ImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk... Show moreImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk stratification and treatment decisions, clinical trial recruitment, and biomarker validation. ObjectiveTo derive and externally validate a prediction model for disease progression in IgAN that can be applied at the time of kidney biopsy in multiple ethnic groups worldwide. Design, Setting, and ParticipantsWe derived and externally validated a prediction model using clinical and histologic risk factors that are readily available in clinical practice. Large, multi-ethnic cohorts of adults with biopsy-proven IgAN were included from Europe, North America, China, and Japan. Main Outcomes and MeasuresCox proportional hazards models were used to analyze the risk of a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, and were evaluated using the R-D(2) measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots. ResultsThe study included 3927 patients; mean age, 35.4 (interquartile range, 28.0-45.4) years; and 2173 (55.3%) were men. The following prediction models were created in a derivation cohort of 2781 patients: a clinical model that included eGFR, blood pressure, and proteinuria at biopsy; and 2 full models that also contained the MEST histologic score, age, medication use, and either racial/ethnic characteristics (white, Japanese, or Chinese) or no racial/ethnic characteristics, to allow application in other ethnic groups. Compared with the clinical model, the full models with and without race/ethnicity had better R-D(2) (26.3% and 25.3%, respectively, vs 20.3%) and AIC (6338 and 6379, respectively, vs 6485), significant increases in C statistic from 0.78 to 0.82 and 0.81, respectively (Delta C, 0.04; 95% CI, 0.03-0.04 and Delta C, 0.03; 95% CI, 0.02-0.03, respectively), and significant improvement in reclassification as assessed by the NRI (0.18; 95% CI, 0.07-0.29 and 0.51; 95% CI, 0.39-0.62, respectively) and IDI (0.07; 95% CI, 0.06-0.08 and 0.06; 95% CI, 0.05-0.06, respectively). External validation was performed in a cohort of 1146 patients. For both full models, the C statistics (0.82; 95% CI, 0.81-0.83 with race/ethnicity; 0.81; 95% CI, 0.80-0.82 without race/ethnicity) and R-D(2) (both 35.3%) were similar or better than in the validation cohort, with excellent calibration. Conclusions and RelevanceIn this study, the 2 full prediction models were shown to be accurate and validated methods for predicting disease progression and patient risk stratification in IgAN in multi-ethnic cohorts, with additional applications to clinical trial design and biomarker research. Show less
Significant variation in the course of autosomal dominant polycystic kidney disease ( ADPKD) within families suggests the presence of effect modifiers. Recent studies of the variation within... Show moreSignificant variation in the course of autosomal dominant polycystic kidney disease ( ADPKD) within families suggests the presence of effect modifiers. Recent studies of the variation within families harboring PKD1 mutations indicate that genetic background may account for 32 to 42% of the variance in estimated GFR (eGFR) before ESRD and 43 to 78% of the variance in age at ESRD onset, but the genetic modifiers are unknown. Here, we conducted a high-throughput single-nucleotide polymorphism (SNP) genotyping association study of 173 biological candidate genes in 794 white patients from 227 families with PKD1. We analyzed two primary outcomes: (1) eGFR and (2) time to ESRD (renal survival). For both outcomes, we used multidimensional scaling to correct for population structure and generalized estimating equations to account for the relatedness among individuals within the same family. We found suggestive associations between each of 12 SNPs and at least one of the renal outcomes. We genotyped these SNPs in a second set of 472 white patients from 229 families with PKD1 and performed a joint analysis on both cohorts. Three SNPs continued to show suggestive/significant association with eGFR at the Dickkopf 3 (DKK3) gene locus; no SNPs significantly associated with renal survival. DKK3 antagonizes Wnt/beta-catenin signaling, which may modulate renal cyst growth. Pending replication, our study suggests that genetic variation of DKK3 may modify severity of ADPKD resulting from PKD1 mutations. Show less
