Objective: This study is designed to address the complex interplay between the gut microbiome, environmental factors, and inflammatory diseases, with a specific emphasis on serving as a healthy cohort for several related projects. Primary hypotheses: Since data from this study will be used as control data for four studies, four primary hypothesis will be defined. Hypothesis H1: Levels of intestinal inflammation will be substantially higher in Zimbabweans living in rural areas and low-resource settings (i.e. high-density areas) compared to Zimbabwean and Swiss individuals living in high-resource settings. Hypothesis H2: Bottlenecks and blooms of bacterial strains are less frequent in healthy participants than in inflammatory bowel disease (IBD) patients and bacterial strains will have lower mutation rates in healthy patients when compared to strains from IBD subjects (partner study: BASEC 2021-00871). Hypothesis H3: Longitudinal changes of the faecal microbiome of healthy Swiss individuals differ systematically compared to longitudinal changes of the faecal microbiome of Swiss UC patients with active disease (partner study: BASEC 2022-02008). Hypothesis H4: The HRV of healthy Swiss individuals differ systematically from HRV of Swiss IBD patients and can be associated with differentially abundant bacterial taxa (partner study: BASEC 2022-02008).
Objective: This study investigates the relationship between lifestyle, gut bacteria, and diseases such as colorectal cancer and inflammatory bowel diseases (IBD). The investigators aim to understand how the gut microbiome, influenced by different environments, impacts disease development. Our research focuses on healthy Swiss individuals as a control group for ongoing projects. Primary hypotheses: Since data from this study will be used as control data for four studies, four primary hypothesis will be defined. Hypothesis H1: Levels of intestinal inflammation will be substantially higher in Zimbabweans living in rural areas and low-resource settings (i.e. high-density areas) compared to Zimbabwean and Swiss individuals living in high-resource settings. Hypothesis H2: Bottlenecks and blooms of bacterial strains are less frequent in healthy participants than in IBD patients and bacterial strains will have lower mutation rates in healthy patients when compared to IBD subjects (partner study: BASEC 2021-00871). Hypothesis H3: Longitudinal changes of the faecal microbiome of healthy Swiss individuals differ systematically compared to longitudinal changes of the faecal microbiome of Swiss UC patients with active disease (partner study: BASEC 2022-02008). Hypothesis H4: The heart rate variability (HRV) of healthy Swiss individuals differ systematically from HRV of Swiss IBD patients and can be associated with differentially abundant bacterial taxa (partner study: BASEC 2022-02008). Secondary hypotheses Hypothesis H5: The faecal microbiome composition of healthy Swiss individuals differs systematically from the faecal microbiome composition of healthy Zimbabweans. (O1) Hypothesis H6: The faecal microbiome composition of healthy Swiss individuals differs systematically from the faecal microbiome of Swiss UC patients experiencing a disease flare. Hypothesis H7: The faecal microbiome composition of healthy Swiss individuals differs systematically from the faecal microbiome of Swiss UC patients after achieving disease remission. Hypothesis H8: The faecal microbiome composition of healthy Swiss without symptoms of irritable bowel syndrome (Rome IV criteria) differs systematically from the faecal microbiome of healthy Swiss with symptoms of irritable bowel syndrome. Design: Observational cohort study with 200 healthy Swiss participants. Participants are followed-up during one year. During the study, 12 faecal samples, voluntary blood samples, and comprehensive data are collected from each participant. Assessed data include clinical assessments, detailed socio-economic information and voluntary heart rate variability (HRV) measurements. The study's longitudinal approach comprises 12 defined follow-ups at days 0, 3, 5, and 7; weeks 2, 3, 4, 8, and 12; and months 6, 9, and 12. The faecal samples will be collected by the participants at home with provided vials. In addition, each faecal sample is accompanied by a follow-up questionnaire to filled out by the patient. The questionnaires focus on gastrointestinal symptoms, fatigue, socio-economic variables, emotional well-being, five factor model (personality) assessment and type D personality, and a simple dietary assessment covering a 24-hour period. Participants will mail the stool vials and questionnaires, using a provided envelope, to Inselspital Bern via the Swiss postal service. Blood samples will be acquired only from a subset of the participants primarily at enrolment. Recruitment: Primarily at the Department of Visceral Surgery and Medicine of the University Hospital Bern (Inselspital Bern), and through outreach to the general population.
Study Type
OBSERVATIONAL
Enrollment
200
Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
Bern, Switzerland
RECRUITINGIntestinal inflammation - healthy Swiss vs. healthy Zimbabweans
Difference in calprotectin levels of healthy Swiss individuals and healthy Zimbabweans in high-resource settings compared to calprotectin levels in Zimbabweans in low-resource settings.
Time frame: All sampling timepoints will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.
Evolutionary dynamics of bacterial strains - Swiss healthy vs. Swiss IBD
The evolutionary dynamics of the most frequent and the most abundant bacteria in healthy Swiss individuals compared to Swiss IBD patients by assessing mutation rate per genome per generation. Comment: calculation of mutation rates is only feasible for abundant bacteria which can be found in a high fraction of participants over more than one timepoint. The investigators will thus determine the most suitable bacterial species and focus the analysis on this bacterial species.
Time frame: All timepoints with samples in both groups will be analysed.
Intra-individual microbiome composition changes - Swiss healthy vs. Swiss UC with initial active disease
Difference in absolute dissimilarity (weighted Unifrac index) changes within individuals over time between the faecal microbiomes of healthy Swiss individuals and the faecal microbiomes of Swiss UC patients initially experiencing a disease flare.
Time frame: Samples from enrolment and after 12 months will be analysed. Alternatively, samples from enrolment and a second timepoint (> 1 week later) with the most available samples and relevant metadata will be prioritised.
Heart rate variability - Swiss healthy vs. Swiss IBD
Heart rate variability (the root mean square of successive differences) measurements compared between healthy Swiss individuals and Swiss IBD patients.
Time frame: Measurments from the first timepoint with heart rate variability assessment will be analysed.
Difference in healthy microbiome composition - Swiss vs. Zimbabweans
Dissimilarity (weighted Unifrac index) between the microbiomes of healthy Swiss individuals and the microbiomes of healthy Zimbabweans. (H5) The endpoint will be tested by performing a permutational multivariate analysis of variance of the index with the adonis function of the vegan R package or another appropriate method.
Time frame: All sampling timepoints will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.
Difference in microbiome composition - Swiss healthy vs. Swiss UC active
Dissimilarity (weighted Unifrac index) between the microbiomes of healthy Swiss individuals and the microbiomes of Swiss UC patients with active disease (i.e., in a disease flare). (H6) The endpoint will be tested by performing a permutational multivariate analysis of variance of the index with the adonis function of the vegan R package or another appropriate method.
Time frame: All sampling timepoints of defined subgroups will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.
Difference in microbiome composition - Swiss healthy vs. Swiss UC remission
Dissimilarity (weighted Unifrac index) between the microbiomes of healthy Swiss individuals and the microbiomes of Swiss UC patients with inactive disease (i.e., remission). (H7) The endpoint will be tested by performing a permutational multivariate analysis of variance of the index with the adonis function of the vegan R package or another appropriate method.
Time frame: All sampling timepoints of defined subgroups will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.
Difference in microbiome composition - Swiss healthy no IBS vs. Swiss healthy IBS
Dissimilarity (weighted Unifrac index) between the microbiomes of healthy Swiss with and without symptoms of irritable bowel syndrome (Rome IV criteria). (H8) The endpoint will be tested by performing a permutational multivariate analysis of variance of the index with the adonis function of the vegan R package or another appropriate method.
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Time frame: All sampling timepoints of defined subgroups will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.
Difference in microbiome composition - Swiss low HRV vs. Swiss high HRV
Dissimilarity (weighted Unifrac index) between the microbiomes of individuals with a low heart rate variability compared to individuals with a high HRV. (H8) The endpoint will be tested by performing a permutational multivariate analysis of variance of the index with the adonis function of the vegan R package or another appropriate method.
Time frame: All sampling timepoints of defined subgroups will be analysed, accounting for dependence between samples from the same individual. Alternatively, the timepoint with the most available samples and relevant metadata will be prioritised.