Esther Nkuipou-Kenfack1, Agnieszka Latosinska1, Wen-Yi Yang2,3, Marie-Céline Fournier4, Alice Blet4,5, Blerim Mujaj2, Lutgarde Thijs2, Elodie Feliot4, Etienne Gayat4,5,6, Harald Mischak1, Jan A Staessen2,7, Alexandre Mebazaa4,5,6, Zhen-Yu Zhang8. 1. Mosaiques Diagnostics GmbH, Hannover, Germany. 2. Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 35, Box 7001, 3000, Leuven, Belgium. 3. Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4. Department of Anesthesiology and Intensive Care, Saint Louis-Lariboisière - Fernand Widal University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France. 5. Université de Paris, Paris, France. 6. INSERM UMR-S 942 - MASCOT, Paris, France. 7. Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands. 8. Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 35, Box 7001, 3000, Leuven, Belgium. zhenyu.zhang@kuleuven.be.
Abstract
RATIONALE: The urinary proteome reflects molecular drivers of disease. OBJECTIVES: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. METHODS: In 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 RESULTS: In 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. CONCLUSIONS: The 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.
RATIONALE: The urinary proteome reflects molecular drivers of disease. OBJECTIVES: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. METHODS: In 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 RESULTS: In 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. CONCLUSIONS: The 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.
Entities:
Keywords:
Biomarker; Heart failure; Intensive care medicine; Mortality; Urinary proteomics