Secondary Logo

Journal Logo

Letters to the Editor

Relationship Between Hyperglycemia and the Risk of Tuberculosis in Asian HIV-Positive Individuals in the Antiretroviral Therapy Era: Cohort Study

Achhra, Amit C. MBBS, MPH, PhD*; Pujari, Sanjay MD; Choi, Jun Yong MD; Khusuwan, Suwimol MD§; Van Kinh, Nguyen MD; Phanuphak, Praphan MD; Chaiwarith, Romanee MD#; Lee, Man Po MD**; Saphonn, Vonthanak MD††; Kiertiburanakul, Sasisopin MD‡‡; Thuy, Pham Thanh MD§§; Law, Matthew G. PhD*for the TREAT Asia HIV Observational Database (TAHOD) Cohort

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: August 15th, 2014 - Volume 66 - Issue 5 - p e108-e111
doi: 10.1097/QAI.0000000000000217
  • Free

To the Editors:


Many low- to middle-income countries, including Asia, are now facing the intersection of 2 epidemics of communicable and noncommunicable diseases, including diabetes mellitus (DM) and tuberculosis (TB) specifically.1 DM has been recognized as an important risk factor for TB in the general population, especially in the TB endemic settings.1,2 Poorly controlled diabetic patients (with Hemoglobin A1C (HbA1c) >7%) are the main group at a higher risk of TB.1 Finally, DM is also believed to elevate the risk of serious infections in general.3 Although the mechanisms of these associations are not clear, they are thought to be directly related to hyperglycemia and cellular insulinopenia, as well as indirect effects of hyperglycemia on macrophage and lymphocyte function.1,3

HIV-positive individuals are at high risk of TB.4 Furthermore, diabetes has emerged as one of the important comorbidities in HIV-positive individuals, especially in the antiretroviral therapy (ART) era.5,6 However, the relationship between blood glucose levels and TB in HIV-positive population has been remarkably understudied.7,8 Investigating this relationship in HIV-positive individuals is important not only for better clinical management of HIV but also for TB control in general. In this study, we model the relationship between fasting blood glucose and risk of TB in the TREAT Asia HIV Observational Database (TAHOD), a multi-country Asian cohort of HIV-positive individuals.


Study Population

TAHOD is a clinical cohort study of patients with HIV infection in Asia that began in 2003. The detailed methodology has been published elsewhere.9 Data are collected from 21 clinical sites in 12 countries and territories in Asia and transferred electronically to the Kirby Institute every March and September. Ethical approval was obtained from the University of New South Wales, Sydney, Australia, and all sites' institutional review boards. For this analysis, we included patients from TAHOD who had at least 1 glucose measurement, at least 6 months of further follow-up, and no known TB at or before study entry.

Measurement of Blood Glucose

In TAHOD, blood glucose tests and results only started to be reported from a median year of 2008 [interquartile range (IQR): 2005–2010]. Blood glucose values are measured according to the local sites' standard of care, and when measured, they are captured during routine data transfer. All values are expected to be measured after 8- to 12-hour fasting.


The primary outcome was any diagnosis of TB, including both definitive and presumptive diagnoses. The diagnostic criteria and the validation procedure used in TAHOD have been published elsewhere.9,10

Statistical Analyses

We used Cox proportional hazard regression to model the relationship between glucose and the outcome.11 Time at risk began at the first glucose measurement and ended at the occurrence of the first event or lost to follow-up/death or censorship date of March 31, 2013. The main exposure factors were baseline (at study entry) and time-updated (latest) blood glucose, which were categorized as deciles to assess for any nonlinear relationship with the incidence rate of the outcome. We also analyzed glucose as diabetic range (≥7 mmol/L as per American Diabetes Association guidelines12) ever recorded in the follow-up as the fixed variable. Models were adjusted for key risk factors,10 which included gender, reported injecting drug use (IDU) as mode of transmission, and age at study entry. Time-updated covariates included CD4 count (categorized as <200, 200–350 and >350 cells/mm3), body mass index (BMI) defined as weight (in kilogram)/height (in square meter) and categorized as <18.5, 18.5–25, 25–30, >30 or missing, and receipt of ART. Once started, receipt of ART was analyzed as intention-to-treat. HIV viral load was not used in models because of concerns about the missing data and collinearity. All time-updated variables were carried forward when missing. Models were further stratified by clinical site, which also accounted for varying TB endemicity across the region. Recording of isoniazid primary prophylaxis is not complete in TAHOD. Sensitivity analysis was conducted by either adjusting for or excluding those who received isoniazid prophylaxis where known.


Of the potentially 8488 eligible patients, 4617 (19,000 person-years of the follow-up) were eligible for inclusion in the analysis. At cohort entry, those included (vs. excluded) were more likely to be female, older, not reporting IDU, and from a low TB burden country site as defined by World Health Organization.10,13

At study entry, the mean age (SD) was 38 (9.9) years, 67% were male, 5.4% reported IDU, and 63% were from a high TB burden country. The median CD4 count was 269 (IQR: 140–430) cells per cubic millimeter, BMI was 21.4 (IQR: 19.3–23.7) kg/m2 (missing in 32%), and glucose was 5 (IQR: 4.6–5.5) mmol/L. A majority of patients (95.7%) started ART at some point in the follow-up. Patients had a median (IQR) of 4 (2–8) glucose measurements, and the median interval between glucose measurements was 5 (3–8) months.

There were 303 TB events at a rate of 1.58/100 person-years (95% confidence interval: 1.41 to 1.76). There was a U-shaped or inverse J-shaped relationship between the glucose (baseline or latest) and the outcome, with the highest risk at both the extremes and the lowest risk at the middle categories (Table 1). The risk was about 2-fold in the 10th decile (fasting blood glucose of >6.5 mmol/L) compared with the sixth decile (reference category). Baseline glucose was a significant factor in both unadjusted and adjusted models, stratified by site (Table 1). Latest glucose also showed a similar trend and was a significant factor in unstratified models (data not shown), although the P-value for latest glucose became nonsignificant after stratifying by site due possibly to loss of power.11,14 Furthermore, occurrence of diabetic range of glucose ever in the follow-up was also associated with the higher risk of TB in all models (Table 1). Results were not sensitive to further adjustment or exclusion of those who were known to have received Isoniazid prophylaxis (7.5% of participants) (data not shown).

Unadjusted and Adjusted Analyses for the Relationship Between Glycemia and the TB Outcome


We found that higher levels of glucose are associated with the greater risk of TB. The effect persisted even after analyzing baseline or latest glucose levels, thereby minimizing the possibility of reverse causality (ie, TB affecting the glucose) and suggest that high glucose is likely to be associated with the prospective risk of TB.

Only a few other studies have assessed this relationship in HIV-positive individuals. Two studies from India and Tanzania, respectively, found diabetes to be an important comorbidity in HIV–TB-coinfected patients, although the HIV-positive individuals in these studies were with lower CD4 count and not on ART.15,16

Unexpectedly, we also found risk to be higher at the extreme low end of the measured glucose. People with low glucose were also more likely to be with low BMI. The later possibly reflects that the low glucose could be a marker of cachectic/undernourished state leading to or associated with TB. An undernourished state is known to be an important risk factor for TB.17

There are limitations to our study. First, HbA1C, possibly a better marker of glucose control, was not available in our cohort. Also, information about use of anti-diabetic medications or formally validated information about diabetes diagnosis was not available. However, it could be argued that higher risk of TB in diabetic patients is largely mediated by higher blood glucose, which was measured in our study. Second, nonavailability of blood glucose in all participants could possibly lead to selection bias. Also, in this younger population, there were only a few participants in the extreme categories of the glucose, possibly leading to loss of power in some of the models. Glucose was not measured in a standardized fashion, and it is possible that, it may not be a postfasting level for some patients. Finally, important variables such as smoking, diet details, and isoniazid prophylaxis were not available. These limitations would likely cause bias toward the null and would underestimate relationships. Our findings nevertheless need to be verified in larger studies.

Blood glucose control could play an important role in modifying the risk of TB in treated HIV-positive individuals. Indeed, studies in general population from China and India have suggested that more vigilant screening for TB in diabetic patients could be a worthwhile approach.18–20 Future studies should investigate whether such an approach (and possibly enhanced screening for DM in TB patients7,19) in treated HIV-positive individuals further improves clinical outcomes.


The study team would like to acknowledge TAHOD-TASER study members, Steering committee, and patients for their support.


1. Dooley KE, Chaisson RE. Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infect Dis. 2009;9:737–746.
2. Jeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med. 2008;5:e152.
3. Shah BR, Hux JE. Quantifying the risk of infectious diseases for people with diabetes. Diabetes Care. 2003;26:510–513.
4. Raviglione MC, Snider DE Jr, Kochi A. Global epidemiology of tuberculosis: morbidity and mortality of a worldwide epidemic. JAMA. 1995;273:220–226.
5. Petoumenos K, Worm SW, Fontas E, et al.. Predicting the short-term risk of diabetes in HIV-positive patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. J Int AIDS Soc. 2012;15:17426.
6. Samarasinghe YP. HIV and diabetes. Prim Care Diabetes. 2007;1:99–101.
7. Oni T, Stoever K, Wilkinson RJ. Tuberculosis, HIV, and type 2 diabetes mellitus: a neglected priority. Lancet Respir Med. 2013;1:356–358.
8. Young F, Critchley J, Johnstone L, et al.. A review of co-morbidity between infectious and chronic disease in sub Saharan Africa: TB and diabetes mellitus, HIV and metabolic syndrome, and the impact of globalization. Glob Health. 2009;5:9.
9. Zhou J, Kumarasamy N, Ditangco R, et al.. The TREAT Asia HIV Observational Database: baseline and retrospective data. J Acquir Immune Defic Syndr. 2005;38:174–179.
10. Zhou J, Elliott J, Li PC, et al.. Risk and prognostic significance of tuberculosis in patients from the TREAT Asia HIV Observational Database. BMC Infect Dis. 2009;9:46.
11. Therneau T, Grambsch P. Modelling Survival Data: Extending the Cox Model. New York, NY: Springer; 2001.
12. Summary of revisions for the 2013 clinical practice recommendations. Diabetes Care. 2013;36(suppl 1):S3.
13. World Health Organization. Tuberculosis Control in South-East Asia and Western Pacific Regions. 2005. Available at: Accessed June 4, 2014.
14. Feng C, Wang H, Tu MX. Power loss of stratified log-rank test in homogeneous samples. Int J Qual Stat Reliability. 2010; Vol. 2010, Article ID 942184, 4 pages. doi:10.1155/2010/942184.
15. Gupta S, Shenoy VP, Bairy I, et al.. Diabetes mellitus and HIV as co-morbidities in tuberculosis patients of rural south India. J Infect Public Health. 2011;4:140–144.
16. Faurholt-Jepsen D, Range N, Praygod G, et al.. Diabetes is a risk factor for pulmonary tuberculosis: a case-control study from Mwanza, Tanzania. PLoS One. 2011;6:e24215.
17. Lönnroth K, Williams BG, Cegielski P, et al.. A consistent log-linear relationship between tuberculosis incidence and body mass index. Int J Epidemiol. 2010;39:149–155.
18. India Diabetes Mellitus–Tuberculosis Study Group. Screening of patients with diabetes mellitus for tuberculosis in India. Trop Med Int Health. 2013;18:646–654.
19. Harries AD, Billo N, Kapur A. Links between diabetes mellitus and tuberculosis: should we integrate screening and care? Trans R Soc Trop Med Hyg. 2009;103:1–2.
20. Zhao W, Shi L, Fonseca VA, et al.. Screening patients with type 2 diabetes for active tuberculosis in communities of China. Diabetes Care. 2013;36:e159–e160.

TAHOD-TASER Study Members

  • A. Kamarulzaman, Sharifah Faridah Syed Omar, Sasheela Vanar, Iskandar Azwa, and L.Y. Ong, University Malaya Medical Center, Kuala Lumpur, Malaysia;
  • C.K.C. Lee, B.L.H. Sim, and R. David, Hospital Sungai Buloh, Sungai Buloh, Malaysia;
  • C.V. Mean, V. Saphonn, and V. Khol, National Center for HIV/AIDS, Dermatology and STDs, Phnom Penh, Cambodia;
  • E. Yunihastuti*, D. Imran, and A. Widhani, Working Group on AIDS Faculty of Medicine, University of Indonesia/Cipto Mangunkusumo Hospital, Jakarta, Indonesia;
  • F.J. Zhang, H.X. Zhao, and N. Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China;
  • J.Y. Choi, S. Na, and J.M. Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea;
  • M. Mustafa and N. Nordin, Hospital Raja Perempuan Zainab II, Kota Bharu, Malaysia;
  • N. Kumarasamy, S. Saghayam, and C. Ezhilarasi, YRG Centre for AIDS Research and Education, Chennai, India;
  • O.T. Ng, P.L. Lim, L.S. Lee, and M.T. Tan, Tan Tock Seng Hospital, Singapore, Singapore;
  • M.P. Lee, P.C.K. Li, W. Lam, and Y.T. Chan, Queen Elizabeth Hospital and KH Wong, Integrated Treatment Centre, Hong Kong, China;
  • P. Kantipong and P. Kambua, Chiangrai Prachanukroh Hospital, Chiang Rai, Thailand;
  • P. Phanuphak, K. Ruxrungtham, A. Avihingsanon, P. Chusut, and S. Sirivichayakul, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand;
  • R. Ditangco*, E. Uy, and R. Bantique, Research Institute for Tropical Medicine, Manila, Philippines;
  • R. Kantor, Providence, Brown University, RI;
  • S. Oka, J. Tanuma, and T. Nishijima, National Center for Global Health and Medicine, Tokyo, Japan;
  • S. Pujari, K. Joshi, and A. Makane, Institute of Infectious Diseases, Pune, India;
  • S. Kiertiburanakul, S. Sungkanuparph, L. Chumla, and N. Sanmeema, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand;
  • T.P. Merati†, D.N. Wirawan, and F. Yuliana, Faculty of Medicine, Udayana University and Sanglah Hospital, Bali, Indonesia;
  • R. Chaiwarith, T. Sirisanthana, W. Kotarathititum, and J. Praparattanapan, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand;
  • T.T. Pham, D.D. Cuong, and H.L. Ha, Bach Mai Hospital, Hanoi, Vietnam;
  • V.K. Nguyen, V.H. Bui, and T.T. Cao, National Hospital for Tropical Diseases, Hanoi, Vietnam;
  • W. Ratanasuwan and R. Sriondee, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand;
  • W.W. Wong, W.W. Ku, and P.C. Wu, Taipei Veterans General Hospital, Taipei, Taiwan;
  • Y.M.A. Chen and Y.T. Lin, Kaohsiung Medical University, Kaohsiung City, Taiwan;
  • A.H. Sohn, N. Durier, B. Petersen, and T Singtoroj, TREAT Asia, amfAR—The Foundation for AIDS Research, Bangkok, Thailand;
  • D.A. Cooper, M.G. Law, A. Jiamsakul, and D.C. Boettiger, The Kirby Institute, University of New South Wales, Sydney, Australia.

*Current Steering Committee Chairs.


© 2014 by Lippincott Williams & Wilkins