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Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

ABSTRACT: An assessment of levels of Insulin in cadaveric fluids, to estimate the postmortem interval (PMI) was carried out. To profile postmortem changes of Insulin, it was extracted at different intervals i.e. (0, 3, 6, 12, 24 h), from the heart of 22 human cadavers. The cases included were the subjects of accidental deaths without any prior history of disease and their exact time of death was known. Immunoanalyzer Cobas e-411 instrument was used to detect the relationship between the amount of Insulin and PMI. Level of Insulin was measured in cardiac blood. Statically, significant correlations between levels of Insulin and PMI were studied and correlation coefficients were calculated. SPSS (version 12.0) was used for statistical analysis. Insulin levels in cadaver blood are correlated significantly with PMI with a p value of <0.001. When insulin level increases by 1 unit the duration decreases by 0.93 units. The least square regression line is: [Duration(Y)=22.71-0.93 Insulin level (X)].

SUBMITTER: Kumar S

PROVIDER: S-EPMC4781973 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This 'ForensOMICS' approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.

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Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

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