T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects
•30 SNPs were determined in a Czech Republic population to create genetic risk score.•WSF1 polymorphism were associated with MCVE and CANCAS1, CERS2 and C9 gene with ACM.•Predictive ability of observed endpoints of genetic and clinical model were compared.•Clinical model lightly improves predictive...
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Published in | Gene Vol. 927; p. 148724 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
15.11.2024
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Online Access | Get full text |
ISSN | 0378-1119 1879-0038 1879-0038 |
DOI | 10.1016/j.gene.2024.148724 |
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Abstract | •30 SNPs were determined in a Czech Republic population to create genetic risk score.•WSF1 polymorphism were associated with MCVE and CANCAS1, CERS2 and C9 gene with ACM.•Predictive ability of observed endpoints of genetic and clinical model were compared.•Clinical model lightly improves predictive ability but not statistically significant.
This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE − WSF1 (P = 0.029), several variants were associated with ACM − specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487–0.676) and 0.645 (95 % CI 0.556–0.735), respectively. However, this difference did not reach statistical significance (P = 0.06).
This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population. |
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AbstractList | This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population.This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population. •30 SNPs were determined in a Czech Republic population to create genetic risk score.•WSF1 polymorphism were associated with MCVE and CANCAS1, CERS2 and C9 gene with ACM.•Predictive ability of observed endpoints of genetic and clinical model were compared.•Clinical model lightly improves predictive ability but not statistically significant. This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE − WSF1 (P = 0.029), several variants were associated with ACM − specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487–0.676) and 0.645 (95 % CI 0.556–0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population. This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population. |
ArticleNumber | 148724 |
Author | Galuška, David Řehořová, Jitka Svojanovský, Jan Divácká, Petra Pácal, Lukáš Chalásová, Katarína Hubáček, Jaroslav A. Lánská, Věra Kaňková, Kateřina |
Author_xml | – sequence: 1 givenname: David surname: Galuška fullname: Galuška, David email: 423585@mail.muni.cz organization: Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic – sequence: 2 givenname: Lukáš orcidid: 0000-0003-1118-7424 surname: Pácal fullname: Pácal, Lukáš organization: Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic – sequence: 3 givenname: Katarína surname: Chalásová fullname: Chalásová, Katarína organization: Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic – sequence: 4 givenname: Petra surname: Divácká fullname: Divácká, Petra organization: Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic – sequence: 5 givenname: Jitka surname: Řehořová fullname: Řehořová, Jitka organization: Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic – sequence: 6 givenname: Jan surname: Svojanovský fullname: Svojanovský, Jan organization: Department of Internal Medicine, St. Anne’s University Hospital, Brno, Czech Republic – sequence: 7 givenname: Jaroslav A. orcidid: 0000-0001-6537-1353 surname: Hubáček fullname: Hubáček, Jaroslav A. organization: Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic – sequence: 8 givenname: Věra orcidid: 0000-0002-2832-5689 surname: Lánská fullname: Lánská, Věra organization: Department of Data Science, Institute for Clinical and Experimental Medicine, Prague, Czech Republic – sequence: 9 givenname: Kateřina orcidid: 0000-0002-5574-1768 surname: Kaňková fullname: Kaňková, Kateřina organization: Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic |
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Keywords | eGFR ESRD MCVE T2DM GWAS Diabetes mellitus ACM Genetic risk score uACR Genetic predisposition CVD PRS TG w/uGRS UAE Single nucleotide polymorphism DKD CKD Diabetic kidney disease |
Language | English |
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Snippet | •30 SNPs were determined in a Czech Republic population to create genetic risk score.•WSF1 polymorphism were associated with MCVE and CANCAS1, CERS2 and C9... This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2... |
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SubjectTerms | Aged Diabetes mellitus Diabetes Mellitus, Type 2 - complications Diabetes Mellitus, Type 2 - genetics Diabetic kidney disease Diabetic Nephropathies - genetics Disease Progression Female Genetic predisposition Genetic Predisposition to Disease Genetic Risk Score Genome-Wide Association Study Humans Male Middle Aged Polymorphism, Single Nucleotide Renal Insufficiency, Chronic - genetics Renal Insufficiency, Chronic - pathology Risk Factors Single nucleotide polymorphism Sphingosine N-Acyltransferase - genetics |
Title | T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects |
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