Bioinformatics-driven untargeted metabolomic profiling for clinical screening of methamphetamine abuse.

Abstract

Amphetamine-type stimulants are very common, and their usage is becoming a very big social problem all over the world. Thousands of addicts encounter several health problems including mental, metabolic, behavioral and neurological disorders. In addition to these, there are several reports about the elevated risk of tendency on committing criminal cases by addicted persons. Hence, methamphetamine addiction is not only an individual health problem but also a social problem. In our study, we aimed to investigate the pathogenesis of chronic usage of methamphetamine via untargeted metabolomics approach. 38 plasma samples were carefully collected and extracted for untargeted metabolomics assay. A liquid-liquid extraction was performed to get as much metabolite as possible from the samples. After the extraction procedure, samples were transferred into vials and they were evaluated via time of flight mass spectrometry instrument. Significantly, altered metabolites were identified by the fold analysis and Welch's test between the groups. 42 different compounds were annotated regarding to data-dependent acquisition method. Pathway analysis were also performed to understand the hazardous effect of methamphetamine on human body. It has been reported that drug exposure may affect several metabolic pathways for amino acids, fats, energy metabolism and vitamins. An alternative bioinformatic model was also developed and validated in order to predict the chronic methamphetamine drug users in any criminal cases. This generated model passes the ROC curve analysis and permutation test and classify the controls and drug users correctly by evaluating the metabolic alterations between the groups.