全球尸检率在下降,影响死因(CoD)诊断和质量控制。使用4,282例人类病例评估死后代谢组学用于CoD筛查,包括CoD组:酸中毒,药物中毒,悬挂,缺血性心脏病(IHD),和肺炎。将案例以3:1分为训练集和测试集。通过正交偏最小二乘判别分析(OPLS-DA)分析来自股骨血液的高分辨率质谱数据以区分CoD组。OPLS-DA实现了R2=0.52和Q2=0.30,训练集和测试集的真正预测率为68%和65%,分别,跨所有群体。特异性优化阈值预测56%的测试用例具有独特的CoD,平均45%灵敏度,和平均96%的特异性。预测准确性各不相同:酸中毒为98.7%,80.5%为药物中毒,悬挂81.6%,IHD为73.1%,肺炎占93.6%。这项研究证明了大规模死后代谢组学用于CoD筛查的潜力,在人类死亡调查中提供高特异性并增强吞吐量和决策。
Autopsy rates are declining globally, impacting cause-of-death (CoD) diagnoses and quality control. Postmortem metabolomics was evaluated for CoD screening using 4,282 human cases, encompassing CoD groups: acidosis, drug intoxication, hanging, ischemic heart disease (IHD), and pneumonia. Cases were split 3:1 into training and test sets. High-resolution mass spectrometry data from femoral blood were analyzed via orthogonal-partial least squares discriminant analysis (OPLS-DA) to discriminate CoD groups. OPLS-DA achieved an R2 = 0.52 and Q2 = 0.30, with true-positive prediction rates of 68% and 65% for training and test sets, respectively, across all groups. Specificity-optimized thresholds predicted 56% of test cases with a unique CoD, average 45% sensitivity, and average 96% specificity. Prediction accuracies varied: 98.7% for acidosis, 80.5% for drug intoxication, 81.6% for hanging, 73.1% for IHD, and 93.6% for pneumonia. This study demonstrates the potential of large-scale postmortem metabolomics for CoD screening, offering high specificity and enhancing throughput and decision-making in human death investigations.