Skip to main content
Download PDF

Associations between type 1 diabetes and pulmonary tuberculosis: a bidirectional mendelian randomization study

Diabetology & Metabolic Syndrome volume 16, Article number: 60 (2024) Cite this article

Abstract

Background

Type 1 diabetes mellitus (T1DM) has been associated with higher pulmonary tuberculosis (PTB) risk in observational studies. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect between T1DM and PTB using bidirectional Mendelian randomization (MR) analysis.

Methods

Single nucleotide polymorphisms (SNPs) of T1DM and PTB were extracted from the public genetic variation summary database. In addition, GWAS data were collected to explore the causal relationship between PTB and relevant clinical traits of T1DM, including glycemic traits, lipids, and obesity. The inverse variance weighting method (IVW), weighted median method, and MR‒Egger regression were used to evaluate the causal relationship. To ensure the stability of the results, sensitivity analyses assess the robustness of the results by estimating heterogeneity and pleiotropy.

Results

IVW showed that T1DM increased the risk of PTB (OR = 1.07, 95% CI: 1.03–1.12, P < 0.001), which was similar to the results of MR‒Egger and weighted median analyses. Moreover, we found that high-density lipoprotein cholesterol (HDL-C; OR = 1.28, 95% CI: 1.03–1.59, P = 0.026) was associated with PTB. There was no evidence of an effect of glycemic traits, remaining lipid markers, or obesity on the risk of PTB. In the reverse MR analysis, no causal relationships were detected for PTB on T1DM and its relevant clinical traits.

Conclusion

This study supported that T1DM and HDL-C were risk factors for PTB. This implies the effective role of treating T1DM and managing HDL-C in reducing the risk of PTB, which provides an essential basis for the prevention and comanagement of concurrent T1DM and PTB in clinical practice.

Introduction

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by absolute insulin deficiency and hyperglycemia [1]. It usually occurs in childhood and adolescence, accounting for approximately 5–10% of all diabetes cases [2, 3]. There is a growing trend in the prevalence of T1DM globally, with an increase of approximately 2–3% per year. It is predicted that 13.5–17.4 million people worldwide will have T1DM by 2040 (60–107% higher than in 2021), leading to a heavy burden on families and economies [4, 5]. Pulmonary tuberculosis (PTB) is a chronic infectious disease caused by Mycobacterium tuberculosis(Mtb) [6]. According to the World Health Organization (WHO), approximately 10.6 million people suffered from TB in 2021, an increase of 4.5% compared to 2020 [7, 8]. As the complication rates of diabetes and tuberculosis increase, it is necessary to further elucidate the association to better reduce the burden of disease.

Several recent studies have suggested that T1DM is a primary risk factor for PTB. For example, a hospital-based cross-sectional study reported that the prevalence of Mtb infection in children and adolescents with T1DM was 29.8% (95% CI 24.2–35.4) [9]. Another case‒control study demonstrated that patients with diabetes had 2.66 times the risk of PTB compared to the general population [10]. A systematic evaluation of 13 observational studies identified a statistically significant association between diabetes and latent tuberculosis infection [11]. However, the results of observational studies may be affected by reverse causality and confounding factors, as well as the fact that most of the studies did not specify diabetes phenotypes. Given the differences between T1DM and T2DM in terms of etiology, pathogenesis, and underlying genetic factors, direct evidence of a causal relationship between T1DM and PTB is still currently lacking.

T1DM is frequently accompanied by disorders of glucose and lipid metabolism and obesity [12, 13]. Some studies have concluded that diabetic patients are susceptible to PTB infection, which is related to disturbed glucose and lipid metabolism in diabetic patients [14]. A cohort study indicated that an estimated 7.5% of TB occurrences were attributed to poor glycemic control [15]. Besides, it has been observed that serum high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) concentrations were lower in patients with PTB [16]. A systematic evaluation identified a consistent log-linear relationship between BMI and tuberculosis incidence, with a decrease in TB incidence of approximately 14% per unit increase in body mass index (BMI) [17]. Nevertheless, there are few studies and mostly observational trials. Further research is necessary to support a causal relationship between glucose, lipids, obesity, and PTB.

Mendelian randomization (MR) utilizes genetic variants as instrumental variables (IVs) to elucidate the causal relationship between exposure and outcome [18]. Due to the random assignment of genetic variants during meiosis, MR is effective in reducing biases caused by confounders and reverse causality [19]. Compared with prospective experiments, MR analysis reveals causal associations in a time-saving and cost-effective manner [20].

In the present study, we performed the first bidirectional two-sample MR analysis to assess the causal effects between T1DM and PTB.

Methods

Study design

A brief description of the bidirectional MR analysis is demonstrated in Fig. 1. The MR analysis was performed to explore potential causal relationships between T1DM and PTB. In addition, the relevant clinical traits of T1DM, including glycemic traits, blood lipids, and obesity, were also investigated for potential causality with PTB to explore the underlying genetic mechanisms between T1DM and PTB. There are three core assumptions of study design: first, the genetic variations used as IVs should be robustly associated with the exposure; second, the genetic variations should be independent of confounders; and third, the genetic variations should affect the risk of the outcome merely through the exposure and not through any other pathways.

Fig. 1
figure 1

Overview of the bidirectional MR analysis. SNPs, single-nucleotide polymorphisms; T1DM, type 1 diabetes; FBG, fasting blood glucose; FI, fasting insulin; HbA1c, glycosylated hemoglobin; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; WHR, waist-to-hip ratio

Full size image

Data sources

Table 1 displays the characteristics of the results from the Genome-Wide Association Study (GWAS).

T1DM

The genetic instruments from the latest GWAS meta-analysis for T1DM were based on 520,580 individuals (18,942 T1DM cases and 501,638 controls) of European ancestry from the European Bioinformatics Institute (EBI) database [21].

Hyperglycemia

Summary statistics on glycemic traits [fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c)] were extracted from the Meta-Analyses of Glucose and Insulin-related Traits Consortium (MAGIC), including 133,010 and 46,368 nondiabetic individuals of European ancestry [22, 23]. For fasting insulin (FI), extracted from the EBI database, the data consisted of 151,013 individuals of European ancestry [24].

Blood lipid

Data from the Global Lipids Genetic Consortium (GLGC) were used to identify genetic loci for blood lipids [HDL-C, LDL-C, TC, triglycerides (TG)], including 188,578 individuals of European ancestry and 7,898 individuals of non-European ancestry [25].

Obesity

Results from the Genetic Investigation of Anthropometric Traits consortium (GIANT) were used to identify genetic instruments for BMI and waist-to-hip (WHR), including 322,154 and 210,088 individuals of European ancestry [26, 27].

PTB

We extracted genetic variation data for respiratory tuberculosis from the FinnGen Consortium. The summary-level statistics are from a large study involving 377,277 individuals (2,432 cases and 374,845 controls); all participants were of European ancestry [28].

Table 1 The characteristics of the GWAS
Full size table

IV selection

Based on the three core assumptions, the IVs for MR studies were chosen: (1) single nucleotide polymorphisms (SNPs) were robustly associated with the exposure (p < 5e-8). Since no SNP were selected as genetic instruments for PTB that reached genome-wide significance, we adopted a less stringent threshold (P < 1e–5) to identify more SNPs for PTB [29]; (2) the independent SNPs were selected after excluding linkage disequilibrium (kb = 10,000, r2 < 0.001); (3) the F statistic was used to assess the strength of the IVs, which is calculated as (\( \frac{{R}^{2}}{1-{R}^{2}}\))×(\( \frac{N-K-1}{K}\)), where R2 is the degree of exposure explained by the instrument, n represents the sample size, and k is the number of SNPs. With an F statistic > 10, IVs can be identified to minimize potential weak instrumental bias [30]. (4) To avoid potential pleiotropic effects caused by confounders, we assessed the confounders influencing the incidence of PTB from previous meta-analysis studies, including smoking [31], alcohol consumption [32], anemia [33], and HIV [34]. Potentially confounding SNPs were removed via the PhenoScanner website (http://www.phenoscanner.medschl.cam.ac.uk/), as presented in Table S1.

Statistical analysis

Several analytical methods, including inverse variance-weighted (IVW), MR‒Egger, and weighted median, were used in the MR analyses [35]. Among them, IVW was used as the primary statistical method, which provides the most accurate causal estimates assuming that all SNPs are valid instrumental variables. MR‒Egger analysis can generate estimates if significant horizontal pleiotropy is detected [36]. The weighted median method can provide accurate causal estimates when at least 50% of the weights come from valid instrumental variables [37]. The Q statistics (Cochran’s Q for IVW and Rücker’s Q for MR‒Egger) were used to assess heterogeneity in the estimates of SNPs, with p values less than 0.05 indicating the presence of heterogeneity [38]. The MR‒Egger regression intercept examination was used to estimate possible horizontal pleiotropy, and a deviation from zero indicated directional pleiotropy [36, 39]. The MR-pleiotropy residual sum and outlier (MR-PRESSO) was used to detect outlying SNPs and provide estimates after removing the outliers [40]. Statistical analysis was performed using the R packages “TwoSampleMR” and “MRPRESSO” in R software (version 4.0.2).

Results

Bidirectional causal effect between T1DM and PTB

Figure 2 demonstrates the MR estimates between T1DM and PTB. Using 74 associated SNPs as IVs, IVW analysis showed that T1DM was correlated with the risk of PTB (OR = 1.07, 95% CI: 1.03–1.12, P < 0.001), with one SNP removed as an outlier (rs1794269). The results of other MR methods were consistent with IVW analysis (Fig. 2), and no heterogeneity or directional pleiotropy was detected (Table S2, Figure S1 in the Supplement). There was no evidence of reverse causation effect of PTB on T1DM (OR = 1.19, 95% CI: 0.76–1.86, P = 0.442). MR-PRESSO showed horizontal pleiotropy (P < 0.001), with four SNPs identified as outliers (rs185377955, rs2395516, rs6708458, rs72836185). When the outliers were removed, there was no significant change in the reverse causality of PTB on T1DM (OR = 0.98, 95% CI: 0.93–1.03, P = 0.427), which was consistent across the MR‒Egger and weighted median analyses (Fig. 2). The Cochran’s Q test and MR‒Egger regression intercept for the relationship did not reveal any evidence of directional pleiotropy or heterogeneity (Table S3, Figure S2 in the Supplement).

Fig. 2
figure 2

MR results for the bidirectional causal effect between T1DM and PTB. T1DM, type 1 diabetes; FBG, fasting blood glucose; FI, fasting insulin; HbA1c, glycosylated hemoglobin; SNPs, single-nucleotide polymorphisms; MR, Mendelian randomization; CI, confidence interval; OR, odds ratio; IVW, inverse variance-weighted; WM, weighted median; P value for heterogeneity based on Cochran’s Q statistic for IVW and Rücker’s Q for MR‒Egger

Full size image

Causal effect of metabolic factors and obesity on PTB

Figure 3 demonstrates the MR estimates of metabolic factors and obesity on PTB. Genetic predisposition to HDL-C was associated with an increased risk of PTB (OR = 1.28, 95% CI: 1.03–1.59, P = 0.026; Fig. 3, Table S8, Figure S7 in the Supplement). Evidence that other metabolic factors and obesity had any effect on PTB was not identified by IVW, which was similar to the results of MR‒Egger and weighted median analyses (Fig. 3). There was no evidence of any directional pleiotropy or heterogeneity detected by Cochran’s Q test and MR‒Egger regression intercepts (Table S4-12, Figure S3-11 in the Supplement).

Fig. 3
figure 3

MR results for the causal effect of metabolic factors and obesity on PTB. PTB, pulmonary tuberculosis; FBG, fasting blood glucose; HbA1c, glycosylated hemoglobin; FI, fasting insulin; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol; BMI, body mass index; WHR, waist-to-hip ratio; SNPs, single-nucleotide polymorphisms; MR, Mendelian randomization; CI, confidence internal; OR, odds ratio; IVW, inverse variance-weighted; WM, weighted median; P value for heterogeneity based on Cochran’s Q statistic for IVW, and Rücker’s Q for MR‒Egger

Full size image

Causal effect of PTB on metabolic factors and obesity

Figure 4 demonstrates the MR estimates of PTB on metabolic factors and obesity. To assess reverse causal effects, we extracted 20 SNPs strongly and independently associated with PTB at a significance of P <1e − 5. No eligible SNPs were identified between PTB and FBG. There was no statistically significant genetic risk association of PTB for metabolic factors and obesity in any analyses (Fig. 4).MR-PRESSO global test and the MR-Egger intercept test showed no evidence for horizontal pleiotropy and Cochran Q tests did not detect heterogeneity (Fig. 4, Table S13-20, Figure S12-19 in the Supplement).

Fig. 4
figure 4

MR results for the causal effect of PTB on metabolic factors and obesity. PTB, pulmonary tuberculosis; FBG, fasting blood glucose; HbA1c, glycosylated hemoglobin; FI, fasting insulin; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol; BMI, body mass index; WHR, waist-to-hip ratio; SNPs, single-nucleotide polymorphisms; MR, Mendelian randomization; CI, confidence interval; OR, odds ratio; IVW, inverse variance-weighted; WM, weighted median; P value for heterogeneity based on Cochran’s Q statistic for IVW and Rücker’s Q for MR‒Egger

Full size image

Discussion

Based on large-scale GWAS data from MR analyses, our study provided strong evidence that genetic susceptibility to T1DM was associated with an increased risk of PTB, although reverse causality was insufficiently supported. We discovered HDL-C was correlated with the risk of PTB. There was no evidence of an association between other relevant clinical traits of T1DM and PTB.

Our study corroborated that the genetic predisposition to T1DM was associated with an increased risk of PTB, which is consistent with previous studies. Animal studies have indicated an increased susceptibility to Mtb infection in T1DM rats [41]. Several previous studies have revealed significant heterogeneity in diabetes populations with PTB concerning age, sex, ethnicity, and socioeconomic status, among others [42]. Nevertheless, it seemed that T1DM remained correlated with PTB after adjusting for potential confounders. A cross-sectional study of children and youth with T1DM indicated that the prevalence of latent tuberculosis was 14.9%, with females slightly higher than males (P > 0.05), and that the duration of T1DM and age at diagnosis had no significant effect (P > 0.05) [43]. Another population-based cohort study showed that patients with T1DM were at an increased risk of tuberculosis, which was higher in men than in women (4.62 vs. 3.59), and in adults than in children (4.06 vs. 3.37), but not significantly [44]. Nonetheless, these studies were not immune to other confounding factors, and our inability to stratify by gender, age, and other factors was a potential limitation of this study. Moreover, it has been demonstrated that impaired Interleukin-1beta (IL-1β), Interleukin-6 (IL-6), and Interferon gamma (IFN-γ) production in patients with T1DM may lead to increased susceptibility to tuberculosis [45, 46]. Thus, immune dysfunction may have a significant role in tuberculosis susceptibility. As alveolar macrophages perform a cardinal function in Mtb infection and replication, IFN-γ determines macrophage activation [47, 48]. IL-1β induces eicosanoids to promote bacterial control and limits type 1 IFN-γ production, which reduces the effect of macrophages and increases tuberculosis susceptibility in patients with T1DM [49]. Regarding the opposite direction, our MR analyses revealed no evidence to support a causal effect of PTB on T1DM. Despite the studies that have reported a greater risk of diabetes in patients with PTB, evidence is scarce [50]. Banyai has proposed from animal experiments that Mtb infection can promote necrosis and atrophy of the pancreas to affect diabetes [51]. Given that T2DM comprises 95% of diabetes, patients in a majority of studies appeared to be T2DM, with the TB-T1DM association currently understudied. Moreover, the results may be attributed to transient hyperglycemia as a result of febrile manifestations of PTB [52]. A prospective cohort study indicated that PTB could promote transient glycemia, without conclusively demonstrating the promotion of chronic glycemic abnormalities [53].

The study also explored the correlation between PTB and relevant metabolic characteristics of T1DM. Our purpose was to confirm the influencing factors further by investigating the causal association between glycemic traits and PTB. However, we did not identify a causal correlation between PTB and FBG, HbA1c, and FI, which was different from some previous observational studies [15, 54]. The difference may be explained as follows: (1) The results obtained from observational studies may be affected by bias, such as confounding factors or reverse causality. (2) Different studies have reached inconsistent conclusions. For example, the cohort study by Pin-Hui Lee et al. indicated that poor glycemic control had a significantly higher hazard of tuberculosis [15]. Conversely, the opposite conclusion was reported in a Danish population-based case‒control study, which did not demonstrate a significant correlation between glycemic traits and tuberculosis [55].

In addition, we used several mediators associated with lipid metabolism, including HDL-C, LDL-C, TG, and TC. We found that elevated HDL-C levels increased the risk of tuberculosis. Lipid metabolism, as one of the essential metabolic pathways, can serve as a secondary source of nutrients for PTB infection, favoring growth and multiplication against Mtb. Mtb induces macrophage differentiation into lipid-loaded foam cells and acquires a dormant-like phenotype [56]. Mtb infection forms granulomas whose core consists of infected macrophages. Progression of granuloma infection is frequently accompanied by dysregulation of lipid metabolism [57]. It has been suggested that HDL-C enhances Mtb infection in macrophages [58]. Interestingly, HDL-C plays a dual role in the prevention and regulation of PTB infection. It has been demonstrated that HDL levels are reduced after infection with tuberculosis [16]. This is probably due to the capacity of HDL to inhibit the production of tumor necrosis factor-alpha (TNF-α), which is critical in the immune defense against TB [58]. Therefore, further research is still necessary to elucidate the role of HDL-C in different states of TB, such as uninfected, initial infection, asymptomatic state, and active disease.

However, a causal relationship between obesity and tuberculosis was not identified, which differed from several previous studies. A systematic review showed a negative correlation between BMI and the incidence of tuberculosis [59]. However, results from observational studies may be subject to confounders and reverse causality. For instance, it has been demonstrated that there was a significant correlation between BMI and anemia in patients with PTB, while anaemia as a risk factor for PTB was excluded as a confounder in this study [60, 61]. Consequently, the MR analysis draws more robust and substantiated conclusions without these issues.

To our knowledge, the study is the first to reveal a causal association between T1DM and PTB using bidirectional MR analysis. Nevertheless, our study has some limitations as well. First, most of the statistics in GWAS were from individuals of European ancestry, which may have raised concerns about the generalizability of the findings to other populations. Ideally, we would repeat this association analysis in large GWAS data from regions with high PTB prevalence (e.g., Africa and South Asia). However, large populations with relevant genomic data are not yet available for further study. Second, despite our efforts to minimize pleiotropy, it was unlikely that all instances of pleiotropy would be eliminated in an MR analysis, which could have biased our results. Third, a potential limitation of the study was the inability to stratify the analyses based on gender, age, and duration of T1DM, among other significant variables.

Conclusion

In conclusion, our bidirectional MR study provided strong evidence of the causal relationship between T1DM, HDL-C, and PTB, while the reverse direction indicated no causal associations. Thus, prevention strategies for PTB should include treatment of T1DM and control of HDL-C levels, providing an essential basis for prevention and comanagement of concurrent T1DM and PTB in clinical practice.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Abbreviations

T1DM:

Type 1 diabetes mellitus

PTB:

Pulmonary tuberculosis

Mtb:

Mycobacterium tuberculosis

FBG:

Fasting blood glucose

FI:

Fasting insulin

HbA1c:

Glycosylated hemoglobin

TG:

Triglycerides

TC:

Total cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

BMI:

Body mass index

WHR:

Waist-to-hip ratio

MR:

Mendelian randomization

SNPs:

Single nucleotide polymorphisms

IVW:

Inverse variance weighting method

WM:

Weighted median

MR-PRESSO:

MR-pleiotropy residual sum and outlier

IVs:

Instrumental variables

OR:

Odds ratio

CI:

Confidence interval

References

  1. DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet. 2018;391:2449–62.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414:782–7.

    Article  PubMed  ADS  CAS  Google Scholar 

  3. Green A, Hede SM, Patterson CC, Wild SH, Imperatore G, Roglic G, et al. Type 1 diabetes in 2017: global estimates of incident and prevalent cases in children and adults. Diabetologia. 2021;64:2741–50.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Guo SJ, Shao H. Growing global burden of type 1 diabetes needs multitiered precision public health interventions. Lancet Diabetes Endocrinol. 2022;10:688–9.

    Article  PubMed  Google Scholar 

  5. Gregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C, et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. 2022;10:741–60.

    Article  PubMed  Google Scholar 

  6. Furin J, Cox H, Pai M, Tuberculosis. Lancet. 2019;393:1642–56.

    Article  PubMed  Google Scholar 

  7. Chakaya J, Petersen E, Nantanda R, Mungai BN, Migliori GB, Amanullah F, et al. The WHO Global Tuberculosis 2021 report– not so good news and turning the tide back to end TB. Int J Infect Dis. 2022;124:26–9.

    Article  Google Scholar 

  8. Vasiliu A, Martinez L, Gupta RK, Hamada Y, Ness T, Kay A et al. Tuberculosis prevention: current strategies and future directions. Clin Microbiol Infect. 2023;S1198-743X(23)00533-5.

  9. Webb EA, Hesseling AC, Schaaf HS, Gie RP, Lombard CJ, Spitaels A, et al. High prevalence of Mycobacterium tuberculosis infection and disease in children and adolescents with type 1 diabetes mellitus. Int J Tuberc Lung Dis. 2009;13:868–74.

    PubMed  CAS  Google Scholar 

  10. Coker R, McKee M, Atun R, Dimitrova B, Dodonova E, Kuznetsov S, et al. Risk factors for pulmonary tuberculosis in Russia: case-control study. BMJ. 2006;332:85–7.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lee M-R, Huang Y-P, Kuo Y-T, Luo C-H, Shih Y-J, Shu C-C, et al. Diabetes Mellitus and latent tuberculosis infection: a systemic review and metaanalysis. Clin Infect Dis. 2017;64:719–27.

    PubMed  Google Scholar 

  12. Corbin KD, Driscoll KA, Pratley RE, Smith SR, Maahs DM, Mayer-Davis EJ, et al. Obesity in type 1 diabetes: pathophysiology, clinical impact, and mechanisms. Endocr Rev. 2018;39:629–63.

    Article  PubMed  Google Scholar 

  13. Vergès B. Lipid disorders in type 1 diabetes. Diabetes Metab. 2009;35:353–60.

    Article  PubMed  Google Scholar 

  14. Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med. 2009;68:2240–6.

    Article  PubMed  Google Scholar 

  15. Lee P-H, Fu H, Lai T-C, Chiang C-Y, Chan C-C, Lin H-H. Glycemic Control and the risk of tuberculosis: a Cohort Study. PLoS Med. 2016;13:e1002072.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Deniz O, Gumus S, Yaman H, Ciftci F, Ors F, Cakir E, et al. Serum total cholesterol, HDL-C and LDL-C concentrations significantly correlate with the radiological extent of disease and the degree of smear positivity in patients with pulmonary tuberculosis. Clin Biochem. 2007;40:162–6.

    Article  PubMed  CAS  Google Scholar 

  17. Lönnroth K, Williams BG, Cegielski P, Dye C. A consistent log-linear relationship between tuberculosis incidence and body mass index. Int J Epidemiol. 2010;39:149–55.

    Article  PubMed  Google Scholar 

  18. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63.

    Article  MathSciNet  PubMed  Google Scholar 

  19. Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization JAMA. 2017;318:1925–6.

    Article  PubMed  Google Scholar 

  20. Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2:6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Chiou J, Geusz RJ, Okino M-L, Han JY, Miller M, Melton R, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594:398–402.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  22. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44:991–1005.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, et al. Common variants at 10 Genomic Loci Influence Hemoglobin A1C Levels via glycemic and nonglycemic pathways. Diabetes. 2010;59:3229–39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021;53:840–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

    Article  Google Scholar 

  26. The LifeLines Cohort Study, The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GLGC, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.

  27. The ADIPOGen Consortium, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, Consortium TGEFOS, The GENIE Consortium, The GLGC, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518:187–96.

    Article  Google Scholar 

  28. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  29. Xie J, Huang H, Liu Z, Li Y, Yu C, Xu L, et al. The associations between modifiable risk factors and nonalcoholic fatty liver disease: a comprehensive mendelian randomization study. Hepatology. 2023;77:949–64.

    Article  PubMed  Google Scholar 

  30. Pierce BL, Burgess S. Efficient design for mendelian randomization studies: Subsample and 2-Sample instrumental variable estimators. Am J Epidemiol. 2013;178:1177–84.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lin H-H, Ezzati M, Murray M. Tobacco smoke, indoor Air Pollution and Tuberculosis: a systematic review and Meta-analysis. PLoS Med. 2007;4:e20.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Imtiaz S, Shield KD, Roerecke M, Samokhvalov AV, Lönnroth K, Rehm J. Alcohol consumption as a risk factor for tuberculosis: meta-analyses and burden of disease. Eur Respir J. 2017;50:1700216.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gelaw Y, Getaneh Z, Melku M. Anemia as a risk factor for tuberculosis: a systematic review and meta-analysis. Environ Health Prev Med. 2021;26:13.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar J-W. Risk factors of multidrug-resistant tuberculosis: a global systematic review and meta-analysis. J Infect. 2018;77:469–78.

    Article  PubMed  Google Scholar 

  35. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using Summarized Data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith G. Best (but oft-forgotten) practices: the design, analysis, and interpretation of mendelian randomization studies. Am J Clin Nutr. 2016;103:965–78.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Burgess S, Thompson SG. Interpreting findings from mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Sugawara I, Mizuno S. Higher susceptibility of type 1 Diabetic rats to Mycobacterium tuberculosis infection. Tohoku J Exp Med. 2008;216:363–70.

    Article  PubMed  Google Scholar 

  42. Cadena J, Rathinavelu S, Lopez-Alvarenga JC, Restrepo BI. The re-emerging association between tuberculosis and diabetes: lessons from past centuries. Tuberculosis (Edinb). 2019;116(Suppl):89–97.

    Article  Google Scholar 

  43. Majaliwa ES, Muze K, Godfrey E, Byashalira K, Mmbaga BT, Ramaiya K, et al. Latent tuberculosis in children and youth with type 1 diabetes mellitus in Dar Es Salaam, Tanzania: a cross section survey. BMC Infect Dis. 2023;23:740.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Shen T-C, Lin C-L, Wei C-C, Liao W-C, Chen W-C, Chen C-H, et al. Increased risk of tuberculosis in patients with type 1 diabetes mellitus: results from a population-based cohort study in Taiwan. Med (Baltim). 2014;93:e96.

    Article  CAS  Google Scholar 

  45. Lachmandas E, Thiem K, van den Heuvel C, Hijmans A, de Galan BE, Tack CJ, et al. Patients with type 1 diabetes mellitus have impaired IL-1β production in response to Mycobacterium tuberculosis. Eur J Clin Microbiol Infect Dis. 2018;37:371–80.

    Article  PubMed  CAS  Google Scholar 

  46. Ohno Y, Aoki N, Nishimura A. In vitro production of interleukin-1, interleukin-6, and tumor necrosis factor-alpha in insulin-dependent diabetes mellitus. J Clin Endocrinol Metabolism. 1993;77:1072–7.

    CAS  Google Scholar 

  47. Flynn JL, Chan J. Immunology of Tuberculosis. Annu Rev Immunol. 2001;19:93–129.

    Article  PubMed  CAS  Google Scholar 

  48. Bisht MK, Dahiya P, Ghosh S, Mukhopadhyay S. The cause–effect relation of tuberculosis on incidence of diabetes mellitus. Front Cell Infect Microbiol. 2023;13:1134036.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Mayer-Barber KD, Andrade BB, Oland SD, Amaral EP, Barber DL, Gonzales J, et al. Host-directed therapy of tuberculosis based on interleukin-1 and type I interferon crosstalk. Nature. 2014;511:99–103.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  50. Critchley JA, Restrepo BI, Ronacher K, Kapur A, Bremer AA, Schlesinger LS, et al. Defining a Research Agenda to address the converging epidemics of tuberculosis and diabetes: part 1: epidemiology and clinical management. Chest. 2017;152:165–73.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Banyai AL. Diabetes and tuberculosis. Dis Chest. 1959;36:238–42.

    Article  PubMed  CAS  Google Scholar 

  52. Boillat-Blanco N, Ramaiya KL, Mganga M, Minja LT, Bovet P, Schindler C, et al. Transient hyperglycemia in patients with tuberculosis in Tanzania: implications for diabetes screening algorithms. J Infect Dis. 2016;213:1163–72.

    Article  PubMed  CAS  Google Scholar 

  53. Kornfeld H, Procter-Gray E, Kumpatla S, Kane K, Li W, Magee MJ, et al. Longitudinal trends in glycated hemoglobin during and after tuberculosis treatment. Diabetes Res Clin Pract. 2023;196:110242.

    Article  PubMed  CAS  Google Scholar 

  54. Leung CC, Lam TH, Chan WM, Yew WW, Ho KS, Leung GM, et al. Diabetic Control and Risk of Tuberculosis: a Cohort Study. Am J Epidemiol. 2008;167:1486–94.

    Article  PubMed  Google Scholar 

  55. Leegaard A, Riis A, Kornum JB, Prahl JB, Thomsen VØ, Sørensen HT, et al. Diabetes, Glycemic Control, and risk of tuberculosis. Diabetes Care. 2011;34:2530–5.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Daniel J, Maamar H, Deb C, Sirakova TD, Kolattukudy PE. Mycobacterium tuberculosis uses host triacylglycerol to accumulate lipid droplets and acquires a Dormancy-Like phenotype in lipid-loaded macrophages. PLoS Pathog. 2011;7:e1002093.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Kim M-J, Wainwright HC, Locketz M, Bekker L-G, Walther GB, Dittrich C, et al. Caseation of human tuberculosis granulomas correlates with elevated host lipid metabolism. EMBO Mol Med. 2010;2:258–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Inoue M, Niki M, Ozeki Y, Nagi S, Chadeka EA, Yamaguchi T, et al. High-density lipoprotein suppresses tumor necrosis factor alpha production by mycobacteria-infected human macrophages. Sci Rep. 2018;8:6736.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  59. Badawi A, Gregg B, Vasileva D. Systematic analysis for the relationship between obesity and tuberculosis. Public Health. 2020;186:246–56.

    Article  PubMed  CAS  Google Scholar 

  60. Mukherjee A, Kaeley N, Dhar M, Kumar S, Bhushan B. Prevalence, characteristics, and predictors of tuberculosis associated anemia. J Family Med Prim Care. 2019;8:2445–9.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Lee DJ. The relationship between TIGIT + regulatory T cells and autoimmune disease. Int Immunopharmacol. 2020;83:106378.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all investigators for sharing these data.

Funding

This work was supported by the National Administration of Traditional Chinese Medicine Young Qi Huang Scholars Support Project (National Traditional Chinese Medicine Human Education Development [2020] No. 7).

Author information

Authors and Affiliations

  1. Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China

    Yijia Jiang, Wenhua Zhang, Maoying Wei, Dan Yin, Yiting Tang, Weiyu Jia, Churan Wang, Jingyi Guo, Aijing Li & Yanbing Gong

Authors
  1. Yijia Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maoying Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiting Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Weiyu Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Churan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jingyi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aijing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yanbing Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design: JYJ, WHZ, YBG; collection and assembly of data: JYJ, MYW, YTT, DY; data analysis and interpretation: JYJ, WYJ, CRW, JYG, AJL; manuscript writing: all authors; final approval of manuscript: all authors.

Corresponding author

Correspondence to Yanbing Gong.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Competing interests

The authors declare no competing interests.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Y., Zhang, W., Wei, M. et al. Associations between type 1 diabetes and pulmonary tuberculosis: a bidirectional mendelian randomization study. Diabetol Metab Syndr 16, 60 (2024). https://doi.org/10.1186/s13098-024-01296-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13098-024-01296-x

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us