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Introduction: Calciphylaxis/calcific uremic arteriolopathy affects mainly end-stage kidney disease patients but is also associated with malignant disorders such as myeloma, melanoma and breast cancer. Genetic risk factors of calciphylaxis have never been studied before.
Methods: We investigated 10 target genes using a tagging SNP approach: the genes encoding CD73/ ecto-5'-nucleotidase (purinergic pathway), Matrix Gla protein, Fetuin A, Bone Gla protein, VKORC1 (all related to intrinsic calcification inhibition), calcium-sensing receptor, FGF23, Klotho, vitamin D receptor, stanniocalcin 1 (all related to CKD-MBD). 144 dialysis patients from the German calciphylaxis registry were compared with 370 dialysis patients without history of CUA. Genotyping was performed using iPLEX Gold MassARRAY(Sequenom, San Diego, USA), KASP genotyping chemistry (LGC, Teddington, Middlesex, UK) or sequencing. Statistical analysis comprised logistic regression analysis with adjustment for age and sex.
Results: 165 SNPs were finally analyzed and 6 SNPs were associated with higher probability for calciphylaxis (OR>1) in our cohort. Nine SNPs of three genes (CD73, FGF23 and Vitamin D receptor) reached nominal significance (p< 0.05), but did not reach statistical significance after correction for multiple testing. Of the CD73 gene, rs4431401 (OR = 1.71, 95%CI 1.08-2.17, p = 0.023) and rs9444348 (OR = 1.48, 95% CI 1.11-1.97, p = 0.008) were associated with a higher probability for CUA. Of the FGF23 and VDR genes, rs7310492, rs11063118, rs13312747 and rs17882106 were associated with a higher probability for CUA.
Conclusion: Polymorphisms in the genes encoding CD73, vitamin D receptor and FGF23 may play a role in calciphylaxis development. Although our study is the largest genetic study on calciphylaxis, it is limited by the low sample sizes. It therefore requires replication in other cohorts if available.
Late-stage age-related macular degeneration (AMD) is a common sight-threatening disease of the central retina affecting approximately 1 in 30 Caucasians. Besides age and smoking, genetic variants from several gene loci have reproducibly been associated with this condition and likely explain a large proportion of disease. Here, we developed a genetic risk score (GRS) for AMD based on 13 risk variants from eight gene loci. The model exhibited good discriminative accuracy, area-under-curve (AUC) of the receiver-operating characteristic of 0.820, which was confirmed in a cross-validation approach. Noteworthy, younger AMD patients aged below 75 had a significantly higher mean GRS (1.87, 95% CI: 1.69-2.05) than patients aged 75 and above (1.45, 95% CI: 1.36-1.54). Based on five equally sized GRS intervals, we present a risk classification with a relative AMD risk of 64.0 (95% CI: 14.11-1131.96) for individuals in the highest category (GRS 3.44-5.18, 0.5% of the general population) compared to subjects with the most common genetic background (GRS -0.05-1.70, 40.2% of general population). The highest GRS category identifies AMD patients with a sensitivity of 7.9% and a specificity of 99.9% when compared to the four lower categories. Modeling a general population around 85 years of age, 87.4% of individuals in the highest GRS category would be expected to develop AMD by that age. In contrast, only 2.2% of individuals in the two lowest GRS categories which represent almost 50% of the general population are expected to manifest AMD. Our findings underscore the large proportion of AMD cases explained by genetics particularly for younger AMD patients. The five-category risk classification could be useful for therapeutic stratification or for diagnostic testing purposes once preventive treatment is available.
This study should contribute to the important field of pharmacogenetics by: firstly, establishing an easy and safe phenotyping method that combines the activity determination of all three previously mentioned CYPs (CYP2D6, CYP2C9, and CYP2C19) into one phenotyping cocktail and secondly, improving the knowledge about the predictive power of the genotype for the measured phenotype. It was indeed possible to develop a save, easy-to-use, fast and simultaneous phenotyping procedure for the important genetic polymorphic enzymes CYP2D6 and CYP2C9. To accomplish that, interaction studies with the chosen probe drugs dextromethorphan (DEX, CYP2D6), flurbiprofen (FLB, CYP2C9) and omeprazole (OME, CYP2C19) were conducted. It could be proven that DEX and FLB can be administered in combination, whereas OME alters the phenotyping results of CYP2C9. This is a new finding as in 2004 a phenotyping cocktail was published that used FLB and OME in combination. However, to our knowledge, no interaction tests were carried in that study. The new phenotyping procedure is not only verified by prior probe drug interaction studies, it also has other advantages over phenotyping cocktails found in literature. Firstly, save probe drugs are used in very small doses. This is possible due to the new sensitive LC-MS/MS methods that were evaluated. Secondly, the new phenotyping procedure is very fast and on-invasive. Urine has to be collected only for 2 h and the results also suggest that the time consuming glucuronide cleavage of the CYP2D6 dependent metabolite dextrorphan, usually carried out before CYP2D6 phenotyping, may be unnecessary. Most importantly, however, new insights into the phenotype prediction from genotype for CYP2C9 and CYP2D6 could be gained within this study. Nearly 300 phenotyped Caucasian subjects were also genotyped for the most important known variant alleles for CYP2D6, CYP2C9 and CYP2C19 using several established and newly developed genoptyping methods. Therefore, a direct correlation between phenotype and genotype could be conducted for CYP2D6 and CYP2C9. Employing linear modeling, it was possible to assign activity coefficients to each of the detected CYP2D6 and CYP2C9 alleles, thereby estimating their contribution to the resulting enzyme activity. This might facilitate the prediction of the CYP2D6 and CYP2C9 metabolic status of a subject knowing only its respective genotypes. Especially the new CYP2D6 genotype phenotype correlation model might allow for more precise phenotype prediction for the included variant alleles than was possible until now. Taken together, this study substantially contributes to the important research field of pharmacogenetics by (i) developing a save and easy-to-use phenotyping combination for CYP2D6 and CYP2C9, and (ii) by establishing activity coefficients for each of the detected CYP2D6 and CYP2C9 alleles, thereby allowing for a more precise prediction of the phenotype from genotype.