Diabetes Mellitus–Tuberculosis Comorbidity in Low–Resource Settings Diabetes Mellitus–Tuberculosis Comorbidity in Low–Resource Settings Victor Murphy Williams
Diabetes Mellitus–Tuberculosis Comorbidity in Low–Resource Settings Victor Murphy Williams
Diabetes Mellitus–Tuberculosis Comorbidity in Low–Resource Settings ISBN: 978-94-6483-899-2 Cover design: Joey Roberts | www.ridderprint.nl Layout and printing: Ridderprint | www.ridderprint.nl ©Copyright ©2024, Victor Murphy Williams All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without prior permission of the author or publishers of the scientific articles. Financial support for the studies presented in the thesis and printing of this thesis by the Global Health Support Program, University Medical Center Utrecht, is greatly appreciated.
Diabetes Mellitus–Tuberculosis Comorbidity in Low–Resource Settings De interactie tussen diabetes mellitus en tuberculose in een omgeving met een laag inkomen (met een samenvatting in het Nederlands) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof. dr. H.R.B.M. Kummeling, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op dinsdag 9 april 2024 des middags te 12.15 uur door Victor Murphy Williams geboren op 27 augustus, 1981 te Etinan, Nigeria
Promotoren Prof. dr. D.E. Grobbee Dr. K. Klipstein-Grobusch Copromotoren Dr. K. Otwombe Dr. A.G. Vos Beoordelingscommissie Prof. dr. O.H. Franco Duran Prof. dr. N. Levitt Prof. dr. M. van der Sande Prof. dr. F.L.J. Visseren Prof. dr. D.L.M. Zwart
Contents Chapter 1 General Introduction 9 Chapter 2 Tuberculosis Treatment and Resulting Abnormal Blood Glucose: A Scoping Review of Studies from 1980 to 2021 21 Chapter 3 Epidemiology and Control of Diabetes-Tuberculosis Comorbidity in Eswatini: Protocol for the Prospective Study of Tuberculosis Patients on Predictive Factors, Treatment Outcomes and Patient Management Practices 45 Chapter 4 Diabetes – Tuberculosis Comorbidity in a Low-Income Setting: Findings from a Prospective Cohort Study in Eswatini 89 Chapter 5 Diabetes – Tuberculosis Care in Eswatini: A Qualitative Study of Opportunities and Recommendations for Effective Services Integration 115 Chapter 6 Tuberculosis Services during the Covid-19 Pandemic: A Qualitative Study on the Impact of Covid-19 and Practices for Continued Services Delivery in Eswatini 139 Chapter 7 General Discussion 155 Chapter 8 Summary Samenvatting 168 172 Appendices Acknowledgement List of Authors Author Biography List of Publications 178 181 182 183
Chapter 1 General Introduction
10 Chapter 1 Global Tuberculosis Burden and Risk Factors Despite investments in tuberculosis (TB) control, 10.6 million people were ill with TB in 2022, with 1.3 million TB-related deaths [1]. Country programs were on track to end TB in line with the United Nations Sustainable Development Goals (SDG) 2030 targets, but the measures aimed at contending the COVID-19 pandemic halted these gains [2,3]. Recent global TB reports indicate a surge in TB-related deaths in 2020 and 2021 back to the 2017 levels, with a gap in the number of people with TB disease accessing treatment and reduced spending on TB-related prevention and treatment activities [4]. Consequently, ending TB by 2030 is presently out of reach, except innovative approaches are developed to increase TB prevention services, TB active case finding, and treatment of new and relapse TB cases to match and supersede pre-COVID-19 pandemic levels. Although TB is present in all countries, the incidence is highest in Southeast Asia, followed by Africa and the Western Pacific (Figure 1). Eight countries contribute to 68% of new global TB cases, while 30 contribute to 85% of all new global TB cases [4,5]. Seventeen of the thirty high-burden TB countries are from Sub-Saharan Africa. This high incidence has been attributed to the high HIV prevalence, undernourishment, alcohol use, smoking, diabetes, overcrowding, and poor housing infrastructure in Sub-Saharan Africa [1,6,7]. Figure 1: Estimated TB incidence rates per 100,000 population in 2022. Countries in the WHO African region had the highest TB incidence rates (WHO 2023 Global Tuberculosis Report - https://www.who.int/teams/ global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023)
11 1 General Introduction The role of HIV in propagating the TB epidemic in Sub-Saharan Africa and other regions of the world is well documented. It constitutes the most important driver of TB disease and poor treatment outcomes in the last decade [8–10]. People living with HIV have increased susceptibility to TB relapse and new infections, delayed sputum conversion and poor treatment outcomes [9]. The rollout of antiretroviral therapy (ART) in most Sub-Saharan African countries and improvement in TB infection prevention and treatment services has contributed to a noted decline in new TB infections [11,12]. Increasing Diabetes Mellitus Burden in Low- and Middle-Income Countries Non-communicable diseases (NCDs) surpassed infectious diseases as the most common cause of death globally. An estimated 41 million people die from NCDs (74% of all deaths) annually, with 77% of all NCD-related deaths occurring in low- and middle-income countries (LMICs ) [13] (Figure 2). Similarly, about 86% of 17 million people who die prematurely annually (before age 70) are from LMICs [13]. Most of these NCD-related deaths are from cardiovascular diseases (44%), cancers (23%), chronic respiratory diseases (10%) and diabetes (5%) [13]. Physical inactivity, unhealthy diet, smoking, alcohol, overweight and obesity, elevated blood pressure and uncontrolled blood glucose are known risks of NCDs [13–15]. War and political instability, insecurity, lack of infrastructure for physical activities, natural disasters, and air pollution are external factors contributing to increased NCD prevalence and death in LMICs, particularly in Sub-Saharan Africa [16–18].
12 Chapter 1 Figure 2: Percentage of Non-communicable disease deaths occurring under 70 years in 2019. Countries in the WHO African Region have the highest percentage of deaths (WHO Non-communicable Diseases Data Portal, 2023: https://ncdportal.org/)) Globally, an estimated 537 million adults live with diabetes, and over three-quarters reside in LMICs [19]. Already responsible for 6.7 million deaths annually, the International Diabetes Federation (IDF) estimates that 783 million people will be living with diabetes by 2045 [19]. An estimated 24 million adults live with diabetes in Sub-Saharan Africa, and 54% of these are undiagnosed [20]. In 2021, 416,000 diabetes-related deaths occurred in Sub-Saharan Africa [20]. Furthermore, the number of people living with diabetes in the region is projected to increase to 55 million by 2045 [20,21] (Figure 3). This projected increase in diabetes cases will significantly burden diabetes control, increase healthcare costs, and impact residents’ quality of life and socioeconomic status. Substantial expenses will go to diabetes care as most people in Sub-Saharan Africa pay out of pocket, thus elevating catastrophic costs for healthcare [22,23]. With weak health systems, limited health infrastructure, financing, and capacity for diabetes care compared to high-income countries, increased diabetes prevalence will increase the risks for diabetic complications and death in low-resource settings, particularly in Sub-Saharan Africa [24,25].
13 1 General Introduction Figure 3: Incidence and death due to diabetes mellitus in Sub-Saharan African countries from 1990 to 2019 (Global Burden of Disease 2022; https://vizhub.healthdata.org/gbd-results/). To mitigate the impending diabetes crises in Sub-Saharan Africa and other low-resource settings, understanding the drivers of diabetes and identifying other less-known risk factors can provide a critical starting point for designing context-specific interventions [21,26]. Another consideration is determining how the epidemiology of diabetes and other NCDs vary in the context of highly prevalent infectious diseases such as HIV, malaria, and TB. This can inform joint NCD-infectious disease intervention strategies and identify service integration points, lowering healthcare service delivery costs and increasing patient access to infectious diseases and NCD services. Diabetes Mellitus – Tuberculosis Comorbidity Diabetes is a known risk factor for TB. The risk of developing TB is two to three times higher in individuals with diabetes than in those without diabetes, and the risk of death during treatment and relapse after treatment is also higher in people with diabetes [27]. Globally, about 15% of people receiving treatment for TB have diabetes [28,29]. In SubSaharan Africa, the prevalence of DM–TB comorbidity is 6.7 – 15% [28–30]. In diabetes, there is impaired blood glucose regulation, limiting lymphocyte functions and rendering individuals susceptible to reactivation of latent TB and new infections [31]. Complications associated with diabetes, including renal impairment, may contribute to poor TB treatment outcomes in people with diabetes receiving treatment for tuberculosis [25].
14 Chapter 1 Some patients without a previous history of diabetes have been diagnosed with impaired blood glucose or diabetes at TB diagnosis. This observation is termed transient or stress hyperglycemia; the blood glucose normalises a few weeks after the patient has commenced TB treatment [32]. An elevation in stress hormones and cytokines in response to TB infection is proposed as being responsible for this [32]. This thesis focuses on DM–TB epidemiology, not transient or stress hyperglycemia. Rationale TB burden and TB-related deaths are high in Sub-Saharan Africa, with TB diagnosis and treatment challenges. Concurrently, the highest increase in diabetes mellitus cases will occur in Sub-Saharan Africa by 2045. Available evidence indicates an ongoing epidemiological transition in Sub-Saharan Africa, with changes in lifestyle, inactivity, dietary patterns, smoking, alcohol, and ageing patterns, all contributing to increased diabetes risk with high burdens of tuberculosis and HIV. With a limited understanding of DM–TB comorbidity in Sub-Saharan Africa, this thesis describes the epidemiology of DM–TB in a low-resource setting, the effect of blood glucose on TB treatment outcomes, the opportunities to improve TB patient management practices, the impact of COVID-19 on TB services and recommendations to improve integrated care for DM and TB. Setting The research described in this thesis was conducted in Eswatini (formerly Swaziland). Eswatini is a landlocked country in Southern Africa. It is surrounded by South Africa, except in the northeast, where it shares a border with Mozambique. It has a population of 1.2 million people [33] with four administrative regions – Hhohho, Lubombo, Manzini and Shiselweni (Figure 4). It is classified as a lower middle-income country by the World Bank [33], with a GDP per capita of $3,987 [33]. It has one of the highest global HIV prevalence at 24.8% and an annual TB incidence of 348/100,000 population [34,35].
15 1 General Introduction Figure 4: Map of Africa indicating the location of Eswatini where the study was conducted (http://www. vidiani.com/large-detailed-contour-political-map-of-africa/; https://www.worldatlas.com/ upload/92/63/ d4/regions-of-Eswatini-map.png) Eswatini and Switzerland were the first countries to achieve the Joint United Nations AIDS Program (UNAIDS) targets of 95-95-95 [36], indicating a robust response to the HIV epidemic. The government funds healthcare with additional support for different healthcare and development interventions from the United States Government, the Global Fund for AIDS, Tuberculosis and Malaria (GFATM), the World Bank and United Nations agencies. Private healthcare services are available and accessed through individual or employer-based medical aid or cash payments.
16 Chapter 1 Thesis Outline This thesis consists of eight chapters listed in the outline below: Chapter 1: Introduction Chapter 2: Scoping review of studies on the occurrence of abnormal blood glucose during tuberculosis treatment. Chapter 3: Protocol for the prospective study of a cohort of patients on tuberculosis-diabetes comorbidity, treatment outcomes and opportunities to improve integrated care. Chapter 4: Epidemiology of diabetes-tuberculosis comorbidity, tuberculosis treatment outcomes and predictors of poor tuberculosis treatment outcomes. Chapter 5: Opportunities and recommendations for improving diabetestuberculosis integrated services in a low- and middle-income country. Chapter 6: Impact of the COVID-19 pandemic on tuberculosis service delivery and approaches adopted by healthcare workers for continued service delivery. Chapter 7: General Discussion Chapter 8: Summary
17 1 General Introduction References 1. World Health Organisation. Tuberculosis (TB): Key Facts [Internet]. Geneva: WHO; 2023 Nov [cited 2023 Nov 29]. Available from: https://www.who.int/publications/m/item/global-tuberculosisreport-factsheet-2023 2. Dheda K, Perumal T, Moultrie H, Perumal R, Esmail A, Scott AJ, et al. The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions. Lancet Respir Med. 2022 Jun;10(6):603–22. 3. Zimmer AJ, Klinton JS, Oga-Omenka C, Heitkamp P, Nyirenda CN, Furin J, et al. Tuberculosis in times of COVID-19. J Epidemiol Community Health [Internet]. 2022 Mar 1 [cited 2023 Oct 18];76(3):310–6. Available from: https://jech.bmj.com/content/76/3/310 4. World Health Organisation. Global Tuberculosis Report 2022 [Internet]. Geneva: WHO; 2022 [cited 2023 Sep 30]. Available from: https://www.who.int/teams/global-tuberculosis-programme/tbreports/global-tuberculosis-report-2022 5. World Health Organisation. WHO global lists of high-burden countries for TB, multidrug/ rifampicin-resistant TB (MDR/RR-TB) and TB/HIV, 2021–2025. [Internet]. Geneva: WHO; 2021 [cited 2023 Oct 1]. Available from: https://www.who.int/news/item/17-06-2021-who-releasesnew-global-lists-of-high-burden-countries-for-tb-hiv-associated-tb-and-drug-resistant-tb 6. Schmidt CW. Linking TB and the Environment: An Overlooked Mitigation Strategy. Environ Health Perspect [Internet]. 2008 Nov [cited 2023 Oct 1];116(11):A478–85. Available from: https://www. ncbi.nlm.nih.gov/pmc/articles/PMC2592293/ 7. Narasimhan P, Wood J, MacIntyre CR, Mathai D. Risk Factors for Tuberculosis. Pulm Med [Internet]. 2013 [cited 2023 Oct 1];2013:828939. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3583136/ 8. De Cock KM, Soro B, Coulibaly IM, Lucas SB. Tuberculosis and HIV Infection in Sub-Saharan Africa. JAMA [Internet]. 1992 Sep 23 [cited 2023 Oct 1];268(12):1581–7. Available from: https:// doi.org/10.1001/jama.1992.03490120095035 9. Bruchfeld J, Correia-Neves M, Källenius G. Tuberculosis and HIV coinfection. Cold Spring Harbor perspectives in medicine. 2015;a017871. 10. Pawlowski A, Jansson M, Sköld M, Rottenberg ME, Källenius G. Tuberculosis and HIV Co-Infection. PLOS Pathogens [Internet]. 2012 Feb 16 [cited 2023 Oct 1];8(2):e1002464. Available from: https:// journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1002464 11. Surie D, Borgdorff MW, Cain KP, Click ES, DeCock KM, Yuen CM. Assessing the impact of antiretroviral therapy on tuberculosis notification rates among people with HIV: a descriptive analysis of 23 countries in sub-Saharan Africa, 2010–2015. BMC Infect Dis [Internet]. 2018 Sep 26 [cited 2023 Oct 1];18(1):481. Available from: https://doi.org/10.1186/s12879-018-3387-z 12. Golub JE, Saraceni V, Cavalcante SC, Pacheco AG, Moulton LH, King BS, et al. The impact of antiretroviral therapy and isoniazid preventive therapy on tuberculosis incidence in HIV-infected patients in Rio de Janeiro, Brazil. AIDS [Internet]. 2007 Jul 11 [cited 2023 Oct 1];21(11):1441–8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3063947/ 13. World Health Organisation. Non communicable diseases: Key Facts [Internet]. Geneva: WHO; 2023 Sep [cited 2023 Sep 30]. Available from: https://www.who.int/news-room/fact-sheets/detail/ noncommunicable-diseases
18 Chapter 1 14. Ezzati M, Riboli E. Behavioral and Dietary Risk Factors for Non-communicable Diseases. New England Journal of Medicine [Internet]. 2013 Sep 5 [cited 2023 Sep 30];369(10):954–64. Available from: https://doi.org/10.1056/NEJMra1203528 15. Kontis V, Mathers CD, Rehm J, Stevens GA, Shield KD, Bonita R, et al. Contribution of six risk factors to achieving the 25×25 non-communicable disease mortality reduction target: a modelling study. The Lancet [Internet]. 2014 Aug 2 [cited 2023 Sep 30];384(9941):427–37. Available from: https:// www.thelancet.com/journals/lancet/article/PIIS0140-67361460616-4/fulltext 16. Prüss-Ustün A, van Deventer E, Mudu P, Campbell-Lendrum D, Vickers C, Ivanov I, et al. Environmental risks and non-communicable diseases. Bmj. 2019;364. 17. Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and Prevention Strategies for Non-communicable Diseases (NCDs) and Their Risk Factors. Frontiers in Public Health [Internet]. 2020 [cited 2023 Sep 30];8. Available from: https://www. frontiersin.org/articles/10.3389/fpubh.2020.574111 18. Aebischer Perone S, Martinez E, du Mortier S, Rossi R, Pahud M, Urbaniak V, et al. Noncommunicable diseases in humanitarian settings: ten essential questions. Conflict and Health [Internet]. 2017 Sep 17 [cited 2023 Sep 30];11(1):17. Available from: https://doi.org/10.1186/s13031017-0119-8 19. International Diabetes Federation. IDF diabetes atlas tenth edition 2021 [Internet]. Brussels. Belgium: International Diabetes Federation; 2021 [cited 2023 Aug 21]. Available from: https:// diabetesatlas.org/atlas/tenth-edition/ 20. International Diabetes Federation. Factsheets | IDF Diabetes Atlas [Internet]. Brussels, Belgium: IDF; 2022 [cited 2023 Sep 30]. Available from: https://diabetesatlas.org/regional-factsheets/ 21. Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet [Internet]. 2023 Jul 15 [cited 2023 Sep 30];402(10397):203–34. Available from: https://www.thelancet.com/journals/ lancet/article/PIIS0140-6736(23)01301-6/fulltext 22. Shabangu K, Suleman F. Medicines availability at a Swaziland hospital and impact on patients. Afr J Prim Health Care Fam Med. 2015;7(1):e1–6. 23. Williams V, Vos-Seda AG, Haumba S, Mdluli-Dlamini L, Calnan M, Grobbee DE, et al. DiabetesTuberculosis Care in Eswatini: A Qualitative Study of Opportunities and Recommendations for Effective Services Integration. Int J Public Health. 2023;68:1605551. 24. Jeon CY, Murray MB. Diabetes Mellitus Increases the Risk of Active Tuberculosis: A Systematic Review of 13 Observational Studies. PLOS Medicine [Internet]. 2008 Jul 15 [cited 2023 Sep 30];5(7):e152. Available from: https://journals.plos.org/plosmedicine/article?id=10.1371/journal. pmed.0050152 25. Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol [Internet]. 2022 Sep [cited 2023 Sep 30];18(9):525–39. Available from: https://www.nature.com/articles/s41574-022-00690-7 26. Nyirenda MJ. Non-communicable diseases in sub-Saharan Africa: understanding the drivers of the epidemic to inform intervention strategies. Int Health [Internet]. 2016 May [cited 2023 Sep 30];8(3):157–8. Available from: https://academic.oup.com/inthealth/article-lookup/doi/10.1093/ inthealth/ihw021
19 1 General Introduction 27. World Health Organisation. Global Tuberculosis Report 2021: TB and Diabetes [Internet]. Geneva: WHO; 2021 Oct. Available from: https://www.who.int/publications/digital/global-tuberculosisreport-2021/featured-topics/tb-diabetes 28. Workneh MH, Bjune GA, Yimer SA. Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. PLoS One. 2017;12(4):e0175925. 29. Noubiap JJ, Nansseu JR, Nyaga UF, Nkeck JR, Endomba FT, Kaze AD, et al. Global prevalence of diabetes in active tuberculosis: a systematic review and meta-analysis of data from 2·3 million patients with tuberculosis. Lancet Glob Health. 2019 Apr;7(4):e448–60. 30. Alebel A, Wondemagegn AT, Tesema C, Kibret GD, Wagnew F, Petrucka P, et al. Prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa: a systematic review and meta-analysis of observational studies. BMC Infect Dis. 2019 Mar 13;19(1):254. 31. Szablewski L, Sulima A. The structural and functional changes of blood cells and molecular components in diabetes mellitus. Biological Chemistry [Internet]. 2017 Apr 1 [cited 2023 Oct 1];398(4):411–23. Available from: https://www.degruyter.com/document/doi/10.1515/hsz-20160196/html 32. Magee MJ, Salindri AD, Kyaw NTT, Auld SC, Haw JS, Umpierrez GE. Stress hyperglycemia in patients with tuberculosis disease: epidemiology and clinical implications. Current diabetes reports. 2018;18:1–10.
Chapter 2 Tuberculosis Treatment and Resulting Abnormal Blood Glucose: A Scoping Review of Studies from 1980 to 2021 Victor Williams, Chukwuemeka Onwuchekwa, Alinda G. Vos, Diederick E. Grobbee, Kennedy Otwombe, Kerstin Klipstein-Grobusch Global Health Action. 2022 Vol. 15, 2114146
22 Chapter 2 Abstract Background Hyperglycaemia is a risk factor for tuberculosis. Evidence of changes in blood glucose levels during and after tuberculosis treatment is unclear. Objective To compile evidence of changes in blood glucose during and after tuberculosis treatment and the effects of elevated blood glucose changes on treatment outcomes in previously normoglycaemic patients. Methods Original research studies (1980 to 2021) were identified in PubMed, Web of Science, CINAHL and Embase databases. Results Of the 1,277 articles extracted, 14 were included in the final review. All studies were observational and 50% were prospective. Fasting blood sugar was the commonest clinical test (64%), followed by the glycated haemoglobin test and the oral glucose tolerance test (each 50%). Most tests were conducted at baseline and in the third month of treatment. Twelve studies showed that the prevalence of hyperglycaemia in previously normoglycaemic patients decreased from baseline to follow-up and end of treatment. Three studies showed successful treatment outcomes of 64%, 75% and 95%. Patients with hyperglycaemia at baseline were more likely to develop cavitary lung lesions and poor treatment outcomes and had higher post-treatment mortality. There was no difference in outcomes by human immunodeficiency virus (HIV) status. Conclusion Elevated blood glucose in normoglycaemic patients receiving treatment for tuberculosis decreased by the end of treatment. Positive HIV status did not affect glucose changes during treatment. Further research is needed to investigate post-treatment morbidity in patients with baseline hyperglycaemia and the effects of HIV on the association between blood glucose and tuberculosis. Keywords Diabetes, Hyperglycaemia, Impaired glucose tolerance, Human immunodeficiency virus
23 2 Tuberculosis treatment and abnormal blood glucose. Introduction The World Health Organization estimates that ten million people were infected with tuberculosis (TB) in 2020, with 1.5 million deaths in the same year [1]. Concurrently, the International Diabetes Federation estimates that 537 million adults aged 20 to 79 were living with diabetes mellitus (DM) in 2021, with 75% of these residing in low- and middleincome countries (LMICs) [2, 3]. An estimated 6.7 million people died from DM in 2021, with rising cases of type 2 diabetes mostly from LMICs [2, 3]. Different studies indicate that people with DM are more likely to develop TB with worse treatment outcomes when receiving treatment for TB [4-7]. Therefore, understanding the blood sugar changes in patients during TB treatment is essential to ensure good treatment outcomes. Globally the prevalence of TB amongst DM patients is estimated to be 15.3% [8]. This prevalence varies depending on the age and sex of the population, the burden of DM and TB in the population and human development index scores [8]. The prevalence of DM in active TB is highest in North America and the Caribbean (19.7%), Western Pacific (19.4%) and Southeast Asia (19.0%) compared with Africa (8.0%) [8]. A prevalence of 15%, 11% and 10% has been documented in Nigeria, Tanzania and Ethiopia, respectively [9]. While numerous studies indicate that diabetes is a risk factor for TB, it is not completely clear if TB or its treatment predisposes one to develop DM [10-12]. Available explanation points to an impaired glucose tolerance (IGT) during treatment with anti-TB drugs, which may or may not resolve once the treatment is completed [11, 13-17]. This IGT is thought to be due to underlying undiagnosed diabetes or stress response from infection, resulting in increased levels of stress hormones, interleukin-1, interleukin-6 and TNF-alpha, abnormal functioning of the pancreas and possible TB-induced pancreatitis offsetting endocrine function [10, 11, 18]. Though plausible, these explanations have not been fully verified. Also, a high TB burden has been associated with human immunodeficiency virus (HIV) infection, which results in an immunocompromised state. So, HIV co-infection in TB patients may result in varied immune and endocrine responses with untoward outcomes. Although studies describing the effect of TB treatment on blood glucose are available, these are few in Africa and other LMICs. Additionally, DM-TB studies in resource-poor settings with high HIV burden are required to understand the intersection with HIV. Some available studies have methodological limitations such as small sample size and short follow-up post-TB treatment [19-21] and were conducted before the HIV epidemic. A search of PubMed and the Joanna Briggs Institute (JBI) Database of Systematic Reviews and Implementation Reports conducted on 25 July 2021 indicates few review articles and a systematic review protocol are available [10, 18, 22]. No scoping reviews were identified. The review articles presented useful information on the possible aetiology of abnormal glucose during TB treatment but none on the predictors. The available studies
24 Chapter 2 also focused on blood glucose changes in people with known DM status, not those with a normal blood glucose level before commencing TB treatment. Patients with known blood glucose anomalies will receive special care during TB treatment, but those presumed to have normal blood glucose may have poor treatment outcomes if abnormal changes in blood glucose were missed during treatment. Therefore, the objective of this scoping review was to identify and compile the available evidence on possible abnormalities in blood glucose in previously normoglycaemic patients during and after TB treatment, using studies published from 1980 to 2021. Methods This review was developed using the JBI reviewer's manual and the methodology is based on the framework developed by Arksey and O’Malley [23, 24]. Scoping review questions The following questions were used as a guide to fully describe the topic of this scoping review and the articles included in the review: 1. What methodology has been employed in describing the abnormal blood glucose arising from TB treatment? 2. What approaches have been identified as appropriate for measuring blood glucose during TB treatment? 3. What is the TB treatment outcome for patients who develop abnormal blood glucose while on TB treatment? 4. What factors determine the occurrence of abnormal blood glucose during TB treatment? 5. What is the frequency of abnormal glucose tolerance or DM in patients receiving TB treatment? Information sources and search strategy A search was done for studies describing TB treatment’s effect on patients’ glucose levels from 1 January 1980 to 30 June 2021. This period was chosen to accommodate the increase in HIV infections that led to an increase in the number of new TB cases [25, 26]. We searched the PubMed, Web of Science, CINAHL and Embase databases. A three-step approach was used to identify articles for inclusion in the review [23]. The first step was a preliminary search that involved identifying index terms and MeSH
25 2 Tuberculosis treatment and abnormal blood glucose. terms by searching PubMed and Embase using keywords from the scoping review’s topic (e.g., tuberculosis treatment, TB treatment, abnormal glucose/hyperglycemia/glucose intolerance, diabetes). The second step was to search the databases for articles using all the identified text words and keywords. The PubMed search was done on 8 November 2021 and the search terms used include ("Tuberculosis"[MeSH Terms] OR "tuberculo*"[Title/ Abstract]) AND ("treat*"[Text Word] OR "therap*"[Text Word] OR "drug*"[Title/Abstract] OR "medication*"[Title/Abstract] OR "medicine"[Title/Abstract] OR "therapeutics"[MeSH Terms] OR "drug therapy"[MeSH Subheading]) AND ("hyperglyc*"[Title/Abstract] OR "glucose intoler*"[Title/Abstract] OR "high blood glucose*"[Title/Abstract] OR "glucose tolerance"[Title/Abstract] OR "glycaemic"[Title/Abstract] OR "glycemic"[Title/Abstract] OR "hyperglycemia"[MeSH Terms] OR "blood glucose"[MeSH Terms] OR "Glucose Tolerance Test"[MeSH Terms]). Additional search criteria for other databases are available in Supplementary file 1. The third step involved searching the reference list of the identified articles from the second step for additional articles for inclusion in the list of potential articles. Where there was a need, the authors of primary studies were contacted to obtain additional information regarding their study. A librarian from Utrecht University Library guided the search processes to ensure we used appropriate search terms and obtained relevant articles. Inclusion criteria Included articles were original studies (case-control studies, cross-sectional studies, cohort studies and clinical trials) with participants of all ages from any part of the world. The following additional inclusion criteria were applied: (a) studies that were published from 1 January 1980 to 30 June 2021, (b) articles in English, (c) studies that specifically indicated that blood glucose was done at baseline or before the start of TB treatment and non-diabetic patients were followed-up either during or after treatment or both, and (d) studies that had information on the country where the study was conducted or specifically stated the region covered. Exclusion criteria Excluded articles were those that were outside the study period, non-original studies (case reports, review papers, modelling studies, systematic reviews and meta-analyses, letters to the editor and opinion papers), studies for which the full texts were not accessible, studies with participants already known to be on treatment for DM, studies with no follow-up data and those with outcomes other than TB. Study selection for inclusion The study selection followed two steps. The first step was the title and abstract screening, and the second step was the full-text screening. All identified articles were compiled
26 Chapter 2 and entered into EndNote (Clarivate Analytics, Philadelphia, USA) for deduplication. Once deduplication was complete, the remaining articles were uploaded into Rayyan software for title and abstract screening based on the inclusion criteria [27]. This was done independently by two reviewers (VW, CO). Where there was a conflict and the two reviewers could not agree, a third reviewer (AV) resolved the conflict. The full text of all the articles selected at the title/abstract stage was compiled and entered into EndNote for a full-text review and selection based on the inclusion criteria independently by two reviewers. Articles not meeting the inclusion criteria were excluded at this stage. The two reviewers first discussed and resolved disagreements and only invited the third reviewer when they did not agree. A PRISMA flow diagram (Figure 1) describes the steps adopted during article screening and selection for inclusion in the final study. Data extraction (Charting the results) The information extracted from each article is listed in Box 1 and is based on the JBI reviewer’s manual [23]. A standardised data extraction form to capture the required information was developed in REDCap as a survey [28] (Supplementary file 2). This was validated and updated by two reviewers (VW, CO) using five selected studies per JBI guidance [23]. They independently extracted data from each article into the REDCap survey (each reviewer assigned each study a predetermined code to enable comparison). At the end of data extraction, data from the REDCap spreadsheet were compared, and all discrepancies were resolved before using a merged file for data synthesis and subsequent analysis. For clarity, successful TB treatment outcome was defined as “cured or completed treatment”, while a poor outcome was defined as “relapse/treatment failure, loss to follow-up or death”. Results We identified 1,277 titles from our search (Figure 1). Of these, 945 unique titles were identified for screening after excluding duplicates. In the title and abstract screening, 916 articles did not meet the inclusion criteria, leaving 29 articles for a full-text review. Fourteen articles [13-15, 19-21, 29-36] were included in the final selection, while 15 articles were excluded. Two of five authors with contact information whose main text was unavailable were contacted but did not respond. Contact information was not available for the other three. Description of included studies The general characteristics of the 14 included studies are summarised in Table 1. Studies were conducted between 1984 and 2020, mainly in Asian (50%) and African (36%) countries. One study was conducted in South America and Europe. The articles, though
27 2 Tuberculosis treatment and abnormal blood glucose. varied, all aimed at studying or identifying IGT or hyperglycaemia during TB treatment. The studies were all observational, and 50% (n=7) were prospective cohort studies. Twenty-one per cent (n=3) were case-control studies, and 14% (n=2) were a combination of cross-sectional and prospective cohort studies. The sample size for the studies varied from 21 to 6,312, and participants from ten studies were patients receiving treatment for drug-sensitive TB. Of the remaining four studies, each used either multidrug-resistant TB patients (MDR-TB), HIV-TB co-infected patients, patients attending a private clinic or patients with respiratory symptoms. The participants were mostly males, with the proportion of males ranging from 49% to 78%, and the mean age of all participants ranged from 29.5 to 53 years. Seven out of the 14 studies (50%) included HIV-co-infected participants. The proportion of HIV co-infection in four studies was less than 10%, then 26%, 61% and 100% in the remaining three studies. In ten studies, participants received first-line TB treatment, one was a second-line only and three were all types of treatment. Method of glucose estimation Four main types of glucose estimation tests were used either singly or in combination. These include FBS (64%), glycated haemoglobin test (HbA1c) (50%), oral glucose tolerance test (OGTT) (50%) and random blood sugar test (RBS) (14%). Some studies combined two or more tests to estimate glucose levels: 36% (FBS + OGTT), 21% (FBS + HbA1c), 14% (HbA1c + OGTT), 14% (RBS + HbA1c), 7% (FBS + HbA1c + OGTT) and 7% (RBS + HbA1c + OGTT). Time of glucose estimation Five described the time of glucose estimation in the studies: baseline, three months, six months, end of treatment and post-treatment (Figure 2, Table 2). Measurements were done at baseline in all 14 studies and a combination of time points thereafter. Two studies (14%) used all five parameters to describe the time of glucose estimation. Glucose changes during tuberculosis treatment Most of the studies defined DM and hyperglycaemia based on the guidance provided by the American Diabetes Association [37]. In this guide, DM is defined as glucose level ≥7.0 mmol/l, ≥11.1 mmol/l or ≥6.5% using FBS, OGTT or HbA1c, respectively. IGT is similarly defined as a glucose level of 5.6 to 6.9 mmol/l, 7.8 to 11.0 mmol/l or 5.7 to 6.4% using FBS, OGTT or HbA1c, respectively. The studies excluded patients with a known diagnosis of DM before conducting a baseline glucose test. With some variability, patients identified with glucose levels consistent with DM and hyperglycaemia had repeat tests at specified periods. Table 2 describes the proportion of participants with DM and hyperglycaemia at baseline and during the follow-up period.
28 Chapter 2 Twelve (86%) studies showed the proportion of previously normoglycaemic patients with glucose values in the DM and IGT range at baseline reduced during treatment follow-up and end of treatment, while only two studies [15, 35] showed an increase (Table 2). DM decreased from 11.9% at baseline to 9.3% at follow-up, while IGT decreased from 46.9% at baseline to 21.5% at follow-up [36] in one of the studies conducted in South Africa. On the contrary, an Iranian cohort study [15] showed that 24% of patients developed DM in the follow-up period, while the proportion with IGT increased from 31% to 34%. Similarly, another study in Pakistan [35] observed that the proportion of IGT increased from 32% at baseline to 42% at follow-up. Most of the follow-up was done at three months (71%) followed by end of treatment (43%). With follow-up at different times, most studies (86%) agree there is a reduction in the glucose level at follow-up compared to baseline and dysglycaemia observed at baseline normalised at follow-up or end of treatment. Glucose levels were higher in older patients, mostly above 40 years, compared to younger patients [13-15]. TB treatment outcome and glucose changes A summary of results with TB treatment outcomes and glucose changes is presented in Table 3. In three studies, 64%, 75% and 95% of the patients had a successful treatment outcome [15, 33, 34]. Two studies indicated that TB patients with DM or IGT were more likely to develop cavitary lung lesions, with one of the studies indicating a 54% prevalence [15, 30]. In one study where patients were followed up to one year after TB treatment, patients with hyperglycaemia had a 48.9% risk of mortality compared to 7.9% in those with euglycaemia [33]. While another study showed that hyperglycaemia at enrolment diagnosed using fasting capillary glucose was associated with poor treatment outcomes such as loss to follow-up, treatment failure or death (aOR 2.46; 95% CI: 1.08 to 5.57) [21], a 2019 study from Mali [34] indicates that blood sugar levels had no impact on TB treatment outcomes. Researchers in Nigeria [31] did not find any difference in HbA1c levels based on HIV status, but a 2017 study in China [32] showed an HIV positive status, DM, smoking cigarettes and presenting to a hospital instead of a clinic were associated with an unstable FBS during TB treatment. Outcomes in TB-HIV co-infected patients Of the seven studies that included HIV co-infected participants, six provided information on glucose changes or their association with TB treatment outcomes based on the HIV status of the participants. The different outcomes are presented in Table 4.
29 2 Tuberculosis treatment and abnormal blood glucose. Discussion This scoping review has compiled findings from different studies on the changes in blood glucose levels of patients receiving treatment for TB. Most of the studies were conducted in Asia and Africa (Table 1), indicating locations with a high prevalence of TB. Consistent with the known epidemiology of TB, there were more male participants in the studies than females and glucose levels were higher in older participants. The FBG test was the commonest method for estimating blood sugar, followed by OGTT and HbA1c. There was no standardised approach to estimating blood sugar for patients, and most studies combined two or more approaches. In the studies where a combination of tests was used, HbA1c had higher values and patients with baseline values in the DM or IGT range were more likely to persist as hyperglycaemia throughout treatment [21]. This further indicates the use of HbA1c in identifying patients with long-term glucose abnormalities. Although all studies conducted baseline blood glucose assessments, subsequent measurements were different across the studies. For glucose screening to identify DM comorbidity during treatment, the timing of blood glucose screening should be standardised to allow for comparison across different patients and country programmes. Some studies only repeated glucose measurements for patients who were not known DM patients but with glucose measurements in the DM or IGT range at baseline, excluding those with normal baseline values [20, 21, 35]. These studies could have primarily aimed at following up on patients with abnormal glucose measurements or adopted as a costsaving measure. A limitation of this approach is that new cases of DM or hyperglycaemia during the follow-up period could be missed. Findings from this review suggest the mean blood glucose levels in patients who were previously not known to have DM but with baseline values in the DM or IGT range decreased once they commenced treatment. The prevalence of elevated blood glucose also decreased during follow-up. This is consistent with earlier findings that the elevated blood glucose at diagnosis may be due to stress hormones’ response to the disease process [10, 11, 18]. However, the elevated blood glucose did not always resolve following treatment, as some studies reported patients with persistent hyperglycaemia after TB treatment (Table 2). This could be people with undiagnosed DM before getting infected with TB or those already with IGT who develop DM due to the extra insulin resistance triggered by infection. Two studies conducted in Iran and Pakistan indicated an increase in blood sugar measurements after treatment [15, 35]. We are cautious of the interpretation of these studies as the number of patients screened at follow-up was lower than the baseline. This reduced number of follow-ups during TB treatment highlights a common problem encountered by TB programmes where patients are lost to follow-up or discontinue treatment due to various reasons such as distance to the health facility,
30 Chapter 2 stigma, treatment fatigue, relocation or treatment costs. Another reason could be down referral of patients once they are stable on treatment from tertiary health facilities to lower-level facilities such as clinics. The development of cavitary lung lesions indicates a severe abnormality in the immune response during TB infection and could be associated with hyperglycaemia [30, 38]. Two studies reported poor treatment outcomes (relapse, death, or loss to follow-up) in patients with DM or hyperglycaemia at enrolment and one-year post-treatment followup [21, 33]. This is consistent with a 2019 systematic review that showed the odds of death (OR 1.88, 95%CI 1.59–2.21) and relapse (OR 1.64, 95%CI 1.29–2.08) were higher in patients with DM receiving TB treatment compared to normoglycaemic TB patients [39]. Similarly, a 2022 multi-centre prospective cohort study from Brazil showed that poor TB treatment outcomes were associated with baseline dysglycaemia and higher HbA1c values [40]. From the studies, it is seen that glucose values improved over time with good TB treatment outcomes. A 2021 study from Ghana shows that though more patients with normoglycaemia had a sputum conversion at two months compared to those with hyperglycaemia, this difference became insignificant at six months, indicating that the observed dysglycaemia at the onset of treatment was temporary [41] and had no association with treatment outcomes. This implies that good treatment outcomes can often be achieved in DM patients with adequate glucose control. This review assessed studies that included HIV-positive participants to ascertain if HIV status affected DM-TB association, but the findings were mixed, tending toward a reduction in hyperglycaemia or no difference based on HIV status (Table 4). This could be because we had only six studies reporting this, and it was not the primary outcome of our study. Despite this, conflicting findings have been reported on the effect of HIV on TB/DM or hyperglycaemia. Studies conducted in Tanzania and Nigeria [42-44] indicate a stronger association among HIV-negative participants, while another study conducted in South Africa [45] indicates a stronger association among people living with HIV. Further research is required to convincingly describe this association as the different blood glucose measurement approaches and medications taken by people living with HIV can influence outcomes [36]. Strengths and Limitations A key strength of this scoping review is the rigorous methodological approach adopted at the different stages to ensure reproducibility, minimal errors and that the included studies met the inclusion criteria. The review team accessed four electronic databases to ensure relevant studies were not excluded. We also expanded the search to cover a period when HIV cases gradually increased and, more recently, to cover the period of the
31 2 Tuberculosis treatment and abnormal blood glucose. COVID-19 pandemic where we expect more screening for diabetes would be done since it is a high-risk factor for COVID-19-associated mortality. Finally, our review team have diverse expertise (infectious disease epidemiologists, biostatisticians, clinicians, public health specialists and non-communicable disease epidemiologists), which served as a useful resource to guide the review process. As a limitation, our review included relatively few articles as most studies in this field assessed glucose changes in known DM patients receiving treatment for TB. Since we did not extract records from all the databases, we may have missed some studies from the databases we did not search. For studies published during the COVID-19 pandemic, bias may likely have been introduced by elevated dysglycaemia from COVID-19 infections. But these are few and published in the early days of the pandemic. No risk of bias assessment was done to ascertain the methodological rigour of the included studies; therefore, recommendations cannot be provided based on the findings of this review. Nevertheless, we have been able to present findings from studies that describe glucose changes in non-DM patients receiving treatment for TB. Conclusion This scoping review aimed to identify and compile the available evidence on possible abnormalities in blood glucose during and after TB treatment. The studies indicated that dysglycaemia in patients receiving treatment for TB normalised after commencing anti-TB medication at the end of treatment, and a positive HIV status was not associated with glucose changes during TB treatment. There was no standardised method and time for testing or screening as the reviewed studies adopted different approaches. Further investigations on patient follow-up after TB treatment for possible signs of glucose changes that may result in high mortality and the impact of HIV on the association between DM and TB are required. This will enable definitive conclusions on the observed high mortality in persons with high glucose post-treatment and any effect of HIV on the association between DM and TB. Paper context Evidence of changes in blood glucose during tuberculosis treatment and the association with HIV is unclear. Our study shows that dysglycaemia identified at the onset of tuberculosis treatment is normalised at follow-up and end of treatment and patients with baseline dysglycaemia have poor outcomes post-treatment compared to normoglycaemic patients. HIV status was not associated with glucose changes during
32 Chapter 2 treatment. Further research is required to understand morbidity post-tuberculosis treatment and its association with HIV. References 1. World Health Organisation. Tuberculosis Fact Sheet Geneva: WHO; 2021 [cited 2022 25 May]. Available from: http://www.who.int/news-room/fact-sheets/detail/tuberculosis. 2. International Diabetes Federation. Diabetic facts and figures Brussels, Belgium: IDF; 2021 [updated 09/12/2021; cited 2022 23 May]. Available from: https://www.idf.org/aboutdiabetes/what-isdiabetes/facts-figures.html. 3. World Health Organisation. Diabetes Key Facts Geneva: WHO; 2021 [updated 10 November 2021; cited 2022 25 May]. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes. 4. Faurholt‐Jepsen D, Range N, PrayGod G, Jeremiah K, Faurholt‐Jepsen M, Aabye MG, et al. Diabetes is a strong predictor of mortality during tuberculosis treatment: a prospective cohort study among tuberculosis patients from Mwanza, Tanzania. Tropical Medicine & International Health. 2013;18(7):822-9. https://doi.org/10.1111/tmi.12120 5. Jiménez-Corona ME, Cruz-Hervert LP, García-García L, Ferreyra-Reyes L, Delgado-Sánchez G, Bobadilla-del-Valle M, et al. Association of diabetes and tuberculosis: impact on treatment and post-treatment outcomes. Thorax. 2013;68(3):214-20. https://doi.org/10.1136/ thoraxjnl-2012-201756 6. Baker MA, Harries AD, Jeon CY, Hart JE, Kapur A, Lönnroth K, et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC medicine. 2011;9(1):1-15. https://doi. org/10.1186/1741-7015-9-81 7. Root HF. The association of diabetes and tuberculosis. New England Journal of Medicine. 1934;210(3):127-47. 8. Noubiap JJ, Nansseu JR, Nyaga UF, Nkeck JR, Endomba FT, Kaze AD, et al. Global prevalence of diabetes in active tuberculosis: a systematic review and meta-analysis of data from 2· 3 million patients with tuberculosis. The Lancet Global Health. 2019;7(4):e448-e60. https://doi.org/10.1016/ S2214-109X(18)30487-X 9. Alebel A, Wondemagegn AT, Tesema C, Kibret GD, Wagnew F, Petrucka P, et al. Prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa: a systematic review and meta-analysis of observational studies. BMC infectious diseases. 2019;19(1):1-10. https://doi. org/10.1186/s12879-019-3892-8 10. Magee MJ, Salindri AD, Kyaw NTT, Auld SC, Haw JS, Umpierrez GE. Stress hyperglycemia in patients with tuberculosis disease: epidemiology and clinical implications. Current diabetes reports. 2018;18(9):1-10. https://doi.org/10.1007/s11892-018-1036-y 11. Luies L, du Preez I. The Echo of Pulmonary Tuberculosis: Mechanisms of Clinical Symptoms and Other Disease-Induced Systemic Complications. Clinical microbiology reviews. 2020;33(4). https:// doi.org/10.1128/CMR.00036-20 12. Cheng P, Wang L, Gong W. Cellular Immunity of Patients with Tuberculosis Combined with Diabetes. Journal of Immunology Research. 2022;2022. https://doi.org/10.1155/2022/6837745
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