Type 2 Diabetes-Related Variants Influence on the Risk of Developing Multiple Myeloma: Results from the Immense Consortium

Abstract Type 2-diabetes (T2D) is thought to be a relevant risk factor for multiple myeloma (MM), but the relationship between both traits is still not well understood. Thus, we decided to conduct a population-based case-control study in a population of 1420 MM patients (705 women and 715 men) and 1858 controls (916 women and 942 men) to evaluate whether 58 genome-wide association studies (GWAS)-identified common variants for T2D influence the risk of developing MM. Logistic regression analyses showed that carriers of the KCNQ1rs2237892T allele or CDKN2A-2Brs2383208G/G, IGF-1rs35767T/T and MADDrs7944584T/T genotypes had an increased risk of MM (OR=1.32, 95%CI 1.01-1.71, P=0.039; OR=1.86, 95%CI 1.12-3.11, P=0.016; OR=2.13, 95%CI 1.35-3.37, P=0.001 and OR=1.33, 95%CI 1.06-1.67, P=0.014, respectively) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a decreased risk for the disease (OR=0.85, 95%CI 0.73-0.99, P=0.38; OR=0.84, 95%CI 0.72-0.99, P=0.034; OR=0.81, 95%CI 0.68-0.98, P=0.032; OR=0.78, 95%CI 0.64-0.95, P=0.013; and OR=0.76, 95%CI 0.58-0.99, P=0.042, respectively). The associations of these T2D-related variants with an increased or decreased risk of MM were due to non-diabetogenic alleles, which suggests a non-diabetogenic mechanism underlying the effect of these variants to determine the risk of the disease. A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348, and NOTCH2rs10923931 SNPs (Pinteraction=0.001 and 0.0004 and Phet=0.19 and 0.60, respectively), which also underlies the importance of considering gender as a factor modifying the risk for MM. Men harbouring the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a decreased risk of MM (OR=0.71, 95%CI 0.54-0.94, P=0.015 and OR=0.66, 95%CI 0.50-0.86, P=0.0019) whereas an opposite but not significant effect was observed in women. Finally, SNP-SNP interaction analysis revealed overall significant two- and three-locus interaction models to increase the risk of MM (FAM148Brs11071657-KCNJ11rs5219, and SLC30A8rs13266634-KCNJ11rs5219-FTOrs8050136; P=0.01 and 0.001, respectively) whereas a significant four-locus model was also found to increase the risk of MM in men (FADS1rs174550-TSPAN8rs7961581-PROX1rs340874-KCNJ11rs5219, P=0.001). Although further studies in independent populations are warranted to replicate these findings, these results suggest that TD2-related variants may influence the risk of developing MM, likely through non-diabetogenic mechanisms. Abstract 2044. Table 1. Demographical characteristics of IMMEnSE cases and controls. CASES CONTROLS Region* Gender M/F (Total) Mean Age (± STD) Gender M/F (Total) Mean Age (± STD) Control type Italy 117/107 (224) 62.60±9.90 127/105 (232) 58.75±10.92 General population Poland 173/198 (371) 62.35±10.39 124/226 (350) 50.68±19.43 Blood donors Spain 139/133 (272) 63.06±11.04 218/192 (410) 63.12±11.94 Hospitalized subjects France 42/33 (75) 55.80±9.04 95/89 (184) 44.07±15.22 Blood donors Portugal 32/35 (67) 65.79±11.16 52/42 (94) 60.88±07.88 Blood donors Hungary 49/87 (136) 65.83±11.19 50/51 (101) 73.18±10.10 Hospitalized subjects Denmark 163/112 (275) 55.20±07.32 276/211 (487) 43.26±11.84 General population Total 715/705 (1420) 61.06±10.57 942/916 (1858) 53.56±16.45 Table 2. Selected type-2 diabetes-related polymorphisms Gene name dbSNP rs# Gene name dbSNP rs# ADAM30 rs2641348 JAZF1 rs864745 ADAMTS9 rs4607103 KCNJ11 rs5215 ADCY5 rs11708067 rs5219 ADRA2A rs10885122 KCNQ1 rs2237897 ARAPI, CENTD2 rs1552224 rs2074196 BCL11A rs10490072 rs2237892 CDC123 rs12779790 rs2237895 CDKAL1 rs7754840 KCNQ1OT1 rs231362 CDKN2A-2B rs564398 LTA rs1041981 rs10811661 MADD rs7944584 rs2383208 MCR4 rs12970134 COL5A1 rs4240702 MTNR1B rs1387153 CRY2 rs11605924 NOTCH2 rs10923931 DCD rs1153188 PKN2 rs6698181 EXT2 rs1113132 PPARG rs1801282 FADS1 rs174550 PRC1 rs8042680 FAM148B rs11071657 PROX1 rs340874 FLJ39370 rs17044137 RBMS1 rs7593730 FTO rs8050136 SLC2A2 rs11920090 G6PC2 rs560887 SLC30A8 rs13266634 GCK rs1799884 TCF2 rs7501939 GCKR rs1260326 TCF7L2 rs7903146 HHEX rs1111875 TCF7L2 rs12255372 HMGA2 rs1531343 THADA rs7578597 HNF1A, TCF1 rs7957197 TP53INP1 rs896854 IGF1 rs35767 TSPAN8 rs7961581 IGF2BP2 rs4402960 VEGFA rs9472138 IL13 rs20541 WFS1 rs734312 IRS1 rs2943641 rs10010131 Disclosures No relevant conflicts of interest to declare.

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Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

Endocrine Related Cancer ◽

10.1530/erc-15-0029 ◽

2015 ◽

Vol 22 (4) ◽

pp. 545-559 ◽

Cited By ~ 6

Author(s):

Rafael Ríos ◽

Carmen Belén Lupiañez ◽

Daniele Campa ◽

Alessandro Martino ◽

Joaquin Martínez-López ◽

...

Keyword(s):

Type 2 Diabetes ◽

Multiple Myeloma ◽

Prediction Models ◽

Disease Risk ◽

Association Studies ◽

Area Under The Curve ◽

Genome Wide Association Studies ◽

Increased Risk ◽

Stratified Analysis

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

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Animal Models of GWAS-Identified Type 2 Diabetes Genes

Journal of Diabetes Research ◽

10.1155/2013/906590 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 21

Author(s):

Gabriela da Silva Xavier ◽

Elisa A. Bellomo ◽

James A. McGinty ◽

Paul M. French ◽

Guy A. Rutter

Keyword(s):

Type 2 Diabetes ◽

Animal Models ◽

Mouse Models ◽

Human Genetics ◽

Association Studies ◽

Genome Wide Association Studies ◽

Risk Genes ◽

Genome Wide ◽

Increased Risk

More than 65loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D). Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notablyTCF7L2andZnT8/SLC30A8, have to date been examined in mouse models. We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics.

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Type 2 diabetes as a determinant of Parkinson’s disease risk and progression

10.1101/2020.11.12.20230474 ◽

2020 ◽

Author(s):

Harneek Chohan ◽

Konstantin Senkevich ◽

Radhika K Patel ◽

Jonathan P Bestwick ◽

Benjamin M Jacobs ◽

...

Keyword(s):

Type 2 Diabetes ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Observational Studies ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Increased Risk ◽

Summary Data

ABSTRACTObjectiveTo investigate type 2 diabetes mellitus (T2DM) as a determinant of Parkinson’s disease (PD) through a meta-analysis of observational and genetic summary data.MethodsA systematic review and meta-analysis of observational studies was undertaken by searching six databases. We selected the highest quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate causal effects of genetic liability towards T2DM on PD risk and progression, using summary data derived from genome-wide association studies.ResultsIn the observational part of the study, nine studies were included in the risk meta-analysis and four studies were included in the progression meta-analysis. Pooled effect estimates revealed that T2DM was associated with an increased risk of PD (OR 1.21, 95% CI 1.07-1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (SMD 0.55, 95% CI 0.39-0.72) and cognitive decline (SMD −0.92, 95% CI −1.50 – −0.34). Using MR we found supportive evidence for a causal effect of diabetes on PD risk (IVW OR 1.08, 95% CI 1.02-1.14; p=0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01-1.20; p=0.032), but not for cognitive progression.ConclusionUsing meta-analysis of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk, and new evidence to support a role in PD progression. Treatment of diabetes may be an effective strategy to prevent or slow progression of PD.

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

Frontiers in Genetics ◽

10.3389/fgene.2020.597876 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhiyong Cui ◽

Hui Feng ◽

Baichuan He ◽

Yong Xing ◽

Zhaorui Liu ◽

...

Keyword(s):

Type 2 Diabetes ◽

Mendelian Randomization ◽

Association Studies ◽

Causal Effects ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Knee Oa ◽

Increased Risk

BackgroundIt remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).MethodsHere, 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.ResultsWe 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, I2 < 0.0001, p = 0.6104).ConclusionOur MR study did not support causal effects of a genetically increased T2D risk, FG, and 2hGlu on hip and knee OA risk.

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Type 2 diabetes and glycemic traits are not causal factors of osteoarthritis: A two-sample Mendelian randomization analysis

10.21203/rs.3.rs-37242/v1 ◽

2020 ◽

Author(s):

Zhiyong Cui ◽

Hui Feng ◽

Baichuan He ◽

Yong Xing ◽

Zhaorui Liu ◽

...

Keyword(s):

Type 2 Diabetes ◽

Mendelian Randomization ◽

Association Studies ◽

Causal Effects ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Knee Oa ◽

Increased Risk

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-hour postprandial glucose (2hGlu) from genome-wide association studies (GWAS) . We used MR inverse variance weighted (IVW), the MR-Egger method, the weighted median (WM) and Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG and 2hGlu with hip and knee OA risk. 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=0.9536, 95% CI 0.5585 to 1.6283, p=0.8629). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR-Egger, intercept=-0.0032, p=0.8518). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR-Egger Q=40.5481, I2=0.1368, p=0.2389). Conclusions: Our MR study did not support causal effects of a genetically increased T2D risk, FG and 2hGlu on hip and knee OA risk.

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Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

Human Molecular Genetics ◽

10.1093/hmg/ddab044 ◽

2021 ◽

Author(s):

Minako Imamura ◽

Atsushi Takahashi ◽

Masatoshi Matsunami ◽

Momoko Horikoshi ◽

Minoru Iwata ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Association Studies ◽

Meta Analysis ◽

Japanese Patients ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p < 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

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A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

International Journal of Epidemiology ◽

10.1093/ije/dyaa262 ◽

2021 ◽

Author(s):

Guanghao Qi ◽

Nilanjan Chatterjee

Keyword(s):

Type 2 Diabetes ◽

Mendelian Randomization ◽

Association Studies ◽

Real Data ◽

Causal Effects ◽

Type I ◽

Genome Wide Association Studies ◽

Simulation Studies ◽

Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

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Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study

BMC Medicine ◽

10.1186/s12916-021-01944-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Shiu Lun Au Yeung ◽

Jie V Zhao ◽

C Mary Schooling

Keyword(s):

Type 2 Diabetes ◽

Genetic Predisposition ◽

Mendelian Randomization ◽

Fasting Glucose ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Inverse Association ◽

Wide Confidence Interval

Abstract Background Observational studies suggest poorer glycemic traits and type 2 diabetes associated with coronavirus disease 2019 (COVID-19) risk although these findings could be confounded by socioeconomic position. We conducted a two-sample Mendelian randomization to clarify their role in COVID-19 risk and specific COVID-19 phenotypes (hospitalized and severe cases). Method We identified genetic instruments for fasting glucose (n = 133,010), 2 h glucose (n = 42,854), glycated hemoglobin (n = 123,665), and type 2 diabetes (74,124 cases and 824,006 controls) from genome wide association studies and applied them to COVID-19 Host Genetics Initiative summary statistics (17,965 COVID-19 cases and 1,370,547 population controls). We used inverse variance weighting to obtain the causal estimates of glycemic traits and genetic predisposition to type 2 diabetes in COVID-19 risk. Sensitivity analyses included MR-Egger and weighted median method. Results We found genetic predisposition to type 2 diabetes was not associated with any COVID-19 phenotype (OR: 1.00 per unit increase in log odds of having diabetes, 95%CI 0.97 to 1.04 for overall COVID-19; OR: 1.02, 95%CI 0.95 to 1.09 for hospitalized COVID-19; and OR: 1.00, 95%CI 0.93 to 1.08 for severe COVID-19). There were no strong evidence for an association of glycemic traits in COVID-19 phenotypes, apart from a potential inverse association for fasting glucose albeit with wide confidence interval. Conclusion We provide some genetic evidence that poorer glycemic traits and predisposition to type 2 diabetes unlikely increase the risk of COVID-19. Although our study did not indicate glycemic traits increase severity of COVID-19, additional studies are needed to verify our findings.

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