AI Article Synopsis
- This study investigates the use of metabolomic methods in forensic science to improve the accuracy of post-mortem interval (PMI) estimations by comparing human serum, aqueous humor (AH), and vitreous humor (VH).
- It finds that metabolomic changes in ocular fluids (AH and VH) occur more gradually than in serum, with specific metabolites like hypoxanthine and choline showing strong time correlations.
- The research concludes that analyzing AH and VH is more effective for PMI estimation than serum, highlighting the potential of these metabolites as biomarkers.
Article Abstract
Introduction: Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation.
Objectives: This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers.
Methods: Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy.
Results: It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed.
Conclusions: The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers.
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| http://dx.doi.org/10.1007/s11306-020-01700-3 | DOI Listing |
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