Predicting membranous nephropathy remission: a nomogram based on early dynamic biomarkers

Area of Science:

• Nephrology
• Immunology
• Clinical Medicine

Background:

• Membranous nephropathy (MN) prognosis traditionally relies on static indicators.
• Dynamic monitoring of early treatment response is crucial for effective MN management.
• Current methods lack the ability to dynamically assess early treatment efficacy in MN.

Purpose of the Study:

• To evaluate if early changes in anti-PLA2R antibody titers, 24-h urine protein, and serum albumin predict 12-month remission in MN.
• To develop a predictive nomogram model for clinical decision-making in MN treatment.
• To assess the utility of early biomarker changes in stratifying MN patient outcomes.

Main Methods:

• Retrospective analysis of 144 patients with PLA2R-related MN treated with rituximab.
• Biomarker analysis (anti-PLA2R antibodies, 24-h urine protein, serum albumin) at 1 and 6 months post-treatment.
• Lasso-Logistic regression for risk factor identification and nomogram construction, validated with ROC curves and calibration plots.

Main Results:

• At 1 month, non-remission patients showed significantly less decline in anti-PLA2R antibodies and urinary protein, and smaller albumin increase compared to remission patients.
• 1-month urinary protein, anti-PLA2R antibody decline, and albumin increase were independent predictors of remission.
• The 1-month nomogram demonstrated excellent discrimination (AUC=0.905) and calibration, comparable to 6-month predictions.

Conclusions:

• Early dynamic changes in anti-PLA2R antibodies, urinary protein, and serum albumin at 1 month effectively predict 12-month remission in MN.
• The developed nomogram model facilitates early risk stratification for MN patients.
• This approach supports the optimization of personalized treatment strategies for improved MN outcomes.