Literature DB >> 28056375

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

Eugenio Alladio1, Agnieszka Martyna2, Alberto Salomone3, Valentina Pirro4, Marco Vincenti5, Grzegorz Zadora6.   

Abstract

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption and provide a suitable gradation to support the judgment.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Alcohol; Empirical cross entropy; EtG; FAEE; Hair analysis; Likelihood ratio

Mesh:

Substances:

Year:  2016        PMID: 28056375     DOI: 10.1016/j.forsciint.2016.12.019

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  5 in total

1.  Alcohol markers in hair: an issue of interpretation.

Authors:  Richard Paul
Journal:  Forensic Sci Med Pathol       Date:  2018-11-30       Impact factor: 2.007

2.  Direct and indirect alcohol biomarkers data collected in hair samples - multivariate data analysis and likelihood ratio interpretation perspectives.

Authors:  Eugenio Alladio; Agnieszka Martyna; Alberto Salomone; Valentina Pirro; Marco Vincenti; Grzegorz Zadora
Journal:  Data Brief       Date:  2017-03-16

3.  Geochemical wolframite fingerprinting - the likelihood ratio approach for laser ablation ICP-MS data.

Authors:  Agnieszka Martyna; Hans-Eike Gäbler; Andreas Bahr; Grzegorz Zadora
Journal:  Anal Bioanal Chem       Date:  2018-04-17       Impact factor: 4.142

4.  Development and Validation of a GC-EI-MS/MS Method for Ethyl Glucuronide Quantification in Human Hair.

Authors:  Alessandro Mattia; Clementina Moschella; Maria Chiara David; Marco Fiore; Sara Gariglio; Alberto Salomone; Marco Vincenti
Journal:  Front Chem       Date:  2022-04-04       Impact factor: 5.545

5.  Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation.

Authors:  Giulia Biosa; Diana Giurghita; Eugenio Alladio; Marco Vincenti; Tereza Neocleous
Journal:  Front Chem       Date:  2020-10-21       Impact factor: 5.221

  5 in total

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