No Casual Relationship Between T2DM and the Risk of Infectious Diseases: A Two-Sample Mendelian Randomization Study.
Ontology highlight
ABSTRACT: Background
In epidemiological studies, it has been proven that the occurrence of type 2 diabetes mellitus (T2DM) is related to an increased risk of infectious diseases. However, it is still unclear whether the relationship is casual.Methods
We employed a two-sample Mendelian randomization (MR) to clarify the causal effect of T2DM on high-frequency infectious diseases: sepsis, skin and soft tissue infections (SSTIs), urinary tract infections (UTIs), pneumonia, and genito-urinary infection (GUI) in pregnancy. And then, we analyzed the genome-wide association study (GWAS) meta-analysis of European-descent individuals and conducted T2DM-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) that were associated with genome-wide significance (p < 5 × 10-8). MR estimates were obtained using the inverse variance-weighted (IVW), the MR-Egger regression, the simple mode (SM), weighted median, and weighted mode.Results
The UK Biobank (UKB) cohort (n > 500,000) provided data for GWASs on infectious diseases. MR analysis showed little evidence of a causal relationship of T2DM with five mentioned infections' (sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy) susceptibility [odds ratio (OR) = 0.99999, p = 0.916; OR = 0.99986, p = 0.233; OR = 0.99973, p = 0.224; OR = 0.99997, p = 0.686; OR, 1.00002, p = 0.766]. Sensitivity analysis showed similar results, indicating the robustness of causality. There were no heterogeneity and pleiotropic bias.Conclusion
T2DM would not be causally associated with high-frequency infectious diseases (including sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy).
SUBMITTER: Wang H
PROVIDER: S-EPMC8435717 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Observational studies showed possible associations between systemic lupus erythematosus and multiple myeloma. However, whether there is a casual relationship between different types of autoimmune diseases (type 1 diabetes mellitus, rheumatoid arthritis, systemic lupus erythematosus, psoriasis, multiple sclerosis, primary sclerosing cholangitis, primary biliary cirrhosis, and juvenile idiopathic arthritis) and multiple myeloma (MM) is not well known. We performed a two-sample Mendelian randomization (MR) study to estimate the casual relationship. Summary-level data of autoimmune diseases were gained from published genome-wide association studies while data of MM was obtained from UKBiobank. The Inverse-Variance Weighted (IVW) method was used as the primary analysis method to interpret the study results, with MR-Egger and weighted median as complementary methods of analysis. There is causal relationship between primary sclerosing cholangitis [OR = 1.00015, 95% CI 1.000048-1.000254, P = 0.004] and MM. Nevertheless, no similar causal relationship was found between the remaining seven autoimmune diseases and MM. Considering the important role of age at recruitment and body mass index (BMI) in MM, we excluded these relevant instrument variables, and similar results were obtained. The accuracy and robustness of these findings were confirmed by sensitivity tests. Overall, MR analysis suggests that genetic liability to primary sclerosing cholangitis could be causally related to the increasing risk of MM. This finding may serve as a guide for clinical attention to patients with autoimmune diseases and their early screening for MM.
| S-EPMC10987346 | biostudies-literature
Project description:PurposeType 1 diabetes mellitus (T1DM) is associated with different types of infections; however, studies on the causal relationship between T1DM and infectious diseases are lacking. Therefore, our study aimed to explore the causalities between T1DM and six high-frequency infections using a Mendelian randomization (MR) approach.MethodsTwo-sample MR studies were conducted to explore the causalities between T1DM and six high-frequency infections: sepsis, acute lower respiratory infections (ALRIs), intestinal infections (IIs), infections of the genitourinary tract (GUTIs) in pregnancy, infections of the skin and subcutaneous tissues (SSTIs), and urinary tract infections (UTIs). Data on summary statistics for T1DM and infections were obtained from the European Bioinformatics Institute database, the United Kingdom Biobank, FinnGen biobank, and Medical Research Council Integrative Epidemiology Unit. All data obtained for summary statistics were from European countries. The inverse-variance weighted (IVW) method was employed as the main analysis. Considering the multiple comparisons, statistical significance was set at p< 0.008. If univariate MR analyses found a significant causal association, multivariable MR (MVMR) analyses were performed to adjust body mass index (BMI) and glycated hemoglobin (HbA1c). MVMR-IVW was performed as the primary analysis, and the least absolute shrinkage and selection operator (LASSO) regression and MVMR-Robust were performed as complementary analyses.ResultsMR analysis showed that susceptibility to IIs increased in patients with T1DM by 6.09% using the IVW-fixed method [odds ratio (OR)=1.0609; 95% confidence interval (CI): 1.0281-1.0947, p=0.0002]. Results were still significant after multiple testing. Sensitivity analyses did not show any significant horizontal pleiotropy or heterogeneity. After adjusting for BMI and HbA1c, MVMR-IVW (OR=1.0942; 95% CI: 1.0666-1.1224, p<0.0001) showed significant outcomes that were consistent with those of LASSO regression and MVMR-Robust. However, no significant causal relationship was found between T1DM and sepsis susceptibility, ALRI susceptibility, GUTI susceptibility in pregnancy, SSTI susceptibility, and UTI susceptibility.ConclusionsOur MR analysis genetically predicted increased susceptibility to IIs in T1DM. However, no causality between T1DM and sepsis, ALRIs, GUTIs in pregnancy, SSTIs, or UTIs was found. Larger epidemiological and metagenomic studies are required to further investigate the observed associations between the susceptibility of certain infectious diseases with T1DM.
| S-EPMC10102543 | biostudies-literature
Project description:BackgroundThe interplay between gut microbiome genera and inflammatory kidney-related diseases, such as nephrotic syndrome, glomerulonephritis, tubulo-interstitial nephritis, and chronic kidney disease, has been observed. However, the causal relationships between specific bacterial genera and these renal diseases have not been fully elucidated.ObjectiveTo investigate the potential causal links between different genera of the gut microbiome and the susceptibility to various renal conditions utilizing two-sample Mendelian randomization (MR) analyses.Materials and methodsGenome-wide association study (GWAS) summary statistics of gut microbiota and inflammatory kidney-related diseases were obtained from published GWASs. Two-sample MR analyses were conducted using methods including inverse-variance weighted (IVW), MR Egger, and others to identify potential causal links between gut microbial genera and renal conditions. Sensitivity analyses, including Cochran's Q test and the MR-PRESSO global test, were performed to validate the robustness of the results and detect horizontal pleiotropy. In addition, a reverse MR analysis was conducted to assess reverse causation possibilities.ResultsBy synthesizing insights from both primary and sensitivity analyses, this study unveiled critical associations of 12 bacterial genera with nephrotic syndrome, 7 bacterial genera with membranous nephropathy, 3 bacterial genera with glomerulonephritis, 4 bacterial genera with acute tubulo-interstitial nephritis, 6 bacterial genera with chronic tubulo-interstitial nephritis, and 7 bacterial genera with chronic kidney disease. Various genera were pinpointed as having either positive or negative causal relationships with these renal conditions, as evidenced by specific ranges of IVW-OR values (all P< 0.05). The congruence of the sensitivity analyses bolstered the primary findings, displaying no marked heterogeneity or horizontal pleiotropy. Notably, the reverse MR analysis with nephritis as the exposure did not reveal any causal relationships, thereby strengthening the resilience and validity of the primary associations.ConclusionThis study explored the causal associations between several gut microbial genera and the risk of several inflammatory kidney-related diseases, uncovering several associations between specific gut microbial genera and nephrotic syndrome, membranous nephropathy, glomerulonephritis, tubulo-interstitial nephritis, and chronic kidney disease. These findings enhance our understanding of the complex interplay between the gut microbiome and kidney diseases, and they will be beneficial for early diagnosis and subsequent treatment.
| S-EPMC10811222 | biostudies-literature
Project description:ObjectiveObservational studies have shown an association between systemic autoimmune disease (AD) and multiple malignancies. However, due to the difficulty indetermining the temporal nature of the order, their causal relationship remains elusive. Based on pooled data from a large population-wide genome-wide association study (GWAS), this study explores the genetic causality between systemic autoimmune disease and renal malignancy.MethodsWe took a series of quality control steps from a large-scale genome-wide association study to select single nucleotide polymorphisms (SNPs) associated with systemic autoimmune disease as instrumental variables(IVs) to analyze genetic causality with renal malignancies. Inverse variance weighting (IVW), MR- Egger, weighted median, simple model and weighted model were used for analysis. The results were mainly based on IVW (Random Effects), followed by sensitivity analysis. Inverse-Variance Weighted(IVW) and MR-Egger were used to test for heterogeneity. MR- Egger is also used for pleiotropic testing. A single SNP analysis was used to identify single nucleotide polymorphisms (SNPs) with potential impact. Odds ratio (OR) and 95% confidence interval (CI) were used to evaluate causality, and sensitivity analysis was performed to evaluate pleiotropy and instrumental validity.ResultsAcute and subacute iridocylitis (P = 0.006, OR = 1.077), Ankylosing spondylitis (P = 0.002, OR = 1.051), and spondyloarthritis (P = 0.009, OR = 1.073) were positively associated with an increased risk of renal malignancy. Coxarthrosis (P = 0.008, OR = 0.483), Juvenile rheumatism (P = 0.011, OR = 0.897), and Systemic lupus erythematosus (P = 0.014, OR = 0.869) were negatively associated with an increased risk of renal malignancy. The results of sensitivity analysis were consistent without heterogeneity or pleiotropy.ConclusionOur study suggests a causal relationship between different systemic autoimmune diseases and renal malignancies. These findings prompt health care providers to take seriously the potential risk of systemic autoimmune disease and provide new insights into the genetics of kidney malignancies.
| S-EPMC10903885 | biostudies-literature
Project description:To investigate the causal relationship between attention deficit and hyperactivity disorder (ADHD) and frozen shoulder using Mendelian randomization (MR). Data were pooled from large-scale genome wide association studies, and genetic loci that were independent of each other and associated with ADHD and frozen shoulder in people of European ancestry were selected as instrumental variables. Three MR analyses, inverse variance weighting, weighted median and MR-Egger, were used to investigate the causal relationship between ADHD and frozen shoulder. Heterogeneity and multiplicity tests were used, and sensitivity analyses were performed using the "leave-one-out" method to explore the robustness of the results. The inverse variance weighting results showed an OR (95 % CI) of 1.12 (1.00-1.25), P = .046, indicating a causal relationship between ADHD and frozen shoulder. And no heterogeneity and multiplicity were found by the test and sensitivity analysis also showed robust results. The present study used a two-sample MR analysis, and by analyzing and exploring the genetic data, the study showed that ADHD is a risk factor for developing frozen shoulder, and patients with ADHD are more likely to suffer from frozen shoulder.
| S-EPMC10627672 | biostudies-literature
Project description:To investigate the causal relationship between obesity and meniscal injuries using Mendelian randomization (MR). Genetic loci independently associated with obesity and meniscal injuries in people of European origin were selected as instrumental variables using pooled data from genome-wide association studies. Three MR analyses, MR-Egger, weighted median and inverse variance weighting, were used to investigate the causal relationship between obesity and meniscal injuries. The results were tested for robustness by heterogeneity and multiplicity tests, and sensitivity analyses were performed using the "leave-one-out" method. The inverse variance weighting results showed an OR (95% CI) of 1.13 (1.04-1.22), P = .003, indicating a causal relationship between obesity and the occurrence of meniscal injuries. And no heterogeneity and multiplicity were found by the test and sensitivity analysis also showed robust results. In this study, genetic data were analyzed and explored using 2-sample MR analysis, and the results showed that obesity is a risk factor for meniscal injuries.
| S-EPMC10695575 | biostudies-literature
Project description:BackgroundEvidence from observational studies and clinical trials suggests that the allergic diseases (ADs) are associated with kidney diseases (KDs). However, the causal association between them remains to be determined. We used bidirectional two-sample Mendelian randomization (MR) analysis to evaluate the potential causality between them.MethodsMendelian randomization (MR) was performed using publicly available genome-wide association study (GWAS) summary datasets. Inverse variance weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods are used to evaluate the causality between ADs and KDs. Sensitivity and heterogeneity analyses were used to ensure the stability of the results.ResultsThe MR results indicated that genetic susceptibility to ADs was associated with a higher risk of CKD [odds ratio (OR) = 1.124, 95% CI = 1.020-1.239, p = 0.019] and unspecified kidney failure (OR = 1.170, 95% CI = 1.004-1.363, p = 0.045) but not with kidney stone, ureter stone or bladder stone (OR = 1.001, 95% CI = 1.000-1.002, p = 0.216), other renal or kidney problem (OR = 1.000, 95% CI = 1.000-1.001, p = 0.339), urinary tract or kidney infection (OR = 1.000, 95% CI = 0.999-1.001, p = 0.604), kidney volume (OR = 0.996, 95% CI = 0.960-1.033, p = 0.812) and cyst of kidney (OR = 0.914, 95% CI = 0.756-1.105, p = 0.354). No causal evidence of KDs on ADs was found in present study.ConclusionResults from MR analysis indicate a causal association between ADs and CKD and unspecified kidney failure. These findings partly suggest that early monitoring of CKD risk in patients with ADs is intentional.
| S-EPMC10963543 | biostudies-literature
Project description:Type 2 diabetes (T2D) frequently co-occurs with respiratory system diseases such as chronic obstructive pulmonary disease (COPD), bronchial asthma, lung cancer, interstitial lung disease, and pulmonary tuberculosis. Although a potential association is noted between these conditions, the available research is limited. To investigate the causal relationship between patients with T2D and respiratory system diseases using two-sample Mendelian randomization analysis. Causal relationships were inferred using a two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies. We employed the variance inverse-weighted method as the primary analytical approach based on three key assumptions underlying MR analysis. To bolster the robustness and reliability of our results, we utilized MR Egger's intercept test to detect potential pleiotropy, Cochran's Q test to assess heterogeneity, funnel plots to visualize potential bias, and "leave-one-out" sensitivity analysis to ensure that our findings were not unduly influenced by any single genetic variant. The inverse variance weighted (IVW) analysis indicated a causal relationship between T2D and COPD [Odds Ratio (OR) = 0.87; 95% Confidence Interval (CI) = 0.82-0.96; p < 0.05]. No significant heterogeneity or pleiotropy were observed through their respective tests (p > 0.05), and the statistical power calculations indicated that the results were reliable. The IVW analysis showed a negative causal relationship between T2D and bronchial asthma [OR = 0.85; 95% CI = 0.81-0.89; p < 0.05]. However, the IVW under the random-effects model indicated heterogeneity (p < 0.05), suggesting instability in the results and requiring cautious interpretation. The study found a positive causal relationship between T2D and pulmonary tuberculosis (OR = 1.24, 95% CI = 1.05-1.45, p < 0.05). However, they exhibited pleiotropy (p < 0.05), indicating their instability. No correlation between T2D and interstitial lung disease or lung cancer was observed. T2D is negatively associated with COPD, suggesting that T2D may reduce the risk of developing COPD. A negative causal relationship between T2D and bronchial asthma has been observed, but the results exhibit heterogeneity. There is a positive causal relationship between T2D and pulmonary tuberculosis, yet the findings suggest the presence of pleiotropy. No significant causal relationship between T2D and lung cancer or interstitial lung disease was observed.
| S-EPMC11291206 | biostudies-literature
Project description:BackgroundPrior observational studies have suggested a potential direct link between psoriasis (PSO) and interstitial lung disease (ILD). Consequently, we applied Mendelian randomization (MR) to further evaluate the bidirectional causal relationships between PSO and its different phenotypes [psoriatic arthritis (PSA)/psoriasis vulgaris (PSV)] and ILD.MethodsData regarding PSO/PSA/PSV and ILD were sourced from publicly accessible genome-wide association studies (GWAS) databases, focusing on European populations. We used five algorithms- MR Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode- to evaluate the causal relationships between PSO/PSA/PSV and ILD, with a primary emphasis on the IVW method.ResultsThe analysis indicated a potential association between PSA and an elevated risk of ILD [IVW odds ratio (OR): 1.035 (95% CI 1.008, 1.064; P = 0.012)], with no evidence of a direct relationship between total PSO and PSV with ILD. Conversely, no substantial evidence emerged from the reverse MR analysis to suggest that ILD significantly affects total PSO or the specific PSA/PSV phenotypes.ConclusionOur findings provide genetic evidence supporting the notion that PSA may be a contributory risk factor for ILD. Further investigations are warranted to explore the underlying mechanisms of this potential causal relationship between PSA and ILD.
| S-EPMC11234683 | biostudies-literature
Project description:BackgroundsMany studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR).MethodsWe collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs.ResultsFrom the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.
| S-EPMC10450337 | biostudies-literature