Clinical phenotypes and renal outcomes in PLA2R-associated membranous nephropathy: a multi-center cohort study with unsupervised cluster analysis

BACKGROUND: Membranous nephropathy (MN) associated with phospholipase A2 receptor (PLA2R) antibodies is the most common cause of nephrotic syndrome in non-diabetic adult patients. This study investigated the relationship between clinical phenotypes and renal outcomes in this population, emphasizing the potential for phenotype-based treatment stratification. METHODS: We conducted a retrospective, multi-center cohort study of PLA2R-positive MN. Unsupervised cluster analysis grouped patients based on clinicopathological characteristics. Primary outcomes included complete or partial remission within 2 years of biopsy and end-stage kidney disease (ESKD) or death during follow-up. RESULTS: Among 178 patients, three distinct clusters emerged (n = 89, 70, and 19). Within 2 years of biopsy, 102 patients (57%) achieved complete or partial remission. Cluster 1, characterized by the mildest disease markers, including the lowest body mass index, serum anti-PLA2R titer, serum creatinine, proteinuria, and triglycerides, had the highest remission rate of 72%, compared with 50% in cluster 2 and 54% in cluster 3. Cluster 2 had significantly lower remission compared to cluster 1 (HR 0.64, 95% CI 0.42-0.96, P = .03); results were similar in cluster 3, albeit not statistically significant (HR 0.50, 95% CI 0.23-1.09, P = .08). Elevated anti-PLA2R levels (>100 U/ml) and proteinuria (>8 g/24-hour) predicted reduced remission (HR 0.56, 95% CI 0.36-0.88, P = .01; HR 0.43, 95% CI 0.26-0.73, P = .002, respectively), while high high-density lipoprotein levels were protective (HR 1.01, 95% CI 1.00-1.02, P = .048). Overall, 21 patients (12%) developed ESKD or died. ESKD-free survival differed significantly across clusters (log-rank test P = .04). CONCLUSIONS: Unsupervised clustering identified distinct clinical phenotypes in PLA2R-positive MN, each associated with different renal prognoses. Phenotype-based risk stratification could enhance treatment precision, improve patient outcomes, and potentially reduce treatment-related adverse effects.