The impact of estimated glomerular filtration rate equations on chronic kidney disease staging in pediatric renal or heart transplant recipients

Background: The aim of this study was to evaluate the performance of selected pediatric estimated glomerular filtration rate (eGFR) equations in relation to the clinical management of children after renal or heart transplantation or post-chemotherapy treatment. Methods: This study was a retrospective cross-sectional analysis of 61 children whose glomerular function (GFR) had been determined using a single-dose inulin clearance (iGFR) method. Eight equations for estimating the GFR were evaluated for bias, agreement, accuracy, and clinical stratification. Results: The outcome of all eight eGFR equations differed from the value determined using the iGFR method, with the mean bias ranging from −3.4 to 20.7 ml/min/1.73 m2 and 90 % accuracy ranging from 16 to 26 %. All eGFR equations overestimated renal function in patients with decreased kidney function as determined by the iGFR method and underestimated renal function in patients with normal kidney function. Consequently, based on the eGFR values, patients with low GFR values according to the iGFR method were staged in a less severe chronic kidney disease (CKD) category, and patients with normal GFR values according to the iGFR method were staged in a more severe CKD category. The percentage of correctly classified patients ranged from 32.6 to 41.6 %. Conclusions: In our cohort we found the CKiDIII equation to be the best alternative to calculating the GFR using the inulin clearance method, closely followed by the Hoste and the revised Grubb equations. The performances of all eight eGFR equations assessed were moderate at best and only slightly better than the easy-to-do bedside Schwartz equation

Primary nasal epithelium exposed to house dust mite extract shows activated expression in allergic individuals

Nasal epithelial cells form the outermost protective layer against environmental factors. However, this defense is not just physical; it has been shown that epithelial cells respond by the production of inflammatory mediators that may affect local immune responses. In this research we set out to characterize potential differences between the responses of nasal epithelium from healthy and allergic individuals to house dust mite (HDM) allergen. These differences will help us to define local mechanisms that could contribute to allergic disease expression. Epithelial cells were cultured from nasal biopsies taken from five healthy and five allergic individuals. These cultures were exposed for 24 hours to culture medium containing HDM allergen, or to culture medium alone. Isolated RNA was used for microarray analysis. Gene-ontology of the response in healthy epithelium revealed mainly up-regulation of chemokines, growth factors, and structural proteins. Moreover, we saw increased expression of two transcription factors (NF-{kappa}B and AP-1) and their regulatory members. The expression pattern of epithelium from allergic individuals in the absence of the HDM stimulus suggests that it is already in an activated state. Most striking is that, while the already activated NF-{kappa}B regulatory pathway remained unchanged in allergic epithelium, the AP-1 pathway is down-regulated upon exposure to HDM allergen; this is contrary to what we see in healthy epithelium. Clear differences in the expression pattern exist between epithelial cells isolated from healthy and allergic individuals at baseline and between their responses to allergen exposure; these differences may contribute to the inflammatory response

Breast cancer risks associated with missense variants in breast cancer susceptibility genes.

BACKGROUND: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility

Breast cancer risks associated with missense variants in breast cancer susceptibility genes

Background Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. Methods We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. Results The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. Conclusions These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.Genome Instability and Cance