Supplementary Material for: Rapid Progression of Autosomal Dominant Polycystic Kidney Disease: Urinary Biomarkers as Predictors

Background: Markers currently used to predict the likelihood of rapid disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD) are expensive and time consuming to assess and often have limited sensitivity. New, easy-to-measure markers are therefore needed that alone or in combination with conventional risk markers can predict the rate of disease progression. In the present study, we investigated the ability of tubular damage and inflammation markers to predict kidney function decline. Methods: At baseline, albumin, immunoglobulin G, kidney injury molecule 1, β2 microglobulin (β2MG), heart-type fatty acid-binding protein, neutrophil gelatinase-associated lipocalin, and monocyte chemotactic protein-1 ­(MCP-1) were measured in 24-h urine samples of patients participating in a study investigating the therapeutic efficacy of lanreotide in ADPKD. Individual change in estimated glomerular filtration rate (eGFR) during follow-up was calculated using mixed-model analysis taking into account 13 ­eGFRs (chronic kidney disease EPIdemiology) per patient. Logistic regression analysis was used to select urinary biomarkers that had the best association with rapidly progressive disease. The predictive value of these selected urinary biomarkers was compared to other risk scores using C-statistics. Results: Included were 302 patients of whom 53.3% were female, with an average age of 48 ± 7 years, eGFR of 52 ± 12 mL/min/1.73 m2, and a height-adjusted total kidney volume (htTKV) of 1,082 (736–1,669) mL/m. At baseline, all urinary damage and inflammation markers were associated with baseline eGFR, also after adjustment for age, sex and baseline htTKV. For longitudinal analyses only patients randomized to standard care were considered (n = 152). A stepwise backward analysis revealed that β2MG and MCP-1 showed the strongest association with rapidly progressive disease. A urinary biomarker score was created by summing the ranking of tertiles of β2MG and MCP-1 excretion. The predictive value of this urinary biomarker score was higher compared to that of the Mayo htTKV classification (area under the curve [AUC] 0.73 [0.64–0.82] vs. 0.61 [0.51–0.71], p = 0.04) and comparable to that of the predicting renal outcomes in ­ADPKD score (AUC 0.73 [0.64–0.82] vs. 0.65 [0.55–0.75], p = 0.18). In a second independent cohort with better kidney function, similar results were found for the urinary biomarker score. Conclusion: Measurement of urinary β2MG and MCP-1 excretion allows selection of ADPKD patients with rapidly progressive disease, with a predictive value comparable to or even higher than that of TKV or PKD mutation. Easy and inexpensive to measure urinary markers therefore hold promise to help predict prognosis in ADPKD.

背景：目前用于预测常染色体显性遗传性多囊肾病（autosomal dominant polycystic kidney disease, ADPKD）患者疾病快速进展风险的现有标志物，检测成本高昂且耗时，且灵敏度往往有限。因此亟需开发新型、易于检测的标志物，可单独或与传统风险标志物联合用于预测疾病进展速率。本研究探讨了肾小管损伤及炎症标志物预测肾功能下降的能力。
方法：本研究纳入接受兰瑞肽（lanreotide）治疗ADPKD疗效评估的受试者，在基线时检测其24小时尿液样本中的白蛋白、免疫球蛋白G、肾损伤分子1、β2微球蛋白（β2 microglobulin, β2MG）、心脏型脂肪酸结合蛋白、中性粒细胞明胶酶相关脂质运载蛋白及单核细胞趋化蛋白-1（monocyte chemotactic protein-1, MCP-1）。采用混合模型分析，结合每位患者的13次估算肾小球滤过率（estimated glomerular filtration rate, eGFR，基于慢性肾脏病流行病学合作研究公式计算），计算随访期间估算肾小球滤过率的个体变化量。采用logistic回归分析筛选与疾病快速进展关联最强的尿液生物标志物。采用C统计量比较上述筛选出的尿液生物标志物与其他风险评分的预测价值。
结果：本研究共纳入302例患者，其中53.3%为女性，平均年龄48±7岁，基线估算肾小球滤过率为52±12 mL/min/1.73 m²，身高校正总肾体积（htTKV）为1082（736~1669）mL/m。基线时，所有尿液损伤及炎症标志物均与基线eGFR相关，在校正年龄、性别及基线htTKV后仍保持显著关联。纵向分析仅纳入标准治疗组患者（n=152）。逐步后退法分析显示，β2MG与MCP-1与疾病快速进展的关联最强。通过对β2MG及MCP-1排泄量的三分位秩和求和，构建尿液生物标志物评分。该尿液生物标志物评分的预测价值高于梅奥htTKV分类（曲线下面积（area under the curve, AUC）0.73 [0.64~0.82] vs 0.61 [0.51~0.71]，p=0.04），与ADPKD肾脏结局预测评分相当（AUC 0.73 [0.64~0.82] vs 0.65 [0.55~0.75]，p=0.18）。在另一项肾功能更佳的独立队列中，尿液生物标志物评分得到了相似的结果。
结论：检测尿液β2MG与MCP-1排泄量可筛选出疾病快速进展的ADPKD患者，其预测价值不低于甚至高于总肾体积或PKD突变检测。因此，易于检测且成本低廉的尿液标志物有望助力ADPKD患者的预后预测。