RationaleThe urinary proteome reflects molecular drivers of disease.ObjectivesTo construct a urinary proteomic biomarker predicting 1-year post-ICU mortality.MethodsIn 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses.Measurements and main resultsIn the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis.ConclusionsThe urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

中文翻译：

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一种新的尿液生物标志物可预测重症监护出院后 1 年的死亡率

基本原理尿蛋白质组反映了疾病的分子驱动因素。目的构建预测 ICU 后 1 年死亡率的尿蛋白质组生物标志物。方法在 1243 名患者中，在 1243 名患者中测量了尿蛋白质组，在入住 ICU 时，使用毛细管电泳结合质谱以及临床变量、循环生物标志物（BNP、hsTnT、活性 ADM 和 NGAL）和尿白蛋白。方法包括用于构建分类器的支持向量建模、Cox 回归、综合判别 (IDI) 和净重分类 (NRI) 改进以及用于评估预测准确性的曲线下面积 (AUC)，以及 Proteasix 和蛋白质-蛋白质组相互作用组分析。测量和主要结果在发现（死亡/幸存者，70/299）和测试（175/699）数据集，新分类器ACM128，主要由胶原碎片组成，产生的 AUC 分别为 0.755（95% CI，0.708-0.798）和 0.688（0.656-0.719）。在考虑研究地点和临床风险因素的同时，1243 名患者的 ACM128 (+ 1 SD) 的风险比为 2.41 (2.00–2.91)，Charlson 合并症指数 (+ 1 分) 的风险比为 1.24 (1.16–1.32)，并且≥ 1.19 (P ≤ 0.022) 其他生物标志物 (+ 1 SD)。ACM128 改善 (P ≤ 0.0001) IDI (≥ + 0.50)、NRI (≥ + 53.7) 和 AUC (≥ + 0.037) 超过临床风险指标和其他生物标志物。Interactome 映射，使用来自 ACM128 中包含的测序肽的亲本蛋白质和计算机预测的蛋白酶，包括/排除尿胶原蛋白片段（63/35 肽），揭示了蛋白质消化和吸收、溶酶体活性和细胞凋亡的顶级分子途径。