Plasma metabolic biomarkers for discriminating individuals with alcohol use disorders from social drinkers and alcohol-naive subjects

• Published in: Journal of substance abuse treatment 2017-06, Vol.77, p.1-5
• Main Authors: Mostafa, Hamza, Amin, Arwa M, Teh, Chin-Hoe, Murugaiyah, Vikneswaran a/l, Arif, Nor Hayati, Ibrahim, Baharudin
• Format: Article
• Language: English
• Subjects: Acetic Acid - blood; Acids; Adult; Alcohol abuse; Alcohol Drinking - blood; Alcohol Drinking - metabolism; Alcohol related disorders; Alcohol use; Alcohol use disorders; Alcoholism; Alcoholism - blood; Alcoholism - diagnosis; Alcoholism - metabolism; Biological markers; Biomarker; Biomarkers; Biomarkers - blood; Case-Control Studies; Cross-Sectional Studies; Discriminant analysis; Discrimination; Drinking behavior; Female; Humans; Least-Squares Analysis; Logistic Models; Magnetic Resonance Spectroscopy - methods; Male; Medical diagnosis; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; NMR; Nuclear magnetic resonance; Plasma; Principal Component Analysis; Principal components analysis; Propionates - blood; Psychiatry; Questionnaires; Reproducibility; Reproducibility of Results; Sensitivity and Specificity; Substance use disorder
• ISSN: 0740-5472; 1873-6483
• DOI: 10.1016/j.jsat.2017.02.015

Abstract Background Alcohol use disorders (AUD) is a phase of alcohol misuse in which the drinker consumes excessive amount of alcohol and have a continuous urge to consume alcohol which may lead to various health complications. The current methods of alcohol use disorders diagnosis such as questionnaires and some biomarkers lack specificity and sensitivity. Metabolomics is a novel scientific field which may provide a novel method for the diagnosis of AUD by using a sensitive and specific technique such as nuclear magnetic resonance (NMR). Methods A cross sectional study was conducted on three groups: individuals with alcohol use disorders ( n = 30), social drinkers ( n = 54) and alcohol-naive controls ( n = 60).1 H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups. Results The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81–1.0). Conclusions The applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.