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Estimation of postmortem interval under different ambient temperatures based on multi-organ metabolomics and machine

Weihao Fan1, Xinhua Dai2, Yi Ye1

  • 1Department of Analytical Toxicology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, PR China.

International Journal of Legal Medicine
|May 27, 2025
PubMed
Summary

Estimating time since death is improved using metabolomics and machine learning. Multi-organ analysis of metabolites in liver, kidney, and muscle provides accurate postmortem interval estimation across temperatures.

Keywords:
Ambient temperatureGC-MSMachine learningMulti-organ metabolomicsPostmortem interval

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Area of Science:

  • Forensic Science
  • Metabolomics
  • Machine Learning

Background:

  • Accurate postmortem interval estimation is crucial in forensic investigations.
  • Existing methods for postmortem interval estimation have limitations.
  • Metabolomics combined with machine learning offers a promising approach for more precise estimations.

Purpose of the Study:

  • To investigate the utility of multi-organ metabolomics for postmortem interval estimation.
  • To develop and validate machine learning models for postmortem interval estimation under varying temperatures.
  • To establish a generalized model for postmortem interval estimation applicable across a specific temperature range.

Main Methods:

  • Gas Chromatography-Mass Spectrometry (GC-MS) was used to analyze metabolites in rat liver, kidney, and muscle tissues.
  • Multivariable statistical analysis identified differential metabolites associated with postmortem interval.
  • Support vector regression models were developed using single-organ and multi-organ metabolomics data for postmortem interval estimation.

Main Results:

  • Differential metabolites associated with postmortem interval were identified in liver (24), kidney (18), and muscle (19) tissues.
  • Multi-organ metabolomics models demonstrated higher accuracy in postmortem interval estimation compared to single-organ models.
  • A comprehensive model integrating multi-organ data and temperature variables accurately estimated postmortem interval between 5-35℃.

Conclusions:

  • Multi-organ metabolomics, coupled with machine learning, provides a robust and accurate method for postmortem interval estimation.
  • The developed models offer a significant advancement for forensic applications, particularly under diverse temperature conditions.
  • This study lays the groundwork for the practical implementation of metabolomics in forensic science for time since death determination.