Biomarker patterns and mechanistic insights into hypothermia from a postmortem metabolomics investigation.

Postmortem metabolomics holds promise for identifying crucial biological markers relevant to death investigations and clinical scenarios. We aimed to assess its applicability in diagnosing hypothermia, a condition lacking definitive biomarkers. Our retrospective analysis involved 1095 postmortem femoral blood samples, including 150 hypothermia cases, 278 matched controls, and 667 randomly selected test cases, analyzed using UHPLC-QTOF mass spectrometry. The model demonstrated robustness with an R2 and Q2 value of 0.73 and 0.68, achieving 94% classification accuracy, 92% sensitivity, and 96% specificity. Discriminative metabolite patterns, including acylcarnitines, stress hormones, and NAD metabolites, along with identified pathways, suggest that metabolomics analysis can be helpful to diagnose fatal hypothermia. Exposure to cold seems to trigger a stress response in the body, increasing cortisol production to maintain core temperature, possibly explaining the observed upregulation of cortisol levels and alterations in metabolic markers related to renal function. In addition, thermogenesis seems to increase metabolism in brown adipose tissue, contributing to changes in nicotinamide metabolism and elevated levels of ketone bodies and acylcarnitines, these findings highlight the effectiveness of UHPLC-QTOF mass spectrometry, multivariate analysis, and pathway identification of postmortem samples in identifying metabolite markers with forensic and clinical significance. The discovered patterns may offer valuable clinical insights and diagnostic markers, emphasizing the broader potential of postmortem metabolomics in understanding critical states or diseases.

Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations.

Autopsy rates are declining globally, impacting cause-of-death (CoD) diagnoses and quality control. Postmortem metabolomics was evaluated for CoD screening using 4,282 human cases, encompassing CoD groups: acidosis, drug intoxication, hanging, ischemic heart disease (IHD), and pneumonia. Cases were split 3:1 into training and test sets. High-resolution mass spectrometry data from femoral blood were analyzed via orthogonal-partial least squares discriminant analysis (OPLS-DA) to discriminate CoD groups. OPLS-DA achieved an R2 = 0.52 and Q2 = 0.30, with true-positive prediction rates of 68% and 65% for training and test sets, respectively, across all groups. Specificity-optimized thresholds predicted 56% of test cases with a unique CoD, average 45% sensitivity, and average 96% specificity. Prediction accuracies varied: 98.7% for acidosis, 80.5% for drug intoxication, 81.6% for hanging, 73.1% for IHD, and 93.6% for pneumonia. This study demonstrates the potential of large-scale postmortem metabolomics for CoD screening, offering high specificity and enhancing throughput and decision-making in human death investigations.

Intralipid as a matrix additive for evaluating hyperlipidemic postmortem blood.

Postmortem whole blood samples can differ greatly in quality where hyperlipemia is a frequent variable that can influence the results of analytical methods. The aim of this study was to investigate the influence of lipemia on postmortem analysis as well as demonstrate the usage of Intralipid in comparison to pooled postmortem lipids as matrix additives for meaningful evaluation and validation of hyperlipidemic postmortem samples. Hyperlipidemic blood samples were simulated by adding different concentrations of Intralipid or pooled authentic postmortem lipids to bovine whole blood. The hyperlipidemic blood samples were spiked with 14 benzodiazepines and five sedative and antianxiety drugs (alprazolam, clonazepam, 7-aminoclonazepam, diazepam, flunitrazepam, 7-aminoflunitrazepam, hydroxyzine, lorazepam, midazolam, nitrazepam, 7-aminonitrazepam, nordazepam, oxazepam, propiomazine, dihydropropiomazine, temazepam, triazolam, zolpidem and zopiclone). Samples were prepared with liquid-liquid extraction followed by ultra-high performance liquid chromatography-mass spectrometry. The effects of lipemia on the recovery of analytes and internal standards (ISs) were evaluated to determine the effect of, and any differences between, the two additives. Lipemia was found to cause major interference when quantifying the analytes. For most analytes, the ISs could compensate for analyte losses. However, the most hydrophilic analytes (7-amino metabolites), together with the most lipophilic analytes (propiomazine and dihydropropiomazine), were greatly affected by lipemia (<50% recovery), and the IS could not compensate for analyte losses. In general, lower analyte recoveries were observed for samples with Intralipid as a lipemic additive in comparison to those containing pooled postmortem lipids. Both Intralipid and pooled postmortem lipids showed marked effects on the analytical results. Intralipid gave a good indication of the effects of lipemia and could be a useful tool for making a meaningful evaluation of hyperlipidemic postmortem samples during the method development and validation.

Postmortem Metabolomics Reveal Acylcarnitines as Potential Biomarkers for Fatal Oxycodone-Related Intoxication.

Postmortem metabolomics has recently been suggested as a potential tool for discovering new biological markers able to assist in death investigations. Interpretation of oxycodone concentrations in postmortem cases is complicated, as oxycodone tolerance leads to overlapping concentrations for oxycodone intoxications versus non-intoxications. The primary aim of this study was to use postmortem metabolomics to identify potential endogenous biomarkers that discriminate between oxycodone-related intoxications and non-intoxications. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry data from 934 postmortem femoral blood samples, including oxycodone intoxications and controls positive and negative for oxycodone, were used in this study. Data were processed and evaluated with XCMS and SIMCA. A clear trend in group separation was observed between intoxications and controls, with a model sensitivity and specificity of 80% and 76%. Approximately halved levels of short-, medium-, and long-chain acylcarnitines were observed for oxycodone intoxications in comparison with controls (p < 0.001). These biochemical changes seem to relate to the toxicological effects of oxycodone and potentially acylcarnitines constituting a biologically relevant biomarker for opioid poisonings. More studies are needed in order to elucidate the potential of acylcarnitines as biomarker for oxycodone toxicity and their relation to CNS-depressant effects.

Postmortem Metabolomics of Insulin Intoxications and the Potential Application to Find Hypoglycemia-Related Deaths.

Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications.

Post-Mortem Metabolomics: A Novel Approach in Clinical Biomarker Discovery and a Potential Tool in Death Investigations.

Metabolomics can be defined as the scientific field aiming at characterizing all low-weight molecules (so-called metabolites) in a biological system. At the time of death, the level and type of metabolites present will most likely reflect the events leading up to death.In this proof of concept study, we investigated the potential of post-mortem metabolomics by identifying post-mortem biomarkers, correlated these identified biomarkers with those reported in clinical metabolomics studies, and finally validated the models predictability of unknown autopsy cases. In this post-mortem metabolomics setting, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry data from 404 post-mortem samples, including pneumonia cases and control cases, were processed using XCMS (R). Potential biomarkers were evaluated using principal component analysis and orthogonal partial least squares-discriminant analysis. Biomarkers were putatively annotated using an in-house database and the online databases METLIN and HMDB. The results showed that clear group separation was observed between pneumonia cases and control cases. The metabolites responsible for group separation belonged to a broad set of biological classes, such as amino acids, carnitines, lipids, nicotinamides, nucleotides, and steroids. Many of these metabolites have been reported as important in clinical manifestation of pneumonia. For the unknown autopsy cases, the sensitivity and specificity were 86 and 84%, respectively. This study successfully investigated the robustness and usability of post-mortem metabolomics in death investigations. The identified post-mortem biomarkers correlated well with biomarkers reported and identified through clinical research.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts.

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.

Evaluation of analytical procedures, especially in regards to measuring chromatographic and signal selectivity, is highly challenging in untargeted metabolomics. The aim of this study was to suggest a new straightforward approach for a systematic examination of chromatographic and signal selectivity in LC-MS-based metabolomics. By calculating the ratio between each feature and its co-eluting features (the co-features), a measurement of the chromatographic selectivity (i.e. extent of co-elution) as well as the signal selectivity (e.g. amount of adduct formation) of each feature could be acquired, the co-feature ratio. This approach was used to examine possible differences in chromatographic and signal selectivity present in samples exposed to three different sample preparation procedures. The capability of the co-feature ratio was evaluated both in a classical targeted setting using isotope labelled standards as well as without standards in an untargeted setting. For the targeted analysis, several metabolites showed a skewed quantitative signal due to poor chromatographic selectivity and/or poor signal selectivity. Moreover, evaluation of the untargeted approach through multivariate analysis of the co-feature ratios demonstrated the possibility to screen for metabolites displaying poor chromatographic and/or signal selectivity characteristics. We conclude that the co-feature ratio can be a useful tool in the development and evaluation of analytical procedures in LC-MS-based metabolomics investigations. Increased selectivity through proper choice of analytical procedures may decrease the false positive and false negative discovery rate and thereby increase the validity of any metabolomic investigation.

NMR spectroscopy-based metabolic profiling of drug-induced changes in vitro can discriminate between pharmacological classes.

Drug-induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances, as well as to support drug repurposing, i.e., identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the "Connectivity Map" (CMap). However, the potential of similar exercises at the metabolite level is still largely unexplored. Only recently, the first high-throughput metabolomic assay pilot study was published, which involved screening the metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here, we report results from a separately developed metabolic profiling assay, which leverages (1)H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of the most-tested drugs, according to their respective pharmacological class. A more-advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (3 metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this type of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information-rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

The cyanobacterial amino acid ß-N-methylamino-l-alanine perturbs the intermediary metabolism in neonatal rats.

The neurotoxic amino acid ß-N-methylamino-l-alanine (BMAA) is produced by most cyanobacteria. BMAA is considered as a potential health threat because of its putative role in neurodegenerative diseases. We have previously observed cognitive disturbances and morphological brain changes in adult rodents exposed to BMAA during the development. The aim of this study was to characterize changes of major intermediary metabolites in serum following neonatal exposure to BMAA using a non-targeted metabolomic approach. NMR spectroscopy was used to obtain serum metabolic profiles from neonatal rats exposed to BMAA (40, 150, 460mg/kg) or vehicle on postnatal days 9-10. Multivariate data analysis of binned NMR data indicated metabolic pattern differences between the different treatment groups. In particular five metabolites, d-glucose, lactate, 3-hydroxybutyrate, creatine and acetate, were changed in serum of BMAA-treated neonatal rats. These metabolites are associated with changes in energy metabolism and amino acid metabolism. Further statistical analysis disclosed that all the identified serum metabolites in the lowest dose group were significantly (p<0.05) decreased. The neonatal rat model used in this study is so far the only animal model that displays significant biochemical and behavioral effects after a low short-term dose of BMAA. The demonstrated perturbation of intermediary metabolism may contribute to BMAA-induced developmental changes that result in long-term effects on adult brain function.

Screening of counterfeit corticosteroid in creams and ointments by NMR spectroscopy.

It has been shown that NMR spectroscopy is an effective analytical method to rapidly screen creams and ointments for counterfeit corticosteroids. Extraction and NMR procedures have been developed. Ten over the counter creams and ointments sold in health care shops were screened and two creams were found to contain counterfeited corticosteroids.