Diagnosis & Person-Centered Interventions

TB Diagnosis & Xpert MTB/RIF

• TB diagnosis in high-burden settings traditionally relies on sputum smear microscopy—low sensitivity, especially in HIV patients.
• Culture methods are accurate but slow and costly, limiting their use in resource-poor areas.
• Xpert MTB/RIF introduced rapid, automated TB and rifampicin resistance detection via real-time PCR. Delivers results in under two hours. User-friendly; recommended by WHO since 2010 as the initial diagnostic for suspected MDR-TB and HIV-associated TB. Impactful in southern Africa, improving detection of smear-negative TB and early treatment.
• Xpert alone is insufficient for long-term TB incidence reduction due to latent TB pools and low transmission from smear-negative cases.
• Widespread adoption strains healthcare systems (increased need for treatment, HIV management, and second-line drugs). See also: Lin TB Lab

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TB Drug Resistance & Diagnostic Technologies

• MTB drug resistance: Intrinsic (cell wall permeability, efflux pumps) and acquired (gene mutations like inhA, rrs). Capreomycin resistance linked to tlyA and rrs mutations.
• Diagnostic methods: Roche™ solid ratio method: cost-effective but slow. Nitrate reductase test: faster MTB sensitivity assessment. Line Probe Assay (LPA): accurate rifampicin resistance detection, WHO-approved for MDR-TB. Digital PCR & NGS offer high sensitivity but may miss resistance data.
• Advanced resistance testing: Phenotypic DSTs are the gold standard but slow (up to 6 weeks). Automated systems (BACTEC MGIT 960, Sensititre) streamline phenotypic DST. Colorimetric assays (Alamar Blue, Thin-Layer Agar) provide low-cost alternatives. Molecular DSTs (Xpert MTB/XDR, LPAs like MTBDRplus/sl) offer rapid mutation detection. WGS provides comprehensive resistance profiles but is expensive and complex. MALDI-TOF MS and QuantaMatrix (QMAP) refine resistance detection via protein and magnetic analysis.
• Bioinformatics tools (TBProfiler, Resistance Sniffer) enhance accuracy and reduce turnaround times.
• Combining molecular, sequencing, and phenotypic methods ensures comprehensive MTB resistance surveillance. See also: Benang Merah

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TB Socioeconomic Impact & Person-Centered Interventions

• TB disproportionately affects low-income groups, causing financial hardship and reducing productivity.
• In 2021, 4 million of 10 million new TB cases were "missing"; diagnosis completion hindered by economic barriers.
• Cash transfers: Help TB patients afford transportation and attend appointments. Even one-time transfers improved diagnostic process completion by 2–4 times. Did not significantly impact treatment initiation but supported diagnostic adherence.
• Conditional cash transfers plus TB counseling: Reduced loss to follow-up. Increased treatment success rate to 82% vs. 66.9% in control. 93.8% success in issuing cash transfers despite logistical issues.
• Full-time employment and food security linked to lower risk of unsuccessful TB treatment outcomes.
• Policy implications: integrating financial and behavioral support into TB programs improves retention and health outcomes.

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TB and Diabetes Mellitus (DM) Co-Morbidity

• TB and DM co-exist frequently in low- and middle-income countries (95% TB, 75% DM cases).
• DM weakens immunity, increasing susceptibility to TB; TB worsens glycemic control via stress hyperglycemia.
• Prevalence of DM among TB patients: 8.5%–11%, up to 45% in some regions.
• TB-DM patients experience: Prolonged smear and culture positivity. Higher risks of complications, relapse, and mortality.
• Immune dysfunction: Impaired T-cell and macrophage function. Hyperglycemia promotes M2 macrophages, reducing phagocytosis and neutrophil migration. Chronic inflammation and elevated cortisol worsen insulin resistance.
• Metformin benefits: Improves TB treatment outcomes (faster sputum conversion, reduced mortality). Potential to shorten TB treatment duration.
• Drug interactions complicate DM management (e.g., rifampin alters metabolism of sulfonylureas).

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Molecular & Cellular Insights into TB-DM

• Multi-omics (genomics, transcriptomics, proteomics, lipidomics, metabolomics) reveal: Chronic inflammation. Disrupted immune and metabolic pathways. Genetic predispositions (e.g., IL-6, IL-18 polymorphisms).
• DM-related factors influence TB severity (age, socio-economic status, hypertension).
• Regional disparities in TB-DM under-researched—need molecular epidemiology.
• DM increases mycobacterial load, distinctive lung damage, and disease progression.
• Integrated strategies: Routine reciprocal screening (TB in DM patients and vice versa). Research into multimorbidities and inflammation.
• Single-cell analysis and multi-omics integration promise predictive models and new therapeutic targets.
• Prospects for TB vaccines tailored for immunocompromised (TB-DM-specific).
• Comprehensive risk scores (including lifestyle, socio-demographic, clinical data) for precision public health.

(Yoseph Leonardo Samodra)

References:

1. Xiong, X.S., Zhang, X.D., Yan, J.W., Huang, T.T., Liu, Z.Z., Li, Z.K., Wang, L. and Li, F., 2024. Identification of Mycobacterium tuberculosis resistance to common antibiotics: an overview of current methods and techniques. Infection and Drug Resistance, pp.1491-1506.
2. Sanchini, A., Lanni, A., Giannoni, F. and Mustazzolu, A., 2024. Exploring Diagnostic Methods for Drug-Resistant Tuberculosis: A Comprehensive Overview. Tuberculosis, p.102522.
3. Menzies, N.A., Cohen, T., Lin, H.H., Murray, M. and Salomon, J.A., 2012. Population health impact and cost-effectiveness of tuberculosis diagnosis with Xpert MTB/RIF: a dynamic simulation and economic evaluation. PLoS medicine, 9(11), p.e1001347.
4. Ismail, Nazir, Harry Moultrie, Judith Mwansa-Kambafwile, Andrew Copas, Alane Izu, Sizulu Moyo, Donald Skinner et al. "Effects of conditional cash transfers and pre-test and post-test tuberculosis counselling on patient outcomes and loss to follow-up across the continuum of care in South Africa: a randomised controlled trial." The Lancet Infectious Diseases (2025).
5. Shete, P.B., Kadota, J.L., Nanyunja, G., Namale, C., Nalugwa, T., Oyuku, D., Turyahabwe, S., Kiwanuka, N., Cattamanchi, A. and Katamba, A., 2023. Evaluating the impact of cash transfers on tuberculosis (ExaCT TB): a stepped wedge cluster randomised controlled trial. ERJ open research, 9(3).
6. Araujo-Pereira, M., Vinhaes, C.L., Barreto-Duarte, B., Villalva-Serra, K., Queiroz, A.T.L., & Andrade, B.B. (2024). Intersecting epidemics: Deciphering the complexities of tuberculosis-diabetes comorbidity. Frontiers in Tuberculosis, 2, Article 1487793.
7. Boadu, A.A., Yeboah-Manu, M., Osei-Wusu, S. and Yeboah-Manu, D., 2024. Tuberculosis and diabetes mellitus: The complexity of the comorbid interactions. International Journal of Infectious Diseases, p.107140.

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