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Article

A Causal Relationship between Type 2 Diabetes and Candidiasis through Two-Sample Mendelian Randomization Analysis

by
Juan Xiong
,
Hui Lu
* and
Yuanying Jiang
*
Department of Pharmacy, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai 200092, China
*
Authors to whom correspondence should be addressed.
Microorganisms 2024, 12(10), 1984; https://doi.org/10.3390/microorganisms12101984
Submission received: 16 August 2024 / Revised: 17 September 2024 / Accepted: 19 September 2024 / Published: 30 September 2024
(This article belongs to the Special Issue Overview of Healthcare-Associated Infections)
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Abstract

:
The potential relationship between type 2 diabetes (T2D) and candidiasis is of concern due to the respective characteristics of these conditions, yet the exact causal link between the two remains uncertain and requires further investigation. In this study, the inverse-variance-weighted (IVW) analysis indicated a significant genetic causal relationship between T2D and candidiasis (p = 0.0264, Odds Ratio [OR], 95% confidence interval [CI] = 1.1046 [0.9096–1.2996]), T2D (wide definition) and candidiasis (p = 0.0031, OR 95% [CI] = 1.1562 [0.8718–1.4406]), and severe autoimmune T2D and candidiasis (p = 0.0041, OR 95% [CI] = 1.0559 [0.9493–1.1625]). Additionally, the MR-Egger analyses showed a significant genetic causal relationship between T2D (wide definition) and candidiasis (p = 0.0154, OR 95% [CI] = 1.3197 [0.7760–1.8634]). The weighted median analyses showed a significant genetic causal relationship between severe autoimmune T2D and candidiasis (p = 0.0285, OR 95% [CI] = 1.0554 [0.9498–1.1610]). This Mendelian randomization (MR) study provides evidence for a genetic correlation between T2D and candidiasis.

1. Introduction

The prevalence and fatality rates of candidiasis caused by Candida species have been increasing annually, resulting in significant detrimental effects to human health and imposing a substantial burden on the healthcare system [1]. Recent epidemiological data suggest that an estimated 1.56 million individuals are afflicted with candidemia or invasive candidiasis annually, leading to approximately 100,000 fatalities [2]. The complexity of treating candidiasis is attributed to the similarities in cellular characteristics between Candida species and human cells, thereby restricting available therapeutic interventions [3,4]. Current treatment modalities predominantly rely on azole antifungal drugs, yet these are becoming less effective as resistance and tolerance spreads [4,5,6]. Furthermore, the delay in the diagnosis of candidiasis is frequently attributed to the non-specific nature of its symptoms, allowing the pathogen to proliferate and spread within the host [7]. Consequently, there is a pressing necessity for the enhanced comprehension of the risk factors associated with candidiasis, as they may offer novel opportunities for therapeutic strategies.
Type 2 diabetes (T2D) is a metabolic syndrome characterized by inadequate insulin secretion and the reduced sensitivity of target organs to insulin, resulting in various metabolic disturbances, including disruptions in fat, protein, water, electrolyte balance, and other metabolic processes [8]. According to data from the Global Burden of Disease (GBD), the age-standardized global prevalence of T2D was approximately 6.0% in men and 5.0% in women in 2019 [9]. In T2D mellitus patients, invasive fungal disease poses a significant risk to life, primarily attributed to the pathogens Candida albicans (C. albicans), Cryptococcus neoformans, and Aspergillus fumigatus [10]. Female diabetics are often susceptible to vulvovaginal candidiasis, with C. albicans being the predominant species identified [11]. The occurrence and vulnerability of individuals to vulvovaginal candidiasis when linked to sodium–glucose cotransporter 2 inhibitors in clinical settings for women diagnosed with T2D were explored [12]. A case–control study involving 250 individuals with T2D and 81 nondiabetic controls in Sri Lanka revealed a notably higher prevalence of oral Candida colonization in diabetic patients compared to their healthy counterparts, with the coexistence of multiple yeast species being a frequent observation in the study cohort [13]. Hence, there is concern among individuals regarding T2D as a risk factor for candidiasis [14,15,16].
Investigating the causal connections between T2D and candidiasis is essential for preventing candidiasis through tailored education for individuals with T2D. Individuals with T2D face an elevated risk of developing candidiasis, influenced by factors such as hyperglycemia that can foster an environment favorable for the proliferation of Candida species [17]. This yeast flourishes in high-sugar conditions, and suboptimal glycemic control can amplify the risk of infection [18]. A grasp of these causal relationships enables healthcare providers to instruct patients on the significance of sustaining blood glucose levels in a healthy range to mitigate the risk of candidiasis. However, the precise causal relationship between the two conditions remains ambiguous and necessitates additional research.
Observational studies aimed at estimating causal inference are subject to various inherent limitations, including restrictions to identifying and accurately measuring confounders [19]. The Mendelian randomization (MR) research design adheres to the principles of Mendelian genetics, including specifically the random assignment of parental alleles to offspring. By positing that genotype determines phenotype, the MR method utilizes genotypes as instrumental variables (IVs) to infer associations between phenotypes and diseases [20,21]. This approach employs genetic variation as an IV to construct a model and ascertain causal effects. Currently, the MR method is extensively employed to evaluate causal relationships between traits and diseases, as well as between different diseases. In this study, we employed a two-sample MR approach to investigate the potential causal relationship between T2D and candidiasis, thereby enhancing our understanding of the prevention and treatment of candidiasis.

2. Materials and Methods

2.1. Study Design

In this study, we considered T2D as an exposure factor, we identified SNPs significantly associated with T2D as instrumental variables, and we evaluated the impact of these SNPs on candidiasis outcomes using two-sample MR analysis. This study adhered rigorously to the three assumptions of MR analysis: (1) ensuring that selected IVs were associated with T2D; (2) confirming that IVs were not correlated with any potential confounding factors; (3) verifying that IVs could only impact candidiasis through T2D (Figure 1). However, the testing of assumptions (2) and (3) presents challenges as they are pertinent to associations involving unidentified confounders. Consequently, we utilized the MR-Egger regression coefficient estimation method to assess the existence of a horizontal pleiotropic effect and investigated if the intercept significantly differed from zero. As this study involved a re-analysis of existing data, no additional ethical approval was required.

2.2. GWAS Data Sources

In this study, we selected “T2D”, “T2D (wide definition)”, and “severe autoimmune T2D” as representative forms of T2D and considered “candidiasis” as representative forms of candidiasis. T2D is a chronic metabolic disorder characterized by insulin resistance and a relative deficiency of insulin. It is marked by hyperglycemia, which occurs because the body does not effectively utilize insulin. The “wide definition” of T2D encompasses a more comprehensive understanding or classification, including not only individuals with traditional clinical diagnoses based on blood glucose levels but also those with prediabetes or at high risk of developing T2D. This wider perspective considers a range of metabolic abnormalities, such as impaired glucose tolerance and insulin resistance, which may not meet the criteria for a formal T2D diagnosis but still pose significant health risks [22]. Genome-wide association studies (GWAS) summary data for “T2D” and “severe autoimmune T2D” were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) (accessed on 10 May 2024) with the study accession IDs GCST90018926 [23] and GCST90026412 [24]. The GWAS summary data for “T2D (wide definition)” and candidiasis were obtained from the FinnGen consortium (https://r10.finngen.fi/) (accessed on 10 May 2024) [22]. The study population was exclusively composed of individuals of European descent, ensuring the elimination of any potential bias caused by factors related to racial admixture. The details regarding the data sources utilized and the demographic profiles of T2D and candidiasis are presented in Table 1.

2.3. Selection Criteria for IVs Selection

A series of stringent quality control measures were implemented in the selection of IVs for MR analysis to adhere to the three assumptions of MR and ensure the robustness and reliability of the analysis. Firstly, SNPs associated with T2D (p < 5 × 10−8), T2D (wide definition) (p < 5 × 10−6), and severe autoimmune T2D (p < 5 × 10−6) were identified. Secondly, the linkage disequilibrium (LD) between SNPs was addressed to mitigate potential bias (r2 < 0.001, clumping distance = 10,000 kb). Thirdly, SNPs linked to candidiasis were excluded (p < 1 × 10−5). Fourth, F statistics were computed to assess the impact of sample overlap and weak instrument bias, with an F value below 10 indicating potential bias. To ensure a robust association with the exposure variable, SNPs with an F statistic greater than 10 were chosen as IVs [25,26]. The F statistic was calculated using the following formula: F = R2× (N − K − 1)/K× (1 − R2), where R2 represents the cumulative explained variance of selected SNPs on T2D, K is the number of selected SNPs, and N is the sample size. The calculation of R2 was performed according to the following formula: R2 = (2 × EAF × (1 − EAF) × Beta2)/[(2 × EAF × (1 − EAF) × Beta2) + (2 × EAF × (1 − EAF) × N × SE2)], where EAF denotes the effect allele frequency, Beta signifies the effect size, and SE represents the standard error of the effect size [27].

2.4. Mendelian Randomization Analysis

Utilizing the specified IVs, a two-sample MR analysis was conducted on the relationship between T2D and candidiasis using the TwoSampleMR package (version 0.5.5, Stephen Burgess, Chicago, IL, USA) in R (version 4.0.3). The analysis encompassed five distinct approaches: inverse-variance-weighted (IVW, random effects) as the primary method [28], supplemented by MR-Egger, the weighted median, and weighted mode. The IVW method was deemed accurate if the assumption of all included SNPs serving as effective IVs was satisfied. The MR-Egger regression method has the capability to identify and correct for pleiotropy; however, it is noted for its low estimation accuracy [29]. The weighted median method offers a precise estimation under the condition that a minimum of 50% of IVs are valid [30]. The weighted mode method is susceptible to challenges in selecting an appropriate bandwidth for mode estimation [31]. The study protocol and details were not pre-registered.

2.5. Sensitivity Analysis

Cochran’s Q statistic was utilized for IVW analysis to assess the heterogeneity of SNP effects on T2D-related candidiasis. A p-value greater than 0.05 signifies the absence of heterogeneity [32]. The intercept test of MR-Egger analysis was employed to detect potential pleiotropy and assess its impact on risk estimation in the intercept test, with a p-value above 0.05 indicating no pleiotropy [29]. The “Leave-one-out” analysis was conducted to examine the influence of individual SNPs on the causal relationship between T2D and candidiasis [33].

3. Results

3.1. IVs Selection

Following the removal of linkage disequilibrium (LD), a subset of 170 SNPs was found to be associated with T2D (p < 5 × 10−8) (Table S1), a subset of 84 SNPs was found to be associated with T2D (wide definition) (p < 5 × 10−6) (Table S2), and a subset of 13 SNPs was found to be associated with severe autoimmune T2D (p < 5 × 10−6) (Table S3) (Figure 1).

3.2. MR Analysis

The MR-Egger, weighted median, and weighted mode analyses did not show a significant genetic causal relationship between T2D and candidiasis, but the IVW analysis indicated a significant genetic causal relationship between T2D and candidiasis (p = 0.0264, Odds Ratio [OR] 95% confidence interval [CI] = 1.1046 [0.9096–1.2996]) (Table 2). The weighted median and weighted mode analyses did not show a significant genetic causal relationship between T2D (wide definition) and candidiasis, but the IVW (p = 0.0031, OR 95% [CI] = 1.1562 [0.8718–1.4406]) and the MR-Egger (p = 0.0154, OR 95% [CI] = 1.3197 [0.7760–1.8634]) analyses indicated a significant genetic causal relationship T2D (wide definition) and candidiasis (Table 2). The MR-Egger and weighted mode analyses did not show a significant genetic causal relationship between autoimmune T2D and candidiasis, but the IVW (p = 0.0041, OR 95% [CI] = 1.0559 [0.9493–1.1625]) and weighted median (p = 0.0285, OR 95% [CI] = 1.0554 [0.9498–1.1610]) analyses indicated a significant genetic causal relationship between severe autoimmune T2D and candidiasis (Table 2). Furthermore, the beta values for the MR-Egger, IVW, weighted median, and weighted mode showed positive associations (Figure 2). These results indicate that T2D is a genetic causal risk factor for candidiasis.
There is a consensus that individuals with diabetes face a heightened risk of developing candidiasis, a condition exacerbated by elevated blood sugar levels and the subsequent pathological changes induced by hyperglycemia. Our MR study indicates a potential genetic link between T2D and candidiasis, suggesting that susceptibility to infections caused by Candida species in individuals with diabetes may be influenced not only by elevated blood glucose levels but also by genetic factors. Consequently, while maintaining blood sugar levels within a healthy range in diabetic patients can help decrease the incidence of candidiasis, this strategy alone may not be fully effective. This underscores the need for diabetic patients with well-managed blood sugar levels to remain vigilant for signs of candidiasis and consider drug prophylaxis when it is deemed appropriate.

3.3. Sensitivity Analysis Results

Cochran’s Q statistic (IVW) indicated that there was no significant heterogeneity in the MR analyses of T2D, T2D (wide definition), and severe autoimmune T2D and candidiasis (p > 0.05) (Table 3). The intercept test of MR-Egger analysis further confirmed the absence of horizontal pleiotropy in the MR analyses of T2D, T2D (wide definition), and severe autoimmune T2D and candidiasis (p > 0.05) (Table 3). The funnel plot illustrates that the distribution of points representing causal effects is symmetrical when a single SNP is utilized as an IV, suggesting a lower susceptibility to potential bias (Figure 3). In the leave-one-out analysis, the exclusion of individual SNPs minimally impacts the results, implying that no single SNP exerts a substantial influence on the overall estimation of causal effects (Figure 4).

4. Discussion

This study represents the first investigation utilizing MR to examine the impact of T2D on candidiasis. Our findings establish a causal relationship between T2D and candidiasis based on the results of our MR analysis. The fundamental assumption in MR is that the IVs are linked to candidiasis solely through their association with T2D. Employing a two-sample study design, we were able to generate cost-effective and unbiased estimates to evaluate the influence of T2D on the risk of candidiasis. The F values of the independent variables suggested that they meet the strong relevance assumption of MR, with a weak instrumental bias that has minimal impact on causal effect estimates. The MR-Egger method was employed to identify and correct for pleiotropy in the genetic variants. Heterogeneity analysis showed no significant differences between SNPs, enhancing the credibility of the MR results. Our primary MR analysis utilizing SNPs associated with T2D, T2D (wide definition), and severe autoimmune T2D indicated a positive association between T2D and candidiasis according to the IVW method. Additionally, the MR-Egger analyses show a significant genetic causal relationship between T2D (wide definition) and candidiasis. The weighted median analyses showed a significant genetic causal relationship between severe autoimmune T2D and candidiasis. Consequently, we posit that T2D is a genetic risk factor for candidiasis.
Candida species exhibit a wide distribution across various hosts, including humans, domestic and wild animals, and diverse environments such as hospitals [34]. These species are a constituent of the typical human microflora and demonstrate the ability to colonize mucosal surfaces in areas such as the oral cavity, gastrointestinal tract, respiratory tract, and genitourinary tract [34]. Despite their commensal nature, Candida species have the capacity to transition from symptomless colonization to infective states [34]. The pathogenicity of Candida species and their colonization factors are influenced by host immune factors, establishing a complex interplay between the fungi and the host’s immune status, which ultimately determines the nature of their relationship as either commensal or parasitic [35]. The development of candidiasis in individuals with T2D is complex, with several hypotheses explaining this phenomenon. One such hypothesis posits that sustained hyperglycemia creates an ideal environment for Candida species by serving as a preferred energy source, thereby facilitating their survival and colonization [36]. In the context of Candida species infection, elevated glucose levels within infected tissues have been demonstrated to increase Candida species’ adherence and invasion capabilities [36,37]. Glucose also plays a role in modulating the morphological transition of C. albicans from yeast to hyphal forms [38], as well as in the formation of biofilms by Candida species cells [39]. Furthermore, the ability to detect sugars is crucial for various virulence factors, including adhesion, resistance to oxidative stress, invasion, and tolerance to antifungal medications [40]. Another hypothesis suggests that diabetes mellitus, as a prevalent endocrine disorder, enhances susceptibility to infections due to immune system impairment. This susceptibility is influenced by various factors, such as reduced T-lymphocyte counts, compromised neutrophil function, heightened leukocyte apoptosis, and diminished cytokine secretion [41]. Elevated levels of IL-10 or reduced levels of IFN-γ in individuals with diabetes may be associated with an increased risk of oral candidiasis [42]. A third hypothesis is that diabetes can induce changes in the normal microbiota, particularly in the oral and vaginal areas [43,44,45]. These changes can disrupt the equilibrium of microbial communities, allowing opportunistic pathogens like Candida species to proliferate. Lastly, diabetic patients are often prescribed antibiotics and steroids more frequently, which can disturb the normal microbial flora and immune function, respectively [46,47,48], fostering an environment that favors the overgrowth of Candida species. Upon examining these results, it becomes evident that there is a consensus that individuals with diabetes are prone to developing candidiasis due to elevated blood sugar levels and the pathological alterations that result from such hyperglycemia. According to this perspective, the risk of candidiasis can be mitigated by effectively managing the blood sugar levels of diabetic patients. Nevertheless, the extent to which the genetic background of individuals with diabetes contributes to the onset of candidiasis remains an area insufficiently explored by current research.
The MR study is an epidemiological method that employs genetic variants as tools to determine causal links between modifiable risk factors and health outcomes, addressing issues like confounding and reverse causation in observational studies. The key principles of MR include the use of genetic variants, such as SNPs, which are linked to risk factors of interest but not directly to the outcome, except through the risk factor. This approach is like random allocation in controlled trials, which helps evenly distribute confounding factors and reduce bias. By analyzing the relationship between these genetic variants and health outcomes, researchers can infer causality [20]. Our MR study indicates a potential genetic link between T2D and candidiasis, suggesting that individuals with diabetes may be more susceptible to infections caused by Candida species, not only because of elevated blood glucose levels and compromised immune function but also due to genetic factors. Therefore, the effective management of blood sugar levels and immune system health in diabetic patients may help reduce the likelihood of developing candidiasis, but this may not be enough on its own. This implies that diabetic patients with well-controlled blood sugar levels should remain vigilant for the development of candidiasis, and drug prophylaxis may be necessary when appropriate.
This study is subject to several limitations. Firstly, the analysis was limited to populations from Europe; therefore, caution must be exercised when generalizing the conclusions to other populations. Secondly, gender was not considered in this study, necessitating the consideration of potential differences when applying the results to male or female populations separately. Thirdly, this study exclusively examined the relationship between T2D and candidiasis without differentiating between candidiasis attributed to various Candida species, including C. albicans, Candida parapsilosis, and Candida auris. Moreover, there was no distinction made between different manifestations of candidiasis, such as oral candidiasis, vulvovaginal candidiasis, candidemia, and invasive candidiasis, among others. Lastly, the occurrence and progression of T2D is a multifaceted process, and the analysis did not consider the impact of T2D resulting from different etiologies based on susceptibility to candidiasis.

5. Conclusions

In conclusion, the findings from our two-sample MR analysis suggest that T2D is a substantial genetic risk factor for candidiasis. Individuals with T2D should remain vigilant for the development of candidiasis, and pharmacological interventions may be warranted to mitigate the risk of candidiasis onset.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms12101984/s1, Tables S1–S3 provide a summary of SNPs associated with T2D. (T2D has a wide definition, including severe autoimmune T2D in this research).

Author Contributions

Conceptualization, H.L. and Y.J.; methodology, J.X. and H.L.; software, J.X.; formal analysis, J.X.; data curation, H.L.; writing—original draft preparation, J.X.; writing—review and editing, H.L. and Y.J.; visualization, J.X.; supervision, H.L. and Y.J.; project administration, H.L. and Y.J.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Innovation Program of Shanghai Municipal Education Commission (202101070007-E00094) and the National Natural Science Foundation of China (No. 82020108032 and No. 82404705).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.

Acknowledgments

We acknowledge the participants and investigators of the FinnGen study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lu, H.; Hong, T.; Jiang, Y.; Whiteway, M.; Zhang, S. Candidiasis: From cutaneous to systemic, new perspectives of potential targets and therapeutic strategies. Adv. Drug Deliv. Rev. 2023, 199, 114960. [Google Scholar] [CrossRef]
  2. Denning, D.W. Global incidence and mortality of severe fungal disease. Lancet Infect. Dis. 2024, 24, E428–E438. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, L.; Lu, H.; Jiang, Y. Natural Polyketides Act as Promising Antifungal Agents. Biomolecules 2023, 13, 1572. [Google Scholar] [CrossRef]
  4. Xiong, J.; Wang, L.; Feng, Y.; Zhen, C.; Hang, S.; Yu, J.; Lu, H.; Jiang, Y. Geldanamycin confers fungicidal properties to azole by triggering the activation of succinate dehydrogenase. Life Sci. 2024, 348, 122699. [Google Scholar] [CrossRef]
  5. Lu, H.; Li, W.; Whiteway, M.; Wang, H.; Zhu, S.; Ji, Z.; Feng, Y.; Yan, L.; Fang, T.; Li, L.; et al. A Small Molecule Inhibitor of Erg251 Makes Fluconazole Fungicidal by Inhibiting the Synthesis of the 14alpha-Methylsterols. mBio 2023, 14, e0263922. [Google Scholar] [CrossRef]
  6. Feng, Y.; Lu, H.; Whiteway, M.; Jiang, Y. Understanding fluconazole tolerance in Candida albicans: Implications for effective treatment of candidiasis and combating invasive fungal infections. J. Glob. Antimicrob. Resist. 2023, 35, 314–321. [Google Scholar] [CrossRef] [PubMed]
  7. Mendonca, A.; Santos, H.; Franco-Duarte, R.; Sampaio, P. Fungal infections diagnosis—Past, present and future. Res. Microbiol. 2022, 173, 103915. [Google Scholar] [CrossRef] [PubMed]
  8. Ma, Q.; Li, Y.; Li, P.; Wang, M.; Wang, J.; Tang, Z.; Wang, T.; Luo, L.; Wang, C.; Wang, T.; et al. Research progress in the relationship between type 2 diabetes mellitus and intestinal flora. Biomed. Pharmacother. 2019, 117, 109138. [Google Scholar] [CrossRef]
  9. Tinajero, M.G.; Malik, V.S. An Update on the Epidemiology of Type 2 Diabetes: A Global Perspective. Endocrinol. Metab. Clin. North. Am. 2021, 50, 337–355. [Google Scholar] [CrossRef]
  10. Lao, M.; Li, C.; Li, J.; Chen, D.; Ding, M.; Gong, Y. Opportunistic invasive fungal disease in patients with type 2 diabetes mellitus from Southern China: Clinical features and associated factors. J. Diabetes Investig. 2020, 11, 731–744. [Google Scholar] [CrossRef] [PubMed]
  11. Goswami, D.; Goswami, R.; Banerjee, U.; Dadhwal, V.; Miglani, S.; Lattif, A.A.; Kochupillai, N. Pattern of Candida species isolated from patients with diabetes mellitus and vulvovaginal candidiasis and their response to single dose oral fluconazole therapy. J. Infect. 2006, 52, 111–117. [Google Scholar] [CrossRef] [PubMed]
  12. Yokoyama, H.; Nagao, A.; Watanabe, S.; Honjo, J. Incidence and risk of vaginal candidiasis associated with sodium-glucose cotransporter 2 inhibitors in real-world practice for women with type 2 diabetes. J. Diabetes Investig. 2019, 10, 439–445. [Google Scholar] [CrossRef] [PubMed]
  13. Sampath, A.; Weerasekera, M.; Dilhari, A.; Gunasekara, C.; Bulugahapitiya, U.; Fernando, N.; Samaranayake, L. Type 2 diabetes mellitus and oral Candida colonization: Analysis of risk factors in a Sri Lankan cohort. Acta Odontol. Scand. 2019, 77, 508–516. [Google Scholar] [CrossRef]
  14. Martorano-Fernandes, L.; Dornelas-Figueira, L.M.; Marcello-Machado, R.M.; Silva, R.B.; Magno, M.B.; Maia, L.C.; Del Bel Cury, A.A. Oral candidiasis and denture stomatitis in diabetic patients: Systematic review and meta-analysis. Braz. Oral Res. 2020, 34, e113. [Google Scholar] [CrossRef] [PubMed]
  15. Mussi, M.C.M.; Fernandes, K.S.; Gallottini, M.H.C. A call for further research on the relation between type 2 diabetes and oral candidiasis. Oral Surg. Oral. Med. Oral Pathol. Oral Radiol. 2022, 134, 206–212. [Google Scholar] [CrossRef] [PubMed]
  16. Mohammed, L.; Jha, G.; Malasevskaia, I.; Goud, H.K.; Hassan, A. The Interplay Between Sugar and Yeast Infections: Do Diabetics Have a Greater Predisposition to Develop Oral and Vulvovaginal Candidiasis? Cureus 2021, 13, e13407. [Google Scholar] [CrossRef]
  17. Hostetter, M.K. Handicaps to host defense. Effects of hyperglycemia on C3 and Candida albicans. Diabetes 1990, 39, 271–275. [Google Scholar] [CrossRef]
  18. Khanna, M.; Challa, S.; Kabeil, A.S.; Inyang, B.; Gondal, F.J.; Abah, G.A.; Minnal Dhandapani, M.; Manne, M.; Mohammed, L. Risk of Mucormycosis in Diabetes Mellitus: A Systematic Review. Cureus 2021, 13, e18827. [Google Scholar] [CrossRef]
  19. Greenland, S.; Morgenstern, H. Confounding in health research. Annu. Rev. Public. Health 2001, 22, 189–212. [Google Scholar] [CrossRef]
  20. Birney, E. Mendelian Randomization. Cold Spring Harb. Perspect. Med. 2022, 12, a041302. [Google Scholar] [CrossRef]
  21. Burgess, S.; Davey Smith, G.; Davies, N.M.; Dudbridge, F.; Gill, D.; Glymour, M.M.; Hartwig, F.P.; Kutalik, Z.; Holmes, M.V.; Minelli, C.; et al. Guidelines for performing Mendelian randomization investigations: Update for summer 2023. Wellcome Open Res. 2019, 4, 186. [Google Scholar] [CrossRef] [PubMed]
  22. Kurki, M.I.; Karjalainen, J.; Palta, P.; Sipila, T.P.; Kristiansson, K.; Donner, K.M.; Reeve, M.P.; Laivuori, H.; Aavikko, M.; Kaunisto, M.A.; et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023, 613, 508–518. [Google Scholar] [CrossRef] [PubMed]
  23. Sakaue, S.; Kanai, M.; Tanigawa, Y.; Karjalainen, J.; Kurki, M.; Koshiba, S.; Narita, A.; Konuma, T.; Yamamoto, K.; Akiyama, M.; et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 2021, 53, 1415–1424. [Google Scholar] [CrossRef] [PubMed]
  24. Mansour Aly, D.; Dwivedi, O.P.; Prasad, R.B.; Karajamaki, A.; Hjort, R.; Thangam, M.; Akerlund, M.; Mahajan, A.; Udler, M.S.; Florez, J.C.; et al. Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes. Nat. Genet. 2021, 53, 1534–1542. [Google Scholar] [CrossRef] [PubMed]
  25. Burgess, S.; Thompson, S.G.; Collaboration, C.C.G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 2011, 40, 755–764. [Google Scholar] [CrossRef] [PubMed]
  26. Hemani, G.; Zheng, J.; Elsworth, B.; Wade, K.H.; Haberland, V.; Baird, D.; Laurin, C.; Burgess, S.; Bowden, J.; Langdon, R.; et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018, 7, e34408. [Google Scholar] [CrossRef] [PubMed]
  27. Yang, M.; Wan, X.; Zheng, H.; Xu, K.; Xie, J.; Yu, H.; Wang, J.; Xu, P. No Evidence of a Genetic Causal Relationship between Ankylosing Spondylitis and Gut Microbiota: A Two-Sample Mendelian Randomization Study. Nutrients 2023, 15, 1057. [Google Scholar] [CrossRef] [PubMed]
  28. Mounier, N.; Kutalik, Z. Bias correction for inverse variance weighting Mendelian randomization. Genet. Epidemiol. 2023, 47, 314–331. [Google Scholar] [CrossRef]
  29. Burgess, S.; Thompson, S.G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 2017, 32, 377–389. [Google Scholar] [CrossRef]
  30. Bowden, J.; Davey Smith, G.; Haycock, P.C.; Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet. Epidemiol. 2016, 40, 304–314. [Google Scholar] [CrossRef]
  31. Hartwig, F.P.; Davey Smith, G.; Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 2017, 46, 1985–1998. [Google Scholar] [CrossRef] [PubMed]
  32. Greco, M.F.; Minelli, C.; Sheehan, N.A.; Thompson, J.R. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat. Med. 2015, 34, 2926–2940. [Google Scholar] [CrossRef] [PubMed]
  33. Zhang, J. Mendelian Randomization Study Implies Causal Linkage Between Telomere Length and Juvenile Idiopathic Arthritis in a European Population. J. Inflamm. Res. 2022, 15, 977–986. [Google Scholar] [CrossRef]
  34. Goncalves, B.; Ferreira, C.; Alves, C.T.; Henriques, M.; Azeredo, J.; Silva, S. Vulvovaginal candidiasis: Epidemiology, microbiology and risk factors. Crit. Rev. Microbiol. 2016, 42, 905–927. [Google Scholar] [CrossRef] [PubMed]
  35. Singh, S.; Fatima, Z.; Hameed, S. Predisposing factors endorsing Candida infections. Infez. Med. 2015, 23, 211–223. [Google Scholar] [PubMed]
  36. Bassyouni, R.H.; Wegdan, A.A.; Abdelmoneim, A.; Said, W.; AboElnaga, F. Phospholipase and Aspartyl Proteinase Activities of Candida Species Causing Vulvovaginal Candidiasis in Patients with Type 2 Diabetes Mellitus. J. Microbiol. Biotechnol. 2015, 25, 1734–1741. [Google Scholar] [CrossRef] [PubMed]
  37. Berbudi, A.; Rahmadika, N.; Tjahjadi, A.I.; Ruslami, R. Type 2 Diabetes and its Impact on the Immune System. Curr. Diabetes Rev. 2020, 16, 442–449. [Google Scholar] [CrossRef]
  38. Sabina, J.; Brown, V. Glucose sensing network in Candida albicans: A sweet spot for fungal morphogenesis. Eukaryot. Cell 2009, 8, 1314–1320. [Google Scholar] [CrossRef]
  39. Dornelas Figueira, L.M.; Ricomini Filho, A.P.; da Silva, W.J.; Del Be, L.C.A.A.; Ruiz, K.G.S. Glucose effect on Candida albicans biofilm during tissue invasion. Arch. Oral Biol. 2020, 117, 104728. [Google Scholar] [CrossRef] [PubMed]
  40. Van Ende, M.; Wijnants, S.; Van Dijck, P. Sugar Sensing and Signaling in Candida albicans and Candida glabrata. Front. Microbiol. 2019, 10, 99. [Google Scholar] [CrossRef]
  41. Saud, B.; Bajgain, P.; Paudel, G.; Shrestha, V.; Bajracharya, D.; Adhikari, S.; Dhungana, G.; Awasthi, M.S. Fungal Infection among Diabetic and Nondiabetic Individuals in Nepal. Interdiscip. Perspect. Infect. Dis. 2020, 2020, 7949868. [Google Scholar] [CrossRef] [PubMed]
  42. Halimi, A.; Mortazavi, N.; Memarian, A.; Zahedi, M.; Niknejad, F.; Sohrabi, A.; Sarraf, S.J. The relation between serum levels of interleukin 10 and interferon-gamma with oral candidiasis in type 2 diabetes mellitus patients. BMC Endocr. Disord. 2022, 22, 296. [Google Scholar] [CrossRef]
  43. Graves, D.T.; Correa, J.D.; Silva, T.A. The Oral Microbiota Is Modified by Systemic Diseases. J. Dent. Res. 2019, 98, 148–156. [Google Scholar] [CrossRef]
  44. Chen, T.; Qin, Y.; Chen, M.; Zhang, Y.; Wang, X.; Dong, T.; Chen, G.; Sun, X.; Lu, T.; White, R.A., 3rd; et al. Gestational diabetes mellitus is associated with the neonatal gut microbiota and metabolome. BMC Med. 2021, 19, 120. [Google Scholar] [CrossRef]
  45. Hu, Y.; Peng, J.; Li, F.; Wong, F.S.; Wen, L. Evaluation of different mucosal microbiota leads to gut microbiota-based prediction of type 1 diabetes in NOD mice. Sci. Rep. 2018, 8, 15451. [Google Scholar] [CrossRef] [PubMed]
  46. Liu, S.; Cao, R.; Liu, L.; Lv, Y.; Qi, X.; Yuan, Z.; Fan, X.; Yu, C.; Guan, Q. Correlation between Gut Microbiota and Testosterone in Male Patients with Type 2 Diabetes Mellitus. Front. Endocrinol. 2022, 13, 836485. [Google Scholar] [CrossRef] [PubMed]
  47. Diviccaro, S.; Falvo, E.; Piazza, R.; Cioffi, L.; Herian, M.; Brivio, P.; Calabrese, F.; Giatti, S.; Caruso, D.; Melcangi, R.C. Gut microbiota composition is altered in a preclinical model of type 1 diabetes mellitus: Influence on gut steroids, permeability, and cognitive abilities. Neuropharmacology 2023, 226, 109405. [Google Scholar] [CrossRef]
  48. Du, F.; Ma, J.; Gong, H.; Bista, R.; Zha, P.; Ren, Y.; Gao, Y.; Chen, D.; Ran, X.; Wang, C. Microbial Infection and Antibiotic Susceptibility of Diabetic Foot Ulcer in China: Literature Review. Front. Endocrinol. 2022, 13, 881659. [Google Scholar] [CrossRef]
Microorganisms 12 01984 g001
Figure 1. Schematic diagram illustrating the underlying assumptions of Mendelian randomization analysis and workflow in this study. The red crosses signify that the hypothesis is invalid.
Figure 1. Schematic diagram illustrating the underlying assumptions of Mendelian randomization analysis and workflow in this study. The red crosses signify that the hypothesis is invalid.
Microorganisms 12 01984 g001
Microorganisms 12 01984 g002aMicroorganisms 12 01984 g002bMicroorganisms 12 01984 g002c
Figure 2. A scatter plot depicting the genetic correlations between type 2 diabetes (T2D) and candidiasis, employing various Mendelian randomization (MR) methodologies. (A) The causal effect estimates of T2D on candidiasis. (B) The causal effect estimates of T2D (wide definition) on candidiasis. (C) The causal effect estimates of severe autoimmune T2D on candidiasis. The slopes of the lines in the plot represent the estimated causal effects of each method. The individual SNP effects on the outcome (candidiasis) (depicted as points with vertical lines) versus the effects on exposure (T2D, T2D (wide definition), and severe autoimmune T2D (depicted as points with horizontal lines) are detailed in the background.
Figure 2. A scatter plot depicting the genetic correlations between type 2 diabetes (T2D) and candidiasis, employing various Mendelian randomization (MR) methodologies. (A) The causal effect estimates of T2D on candidiasis. (B) The causal effect estimates of T2D (wide definition) on candidiasis. (C) The causal effect estimates of severe autoimmune T2D on candidiasis. The slopes of the lines in the plot represent the estimated causal effects of each method. The individual SNP effects on the outcome (candidiasis) (depicted as points with vertical lines) versus the effects on exposure (T2D, T2D (wide definition), and severe autoimmune T2D (depicted as points with horizontal lines) are detailed in the background.
Microorganisms 12 01984 g002aMicroorganisms 12 01984 g002bMicroorganisms 12 01984 g002c
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Figure 3. Funnel plots illustrating genetically predicted associations with (A) type 2 diabetes (T2D), (B) T2D (using a broad definition), and (C) severe autoimmune T2D in relation to candidiasis, employing the inverse-variance-weighted (IVW) and MR-Egger methodologies.
Figure 3. Funnel plots illustrating genetically predicted associations with (A) type 2 diabetes (T2D), (B) T2D (using a broad definition), and (C) severe autoimmune T2D in relation to candidiasis, employing the inverse-variance-weighted (IVW) and MR-Egger methodologies.
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Figure 4. “Leave-one-out” sensitivity analysis. The MR outcomes for the remaining instrumental variables (IVs) were computed by sequentially excluding each IV. (A) Type 2 diabetes (T2D), (B) T2D (wide definition), and (C) severe autoimmune T2D. The red lines represent the analysis outcomes using the random effects inverse-variance-weighted (IVW) method.
Figure 4. “Leave-one-out” sensitivity analysis. The MR outcomes for the remaining instrumental variables (IVs) were computed by sequentially excluding each IV. (A) Type 2 diabetes (T2D), (B) T2D (wide definition), and (C) severe autoimmune T2D. The red lines represent the analysis outcomes using the random effects inverse-variance-weighted (IVW) method.
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Table 1. Data sources utilized in the present study.
Table 1. Data sources utilized in the present study.
Exposures or OutcomeNumber of CaseNumber of ControlAncestryGWAS ID
Type 2 diabetes38,841451,248Europeanebi-a-GCST90018926
Type 2 diabetes (wide definition)17,268184,778Europeanfinn-b-T2D_WIDE
Severe autoimmune type 2 diabetes4522744Europeanebi-a-GCST90026412
Candidiasis2015214,816Europeanfinn-b-AB1_CANDIDIASIS
Table 2. MR results.
Table 2. MR results.
ExposureMethodsNo. of SNPsp-ValueOROR_LCI95OR_UCI95
T2DMR Egger1700.21001.11930.89841.3402
Weighted median1700.44901.05830.94731.1693
Inverse-variance-weighted1700.02641.10460.90961.2996
Weighted mode1700.27751.10570.90871.3027
T2D, wide definitionMR Egger840.01541.31970.77601.8634
Weighted median840.07781.13700.88531.3887
Inverse-variance-weighted840.00311.15620.87181.4406
Weighted mode840.28011.13200.88901.3751
Severe autoimmune T2DMR Egger130.22701.06880.93841.1991
Weighted median130.02851.05540.94981.1610
Inverse-variance-weighted130.00411.05590.94931.1625
Weighted mode130.09841.05440.95061.1582
Table 3. MR sensitivity analyses.
Table 3. MR sensitivity analyses.
ExposuresOutcomesNo. of SNPsCochran’s Heterogeneity TestPleiotropy Test
Single-SNP IVWMR-Egger Intercept
Qp-ValueInterceptp-Value
T2DCandidiasis170193.10.09842−0.00110.865
T2D (wide definition)Candidiasis84101.20.08546−0.0150.194
Severe autoimmune T2DCandidiasis136.4230.8933−0.00780.807
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Xiong, J.; Lu, H.; Jiang, Y. A Causal Relationship between Type 2 Diabetes and Candidiasis through Two-Sample Mendelian Randomization Analysis. Microorganisms 2024, 12, 1984. https://doi.org/10.3390/microorganisms12101984

AMA Style

Xiong J, Lu H, Jiang Y. A Causal Relationship between Type 2 Diabetes and Candidiasis through Two-Sample Mendelian Randomization Analysis. Microorganisms. 2024; 12(10):1984. https://doi.org/10.3390/microorganisms12101984

Chicago/Turabian Style

Xiong, Juan, Hui Lu, and Yuanying Jiang. 2024. "A Causal Relationship between Type 2 Diabetes and Candidiasis through Two-Sample Mendelian Randomization Analysis" Microorganisms 12, no. 10: 1984. https://doi.org/10.3390/microorganisms12101984

APA Style

Xiong, J., Lu, H., & Jiang, Y. (2024). A Causal Relationship between Type 2 Diabetes and Candidiasis through Two-Sample Mendelian Randomization Analysis. Microorganisms, 12(10), 1984. https://doi.org/10.3390/microorganisms12101984

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