| Title: | Albuminuria-Related Genetic Biomarkers: Replication and Predictive Evaluation in Individuals with and without Diabetes from the UK Biobank |
| Journal: | International Journal of Molecular Sciences |
| Published: | 7 Jul 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37446387/ |
| DOI: | https://doi.org/10.3390/ijms241311209 |
| URL: | https://www.mdpi.com/1422-0067/24/13/11209/pdf?version=1688721017 |
Publication 10248
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Abstract
Increased albuminuria indicates underlying glomerular pathology and is associated with worse renal disease outcomes, especially in diabetic kidney disease. Many single nucleotide polymorphisms (SNPs), associated with albuminuria, could be potentially useful to construct polygenic risk scores (PRSs) for kidney disease. We investigated the diagnostic accuracy of SNPs, previously associated with albuminuria-related traits, on albuminuria and renal injury in the UK Biobank population, with a particular interest in diabetes. Multivariable logistic regression was used to evaluate the influence of 91 SNPs on urine albumin-to-creatinine ratio (UACR)-related traits and kidney damage (any pathology indicating renal injury), stratifying by diabetes. Weighted PRSs for microalbuminuria and UACR from previous studies were used to calculate the area under the receiver operating characteristic curve (AUROC). CUBN-rs1801239 and DDR1-rs116772905 were associated with all the UACR-derived phenotypes, in both the overall and non-diabetic cohorts, but not with kidney damage. Several SNPs demonstrated different effects in individuals with diabetes compared to those without. SNPs did not improve the AUROC over currently used clinical variables. Many SNPs are associated with UACR or renal injury, suggesting a role in kidney dysfunction, dependent on the presence of diabetes in some cases. However, individual SNPs or PRSs did not improve the diagnostic accuracy for albuminuria or renal injury compared to standard clinical variables.</p>
9 Authors
- Marisa Cañadas-Garre
- Andrew T. Kunzmann
- Kerry Anderson
- Eoin P. Brennan
- Ross Doyle
- Christopher C. Patterson
- Catherine Godson
- Alexander P. Maxwell
- Amy Jayne McKnight
1 Application
| Application ID | Title |
|---|---|
| 14259 | Multi-omic analyses for association with kidney disease |
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