Alladio Eugenio, Martyna Agnieszka, Salomone Alberto, Pirro Valentina, Vincenti Marco, Zadora Grzegorz
Dipartimento di Chimica, Università degli Studi di Torino, Via P. Giuria 7, 10125 Torino, Italy; Centro Regionale Antidoping e di Tossicologia "A. Bertinaria", Regione Gonzole 10/1, 10043 Orbassano, Torino, Italy.
Department of Analytical Chemistry, Chemometric Research Group, Institute of Chemistry, The University of Silesia, Szkolna 9, 40-006 Katowice, Poland.
Forensic Sci Int. 2017 Feb;271:13-22. doi: 10.1016/j.forsciint.2016.12.019. Epub 2016 Dec 21.
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.
通过检测头皮毛发中的直接乙醇代谢产物,如葡萄糖醛酸乙酯(EtG)和脂肪酸乙酯(FAEEs),并采用毛发检测协会建议的特定临界值,被认为是有效识别慢性酒精滥用的最佳策略。然而,一些因素(如头发处理)可能会改变酒精摄入量与生物标志物浓度之间的相关性,这可能会在解释过程和结论中引入偏差。对125名有不同饮酒习惯的受试者进行了血液和毛发采样,以测定间接(如CDT)和直接酒精生物标志物。使用多种多元统计方法对总体数据进行了研究。首次使用似然比(LR)方法,基于直接和间接酒精生物标志物的不同组合,为酒精滥用的诊断提供预测模型。与直接与临界值比较相比,LR策略提供了更可靠的结果,因为分析结果的微小变化可能导致解释方式的巨大差异。一个结合EtG和FAEEs毛发浓度的LR模型被证明能够以理想的正确分类率区分非慢性饮酒者和慢性饮酒者,而间接生物标志物的贡献被证明可以忽略不计。使用一种将LR方法与多元统计相结合的新方法观察到了最佳结果。特别是,LR方法与主成分分析(PCA)或线性判别分析(LDA)的组合被证明成功地区分了慢性饮酒者和非慢性饮酒者。随后在一个由43名个体组成的独立数据集上对这些LR模型进行了测试,证实了它们的高效率。这些模型被证明比单独考虑的EtG和FAEEs更不易产生偏差。总之,LR模型可能是一种有效的策略,有助于慢性酒精消费的诊断,并提供合适的分级来支持判断。