Dataset Information

Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis.

ABSTRACT:

Background

It remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).

Methods

Here, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single-nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG), and 2-h postprandial glucose (2hGlu) from genome-wide association studies (GWAS). We used the MR inverse variance weighted (IVW), the MR-Egger method, the weighted median (WM), and the Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG, and 2hGlu with hip and knee OA risks. Sensitivity analyses were also conducted to verify whether heterogeneity and pleiotropy can bias the MR results.

Results

We did not find statistically significant causal effects of genetically increased T2D risk, FG, and 2hGlu on hip and knee OA (e.g., T2D and hip OA, MR-Egger OR = 1.1708, 95% CI 0.9469-1.4476, p = 0.1547). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR-Egger, intercept = -0.0105, p = 0.1367). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR-Egger Q = 30.1362, I ² < 0.0001, p = 0.6104).

Conclusion

Our MR study did not support causal effects of a genetically increased T2D risk, FG, and 2hGlu on hip and knee OA risk.

SUBMITTER: Cui Z

PROVIDER: S-EPMC7838644 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis.

Cui Zhiyong Z Feng Hui H He Baichuan B Xing Yong Y Liu Zhaorui Z Tian Yun Y

Frontiers in genetics 20210113

<h4>Background</h4>It remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).<h4>Methods</h4>Here, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single-nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG), and 2-h postprandial glucose (2hGlu) from genome-wide association studies (GWAS). We used the MR inv ...[more]

PMID: 33519901

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Project description:ObjectiveTo investigate the genetic underpinnings of the association between type 2 diabetes (T2D), glycemic indicators such as fasting glucose (FG), fasting insulin (FI), and glycated hemoglobin (GH), and venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), thereby contributing novel insights to the scholarly discourse within this domain.MethodsGenome-wide association study (GWAS) summary data pertaining to exposures (T2D, FG, FI, GH) and outcomes (VTE, DVT, PE) were acquired from the IEU Open GWAS database, encompassing participants of European descent, including both male and female individuals. Two-sample Mendelian randomization (MR) analyses were conducted utilizing the TwoSampleMR and MRPRESSO packages within the R programming environment. The primary analytical approach employed was the random-effects inverse variance weighted (IVW) method. Heterogeneity was assessed via Cochran's Q statistic for MR-IVW and Rucker's Q statistic for MR-Egger. Horizontal pleiotropy was evaluated using the intercept test of MR Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, with the latter also employed for outlier detection. Additionally, a "Leave one out" analysis was conducted to ascertain the influence of individual single nucleotide polymorphisms (SNPs) on MR results.ResultsThe random-effects IVW analysis revealed a negative genetic causal association between T2D) and VTE (P = 0.008, Odds Ratio [OR] 95% confidence interval [CI] = 0.896 [0.827-0.972]), as well as between FG and VTE (P = 0.002, OR 95% CI = 0.655 [0.503-0.853]), GH and VTE (P = 0.010, OR 95% CI = 0.604 [0.412-0.884]), and GH and DVT (P = 0.002, OR 95% CI = 0.413 [0.235-0.725]). Conversely, the random-effects IVW analysis did not detect a genetic causal relationship between FI and VTE (P > 0.05), nor between T2D, FG, or FI and DVT (P > 0.05), or between T2D, FG, FI, or GH and PE (P > 0.05). Both the Cochran's Q statistic for MR-IVW and Rucker's Q statistic for MR-Egger indicated no significant heterogeneity (P > 0.05). Moreover, the intercept tests of MR Egger and MR-PRESSO suggested the absence of horizontal pleiotropy (P > 0.05). MR-PRESSO analysis identified no outliers, while the "Leave one out" analysis underscored that the MR analysis was not influenced by any single SNP.ConclusionOur investigation revealed that T2D, FG, and GH exhibit negative genetic causal relationships with VTE at the genetic level, while GH demonstrates a negative genetic causal relationship with DVT at the genetic level. These findings furnish genetic-level evidence warranting further examination of VTE, DVT, and PE, thereby making a contribution to the advancement of related research domains.

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.

Dataset Information

Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis.

Background

Methods

Results

Conclusion

Publications

Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis.

Similar Datasets

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