A multiparametric microbial and biochemical model for predicting postmortem interval

Abstract

Background: Postmortem interval (PMI) estimation remains a major challenge in forensic science due to the influence of multiple factors. Traditional methods are often limited to early postmortem stages and lack precision in advanced decomposition [1,2]. Emerging approaches based on postmortem microbiology (thanatomicrobiome) and biochemical alterations (thanatochemistry) show promise [3,4], although their combined application remains underexplored. Objective: This study aimed to perform a combined microbial and biochemical analysis of different organs of mice over time since death to develop an accurate mathematical model for PMI estimation. Methods: Organs (lungs, heart, kidneys, liver, and brain) from male C57BL/6J specific pathogen-free mice were collected at six PMI (0, 12, 24, 48, 72, and 96 h; n=3 animals/timepoint) under aseptic conditions. For microbial analysis, organs were homogenized in buffered peptone water and cultured aerobically on Blood Agar, MacConkey, or Slanetz–Bartley media to quantify [colony-forming units/g of tissue] total bacteria, Escherichia coli, and Enterococcus faecalis, respectively. For biochemical analysis, tissue homogenates in phosphate buffer (pH 7.4) were analyzed for urea, lactate, uric acid, glucose, total proteins, magnesium, ethanol, and iron using the Accent MC240 autoanalyzer (Cormay^®). For PMI model development, markers were normalized to magnesium, and those with consistent temporal trends and R² > 0.900 were selected. Results: Microbial dynamics were strongly tissue- and time-dependent, with total bacterial loads peaking at 72 h in most organs and later in the brain (96 h). E. coli was absent in the liver, heart, and brain, while E. faecalis showed consistent colonization in the kidneys, lungs, and the brain, particularly from 24 h onwards. Biochemical markers also exhibited distinct temporal patterns: lactate increased early, whereas uric acid and urea correlated with later PMI stages. Iron showed a progressive tissue-dependent increase. Based on these results, a mathematical model for PMI estimation was developed (Figure 1). Conclusions: The integration of culture-based microbiological data with biochemical markers provides a robust multiparametric framework for PMI estimation. In particular, E. faecalis colonization and the temporal dynamics of lactate, uric acid, urea, and iron emerged as reliable indicators of postmortem progression. Further validation in complex forensic scenarios will be required.

Figure 1. Global model for PMI estimation. Equation A allows for the calculation of the PMI from the mean value of the following magnesium-normalized parameters: uric acid and iron in the kidneys; urea in the liver; glucose and urea in the lungs; glucose, lactate, uric acid, and iron in the heart; and lactate, uric acid, and urea in the brain. Equation B estimates the error.