Metformin use and tuberculosis

Metformin shows potential as a supportive therapy for tuberculosis (TB), especially in diabetic patients. It may help limit the growth of Mycobacterium tuberculosis by activating certain cellular pathways. Studies suggest that diabetic patients taking metformin have a lower risk of developing active TB. Additionally, metformin appears to improve treatment outcomes by increasing the chances of clearing the infection and reducing TB-related deaths. However, it does not seem to have a significant effect on preventing latent TB infection, indicating that its role is more in strengthening the body’s immune response rather than directly targeting the bacteria.[2]

Despite its benefits, metformin does not appear to reduce the likelihood of TB relapse after treatment. Differences in study methods, particularly in diagnosing latent TB, may explain some of the conflicting findings. TB often remains dormant in the body until the immune system weakens, making it difficult to assess metformin’s full protective effect. However, since metformin enhances the body's defense against TB, it may help prevent latent infections from becoming active disease rather than stopping infection in the first place. This supports the idea that metformin works by boosting the immune system rather than attacking the bacteria directly.[2]

From a study in Korea, the metformin user group had a higher treatment success rate (90.3% vs. 87.6%, p < 0.01) and lower all-cause mortality (9.5% vs. 12.4%, p < 0.01) compared to non-users. No difference was found in TB-related vs. non-TB-related deaths among deceased patients. However, survival probability differed significantly for non-TB-related deaths (p < 0.01). In the PS-matched cohort, metformin users had a lower risk of death (HR 0.77, 95% CI 0.68–0.86, p < 0.01). This protective effect remained consistent across gender and pre-PS matching analyses. Metformin use was linked to reduced all-cause mortality during TB treatment, suggesting a potential role as a host-directed therapy (HDT) in TB–DM patients.[1] 

References:

1. Chung, E., Jeong, D., Mok, J., Jeon, D., Kang, H.Y., Kim, H., Kim, H., Choi, H. and Kang, Y.A., 2024. Relationship between metformin use and mortality in tuberculosis patients with diabetes: a nationwide cohort study. The Korean Journal of Internal Medicine, 39(2), p.306.

2. Yu, X., Li, L., Xia, L., Feng, X., Chen, F., Cao, S. and Wei, X., 2019. Impact of metformin on the risk and treatment outcomes of tuberculosis in diabetics: a systematic review. BMC infectious diseases, 19, pp.1-11.

Association of obesity and risk of tuberculosis

A study was conducted in Shandong Province, China, to investigate the association between Body Mass Index (BMI) and primary drug-resistant tuberculosis (DR-TB) among 8,957 newly diagnosed pulmonary TB cases. This retrospective, observational analysis grouped participants into underweight, normal weight, overweight, and obese categories, then examined clinical characteristics, drug-resistance profiles, and potential risk factors associated with DR-TB.[1]

Underweight cases tended to include both younger (15–24 years) and older (>65 years) individuals, alongside higher rates of asthma and COPD but lower diabetes prevalence. Overweight and obese patients, on the other hand, were generally middle-aged (45–64 years) and displayed a heightened prevalence of diabetes, hypertension, and other comorbid conditions, such as cancer and cavities. Obese groups also showed a larger proportion of female patients.[1]

Drug-resistance patterns varied across BMI categories. While the overall proportion of DR-TB reached 18.86% in normal-weight patients, it was higher in overweight (20.38%) and obese (23.91%) individuals. Notably, obese patients showed a substantially elevated rate of MDR-TB (17.39%), compared to 11.19% in normal-weight patients. Underweight cases were especially prone to isoniazid (INH) resistance, indicating that lower BMI may be linked with selective drug-resistant patterns.[1]

Further risk analysis revealed that being overweight increased the likelihood of MDR-TB (adjusted OR ~1.54), though the effect size was borderline significant. Males and individuals with certain comorbidities emerged as important risk factors for DR-TB across multiple BMI categories, suggesting that both physiological and demographic variables could influence the development of drug-resistant strains.[1]

These findings underscore the relevance of BMI in shaping TB clinical characteristics and drug-resistance profiles. Overweight and obese patients showed notably higher proportions of both DR-TB and MDR-TB, whereas underweight patients displayed unique vulnerabilities, particularly INH resistance. By identifying the demographic and clinical risk factors most associated with DR-TB within each BMI category, the study offers valuable insights for tailoring TB control strategies and addressing specific needs among high-risk groups.[1]

Despite the recognized link between high BMI and diabetes—as well as the established role of diabetes as a risk factor for tuberculosis (TB)—epidemiological data indicate a paradoxical inverse association between obesity and TB. In study cohorts from the NHIS and NTC, individuals with higher BMI were more likely to be male, older, current users of tobacco and alcohol, have lower educational attainment, higher diabetes prevalence, and in the NHIS cohort, lower household income. Mediation analysis showed that while higher BMI significantly increased the odds of diabetes and diabetes elevated the odds of active TB, obesity itself remained directly protective: obese individuals experienced approximately a two-thirds reduction in TB risk compared to normal-weight individuals, with 71.9% reduced odds in the NHIS cohort and 67.3% in the NTC cohort. Overall, the harmful effect of high BMI mediated through diabetes was overshadowed by this strong protective effect, resulting in a net lower TB risk among obese individuals—even those who also had diabetes—compared to normal-weight peers without diabetes.[2]

These findings were robust after accounting for potential confounding factors, with baseline BMI data collection ruling out the possibility of reverse causation (in which TB-induced weight loss could falsely lower BMI). While residual confounding by socioeconomic status cannot be entirely excluded, the study results reinforce that higher BMI exerts a paradoxical yet significant protective influence against TB, counterbalancing the heightened risk posed by diabetes.[2]

In another study, underweight individuals face a significantly higher risk of developing TB, with the highest incidence observed in those who are both underweight and have DM (5.35 cases per 1,000 person-years). In contrast, individuals with normal weight but DM also have an elevated risk (2.26 cases per 1,000 person-years), while those who are overweight or obese show a reduced risk of TB, with the lowest incidence among the severely obese. This pattern suggests a complex interaction between metabolic status and TB susceptibility.[3]

Further analysis of hazard ratios (HRs) reveals that underweight individuals with DM face a 3.24 times higher TB risk compared to those with normal weight, while even underweight individuals without DM have a 2.21 times increased risk. Interestingly, obesity appears to provide a protective effect, with severely obese individuals having a 71% lower TB risk compared to those with normal weight. Subgroup analyses highlight additional risk factors, including gender, age, smoking, and alcohol consumption, with male underweight diabetics and heavy drinkers facing particularly high TB risks. Notably, diabetes control (fasting glucose levels) does not significantly alter the BMI-TB association, reinforcing the idea that maintaining a healthy weight may be as crucial as diabetes management in TB prevention. These findings emphasize the need for integrated public health strategies that address both malnutrition and metabolic diseases to reduce TB burden.[3]

References:

1. Song, W.M., Guo, J., Xu, T.T., Li, S.J., Liu, J.Y., Tao, N.N., Liu, Y., Zhang, Q.Y., Liu, S.Q., An, Q.Q. and Li, Y.F., 2021. Association between body mass index and newly diagnosed drug-resistant pulmonary tuberculosis in Shandong, China from 2004 to 2019. BMC pulmonary medicine, 21, pp.1-14.

2. Lin, H.H., Wu, C.Y., Wang, C.H., Fu, H., Lönnroth, K., Chang, Y.C. and Huang, Y.T., 2018. Association of obesity, diabetes, and risk of tuberculosis: two population-based cohorts. Clinical Infectious Diseases, 66(5), pp.699-705.

3. Choi, H., Yoo, J.E., Han, K., Choi, W., Rhee, S.Y., Lee, H. and Shin, D.W., 2021. Body mass index, diabetes, and risk of tuberculosis: a retrospective cohort study. Frontiers in nutrition, 8, p.739766.

Smoking, DM, and TB

• Smoking and TB Risk: Current smokers exhibit a higher TB incidence rate (0.60%) compared to never smokers (0.56%) and former smokers (0.59%). After adjusting for confounders, current smokers have a significantly higher TB risk (aHR 1.158), while former smokers show a reduced risk (aHR 0.947). TB risk increases with smoking intensity and duration. Smoking cessation is recommended to reduce TB risk, with emphasis on maintaining weight after quitting. Weight loss post-cessation increases TB risk. See also: Lin TB Lab
• Smoking and Active TB Risk: Current smoking doubles the risk of active TB compared to never smokers, with risk escalating based on cigarette consumption and years of smoking. Smoking contributes to 17% of TB cases in Taiwan, highlighting its significant role in TB incidence. The impact of smoking-related TB is more pronounced in individuals under 65 years. See also: Australia Scholarships
• Diabetes and TB in Indonesia: Indonesia has a high TB incidence and a growing diabetes prevalence, with studies showing strong connections between the two diseases. Routine diabetes screening for TB patients, especially those over 35, is recommended to improve management and treatment outcomes. Metformin use in TB-DM patients is linked to a higher treatment success rate and reduced all-cause mortality.
• Mtb Sensitization and Type 2 Diabetes: Mtb sensitization increases the risk of type 2 diabetes (T2DM), primarily through insulin resistance. Insulin resistance explained 18.3% of the Mtb-T2DM association, while β-cell dysfunction was not a significant factor.
• TB Screening in Diabetic Patients: Mass TB screening among persons with diabetes is feasible but not cost-efficient in low detection settings. Targeted screening in high TB incidence areas is more effective, with risk-stratified approaches recommended for lower-burden settings. Successful TB screening implementation requires integration with existing community-based diabetes screening efforts.
• Diabetes and TB Risk in Spain: Diabetic patients with TB are younger and have higher triglycerides, and are more frequently from Hindustan. TB incidence is higher among diabetic patients with elevated HbA1c levels, peaking at HbA1c ≥ 9%. No significant difference in TB localization, radiography, or skin test results based on HbA1c levels, but TB incidence increased with higher HbA1c.
• Diabetes and TB Disease Vulnerability: DM increases the risk of TB, with more severe DM increasing susceptibility. Effective TB elimination strategies should address both TB and DM management, especially in regions with high TB burdens and rising DM prevalence.

Recommendations:

• Smoking cessation and weight maintenance are crucial to reduce TB risk.
• Diabetic TB patients should be regularly screened and managed to improve treatment outcomes.
• Risk-based TB screening in diabetic populations, especially in high-burden areas, is recommended.

References:

1. Kim, S.H., Park, Y.M., Han, K., Ko, S.H., Kim, S.Y., Song, S.H., Kim, C.H., Hur, K.Y. and Kim, S.K., 2022. Association of weight change following smoking cessation with the risk of tuberculosis development: A nationwide population-based cohort study. Plos one, 17(4), p.e0266262.
2. Lin, H.H., Ezzati, M., Chang, H.Y. and Murray, M., 2009. Association between tobacco smoking and active tuberculosis in Taiwan: prospective cohort study. American journal of respiratory and critical care medicine, 180(5), pp.475-480.
3. Magodoro, I.M., Aluoch, A., Claggett, B., Nyirenda, M.J., Siedner, M.J., Wilkinson, K.A., Wilkinson, R.J. and Ntusi, N.A., 2024, October. Association Between Mycobacterium tuberculosis Sensitization and Insulin Resistance Among US Adults Screened for Type 2 Diabetes Mellitus. In Open Forum Infectious Diseases (Vol. 11, No. 10, p. ofae568). US: Oxford University Press.
4. Liu, Q., You, N., Wen, J., Wang, J., Ge, Y., Shen, Y., Ding, X., Lu, P., Chen, C., Zhu, B. and Zhu, L., 2023. Yield and efficiency of a population-based mass tuberculosis screening intervention among persons with diabetes in Jiangsu Province, China. Clinical Infectious Diseases, 77(1), pp.103-111.
5. Alisjahbana, B., Sahiratmadja, E., Nelwan, E.J., Purwa, A.M., Ahmad, Y., Ottenhoff, T.H., Nelwan, R.H., Parwati, I., Meer, J.W.V.D. and Crevel, R.V., 2007. The effect of type 2 diabetes mellitus on the presentation and treatment response of pulmonary tuberculosis. Clinical infectious diseases, 45(4), pp.428-435.
6. Chung, E., Jeong, D., Mok, J., Jeon, D., Kang, H.Y., Kim, H., Kim, H., Choi, H. and Kang, Y.A., 2024. Relationship between metformin use and mortality in tuberculosis patients with diabetes: a nationwide cohort study. The Korean Journal of Internal Medicine, 39(2), p.306.
7. Antonio-Arques, V., Caylà, J.A., Real, J., Moreno-Martinez, A., Orcau, À., Mauricio, D., Mata-Cases, M., Julve, J., Navas Mendez, E., Puig Treserra, R. and Millet, J.P., 2022. Glycemic control and the risk of tuberculosis in patients with diabetes: A cohort study in a Mediterranean city. Frontiers in public health, 10, p.1017024.
8. Baker, M.A., Lin, H.H., Chang, H.Y. and Murray, M.B., 2012. The risk of tuberculosis disease among persons with diabetes mellitus: a prospective cohort study. Clinical Infectious Diseases, 54(6), pp.818-825.

 TBC 045

Transmission Control and Treatment Access Challenges

1. Clinical and Biological Predictors of Treatment Outcomes

• Key Findings: Early culture conversion is the strongest predictor of successful MDR-TB treatment. Younger age, male sex, normal BMI, no prior TB treatment, and absence of HIV, CKD, or cavitary lung lesions improve outcomes. Prediabetes is linked to delayed sputum conversion and poor prognosis if HbA1c levels are high. See also: Lin TB Lab NTU
• Action Plan: Implement Early Monitoring Protocols: Introduce routine and rapid culture conversion testing within the first two months of treatment. Incorporate regular HbA1c testing for TB patients to identify and manage prediabetes early, potentially through lifestyle interventions or medications.

2. Diagnostic and Treatment Access Challenges in Urban Settings

• Key Issues: Low identification and diagnosis rates for DR-TB in urban Indonesia. Significant delays between diagnosis and treatment, particularly for marginalized populations. Limited diagnostic support and reliance on phenotypic drug susceptibility testing (pDST).
• Action Plan: Enhance Diagnostic Capacity and Accessibility: Scale up active case-finding strategies, particularly in high-density and underserved areas. Establish more accessible diagnostic centers with faster molecular testing (e.g., GeneXpert) to reduce delays and improve linkage to care.

3. Geographic Hotspots and Transmission Control

• Critical Insights: MDR-TB hotspots exhibit higher transmission rates and specific genotypic clustering (e.g., LAM sublineage). Direct transmission is a significant contributor to MDR-TB in hotspot regions, even among treatment-naïve individuals. Spatial clustering indicates the need for geographically targeted interventions.
• Action Plan: Targeted Public Health Interventions: Deploy resources to identified hotspots with enhanced screening, treatment adherence support, and community education. Integrate geographic and mathematical modeling to adapt strategies dynamically based on emerging data.

References:

1. Soeroto, A.Y., Pratiwi, C., Santoso, P. and Lestari, B.W., 2021. Factors affecting outcome of longer regimen multidrug-resistant tuberculosis treatment in West Java Indonesia: A retrospective cohort study. PloS one, 16(2), p.e0246284.
2. Lestari, B.W., Nijman, G., Larasmanah, A., Soeroto, A.Y., Santoso, P., Alisjahbana, B., Chaidir, L., Andriyoko, B., Van Crevel, R. and Hill, P.C., 2024. Management of drug-resistant tuberculosis in Indonesia: a four-year cascade of care analysis. The Lancet Regional Health-Southeast Asia, 22:100294.
3. Viswanathan, V., Devarajan, A., Kumpatla, S., Dhanasekaran, M., Babu, S. and Kornfeld, H., 2023. Effect of prediabetes on tuberculosis treatment outcomes: A study from South India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17(7), p.102801.
4. Zelner, J.L., Murray, M.B., Becerra, M.C., Galea, J., Lecca, L., Calderon, R., Yataco, R., Contreras, C., Zhang, Z., Manjourides, J. and Grenfell, B.T., 2016. Identifying hotspots of multidrug-resistant tuberculosis transmission using spatial and molecular genetic data. The Journal of infectious diseases, 213(2), pp.287-294.
5. Manjourides, J., Lin, H.H., Shin, S., Jeffery, C., Contreras, C., Santa Cruz, J., Jave, O., Yagui, M., Asencios, L., Pagano, M. and Cohen, T., 2012. Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis, 92(3), pp.273-279.
6. Wulandari, D.A., Hartati, Y.W., Ibrahim, A.U. and Pitaloka, D.A.E., 2024. Multidrug-resistant tuberculosis. Clinica Chimica Acta, 559, p.119701.

TBC 044 

The Role of Diabetes Management and Health System Strengthening in Tuberculosis Control

1. Impact of Diabetes on Tuberculosis (TB) Treatment and Outcomes

• No Significant Impact on Treatment Efficacy: Sputum AFB conversion, cure rates, treatment duration, and mortality were similar between diabetic and non-diabetic patients. Blood glucose control (HbA1c levels) did not significantly influence TB treatment success. See also: Lin TB Lab
• Higher Risk of TB Recurrence in Diabetic Patients: DM patients had a 2.5 times higher risk of TB recurrence, with males and younger individuals at greater risk. Moderate blood glucose control (31-50% RBG reduction) was associated with better sputum conversion rates.
• Complex Relationship Between TB and Diabetes: TB treatment may induce temporary hyperglycemia, and severe TB cases correlate with higher blood glucose levels. Standardized glucose screening during TB treatment is essential for early detection and management of DM.
• Integrated DM-TB Management Improves Outcomes: Structured DM-TB case management programs lead to better TB treatment success and lower relapse rates.

2. Challenges and Strategies for TB Control in High-Burden Areas

• Barriers to TB Infection Prevention and Control (TB-IPC) in Papua New Guinea (PNG): Governance gaps, funding delays, cultural stigma, and healthcare worker shortages hinder TB-IPC efforts. Lack of prioritization in national health plans and weak leadership affect policy implementation. See also: NTU GHP
• TB Control Challenges in PNG: High TB incidence (432 per 100,000 people) and low treatment success rate (50%) due to poor healthcare access. Loss to follow-up (22%) is common among young adults, leading to higher transmission risks.
• Strategies for Improvement: Strengthening healthcare infrastructure, healthcare worker training, and policy commitment. Expanding rapid molecular diagnostics and decentralizing TB services for better accessibility. Community engagement and intersectoral collaboration with NGOs to reduce stigma and misinformation.

References:

1. Suwannacho, R., Anantachina, N. and Sornprom, C., 2024. Glycated hemoglobin level and sputum acid—fast bacilli conversion in pulmonary tuberculosis. The Clinical Academia, 48(3), pp.84-91.
2. Septa, D. and Surjadi, L.M., 2023. Glycemic Control Effect on Acid-Fast Bacteria Conversion in Diabetic Patients with Tuberculosis. Jurnal Biomedika dan Kesehatan, 6(1), pp.62-70.
3. Marme, G., Kuzma, J., Zimmerman, P.A., Harris, N. and Rutherford, S., 2024. Investigating socio-ecological factors influencing implementation of tuberculosis infection prevention and control in rural Papua New Guinea. Journal of Public Health, 46(2), pp.267-276.
4. Charles, F., Lin, Y.D., Greig, J., Gurra, S., Morikawa, R., Graham, S.M. and Maha, A., 2024. Loss to follow-up among adults with drug-resistant TB in Papua New Guinea. Public Health Action, 14(3), pp.85-90.
5. Wang, Y., Shi, J., Yin, X., Tao, B., Shi, X., Mao, X., Wen, Q., Xue, Y. and Wang, J., 2024. The impact of diabetes mellitus on tuberculosis recurrence in Eastern China: a retrospective cohort study. BMC Public Health, 24(1), p.2534.
6. Lee, P.H., Lin, H.C., Huang, A.S.E., Wei, S.H., Lai, M.S. and Lin, H.H., 2014. Diabetes and risk of tuberculosis relapse: nationwide nested case-control study. PloS one, 9(3), p.e92623.
7. Williams, V., Onwuchekwa, C., Vos, A.G., Grobbee, D.E., Otwombe, K. and Klipstein-Grobusch, K., 2022. Tuberculosis treatment and resulting abnormal blood glucose: a scoping review of studies from 1981-2021. Global Health Action, 15(1), p.2114146.
8. Lo, H.Y., Yang, S.L., Lin, H.H., Bai, K.J., Lee, J.J., Lee, T.I. and Chiang, C.Y., 2016. Does enhanced diabetes management reduce the risk and improve the outcome of tuberculosis?. The International Journal of Tuberculosis and Lung Disease, 20(3), pp.376-382.

TBC 043

Temporal trends in mortality of TB attributable to HFPG

TB patients with diabetes or hyperglycemia face a higher likelihood of experiencing more severe disease and unfavorable treatment outcomes compared to those without co-morbidities. Long-term elevated blood glucose levels can impair immune cells crucial for combating TB bacteria, weakening the immune response and enabling TB bacteria to multiply and spread throughout the body, thereby increasing the risk of developing active TB. Additionally, diabetes and hyperglycemia can reduce the body's ability to effectively treat TB infections.[2]

See also: https://tbreadingnotes.blogspot.com/2024/07/exposure-to-secondhand-smoke-and-risk.html

In China, the age-standardized mortality rates (ASMRs) for TB related to hyperglycemia were lower than the global average. Although men showed higher TB mortality rates, the reduction in mortality was smaller in men compared to women. Notably,  high fasting plasma glucose (HFPG)-related TB mortality initially increased and then decreased with age, with the most significant decrease in the average annual percentage change (AAPC) observed in the 60–64 age group. This suggests that this age range is a critical period for HFPG’s impact on TB mortality, indicating that interventions focused on managing diabetes and hyperglycemia during this life stage could significantly improve TB health outcomes.[2]

See also: https://tbreadingnotes.blogspot.com/2024/07/the-relationship-between-malnutrition.html

In older adults, TB cases are often due to the reactivation of dormant TB lesions, which can be attributed to age-related changes in the immune system, such as a reduced ability to reactivate previously acquired immunity. Elevated TB mortality in men may also be linked to biological factors, including differences in sex hormone effects on macrophage activation. For example, oestradiol, a female sex hormone, enhances macrophage activation, whereas androgen does not have a similar effect.[2]

See also: https://tbreadingnotes.blogspot.com/2024/07/ending-tb-in-southeast-asia.html

Mortality related to TB is associated with harmful health habits, which may be influenced by gender-specific norms and behaviors. Men often exhibit delayed healthcare-seeking behavior and poorer treatment adherence compared to women. A crucial obstacle in managing glucose levels among TB patients is adherence to medication; thus, strategies for controlling HFPG should particularly focus on men.[2]

A meta-analysis found that poorly controlled glucose (HbA1c > 7.0%) increased the odds of TB by 2.05 times (95% CI: 1.65–2.55) compared to good glycemic control (HbA1c < 7.0%). Ten studies showed a rising trend in HbA1c levels from non-DM to DM patients, with DM-TB patients having significantly higher HbA1c than DM-only patients (P = 0.002). These findings highlight the vulnerability of diabetic patients with poor glucose control to TB, emphasizing the need for TB control strategies targeting this population.[1]

Reference:

1. Chen, Z., Liu, Q., Song, R., Zhang, W., Wang, T., Lian, Z., Sun, X. and Liu, Y., 2021. The association of glycemic level and prevalence of tuberculosis: a meta-analysis. BMC Endocrine Disorders, 21(1), p.123. 

2. Wang C, Yang X, Zhang H, Zhang Y, Tao J, Jiang X and Wu C (2023) Temporal trends in mortality of tuberculosis attributable to high fasting plasma glucose in China from 1990 to 2019: a joinpoint regression and age-period-cohort analysis. Front. Public Health 11:1225931.

 

Tuberculosis in Southeast Asia

The incidence of tuberculosis (TB) in ASEAN declined from 2002 to 2017. Six out of ten ASEAN countries—Cambodia, Myanmar, Indonesia, Vietnam, Laos, and Thailand—showed a continuous decrease in TB cases. The Philippines experienced an initial decline in TB incidence until 2007, followed by an increasing trend. Meanwhile, Malaysia, Singapore, and Brunei exhibited fluctuations in TB case trends, with Malaysia showing a rising trend from 2009 onward. Regarding health-related expenditures, Singapore had the highest per capita spending at US$1,550.42, followed by Brunei at US$716.15. These two countries also reported the lowest mean number of TB cases. In contrast, Myanmar had the second-highest mean number of TB cases while spending the least per capita on health compared to other ASEAN nations.[1] See also: https://tbreadingnotes.blogspot.com/2024/08/achieving-universal-social-protection.html

TB loss to follow-up (LTFU) refers to patients who begin tuberculosis treatment but fail to complete it or attend follow-up appointments. According to the WHO, TB LTFU includes patients who discontinue treatment for more than eight consecutive weeks after undergoing at least four weeks of therapy. Several socioeconomic factors contribute to TB LTFU, including low education levels, short-term migration—particularly across provinces—and limited access to healthcare services. Additionally, individuals from lower-income backgrounds and those who are unemployed face a higher risk of discontinuing treatment, especially when healthcare services are not easily accessible.[2] See also: https://tbreadingnotes.blogspot.com/2024/08/cost-effectiveness-and-budget-impact-of.html

Behavioral and community-related factors also play a significant role in TB LTFU. Alcohol consumption and smoking have been linked to an increased likelihood of treatment default, with alcohol impairing judgment, disrupting adherence to treatment schedules, and reducing the effectiveness of TB medications through drug interactions. Similarly, smoking may exacerbate pulmonary symptoms, complicating the treatment process and potentially diminishing TB medication efficacy. Furthermore, household economic conditions influence treatment adherence, with self-employed household heads being more likely to default on treatment compared to government employees. In contrast, having health insurance and access to travel support significantly reduces the risk of LTFU by alleviating financial and logistical burdens associated with treatment.[2]

Migrants face unique challenges that heighten their risk of TB LTFU, including unstable housing, irregular employment, and difficulties accessing consistent healthcare services. Their transient nature often results in treatment interruptions due to relocation and barriers to continuity of care. Financial constraints further exacerbate treatment adherence issues, as out-of-pocket payments may deter individuals from continuing TB therapy. Conversely, individuals with health insurance are more likely to adhere to TB treatment protocols, underscoring the crucial role of financial stability in ensuring treatment completion and reducing LTFU rates.[2]

References:

1. Shanmuham, V., Shetty, J.K. and Naik, V.R., 2022. Incidence of tuberculosis in the association of South-East Asia Nation (ASEAN) countries and its relation with health expenditure: a secondary data analysis. Manipal Journal of Nursing and Health Sciences, 8(1), p.7.

2. Rani, A.Y.A., Ismail, N., Zakaria, Y. and Isa, M.R., 2024. A scoping review on socioeconomic factors affecting tuberculosis loss to follow-up in Southeast Asia. Med J Malaysia, 79(4), pp.470-476. 

Tuberculosis in Yogyakarta

A study conducted at Panembahan Senopati Hospital and Yogyakarta Respira Lung Hospital from January to March 2023 found no significant relationship between age, gender, education, employment, marital status, family TB history, or prior TB history with pulmonary TB in DM patients. However, low-income DM patients had a 0.43 times higher risk of developing pulmonary TB, and BMI status was significantly associated with TB incidence. Additionally, DM patients experiencing TB-related symptoms had a higher risk, but no association was found between TB incidence and alcohol consumption, smoking, family support, or home environmental conditions.[1]

Gender emerged as a key risk factor in the multivariate analysis, with female DM patients showing a 9.60 times higher risk of developing TB compared to males. However, the confidence interval (CI = 0.10-1.01) suggests borderline statistical significance. Interestingly, multivariate analysis did not confirm an association between clinical symptoms and TB incidence.[1]

The study highlights BMI, income level, and gender as significant risk factors for pulmonary TB in DM patients. However, the small sample size (n=52) limits the study’s power, making some findings less stable. Additionally, the study does not account for potential interactions between variables, such as gender and BMI, which may have influenced the results.[1]

References:

1. Nuraisyah, F., Juliana, N., Astaria, D., Khalisah, N., Al Fatih, D.M.F., Dewi, S.K. and Marwati, T., 2024. Risk Factors of Pulmonary Tuberculosis in Type 2 Diabetes Mellitus in Yogyakarta. Journal of Epidemiology and Public Health, 9(2), pp.194-203.

Health and Economic Outcomes of Racial and Ethnic Tuberculosis Disparities

Significant progress has been made in reducing tuberculosis (TB) incidence in the United States, achieving one of the lowest rates globally. However, racial and ethnic disparities remain among US-born individuals, with higher TB incidence and case-fatality rates observed in marginalized communities. These disparities stem from systemic health inequities influenced by social, economic, and environmental disadvantages.[1]

Data from the National Tuberculosis Surveillance System (NTSS) from 2010 to 2019 highlight the disproportionate TB burden among racial and ethnic groups. Of the 31,811 reported TB cases in US-born persons, Black individuals accounted for 38%, followed by Hispanic (21%) and White (32%) populations. Case-fatality rates were also disproportionately high, with Black individuals experiencing 42% of TB-related deaths. Limited access to prevention services, delayed medical care, and lower quality of healthcare contribute to these disparities.[1]

Projections for 2023-2035 estimate 26,203 TB cases and 3,264 TB deaths among US-born persons, with case-fatality rates increasing by 7% due to age-related factors. Nearly half of TB cases (45%) are expected to be linked to racial and ethnic disparities, with Native Hawaiian or Other Pacific Islander persons experiencing the highest proportion of disparity-associated cases at 75%. Black and American Indian/Alaska Native individuals will bear the greatest loss in quality-adjusted life years (QALYs), reflecting the severe health burden of TB inequities.[1]

The economic impact of TB disparities is substantial, with projected costs reaching $1.397 billion between 2023 and 2035. Racial and ethnic disparities will account for up to 66% of these costs, highlighting the urgent need for targeted public health interventions. Addressing these inequities through improved access to healthcare, early detection, and prevention strategies is critical to reducing TB incidence and ensuring health equity for all US-born populations.[1] 

References:

1. Swartwood, N.A., Li, Y., Regan, M., Marks, S.M., Barham, T., Asay, G.R.B., Cohen, T., Hill, A.N., Horsburgh, C.R., Khan, A.D. and McCree, D.H., 2024. Estimated Health and Economic Outcomes of Racial and Ethnic Tuberculosis Disparities in US-Born Persons. JAMA Network Open, 7(9), pp.e2431988-e2431988.

Tuberculosis in Brunei Darussalam

A retrospective cohort study examined the contact investigation process for latent tuberculosis infection (LTBI) in Brunei, analyzing data from 10,537 contacts of 1,048 pulmonary TB (PTB) index cases between 2009 and 2018. Data were obtained from the National Tuberculosis Coordinating Centre (NTCC), which oversees TB prevention and control. The study aimed to assess LTBI diagnosis, treatment initiation and completion, and the progression to active TB disease, considering factors such as demographics, exposure type, and index case characteristics.[1]

Key findings revealed that 9.9% of contacts were diagnosed with LTBI, with higher odds among male contacts, household contacts, and those exposed to smear-positive PTB cases. However, only 43.0% of LTBI cases initiated treatment, with foreign nationals and young children being less likely to start therapy. Of those who initiated treatment, 74.0% completed it, with higher completion rates observed among local residents and those exposed to smear-positive index cases. The study also found that only 0.5% of LTBI cases progressed to active TB, mostly within eight years post-diagnosis, despite most having completed treatment.[1]

These findings highlight disparities in LTBI diagnosis and treatment uptake, particularly among foreign nationals, and underscore the need for targeted interventions to improve treatment initiation. Strategies such as increased awareness, enhanced follow-up, and addressing barriers to treatment can help strengthen TB control efforts. Additionally, further research on reasons for treatment non-initiation and long-term monitoring of LTBI cases is crucial for reducing TB transmission in Brunei.[1]

The annual incidence rate of LTBI in health-care workers in the government sector in Brunei Darussalam ranged from 8.1 to 24.6, with an average of 14.6 over the 4-year period. When comparing treatment acceptance among subgroups, only gender showed statistical significance, with females demonstrating significantly higher treatment acceptance.[2]

References:

1. Chaw, L., Hamid, R.A., Koh, K.S. and Thu, K., 2022. Contact investigation of tuberculosis in Brunei Darussalam: Evaluation and risk factor analysis. BMJ open respiratory research, 9(1).

2. Syafiq, N.J.M., Trivedi, A.A., Lai, A., Fontelera, M.P.A. and Lim, M.A., 2023. Latent tuberculosis infection in health-care workers in the government sector in Brunei Darussalam: A cross-sectional study. Journal of Integrative Nursing, 5(3), pp.197-202.

Mechanisms underlying TB-DM comorbidity

Tuberculosis (TB) and diabetes mellitus (DM) frequently co-exist, particularly in low- and middle-income countries, where 95% of TB and 75% of DM cases occur. DM weakens the immune system, increasing susceptibility to active TB, while TB-induced stress hyperglycemia complicates DM management. Between 2016 and 2018, the prevalence of DM among TB patients ranged from 8.5% to 11%, reaching up to 45% in some cases. TB patients with DM experience prolonged smear and culture positivity, higher risks of complications, relapse, and mortality. Impaired immune responses, particularly dysfunctional T-cell activity and macrophage polarization, contribute to increased bacterial survival and dissemination. Hyperglycemia further disrupts immune defenses by promoting M2 macrophage polarization, reducing phagocytic activity, and impairing neutrophil migration due to glycated collagen buildup.[2] See also: https://lintblab.weebly.com/

The interplay between TB and DM extends beyond immune dysfunction to metabolic regulation. Chronic TB infections trigger prolonged inflammation and cortisol release, suppressing T-cell function and weakening infection control. Both diseases activate the hypothalamic-pituitary-adrenal (HPA) axis, leading to elevated cortisol levels that exacerbate insulin resistance and worsen glycemic control. Furthermore, hyperglycemia generates Advanced Glycation End Products (AGEs), fueling chronic inflammation and further increasing TB susceptibility. Reduced production of key cytokines, such as interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), compromises MTB containment, creating a cycle of immune impairment and increased disease severity.[2]

TB treatment also complicates DM management due to drug interactions. Isoniazid and rifampin, essential anti-TB drugs, influence the metabolism of several anti-diabetic agents, including sulfonylureas and HIV medications. Rifampin alters drug transport and metabolism via uridine diphosphate-glucuronosyltransferases, affecting glycemic control. However, metformin, with minimal impact on cytochrome P450 enzymes, has shown potential benefits in TB treatment by improving treatment success rates, reducing mortality, accelerating sputum culture conversion, and inhibiting latent TB reactivation. Moreover, metformin may help shorten TB treatment duration. Despite overlapping side effects of TB and DM medications—including neuropathy, hepatotoxicity, gastrointestinal issues, fluid retention, and hypoglycemia—metformin's promising anti-TB properties highlight its therapeutic potential in managing TB-DM comorbidity.[2]

The intricate cellular and molecular mechanisms underlying TB-DM comorbidity highlight a complex interplay that significantly impacts global health outcomes. Multi-omic approaches—including genomics, transcriptomics, proteomics, lipidomics, and metabolomics—offer crucial insights into immune dysfunction and inflammatory pathways in TB-DM patients. Cellular immunology studies reveal that DM impairs macrophage and neutrophil function, weakens cytokine signaling, and alters T-cell responses, increasing susceptibility to TB. Genetic variants, such as polymorphisms in IL-6 and IL-18, may predispose individuals to TB-DM, emphasizing the need for personalized medicine. Transcriptomic research indicates that TB-DM is characterized by chronic inflammation and dysregulated immune pathways, while proteomic studies suggest that changes in complement and coagulation cascades may be linked to lipid metabolism abnormalities. Lipidomic and metabolomic analyses further highlight disruptions in glycerophospholipids, bile acids, and carbohydrate metabolism, underscoring DM’s profound impact on TB pathophysiology.[1]

Beyond molecular mechanisms, epidemiological data underscore the significant prevalence of DM among TB patients, influenced by factors such as age, socio-economic status, lifestyle, family history, and hypertension. However, regional disparities in TB-DM comorbidity remain underexplored, necessitating investments in molecular epidemiology to tailor public health interventions. The bidirectional nature of TB-DM complicates clinical management, as DM correlates with higher mycobacterial loads and distinctive lung damage, accelerating disease progression. Integrated health strategies—including routine TB screening for DM patients, reciprocal screening, and expanded research into TB transmission dynamics—are essential. Investigating how DM-related multimorbidities shape inflammatory profiles could refine biomarker discovery and treatment approaches, improving patient outcomes.[1]

Advancements in single-cell analysis and multi-omic integration hold promise for transforming TB-DM management by offering deeper insights into immune dysfunction and potential therapeutic targets. Combining genetic, molecular, and clinical data could enable predictive models for disease progression and treatment response. Additionally, the potential development of TB vaccines tailored for immunocompromised populations may emerge from a refined understanding of TB-DM-specific immune alterations. Comprehensive risk scores incorporating socio-demographic, lifestyle, and clinical data could further optimize precision public health interventions. A multidisciplinary approach that integrates epidemiological, clinical, and multi-omic research is crucial to unravel the complexities of TB-DM, paving the way for more effective diagnostic tools, personalized treatments, and improved global health strategies.[1]

References: 

1. 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. 

2. 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.