Background Fatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting.

Methods High-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability.

Results Here we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia.

Conclusions Fatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

The authors have declared no competing interest.

This work was supported by the Swedish Research Council (Vetenskapsradet; 2013-01407).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was approved by the Swedish Ethical Review Authority (Dnr 2019-04530, 2024-05087-02, 2025-02459-02). Due to the retrospective nature of the study, the need for informed consent was waived by the Swedish Ethical Review Authority. All methods were carried out in accordance with relevant guidelines and regulations.

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Data availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available, due to legal and ethical considerations. Code availability Code used for data preprocessing, analysis, and visualisations are available at GitHub: github.com/LJ-Ward/insulin.

https://www.github.com/LJ-Ward/insulin