Background

A urine ‘biomarker panel’ comprising alpha-1-acid-glycoprotein, ceruloplasmin, transferrin and lipocalin-like-prostaglandin-D synthase performs to an ‘excellent’ level for lupus nephritis identification in children cross-sectionally. The aim of this study was to assess if this biomarker panel predicts lupus nephritis flare/remission longitudinally.

Methods

The novel urinary biomarker panel was quantified by enzyme linked immunoabsorbant assay in participants of the United Kingdom Juvenile Systemic Lupus Erythematosus (UK JSLE) Cohort Study, the Einstein Lupus Cohort, and the South African Paediatric Lupus Cohort. Monocyte chemoattractant protein-1 and vascular cell adhesion molecule-1 were also quantified in view of evidence from other longitudinal studies. Serial urine samples were collected during routine care with detailed clinical and demographic data. A Markov Multi-State model of state transitions was fitted, with predictive clinical/biomarker factors assessed by a corrected Akaike Information Criterion (AICc) score (the better the model, the lower the AICc score).

Results

The study included 184 longitudinal observations from 80 patients. The homogeneous multi-state Markov model of lupus nephritis activity AICc score was 147.85. Alpha-1-acid-glycoprotein and ceruloplasmin were identified to be the best predictive factors, reducing the AICc score to 139.81 and 141.40 respectively. Ceruloplasmin was associated with the active-to-inactive transition (hazard ratio 0.60 (95% confidence interval [0.39, 0.93])), and alpha-1-acid-glycoprotein with the inactive-to-active transition (hazard ratio 1.49 (95% confidence interval [1.10, 2.02])). Inputting individual alpha-1-acid-glycoprotein/ceruloplasmin values provides 3, 6 and 12 months probabilities of state transition.

Conclusions

Alpha-1-acid-glycoprotein was predictive of active lupus nephritis flare, whereas ceruloplasmin was predictive of remission. The Markov state-space model warrants testing in a prospective clinical trial of lupus nephritis biomarker led monitoring.

中文翻译：

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儿童狼疮肾炎尿液生物标志物动力学的马尔可夫多状态模型：预测疾病活动的变化

背景

包含α-1-酸-糖蛋白，铜蓝蛋白，转铁蛋白和脂质钙蛋白样前列腺素-D合酶的尿液“生物标志物组”在儿童横断面识别狼疮性肾炎中的表现达到“卓越”水平。这项研究的目的是评估该生物标志物组是否纵向预测了狼疮性肾炎的发作/缓解。

方法

在英国青少年系统性红斑狼疮（UK JSLE）队列研究，爱因斯坦狼疮队列和南非小儿狼疮队列研究的参与者中，通过酶联免疫吸附测定对新型尿液生物标志物组进行了定量。鉴于其他纵向研究的证据，也对单核细胞趋化蛋白1和血管细胞粘附分子1进行了定量。在常规护理期间收集了连续的尿液样本，并提供了详细的临床和人口统计学数据。拟合了状态转换的马尔可夫多状态模型，并通过校正的Akaike信息准则（AICc）评分评估了预测的临床/生物标志物因素（模型越好，AICc评分越低）。

结果

该研究包括对80位患者的184处纵向观察。狼疮性肾炎活动性AICc评分的同质多状态马尔可夫模型为147.85。确定α-1酸-糖蛋白和铜蓝蛋白是最好的预测因素，将AICc评分分别降低至139.81和141.40。铜蓝蛋白与主动-非主动转换（危险比0.60（95％置信区间[0.39，0.93]）相关，而α-1-酸-糖蛋白与主动-非主动转换相关（危险比1.49（95） ％置信区间[1.10，2.02]）。输入单独的α-1-酸-糖蛋白/铜蓝蛋白值可提供3、6和12个月的状态转变概率。

结论

α-1-酸-糖蛋白可预测活动性狼疮性肾炎发作，而铜蓝蛋白可预测缓解。马尔可夫状态空间模型值得在狼疮性肾炎生物标志物主导的监测的前瞻性临床试验中进行测试。