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APOL1 genotype-associated morphologic changes among patients with focal segmental glomerulosclerosis
Jarcy Zee1, Michelle T McNulty2, Jeffrey B Hodgin3
1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine and Children's Hospital of Philadelphia, 423 Guardian Drive, Philadelphia, PA, 19104, USA. Jarcy.Zee@Pennmedicine.upenn.edu.
Background:
The G1 and G2 alleles of apolipoprotein L1 (APOL1) are common in the Black population and associated with increased risk of focal segmental glomerulosclerosis (FSGS). The molecular mechanisms linking APOL1 risk variants with FSGS are not clearly understood, and APOL1's natural absence in laboratory animals makes studying its pathobiology challenging.
Methods:
In a cohort of 90 Black patients with either FSGS or minimal change disease (MCD) enrolled in the Nephrotic Syndrome Study Network (58% pediatric onset), we used kidney biopsy traits as an intermediate outcome to help illuminate tissue-based consequences of APOL1 risk variants and expression. We tested associations between APOL1 risk alleles or glomerular APOL1 mRNA expression and 83 light- or electron-microscopy traits measuring structural and cellular kidney changes.
Results:
Under both recessive and dominant models in the FSGS patient subgroup (61%), APOL1 risk variants were significantly correlated (defined as FDR <0.1) with decreased global mesangial hypercellularity, decreased condensation of cytoskeleton, and increased tubular microcysts. No significant correlations were detected in MCD cohort. Independent of risk alleles, glomerular APOL1 expression in FSGS patients was not correlated with morphologic features.
Conclusions:
While APOL1-associated FSGS is associated with two risk alleles, both one and two risk alleles are associated with cellular/tissue changes in this study of FSGS patients. Our lack of discovery of a large group of tissue differences in FSGS and no significant difference in MCD may be due to the lack of power but also supports investigating whether machine learning methods may more sensitively detect APOL1-associated changes.
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