研究背景和目的：意识障碍（Disorders of Consciousness, DoC）是严重脑损伤后常见的临床综合征，其中无反应觉醒综合征（Unresponsive Wakefulness Syndrome, UWS）与最小意识状态（Minimally Conscious State, MCS）的准确鉴别对治疗决策与预后评估具有重要意义。然而，当前临床主要依赖行为学量表评估，存在较高的误诊率，亟需寻找客观、稳定的生物学标志物以提高诊断准确性。代谢组学作为一种系统揭示病理状态下分子变化特征的技术，为DoC的生物标志物筛选和亚型识别提供了新的思路。本研究首先基于血浆、脑脊液与尿液的代谢组学分析，构建可用于鉴别UWS与MCS的多体液联合诊断模型，明确各类体液中与意识水平密切相关的潜在代谢标志物。其中，尿液作为无创、易采集的体液类型，在疾病监测与辅助诊断中具有重要临床价值。然而，其在样本储存过程中面临代谢物不稳定、易降解等问题，严重影响代谢组学数据的准确性与重复性。因此，本研究在探索意识障碍亚型代谢特征的同时，进一步尝试开发新型尿液代谢物保存方法，为临床诊断提供更可靠的实验依据和技术支持。

研究方法：在意识障碍分型诊断方面，研究团队前瞻性纳入符合国际诊断标准的51例患者，根据昏迷恢复量表修订版评分（Coma Recovery Scale-Revised, CRS-R
）分为UWS组（35例）和MCS组（16例）。采集患者的脑脊液、血清及尿液样本，应用超高效液相色谱-串联质谱技术（Ultra Performance liquid chromatography-mass spectrometry, UHPLC-MS）进行非靶向代谢组学检测，采用正交偏最小二乘判别分析（Orthogonal Partial Least Squares Discriminant Analysis, OPLS-DA）筛选差异代谢物，并通过受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估诊断效能。在尿液样本保存方法研究方面，采用苯乙烯-二乙烯基苯反相磺酸酯（Styrene Divinylbenzene Reversed Phase Sulfonate, SDB-RPS）膜处理尿液样本，通过对比处理组与未处理组在不同采集条件下代谢物鉴定水平、不同温度条件下的代谢物半衰期，以及在不同疾病分类中的诊断效能，系统评估该技术的应用价值。

研究结果：多体液代谢组学分析结果表明，在脑脊液、血清和尿液中分别鉴定到232、229和571个代谢物，经筛选后获得14种（脑脊液）、24种（血清）和22种（尿液）具有显著差异的代谢物。功能分析显示，脑脊液差异代谢物主要参与细胞坏死、凋亡及神经保护过程，血清差异代谢物与脂质代谢和免疫调节密切相关，而尿液差异代谢物则主要涉及细胞信号转导和神经功能调控。诊断效能评估结果显示，脑脊液、血清和尿液代谢标志物组合的ROC曲线下面积（Area Under Curve，AUC）分别为0.85（95%CI：0.73-0.96）、0.94（95%CI：0.86-1.00）和0.93（95%CI：0.79-1.00），展现出良好的诊断价值。在尿液样本保存研究中，经SDB-RPS膜处理与未经处理的尿液样本代谢物鉴定重叠率达到88.26%，且加速降解实验证实该技术可显著延长尿液代谢物在不同低温条件下的保存时间，特别是在-80℃条件下推算保存时间可以达到2136.52年。此外，该技术在肝病分类诊断中的应用使AUC提升了13.7%，为解决代谢组学研究中的尿液样本保存难题提供了创新性解决方案。

研究结论：本研究通过系统的代谢组学分析，建立了基于多体液代谢网络的意识障碍分型诊断体系，不同体液建立的代谢物诊断模型AUC值均在0.85以上，显示出较好的诊断性能，为临床鉴别诊断提供了客观可靠的分子依据。同时，开发的尿液代谢物保存技术显著提高了样本稳定性，为代谢组学研究提供了重要的方法学支持。未来研究将进一步扩大样本量，优化诊断模型，并探索代谢物在疾病发生发展中的作用机制，为更多临床疾病的精准诊疗提供科学依据。

Background and Objective: Disorders of consciousness (DoC) are common clinical syndromes following severe brain injury. Accurate differentiation between unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is crucial for treatment decisions and prognostic evaluation. However, current clinical diagnosis primarily relies on behavioral assessments, which are associated with a high rate of misdiagnosis. Therefore, there is an urgent need to identify objective and reliable biological markers to improve diagnostic accuracy. Metabolomics, as a powerful tool to systematically characterize molecular alterations under pathological conditions, offers new opportunities for biomarker discovery and subtype classification in DoC. In this study, we first performed metabolomic analyses of plasma, cerebrospinal fluid, and urine to establish a multi-biofluid diagnostic model for distinguishing UWS from MCS and to identify potential metabolites closely associated with consciousness levels in different types of body fluids. Among these, urine has particular clinical value due to its non-invasive nature and ease of collection, making it a promising candidate for disease monitoring and auxiliary diagnosis. However, metabolomic studies using urine are challenged by the instability and rapid degradation of urinary metabolites during sample storage, which severely affects data accuracy and reproducibility. Therefore, in addition to exploring metabolic features of DoC subtypes, this study also aimed to develop a novel urine preservation method to enhance sample stability and provide more reliable experimental and technical support for clinical diagnostics.

Methods: For DoC subtyping, 51 patients meeting international diagnostic criteria were prospectively enrolled and stratified into UWS (n=35) and MCS (n=16) groups based on Coma Recovery Scale-Revised (CRS-R) scores. Cerebrospinal fluid (CSF), serum, and urine samples were collected and analyzed using untargeted metabolomics via ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS). Orthogonal partial least squares-discriminant analysis (OPLS-DA) identified differential metabolites, while receiver operating characteristic (ROC) curves evaluated diagnostic performance. For urine preservation, styrene divinylbenzene reversed-phase sulfonate (SDB-RPS) membranes were employed. The study systematically assessed the technology's efficacy by comparing treated versus untreated samples in terms of metabolite identification, stability under varying temperatures (via accelerated degradation experiments), and diagnostic utility in disease classification.

Results: Multi-fluid metabolomics identified 232 (CSF), 229 (serum), and 571 (urine) metabolites, with 14 (CSF), 24 (serum), and 22 (urine) showing significant differences. Functional analysis revealed CSF metabolites were associated with necrosis, apoptosis, and neuroprotection; serum metabolites with lipid metabolism and immune regulation; and urine metabolites with cell signaling and neural function. Diagnostic models demonstrated robust performance: area under the curve (AUC) values were 0.85 (95% CI: 0.73–0.96) for CSF, 0.94 (95% CI: 0.86–1.00) for serum, and 0.93 (95% CI: 0.79–1.00) for urine. SDB-RPS membrane treated samples achieved an 88.26% metabolite identification overlap with untreated samples and extended metabolite preservation, particularly at −80℃ (projected stability: 2136.52 years). Additionally, this technology improved liver disease classification AUC by 13.7%, offering an innovative solution to urine sample stability challenges in metabolomics.

Conclusion: This study established a multi-fluid metabolomic diagnostic framework for DoC with AUC >0.85 across biofluids, providing objective molecular criteria for clinical differentiation. The developed urine preservation method significantly enhanced sample stability, advancing metabolomic research methodologies. Future work will expand sample sizes, refine diagnostic models, and elucidate metabolite roles in disease mechanisms to support precision medicine.