This proof-of-concept investigation assessed whether the commercial Biocrates MxP® Quant 500 kit, developed for biofluids, can be applied to postmortem human left ventricular tissue obtained at forensic autopsy. The aim was to determine analytical feasibility and performance of the kit’s standardized targeted metabolomics workflow in a challenging postmortem matrix that may be affected by variable degradation and incomplete metadata.
Forty forensic autopsy cases were selected to represent a range of myocardial conditions encountered in forensic practice. The cohort comprised 10 decedents with type 2 diabetes (T2D), 20 decedents with ischemic heart disease (IHD) without T2D, and 10 control cases without cardiac pathology. Cases were chosen to enable feasibility assessment across heterogeneous postmortem cardiac tissue rather than to test a specific biomarker hypothesis.
Left ventricular tissue samples were processed and analyzed using the kit’s standard protocol, employing liquid chromatography–tandem mass spectrometry (LC-MS/MS) and flow injection analysis–tandem mass spectrometry (FIA-MS/MS) as prescribed by the manufacturer. The MxP Quant 500 kit targets a predefined panel of 630 endogenous metabolites drawn from diverse biochemical classes, enabling standardized targeted quantification without the need for developing bespoke assays or extensive in-house optimization.
Of the 630 targeted metabolites, 463 (74%) were reported within the kit’s quantifiable range in postmortem cardiac tissue. Retention varied markedly between classes. Lipid-related metabolites showed the highest rates of successful quantification: sphingomyelins were completely retained (100%), phosphatidylcholines showed 93% retention, triacylglycerols 82%, and fatty acids 83%.
In contrast, some metabolite classes exhibited greater variability. For acylcarnitines, 45% of targets were within the quantifiable range, while 47% of acylcarnitine measurements fell below the limit of detection. Amino acids showed instances of measurements exceeding the upper limit of quantification (35% above ULOQ for some amino acids). These class-specific patterns reflect differential detectability and dynamic range when applying a biofluid-validated kit to tissue, and they identify areas where additional sample preparation or dilution strategies might be needed.
Univariate comparisons between the predefined groups identified nominal differences in specific metabolite subclasses with unadjusted p-values < 0.05. However, after correction for multiple testing using false discovery rate procedures, no metabolites remained statistically significant. Multivariate analyses, including PERMANOVA and principal component analysis (PCA), did not demonstrate strong global separation among the T2D, IHD, and control groups in this dataset.
These results indicate that while many metabolites are measurable in postmortem cardiac tissue using the kit, detecting robust group-level biochemical signatures in this sample set was not achieved after correction for multiple comparisons. The study was framed as feasibility testing rather than powered discovery of diagnostic markers.
The authors report that the MxP Quant 500 kit is technically feasible for targeted metabolomics of postmortem human cardiac tissue, particularly for lipidomic profiling where retention was highest. Use of a standardized commercial kit may lower barriers to implementation, provide reproducible quantification across laboratories, and serve as a cost-effective alternative to labor-intensive, custom targeted or untargeted workflows.
For forensic investigations of sudden cardiac death and cardiovascular research more broadly, the ability to quantify a broad panel of metabolites in myocardial tissue could aid biochemical characterization of disease processes. However, class-dependent variability and limits of detection/quantification observed in this study should be considered when interpreting results or planning studies aimed at biomarker discovery.
Limitations noted in the report include variable performance across metabolite classes and the inherent challenges of postmortem tissue analysis, including tissue degradation and incomplete metadata such as exact timing and environmental conditions affecting postmortem interval. The study was not designed or powered to detect definitive biomarker differences; rather, it assessed practical applicability of a commercial kit in a forensic tissue matrix.
Processed metabolomics data and statistical scripts have been made publicly available via a GitHub repository. Individual-level autopsy metadata (age, BMI, postmortem interval, heart weight, left ventricular wall thickness, coronary stenosis) are not publicly accessible due to Danish legal and ethical restrictions; requests for these data must be directed to the institutional Data Access Committee as described by the authors.
Future work would logically include method optimization for tissue-specific issues (for example, addressing metabolites frequently below detection or above quantification limits), larger and better-characterized cohorts to increase power for group comparisons, and cross-laboratory validation to confirm reproducibility. Overall, the study provides a practical foundation for using a standardized targeted kit to quantify a broad range of metabolites in postmortem cardiac tissue, with particular strength in lipid profiling.