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Investigation of metabolites for estimating blood deposition time
Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor’s alibi. Previously,...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748410/ https://www.ncbi.nlm.nih.gov/pubmed/28780758 http://dx.doi.org/10.1007/s00414-017-1638-y |
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author | Lech, Karolina Liu, Fan Davies, Sarah K. Ackermann, Katrin Ang, Joo Ern Middleton, Benita Revell, Victoria L. Raynaud, Florence J. Hoveijn, Igor Hut, Roelof A. Skene, Debra J. Kayser, Manfred |
author_facet | Lech, Karolina Liu, Fan Davies, Sarah K. Ackermann, Katrin Ang, Joo Ern Middleton, Benita Revell, Victoria L. Raynaud, Florence J. Hoveijn, Igor Hut, Roelof A. Skene, Debra J. Kayser, Manfred |
author_sort | Lech, Karolina |
collection | PubMed |
description | Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor’s alibi. Previously, we introduced two circadian hormones for trace deposition timing and recently demonstrated that messenger RNA (mRNA) biomarkers significantly improve time prediction accuracy. Here, we investigate the suitability of metabolites measured using a targeted metabolomics approach, for trace deposition timing. Analysis of 171 plasma metabolites collected around the clock at 2-h intervals for 36 h from 12 male participants under controlled laboratory conditions identified 56 metabolites showing statistically significant oscillations, with peak times falling into three day/night time categories: morning/noon, afternoon/evening and night/early morning. Time prediction modelling identified 10 independently contributing metabolite biomarkers, which together achieved prediction accuracies expressed as AUC of 0.81, 0.86 and 0.90 for these three time categories respectively. Combining metabolites with previously established hormone and mRNA biomarkers in time prediction modelling resulted in an improved prediction accuracy reaching AUCs of 0.85, 0.89 and 0.96 respectively. The additional impact of metabolite biomarkers, however, was rather minor as the previously established model with melatonin, cortisol and three mRNA biomarkers achieved AUC values of 0.88, 0.88 and 0.95 for the same three time categories respectively. Nevertheless, the selected metabolites could become practically useful in scenarios where RNA marker information is unavailable such as due to RNA degradation. This is the first metabolomics study investigating circulating metabolites for trace deposition timing, and more work is needed to fully establish their usefulness for this forensic purpose. |
format | Online Article Text |
id | pubmed-5748410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-57484102018-01-19 Investigation of metabolites for estimating blood deposition time Lech, Karolina Liu, Fan Davies, Sarah K. Ackermann, Katrin Ang, Joo Ern Middleton, Benita Revell, Victoria L. Raynaud, Florence J. Hoveijn, Igor Hut, Roelof A. Skene, Debra J. Kayser, Manfred Int J Legal Med Original Article Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor’s alibi. Previously, we introduced two circadian hormones for trace deposition timing and recently demonstrated that messenger RNA (mRNA) biomarkers significantly improve time prediction accuracy. Here, we investigate the suitability of metabolites measured using a targeted metabolomics approach, for trace deposition timing. Analysis of 171 plasma metabolites collected around the clock at 2-h intervals for 36 h from 12 male participants under controlled laboratory conditions identified 56 metabolites showing statistically significant oscillations, with peak times falling into three day/night time categories: morning/noon, afternoon/evening and night/early morning. Time prediction modelling identified 10 independently contributing metabolite biomarkers, which together achieved prediction accuracies expressed as AUC of 0.81, 0.86 and 0.90 for these three time categories respectively. Combining metabolites with previously established hormone and mRNA biomarkers in time prediction modelling resulted in an improved prediction accuracy reaching AUCs of 0.85, 0.89 and 0.96 respectively. The additional impact of metabolite biomarkers, however, was rather minor as the previously established model with melatonin, cortisol and three mRNA biomarkers achieved AUC values of 0.88, 0.88 and 0.95 for the same three time categories respectively. Nevertheless, the selected metabolites could become practically useful in scenarios where RNA marker information is unavailable such as due to RNA degradation. This is the first metabolomics study investigating circulating metabolites for trace deposition timing, and more work is needed to fully establish their usefulness for this forensic purpose. Springer Berlin Heidelberg 2017-08-05 2018 /pmc/articles/PMC5748410/ /pubmed/28780758 http://dx.doi.org/10.1007/s00414-017-1638-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Lech, Karolina Liu, Fan Davies, Sarah K. Ackermann, Katrin Ang, Joo Ern Middleton, Benita Revell, Victoria L. Raynaud, Florence J. Hoveijn, Igor Hut, Roelof A. Skene, Debra J. Kayser, Manfred Investigation of metabolites for estimating blood deposition time |
title | Investigation of metabolites for estimating blood deposition time |
title_full | Investigation of metabolites for estimating blood deposition time |
title_fullStr | Investigation of metabolites for estimating blood deposition time |
title_full_unstemmed | Investigation of metabolites for estimating blood deposition time |
title_short | Investigation of metabolites for estimating blood deposition time |
title_sort | investigation of metabolites for estimating blood deposition time |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748410/ https://www.ncbi.nlm.nih.gov/pubmed/28780758 http://dx.doi.org/10.1007/s00414-017-1638-y |
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