeba histolytica ova (Appendix H). 38  39  2.3.5 Indian Migrants Lose Prevotella spp. Abundance over Generations   When assessing relative abundances of taxa in each cohort in both 16S amplicon data and shotgun data, a similar pattern was observed of high Prevotella spp. abundance detected in Indian samples, and a gradual decline in abundance in Indo-Immigrants to Indo-Canadians (Figure 7). LEfSe analysis with shotgun sequence data identified Prevotella copri (clade A SGB1626) to be over five times more abundant in Indians (Figure 6B). Other differentially abundant bacteria in Indians included Phocaeicola plebeius (SGB1903) (LDA score = 4.66, p = 2.40e-12), Megasphaera sp. (BL7 SGB5858) (LDA score = 4.46, p = 2.04e-10), Dialister hominis (SGB5803) (LDA score = 4.15, p = 5.42e-06), and Escherichia coli (SGB10068) (LDA score = 4.16, p = 4.21e-12) (Table 4). In Indo-Immigrants, the top differentially abundant bacteria were Dorea longicatena (SGB4581) (LDA score = 4.30, p = 2.04e-07), Faecalibacterium prausnitizii (SGB15342) (LDA score = 4.24, p = 3.18e-11) and Lachnospiraceae bacterium (WCA3 601 WT 6H SGB4910) (LDA score = 4.08, p = 0.0099). Several bacteria were identified to be differentially abundant in Indo-Canadians, most notably two Ruminoccocus torques SGBs were identified, along with two F. prausnitzii SGBs. Other taxa detected in high abundance in Indo-Canadians included Blautia wexlerae (LDA score = 4.66, p = 5.37e-21), Eubacterium rectale (LDA score = Figure 6. Significantly Different Gut Bacterial Abundances Detected Across Cohorts LEfSe (Linear discriminate analysis Effect Size) results of top differentially abundant bacteria reveals distinct patterns in bacterial abundances in those living in Canada versus India. (A) Heatmap generated in MicrobiomeAnalyst 2.0, displaying taxa identified by Random Forest as key features that contribute to the predictive accuracy of classifying samples into their respective groups (Appendix I). Features were filtered for minimum 4 counts in 20% or more samples, and low variance filter of 10%. Bars on the top represent the cohorts that group the individual sample columns displayed. Taxa are labelled in rows, with taxonomic rank noted before the bacteria name. Colours on heatmap represent the relative abundances of each bacteria in a given sample. This heatmap shows a distinct pattern: bacterial species that are abundant in the cohorts residing in Canada tend to be less abundant in the Indian cohort. Conversely, bacteria that are highly abundant in the Indian cohort are found in lower abundances in the more westernized groups. The Indo-Immigrant cohort exhibits a gradient of abundances that bridges the gap between the Indian and Canadian cohorts. (B) LEfSe was used to test for differentially abundance bacteria and effect size estimations of bacterial abundances. A Kruskal-Wallis test was performed with a significance (\uf061) at 0.05 for one-against-all comparisons. Differentially abundant bacteria were detected using a Linear Discriminate Analysis (LDA) score, with an LDA score equal to or greater than 3.5. In Indians, Prevotella copri (clade A SGB1626) was over 5 times more abundant (LDA score = 4.73, p = 9.25e-13). In Indo-Immigrants, the top differentially abundant bacteria were Dorea longicatena (SGB4581) (LDA score = 4.30, p = 2.04e-07), Faecalibacterium prausnitizii (SGB15342) (LDA score = 4.24, p = 3.18e-11) and Lachnospiraceae bacterium (WCA3 601 WT 6H SGB4910) (LDA score = 4.08, p = 0.0099). In Indo-Canadians, two Ruminoccocus torques SGBs were identified, along with two Faecalibacterium prausnitzii SGBs. 40 4.66, p = 5.36e-05), Blautia massiliensis (SGB4826) (LDA score = 4.23, p = 1.82e-12), and Anaerostipes hadrus (SGB4540) (LDA score = 4.19, p = 3.45e-19). Taxa enriched in Euro-Canadians included Phocaeicola vulgatus (SGB1814) (LDA score =  4.49, p = 5.66e-05), Lachnospiraceae bacterium (LDA score = 4.27, p = 3.12e-16), Clostridium sp. (AF34 10BH SGB4914) (LDA score = 4.08, p = 0.00767), and Blautia faecis (SGB4820) (LDA score = 3.91, p = 3.08e-19). Taxa identified in higher abundances in Euro-Immigrants included Fusicatenibacter saccharivorans (SGB4874) (LDA score = 4.15, p = 3.84e-14), Clostridia bacterium (LDA score = 4.52, p = 3.81e-08), Lacrimispora celerecrescens SGB4868) (LDA score = 3.96, p = 2.47e-08), and Blautia obeum (LDA score = 3.92, p = 5.24e-15).       41 Figure 7. Pattern of Prevotella spp. Loss Observed in Indian Migrants Taxonomic stacked bar plots depicting relative abundances of gut microbiota of each sample (N = 174), grouped into their respective cohort. Legends display the top 18 most abundant taxa present across all samples. Both (A) 16S sequence data and (B) shotgun sequence data demonstrate a high abundance of Prevotella spp. in Indians that declines in Indian migrant cohorts. Overall gut bacterial composition is both distinct and more heterogenous in Indians, when compared to all other cohorts. 42 Table 4. Differentially Abundant Bacteria Calculated with LEfSe Taxon Log Score Class LDA  P value Veillonellaceae GGB4266 (SGB5809) 4.023 Indian 3.680 0.003000 Phocaeicola plebeius (SGB1903) 4.658 Indian 4.361 2.40E-12 Prevotella copri clade A (SGB1626) 5.034 Indian 4.735 9.25E-13 Escherichia coli (SGB10068) 4.157 Indian 3.801 4.21E-12 Megasphaera sp. (BL7 SGB5858) 4.456 Indian 4.159 2.04E-10 Dialister hominis (SGB5803) 4.147 Indian 3.838 5.42E-06 Acidaminococcus intestini (SGB5736) 3.758 Indian 3.544 0.001382 Dorea longicatena 4.390 Indo-Immigr 4.019 2.99E-16 Faecalibacterium prausnitzii (SGB15342) 4.242 Indo-Immigr 3.852 3.18E-11 Lachnospiraceae bacterium (WCA3 601 WT 6H SGB4910) 4.079 Indo-Immigr 3.685 0.009908 Dorea longicatena (SGB4581) 4.301 Indo-Immigr 3.922 2.04E-07 Faecalibacterium prausnitzii 4.930 Indo-Can 4.513 1.07E-16 Ruminococcus torques 4.410 Indo-Can 4.083 4.02E-16 Anaerobutyricum hallii 4.033 Indo-Can 3.693 3.48E-21 Collinsella aerofaciens (SGB14546) 4.478 Indo-Can 4.099 1.69E-11 Dialister invisus (SGB5825) 4.270 Indo-Can 3.963 4.37E-08 Blautia wexlerae (SGB4837) 4.657 Indo-Can 4.292 5.37E-21 Gemmiger formicilis (SGB15300) 4.422 Indo-Can 4.001 3.60E-06 Faecalibacterium prausnitzii (SGB15318) 4.436 Indo-Can 4.108 3.78E-14 Blautia massiliensis (SGB4826) 4.232 Indo-Can 3.925 1.82E-12 Anaerostipes hadrus (SGB4540) 4.189 Indo-Can 3.849 3.45E-19 Ruminococcus torques (SGB4563) 4.172 Indo-Can 3.843 1.32E-10 Faecalibacterium prausnitzii (SGB15332) 4.368 Indo-Can 4.007 2.44E-09 Anaerobutyricum hallii (SGB4532) 3.918 Indo-Can 3.583 6.69E-20 Eubacterium rectale (SGB4933) 4.656 Indo-Can 4.179 5.36E-05 Clostridia bacterium (SGB14861) 4.027 Indo-Can 3.739 7.03E-16 Ruminococcus torques (SGB4608) 4.035 Indo-Can 3.710 2.92E-13 Lachnospiraceae bacterium 4.275 Euro-Can 3.861 3.12E-16 Blautia faecis (SGB4820) 3.909 Euro-Can 3.564 3.08E-19 Phocaeicola vulgatus (SGB1814) 4.488 Euro-Can 4.056 5.66E-05 Clostridium sp (AF34 10BH SGB4914) 4.083 Euro-Can 3.654 0.007673 Ruminococcaceae GGB9635 (SGB15106) 3.871 Euro-Immigr 3.588 0.014807 Clostridia bacterium 4.522 Euro-Immigr 4.047 3.81E-08 Blautia obeum 3.917 Euro-Immigr 3.566 5.24E-15 Fusicatenibacter saccharivorans (SGB4874) 4.485 Euro-Immigr 4.154 3.84E-18 Lacrimispora celerecrescens (SGB4868) 3.960 Euro-Immigr 3.635 2.47E-08  43 2.3.6 Indian Gut Microbiome Predicted to be More Robust Using BugBase, the Kruskal-Wallis with Mann-Whitney-Wilcoxon pairwise comparisons followed by False Discovery Rate (FDR) correction was performed to estimate the differences in phenotypic potential in the gut microbiomes of each cohort from the 16S amplicon data. The Indian microbiome was predicted to have a significantly higher potential for stress tolerance in their gut when compared to all other cohorts (pFDR = 2.07e-18) (Figure 8). The potential for pathogenic bacteria was also predicted to be highest in Indians, then second highest in Indo-Immigrants, however, Indo-Canadians dropped down to similar levels as the European cohorts. Indians displayed a significantly higher level of pathogenic potential than Indo-Immigrants (pFDR = 1.03e-07), and Indo-Immigrants showed a significantly higher level than Indo-Canadians (pFDR = 5.78e-04). Gram negative bacteria was also predicted at significantly higher abundances in Indians and Indo-Immigrants when compared to the westernized cohorts. Indians showed a significantly higher prediction of Gram-negative bacteria in their gut than Indo-Immigrants (pFDR = 1.94e-09), and Indo-Immigrants showed a higher level than Indo-Canadians (pFDR = 3.32e-06).   The high stress, high pathogenic potential may at first seem to be driven by the high Prevotella spp. abundances in Indians and Indo-Immigrants. However, when examining the taxa that drove these phenotypic predictions, Proteobacteria abundance was the main driver in the prediction of significantly higher stress tolerance. Gram negative and potentially pathogenic predictions were driven mainly by a combination of Proteobacteria, Bacteroidetes, and Firmicutes. Additional phenotypic predictions from BugBase can be found in (Appendix J).      44 Figure 8. BugBase Predicts Higher Pathogenic Potential and Stress-Tolerant Microbiome in Indians A Kruskal-Wallis test was performed in BugBase followed by Mann-Whitney-Wilcoxon tests for pairwise comparisons with adjusted P values shown. Significant phenotypic differences were predicted from the 16S sequence data. Relative abundance is presented on the y-axis. (A) Stress tolerance was significantly higher in Indians (pFDR = 2.40e-12) compared to all cohorts. (B) Potentially pathogenic bacteria was significantly higher in Indians (pFDR = 2.07e-18) compared to all cohorts. Indo-Immigrants also showed significantly higher pathogenic potential than Indo-Canadians (pFDR = 5.78e-04), Euro-Canadians (pFDR = 1.41e-04) and Euro-Immigrants (pFDR = 2.34e-04). (C) Gram negative bacteria were estimated to be significantly higher in Indians than all other cohorts (pFDR = 2.01e-23). Indo-Immigrants were predicted to have a significantly higher Gram-negative bacteria abundance than Indo-Canadians (pFDR = 3.32e-06), Euro-Canadians (pFDR = 7.96e-10), and Euro-Immigrants (pFDR = 1.81e-05). Taxonomic contributions are also displayed for each phenotype prediction: (D) Stress tolerance was driven by higher abundances of Proteobacteria in Indians. (E) Potentially pathogenic bacteria was predicted by higher abundances of Proteobacteria and Bacteroidetes. (F) Gram negative bacteria prediction was influenced by abundances of Proteobacteria, Firmicutes and Bacteroidetes.                        45 2.4 Discussion 2.4.1 Indian Gut Microbiota is Distinctive from Westernized Populations Previous investigations of the Indian gut microbiota have also shown a distinct composition compared to the well-studied westernized gut (Bhute et al., 2016; Dhakan et al., 2019). Highly abundant genera in our Indian cohort included Dialister, Megasphaera, Prevotella, Succinivibrio, and Acidaminococcus, which have been previously detected at high abundances in the Indian gut (Arumugam et al., 2011; Bhute et al., 2016; Chaudhari et al., 2020; Dhakan et al., 2019; Jain et al., 2018; A. Kulkarni et al., 2019; Kumbhare et al.; Tandon et al., 2018). These taxa were not commonly found in our westernized European groups, and instead they harboured high abundances of microbes common in the westernized gut such as Bacteroides spp. (Yatsunenko et al., 2012), P. vulgatus (formerly Bacteroides vulgatus) (Bodykevich et al., 2023), and B. obeum (Piquer-Esteban et al., 2022). Furthermore, the high level of taxonomic variation observed in Indians has been previously described (Prasoodanan PK et al., 2021), which may reflect complex interactions between the wide spectrum of dietary practices in India, along with the current Western influence. The stark differences in taxonomic composition in the gut of Indian residents versus those born in a westernized country further demonstrates the key roles that geographic location and early-life environment have on the succession of species that develop in the gut.   2.4.2 Indian Migrants Transition Within One Generation Our taxonomic data revealed high abundances of VANISH taxa in Indian residents and Indo-Immigrants, and enrichment of BloSSUM taxa in European westernized controls. Whereas Indo-Canadians exhibited a transition-state microbiota containing both VANISH and BloSSUM taxa, a trend that has previously been detected in infants living in transitional societies (Olm et al., 2022).  A key genus that is distinctive in Indo-Canadians is the loss of Prevotella spp., which has been previously observed in immigrant generations (Copeland et al., 2021; Peters et al., 2020; van der Vossen et al., 2023; Vangay et al., 2018). In fact, this contrast in abundance of 46 Prevotella spp. in non-westernized versus westernized groups has sparked interest both in its relation to lifestyle and disease.  Prevotella spp. in the gut has been linked to various diseases including IBD (Hertz et al., 2022; Hjorth et al., 2018), obesity (Moreno-Indias et al., 2016), rheumatoid arthritis (Scher et al., 2013), high blood pressure and impaired glucose metabolism (Egshatyan et al., 2016). In particular, P. copri was associated with increased risk of laboratory-induced colitis in mice (Scher et al., 2013). Another study showed Prevotella intestinalis abundance led to exacerbated inflammation upon induced colitis (Iljazovic et al., 2020). Furthermore, a significantly lower level of butyrate was found in a Prevotella-dominant gut (Chen et al., 2017), and this lack of butyrate can degrade the mucus barrier and lead to intestinal dysbiosis (Moreno-Indias et al., 2016). Despite the association of this genus with diseases in the West, contradictory studies also showed beneficial health effects of Prevotella spp. abundance, such as its correlation with reduced risks of cardiovascular disease (CVD)  (Wang et al., 2016) and diabetes (Bibb\u00f2 et al., 2017),  and a low Prevotella spp. abundance was associated with obesity (Borgo et al., 2017). Additionally, P. copri enrichment has also been associated with improved glucose metabolism (Kovatcheva-Datchary et al., 2015). These contradictory studies present the current uncertainty around Prevotella  spp.-dominance in the gut and their role in disease.  Indians are known to possess some of the highest worldwide abundances of Prevotella spp. in their gut (Prasoodanan PK et al., 2021), as was reflected in our data. This detection of high abundances in healthy individuals highlights the importance of context when investigating the role that microbes have in the gut, as bacterial strains may divergently evolve to interact with its host depending on their genetics, geography, and lifestyle. In fact, these factors may have already driven evolutionary changes in how Prevotella spp. strains interact with different host populations. A recent study isolated P. copri from Indians and other non-westerners, and found 47 these strains demonstrated traits that aid in digestion of plant-based carbohydrates, where P. copri strains obtained from westernized populations were enriched with virulence factors and resistance genes (Prasoodanan PK et al., 2021). These findings demonstrate how specific strains can evolve with its host, and we must be wary of this when drawing general associations between bacterial species and diseases. Furthermore, the loss of P. copri we observed over generations in Indian immigrants raises questions on the implications this has on their gut health, and future research should explore the strain-specific P. copri-host interactions present within this demographic.  A previous study analyzing the gut microbiome in Indian migrants reported significantly higher levels of D. succinatiphilus and Megasphaera in Indo-Immigrants, whereas Indo-Canadians had a significantly higher abundance of D. invisus. Our data also showed a similar trend with these taxa, with D. succinatiphilus enriched in Indo-Immigrants, D. invisus in Indo-Canadians, and Megasphaera in our Indian cohort. While little is known about these taxa, a relationship between a carbohydrate-rich diet and succinate-utilizing bacteria has been previously discussed (Nakayama et al., 2017), with D. succinatiphilus being a well-known species in this category. R. torques, a known mucin degrader (Png et al., 2010), was also notably enriched in Indo-Canadians, and has been previously detected in high abundances in IBD patients (Ali et al., 2020). Additionally, a recent paper from the Crohn\u2019s and Colitis Canada Genetic, Environmental, and Microbial project (GEM) created a microbiome-risk score for Crohn\u2019s Disease (CD), of which R. torques was identified as the top taxa to contribute to this score (Garay et al., 2023). Hence, deeper investigations should explore the role of specific R. torques strains in the gut and its implications in IBD.  As IBD is becoming increasingly more prevalent in India, future research should also aim to compare the gut microbiomes of IBD patients in India versus the West. While few studies have 48 investigated the IBD microbiota in Indians, a reduction in Bacteroides spp., Lactobacillus spp., Ruminococcus spp., and Bifidobacterium spp. was observed in both UC and CD patients from North India, compared to controls (Verma et al., 2010). Furthermore, increased abundances of Methanobrevibacter smithii and sulphate-reducing bacteria were found in both UC and CD patients (Verma et al., 2010). In Tamil Nadu, IBD patients were compared to controls, and found an enrichment of Bacteroides-Prevotella-Porphyromonas and E. coli in UC patients (Kabeerdoss et al., 2015). Decreases in Clostridium coccoides and Clostridium leptum clusters have also been observed in IBD patients from North India and Tamil Nadu (Kabeerdoss et al., 2015; Kabeerdoss et al., 2013; Kumari et al., 2013), along with a reduction in butyrate levels observed in UC patients from North India (Kumari et al., 2013). This reduction in beneficial bacteria in Indians with IBD aligns with what is observed in IBD patients in the West (Aldars-Garc\u00eda et al., 2021), however further exploration is still required, with larger-scale studies using consistent methodologies.  Given that a dysfunctional gut barrier precedes IBD (Garay et al., 2023), understanding normal barrier function in Indians and how this might change upon immigration could provide mechanistic clues to their increased risk for IBD. Currently, there are limited findings examining gut barrier function in Indian populations; however, microbes with a higher potential for mucin degradation, such as Bifidobacterium longum, Bacteroides spp., Alistipes spp., Clostridia spp. and Negativicutes spp., were also previously detected in the Indo-Canadian gut (Copeland et al., 2021). These bacteria are mainly acquired in the westernized lifestyle, and the adoption of these species along with the sudden change in environment could be driving gut dysbiosis in immigrants. Further investigations should examine if this outcome is occurring in Indian immigrants and if so, this may be an underlying risk to IBD. Additionally, now that evidence has shown the gut microbiome of those living in India differ from a westernized gut, future microbiome-based therapies are unlikely to look similar across global populations. Therapies that aim to 49 restore a \u201chealthy\u201d microbiome will therefore need to be tailored to the individual, including their lifestyle, diet and cultural practices.    50 Chapter 3: The Impact of Westernization on the Gut Microbiome in Indian Migrants  3.1 Overview The primary focus of this chapter is to delve into the lifestyle-driven alterations in the gut microbiome, as was observed in Chapter 2. We first assessed whether the dietary patterns in Indian migrants transition towards a more westernized diet. Next, using shotgun sequence data we characterized the functional potential in the gut microbiomes of each cohort using three distinct annotation approaches. Since humans lack many of the enzymes required to break down carbohydrates, the bacteria in our gut play a crucial role in this process by expressing carbohydrate-active enzymes (CAZymes) (Wardman et al., 2022). Studies have shown specific CAZyme profiles that help to break down plant-based polysaccharides in those who have a Prevotella-dominant gut microbiota (Aakko et al., 2020), hence these CAZymes were a very relevant aspect to investigate in our cohort, as Indians display some of the highest abundances of Prevotella spp. worldwide (Prasoodanan PK et al., 2021). Additionally, we analyzed for Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, which is a commonly used database that provides a collection of orthologous groups of genes that are functionally conserved across certain species (Kanehisa & Goto, 2000). Lastly, we investigated our shotgun sequence data in the context of the MetaCyc database, which annotates metabolic functions from metagenomic data (Caspi et al., 2016). The integration of CAZyme activity profiles, KEGG orthology, and MetaCyc pathways allowed us to explore deeper into the relationships between diet and lifestyle on the potential microbial metabolic activity in the gut of Indian migrants.   51 3.2 Methods 3.2.1 Dietary Data Collection Participants filled out a food log for 3 consecutive days prior to their stool collection (Appendix K). Information from the food logs were retrospectively added into the ESHA Food Processor\u00ae Nutritional Analysis software (Version 11.11.0).  3.2.2 Nutritional Analysis ESHA generated a summary of the nutrients consumed by each participant, and each nutrient was averaged over the 3 days of dietary data provided. Several data cleaning steps were untaken to ensure food intake was inputted into ESHA to accurately reflect the participant\u2019s food log (i.e. calories, proportions, recipes, etc.). One researcher had conducted 4 rounds of data cleaning and three additional researchers reviewed ESHA data for outliers such as abnormally high caloric or nutrient intake. Each nutrient was averaged over 3 days, and results were reported both as absolute values (males and females separate) and as values normalized to 1000 kcal. To account for potential biases in using a North American nutritional software, we inputted food logs into the Madras Diabetes Research Foundation (MDRF) EpiNu\u00ae Nutritional software, which was designed to represent the Indian diet. If subjects did not specify the type of cooking oils, the cooking oil type was adjusted to reflect the dominant cooking oil used in the subject\u2019s region.    To calculate the percentage of daily caloric intake from ultra-processed foods (UPFs), dietary data for each individual was examined in ESHA to flag any products that were classified as UPF according to the NOVA classification. Calories from UPFs were added up for each participant, averaged over 3 days, then divided by the average caloric intake of each participant multiplied by 100. To determine participants who were vegetarian or pescetarian, participants were asked in the demographic questionnaire if they had any dietary restrictions. In addition to the subject\u2019s reported diet, the food logs themselves were scanned for validation. Pescatarians 52 were counted if their food logs reported to only be eating fish, but not meat. Whereas vegetarians were counted if their food records did not contain any meat, fish, but did include eggs (ovo-vegetarian) and\/or dairy (lacto-vegetarian).   3.2.3 Functional Profiling Shotgun reads were analyzed for functional potential using HUMAnN 3.6 (Beghini et al., 2021). Gene family and pathway abundances were annotated using the UniRef90 (Suzek et al., 2007) and MetaCyc (Caspi et al., 2014) databases, respectively, then normalized to reads per kilobase to account for gene length. Both tables were further normalized to relative abundance to account for sampling depth. The normalized gene family abundance output was then regrouped to the CAZy and KEGG databases using the humann_regroup_table function. To determine differentially abundant genes\/pathways across cohorts, the CAZy, KEGG, and pathway tables were collapsed to perform LEfSe analysis on the unstratified data using a one-against-all strategy. A Kruskal-Wallis test was performed with a significance (\uf061) at 0.05 and LDA score threshold of 3.0 (Segata et al., 2011). Differentially abundant features were then plotted as a heatmap using MicrobiomeAnalyst 2.0 (Lu et al., 2023).  3.2.4 Statistical Analyses Statistical analyses for dietary data were performed using GraphPad Prism (Version 10.0.3) for macOS (GraphPad Software, Boston, Massachusetts USA, www.graphpad.com). We applied the Kruskal-Wallis test coupled with Dunn\u2019s post hoc analysis for nonparametric data. For data that passed normality, an ordinary one-way ANOVA with post hoc Tukey\u2019s multiple comparisons test was performed. Results were reported as median values and IQR, unless otherwise specified. To understand associations between the distinctions in beta diversity from the taxonomic data with the dietary patterns and baseline characteristics that were significantly different,  a distance-based redundancy analysis (dbDRA) was executed in R statistical software 53 Figure 9. Indian Migrants Consume Significantly More Ultra-Processed Food than Indians Calories from Group 4 Ultra-Processed Food (UPF) was counted for each subject from the ESHA dietary reports then compared to total average daily caloric intake. A Kruskal-Wallis test was performed, followed by Dunn\u2019s multiple comparisons, with adjusted p values displayed. Middle bands are the median values, top and bottom boxes display the first and third quartiles, and whiskers are the max and min values. (A) Percentage of daily caloric intake from Group 4 UPF was highest in Indo-Canadians and Indians consumed a significantly lower amount than all other cohorts (p = <0.0001). (B) Bar graph displaying the ratio between average total caloric intake vs. average total kcal from Group 4 UPF in each cohort. (4.2.2) (R Core Team, 2018), using the Weighted UniFrac plot generated from 16S amplicon data in QIIME 2.  3.3 Results 3.3.1 Indo-Canadian Diet had Highest Levels of Ultra-Processed Food Intake Indo-Canadians had the highest intake of Group 4 Ultra-Processed Food (UPF) at 61% of their daily caloric intake (95% CI = 467.9 \u2013 780.5 kcal, per 1000 kcal), while Indians consumed significantly less than all other cohorts, reporting 12% of their daily intake from Group 4 UPF (95% CI = 63.66 \u2013 127.1 kcal per 1000 kcal) (Figure 9). Euro-Canadians had the second highest consumption at 51% (95% CI = 399.6 \u2013 582.9 kcal, per 1000 kcal) and Indo-Immigrants reported 44% (95% CI = 323.7 \u2013 581.1 kcal, per 1000 kcal). These results indicate that Indo-Immigrants and Indo-Canadians significantly increase their intake of Group 4 UPF compared to Indians, with Indo-Canadians consuming even more than the European westernized controls.             54 3.3.2 Indian Cohort had the Highest Proportion of Non-Meat Eaters The highest proportion of non-meat eaters were Indians at 60% (n = 36\/61, p = <0.0001), with 30% reporting to be vegetarian and 30% reporting pescetarian. In Indo-Immigrants, 19% (n = 6\/32) reported to not eat meat. In the westernized cohorts, 12% (n = 2\/17) of Indo-Canadians, 10% (n = 4\/41) Euro-Canadians and 39% (n = 9\/23) of Euro-Immigrants were non-meat eaters. These results align with our expectation that Indian residents would have the highest proportion of non-meat eaters.   3.3.3 Low Variety of Cooking Oils Were Used by Westernized Groups A total of 70% (n = 43\/61) of Indians, 56% (n = 18\/32) of Indo-Immigrants, 53% (n = 9\/17) Indo-Canadians, 85% (n = 35\/41) Euro-Canadians, and 78% (n = 18\/23) of Euro-Immigrants reported the types of oils they used when cooking (Figure 10). In Indians, the main cooking oil\/fat reported was sunflower oil (30%) followed by ghee (12%) and mustard oil (11%). The three top cooking oils in Indo-Immigrants were olive oil (28%), butter (24%) and ghee (16%). In all three westernized cohorts the top cooking oil used was olive oil, followed by butter. When compared to Indians, Indian migrants appear to reduce their variety of cooking oils. Overall, Indians used a greater variety of cooking fats, whereas westernized cohorts were more limited, using mainly olive oil, butter, canola oil, coconut oil, and vegetable oil (Table 5).  55 Figure 10. Decreased Variability in Cooking Oils are Used by Indian Migrants Food logs were reviewed for participants who reported their use of cooking oils\/fats. A total of 70% (n = 43\/61) of Indians, 56% (n = 18\/32) of Indo-Immigrants, 53% (n = 9\/17) Indo-Canadians, 85% (n = 35\/41) Euro-Canadians, and 78% (n = 18\/23) of Euro-Immigrants reported the types of fats they used when cooking. This plot displays the number of subjects in each group who reported their use of the listed cooking oils\/fats. Oils that were reported less than 5 times across all cohorts were grouped into the \u201cother\u201d category. In Indians, the most dominant cooking fats reported were sunflower oil and ghee. Compared to Indians, both Indian migrant cohorts reported much less variety in cooking oils. Indo-Immigrants, Indo-Canadians, and both westernized control groups predominately reported olive oil and butter.        Table 5. Percentages of Respondents Who Reported Use of Each Cooking Oil           Oil Type Indian n = 43 Indo-Immigr n = 18 Indo-Can n = 9 Euro-Can n = 35 Euro-Immigr n = 18 Butter 5% 24% 27% 24% 26% Ghee 12% 16% 0% 0% 0% Coconut 9% 4% 0% 5% 10% Olive 1% 28% 45% 44% 39% Sunflower 30% 0% 0% 2% 0% Canola 0% 12% 0% 13% 6% Vegetable 5% 4% 18% 2% 10% Mustard 11% 8% 0% 0% 0% Avocado 0% 0% 0% 5% 6% Groundnut 8% 0% 0% 0% 0% Rice 8% 0% 0% 0% 0% Other * 9% 4% 9% 4% 3% * Oils reported less than 5 times across all cohort were grouped into \u201cOther\u201d  56 3.3.4 Macronutrient Composition Differs from Indians and Westernized Cohorts Absolute macronutrient intake for males and females were independently examined in Table 6 and Table 7, respectively. Pairwise comparisons of dietary data can be found in Appendix L. Total energy intake was significantly higher in Euro-Canadian males vs. Indo-Immigrant males (p = 0.0144, 95% CI = -1461 \u2013 -110.0 kcal). There were no other significant differences in caloric intake between groups. When assessing macronutrient composition, median percentage of protein intake was highest in Euro-Canadians (16.1%, 95% CI = 13.2 \u2013 17.5%) and lowest protein intake was in Indians (12.5%, 95% CI = 11.7 \u2013 13.3). Indians consumed the highest percentage of carbohydrates in their diet (56.5%, 95% CI = 54.7% - 59.0%) and lowest carbohydrate intake was in Indo-Canadians (44.0%, 95% CI = 40.6% \u2013 50.6%). The highest percentage of fat consumption was in Indo-Canadians (39.7%, CI 95% 33.8% \u2013 42.2%) and the lowest was in Indians (31.1%, 95% CI = 28.5% \u2013 34.4%). As expected, these results highlight that Indians consume a high carbohydrate and low protein diet, whereas Euro-Canadians generally consume a high protein diet.  3.3.4.1 Continual Reduction in Carbohydrate Intake in Indo-Immigrants and Indo-Canadians  When examining the median carbohydrate intake normalized to 1000 kcal, several significant differences were observed amongst groups. Indians had the highest carbohydrate consumption (141.2g per 1000 kcal, 95% CI = 136.8 \u2013 147.5) when compared to Indo-Immigrants (131.5g per 1000 kcal, 95% CI = 122.0 \u2013 141.6, p = 0.0129), Indo-Canadians (110.1g per 1000 kcal, 95% CI = 101.4 \u2013 126.6, p = <0.0001), and Euro-Canadians (113.5g per 1000 kcal, 95% CI = 104.5 \u2013 124.4, p = <0.0001). Carbohydrate consumption reduced over generations, with Indo-Immigrants consuming 17.09g more carbohydrates per 1000 kcal than Indo-Canadians (95% CI of diff: 0.3789 \u2013 33.81, p = 0.0423), suggesting a transition away from their traditional dietary patterns. Moreover, no significant differences were observed between Indo-Canadians, Euro-57 Canadians and Euro-Immigrants, indicating these groups consumed a relatively similar amount of carbohydrates.   3.3.4.2 Indians Report the Lowest Intake of Total Dietary Fat Comparisons between median fat intake normalized to 1000 kcal revealed Indians consumed the lowest amount of fat (34.6g per 1000 kcal, 95% CI = 31.7 \u2013 38.2) compared to Indo-Canadians (44.1g per 1000 kcal, 95% CI = 37.6 \u2013 46.8), and Euro-Canadians (42.9g per 1000 kcal, 95% CI = 39.9 \u2013 45.4). Tukey\u2019s post hoc analysis indicated significant differences in pairwise comparisons, with Indians consuming an average of 7.42g less fat for every 1000 kcal than Indo-Canadians (95% CI of diff: -13.26 \u2013 -1.580, p = 0.0053), and 8.17g less fat than Euro-Canadians (95% CI of diff: -12.47 \u2013 -3.875, p = <0.0001). No significant differences were observed in pairwise comparisons involving Indo-Immigrants, indicating that their fat intake occupies an intermediate level- higher than that of westernized groups, yet lower than the intake of Indians.  Macronutrient values were also compared between the North American ESHA and Indian EpiNu nutritional software. Overall macronutrient composition in Indian and Indo-Immigrants were not significantly different between these two software (Figure 11A). However, EpiNu calculated a significantly higher average intake of PUFA (EpiNu = 13.49g\/1000kcal per 1000, ESHA = 6.370\/1000kcal; p = <0.0001) and Monounsaturated Fatty Acid (MUFA) (EpiNu = 13.09g\/1000kcal, ESHA = 8.952g\/1000kcal; p = <0.0001). Additionally, a significantly lower intake of Saturated Fatty Acid (SFA) in Indians was detected in EpiNu (2.597g per 1000 kcal) compared to ESHA (10.53g per 1000 kcal, p = <0.0001) (Figure 11B). Overall, these results suggest that while macronutrient composition was in agreement with both software, the types of dietary fat differed amongst EpiNu and ESHA.  58 Table 6. Male Participant Absolute Macronutrient Intake                   Macronutrient RDA\/AMDR Indian n = 37 Indo-Immigr n = 18 Indo-Can n = 6 Euro-Can n = 16 Euro-Immigr n = 10 P value Energy, kcal (IQR) - 2329 (1187) 2086 (933) 2274 (532) 2911 (1342) 2668 (906) 0.0343a Protein             g (IQR) 56g 82.9 (41.3) 78.8 (39.1) 138 (37.2) 119 (62.6) 103 (42.5) <0.0001a      % of total energy  10-35% 13.1% 14.3% 23.1% 17.8% 16.4%       g per 1000 kcal - 31.3 35.1 37.5 40.4 37.6  Fat             g (IQR) - 80.2 (43.1) 76.5 (40.8) 109 (39.9) 113 (63.9) 98.5 (45.2) 0.0109 a      % of total energy  20-35% 29.2% 34.6% 39.2% 38.2% 37.1%       g per 1000 kcal - 34.6 38.2 44.1 42.9 40.7  PUFA, g (IQR) - 13.1 (13.7) 10.6 (6.33) 13.7 (6.32) 22.2 (13.3) 11.7 (9.35) 0.0254 b Omega-3, g (IQR) 1.6 1.44 (1.77) 0.93 (0.53) 1.24 (1.01) 2.31 (3.44) 0.85 (1.63) 0.0080 b Omgea-6, g (IQR) 17 11.5 (10.9) 8.77 (4.64) 10.5 (5.93) 16.7 (10.8) 9.20 (7.15) 0.0127 b MUFA, g (IQR) - 17.8 (14.3) 17.7 (11.8) 17.2 (22.3) 33.0 (24.0) 24.6 (13.7) 0.0100 b SFA, g (IQR) - 26.4 (20.0) 23.0 (12.2) 34.5 (15.0) 37.3 (22.3) 30.1 (19.0) 0.0055 a Carbohydrate             g (IQR) 130g 336 (58.6) 267 (51.8) 207 (39.7) 268 (41.1) 284 (43.0) <0.0001a      % of total energy  45-65% 58.6% 51.8% 39.8% 41.1% 43.0%       g per 1000 kcal - 141 131 110 113 112  Fibre, g (IQR) 38g 34.9 (17.4) 28.3 (20.1) 13.1 (9.70) 23.9 (15.5) 22.4 (22.6) 0.0016 b      Soluble fiber  0.37 0.59 0.03 0.66 0.56 0.1223 b Abbreviations: aANOVA, b Kruskal-Wallis. Median and IQR reported.  59 Table 7. Female Participant Absolute Macronutrient Intake                Macronutrient RDA\/AMDR Indian n = 24 Indo-Immigr n = 14 Indo-Can n = 11 Euro-Can n = 25 Euro-Immigr n = 13 P value Energy, kcal (IQR) - 1747 (630) 1976 (1136) 1812 (527) 2265 (910) 2227 (1060) 0.0332* Protein             g (IQR) 46g 51.5 (21.8) 65.3 (47.9) 63.1 (20.5) 81.0 (20.8) 66.6 (50.7) 0.0004 b      % of total energy  10-35% 12.3% 13.8% 12.9% 14.1% 14.0%       g per 1000 kcal - 31.3 35.1 37.5 40.4 37.6  Fat             g (IQR) - 62.7 (32.3) 69.8 (38.0) 73.1 (27.5) 89.2 (40.4) 103 (57.0) 0.0018 b      % of total energy  20-35% 33.4% 34.4% 39.7% 38.7% 36.7%       g per 1000 kcal - 34.6 38.2 44.1 42.9 40.7  PUFA, g (IQR) - 12.1 (9.49) 12.9 (11.0) 8.06 (4.33) 13.3 (8.90) 9.87 (13.0) ns Omega-3, g (IQR) 1.1 1.24 (0.85) 1.46 (1.02) 0.84 (0.44) 1.42 (1.25) 1.06 (1.16) ns Omgea-6, g (IQR) 12 10.5 (8.72) 8.80 (8.25) 7.00 (4.04) 10.0 (8.92) 8.73 (7.67) ns MUFA, g (IQR) - 16.5 (10.5) 15.6 (13.4) 15.3 (9.77) 23.8 (13.7) 19.0 (23.6) ns SFA, g (IQR) - 18.3 (14.9) 23.4 (10.6) 22.9 (5.66) 30.6 (14.3) 41.7 (35.1) ns Carbohydrate             g (IQR) 130g 258 (86.4) 256 (116) 218 (99.5) 272 (119) 248 (141) ns      % of total energy  45-65% 55.7% 53.6% 38.4% 47.6% 49.4%       g per 1000 kcal - 141 131 110 113 112  Fibre, g (IQR) 25g 24.6 (17.8) 23.1 (18.7) 14.7 (6.30) 27.0 (15.2) 28.8 (28.4) 0.0163 a              Soluble fiber  0.21 0.69 0.05 0.75 0.45 0.0003 b Abbreviations: aANOVA, b Kruskal-Wallis. Median and IQR reported. *Tukey\u2019s post hoc analysis revealed no significant differences between groups  60 Figure 11. Dietary Fat Types are Significantly Different Between EpiNu vs. ESHA (A) Percentages of fat, protein and carbohydrates were calculated in ESHA and EpiNu for each participant, then mean values were calculated for each cohort. An ordinary one-way Analysis of Variance (ANOVA) was used, and no significant differences in macronutrient intake were found between ESHA and EpiNu. The macronutrient compositions from ESHA were  30.91% Fat : 12.72% Protein : 56.37% Carbohydrate for Indians and 34.77% Fat : 14.27% Protein : 50.97% Carbohydrate for Indo-Immigrants. The macronutrient compositions from EpiNu were  30.74% Fat : 12.22% Protein : 57.04% Carbohydrate for Indians and 37.13% Fat : 13.85% Protein : 49.02% Carbohydrate for Indo-Immigrants. (B) Significant differences in types of fat consumed by Indians and Indo-Immigrants were calculated between EpiNu vs. ESHA. Dietary fats displayed in grams per 1000 Calories (kcal). Kruskal-Wallis tests were conducted to determine differences between groups. The middle band inside of the boxes is the median, the bottom and top boxes are the first and third quartiles, and the whiskers are the min to max values. When compared to EpiNu, ESHA calculated a significantly higher saturated fatty acid (SFA) intake in Indians (p = <0.0001). EpiNu calculated significantly higher monounsaturated fatty acid (MUFA) intake in both Indians and Indo-Immigrants (p = <0.0001). Compared to ESHA, EpiNu calculated a significantly higher intake of polyunsaturated fatty acid (PUFA) in Indians.                       3.3.4.3 Indian Diet Was Lowest in Protein Intake Relative protein intake was highest in Euro-Canadians (40.4g per 1000 kcal, 95% CI = 32.3 \u2013 43.6) and Indians had the lowest protein consumption (31.3g per 1000 kcal, 95% CI = 29.3 \u2013 33.2). Dunn\u2019s post hoc analysis show that Indians demonstrated a lower mean rank than Indo- 61 Canadians (-39.14, p = 0.0461), Euro-Canadians (-43.21, p = 0.0002) and Euro-Immigrants (-45.29, p = 0.0024). No significant differences in protein intake were detected between westernized groups and Indo-Immigrants, indicating a relatively consistent consumption of protein.   3.3.4.4 Fibre Intake Dramatically Reduced in Indo-Canadians Median total fibre intake was lowest in Indo-Canadians (6.80g per 1000 kcal, 95% CI = 5.34 \u2013 9.00) and highest in Indians (13.9, per 1000 kcal, 95% CI = 11.8 \u2013 15.3). Tukey\u2019s post hoc analysis revealed significant differences in pairwise comparisons with Indo-Canadians. On average, Indo-Canadians consumed 7.13g less fibre per 1000 kcal than Indians (95% CI = 3.040 \u2013 11.22, p = <0.0001), 3.34g less fibre than Indo-Immigrants (95% CI = 1.506 \u2013 10.46, p = 0.0028) and 5.67g less than Euro-Immigrants (95% CI = -10.44 \u2013 -0.9008, p = 0.0110). Total fibre intake was also significantly higher in Indians versus Euro-Canadians (95% CI = 0.3316 \u2013 6.356, p = 0.0213). However, when examining soluble fibre, Euro-Canadians consumed the highest level (0.36g per 1000 kcal, 95% CI = 0.1678 \u2013 0.4685) and Indo-Canadians the lowest (0.023g per 1000 kcal, 95% CI = 0.000 \u2013 0.056). Indians had the second lowest intake of soluble fibre (0.1412g per 1000 kcal, 95% CI = 0.09022 \u2013 0.2332), indicating majority of their total fibre consumption was from insoluble fibre such as cellulose, hemicellulose and lignin, found in foods such as roti, cauliflower and pulses. Dunn\u2019s post hoc analysis showed Indo-Canadians reported significantly lower mean rank versus Indo-Immigrants (65.74, p = 0.0001), Euro-Canadians (-56.54, p = 0.0010) and Euro-Immigrants (-47.90, p = 0.0291). Micronutrient intake was also assessed amongst groups, which can be found in Appendix M.  3.3.5 Functional Potential in the Indian Gut is Distinctive from Westernized Groups   When assessing the functional potential from the shotgun sequence data, significant differences in pathway abundances were observed between classes (Appendix N). Pathway annotation using MetaCyc revealed that a pathway involved in production of lipopolysaccharide  62 (LPS), PWY-1269-CMP-3-deoxy-D-manno-octulosonate biosynthesis, was significantly more abundant in Indians (LDA score = 3.179, p = 3.37e-17). Other pathways enriched in Indians included 5 for peptidoglycan synthesis, 6 involved with microbial growth\/metabolism, 4 for protein synthesis and 2 for DNA synthesis\/repair (Figure 12). In Indo-Canadians, indications of higher usage of glucose storage was detected via PWY-5941-glycogen degradation II (LDA = 3.369, p = 9.27e-20) and the use of simple sugars through PWY-6317-D-galactose degradation I - Leloir pathway (LDA = 3.126, p = 2.96e-18). Other pathways enriched in Indo-Canadians included PWY-6823-Molybdoprerin biosynthesis (LDA = 3.359, p = 1.32e-22), pentose phosphate pathway non-oxidative branch I (LDA = 3.098, p = 5.27e-10) and II (LDA = 3.039, p = 1.07e-11), and P41_PWY-pyruvate fermentation to acetate and (S) - lactate I (LDA = 3.018, p = 3.48e-16). Euro-Canadians had increased pathways related to fatty acid biosynthesis via PWY66-429 \u2013 fatty acid biosynthesis initiation (mitochondria) (LDA = 3.403, p = 1.07e-21) and CDP Diacylglycerol Biosynthesis I and II (LDA = 3.941, p = 2.16e-15). Euro-Immigrants also expressed pathways metabolizing sugars such as PWY-7238 \u2013 sucrose biosynthesis II (LDA = 3.555, p = 4.44e-21) and GLYCOGENSYTHN-PWY \u2013 glycogen biosynthesis I \u2013 from ADP-D glucose (LDA = 3.468, p = 1.52e-21).  When measuring CAZyme gene families across cohorts, several differentiated CAZy families were detected in the Indian gut including Glycoside Hydrolase (GH) GH43 (LDA score = 4.417, p = 1.57e-11), GH51 (LDA score = 4.047, p = 2.57e-11), GH10 (LDA score = 3.921, p = 1.37e-18), all of which are well known for their degradation of plant-based polysaccharides (Appendix O). Additionally, the top taxa contributing to the higher abundances of these CAZy families was from P. copri (Figure 13). KEGG genes were also differentially detected across groups, with K00561 abundant in Indians (LDA score = 3.187, p = 3.15e-18) and K18220 in Indo-Immigrants (LDA score = 3.129, p = 1.19e-05), both identified as antimicrobial resistance genes. Taxa contributions to each of these antimicrobial resistance genes are indicated in Appendix P 63 Figure 12. Microbial Metabolic Pathways Show Distinction in Functional Potential of Indian Gut Microbiome Pathway abundances were annotated using MetaCyc, then normalized to account for gene length and sampling depth. LEfSe (Linear discriminate analysis Effect Size) was used to test for differentially abundant microbial metabolic pathways using unstratified data. A Kruskal-Wallis test was performed with a significance (\uf061) at 0.05 for one-against-all comparisons. Discriminate features were identified using a Linear Discriminate Analysis (LDA) score, with an LDA score equal to or greater than 3.0. Using MicrobiomeAnalyst 2.0, a heatmap was generated to display the differentially abundant pathways identified from LEfSe. Each column is a sample, and the coloured bars on top represent the cohorts. Metabolic pathways are labelled in rows and annotated with a colour that describes their general function. Colours on the heatmap represent the relative abundances of each pathway. This heatmap displays a clear distinction in the microbial genetic content of metabolic pathway abundances in Indians versus cohorts living in Canada. * L-orthinine biosynthesis I is indirectly linked with protein synthesis and also involved with urea cycle; * CMP-3-deoxy-D-manno-octulosonate biosynthesis is directly involved in lipopolysaccharide (LPS) production.  64  Figure 13. Prevotella copri is the Top Taxa Contributing to CAZy Families Abundant in Indians Normalized gene family abundance table was regrouped to CAZy database using humann_regroup_table. Bar plots for stratified taxa contributions for each CAZy gene family was generated using humann_barplot. Prevotella copri is the top contributor to CAZyme families (A) GH10 (B) GH43 (C) GH51, all which were enriched in Indians.  65 3.3.6 Processed Food & Alcohol Consumption are Drivers in the Distinction between Westernized and Indian cohorts The dbRDA model incorporated demographic variables and dietary patterns identified as significantly different at baseline. To ensure minimal collinearity among predictors, the variance inflation factor values were calculated, with values ranging from 1.14 for vegetarian to 1.38 for pescetarian, indicating each variable uniquely contributed to the model. The explanatory variables considered in this model elucidated 16.36% of the microbial variation on the first 2 axes (Figure 14). PERMANOVA revealed that collectively these explanatory variables were significant (F = 6.8528, p = 0.001).            Upon further analysis, the higher intake of Group 4 UPF and alcohol were identified as principal determinants in differentiating the microbiota in westernized cohorts, with adjusted p-Figure 14. Ultra-Processed Foods and Alcohol Consumption Drives Differences in Gut Microbiota Distance-based redundancy analysis (dbRDA) plot was generated using Weighted UniFrac distance matrix from 16S amplicon data. This dbRDA plot depicts the distribution of samples and their associations with lifestyle factors. Each dot is a sample, colour coded by cohort. Variance inflation factor values ranged between 1.14 \u2013 1.38, and this model elucidated 16.36% of the microbial variation on the first two axes (F = 6.8528, p = 0.001). Permutational multivariate ANOVA (PERMANOVA) test reveals these explanatory variables collectively were significant (F = 6.8528, p = 0.001), with Group 4 UPF (pFDR = 0.0025) and alcohol (pFDR = 0.0025) driving distinctions in the microbiota of westernized cohorts. Distinct clustering is shown in Indians, with advancing age being a significant contribution (pFDR = 0.04333).  66 values of pFDR = 0.0025 for both, using the Benjamini-Hochberg procedure for FDR adjustments. Contrastingly, there was a notable dispersion of microbial composition in the Indian cohort. The pescetarian diet appeared to influence a clustering of a subset of the Indian samples (pFDR = 0.05875), albeit not significant. Advancing age (pFDR = 0.04333) influenced another distinct clustering, though vegetarian dietary practice was not a significant driver (pFDR = 0.375). This divergence in the Indian cohort highlights the heterogeneity of microbiome compositions that exist within the country (Prasoodanan PK et al., 2021).   3.4 Discussion 3.4.1 Differences in Indian Diet is Reflective in CAZyme Activity  Indians, known for their high complex carbohydrate diet, have been previously indicated to exhibit an increased expression of carbohydrate metabolism genes for complex polysaccharides in their gut (Bhute et al., 2016; Dhakan et al., 2019; A. Kulkarni et al., 2019). Alongside this, a Prevotella-dominant gut microbiota has shown to exhibit CAZyme profiles that facilitate breakdown of plant-based foods (Aakko et al., 2020). Our findings reflected this relationship, as we detected enrichment of CAZy gene families that aid in the digestion of xylan and xyloglucan, commonly found in legumes, peppers and tomatoes. Furthermore, P. copri emerged as the top contributing taxa to these enriched CAZymes. A similar pattern was in fact detected in another study on immigrants who migrated from Thailand to the US, where a decrease in Prevotella spp. abundance corresponded with a loss of CAZymes that break down plant fibre (Vangay et al., 2018). The subsequent loss of P.copri in our Indo-Immigrant and Indo-Canadian cohorts aligned with their reduction of a high carbohydrate, high fibre diet, which was a trend previously explored among Hadza and Nepali groups (Gellman et al., 2023). Plant-derived microbiota-accessible carbohydrates (MACs) are required to maintain abundances of P.copri, whereas other species such as Bacteroides thetaiotaomicron can utilize both plant and animal- 67 derived MACs, allowing for persistence in the gut (Gellman et al., 2023). This relationship therefore suggests that the Prevotella spp. abundances in the Indian gut is highly linked to their dietary patterns, and the observed loss of Prevotella spp. in Indian migrants is likely a direct result of their reduction of plant-based foods.  3.4.2 India is Currently Industrializing As we discuss the differences in lifestyle between India and the West, it is important we note the effect of globalization on India, as was reflected in the cooking habits reported by Indians in our study (Persaud & Landes, 2007). Overall, westernized populations are known for their use of white oils, yet in our Indian cohort, sunflower oil was the most reported cooking fat, which does not align with what is used in traditional Indian dishes. In the 1990s, dietary advice in India was to reduce saturated fat such as ghee, coconut oil and groundnut oils, and instead begin using PUFA-rich oils like sunflower and safflower oils (Mani & Kurpad, 2016). Over time, these western oils became cheaper and more readily available in India, therefore they began to move away from their traditional fats (Mani & Kurpad, 2016). Coinciding with this transition of increasing PUFA consumption is the rising epidemic of several modern diseases such as IBD (Snell et al., 2020), type-2 diabetes, and metabolic syndrome (MS) (Lakshmipriya et al., 2013). India is now in an accelerating incidence phase of IBD (Kaplan & Windsor, 2021); from 1990 to 2019, the number of IBD patients doubled (Dharni et al., 2023). This disease is becoming an ever growing concern in India, that they even established the Colitis and Crohn\u2019s Foundation (India) to monitor the epidemiology and clinical presentations of IBD in Indians (Sood et al., 2021). While dietary studies in Indian IBD patients are limited, previous research has shown that increased intake of linolenic (n-6 PUFA) was associated with an increased risk for UC, whereas increased intake of \uf061-linolenic acid (n-3 PUFA) was associated with reduced UC-risk (Ananthakrishnan et al., 2014; Investigators, 2009). Furthermore, a previous study out of Chennai, India found that Indians who predominately used sunflower oil showed significantly higher prevalence of MS, whereas those  68 who used traditional oils had lower PUFA : SFA and linoleic acid : \uf061-linolenic acid ratios, alongside a lower prevalence of MS (Lakshmipriya et al., 2013). These findings highlight how the westernization of oils used in India may be a contributing factor to their increasing incidence of IBD.   Surprisingly, the westernized cohorts in our study reported to cook mainly with olive oil and butter, and few reported to cook with white oils. Though these reports do not entirely capture what these individuals were consuming, such as pre-made foods or eating at restaurants, it is worth highlighting that while India has adopted the use of white oils, the West may currently be shifting away from them. For decades, the westernized diet has been characterized by a much higher intake of omega-6 PUFA, leading to omega-6:omega-3 ratios of 20:1, which is far from the estimated ratio of 1:1 of our ancestors (Simopoulos, 2016). This excessive intake of omega-6 PUFAs has shown to promote pro-inflammatory conditions (Simopoulos, 1991), CVD (De Lorgeril et al., 1994; Simopoulos, 2004; Yang et al., 2023), cancer (De Lorgeril & Salen, 2012), and autoimmune diseases (Simopoulos, 2006). Furthermore, with previous large-scale studies and meta-analyses finding no significant association between SFA and CVD (Astrup et al., 2020; Dehghan et al., 2017; Ho et al., 2020; Siri-Tarino et al., 2010) and the increasing popularity surrounding the health benefits of the Mediterranean Diet (Vetrani et al., 2022), a cultural shift in the West may be occurring, where they are preferring more traditional fats like olive oil and butter (Fernandes et al., 2020), an observation also recently reported from an Australian cohort (Wilson et al., 2023).   Overall, with our data obtained in 2018, we effectively captured how India is slowly adopting westernized dietary habits and may be why we observed the unexpected highest consumption of PUFAs in Indian residents. The differences in dietary fats detected between  69 EpiNu and ESHA can be explained through the more accurate approach taken with EpiNu, in which the common cooking oil used is adjusted based on the region the Indian subjects are residing in. This discrepancy suggests that North American nutritional databases are not properly accounting for the cultural differences that exist in cooking practices throughout India. Additionally, since this higher consumption of PUFAs in India has been previously linked to the rising prevalence of modern diseases, there is an increasing demand for awareness around the health implications of dietary fats. Furthermore, to understand how changes in fat consumption may be influencing risk to modern diseases, the consequential shifts in the gut microbiome may also help to understand the mechanistic factors driving disease development. Future investigations should aim to identify changes in the gut microbiome that are directly linked to the consumption of western oils in Indians as they are continuing to westernize.   Another factor of industrialization that is common in India is the overuse of antibiotics. India has one of the highest threats for antimicrobial resistance due to several factors including easy access to antibiotics and perceived high patient demand from healthcare providers (Fazaludeen Koya et al., 2022; Gandra et al., 2017). The over-use of antibiotics in India was reflective in our cohorts, as KEGG identities that contain signatures for antibiotic resistance genes were differentially abundant in Indians and Indo-Immigrants. Previous studies also report Indians have a higher potential for xenobiotic metabolism in their gut (Bhute et al., 2016; Das et al., 2018), which was reflected by a high level of antibiotic-resistant strains detected in healthy Indian subjects (Gupta et al., 2019). The widespread use of antibiotics in India not only increases the prevalence of antibiotic resistance, but also accelerates microbiome turnover in the gut, a pattern that was evident in our Indian cohort. Several pathways indicative of microbiota turnover were enriched in Indians including peptidoglycan and LPS synthesis, microbial growth\/metabolism, and DNA synthesis\/repair. While common usage of antibiotics is a likely catalyst for bacterial turnover,  70 the higher risk for infectious disease via water contamination and inadequate sanitation in India may also serve as environmental triggers. Our findings highlight India\u2019s unique transition phase, balancing Western influences with traditional lifestyle practices, all of which are shaping their gut microbiome. Future research should delve deeper into how westernization is currently influencing the gut microbiome in Indians, as this will likely pose future health challenges.  3.4.3 Acculturation of Westernized Diet Drives Functional Shifts in Microbiome Dietary acculturation is a frequent trend observed in immigrants living in North America (Peters et al., 2020; Satia-Abouta et al., 2002). An increasing shift away from the Indian diet was observed in our study, where there was a reduction in total carbohydrate intake in Indo-Immigrants, followed by a more significant decline in Indo-Canadians. In fact, the Carbohydrate: Protein ratio has been previously examined (Copeland 2021), also finding that while recent Indian migrants consumed a high proportion of carbohydrates, Indo-Canadians consumed comparable ratios to that of Americans. Additionally, we found the strongest indication of dietary acculturation in Indian migrants to be their intake of Group 4 UPFs. Previous studies on South Asian immigrants in Canada have highlighted significant dietary shifts, particularly an increase in sweets, fast food, sugared drinks, and eating outside of the home, which often involves more UPFs (Lesser et al., 2014; Noor et al., 2020). When compared to Indian residents, we found that the proportion of daily caloric intake coming from Group 4 UPFs increased by 49% in Indo-Canadians, along with a significant reduction in soluble fibre intake. This poor diet quality may also explain why pathways were enriched in this cohort that were tapping into glycogen storage for energy, suggesting these microbes have inadequate nutrient availability from the host diet (Esteban-Torres et al., 2023). Overall, such a shift to high ultra-processed food consumption raises concern for its implications on gut health and disease risk in this demographic.    71 Increasing UPF consumption has been linked to higher risks for CVD, metabolic disorders, micronutrient deficiencies, and anxiety and depression (Tristan Asensi et al., 2023). Risks to such health conditions may also in part be due to imbalances in the gut microbiome that are a triggered from UPFs. In fact, consuming UPFs such as sweets and fried foods has shown to modulate the gut microbiota in humans (Partula et al., 2019). A study following 116, 087 adults from various countries, of which 467 participants developed IBD found higher intake of UPFs was associated with a higher risk for incident IBD (Narula et al., 2021). Additionally, a meta-analysis reviewing subjects from five cohort studies (N = 1,068,425) found a higher consumption of UPFs led to an increased risk for CD, but not UC (Narula et al., 2023). Furthermore, increased consumption of UPFs has previously been linked to elevated intestinal permeability markers, suggesting an association with inflammation in the gut (Um et al., 2022). A similar finding was found in another cohort, with those who adhered less to the Mediterranean Diet and instead consumed more junk food also had higher levels of intestinal permeability (Di Palo et al., 2020). Moreover, a Mediterranean Diet intervention study in IBD patients that ultimately consumed less UPF also showed a protective role in reducing inflammation, demonstrating elevated short-chain fatty acid production and increased abundances of bacterial species known to be protective in colitis (Haskey et al., 2023). While there are several potential culprits involved in the ultra-processing of foods that may have mechanistic interactions with the gut, it is likely an interplay of several factors surrounding the consumption of UPFs.   Group 4 UPFs are typically low-cost and more accessible to consumers; however, they often contain excessive calories, sugar, and sodium, and seldom contribute to the recommended daily intake for fibre, protein or micronutrients (Tristan Asensi et al., 2023). The manufacturing of UPFs mainly involves industrially made ingredients with non-nutritive components such as emulsifiers, used to mix ingredients in UPFs, as well as flavouring and sweeteners. These additives not only help to increase shelf-life, but make these foods highly palatable (Tristan Asensi  72 et al., 2023). A likely contributing factor to the negative health consequences observed is simply the overconsumption of UPFs resulting in excessive caloric intake, often replacing nutritious meals (Dinu et al., 2022). Additionally, some studies have also shown that these additives may affect the gut microbiome and pro-inflammatory conditions. For example, emulsifiers, used to mix components in UPF, have been extensively studied in murine models, showing their ability to modulate the gut microbiota, reduce mucus thickness and increase intestinal permeability, bacterial translocation and inflammation (Bancil et al., 2021). While studies translating these same mechanistic effects of emulsifiers in humans are scarce, a short-term prospective study (N = 588) found a positive association between dietary emulsifiers and the inflammatory biomarker glycoprotein acetyls (Um et al., 2022). Moreover, a randomized double-blind controlled-feeding study (N = 16) with the emulsifier carboxymethylcellulose (CMC) found varied responses in the treatment group, with two subjects exhibiting significant alterations in gut composition and microbial encroachment, whereas other participants seemed non-sensitive to CMC (Chassaing et al., 2022; Daniel et al., 2024). Although constrained by a small sample size, these findings prompt the need to further investigate in large-scale cohorts, as this may in part explain the inconsistent outcomes observed when transitioning from murine to human studies. Overall, these findings underscore that emulsifiers may be one of many components in UPF that may affect the gut microbiome, however more human studies are required to fully understand the complex role of UPF and its effects on the gut microbiome and inflammation. Future studies should also consider examining the synergistic effects of multiple food additives to mirror the composition of UPFs typically consumed by humans.    73 Chapter 4: Conclusion  4.1 Summary This thesis sought to characterize the differences between the Indian and westernized gut microbiome, along with the impact that immigration has on the gut microbiome in Indian migrants in Canada. Given that previous research has aimed to characterize the gut microbiome in Indians, we first reviewed the literature in Chapter 1 to provide a comprehensive overview of what is currently known about the Indian gut microbiome and how it may be influenced by immigration. In Chapter 2, we defined the gut microbiota in Indians, Indian immigrants and Canadian-born Indians. Beta diversity analysis detected a notable distinction in the gut microbiota of Indians living in India, along with a second clustering of Indo-Immigrants, whereas all three westernized cohorts clustered together. Differential abundance analysis revealed a high relative abundance of Prevotella spp. in Indians, along with other genera frequently detected in the Indian gut. Next, we show that immigration to Canada resulted in a shift away from the typical Indian gut microbiome and instead adopted microbes more common in a westernized gut. Microbiome phenotype predictions through BugBase estimated that the Indian gut microbiome has a higher pathogenic potential and stress tolerance, with these characteristics tapering down from Indo-Immigrants to Indo-Canadians, suggesting Indians may harbour a more robust microbiota, which is seen to be lost once migrated to Canada.  Chapter 3 provides a deeper exploration into the acculturation of westernized practices in Indian migrants, which revealed a significant increase in consumption of Group 4 UPF in Indians living in Canada, specifically in those Canadian-born. A notable pattern of either sequential decline or incline was observed in Indian migrant generations, where we observed a decline in the consumption of carbohydrates and fibre, and an inclination towards more protein, which further demonstrates the westernization of their dietary practices. Finally, by assessing the   74 Figure 15. Adoption of Westernized Dietary Practices is Associated with a Transition Away from the Traditional Indian Gut Microbiome Graphical representation highlighting the main findings from our study. As Indo-Immigrants and Indo-Canadians  subsequently increased ultra-processed food intake, their fibre consumption dramatically reduced. This change in dietary pattern was observed alongside a transition away from high Prevotella spp. and VANISH (Volatile and\/or Associated Negatively with Industrialized Societies of Humans) taxa, which were found abundant in the Indian gut. Instead, Indo-Canadians adopted BloSSUM (Bloom or Selected in Societies of Urbanization\/Modernization) taxa, which are commonly found in the westernized microbiome. High Prevotella copri abundance in the Indian gut contributed to enriched carbohydrate-active enzymes (CAZymes) that are specialized to degrade complex carbohydrates common in their diet, which decline in Indian migrant cohorts. Additionally, the Indian microbiome displayed characteristics of a more robust gut, with predictive functions of higher stress tolerance and increased microbial cell turnover.                            75 functional potential from our metagenomic data, we showed that while Indians harbour a CAZyme profile more tailored for plant-based polysaccharide degradation, these CAZyme families decrease in abundances in Indian migrants. Furthermore, Indians may be facing more environmental stressors on the gut, as was indicated through their enrichment in several microbial pathways linked to microbiota regeneration.  Overall, our findings illuminate a clear distinction between the Indian gut microbiome from a westernized Indian gut microbiome. In concordance with previous research, we show that the Indian gut contains significantly high Prevotella spp. abundances, which enriches the gut with CAZymes tailored to break down plant-based foods, aligning with their high carbohydrate, high fibre diet. In addition, a new finding we elucidated was that the gut microbiome in Indians displayed characteristics of higher bacterial cell turnover, and predictions of higher pathogenic potential and stress tolerance in the gut, which all together suggest the Indian microbiome is more robust and capable of handling stressors posed on the gut. Lastly, we established the gradient of change towards a more westernized microbiome in Indian migrants, which was associated with a similar pattern of increasing dietary acculturation in each generation (Figure 15). These results align with our understanding of how the gut microbiome develops based on lifestyle and this highlights the demand to improve representation of non-westernized and transitional populations in microbiome research.  4.2 Limitations  One of the primary challenges encountered in our study was the inadequate representation of specific cultural diets during the collection, processing, and analysis of our dietary data. Firstly, the reliance on self-reported food diaries may have introduced biases, including unreported items or imprecise portion sizes consumed. Although ASA-24 was initially  76 chosen for its potential to more accurately capture detailed food intake, it lacked a sufficient database for Indian cuisine, which would likely have led to a loss of data due to the increased burden on participants of entering recipes manually. We therefore employed ESHA, which offered a broader range of dishes, however, the standard recipes for Indian dishes within ESHA did not accurately reflect authentic Indian cooking practices. The scarcity of data on non-Western diets poses a significant obstacle in dietary research. This shortfall in the field\u2019s available resources led us to re-enter the data from our Indian and Indo-Immigrant cohorts into EpiNu Nutritional software, designed to cater to the Indian diet.   Secondly, it is worth noting that the transportation methods differed in our Indian cohort, since the DNA samples were extracted in India and was then transported on dry ice to Canada. However, DNA has shown to maintain stability during transport (Ezzy et al., 2019) and long-term storage in -80\u00b0C (Matange et al., 2021), and these reads still maintained sufficient quality upon sequencing. Also, while DNA extractions were performed by the same researcher in Canada, we required other scientists to extract DNA in India, which may have been a source of variation, however, studies have shown this effect is likely minimal (Wagner Mackenzie et al., 2015). Another potential limitation is the significant differences detected in our beta diversity measures between the two locations collected in India, however, the focus of our study was to compare against immigrant and westernized groups in which we found significant comparisons from both sites in India. Moreover, most individuals in our Indian cohort were born in South or West India, while majority of Indo-Immigrants were born in North India, which may also have introduced variability in the microbiomes observed between cohorts (Appendix Q).   Another limitation was that the depth of shotgun sequencing was too shallow to effectively identify strain levels of certain bacteria. For example, previous studies have discussed the clinical relevance of specific P. copri strains, where some are associated with disease conditions,  77 whereas other strains are beneficial to the gut (Prasoodanan PK et al., 2021). The methods deployed for our study did not resolve to the strain level for P. copri, which could have provided further insight into potential differences in the strains prevalent in global populations. Furthermore, we also wanted to explore the parasite populations in our cohorts, however our sequencing depth and taxonomic classification methods used did not allow us to characterize parasite communities in the gut. As this project focused on the relative abundance of Prevotella spp., quantitative PCR could also help to determine absolute abundances present in the gut, as this may also be a key factor influencing disease risk (Vandeputte et al., 2017). Additionally, while metagenomics can provide important insights into the genetic content present within the gut, these data only represent the functional potential of the gut and does not provide us information regarding genetic expression and actual function of microbiome communities. Overall, future work should aim to resolve strain-level analysis and parasite communities in the microbiomes of this population along with deeper exploration into absolute abundances of taxa and the functional activity present to better understand their increased risk to IBD.  Lastly, the lower sample size of Indo-Canadians ultimately influenced the power of our study and the conclusions drawn from this group. Despite extensive recruitment efforts specifically for the Indo-Canadian population in Kelowna, there was significant difficulty recruiting participants in this demographic compared to other cohorts. The likely reasons for this may be that majority of the adult Indian population in Kelowna are Indo-Immigrants, and many of the second-generation Indo-Canadians are children and adolescents. Furthermore, as a Euro-Canadian, there were inherent social and cultural barriers between myself and the Indian community, which made it even more difficult to draw interest in the study. It is important that future studies focused on a particular demographic ensure that researchers on the team are either a part of this demographic, or well-connected to the community prior to beginning the research.    78 4.3 Knowledge Translation Due to the overrepresentation of European\/westernized populations in the gut microbiome research, the current demand for gut microbiome studies from less industrialized nations has led this study to be presented in a timely manner (Abdill et al., 2022; Parizadeh & Arrieta, 2023). Our work helps to improve our understanding of what a healthy gut microbiome looks like in Indians not only living in India, but Indian immigrants who are now living in a westernized country. The findings of this study has and will continue to be communicated to the science community and the general public via conferences presentations, manuscripts, news articles, and blog posts.  4.4 Future Work Our study has provided a steppingstone for many future investigations into the role that environment and lifestyle has on the westernization of the gut microbiome, along with providing opportunity to explore deeper into the underlying mechanisms that are increasing the risk of IBD in Indians. A subset of participants were instructed to collect stool in glycerol for future experimentation in gnotobiotic mice, which will allow us to further reveal potential immunological and metabolic differences when mice harbour an Indo-Immigrant, Indo-Canadian or Euro-Canadian microbiome. To explore deeper into the specific nature of IBD in Indians, samples obtained from IBD patients in India would provide invaluable insight, as these data are sparse, but required to compare communities to healthy controls (Verma et al., 2010).  4.5 Concluding Remarks As the world becomes increasingly industrialized, understanding the impact of this shift on human health is crucial. This is particularly important for treating existing diseases and anticipating the rise of conditions like IBD in transitional countries. Our study sheds light on the  79 gut microbiome changes in a population undergoing westernization, using India as a prime example. We also show a drastic transition within the gut microbiomes of Indo-Immigrants and Indo-Canadians, extending our knowledge of the strong effect of immigration and westernization on the gut microbiome. This transition is indicative of a microbial adaptation to changing dietary patterns and environment exposures, which provides insight into the dynamic nature of the gut microbiome.  Future research should explore the implications that this microbiome transition has on immigrants, especially as many non-westernized populations are now immigrating to westernized countries. Increasing globalization is also resulting in the westernization of many nations like India, which also poses risk for the industrialization of their gut microbiome and an increased prevalence of modern diseases. Previous studies have already noted health consequences following the assimilation into the westernized culture (Parizadeh & Arrieta, 2023; Vangay et al., 2018). 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Posters and Pamphlets Used for Recruitment in Canada                    Front cover for Indian & Bengali communities Front cover for Indian & Punjabi communities  95                       96 Appendix B. Demographics Questionnaire Provided to Participants    97                       98                       99                     100 Appendix C. Lifestyle Survey Provided to Subjects (Indo-Immigrant-Specific)   101                       102                       103                       104                       105 Appendix D. Stool Collection Instructions Provided to Participants                      106                      107 Appendix E. PERMANOVA Results from Beta Diversity Analysis     Bray Curtis  Weighted UniFrac Pair pseudo-F  P value pseudo-F P value Indo-Immigr vs. Indo-Can 4.215 0.01 4.671 0.01 Indo-Immigr vs. Euro-Can 8.977 0.01 11.93 0.01 Indo-Immigr vs. Euro-Immigr 5.314 0.01 6.903 0.01 Indo-Immigr vs. Indian 9.132 0.01 25.98 0.01 Indo-Can vs. Euro-Can 3.084 0.01 5.409 0.01 Indo-Can vs. Euro-Immigr 2.167 0.01 2.361 0.10 Indo-Can vs. Indian 8.661 0.01 25.21 0.01 Euro-Can vs. Euro-Immigr 1.164 1.00 1.116 1.00 Euro-Can vs. Indian 18.28 0.01 49.61 0.01 Euro-Immigr vs. Indian 11.18 0.01 31.57 0.01             Adjusted P values are displayed, which were obtained using 999 permutations and Bonferroni correction applied. Beta diversity data shown here is from shotgun sequence data.     108 Appendix F. Beta-Diversity Results To determine if there were differences between the samples collected in Manipal and Kolkata in India, we performed additional PERMANOVA tests for the Bray Curtis and Weighted UniFrac distance matrices of the shotgun data. Significant differences were found between sites in India both in Bray Curtis (pBONF = 0.03) and Weighted UniFrac (pBONF = 0.01). However, pairwise PERMANOVA comparisons between each sample site to the additional cohorts still individually showed significance.         Beta diversity analyses were also conducted on the 16S sequence data. Bray Curtis analysis reveal Indians show significant distinctions in their gut microbiota when compared to Indo-Immigrants (pseudo-F = 6.585, pBONF = 0.001), Indo-Canadians (pseudo-F = 8.203, pBONF = 0.001), and Euro-Canadian controls (pseudo-F = 21.60, pBONF = 0.001). Indo-Immigrants were also significantly distinct from Indo-Canadians (pseudo-F = 6.094, pBONF = 0.001). Indo-Canadians were significantly distinct from Euro-Canadians (pseudo-F = 2.918, pBONF = 0.001). Weighted UniFrac analysis also show similar findings with Indians being significantly distinct from Indo-Immigrants (pseudo-F = 7.707, pBONF = 0.001), Indo-Canadians (pseudo-F = 15.95, pBONF = 0.001), and Euro-Canadians (pseudo-F = 49.18, pBONF = 0.001). Indo-Canadians were also distinct from Indo-Immigrants (pseudo-F = 12.86, pBONF = 0.001) and Euro-Canadians (pseudo-F = 6.748, pBONF = 0.001).         109 Appendix G. LEfSe Results from 16S Sequence Data  Taxon Log Score Class LDA  P value Succinivibrio sp. (000431835) 4.212 Indian 3.919 9.73E-05 Prevotella hominis 4.400 Indian 4.087 1.34E-07 Prevotella stercorea 4.236 Indian 3.883 1.08E-10 Prevotella copri 5.426 Indian 5.103 2.12E-17 Megasphaera A (38685) 4.820 Indian 4.508 5.08E-15 Dialister hominis 4.141 Indian 3.832 5.98E-06 Mitsuokella multacida 3.832 Indian 3.535 8.44E-14 Ligilactobacillus ruminis 3.872 Indian 3.600 2.37E-18 Acidaminococcus 3.937 Indian 3.584 2.83E-08 Catenibacterium sp. (000437715) 3.880 Indo-Immigr 3.555 2.15E-10 Dorea A longicatena 4.144 Indo-Immigr 3.720 6.21E-14 Dialister succinatiphilus 4.022 Indo-Immigr 3.754 0.000355 Anaerostipes hadrus 4.310 Indo-Can 3.964 8.58E-20 Anaerobutyricum 4.093 Indo-Can 3.737 2.33E-20 Streptococcus 4.087 Indo-Can 3.681 9.80E-08 Blautia A (141781) 4.759 Indo-Can 4.403 2.25E-24 Fusicatenibacter saccharivorans 4.371 Indo-Can 4.028 2.86E-17 Mediterraneibacter A (155507) faecis 4.048 Indo-Can 3.695 2.93E-11 Gemmiger A (73129) 4.428 Indo-Can 4.039 1.35E-08 Blautia A (141781) massiliensis 4.413 Indo-Can 4.054 1.06E-14 Collinsella 4.539 Indo-Can 4.080 4.80E-09 Agathobacter rectalis 4.607 Indo-Can 4.162 3.44E-05 Mediterraneibacter A (155507) 4.240 Indo-Can 3.902 1.48E-17 Peptostreptococcaceae (256921) 4.067 Indo-Can 3.509 5.83E-09 Faecalibacterium prausnitzii C (71358) 4.755 Euro-Can 4.285 6.92E-13 Blautia A (141781) faecis 4.165 Euro-Can 3.826 2.80E-21 Paramuribaculum sp. (900551515) 3.962 Euro-Can 3.579 4.55E-08 Bacteroides H 4.268 Euro-Can 3.893 2.75E-09 Phocaeicola A (858004) vulgatus 4.728 Euro-Can 4.359 2.12E-08 Gemmiger A (73129) qucibialis 4.207 Euro-Can 3.800 3.94E-10 Faecalibacterium 5.026 Euro-Can 4.607 3.71E-20 Alistipes A (871400) 4.077 Euro-Immigr 3.696 1.68E-13 Coprococcus A (121497) 4.257 Euro-Immigr 3.869 0.010928   110        111 Appendix H. Suspected Parasites Detected from Stool Microscope Examination                  112 Appendix I. Random Forest Results from Shotgun Data     Random Forest results generated in MicrobiomeAnalyst 2.0 using MetaPhlAn4 relative abundance table output. Features are ranked by their contributions to classification accuracy (Mean Decrease Accuracy), indicating the extent to which the presence\/absence of each bacteria influences the overall accuracy of classifying samples into their respective cohorts.             113 Appendix J. BugBase Results   114 Appendix K. Dietary Log Provided to Participants                       115                       116                        117                            118 Appendix L. Pairwise Comparisons of Macronutrient Intake in Participants Macronutrient P value Protein      Indian vs. Euro-Can 0.0008 a     Indian vs. Euro-Immigr 0.0143 a Fat      Indian vs. Euro-Can 0.0034 a     Indian vs. Euro-Immigr 0.0212 a Fibre      Indian vs. Indo-Can 0.0487 a     Indo-Can vs. Euro-Can 0.0080 a Macronutrient P value Energy      Indo-Immigr vs. Euro-Can 0.0144 c Protein      Indian vs. Indo-Can 0.0017b     Indian vs. Euro-Can <0.0001b     Indo-Immigr vs. Indo-Can 0.0009b     Indo-Immigr vs. Euro-Can <0.0001b Fat      Indian vs. Euro-Can 0.0175c     Indo-Immigr vs. Euro-Can 0.0084c PUFA      Indo-Immigr vs. Euro-Can 0.0140 a Omega-3      Indo-Immigr vs. Euro-Can 0.0064 a Omgea-6      Indo-Immigr vs. Euro-Can 0.0096 a MUFA      Indian vs. Euro-Can 0.0112a     Indo-Immigr vs. Euro-Can    0.0154a SFA      Indian vs. Euro-Can 0.0312c      Indo-Immigr vs. Euro-Can 0.0025c Carbohydrate      Indian vs. Indo-Immigr 0.0228 c     Indian vs. Indo-Can 0.0177 c Fibre      Indian vs. Indo-Can 0.0014 a Male Macronutrient Intake Pairwise Comparisons Female Macronutrient Intake Pairwise Comparisons Multiple Comparisons Test: aDunn\u2019s; bHolm-Sidak\u2019s, cTukey\u2019s Abbreviations: polyunsaturated fatty acid (PUFA); monounsaturated fatty acid (MUFA); saturated fatty acid (SFA)  Multiple Comparisons Test: aDunn\u2019s   119 Appendix M. Micronutrient Intake in Males and Females  Male Participant Absolute Micronutrient Intake             Macronutrient RDA\/AMDR Indian n = 24 Indo-Immigr n = 14 Indo-Can n = 11 Euro-Can n = 25 Euro-Immigr n = 13 P value Vitamin A, mcg (IQR) 600 243 (279) 411 (278) 365 (332) 697 (913) 404 (661) 0.0007 a Vitamin B1, mg (IQR) 1.1 1.43 (0.83) 1.03 (0.67) 0.73 (0.33) 0.94 (0.43) 1.03 (0.68) 0.0024 a Vitamin B2, mg (IQR) 1.1 1.01 (0.50) 0.89 (0.60) 0.97 (0.29) 1.50 (0.99) 1.21 (0.86) 0.0181* a Vitamin B6, mg (IQR) 1.3 1.24 (0.90) 1.07 (0.57) 0.78 (0.45) 1.40 (0.90) 1.64 (1.39) 0.0276 a Vitamin B12, mcg (IQR) 2.4 1.24 (1.00) 1.58 (1.57) 2.11 (1.91) 2.11 (1.47) 3.06 (2.23) 0.0357* a Vitamin C, mg (IQR) 75 32.4 (32.4) 84.3 (74.0) 42.7 (54.8) 94.7 (97.5) 56.2 (130) 0.0002 a Vitamin D, mcg (IQR) 15 2.13 (2.25) 2.53 (2.43) 1.91 (3.03) 1.75 (2.65) 2.39 (3.15) ns Zinc, mg (IQR) 8 6.09 (3.46) 5.22 (2.83) 4.28 (2.88) 6.55 (2.71) 5.58 (4.13) 0.0387* a Iron, mg (IQR) 18 13.6 (9.49) 11.7 (11.2) 8.73 (3.32) 13.0 (3.15) 13.4 (6.19) 0.0067 a Folate, mcg (IQR) 400 365 (223) 263 (199) 163 (134) 267 (210) 246 (513) 0.0191 a Magnesium, mg (IQR) 310 255 (153) 188 (152) 130 (47.5) 263 (152) 207 (162) 0.0027 a Calcium, mg (IQR) 1000 640 (411) 752 (690) 631 (271) 852 (756) 767 (780) ns Sodium, mg (IQR) 1500 2584 (1809) 3059 (1949) 3046 (1394) 3256 (2027) 4245 (2663) ns Abbreviations: aKruskal-Wallis, b ANOVA. Median and IQR reported.  120  Female Participant Absolute Micronutrient Intake             Macronutrient RDA\/AMDR Indian n = 24 Indo-Immigr n = 14 Indo-Can n = 11 Euro-Can n = 25 Euro-Immigr n = 13 P value Vitamin A, mcg (IQR) 600 243 (279) 411 (278) 365 (332) 697 (913) 404 (661) 0.0007 a Vitamin B1, mg (IQR) 1.1 1.43 (0.83) 1.03 (0.67) 0.73 (0.33) 0.94 (0.43) 1.03 (0.68) 0.0024 a Vitamin B2, mg (IQR) 1.1 1.01 (0.50) 0.89 (0.60) 0.97 (0.29) 1.50 (0.99) 1.21 (0.86) 0.0181* a Vitamin B6, mg (IQR) 1.3 1.24 (0.90) 1.07 (0.57) 0.78 (0.45) 1.40 (0.90) 1.64 (1.39) 0.0276 a Vitamin B12, mcg (IQR) 2.4 1.24 (1.00) 1.58 (1.57) 2.11 (1.91) 2.11 (1.47) 3.06 (2.23) 0.0357* a Vitamin C, mg (IQR) 75 32.4 (32.4) 84.3 (74.0) 42.7 (54.8) 94.7 (97.5) 56.2 (130) 0.0002 a Vitamin D, mcg (IQR) 15 2.13 (2.25) 2.53 (2.43) 1.91 (3.03) 1.75 (2.65) 2.39 (3.15) ns Zinc, mg (IQR) 8 6.09 (3.46) 5.22 (2.83) 4.28 (2.88) 6.55 (2.71) 5.58 (4.13) 0.0387* a Iron, mg (IQR) 18 13.6 (9.49) 11.7 (11.2) 8.73 (3.32) 13.0 (3.15) 13.4 (6.19) 0.0067 a Folate, mcg (IQR) 400 365 (223) 263 (199) 163 (134) 267 (210) 246 (513) 0.0191 a Magnesium, mg (IQR) 310 255 (153) 188 (152) 130 (47.5) 263 (152) 207 (162) 0.0027 a Calcium, mg (IQR) 1000 640 (411) 752 (690) 631 (271) 852 (756) 767 (780) ns Sodium, mg (IQR) 1500 2584 (1809) 3059 (1949) 3046 (1394) 3256 (2027) 4245 (2663) ns Abbreviations: aKruskal-Wallis. Median and IQR reported. * Dunn\u2019s multiple comparisons revealed no significant differences between groups.     121   Macronutrient P value Vitamin A      Indian vs. Euro-Can 0.0004 a     Indo-Immigr vs. Euro-Can 0.0108 a Vitamin B1      Indian vs. Indo-Immigr 0.0021b     Indo-Immigr vs. Euro-Can 0.0383b Vitamin B2      Indian vs. Indo-Can 0.0038 b     Indian vs. Euro-Can <0.0001b     Indian vs. Euro-Immigr 0.0172 b     Indo-Immigr vs. Indo-Can 0.0076 b     Indo-Immigr vs. Euro-Can <0.0001b     Indo-Immigr vs. Euro-Immigr 0.0373 b Vitamin B12      Indian vs. Euro-Can 0.0099 a     Indo-Immigr vs. Euro-Can 0.0194 a Vitamin D      Indian vs. Euro-Can 0.0112 a Zinc      Indian vs. Indo-Immigr <0.0001 a     Indian vs. Indo-Can 0.0022 a     Indian vs. Euro-Can 0.0134 a     Indian vs. Euro-Immigr <0.0001 a Iron      Indian vs. Indo-Immigr 0.0048 b Folate      Indian vs. Indo-Immigr 0.0047 b     Indian vs. Euro-Immigr 0.0304 b Magnesium  Male Micronutrient Intake Pairwise Comparisons  122 Macronutrient P value     Indo-Immigr vs. Euro-Can 0.0173 b Calcium      Indian vs. Euro-Can 0.0304 a                                          Macronutrient P value Vitamin A      Indian vs. Euro-Can 0.0001a Vitamin B1      Indian vs. Indo-Can 0.0007a Vitamin B6      Indo-Can vs. Euro-Immigr 0.0268 a Vitamin C      Indian vs. Indo-Immigr 0.0463 a     Indo-Immigr vs. Euro-Can 0.0001 a Iron      Indian vs. Indo-Can 0.0284 a     Indo-Can vs. Euro-Can 0.0148 a     Indo-Can vs. Euro-Immigr 0.0070 a Folate      Indian vs. Indo-Can 0.0062 a Magnesium      Indian vs. Indo-Can 0.0070 a     Indo-Can vs. Euro-Can 0.0026 a     Indo-Can vs. Euro-Immigr 0.0496 a Female Micronutrient Intake Pairwise Comparisons Multiple Comparisons Test: aDunn\u2019s, bTukey\u2019s Multiple Comparisons Test: aDunn\u2019s  123 Appendix N. Differential Microbial Metabolic Pathways Across Cohorts Pathway Log Score Class LDA  P value Superpathway of L-Aspartate and L-Asparagine Biosynthesis  3.758 Indian 3.067 2.09E-13 Glycolysis I (from Glucose-6-Phosphate)  3.739 Indian 3.091 3.70E-11 Peptidoglycan Biosynthesis I  4.134 Indian 3.138 0.000606 3-Deoxy-D-Manno-Octulosonate Biosynthesis  3.647 Indian 3.179 3.37E-17 Folate Transformations II (Plants)  4.057 Indian 3.001 0.000615 Inosine 5-Phosphate Degradation  4.059 Indian 3.052 1.49E-07 Cis-Vaccenate Biosynthesis  3.690 Indian 3.022 1.73E-09 Superpathway of Guanosine Nucleotides De Novo Biosynthesis II  3.706 Indian 3.076 5.52E-10 Superpathway of Adenosine Nucleotides De Novo Biosynthesis II  3.778 Indian 3.091 5.73E-10 Hydroxymethyl-Dihydropterin Diphosphate Biosynthesis I  3.693 Indian 3.171 3.78E-12 Peptidoglycan Biosynthesis III (Mycobacteria)  4.127 Indian 3.236 4.29E-06 UDP-N-Acetylmuramoyl-Pentapeptide Biosynthesis II (Lysine Containing)  4.141 Indian 3.119 0.001619 UDP-N-Acetylmuramoyl-Pentapeptide Biosynthesis I  4.138 Indian 3.149 0.000520 Queuosine Biosynthesis I (De Novo)  4.096 Indian 3.276 7.39E-07 Pre Q0 Biosynthesis  3.821 Indian 3.183 1.54E-11 Pyrimidine Deoxyribonucleosides Salvage  3.968 Indian 3.223 2.97E-06 Superpathway of Pyrimidine Nucleobases Salvage  3.655 Indian 3.011 4.06E-08 Guanosine Ribonucleotides De Novo Biosynthesis  4.185 Indian 3.311 6.22E-06 Superpathway of Guanosine Nucleotides De Novo Biosynthesis I  3.765 Indian 3.118 2.33E-10 Superpathway of Adenosine Nucleotides De Novo Biosynthesis I  3.868 Indian 3.117 1.42E-10 UDP-N-Acetylmuramoyl-Pentapeptide Biosynthesis III  4.111 Indian 3.216 5.60E-06 Peptidoglycan Maturation  3.957 Indian 3.148 1.04E-07 L-Valine Biosynthesis  4.135 Indian 3.024 0.001861 L-Arginine Biosynthesis I (via L-Ornithine)  4.010 Indo-Immigr 3.229 5.15E-17 L-Ornithine Biosynthesis I  4.015 Indo-Immigr 3.361 3.48E-22 Thiamine Phosphate Formation from Pyrithiamine and Oxythiamine (Yeast)  3.950 Indo-Immigr 3.037 1.73E-08 Superpathway of Thiamine Diphosphate Biosynthesis III (Eukaryotes)  3.691 Indo-Immigr 3.065 8.88E-15 Superpathway of Adenosylcobalamin Salvage from Cobinamide I  3.831 Indo-Can 3.360 3.32E-22 Pentose Phosphate Pathway (Non-Oxidative Branch I)  3.887 Indo-Can 3.098 5.27E-10 Pyruvate Fermentation to Acetate and (S)-Lactate I  3.676 Indo-Can 3.018 3.48E-16 Glycogen Degradation II  4.067 Indo-Can 3.369 9.27E-20 D-Galactose Degradation I (Leloir Pathway)  3.883 Indo-Can 3.126 2.96E-18 Molybdopterin Biosynthesis  3.867 Indo-Can 3.359 1.32E-22  124       Pathway Log Score Class LDA  P value Pentose Phosphate Pathway (Non-Oxidative Branch II)  3.922 Indo-Can 3.039 1.07E-11 Diacylglycerol Biosynthesis I 3.941 Euro-Can 3.166 2.16E-15 Adenine and Adenosine Salvage III  4.166 Euro-Can 3.070 0.000548 Purine Ribonucleosides Degradation  4.074 Euro-Can 3.401 5.14E-18 Diacylglycerol Biosynthesis II 3.941 Euro-Can 3.166 2.16E-15 Fatty Acid Biosynthesis Initiation (Mitochondria) 4.045 Euro-Can 3.403 1.07E-21 L-Arginine Biosynthesis II (Acetyl Cycle) 4.032 Euro-Immigr 3.344 2.48E-20 Glycogen Biosynthesis I (from ADP-D-Glucose) 4.117 Euro-Immigr 3.468 1.52E-21 Aminoimidazole Ribonucleotide Biosynthesis I 4.040 Euro-Immigr 3.206 9.73E-19 Isoprene Biosynthesis I  3.927 Euro-Immigr 3.310 2.74E-18 Sucrose Biosynthesis II  4.183 Euro-Immigr 3.555 4.44E-21 Arginine Biosynthesis IV (Archaebacteria)  3.769 Euro-Immigr 3.101 3.81E-09 Flavin Biosynthesis I (Bacteria and Plants)  3.898 Euro-Immigr 3.162 2.23E-17  125 Appendix O. Differential Abundances of CAZyme Gene Families Across Cohorts CAZyme Log Score Class LDA  P value GH10 4.243 Indian 3.921 1.37E-18 GH106 4.100 Indian 3.596 3.24E-05 GH110 3.968 Indian 3.628 1.18E-09 GH28 4.272 Indian 3.724 0.000284 GH43 5.017 Indian 4.417 1.57E-11 GH51 4.586 Indian 4.047 2.57E-11 GH53 3.882 Indian 3.414 0.000149 GT19 3.662 Indian 3.220 0.003873 GT51 4.357 Indian 3.866 1.13E-10 GT83 3.530 Indian 3.204 4.40E-18 PL1 4.235 Indian 3.858 3.30E-09 GH1 4.351 Indo-Immigr 3.922 8.19E-14 CBM48 4.640 Indo-Can 4.094 1.97E-13 GH113 3.549 Indo-Can 3.223 3.11E-07 GH13 4.995 Indo-Can 4.381 7.49E-12 GH31 4.305 Indo-Can 3.801 3.41E-11 GH4 3.882 Indo-Can 3.365 2.79E-07 GH42 3.811 Indo-Can 3.300 1.36E-05 GH77 4.464 Indo-Can 3.944 3.66E-14 GH78 4.074 Indo-Can 3.474 9.43E-08 GT28 4.477 Indo-Can 3.767 2.37E-06 GT35 4.497 Indo-Can 4.019 1.28E-15 GH109 4.300 Euro-Can 3.897 1.03E-10 GH23 4.425 Euro-Can 3.706 1.49E-05 GH88 3.898 Euro-Can 3.438 1.96E-07 GH92 4.248 Euro-Can 3.827 1.03E-09 GT2 5.072 Euro-Can 4.359 8.34E-11 GT30 3.587 Euro-Can 3.065 3.26E-06 GT4 4.571 Euro-Can 3.705 0.012273 GT5 4.397 Euro-Can 3.858 2.33E-13 PL10 3.837 Euro-Can 3.421 1.19E-08 PL12 3.557 Euro-Can 3.169 2.91E-08  126       CAZyme Log Score Class LDA  P value PL8 3.580 Euro-Can 3.148 3.04E-06 CBM13 3.499 Euro-Immigr 3.083 8.06E-14 CBM58 3.411 Euro-Immigr 3.040 1.71E-13 GH112 3.927 Euro-Immigr 3.535 9.66E-10 GH32 4.296 Euro-Immigr 3.805 2.93E-13  127 Appendix P. Top Taxa Contributions to Antimicrobial Resistance-Related KEGG Orthologies Abundant in Indians and Indo-Immigrants     128 Appendix Q. Participant Overview (ethnicity and\/or region born)    Indian  During 2017-2019, samples were collected in India: (n = 19) were recruited from Kolkata and (n = 42) were recruited from Manipal. All participants were Indian in this cohort, but some subjects additionally identified themselves as: Hindu (n = 15), Bengali (n = 6), Muslim (n = 1), Jewish (n = 1), and Tamil\/Dravidian (n = 1). Based on city of birth, (n = 6) were born in North India, (n = 27) in South India, (n = 17) East India, (n = 6) West India, and (n = 6) did not specify.  Indian Immigrant  All Indian immigrants were recruited in Canada (2021-2022) and (n = 30) identified as Indian and\/or Punjabi and (n = 2) identified as Bangladeshi. Based on region of birth, (n = 19) were from North India, (n = 3) from South India, (n = 2) from East India, (n = 4) from West India, and (n = 2) from Bangladesh. Additionally, 1 subject was born in the United Arab Emirates with Indian ancestry then raised in India, and another subject was born in Indonesia with Indian ancestry.   Indo-Canadian  All Indo-Canadians were recruited in Canada (2021-2022) and confirmed Canada as their country of birth. All participants identified as Indian and\/or Punjabi, and (n = 2) were half Indian, half Caucasian.   Euro-Canadian All Euro-Canadians were confirmed to have European ancestry during screening, and reported combinations of European\/Caucasian, Italian, Scottish, Irish, Polish, Russian, French, Ukrainian, Finnish, Welsch, British, Francophone, Dutch, Hungarian, Greek, and German. All samples were collected between 2021-2022.  Euro-Immigrant  During screening, all Euro-Immigrants were confirmed to have European ancestry and to have migrated from a westernized city. Countries of birth were as follows: United States (n = 10), England (n = 5), Australia (n = 2), Germany (n = 1), Russia (n = 1), Czech Republic (n = 1), Switzerland (n = 1), Netherlands (n = 1). All samples were collected between 2021-2022.           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