Cittadella Universitaria di Monserrato, 09042, Monserrato (CA), Italy.
Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy.
Int J Legal Med. 2023 Nov;137(6):1875-1885. doi: 10.1007/s00414-023-03050-w. Epub 2023 Jul 4.
Due to its peculiar anatomy and physiology, the pericardial fluid is a biological matrix of particular interest in the forensic field. Despite this, the available literature has mainly focused on post-mortem biochemistry and forensic toxicology, while to the best of authors' knowledge post-mortem metabolomics has never been applied. Similarly, estimation of the time since death or post-mortem interval based on pericardial fluids has still rarely been attempted.
We applied a metabolomic approach based on H nuclear magnetic resonance spectroscopy to ascertain the feasibility of monitoring post-mortem metabolite changes on human pericardial fluids with the aim of building a multivariate regression model for post-mortem interval estimation.
Pericardial fluid samples were collected in 24 consecutive judicial autopsies, in a time frame ranging from 16 to 170 h after death. The only exclusion criterion was the quantitative and/or qualitative alteration of the sample. Two different extraction protocols were applied for low molecular weight metabolites selection, namely ultrafiltration and liquid-liquid extraction. Our metabolomic approach was based on the use of H nuclear magnetic resonance and multivariate statistical data analysis.
The pericardial fluid samples treated with the two experimental protocols did not show significant differences in the distribution of the metabolites detected. A post-mortem interval estimation model based on 18 pericardial fluid samples was validated with an independent set of 6 samples, giving a prediction error of 33-34 h depending on the experimental protocol used. By narrowing the window to post-mortem intervals below 100 h, the prediction power of the model was significantly improved with an error of 13-15 h depending on the extraction protocol. Choline, glycine, ethanolamine, and hypoxanthine were the most relevant metabolites in the prediction model.
The present study, although preliminary, shows that PF samples collected from a real forensic scenario represent a biofluid of interest for post-mortem metabolomics, with particular regard to the estimation of the time since death.
由于其独特的解剖结构和生理学特性，心包液是法医学领域中特别感兴趣的生物基质。尽管如此，现有文献主要集中在后mortem 生物化学和法医毒理学上，而据作者所知，死后代谢组学从未被应用过。同样，基于心包液估计死亡时间或死后间隔时间的尝试也很少。
我们应用基于 H 核磁共振波谱的代谢组学方法，确定监测人心包液死后代谢物变化的可行性，旨在建立死后间隔时间估计的多元回归模型。
在 24 例连续司法解剖中收集心包液样本，死亡后时间范围为 16 至 170 小时。唯一的排除标准是样本的定量和/或定性改变。为了选择低分子量代谢物，应用了两种不同的提取方案，即超滤和液液萃取。我们的代谢组学方法基于 H 核磁共振和多变量统计数据分析的使用。
用两种实验方案处理的心包液样本在检测到的代谢物分布上没有显著差异。基于 18 例心包液样本的死后间隔时间估计模型在 6 例独立样本中进行了验证，根据使用的实验方案，预测误差为 33-34 小时。通过将窗口缩小到死后间隔时间低于 100 小时，根据提取方案的不同，模型的预测能力得到了显著提高，误差为 13-15 小时。胆碱、甘氨酸、乙醇胺和次黄嘌呤是预测模型中最相关的代谢物。
尽管本研究初步，但表明从真实法医场景中收集的 PF 样本代表了死后代谢组学感兴趣的生物流体，特别是在估计死亡时间方面。
