Inflammatory bowel disease in sub-Saharan Africa: a protocol of a prospective registry with a nested case-control study

Leolin Katsidzira, Wisdom F Mudombi, Rudo Makunike-Mutasa, Bahtiyar Yilmaz, Annika Blank, Gerhard Rogler, Andrew Macpherson, Stephan Vavricka, Innocent Gangaidzo, Benjamin Misselwitz, Leolin Katsidzira, Wisdom F Mudombi, Rudo Makunike-Mutasa, Bahtiyar Yilmaz, Annika Blank, Gerhard Rogler, Andrew Macpherson, Stephan Vavricka, Innocent Gangaidzo, Benjamin Misselwitz

Abstract

Introduction: The epidemiology of inflammatory bowel disease (IBD) in sub-Saharan Africa is poorly documented. We have started a registry to determine the burden, phenotype, risk factors, disease course and outcomes of IBD in Zimbabwe.

Methods and analysis: A prospective observational registry with a nested case-control study has been established at a tertiary hospital in Harare, Zimbabwe. The registry is recruiting confirmed IBD cases from the hospital, and other facilities throughout Zimbabwe. Demographic and clinical data are obtained at baseline, 6 months and annually. Two age and sex-matched non-IBD controls per case are recruited-a sibling or second-degree relative, and a randomly selected individual from the same neighbourhood. Cases and controls are interviewed for potential risk factors of IBD, and dietary intake using a food frequency questionnaire. Stool is collected for 16S rRNA-based microbiota profiling, and along with germline DNA from peripheral blood, is being biobanked. The estimated sample size is 86 cases and 172 controls, and the overall registry is anticipated to run for at least 5 years. Descriptive statistics will be used to describe the demographic and phenotypic characteristics of IBD, and incidence and prevalence will be estimated for Harare. Risk factors for IBD will be analysed using conditional logistic regression. For microbial analysis, alpha diversity and beta diversity will be compared between cases and controls, and between IBD phenotypes. Mann-Whitney U tests for alpha diversity and Adonis (Permutational Multivariate Analysis of Variance) for beta diversity will be computed.

Ethics and dissemination: Ethical approval has been obtained from the Parirenyatwa Hospital's and University of Zimbabwe's research ethics committee and the Medical Research Council of Zimbabwe. Findings will be discussed with patients, and the Zimbabwean Ministry of Health. Results will be presented at scientific meetings, published in peer reviewed journals, and on social media.

Trial registration number: NCT04178408.

Keywords: adult gastroenterology; inflammatory bowel disease; tropical medicine.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

References

    1. Cosnes J, Gower-Rousseau C, Seksik P, et al. . Epidemiology and natural history of inflammatory bowel diseases. Gastroenterology 2011;140:1785–94. 10.1053/j.gastro.2011.01.055
    1. Zhou B, Lu Y, Hajifathalian K, et al. . Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 2016;387:1513–30. 10.1016/S0140-6736(16)00618-8
    1. Price AJ, Crampin AC, Amberbir A, et al. . Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol 2018;6:208–22. 10.1016/S2213-8587(17)30432-1
    1. Katsidzira L, Gangaidzo I, Thomson S, et al. . The shifting epidemiology of colorectal cancer in sub-Saharan Africa. Lancet Gastroenterol Hepatol 2017;2:377–83. 10.1016/S2468-1253(16)30183-2
    1. Bernstein CN, Eliakim A, Fedail S, et al. . World gastroenterology organisation global guidelines inflammatory bowel disease: update August 2015. J Clin Gastroenterol 2016;50:803–18. 10.1097/MCG.0000000000000660
    1. Kaplan GG, Ng SC. Understanding and preventing the global increase of inflammatory bowel disease. Gastroenterology 2017;152:313–21. 10.1053/j.gastro.2016.10.020
    1. Ng SC, Tang W, Leong RW, et al. . Environmental risk factors in inflammatory bowel disease: a population-based case-control study in Asia-Pacific. Gut 2015;64:1063–71. 10.1136/gutjnl-2014-307410
    1. Halfvarson J, Jess T, Magnuson A, et al. . Environmental factors in inflammatory bowel disease: a co-twin control study of a Swedish-Danish twin population. Inflamm Bowel Dis 2006;12:925–33. 10.1097/
    1. Vind I, Riis L, Jespersgaard C, et al. . Genetic and environmental factors as predictors of disease severity and extent at time of diagnosis in an inception cohort of inflammatory bowel disease, Copenhagen County and City 2003-2005. J Crohns Colitis 2008;2:162–9. 10.1016/j.crohns.2008.01.004
    1. Merchant AT, Dehghan M, Chifamba J, et al. . Nutrient estimation from an FFQ developed for a black Zimbabwean population. Nutr J 2005;4:37. 10.1186/1475-2891-4-37
    1. Katsidzira L, Laubscher R, Gangaidzo IT, et al. . Dietary patterns and colorectal cancer risk in Zimbabwe: a population based case-control study. Cancer Epidemiol 2018;57:33–8. 10.1016/j.canep.2018.09.005
    1. Yakubu A, Tindana P, Matimba A, et al. . Model framework for governance of genomic research and biobanking in Africa - a content description. AAS Open Res 2018;1:13. 10.12688/aasopenres.12844.1
    1. Piovani D, Danese S, Peyrin-Biroulet L, et al. . Environmental risk factors for inflammatory bowel diseases: an umbrella review of meta-analyses. Gastroenterology 2019;157:647–59. 10.1053/j.gastro.2019.04.016
    1. Zimbabwe National Statistical Agency and ICF International Zimbabwe demographic and health survey 2015: final report. Rockville, Maryland: Zimbabwe National Statistical Agency (ZIMSTAT) and ICF International, 2016.
    1. Dupont WD, Plummer WD. Power and sample size calculations for studies involving linear regression. Control Clin Trials 1998;19:589–601. 10.1016/S0197-2456(98)00037-3
    1. Pittet V, Juillerat P, Mottet C, et al. . Cohort profile: the Swiss inflammatory bowel disease cohort study (SIBDCS). Int J Epidemiol 2009;38:922–31. 10.1093/ije/dyn180
    1. Caporaso JG, Kuczynski J, Stombaugh J, et al. . QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335–6. 10.1038/nmeth.f.303
    1. Callahan BJ, Sankaran K, Fukuyama JA, et al. . Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Res 2016;5:1492. 10.12688/f1000research.8986.1

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