Revealing Metabolic Perturbation Following Heavy Methamphetamine Abuse by Human Hair Metabolomics and Network Analysis

Abstract

Methamphetamine (MA) is a highly addictive central nervous system stimulant. Drug addiction is not a static condition but rather a chronically relapsing disorder. Hair is a valuable and stable specimen for chronic toxicological monitoring as it retains toxicants and metabolites. The primary focus of this study was to discover the metabolic effects encompassing diverse pathological symptoms of MA addiction. Therefore, metabolic alterations were investigated in human hair following heavy MA abuse using both targeted and untargeted mass spectrometry and through integrated network analysis. The statistical analyses (t-test, variable importance on projection score, and receiver-operator characteristic curve) demonstrated that 32 metabolites (in targeted metabolomics) as well as 417 and 224 ion features (in positive and negative ionization modes of untargeted metabolomics, respectively) were critically dysregulated. The network analysis showed that the biosynthesis or metabolism of lipids, such as glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids, as well as the metabolism of amino acids (glycine, serine and threonine; cysteine and methionine) is affected by heavy MA abuse. These findings reveal crucial metabolic effects caused by MA addiction, with emphasis on the value of human hair as a diagnostic specimen for determining drug addiction, and will aid in identifying robust diagnostic markers and therapeutic targets.

Metabolic phenotyping of opioid and psychostimulant addiction: A novel approach for biomarker discovery and biochemical understanding of the disorder

“…In the urine, ion features corresponding to potential urine markers of methamphetamine addiction were detected but only putatively assigned. In the hair, a decrease in deoxycorticosterone suggests altered central production of neurosteroids (Mellon & Griffin, 2002), A study conducted on the hair metabolome of methamphetamine users revealed additional abnormalities in the abundance of amino acids and lipids (Kim et al, 2020). The amino acids arginine and methionine, both known ROS scavengers (Liang et al, 2018;Luo & Levine, 2009), were down-regulated in the hair of drug abusers, which may indicate higher susceptibility to oxidative stress.…”

“…A study conducted on the hair metabolome of methamphetamine users revealed additional abnormalities in the abundance of amino acids and lipids (Kim et al, 2020). The amino acids arginine and methionine, both known ROS scavengers (Liang et al, 2018; Luo & Levine, 2009), were down‐regulated in the hair of drug abusers, which may indicate higher susceptibility to oxidative stress.…”

“…Biofluids including urine, blood and hair samples have also been studied to understand the metabolic consequences of exposure (Choi et al, 2017; Goodwin et al, 2014; Olesti et al, 2019; Sánchez‐López et al, 2017; Yao et al, 2013), abstinence (Kim et al, 2019; Shima et al, 2011; Zhang, Chiu, et al, 2016; Zheng et al, 2014) and the establishment of drug reinforcement (Zaitsu et al, 2014). Human studies investigating the impact of psychostimulants on hair (Kim et al, 2020) and serum/plasma metabolome (Costa et al, 2019; Lin et al, 2019) and the effect of abstinence from cocaine (Patkar et al, 2009) will also be reviewed. These studies are summarised in Table 1.…”

Untargeted metabolomics approaches to improve casework in clinical and forensic toxicology—“Where are we standing and where are we heading?”

“…Supervised multivariate approaches are prone to overfitting and therefore cross‐validation is crucial to assess the discriminative capacity of the revealed markers (Broadhurst & Kell, 2006; Cuykx et al, 2018; Gromski et al, 2014). Univariate analysis (parametric or nonparametric t ‐test or ANOVA/Kruskal–Wallis) are applied to identify significantly changed metabolites, often in combination with fold‐change analysis as, for example, used in references (Kim et al, 2020; Patkar et al, 2009; Steuer et al, 2020; Steuer, Raeber, et al, 2019). Volcano plots (log2 fold‐change vs. –log10 p ‐value) provide a one‐glance overview of the distribution and magnitude of changes, and their significance.…”

Metabolic Alterations Associated with γ-Hydroxybutyric Acid and the Potential of Metabolites as Biomarkers of Its Exposure

“…To investigate the maximum number of metabolites possible, non-selective approaches have been adopted for sample preparation and instrumental analysis [ 2 , 4 ]. In particular, metabolomics is currently being used to identify endogenous metabolites generated after exposure to addictive drugs [ 8 , 9 , 10 , 11 ]. This metabolomics approach can provide a new foundation for identifying effective diagnostic markers or therapeutic targets, if the knowledge of the mechanisms of a drug’s pharmacodynamic or pharmacokinetic properties is limited [ 12 ].…”