Multifactor Strategies for TB Prevention and Control

1. Nutritional Status and TB Risk Evidence from a large Chinese cohort shows that higher BMI is independently protective against TB, with each one-unit increase lowering incidence by nearly 8%. Overweight and obese individuals had the lowest TB risk, while underweight participants experienced the highest, though statistical significance was limited after adjustment. This protective association held across age and sex groups, reinforcing the role of nutritional status in TB susceptibility. The findings align with prior research linking malnutrition to greater TB risk and suggest that improving BMI may have contributed to China’s recent TB declines.

See also: Yoseph Samodra

Policy implications are clear: screening should target individuals with low BMI, particularly in high-burden settings. Nutritional interventions at the community level could serve as effective TB prevention strategies alongside conventional case detection and treatment programs. By integrating dietary support into TB control frameworks, public health efforts could address both malnutrition and infection risk in a single, cost-effective approach.

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2. Community-Based TB Detection and Treatment Uganda’s national TB campaigns in 2022 demonstrated the power of community-driven interventions. Within a year, coverage expanded from 76% to 100% of districts, diagnostic unit participation doubled, and reach scaled from 2.9% to 11.6% of the population. Case notification rates jumped by 24% in the first campaign and 59% in the second, aided by mobile diagnostics, preventive therapy for 23,000 high-risk contacts, and integration of leprosy screening. Strong governmental and partner support enabled rapid expansion and ensured operational sustainability.

These results highlight that community mobilization, when coupled with logistical innovations, can dramatically improve TB detection and treatment initiation. Sustaining funding, refining operational tools, and maintaining political commitment are essential to ensuring these gains translate into lasting reductions in TB burden, especially in remote and underserved areas.

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3. TB–Diabetes Interaction and Immune Dysfunction TB patients with diabetes mellitus (TB-DM) display a distinct biological profile—marked by elevated inflammatory proteins, atherogenic lipid patterns, and sustained immune activation even after two months of therapy. These abnormalities correlate with worse outcomes, including persistent sputum positivity, suggesting prolonged infection and elevated cardiovascular risk. Hyperglycemia exacerbates immune dysfunction by impairing macrophage oxidative and cytokine responses, downregulating critical receptors, and dampening pathogen clearance.

Managing TB-DM requires a tailored approach. Beyond standard TB therapy, clinicians should consider blood glucose control, anti-inflammatory agents, and lipid-lowering treatments such as statins. Biomarkers could help predict treatment response, enabling early intervention for high-risk patients. Viewing TB-DM as a distinct phenotype, rather than a coincidental comorbidity, may improve both infection control and long-term health outcomes.

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4. Environmental and Microbiome Factors in TB Control Research in high-density urban settings confirms that overcrowded housing and close contact with TB patients are significant risk factors for infection, independent of individual knowledge or demographics. Structural interventions—improving ventilation, reducing crowding, and enhancing contact tracing—are therefore essential for disrupting transmission in these environments.

The gut microbiome also plays a critical role in TB immunity, influencing T cell activity and treatment outcomes. Long-term use of anti-TB antibiotics can disrupt this microbial balance, potentially weakening host defenses and compounding risks in patients with structural lung damage from previous infections. Integrating microbiome preservation strategies, along with post-treatment lung health monitoring, could prevent reinfection, reduce opportunistic pathogens, and improve quality of life for TB survivors.

References:

1. Chen, J., Zha, S., Hou, J., Lu, K., Qiu, Y., Yang, R., Li, L., Yang, Y. and Xu, L., 2022. Dose–response relationship between body mass index and tuberculosis in China: a population-based cohort study. BMJ open, 12(3), p.e050928.
2. Turyahabwe, S., Bamuloba, M., Mugenyi, L., Amanya, G., Byaruhanga, R., Imoko, J.F., Nakawooya, M., Walusimbi, S., Nidoi, J., Burua, A. and Sekadde, M., 2024. Community tuberculosis screening, testing and care, Uganda. Bulletin of the World Health Organization, 102(6), p.400.
3. Brake, J., Ajie, M., Sumpter, N.A., Koesoemadinata, R.C., Soetedjo, N.N., Santoso, P., Alisjahbana, B., Ruslami, R., Hill, P. and van Crevel, R., 2025. Inflammation and dyslipidaemia in combined diabetes and tuberculosis; a cohort study. iScience, 28(6).
4. Sopiani, P., Maemun, S., Azijah, I., Pratiwi, T.Z. and Saputra, R., 2025. Analysis of Risk Factors for Pulmonary Tuberculosis in Cirascas District, East Jakarta, 2022. The Indonesian Journal of Infectious Diseases, 11(1), pp.42-51.
5. Chaubey, G.K., Modanwal, R., Dilawari, R., Talukdar, S., Dhiman, A., Chaudhary, S., Patidar, A., Raje, C.I. and Raje, M., 2024. Chronic hyperglycemia impairs anti-microbial function of macrophages in response to Mycobacterium tuberculosis infection. Immunologic Research, 72(4), pp.644-653.
6. Wu, Y., Wang, C. and Li, Y., 2025. Status and outlook of pulmonary tuberculosis coinfection. Journal of Research in Medical Sciences, 30(1), p.34.

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Diagnostic Advancements and Clinical Prognostic Indicators

1. Clinical Prognostic Indicators & Treatment Monitoring

• BMI recovery during M/XDR-TB treatment was a strong predictor of survival; nearly 70% of patients gained weight, and lack of weight gain (especially among underweight and normal BMI patients) was linked to a fivefold increase in mortality.
• Early weight change during treatment (3–6 months) emerged as a potential independent prognostic factor for survival.
• Highlights the importance of integrating BMI monitoring and nutritional interventions into TB programs.

See also: Hsien-Ho Lin TB Lab

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2. Diagnostic Advancements and Test Performance

• GeneXpert enabled rapid, accurate TB and rifampicin resistance detection within two hours, supporting early treatment.
• Second-generation IGRAs showed superior sensitivity (~90%) and fewer indeterminate results compared to traditional IGRAs, especially in smear-negative and extrapulmonary TB cases.
• Lower false negatives and improved performance across HIV and diabetes patients make these new IGRAs promising for routine use.

See also: Jago Beasiswa

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3. TB and Comorbidity Management (Diabetes Focus)

• Historical and current data affirm that diabetes significantly increases TB risk, especially with poor glycemic control.
• Dual burden (TB-DM) remains prevalent in high-incidence regions, necessitating integrated care approaches.
• Evidence also suggests bidirectional interaction, with TB potentially worsening glucose tolerance.

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4. Early Detection Strategies & Screening Value

• Two-way TB screening among DM patients using symptom checklists and imaging uncovered TB cases that might be missed in routine care.
• Even with a low confirmation rate, the study demonstrated operational feasibility and value of systematic screening in high-risk populations.
• Reinforces the need to combine symptoms, imaging, and sputum tests for robust case identification.

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5. Biomarker and Immunologic Insights

• A 16-gene transcriptomic signature (COR) was successfully validated for predicting TB progression in two African cohorts.
• The COR signature, regulated by type I and II interferons, suggests interferon activity as a preclinical marker for TB.
• Transition from RNA-seq to PCR-based platforms makes gene expression tools more accessible for broader screening and risk stratification.

References:

1. Chakhaia, T., Blumberg, H.M., Kempker, R.R., Luo, R., Dzidzikashvili, N., Chincharauli, M., Tukvadze, N., Avaliani, Z., Stauber, C. and Magee, M.J., 2025. Lack of weight gain and increased mortality during and after treatment among adults with drug-resistant tuberculosis: a retrospective cohort study in Georgia, 2009–2020. ERJ Open Research.
2. Hariyanto, S.W., Avidati, H., Ulfah, U., Nurlaily, A.N. and Tejaningrum, K.D., Tuberculosis Screening in Patients with Diabetes Mellitus at the Internal Medicine Clinic of UGM Academic Hospital: Descriptive Study. Academic Hospital Journal, 7(1), p.8.
3. Petruccioli, E., Scriba, T.J., Petrone, L., Hatherill, M., Cirillo, D.M., Joosten, S.A., Ottenhoff, T.H., Denkinger, C.M. and Goletti, D., 2016. Correlates of tuberculosis risk: predictive biomarkers for progression to active tuberculosis. European Respiratory Journal, 48(6), pp.1751-1763.
4. Wati, N., Mu’awanah, I.A.U. and Amalia, A.A., 2024. Pulmonary Tuberculosis Incidence Rate with Genexpert Examination Method at Mlati II Public Health Center, Sleman In 2020-2023. International Journal of Health, Economics, and Social Sciences, 6(4), pp.1124-1129.
5. Cadena, J., Rathinavelu, S., Lopez-Alvarenga, J.C. and Restrepo, B.I., 2019. The re-emerging association between tuberculosis and diabetes: lessons from past centuries. Tuberculosis, 116, pp.S89-S97.
6. Whitworth, H.S., Badhan, A., Boakye, A.A., Takwoingi, Y., Rees-Roberts, M., Partlett, C., Lambie, H., Innes, J., Cooke, G., Lipman, M. and Conlon, C., 2019. Clinical utility of existing and second-generation interferon-γ release assays for diagnostic evaluation of tuberculosis: an observational cohort study. The Lancet Infectious Diseases, 19(2), pp.193-202.

Yoseph Leonardo Samodra

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