Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Download PDF

Interaction between type 2 diabetes and past COVID-19 on active tuberculosis

BMC Infectious Diseases volume 24, Article number: 1383 (2024) Cite this article

Abstract

Background

The global setback in tuberculosis (TB) prevalence and mortality in the post-COVID-19 era has been partially attributed to pandemic-related disruptions in healthcare systems. The additional biological contribution of COVID-19 to TB is less clear. The goal of this study was to determine if there is an association between COVID-19 in the past 18 months and a new TB episode, and the role played by type 2 diabetes mellitus (DM) comorbidity in this relationship.

Methods

A cross-sectional study was conducted among 112 new active TB patients and 373 non-TB controls, identified between June 2020 and November 2021 in communities along the Mexican border with Texas. Past COVID-19 was based on self-report or positive serology. Bivariable/multivariable analysis were used to evaluate the odds of new TB in hosts with past COVID-19 and/or DM status.

Results

The odds of new TB were higher among past COVID-19 cases vs. controls, but only significant among DM patients (aOR 2.3). The odds of TB in people with DM was 2.7-fold higher among participants without past COVID-19 and increased to 7.9-fold among those with past COVID-19.

Conclusion

DM interacts with past COVID-19 synergistically to magnify the risk of TB. Latent TB screening and prophylactic treatment, if positive, is recommended in past COVID-19 persons with DM. Future studies are warranted with a longitudinal design and larger sample size to confirm our findings.

Peer Review reports

Introduction

Tuberculosis (TB), a lung infection caused by Mycobacterium tuberculosis, is a healthcare challenge in low- and middle-income countries. The World Health Organization (WHO) has reported a global setback in efforts to control TB following the coronavirus disease 2019 (COVID-19) pandemic [1]. This includes a fall in the number of reported TB cases during 2020, followed by rebounds in 2021 by 28%, and in 2022 by 16%, when it reached levels higher than any year prior to 2019 [1]. During this 2020 to 2022 period, COVID-19 related disruptions caused an excess of half a million deaths due to TB [1]. These excess TB cases and deaths have been attributed in part to disruptions to TB diagnosis and treatment by national healthcare systems [2].

At the biological level there are also potential interactions between having had COVID-19 and TB risk. A weakened immune system may occur after a COVID-19 episode due to persistent inflammation, cytokine dysregulation, and lymphopenia that can make the host permissive to secondary infections [3,4,5]. Individuals with underlying medical conditions such as diabetes mellitus (DM) have an increased risk of COVID-19 development, severity, and mortality [6,7,8,9,10]. The higher vulnerability of DM patients to COVID-19 may be explained by alterations in innate functions (e.g. chemotaxis, phagocytosis, cytotoxicity) and T lymphocytes (e.g. Th17, CD8, Treg) [11, 12]. Given alterations in immune function due to COVID-19 or DM, individuals with poor glucose control and a recent or current COVID-19 episode may provide a ‘perfect storm’ for magnifying TB risk. However, the actual impact of a COVID-19 episode on an individual’s TB risk is not fully understood at the biological level. While some studies have reported cases of TB shortly after or concurrent to a COVID-19 episode [6, 13,14,15], to our knowledge it is still unclear whether there is a statistical association between both pulmonary infections.

The population along the United States (US)-Mexico border counties has more TB when compared to the 2019 national incidences per 100,000 in the US (8.4 vs 2.7) and Mexico (35.0 vs 17.7) [16,17,18,19]. Factors driving vulnerability to develop TB in these border regions include migration, poverty, limited access to healthcare and weakened immune system due to DM [20,21,22].

Given the high vulnerability of the US-Mexico border population for TB [21], this setting is uniquely posited to test the hypothesis that individuals with a recent COVID-19 episode would have a higher risk of developing TB. We conducted a cross-sectional study among newly diagnosed TB patients and non-TB controls to determine whether having had a recent COVID-19 episode would be more likely to result in developing TB. Our findings showed that the odds of TB were higher in participants with past COVID-19, but results were only significant in individuals with DM. We discuss the clinical implications of these findings for mitigating the odds of TB in new COVID-19 patients.

Methods

Study design and participant characterization

A cross-sectional study was conducted during the first 18 months of the COVID-19 pandemic (June 2020 to November 2021) in the Mexican cities of Reynosa and Matamoros, on the border with Texas, United States (US). Participants included identified adults (age ≥ 18 years) who were diagnosed with pulmonary TB at the reference TB clinics from the Secretaría de Salud de Tamaulipas. Patients were excluded if testing positive for HIV or receiving TB treatment for more than 2 weeks. Participants also included close contacts of new TB patients and non-TB exposed community controls.

Newly diagnosed pulmonary TB disease was based on a positive sputum smear for acid-fast bacilli, culture for M. tuberculosis, or clinical diagnosis (abnormal chest x-rays and symptoms). Close contacts were defined as persons who had shared at least 5 h of airspace with a new TB patient but had no evidence of TB. Community controls did not have a history of TB nor known exposure to a TB case in the past 2 years. Evidence of latent TB infection in non-TB controls was defined as having a positive Interferon Gamma Release Assays [IGRA; QuantiFERON-Gold in-Tube (Qiagen) or T.Spot-TB (Oxford Immunotec)]. BCG vaccination was based on scarring.

A past history of COVID-19 was based on self-report of symptoms suggestive of COVID-19 during the past 18 months and/or a positive serology for anti-SARS-CoV-2 nucleocapsid IgG index (hereafter ‘COVID-IgG’; Alinity c test platform, Abbott Laboratories). COVID-19 vaccination became available during the study period and participants were considered vaccinated if they had received at least one dose.

Participant sociodemographics, physical measures, self-reported medical history and laboratory testing were documented as described [23]. DM was based on hyperglycemia (fasting glucose ≥ 126 mg/dL or random ≥ 200 mg/dL) or chronic hyperglycemia (HbA1c ≥ 6.5%) [24]. Macrovascular (heart disease or high blood pressure) and microvascular diseases (neuropathy, kidney disease) were self-reported. Body mass index (BMI) was stratified into underweight or normal (≤ 24.9 kg/m2) and overweight or obese (≥ 25 kg/m2).

Statistical analysis

Data was entered into Microsoft Access and exported to Statistical Analysis System (SAS) version 9.4 for statistical analysis. Pearson’s chi-square was used to determine associations between categorical variables and Fisher’s Exact test was used when any cell sample size was ≤ 5. The t-test was used to analyze differences in means for continuous variables. For multivariable models, variables with p ≤ 0.2 by univariable analysis or of biological interest (e.g. sex, age, BMI) were entered into backwards logistic regression models to identify the predictors of TB in addition to past COVID-19. DM was evaluated as a confounder or through a multiplicative effect modifier assessment. Variables with p ≤ 0.05 were kept in the final reduced model. P values ≤ 0.05 were considered significant and between 0.05 and 0.09 marginally significant.

Results

Definition of past COVID-19

We studied 485 participants (112 TB cases, 284 close contacts, 89 community controls). The defined past COVID-19 based on self-reported disease (n = 478, 98.6% interviewed) and COVID-IgG (n = 445, 91.8% tested; Table S1). When using the COVID-IgG cut-off of 1.4 IU/ml per manufacturer recommendations, self-reported COVID-19 was more prevalent among individuals with a positive vs. a negative serology (50.9% vs. 22.2%; p < 0.001; Fig. 1A). Positive COVID-IgG was more likely among those reporting a COVID-19 history vs. no history (OR 3.64, 95% CI: 2.31–5.75; Table 1). However, 53 individuals had positive serology but no reported COVID-19, suggesting some COVID-19 cases were asymptomatic.

Fig. 1
figure 1

Anti-SARS-CoV-2 serology (COVID-IgG index) in all participants with respect to COVID-19 report, vaccination and episode timing. A Prevalence of self-reported (SR) COVID-19 among individuals with a negative or positive COVID-IgG index cut-off of 1.4 IU/mL per manufacturer recommendations. Error bars, 95% CI. B COVID-IgG index among study participants by COVID-19 self-report (SR + or -) or vaccination status. Dotted horizontal lines show cut-offs of 1.4 IU/mL (manufacturer recommendation) and 2.5 IU/mL (this study). C COVID-IgG index stratified by the time elapsed between enrollment and months elapsed since the reported COVID-19 episode. *, p ≤ 0.05. SR: self-reported COVID-19; COVID-Ig index: anti-SARS-CoV-2-IgG index; Neg: Negative; Pos: Positive

Full size image
Table 1 Relationship between variables documenting SARS-CoV-2 infection, COVID-19 disease and COVID-19 vaccination
Full size table

During this study period, COVID-19 vaccines became available and were received by some of the participants (n = 370, 76.3%; Table S1), so vaccination history was taken into consideration for defining COVID-19 given its possible effect on serology or mitigation of COVID-19 symptoms. We evaluated this possibility and found that COVID-19 vaccinees had higher COVID-IgG (Fig. 1B), and particularly among those who did not report COVID-19 (positive COVID-IgG using the clinical cut-off of ≥ 1.4 IU/mL: 24.2% in vaccinees vs. 16.1% in non-vaccinees; Table 1). These associations were not statistically significant but suggested a partial influence of the COVID-19 vaccine on positive serology. To increase the specificity of the serology, we evaluated a higher IgG index cut-off. We selected 2.5 IU/mL based on visual inspection of the titers in individuals with self-reported COVID-19 and vaccination history (Fig. 1B). This higher IgG index cut-off provided a more specific estimate of infection: a shift from 24.2% to only 9.8% of vaccinees having a positive serology among those with no self-reported COVID-19 (OR 0.64; 95% CI: 0.25–1.66; Table 1).

IgG titers were highest in individuals with recent COVID-19 episodes and waned over time [IgG index median, IQR at 0–6 months = 2.64 (4.40); 6–12 months = 0.75 (1.72), and 12 or more months = 0.60 (1.01); Fig. 1C)]. The proportion of individuals with a positive serology also decreased over time among individuals with reported COVID-19 (0–6 months: 55.6%; 6–12 months: 35.2%; ≥ 12 months: 9.26%; p = 0.004). These findings suggested that as longer times elapsed since a COVID-19 episode, false-negative serology was more likely.

Together, our observations suggested a partial overlap between a positive COVID-19 history and serology, and an influence of vaccination history and timing since COVID-19 on serology titers. Hence, our final classification of subjects with past COVID-19 was based on reported history of disease in the last 18 months or a COVID-IgG cut-off ≥ 2.5 IU/mL. Hereafter, we refer to this new variable of symptomatic and asymptomatic cases as “past COVID-19”.

Characteristics of participants by TB status

For analysis by TB status, we combined the 284 close contacts and 89 community controls into one non-TB control group. Compared to non-TB controls, TB cases had a higher proportion of males (TB 72.3% vs no TB 35.7%; p < 0.001), less education (TB 25.9% vs. non-TB 39.0% with high school degree; p < 0.011) and fewer smokers (TB 6.3% vs. non-TB 17.4%; p < 0.004). TB cases had more DM (TB 56.3% vs. non-TB 23.9%; p < 0.001), and microvascular disease (TB 34.5% vs. non-TB 19.3%; p < 0.001) and lower BMI (TB 75.9% vs. non-TB 22.3%; p < 0.001). TB patients vs non-TB controls had lower lipids, hemoglobin, hematocrit, and lymphocyte counts, but higher monocytes, platelets, and neutrophils (p < 0.001; Table S2).

Association between past COVID-19 and pulmonary TB

We found no significant differences between TB case patients and controls on any COVID-19 related variable, i.e. reported COVID-19, COVID-IgG, vaccination, or the composite past COVID-19 variable (Table S2). However, upon further stratification of participants by past COVID-19 and TB, the features that distinguished past COVID-19 patients were higher odds of ever being married among non-TB, and higher DM or high HbA1c levels among TB patients (Table S3). White blood cell counts did not differ by past COVID-19 and TB status (Table S4), but among non-vaccinated TB contacts, lymphocyte counts were lower in those with past COVID-19 vs a negative COVID-19 history although this difference was not statistically significant (Fig S1).

Role of DM on the association between past COVID-19 and TB

Bivariable analysis suggested that past COVID-19 was an effect modifier of the association between TB and DM (Table 2). Namely, the odds of TB was 4.10 among persons with DM compared to no DM in all participants, 2.71 among those negative for past COVID-19 episode, and 7.85 among those with a past COVID-19 episode. Table 3 shows the independent contribution of past COVID-19 to TB in multivariable models. Model A included all the variables (DM, sex, age distribution, BMI, marital status, COVID-19 vaccine, smoking, microvascular disease, triglyceride levels) and model B was the reduced version. Both models indicated that past COVID-19 was not significantly associated with TB in the overall sample. However, Model C showed a marginally significant interaction between past COVID-19 and DM (past COVID-19 * DM, i.e. in people who have past COVID-19 and DM; p 0.057). Similar models among participants with DM (Model D) or poor glucose control (HbA1c ≥ 7.0% in model E and ≥ 7.5% in model F) showed that the strength of the association between past COVID-19 and TB increased among participants with poor glucose control (DM adjusted OR, aOR 2.33; HbA1c ≥ 7.0 aOR 2.99; HbA1c ≥ 7.5 aOR 3.31). Together, these findings suggest that past COVID-19 alone is not independently associated with TB, but this association is significant among DM patients, and particularly in those with poor glucose control.

Table 2 Odds ratio of TB stratified by past COVID-19 and type 2 diabetes mellitus (DM) statusa
Full size table
Table 3 Multivariable models for the adjusted odds ratio of TB disease in individuals with past COVID-19a
Full size table

Discussion

Our cross-sectional study examined the relationship between a past symptomatic or asymptomatic COVID-19 episode on the odds of active TB. The prevalence of past COVID-19 was higher among newly diagnosed TB patients vs. non-TB controls, but only significant in hosts with DM, and strongest among those with poor glucose control. Likewise, we found that past COVID-19 magnified the strength of the known association between DM and TB. DM increased the odds of TB by threefold among non-COVID-19 participants, which is consistent with pre-COVID-19 pandemic findings in our Mexico border community [22], and elsewhere [25]. However, among persons with DM, the odds of TB increased to nearly eightfold among those with past-COVID-19. Together, these findings indicate an interaction between past COVID-19 and DM, magnifying the odds of TB when compared to COVID-19 or DM alone (Fig. 2).

Fig. 2
figure 2

Summary of findings on the interaction between COVID-19 and DM on the odds of TB. Among all participants the odds of TB were not significantly higher in individuals with past COVID-19, but increased among those with DM (aOR 2.3; 95% 0.97, 5.6), and become significant in those with poor glucose control (aOR 3.3; 95% CI 1.2, 9.2). Additionally, the odds of TB was nearly threefold in DM vs. non-DM patients without past COVID-19 and increased to nearly eightfold among those with past COVID-19. We recommend that individuals with a poorly controlled DM and a recent episode of COVID-19 be screened for latent TB infection, and if positive, to consider LTBI treatment (italic font). Bold numbers, significant or marginally significant associations. Blue text in italics, clinical recommendations. Partially created with Biorender

Full size image

The impact of the COVID-19 pandemic on higher TB incidence and mortality was predicted in the early stages of the COVID-19 pandemic, and partially attributed to programmatic shifts in resources from TB to COVID-19 [2, 26], but its biological impact is less clear [13]. In theory, past COVID-19 patients should be more vulnerable to TB due to reported viral induction of lymphopenia, functional T cell exhaustion, dysregulated innate and adaptive immune responses, and reduced secretion of IFN-γ in response to M. tuberculosis antigens [4, 5, 27]. Accordingly, some studies report cases of TB reactivation or primary TB attributed to a recent or concurrent COVID-19 episode [14, 15]. However, our findings revealed a non-significant increase in the odds of TB in participants with past COVID-19 alone. There are several possible explanations for this low impact of COVID-19 on TB in our study: i) we only included past COVID-19 cases, and missed those with concurrent TB disease which is when highest immune compromise is most likely, and ii) we did not have information to stratify participants by COVID-19 disease severity, which seems critical for conferring TB risk [14].

DM has a known deleterious relationship with the risk and prognosis of COVID-19 or TB [9, 25]. The interaction between DM and COVID-19 on TB had been anticipated [6, 8, 28], and our observational study supports these predictions. The mechanism by which DM and COVID-19 synergize to magnify the risk of TB remains to be elucidated. It is possible that lymphopenia due to COVID-19 synergized with defective monocyte/macrophage effector functions in DM [12, 29] to compromise the cell-mediated immunity that is critical for M. tuberculosis containment [30].

Our findings have clinical implications. In primary care clinics, individuals with a recent COVID-19 episode and poor glucose control should be prioritized for evaluation of a latent TB infection in TB endemic regions, and if positive, offered latent TB infection treatment. During the contact investigations at TB clinics, this recommendation would be important in recent contacts of a new TB cases [31]. Our TB clinics focus current efforts for latent TB infection testing in close contacts who are children under 5 years or people living with HIV. Close contacts with COVID-19 are not evaluated at TB clinics. The independent management of COVID-19 from TB patient cases seems practical to avoid cross-infections, but the relationship between suggests a benefit for improved communication between TB clinics and primary care physicians.

We recognize study limitations. Our sample size is relatively small when considering the changing features of past COVID-19 individuals during the 18-month study period. These included the dwindling of the COVID-IgG titers as time elapsed between the COVID-19 and TB episodes, and the introduction of COVID-19 vaccines. It would have been ideal to stratify participants by these features, but our statistical power was insufficient. Our study design was cross-sectional with participants enrolled at the time of TB diagnosis, so past COVID-19 was based on indirect methods (self-report and serology) with possibilities for non-differential misclassifications. It would have been ideal to have clinical data to stratify COVID-19 severity or details on the vaccine manufacturer, doses, or timing. Our study may be prone to survival bias because we did not include those who succumbed to COVID-19 and/or TB. Overcoming these limitations would be possible in settings where all the medical data is entered electronically in real time, i.e. primary care physicians treating COVID-19 and pulmonary clinics managing the same person for a later episode of TB. This type of documentation would make it possible for a retrospective analysis with longitudinal data on thousands of individuals. Finally, we cannot rule out selection bias since in non-TB controls usually have higher female participation (this is controlled for in our multivariable models), and TB-naive controls were a convenience sample from the community. Despite the limitations of our study, we were able to detect an anticipated interaction between DM and COVID-19 on the odds of TB.

Conclusion

Future studies are warranted to determine whether having COVID-19 in addition to diabetes mellitus is a risk factor for TB, given: i) the interaction of COVID-19 with the immune system or ii) because having COVID-19 may reflect the degree to which DM has already compromised the immune system. Looking forward, it may be uncertain how the COVID-19 pandemic will continue to impact the risk of TB given the acquisition of immunity to the virus through repeated infections in humans, plus the availability of second generation COVID-19 vaccines [32]. However, the emergence of new SARS-CoV-2 variants and low access to annual COVID-19 vaccines in TB endemic areas may retain the threat of higher TB risk, particularly among poorly controlled DM patients. Studies are warranted to confirm our findings with a larger sample size, in different study populations, at different times after a COVID-19 episode, and years after the emergence of the initial COVID-19 pandemic, to identify the actual impact of this viral infection on the risk of TB.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. World-Health-Organization. Global tuberculosis report 2023. Geneva: World Health Organization; 2023. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023.

    Google Scholar 

  2. Migliori GB, Thong PM, Akkerman O, Alffenaar JW, Alvarez-Navascues F, Assao-Neino MM, Bernard PV, Biala JS, Blanc FX, Bogorodskaya EM, et al. Worldwide effects of coronavirus disease pandemic on tuberculosis services, January-April 2020. Emerg Infect Dis. 2020;26(11):2709–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Shuwa HA, Shaw TN, Knight SB, Wemyss K, McClure FA, Pearmain L, Prise I, Jagger C, Morgan DJ, Khan S, et al. Alterations in T and B cell function persist in convalescent COVID-19 patients. Med (N Y). 2021;2(6):720–735 e724.

    CAS  Google Scholar 

  4. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, Chen L, Li M, Liu Y, Wang G, et al. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front Immunol. 2020;11: 827.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Aiello A, Najafi-Fard S, Goletti D. Initial immune response after exposure to Mycobacterium tuberculosis or to SARS-COV-2: similarities and differences. Front Immunol. 2023;14:1244556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Aguillon-Duran GP, Prieto-Martinez E, Ayala D, Garcia J Jr, Thomas JM 3, Garcia JI, Henry BM, Torrelles JB, Turner J, Ledezma-Campos E, et al. COVID-19 and chronic diabetes: the perfect storm for reactivation tuberculosis? A case series. J Med Case Rep. 2021;15(1):621.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nabity SA, Marks SM, Goswami ND, Smith SR, Timme E, Price SF, Gross L, Self JL, Toren KG, Narita M, et al. Characteristics of and deaths among 333 persons with tuberculosis and COVID-19 in cross-sectional sample from 25 jurisdictions, United States. Emerg Infect Dis. 2023;29(10):2016–23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Harries AD, Kumar AMV, Satyanarayana S, Lin Y, Dlodlo RA, Khogali M, Zachariah R, Kapur A. TB and COVID-19: paying attention to diabetes mellitus. Trans R Soc Trop Med Hyg. 2021;115(6):600–2.

    Article  CAS  PubMed  Google Scholar 

  9. Dave JA, Tamuhla T, Tiffin N, Levitt NS, Ross IL, Toet W, Davies MA, Boulle A, Coetzee A, Raubenheimer PJ. Risk factors for COVID-19 hospitalisation and death in people living with diabetes: a virtual cohort study from the Western Cape Province, South Africa. Diabetes Res Clin Pract. 2021;177:108925.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Visca D, Ong CWM, Tiberi S, Centis R, D’Ambrosio L, Chen B, Mueller J, Mueller P, Duarte R, Dalcolmo M, et al. Tuberculosis and COVID-19 interaction: a review of biological, clinical and public health effects. Pulmonology. 2021;27(2):151–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Erener S. Diabetes, infection risk and COVID-19. Mol Metab. 2020;39:101044.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gomez DI, Twahirwa M, Schlesinger LS, Restrepo BI. Reduced Mycobacterium tuberculosis association with monocytes from diabetes patients that have poor glucose control. Tuberculosis. 2013;93(2):192–7.

    Article  CAS  PubMed  Google Scholar 

  13. Dheda K, Perumal T, Moultrie H, Perumal R, Esmail A, Scott AJ, Udwadia Z, Chang KC, Peter J, Pooran A, et al. The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions. Lancet Respir Med. 2022;10(6):603–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kumwichar P, Chongsuvivatwong V. COVID-19 pneumonia and the subsequent risk of getting active pulmonary tuberculosis: a population-based dynamic cohort study using national insurance claims databases. EClinicalMedicine. 2023;56: 101825.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Alemu A, Bitew ZW, Seid G, Diriba G, Gashu E, Berhe N, Mariam SH, Gumi B. Tuberculosis in individuals who recovered from COVID-19: a systematic review of case reports. PLoS One. 2022;17(11):e0277807.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cuentame de Mexico. Poblacion total. Mexico: Instituto Nacional de Estadística y Geografía, INEGI; 2020. https://cuentame.inegi.org.mx/poblacion/habitantes.aspx?tema=P.

  17. Boletines Epidemiologicos Historicos. Mexico, D.F.: Gobierno de Mexico, Secretaria de Salud; 2023. https://www.gob.mx/salud/acciones-y-programas/historico-boletin-epidemiologico.

  18. Texas-Department-of-State-and-Health-Services. Tuberculosis along the Texas-Mexico border. Austin: Texas Department of State and Health Services; 2020. https://www.dshs.texas.gov/sites/default/files/LIDS-TB/publications/TBBorder.pdf.

    Google Scholar 

  19. Schildknecht KR, Pratt RH, Feng PI, Price SF, Self JL. Tuberculosis - United States, 2022. MMWR Morb Mortal Wkly Rep. 2023;72(12):297–303.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Fisher-Hoch SP, Rentfro A, Salinas J, Perez A, Brown H, Reininger B, Restrepo BI, Wilson J, Hossain M, Rahbar M, et al. Socioeconomic status and prevalence of obesity and diabetes in a Mexican American community, Cameron County, Texas, 2004–2007. Prev Chronic Dis. 2010;7(3):1–10.

    Google Scholar 

  21. CDC. Preventing and controlling tuberculosis along the U.S.-Mexico border. MMWR Recomm Rep. 2001;50(RR–1):1–27.

    Google Scholar 

  22. Restrepo BI, Camerlin AJ, Rahbar MH, Wang W, Restrepo MA, Zarate I, Mora-Guzman F, Crespo-Solis JG, Briggs J, McCormick JB, et al. Cross-sectional assessment reveals high diabetes prevalence among newly-diagnosed tuberculosis cases. Bull World Health Organ. 2011;89(5):352–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Scordo JM, Aguillon-Duran GP, Ayala D, Quirino-Cerrillo AP, Rodriguez-Reyna E, Mora-Guzman F, Caso JA, Ledezma-Campos E, Schlesinger LS, Torrelles JB, et al. A prospective cross-sectional study of tuberculosis in elderly hispanics reveals that BCG vaccination at birth is protective whereas diabetes is not a risk factor. PLoS One. 2021;16(7):e0255194.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. American Diabetes Association Professional Practice C, American Diabetes Association Professional Practice C, Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, Freeman R, Green J, Huang E, et al. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(Supplement_1):S17–38.

    Article  Google Scholar 

  25. Huangfu P, Ugarte-Gil C, Golub J, Pearson F, Critchley J. The effects of diabetes on tuberculosis treatment outcomes: an updated systematic review and meta-analysis. Int J Tuberc Lung Dis. 2019;23(7):783–96.

    Article  CAS  PubMed  Google Scholar 

  26. McQuaid CF, McCreesh N, Read JM, Sumner T, Group CCW, Houben R, White RG, Harris RC. The potential impact of COVID-19-related disruption on tuberculosis burden. Eur Respir J. 2020;56(2):2001718.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Palacios-Gutierrez JJ, Rodriguez-Guardado A, Arias-Guillen M, Alonso-Arias R, Palacios-Penedo S, Garcia-Garcia JM, Balbin M, Perez-Hernandez D, Sandoval-Torrientes M, Torreblanca-Gil A, et al. Clinical and epidemiological correlates of low IFN-Gamma responses in mitogen tube of QuantiFERON assay in tuberculosis infection screening during the COVID-19 pandemic: a Population-based marker of COVID-19 mortality? Arch Bronconeumol. 2022;58(9):649–59.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Thong PM, Chong HT, Chang AJW, Ong CWM. COVID-19, the escalation of diabetes mellitus and the repercussions on tuberculosis. Int J Infect Dis. 2023;130 Suppl 1:S30–3.

    Article  PubMed  Google Scholar 

  29. Martinez N, Ketheesan N, West K, Vallerskog T, Kornfeld H. Impaired recognition of Mycobacterium tuberculosis by Alveolar macrophages from diabetic mice. J Infect Dis. 2016;214(11):1629–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cliff JM, Kaufmann SH, McShane H, van Helden P, O’Garra A. The human immune response to tuberculosis and its treatment: a view from the blood. Immunol Rev. 2015;264(1):88–102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Behr MA, Edelstein PH, Ramakrishnan L. Revisiting the timetable of tuberculosis. BMJ. 2018;362:k2738.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Barreiro A, Prenafeta A, Bech-Sabat G, Roca M, Perozo Mur E, March R, Gonzalez-Gonzalez L, Madrenas L, Corominas J, Fernandez A, et al. Preclinical evaluation of a COVID-19 vaccine candidate based on a recombinant RBD fusion heterodimer of SARS-CoV-2. iScience. 2023;26(3):106126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the health professionals at the TB clinics from the Secretaría de Salud (SSA) de Tamaulipas in Reynosa and Matamoros, including Mr. Jorge Perez-Navarro for logistics support. We dedicate this study to the memory of team members we lost to COVID-19, Dr. Francisco Mora-Guzmán and R. Eminé Rodriguez-Reyna. We thank the US Customs and Border Protection, Agriculture Specialists at the Hidalgo and Cameron international bridges for coordination of study logistics.

Funding

This research was funded by the National Institute On Aging of the National Institutes of Health [P01-AG051428 to BIR], National Institute of Allergy and Infectious Diseases [NIAID IN-TRAC P30AI168439 to BIR; R21AI144541 to BIR] and National Institute Of General Medical Sciences of the National Institutes of Health under Award Number T34GM137854 To Mateo Joya-Ayala. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. School of Public Health, University of Texas Health Science Center at Houston, One West University Blvd, SPH Bldg, Brownsville, TX, 78520, USA

    Liz E. Calles-Cabanillas, Genesis P. Aguillón-Durán, Doris Ayala, José A. Caso, Miguel Garza, Miryoung Lee & Blanca I. Restrepo

  2. Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78541, USA

    Mateo Joya-Ayala

  3. Departamento Estatal de Micobacteriosis, Secretaría de Salud de Tamaulipas, Reynosa 88630, Matamoros 87370 and Ciudad Victoria 87000, Tamaulipas, México

    America M. Cruz-Gonzalez, Raul Loera-Salazar, Ericka Prieto-Martinez & Javier E. Rodríguez-Herrera

  4. Unidad Académica Reynosa-Aztlán, Universidad Autónoma de Tamaulipas, Reynosa, Mexico

    Esperanza M. Garcia-Oropesa

  5. South Texas Diabetes and Obesity Institute and Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX, 78541, USA

    John M. Thomas III & Blanca I. Restrepo

  6. Population Health and Host Pathogens Interactions Programs and International Center for the Advancement of Research & Education (I•CARE), Texas Biomedical Research Institute, San Antonio, TX, 78229, USA

    Jordi B. Torrelles & Blanca I. Restrepo

Authors
  1. Liz E. Calles-Cabanillas
    View author publications

    Search author on:PubMed Google Scholar

  2. Genesis P. Aguillón-Durán
    View author publications

    Search author on:PubMed Google Scholar

  3. Doris Ayala
    View author publications

    Search author on:PubMed Google Scholar

  4. José A. Caso
    View author publications

    Search author on:PubMed Google Scholar

  5. Miguel Garza
    View author publications

    Search author on:PubMed Google Scholar

  6. Mateo Joya-Ayala
    View author publications

    Search author on:PubMed Google Scholar

  7. America M. Cruz-Gonzalez
    View author publications

    Search author on:PubMed Google Scholar

  8. Raul Loera-Salazar
    View author publications

    Search author on:PubMed Google Scholar

  9. Ericka Prieto-Martinez
    View author publications

    Search author on:PubMed Google Scholar

  10. Javier E. Rodríguez-Herrera
    View author publications

    Search author on:PubMed Google Scholar

  11. Esperanza M. Garcia-Oropesa
    View author publications

    Search author on:PubMed Google Scholar

  12. John M. Thomas III
    View author publications

    Search author on:PubMed Google Scholar

  13. Miryoung Lee
    View author publications

    Search author on:PubMed Google Scholar

  14. Jordi B. Torrelles
    View author publications

    Search author on:PubMed Google Scholar

  15. Blanca I. Restrepo
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, B.I.R., J.B.T., J.T.; Formal analysis, B.I.R., L.E.C.C., M.L. Investigation, L.E.C.C., G.P.A.D., D.A., J.A.C., M.J.A., M.G.; Writing—original draft preparation, B.I.R., L.E.C.C.; Writing—review and editing, all authors; Project Administration, B.I.R., J.E.R.H., R.L.S., A.C.G., E.P.M., E.M.G.O., J.B.T., J.T.; Funding acquisition, B.I.R., L.S.S., J.B.T. and J.T. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Blanca I. Restrepo.

Ethics declarations

Ethics approval and consent to participate

This study was approved by institutional review boards in Mexico (110/2018/CEI) and the US (HSC-SPH-19–0308; HSC-SPH-14–1007) and written informed consent was obtained from participants.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Calles-Cabanillas, L.E., Aguillón-Durán, G.P., Ayala, D. et al. Interaction between type 2 diabetes and past COVID-19 on active tuberculosis. BMC Infect Dis 24, 1383 (2024). https://doi.org/10.1186/s12879-024-10244-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-024-10244-z

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us