Human-to-Rat Validation of PMI Biomarkers: A Bidirectional Cross-Species Metabolomics Study Reveals Asymmetric Translation
Clinical Snapshot
PICO Framework
| P — Population | 435 medico-legal autopsy cases (PMI 1-10 days) and laboratory rats |
| I — Intervention | LC-MS-based metabolomics analysis of femoral blood samples |
| C — Comparator | Cross-species validation between human autopsy samples and controlled rat model |
| O — Outcomes | PMI estimation accuracy (R²), biomarker identification, and cross-species translation validity |
Bottom Line
This cross-species metabolomics study demonstrates that human-derived PMI biomarkers show better translational validity than animal-derived markers when applied to forensic cases. Using 435 autopsy samples, researchers achieved moderate PMI prediction accuracy (R² = 0.51) with human-derived biomarkers, while rat-derived markers performed poorly (R² = 0.10). The bidirectional validation approach revealed asymmetric translation between species, with 18 of 51 human biomarkers successfully validated in controlled rat models. However, significant limitations include inadequate control for environmental confounders, particularly temperature history, which critically affects post-mortem biochemical changes. While this research advances our understanding of PMI biomarker discovery, clinical application requires further validation studies incorporating comprehensive environmental metadata and standardized collection protocols before implementation in forensic practice.
Key Findings
P Value: Not specified for primary outcomes
Effect Size: Human untargeted model R² = 0.51; rat-derived targeted model R² = 0.10
Primary Outcome: PMI estimation accuracy using metabolomic biomarkers
Nnt Or Sensitivity: 51 consistently selected PMI-informative features; 18/51 features validated across species
Confidence Interval: Not reported
Clinical Application
Requires LC-MS facilities and expertise; currently research-level application Relevant to Australian forensic pathology services; would require validation in local environmental conditions and integration with existing medico-legal protocols Forensic pathology cases requiring PMI estimation
Abstract
Accurately estimating the post-mortem interval (PMI, also known as time since death) remains a major challenge in forensic science due to substantial biological and environmental variability in human cases. While animal models provide controlled conditions for identifying PMI-related metabolic changes, their translational value for human forensics is unclear. This study bridges controlled rat metabolomics with real-world human autopsy samples using LC-MS-based metabolomics and machine learning. We analyzed femoral blood from 435 medico-legal autopsies (PMI 1-10 days) and employed two complementary modeling strategies. An untargeted Lasso model using all 3450 detected features achieved R2 = 0.51 and identified 51 consistently selected PMI-informative molecular features. To assess cross-species robustness, we reanalysed rat samples using the same analytical platform. Eighteen of the 51 human-derived features were detected in rats, showing clear PMI-dependent trends and strong correlations with PMI. Two metabolites achieved level 2 annotation and matched known PMI biomarkers from previous animal studies, demonstrating meaningful human-to-rat biochemical overlap. In contrast, a targeted Lasso model based on biomarkers identified in rats performed poorly in humans (R2 = 0.10), underscoring limited direct rat-to-human translation. Overall, human-derived biomarkers validated robustly in the controlled rat model proved far more reliable. These findings highlight both the potential and limitations of cross-species PMI biomarker discovery and emphasize the need for improved metadata, especially temperature history, to advance PMI modeling in forensic practice.
References
- 1.Marquart Løber, I. M., Ward, L. J., Elmsjö, A., Söderberg, C., Villesen, P., Nielsen, K. L., & Green, H. (2026). Human-to-rat validation of PMI biomarkers: A bidirectional cross-species metabolomics study reveals asymmetric translation. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.6c00202
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