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INTRODUCTION: A C5 polymorphism (rs17611, 2404G>A) exists where the G allele associates with enhanced C5a-like production by neutrophil elastase. This cohort study investigated the influence of this polymorphism as a risk factor for lupus nephritis (LN), and on C5a and membrane attack complex (MAC) levels in LN during flare. METHODS: A cohort of lupus patients (n = 155) was genotyped for the 2404G>A polymorphism. A longitudinal LN subset (n = 66) was tested for plasma and urine levels of C5a and MAC 4 and/or 2 months before and at nonrenal or LN flare. RESULTS: The 2404G allele and 2404-GG genotype were associated with LN in black, but not white, lupus patients. In the longitudinal cohort, neither urine nor plasma C5a levels changed at nonrenal flare regardless of 2404G>A genotype or race. Urine (but not plasma) C5a levels increased at LN flare independent of race, more so in 2404-GG patients where 8 of 30 LN flares exhibited very high C5a levels. Higher proteinuria and serum creatinine levels also occurred in these eight flares. Urine (but not plasma) MAC levels also increased at LN flare in 2404-GG patients and correlated with urine C5a levels. CONCLUSIONS: The C5 2404-G allele/GG genotype is a potential risk factor for LN uniquely in black lupus patients. The GG genotype is associated with sharp increases in urine C5a and MAC levels in a subset of LN flares that correspond to higher LN disease indices. The lack of corresponding changes in plasma suggests these increases reflect intrarenal complement activation.
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Background: Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet CDM at three institutions from January 1, 2009 to January 1, 2018, where a random selection of 50 cases and 50 noncases (individuals not meeting case criteria seen within the same calendar year and within 5 years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 noncases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009 to 2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI, 97% to 99%), 79% (95% CI, 74% to 85%), and 0.9 (0.84 to 0.97), respectively. The most common causes of false positive classification were secondary FSGS (nine out of 39) and lupus nephritis (nine out of 39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.
