TBC 028: Impact of Diabetes on TB and LTBI

Approximately 5–15% of individuals infected with Mycobacterium tuberculosis (MTB) progress to active TB disease within the first 2–5 years. Latent tuberculosis infection (LTBI) is a significant public health challenge, particularly in high TB-burden regions, and is influenced by factors such as diabetes mellitus (DM). The incidence of DM is positively associated with LTBI, with cross-sectional studies showing increased odds of association. Individuals in high TB-burden areas have a greater likelihood of LTBI than those in low-burden areas.

Diagnostic Approaches and Challenges

Diagnostic methods for LTBI primarily include interferon-gamma release assays (IGRA) and the tuberculin skin test (TST). In six studies involving 721 participants across Africa, IGRA was used in all studies (100%), with two studies also employing TST. The pooled prevalence estimates were:

• 48% (95% CI 25–71%, I2 = 98.15%, p < 0.001) using IGRA.
• 17% (95% CI 10–33%, I2 = 94.00%, p < 0.001) using TST.
• The overall pooled prevalence of LTBI was 40% (95% CI 20–60%, I2 = 98.52%, p < 0.001).

High LTBI prevalence was noted among African populations with DM, particularly in individuals aged ≥40 years and those with poor glycemic control (HbA1c > 7%).

TB Diagnosis in Resource-Limited Settings

Rapid TB diagnosis remains challenging in settings with limited access to radiography, microbiological testing, or specialized staff. Empirical diagnosis, often inconsistent and poorly standardized, is critical in such environments but risks inappropriate treatment due to poor correlation with microbiological results.

To address this, a clinical risk score has been developed, incorporating six predictors:

1. Male sex.
2. Age 25–44 years.
3. HIV positivity.
4. Specific WHO-defined TB symptoms (e.g., cough, fever, night sweats, weight loss > 5 kg).
5. Symptom duration >2 weeks.
6. Self-reported history of diabetes.

The score ranges from 1 to 10, is manually calculable, and demonstrated reasonable predictive accuracy. It facilitates immediate diagnosis in settings where diagnostic delays could result in missed treatment opportunities.

Advances in PTB Detection Models

Efforts to enhance pulmonary TB (PTB) detection have focused on clinical predictors such as patient history, physical examination, and TST. Studies incorporating additional factors like CD4 count, BMI, and ART duration in co-infected populations have shown improved diagnostic accuracy. Integrating TST with WHO symptom screening increased sensitivity, underscoring the potential for augmenting existing protocols. However, the WHO symptom screen remains the primary tool in resource-limited settings.

Recommendations for High-Risk Groups

LTBI treatment aims to prevent progression to active TB, particularly in high-risk groups, including people living with HIV, close TB contacts, and patients undergoing immunosuppressive therapies. Socioeconomic factors and increased TB exposure amplify risks for individuals with diabetes, suggesting a need for targeted strategies to address shared risk factors and improve TB outcomes.

By integrating accessible, low-cost tools and addressing gaps in clinical practice, these approaches hold promise for improving TB care and reducing delays in high-burden, resource-constrained settings.

References:

1. Kibirige, D., Andia-Biraro, I., Kyazze, A.P., Olum, R., Bongomin, F., Nakavuma, R.M., Ssekamatte, P., Emoru, R., Nalubega, G., Chamba, N. and Kilonzo, K., 2023. Burden and associated phenotypic characteristics of tuberculosis infection in adult Africans with diabetes: a systematic review. Scientific Reports, 13(1), p.19894.
2. Baik, Y., Rickman, H.M., Hanrahan, C.F., Mmolawa, L., Kitonsa, P.J., Sewelana, T., Nalutaaya, A., Kendall, E.A., Lebina, L., Martinson, N. and Katamba, A., 2020. A clinical score for identifying active tuberculosis while awaiting microbiological results: development and validation of a multivariable prediction model in sub-Saharan Africa. PLoS medicine, 17(11), p.e1003420.
3. Van Wyk, S.S., Lin, H.H. and Claassens, M.M., 2017. A systematic review of prediction models for prevalent pulmonary tuberculosis in adults. The International Journal of Tuberculosis and Lung Disease, 21(4), pp.405-411. See also: https://tbreadingnotes.blogspot.com/2024/07/prediction-models-for-prevalent.html
4. Zhou, G., Guo, X., Cai, S., Zhang, Y., Zhou, Y., Long, R., Zhou, Y., Li, H., Chen, N. and Song, C., 2023. Diabetes mellitus and latent tuberculosis infection: an updated meta-analysis and systematic review. BMC Infectious Diseases, 23(1), p.770.
5. Lee, M.R., Huang, Y.P., Kuo, Y.T., Luo, C.H., Shih, Y.J., Shu, C.C., Wang, J.Y., Ko, J.C., Yu, C.J. and Lin, H.H., 2017. Diabetes mellitus and latent tuberculosis infection: a systemic review and metaanalysis. Clinical Infectious Diseases, 64(6), pp.719-727.?See also: https://tbreadingnotes.blogspot.com/2024/07/diabetes-mellitus-and-latent.html