DM was associated with an increased risk of TB regardless of study design and population. People with DM may be important targets for interventions such as active case finding and treatment of latent TB and efforts to diagnose, detect, and treat DM may have a beneficial impact on TB control.

We searched the PubMed and EMBASE databases to identify observational studies that had reported an age-adjusted quantitative estimate of the association between DM and active TB disease. The search yielded 13 observational studies (n = 1,786,212 participants) with 17,698 TB cases. Random effects meta-analysis of cohort studies showed that DM was associated with an increased risk of TB (relative risk = 3.11, 95% CI 2.27–4.26). Case-control studies were heterogeneous and odds ratios ranged from 1.16 to 7.83. Subgroup analyses showed that effect estimates were higher in non-North American studies.

Several studies have suggested that diabetes mellitus (DM) increases the risk of active tuberculosis (TB). The rising prevalence of DM in TB-endemic areas may adversely affect TB control. We conducted a systematic review and a meta-analysis of observational studies assessing the association of DM and TB in order to summarize the existing evidence and to assess methodological quality of the studies.

Funding: CYJ is supported by a departmental grant from the Department of Epidemiology at Harvard School of Public Health. The department had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2008 Jeon and Murray. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

However, the estimate of this impact is based on three cohort studies from Asia; other studies suggest that the extent of the impact due to diabetes may vary by region and ethnicity. In populations where diabetes affects the risk of tuberculosis to a similar or greater extent, global tuberculosis control might benefit from active case finding and treatment of dormant tuberculosis in people with diabetes and from increased efforts to diagnose and treat diabetes.

These findings support the idea that diabetes increases the risk of tuberculosis, a biologically plausible idea because, in experimental and clinical studies, diabetes was found to impair the immune responses needed to control bacterial infections. The 3-fold increased risk of tuberculosis associated with diabetes that the meta-analysis reveals suggests that diabetes may already be responsible for more than 10% of tuberculosis cases in countries such as India and China, a figure that will likely increase as diabetes becomes more common.

From their search of electronic databases, the researchers found 13 observational studies (nonexperimental investigations that record individual characteristics and health outcomes without trying to influence them in any way) that had examined whether diabetes mellitus increases the risk of active tuberculosis. Diabetes was positively associated with tuberculosis in all but one study, but the estimates of how much diabetes increases the risk of developing active tuberculosis were highly variable, ranging from no effect to an increased risk of nearly 8-fold in one study. The variability may represent true differences between the study populations, as higher increases in risk due to diabetes was found in studies conducted outside of North America, including Central America, Europe, and Asia; or it may reflect differences in how well each study was done. This variability meant that the researchers could not include all of the studies in their meta-analysis. However, the three prospective cohort studies (studies that follow a group of individuals with potential risk factors for a disease over time to see if they develop that disease) that they had identified in their systematic review had more consistent effects estimates, and were included in the meta-analysis. This meta-analysis showed that, compared to people without diabetes, people with diabetes had a 3-fold increased risk of developing active tuberculosis.

Despite this control strategy, tuberculosis remains a major health problem in many countries. To reduce the annual number of new tuberculosis cases (incidence) and the number of people with tuberculosis (prevalence) in such countries, it may be necessary to identify and target factors that increase an individual's risk of developing active tuberculosis. One possible risk factor for tuberculosis is diabetes, a condition characterized by high blood sugar levels and long-term complications involving the circulation, eyes and kidneys, and the body's ability to fight infection. 180 million people currently have diabetes, but this number is expected to double by 2030. Low- to middle-income countries (for example, India and China) have the highest burden of tuberculosis and are experiencing the fastest increase in diabetes prevalence. If diabetes does increase the risk of developing active tuberculosis, this overlap between the diabetes and tuberculosis epidemics could adversely affect global tuberculosis control efforts. In this study, the researchers undertake a systematic review (a search using specific criteria to identify relevant research studies, which are then appraised) and a random effects meta-analysis (a type of statistical analysis that pools the results of several studies) to learn more about the association between diabetes and tuberculosis.

Every year, 8.8 million people develop active tuberculosis and 1.6 million people die from this highly contagious infection that usually affects the lungs. Tuberculosis is caused by Mycobacterium tuberculosis, bacteria that are spread through the air when people with active tuberculosis cough or sneeze. Most infected people never become ill—a third of the world's population is actually infected with M. tuberculosis—because the human immune system usually contains the infection. However, the bacteria remain dormant within the body and can cause disease many years later if host immunity declines because of increasing age or because of other medical conditions such as HIV infection. Active tuberculosis can be cured by taking a combination of several antibiotics every day for at least six months, and current control efforts concentrate on prompt detection and carefully monitored treatment of people with active tuberculosis to prevent further transmission of the bacteria.

Despite the availability of effective therapy, tuberculosis (TB) continues to infect an estimated one-third of the world's population, to cause disease in 8.8 million people per year, and to kill 1.6 million of those afflicted [ 1 ]. Current TB control measures focus on the prompt detection and treatment of those with infectious forms of the disease to prevent further transmission of the organism. Despite the enormous success of this strategy in TB control, the persistence of TB in many parts of the world suggests the need to expand control efforts to identify and address the individual and social determinants of the disease. Since the early part of the 20th century, clinicians have observed an association between diabetes mellitus (DM) and TB, although they were often unable to determine whether DM caused TB or whether TB led to the clinical manifestations of DM [ 2 – 6 ]. Furthermore, these reports did not address the issues of confounding and selection bias. More recently, multiple rigorous epidemiological studies investigating the relationship have demonstrated that DM is indeed positively associated with TB [ 7 – 11 ]. While the investigators suggested that the association reflects the effect of DM on TB, some controversy over the directionality of the association remains due to observations that TB disease induces temporary hyperglycemia, which resolves with treatment [ 12 , 13 ]. A causal link between DM and TB does not bode well for the future, as the global burden of DM is expected to rise from an estimated 180 million prevalent cases currently to a predicted 366 million by 2030 [ 14 ]. Experts have raised concerns about the merging epidemics of DM and TB [ 15 – 17 ], especially in low- to middle-income countries, such as India and China, that are experiencing the fastest increase in DM prevalence [ 18 ] and the highest burden of TB in the world [ 19 ]. Given the public health implications of a causal link between DM and TB, there is a clear need for a systematic assessment of the association in the medical literature. We undertook a systematic review to qualitatively and quantitatively summarize the existing evidence for the association between DM and TB, to examine the heterogeneity underlying the different studies, and to evaluate the methodological quality of the studies. As our aim was to summarize the effect of DM on TB, we did not include studies that investigated the reverse association.

We assessed publication bias using the Begg test and Egger test [ 26 , 27 ]. Statistical procedures were carried out using R version 2.5.1 [ 28 ]. 95% CI of the I 2 value was computed using the “heterogi” module in STATA version 10 [ 29 ].

We explored possible effect modification by age by examining the three studies that reported results by age groups [ 7 , 9 , 25 ]. For this analysis, we graphed the stratum-specific estimates in a forest plot, and tested for heterogeneity of the effects within each study by the Q-test and I 2 value. We also performed meta-regression within each study in which we regressed the log-transformed RRs by the mid-points of the age-bands. For the unbound age group, ≥ 60 y, we added half the range of the neighboring age-band, or 5 y, to the cutoff. We computed the factor reduction in RR with 10 y increases in age, and reported the p-value for significance of trend.

In order to identify possible sources of heterogeneity and to assess the effect of study quality on the reported effect estimates, we performed sensitivity analyses in which we compared pooled effect estimates for subgroups categorized by background TB incidence, geographical region, underlying medical conditions of the population under study, and the following quality-associated variables: time of assessment of DM in relation to TB diagnosis, method of DM assessment (self-report or medical records versus laboratory tests), method of TB assessment (microbiologically confirmed versus other), adjustment for important potential confounders, and the potential duplication of data on the same individuals. To determine whether the effect estimates varied significantly by the above-mentioned factors, we performed univariate meta-regressions, in which we regressed the study-specific log-transformed relative risks (RRs) by the variables representing the study characteristics, weighting the studies by the inverse of the sum of within-study and between-study variance for all studies within the comparison. For background TB incidence, we created an ordinal variable, 1 representing < 10/100,000 person-years to 3 representing ≥ 100/100,000 person-years. Coefficients of meta-regression represent differences in log-transformed RRs between the subgroups; we tested the significance of these coefficients by Student t-test, and significance was set at p < 0.10. We considered studies to be of higher quality if they specified that DM be diagnosed prior to the time of TB diagnosis; used blood glucose tests for diagnosis of DM; used a microbiological definition of TB; adjusted for at least age and sex; were cohort, nested case-control, or population-based case-control studies; or did not have the potential for duplication of data. As the average background incidence rate of TB did not exceed 2 per 100 person-years in any of the of the case-control studies that had not employed incidence density sampling, we assumed TB to be sufficiently rare that the odds ratios would estimate the risk ratios [ 24 ], and that it would therefore be valid to compute summary RR in the sensitivity analyses regardless of the measure of association and design of the study.

We separated the studies by study design and assessed heterogeneity of effect estimates within each group of studies using the Cochrane Q test for heterogeneity [ 21 ] and the I 2 statistic described by Higgins et al. [ 22 ]. We determined the 95% confidence intervals (CIs) for the I 2 values using the test-based methods [ 22 ]. We performed meta-analysis for computation of a summary estimate only for the study design (i.e., cohort) that did not show significant heterogeneity. Effect estimates of other study designs were not summarized due to significant heterogeneity. For those studies that reported age, sex, race, or region stratum-specific effects, we calculated an overall adjusted effect estimate for the study using the inverse-variance weighting method, then included this summary estimate in the meta-analyses and sensitivity analyses. We decided a priori to use the Dersimonian and Laird random effects method to pool the effect estimates across studies for the meta-analyses, because the underlying true effect of DM would be expected to vary with regard to underlying TB susceptibility and the severity of DM, and because it would yield conservative 95% confidence intervals [ 23 ].

The two investigators (CJ, MM) independently read the papers and extracted information on the year and country of the study, background TB incidence, study population, study design, number of exposed/unexposed people or cases/controls, definitions and assessment of DM and TB, statistical methods, effect estimates and their standard errors, adjustment and stratification factors, response rates, the timing of diagnosis of DM relative to that of TB, and the potential duplication of data on the same individuals. Differences were resolved by consensus. For the studies that did not directly report the background TB incidence, we obtained data for the closest matching year and state (or country) made available by public databases (WHO global tuberculosis database, http://www.who.int/globalatlas/dataQuery/ ; CDC Wonder, http://wonder.cdc.gov/TB-v2005.html ).

We searched the PubMed database from 1965 to March 2007 and the EMBASE database from 1974 to March 2007 for studies of the association between DM and TB disease; our search strategy is detailed in Box 1 . We also hand-searched bibliographies of retrieved papers for additional references and contacted experts in the field for any unpublished studies. Since we speculated that studies that examined the association between DM and TB may not have referred to the term “diabetes” in the title or abstract, we also searched for studies that examined any risk factors for active TB. We restricted our analysis to human studies, and placed no restrictions on language. We included studies if they were peer-reviewed reports of cohort, case-control, or cross-sectional studies that either presented or allowed computation of a quantitative effect estimate of the relationship between DM and active TB and that controlled for possible confounding by age or age groups. We also included studies that compared prevalence or incidence of DM or TB of an observed population to a general population as long as they had performed stratification or standardization by age groups. We excluded studies if they were any of the following: case studies and reviews; studies among children; studies that did not provide effect estimates in odds ratios, rate ratios, or risk ratios, or did not allow the computation of such; studies that did not adjust for age; studies that employed different methods for assessing TB among individuals with and without DM or for assessing DM among TB patients and controls; studies that investigated the reverse association of the impact of TB disease or TB treatment on DM; anonymous reports; and duplicate reports on previously published studies.

We conducted our systematic review according to the guidelines set forth by the Meta-analysis of Observational Studies in Epidemiology (MOOSE) group for reporting of systematic reviews of observational studies (see Text S2 for the MOOSE Checklist) [ 20 ].

Results

We identified and screened 3,701 papers by titles and abstracts; of these, 3,378 were excluded because they did not study risk factors for TB, were studies among children, were case reports, reviews, or studies of TB treatment outcome (Figure 1). Of the remaining 323 articles, 232 studies were excluded because they did not report on the association between DM and TB, and 56 studies were excluded because they were review articles (12) or ecological studies (2); studied the clinical manifestations of TB in people with diabetes (11); studied the association of DM and TB treatment outcome (6); assessed latent, relapsed, clustered, or drug-resistant TB as the outcome (6); studied the reverse association of the effect of TB on DM (5); had no comparison group (5); were case reports (3); did not give a quantitative effect estimate (3); had collapsed DM and other chronic diseases into a single covariate (2); or was a study that had been reported elsewhere (1). We contacted the authors of four papers that reported including DM in a multivariate analysis but that did not provide the adjusted effect estimate for DM; we included the papers of the two authors who responded and provided these adjusted estimates [30,31]. Further exclusion of studies that did not adjust for age (11), studies that used a general population as the comparison group for TB incidence or DM prevalence without standardization by age (9), and studies that used different methods for ascertaining TB in the people with diabetes and control group (2), left 13 eligible studies. These included three prospective cohort studies [7,30,32], eight case-control studies [8,11,31–37], and two studies for which study design could not be classified as either cohort or case control, as TB case accrual occurred prospectively while the distribution of diabetes in the population was assessed during a different time period after baseline [9,25]. The studies were set in Canada (1), India (1), Mexico (1), Russia (1), South Korea (1), Taiwan (1), the UK (1), and the US (6), and were all reported in English and conducted in the last 15 y. Two of the cohort studies were among renal transplant patients [30,32], and three of the case-control studies were hospital-based or based on discharge records [8,11,35]. The studies are summarized in Table 1.

Figure 2 summarizes the adjusted effect estimates of the 13 studies categorized by the study design. We found substantial heterogeneity of effect estimates from studies within each study design; between-study variance accounted for 39% of the total variance among cohort studies, 68% of the total variance among case-control studies, and 99% of the total variance in the remaining two studies. Despite this heterogeneity, the forest plot shows that DM is positively associated with TB regardless of study design, with the exception of the study by Dyck et al. [25]. DM was associated with a 3.11-fold (95% CI 2.27–4.26) increased risk of TB in the cohort studies. Of note, the study conducted within a nontransplant population provided greater weight (63%) to the summary estimate than the other two cohort studies combined. The effect estimates in the remaining studies were heterogeneous and varied from a RR of 0.99 to 7.83.

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larger image TIFF original image Download: Figure 2. Forest Plot of the 13 Studies That Quantitatively Assessed the Association between Diabetes and Active Tuberculosis by Study Designs Size of the square is proportional to the precision of the study-specific effect estimates, and the bars indicate the corresponding 95% CIs. Arrows indicate that the bars are truncated to fit the plot. The diamond is centered on the summary RR of the cohorts studies, and the width indicates the corresponding 95% CI. *Other: The studies by Ponce-de-Leon et al. [7] and Dyck et al. [25] were not specified as prospective cohort or case-control. TB case accrual occurred prospectively, while the underlying distribution of diabetes was determined during a different time period after baseline. https://doi.org/10.1371/journal.pmed.0050152.g002

Table 2 shows that there is an increased risk of active TB among people with diabetes regardless of background incidence, study region, or underlying medical conditions in the cohort. In the sensitivity analyses, we noticed that the strength of association increased from a RR of 1.87 to a RR of 3.32 as background TB incidence of the study population increased from < 10/100,000 person-years to ≥ 100/100,000 person-years, but the trend was not significant (trend p = 0.229). Effect estimates were heterogeneous within each category of background TB incidence (I2 = 60%, 98%, and 76% from highest to lowest background TB incidence category).

We also found that the associations of DM and TB in the study populations from Central America [9], Europe [33,37], and Asia [7,30,32] (RR CentralAm = 6.00, RR Europe =4.40, RR Asia = 3.11) were higher than those of North American studies [8,11,33,34–36] (RR NA = 1.46) (meta-regression p CentralAm = 0.006, p Europe = 0.004, p Asia = 0.03). Among North American studies, the pooled estimate of the relative risks for Hispanics from two studies [8,11] was higher (RR = 2.69) than that of non-Hispanics from the same study [8] and other North American studies (RR = 1.23) (meta-regression p = 0.060) (Table 2).

In general, stratification of the studies by quality-associated variables did not reduce the heterogeneity of effect estimates. Nonetheless, DM remained positively associated with TB in all strata. Studies that explicitly reported that DM was diagnosed prior to TB showed stronger associations (RR = 2.73) [7,31–34] than those that did not establish the temporal order of DM and TB diagnosis (RR = 2.10) [8,9,11,25,30,35–37], although the difference was not significant (meta-regression p = 0.483). Associations were stronger in studies that classified DM exposure through empirical testing (RR = 3.89) [7,9,32,34] rather than medical records (RR = 1.61) (meta-regression p = 0.051) [8,11,25,30,31,33]; and in those that confirmed TB status using microbiological diagnosis (RR = 4.91) [7,9,35,37] than in the studies that did not confirm by microbiological tests (RR = 1.66) (meta-regression p = 0.015) [8,11,25,30–34,36]. Among case-control studies, those that were nested in a clearly identifiable population or were population-based also reported stronger associations (RR = 3.36) [31,33,34,37] than those that used hospital based controls (RR = 1.62) [8,11,37], but the difference was not significant (meta-regression p = 0.321). Studies that had adjusted for smoking showed stronger associations (RR = 4.40) [33,37], while studies in which an individual may have contributed more than one observation to the data revealed weaker associations (RR = 1.62) [8,11]. Although these results suggest that higher-quality studies gave stronger estimates of association, we also found that the association was weaker in studies that adjusted for socioeconomic status (RR = 1.66) (Table 2) [8,11,37].

Figure 3 presents the summary measures of the association between DM and TB by age group based on the data from the three studies that presented age-stratified RRs. The plots from Kim et al. [7] and Ponce-de-Leon et al. [9] demonstrate stronger associations of DM and TB under the age of 40 y and declining RR with increasing age in age groups over 40 y (trend p Kim = 0.014, p Ponce-de-Leon = 0.184). Each 10 y increase in age was associated with a 0.6-fold reduction in magnitude of association in the study by Kim et al. [7]. This trend was not apparent in the study by Dyck et al. (Figure 3) [25].

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larger image TIFF original image Download: Figure 3. Forest Plot of Age-Specific Association between Diabetes and Active Tuberculosis from Kim et al. [ Forest Plot of Age-Specific Association between Diabetes and Active Tuberculosis from Kim et al. [ 7 ], Ponce-de-Leon et al. [ 9 ], and Dyck et al. [ 25 Size of the square is proportional to the precision of the study-specific effect estimates, and the bars indicate 95% CI of the effect estimates. Arrows indicate that the bars are truncated to fit the plot. *Meta-regression: Factor reduction in RR with 10 y increase in age; p-values are given for test of linear trend. HR, hazard ratio. https://doi.org/10.1371/journal.pmed.0050152.g003

Both the Egger test and Begg test for publication bias were insignificant (p = 0.37, p = 0.14).