Clinical Pharmacology and Safety Sciences R&D, AstraZeneca US, Gaithersburg, Maryland, USA.
Applied Analytics and AI, BioPharmaceuticals R&D, AstraZeneca US, Gaithersburg, Maryland, USA.
Lupus Sci Med. 2023 Jan;10(1). doi: 10.1136/lupus-2022-000747.
Lupus nephritis (LN) is diagnosed by biopsy, but longitudinal monitoring assessment methods are needed. Here, in this preliminary and hypothesis-generating study, we evaluate the potential for using urine proteomics as a non-invasive method to monitor disease activity and damage. Urinary biomarkers were identified and used to develop two novel algorithms that were used to predict LN activity and chronicity.
Baseline urine samples were collected for four cohorts (healthy donors (HDs, n=18), LN (n=42), SLE (n=17) or non-LN kidney disease biopsy control (n=9)), and over 1 year for patients with LN (n=42). Baseline kidney biopsies were available for the LN (n=46) and biopsy control groups (n=9). High-throughput proteomics platforms were used to identify urinary analytes ≥1.5 SD from HD means, which were subjected to stepwise, univariate and multivariate logistic regression modelling to develop predictive algorithms for National Institutes of Health Activity Index (NIH-AI)/National Institutes of Health Chronicity Index (NIH-CI) scores. Kidney biopsies were analysed for macrophage and neutrophil markers using immunohistochemistry (IHC).
In total, 112 urine analytes were identified from LN, SLE and biopsy control patients as both quantifiable and overexpressed compared with HDs. Regression analysis identified proteins associated with the NIH-AI (n=30) and NIH-CI (n=26), with four analytes common to both groups, demonstrating a difference in the mechanisms associated with NIH-AI and NIH-CI. Pathway analysis of the NIH-AI and NIH-CI analytes identified granulocyte-associated and macrophage-associated pathways, and the presence of these cells was confirmed by IHC in kidney biopsies. Four markers each for the NIH-AI and NIH-CI were identified and used in the predictive algorithms. The NIH-AI algorithm sensitivity and specificity were both 93% with a false-positive rate (FPR) of 7%. The NIH-CI algorithm sensitivity was 88%, specificity 96% and FPR 4%. The accuracy for both models was 93%.
Longitudinal predictions suggested that patients with baseline NIH-AI scores of ≥8 were most sensitive to improvement over 6-12 months. Viable approaches such as this may enable the use of urine samples to monitor LN over time.
狼疮肾炎（LN）通过活检诊断，但需要进行纵向监测评估方法。在这里，在这项初步的、产生假说的研究中，我们评估了将尿液蛋白质组学用作监测疾病活动和损伤的非侵入性方法的潜力。鉴定了尿生物标志物，并用于开发两种新的算法，用于预测 LN 活动和慢性。
收集了四个队列（健康供体（HDs，n=18）、LN（n=42）、SLE（n=17）或非 LN 肾脏疾病活检对照（n=9））的基线尿样，并对 LN 患者（n=42）进行了超过 1 年的随访。LN（n=46）和活检对照组（n=9）均获得基线肾脏活检。使用高通量蛋白质组学平台鉴定出 HD 平均值的尿液分析物≥1.5 SD，然后对其进行逐步、单变量和多变量逻辑回归建模，以开发用于 NIH 活动指数（NIH-AI）/NIH 慢性指数（NIH-CI）评分的预测算法。使用免疫组织化学（IHC）分析肾脏活检中的巨噬细胞和中性粒细胞标志物。
总共从 LN、SLE 和活检对照组患者中鉴定出 112 种尿液分析物，与 HDs 相比，这些分析物均可定量且过表达。回归分析确定了与 NIH-AI（n=30）和 NIH-CI（n=26）相关的蛋白质，两组共有 4 种分析物，表明与 NIH-AI 和 NIH-CI 相关的机制存在差异。对 NIH-AI 和 NIH-CI 分析物的途径分析确定了粒细胞相关和巨噬细胞相关途径，并且在肾脏活检中通过 IHC 证实了这些细胞的存在。为 NIH-AI 和 NIH-CI 分别确定了 4 个标记物，并用于预测算法。NIH-AI 算法的敏感性和特异性均为 93%，假阳性率（FPR）为 7%。NIH-CI 算法的敏感性为 88%，特异性为 96%，FPR 为 4%。两种模型的准确性均为 93%。
纵向预测表明，基线 NIH-AI 评分≥8 的患者对 6-12 个月的改善最敏感。这种可行的方法可能使我们能够随着时间的推移使用尿液样本监测 LN。
