URINE NMR BIOMARKERS OF SEPSIS IN THE ICU

We aimed to identify metabolomic biomarkers of sepsis in urine by 1H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a 1H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data with the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

本研究旨在通过氢核磁共振（1H-NMR）光谱技术，鉴定尿液中的脓毒症代谢组学生物标志物，以评估疾病严重程度并预测预后结局。研究共收集了重症监护病房（ICU）内64例重症脓毒症或感染性休克患者的尿液样本，用于氢核磁共振光谱数据采集。随后对预处理后的光谱数据开展有监督分析，并采用偏最小二乘判别分析（PLS-DA）构建了脓毒症预后（30天死亡率/生存率）预测模型。此外，本研究还将代谢组学数据的预测效能与序贯器官衰竭评估（SOFA）评分进行了对比。有监督多变量分析构建了性能优良的预测模型，可有效区分患者组别并识别特征性代谢模式。预后不良患者的乙醇、葡萄糖及马尿酸盐水平更高，而蛋氨酸、谷氨酰胺、精氨酸与苯丙氨酸水平则显著降低。上述代谢物或可构成ICU患者感染性休克代谢应答及死亡风险的复合生物特征模式的组成部分。内部交叉验证结果表明，本研究构建的代谢预测模型具有良好的稳健性，且相较于SOFA评分具备更优的预测效能。本研究结果提示，核磁共振代谢组学分析有助于早期识别预后不良脓毒症患者的代谢组学表型。基于上述代谢物构建的脓毒症患者病情进展预测模型，相较于公认的器官功能障碍评分SOFA，具备更高的分类灵敏度与特异度。