Lecture

Insulin in Forensic Toxicology: Analytical Advances and Case Insights

  • at -
  • ICM Saal 5
  • Type: Lecture

Lecture description

There is a high demand for reliable postmortem insulin analysis in forensic medicine, as overdoses of insulin or its synthetic analogues are frequently considered in cases of unexplained deaths. Such intoxications may result from unintentional medication errors by patients or medical personnel, as well as from homicide or suicide – particularly among individuals with diabetes. Despite its clear forensic relevance, evidence of insulin uptake and interpretation of postmortem concentrations remain challenging due to rapid degradation, especially in hemolytic blood samples. Meaningful results can be obtained from alternative matrices such as vitreous humor and cerebrospinal fluid; however, insulin concentrations in these matrices are significantly lower, posing additional challenges with regard to sample handling and method sensitivity.
While immunoassays are widely used in clinical settings and provide high sensitivity for human insulin, their limited specificity and cross-reactivity often prevent reliable differentiation between the various structurally similar analogs. Moreover, immunoassays
are generally not validated for postmortem samples or alternative matrices. Thus, they may generate misleading results and are therefore of limited suitability for forensic investigations. In contrast, liquid chromatography coupled with mass spectrometry
enables unambiguous identification of insulin analogues and metabolites in complex postmortem matrices, although some analytical workflows still rely on antibody-based extraction steps. Current methods also differ in their use of tandem or high-resolution
mass spectrometry, and proteomics- as well as metabolomics-based approaches are increasingly explored.
The presentation will discuss key analytical challenges, recent methodological advances, and forensic case examples in which immunoassays failed but were successfully resolved using LC-MS-based protein analysis.
#analytica
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