Fan Weihao, Dai Xinhua, Ye Yi, Yang Hongkun, Sun Yiming, Wu Jingting, Fu Yingqiang, Shi Kaiting, Chen Xiaogang, Liao Linchuan
Department of Analytical Toxicology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, PR China.
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, PR China.
Int J Legal Med. 2025 May 27. doi: 10.1007/s00414-025-03523-0.
In forensic practice, the estimation of postmortem interval has been a persistent challenge. Recently, there has been an increasing utilization of metabolomics techniques combined with machine learning methods for postmortem interval estimation. When examining metabolite changes from a global perspective, rather than relying on specific substance changes, estimating postmortem interval through machine learning methods is more precise and entails fewer errors. Prior studies have investigated the use of metabolomics to estimate postmortem interval. Nevertheless, most of them focused on analyzing the metabolomic properties of a single organ or biofluid concerning a specific temperature. In this study, we employ the GC-MS platform to identify metabolites in the liver, kidney, and quadriceps femoris muscle of mechanically suffocated Sprague Dawley rats at various temperatures. Multivariable statistical analysis was used to determine differential compounds from the original data. The machine learning method was used to establish models for the estimation of postmortem interval under various ambient temperatures. As indicated by the results, liver, kidney, and quadriceps femoris muscle samples were screened for 24, 18, and 19 differential metabolites respectively, associated with postmortem interval under various ambient temperatures. Based on the metabolites listed above, the support vector regression models were established by utilizing single-organ and multi-organ metabolomics data for postmortem interval estimation. The multi-organ model showed a higher estimation accuracy. Also, a comprehensive generalization postmortem interval estimation model was established with multi-organ metabolomics data and temperature variables, which can be used for the postmortem interval estimation within the temperature range of 5-35℃. These results demonstrate that a multi-organ model utilizing metabolomics techniques can accurately estimate the postmortem interval under various ambient temperatures. Meanwhile, this research establishes a strong foundation for the practical application of metabolomics in postmortem interval estimation.
在法医实践中，死后间隔时间的估计一直是个持续存在的难题。最近，代谢组学技术与机器学习方法相结合用于死后间隔时间估计的应用越来越多。从全局角度检查代谢物变化，而不是依赖特定物质的变化，通过机器学习方法估计死后间隔时间更精确且误差更少。先前的研究已经探讨了使用代谢组学来估计死后间隔时间。然而，其中大多数研究都集中在分析特定温度下单个器官或生物流体的代谢组学特性。在本研究中，我们使用气相色谱 - 质谱平台来鉴定在不同温度下机械窒息的斯普拉格 - 道利大鼠肝脏、肾脏和股四头肌中的代谢物。使用多变量统计分析从原始数据中确定差异化合物。利用机器学习方法建立不同环境温度下死后间隔时间估计的模型。结果表明，肝脏、肾脏和股四头肌样本分别筛选出24、18和19种与不同环境温度下死后间隔时间相关的差异代谢物。基于上述代谢物，利用单器官和多器官代谢组学数据建立支持向量回归模型用于死后间隔时间估计。多器官模型显示出更高的估计准确性。此外，利用多器官代谢组学数据和温度变量建立了一个综合的死后间隔时间估计通用模型，该模型可用于5 - 35℃温度范围内的死后间隔时间估计。这些结果表明，利用代谢组学技术的多器官模型可以准确估计不同环境温度下的死后间隔时间。同时，本研究为代谢组学在死后间隔时间估计中的实际应用奠定了坚实基础。
