Integrated Non-targeted and Targeted Metabolomics Uncovers Dynamic Metabolic Effects during Short-Term Abstinence in Methamphetamine Self-Administering Rats

Exploring the progression of drug dependence in a methamphetamine self-administration rat model through targeted and non-targeted metabolomics analyses

Persistent neurochemical and biological disturbances resulting from repeated cycles of drug reward, withdrawal, and relapse contribute to drug dependence. Methamphetamine (MA) is a psychostimulant with substantial abuse potential and neurotoxic effects, primarily affecting monoamine neurotransmitter systems in the brain. In this study, we aimed to explore the progression of drug dependence in rat models of MA self-administration, extinction, and reinstatement through targeted and non-targeted metabolomics analyses. Metabolic profiles were examined in rat plasma during the following phases: after 16 days of MA self-administration (Group M); after 16 days of self-administration followed by 14 days of extinction (Group MS); and after self-administration and extinction followed by a reinstatement injection of MA (Group MSM). Each group of MA self-administration, extinction, and reinstatement induces distinct changes in the metabolic pathways, particularly those related to the TCA cycle, arginine and proline metabolism, and arginine biosynthesis. Additionally, the downregulation of glycerophospholipids and sphingomyelins in Group MSM suggests their potential role in MA reinstatement. These alterations may signify the progressive deterioration of these metabolic pathways, possibly contributing to drug dependence following repeated cycles of drug reward, withdrawal, and relapse. These results provide valuable insights into the metabolic changes associated with MA use at various stages, potentially facilitating the discovery of early diagnostic biomarkers and therapeutic targets for MA use disorders.

Methylphenidate Exposing During Neurodevelopment Alters Amino Acid Profile, Astrocyte Marker and Glutamatergic Excitotoxicity in the Rat Striatum

There is a public health concern about the use of methylphenidate (MPH) since the higher prescription for young individuals and non-clinical purposes is addressed to the limited understanding of its neurochemical and psychiatric consequences. This study aimed to evaluate the impact of early and chronic MPH treatment on the striatum focusing on amino acid profile, glutamatergic excitotoxicity, redox status, neuroinflammation and glial cell responses. Male Wistar rats were treated with MPH (2.0 mg/kg) or saline solution from the 15th to the 44th postnatal day. Biochemical and histological analyses were conducted after the last administration. MPH altered the amino acid profile in the striatum, increasing glutamate and ornithine levels, while decreasing the levels of serine, phenylalanine, and branched-chain amino acids (leucine, valine, and isoleucine). Glutamate uptake and Na+,K+-ATPase activity were decreased in the striatum of MPH-treated rats as well as increased ATP levels, as indicator of glutamatergic excitotoxicity. Moreover, MPH caused lipid peroxidation and nitrative stress, increased TNF alpha expression, and induced high levels of astrocytes, and led to a decrease in BDNF levels. In summary, our results suggest that chronic early-age treatment with MPH induces parallel activation of damage-associated pathways in the striatum and increases its vulnerability during the juvenile period. In addition, data presented here contribute to shedding light on the mechanisms underlying MPH-induced striatal damage and its potential implications for neurodevelopmental disorders.

Variations in the oral microbiome and metabolome of methamphetamine users

Drug addiction can seriously damage human physical and mental health, while detoxification is a long and difficult process. Although studies have reported changes in the oral microbiome of methamphetamine (METH) users, the role that the microbiome plays in the process of drug addiction is still unknown. This study aims to explore the function of the microbiome based on analysis of the variations in the oral microbiome and metabolome of METH users. We performed the 16S rRNA sequencing analysis based on the oral saliva samples collected from 278 METH users and 105 healthy controls (CTL). In addition, the untargeted metabolomic profiling was conducted based on 220 samples. Compared to the CTL group, alpha diversity was reduced in the group of METH users and the relative abundances of Peptostreptococcus and Gemella were significantly increased, while the relative abundances of Campylobacter and Aggregatibacter were significantly decreased. Variations were also detected in oral metabolic pathways, including enhanced tryptophan metabolism, lysine biosynthesis, purine metabolism, and steroid biosynthesis. Conversely, the metabolic pathways of porphyrin metabolism, glutathione metabolism, and pentose phosphate were significantly reduced. It was speculated that four key microbial taxa, i.e., Peptostreptococcus, Gemella, Campylobacter, and Aggregatibacter, could be involved in the toxicity and addiction mechanisms of METH by affecting the above metabolic pathways. It was found that with the increase of drug use years, the content of tryptamine associated with neuropsychiatric disorders was gradually increased. Our study provides novel insights into exploring the toxic damage and addiction mechanisms underlying the METH addiction.IMPORTANCEIt was found that with the increase of drug use years, the content of tryptamine associated with neuropsychiatric disorders gradually increased. The prediction models based on oral microbiome and metabolome could effectively predict the methamphetamine (METH) smoking. Our study provides novel insights into the exploration of the molecular mechanisms regulating the toxic damage and addiction of METH as well as new ideas for early prevention and treatment strategies of METH addiction.

Effect of aerobic exercise on brain metabolite profiles in the mouse models of methamphetamine addiction: LC-MS-based metabolomics study

Methamphetamine (MA) abuse is recognized as a brain disorder, and physical activity has clear benefits for MA use disorders. The specific mechanisms by which physical activity alleviates MA use disorders are currently not fully understood. Based on this, the present study used untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) to analyze the metabolic changes induced by MA in the brains of mice by exercise intervention. It was found that after 2 weeks of treadmill training, aerobic exercise modulated MA-induced brain metabolic disorders, in which 129 metabolites existed that were significantly differentiated in response to MA induction, and 32 metabolites were significantly affected by exercise. These differential metabolites were mainly enriched in glycerophospholipid metabolism, steroid hormone biosynthesis and degradation, and renin-angiotensin system pathways. To our knowledge, this study is the first to use LC-MS to investigate the effects of aerobic exercise on MA-induced brain metabolic profiling. The findings of this study provide new insights into exercise therapy using MA.

Identification of Potential Biomarkers for Diagnosis of Patients with Methamphetamine Use Disorder

The current method for diagnosing methamphetamine use disorder (MUD) relies on self-reports and interviews with psychiatrists, which lack scientific rigor. This highlights the need for novel biomarkers to accurately diagnose MUD. In this study, we identified transcriptome biomarkers using hair follicles and proposed a diagnostic model for monitoring the MUD treatment process. We performed RNA sequencing analysis on hair follicle cells from healthy controls and former and current MUD patients who had been detained in the past for illegal use of methamphetamine (MA). We selected candidate genes for monitoring MUD patients by performing multivariate analysis methods, such as PCA and PLS-DA, and PPI network analysis. We developed a two-stage diagnostic model using multivariate ROC analysis based on the PLS-DA method. We constructed a two-step prediction model for MUD diagnosis using multivariate ROC analysis, including 10 biomarkers. The first step model, which distinguishes non-recovered patients from others, showed very high accuracy (prediction accuracy, 98.7%). The second step model, which distinguishes almost-recovered patients from healthy controls, showed high accuracy (prediction accuracy, 81.3%). This study is the first report to use hair follicles of MUD patients and to develop a MUD prediction model based on transcriptomic biomarkers, which offers a potential solution to improve the accuracy of MUD diagnosis and may lead to the development of better pharmacological treatments for the disorder in the future.

Kratom (M. speciosa) exposure during adolescence caused long-lasting cognitive behavioural deficits associated with perturbated brain metabolism pathways in adult rats

Kratom (M. speciosa Korth) is an herbal plant native to Southeast Asia. The leaves have been widely used to alleviate pain and opioid withdrawal symptoms. However, the increasing trend of recreational use of kratom among youth is concerning because substance abuse may render the adolescent brain more susceptible to neuropathological processes, causing dramatic consequences that persist into adulthood. Therefore, the present study aimed to investigate the long-term effects of mitragynine, the main alkaloid and lyophilized kratom decoction (LKD) exposure during adolescence on cognitive behaviours and brain metabolite profiles in adult rats. Adolescent male Sprague-Dawley rats were given mitragynine (3, 10 or 30mg/kg) or LKD orally for 15 consecutive days during postnatal days 31-45 (PND31-45). Behavioural testing was performed during adulthood (PND70-84) and the brains were subjected to metabolomic analysis. The results show that a high dose of mitragynine impaired long-term object recognition memory. Social behaviour and spatial learning were not affected, but both mitragynine and LKD impaired reference memory. Brain metabolomic study revealed several altered metabolic pathways that may be involved in the cognitive behavioural effects of LKD and mitragynine exposure. These pathways include arachidonic acid, taurine and hypotaurine, pantothenate and CoA biosynthesis, and tryptophan metabolism, while the N-isovalerylglycine was identified as the potential biomarker. In summary, adolescent kratom exposure can cause long-lasting cognitive behavioural deficits and alter brain metabolite profiles that are still evident in adulthood. This finding also indicates that the adolescent brain is vulnerable to the impact of early kratom use.

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg^-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

Striatal miR-183-5p inhibits methamphetamine-induced locomotion by regulating glucocorticoid receptor signaling

MicroRNA (miRNA)-mediated striatal gene regulation may play an important role in methamphetamine (METH) addiction. This study aimed to identify changes in novel miRNAs and their target genes during METH self-administration and investigate their roles in METH-induced locomotion. RNA sequencing analysis revealed that mir-183-5p was upregulated in the striatum of METH self-administered rats, and target gene prediction revealed that the glucocorticoid receptor (GR) gene, Nr3c1, was a potential target gene for mir-183-5p. We confirmed that single and repeated METH administrations increased METH-induced locomotion and plasma corticosterone levels in rats. Additionally, increased miR-185-5p expression and decreased GR gene expression were observed only in the repeated-METH-injection group but not in the single-injection group. We then investigated the effects of miR-183-5p on METH-induced locomotion using a miR-183-5p mimic and inhibitor. Injection of a mir-183-5p mimic in the striatum of rats attenuated METH-induced locomotion, whereas injection of a miR-183-5p inhibitor enhanced the locomotor activity in METH-administered rats. Furthermore, the miR-183-5p mimic reduced the phosphorylation of tyrosine hydroxylase (TH) whereas the inhibitor increased it. Taken together, these results indicate that repeated METH injections increase striatal miR-183-5p expression and regulate METH-induced locomotion by regulating GR expression in rats, thereby suggesting a potential role of miR-183-5p as a novel regulator of METH-induced locomotion.

Exercise modulates central and peripheral inflammatory responses and ameliorates methamphetamine-induced anxiety-like symptoms in mice

Anxiety-like symptoms are common symptoms of methamphetamine (METH) users, especially in the acute withdrawal period, which is an important factor for the high relapse rate during METH acute withdrawal. Exercise has been demonstrated to relieve anxiety-like symptoms during METH withdrawal, but the underlying mechanisms of this anti-anxiety effect are still unclear. Activated microglia and abnormal neuroinflammation play an important role in the pathogenesis of anxiety-like symptoms after METH withdrawal. Moreover, peripheral immune factors were also significantly associated with anxiety symptoms. However, the effects of treadmill exercise on microglial function and neuroinflammation in the striatum and hippocampus during acute METH withdrawal have not been reported. In the current study, we found severe peripheral immune dysfunction in METH users during acute withdrawal, which may in part contribute to anxiety symptoms during METH acute withdrawal. We also showed that 2 weeks of METH exposure induced anxiety-like symptoms in the acute withdrawal period. Additionally, METH exposure resulted in increased microglial activation and proinflammatory cytokines released in the mouse striatum and hippocampus during acute withdrawal. We next evaluated the effects of treadmill exercise in countering anxiety-like symptoms induced by METH acute withdrawal. The results showed that anxiety-like symptoms induced by acute METH withdrawal were attenuated by coadministration of treadmill exercise. In addition, treadmill exercise counteracted METH-induced microglial activation in the mouse striatum and various subregions of the hippocampus. Furthermore, treadmill exercise also reversed the increase in proinflammatory cytokines induced by acute METH withdrawal in the mouse striatum, hippocampus and serum. Our findings suggest that the anti-anxiety effect of treadmill exercise may be mediated by reducing microglial activation and regulating central and peripheral inflammatory responses.

Precise Metabolomics Reveals a Diversity of Aging-Associated Metabolic Features

Mass spectrometry-based metabolomics has emerged as a powerful technique for biomedical research, although technical issues with its analytical precision and structural characterization remain. Herein, a robust non-targeted strategy for accurate quantitation and precise profiling of metabolomes is developed and applied to investigate plasma metabolic features associated with human aging. A comprehensive set of isotope-labeled standards (ISs) covering major metabolic pathways is incorporated to quantify polar metabolites. Matching rules to select ISs for calibration follow a primary criterion of minimal coefficients of variations (COVs). If minimal COVs between specific ISs for a particular metabolite fall within 5% window, a further selection of ISs is conducted based on structural similarities and proximity in retention time. The introduction and refined selection of appropriate ISs for quantitation reduces the COVs of 480 identified metabolites in quality control samples from 14.3% to 9.8% and facilitates identification of additional metabolite. Finally, the precise metabolomics approach reveals perturbations in a diverse array of metabolic pathways across aging that principally implicate steroid metabolism, amino acid metabolism, lipid metabolism, and purine metabolism, which allows the authors to draw correlates to the pathology of various age-related diseases. These findings provide clues for the prevention and treatment of these age-related diseases.

MS-IDF: A Software Tool for Nontargeted Identification of Endogenous Metabolites after Chemical Isotope Labeling Based on a Narrow Mass Defect Filter

Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is an emerging metabolomic strategy for the quantification and characterization of small molecular compounds in biological samples. However, its subsequent data analysis is not straightforward due to a large amount of data produced and interference of biological matrices. In order to improve the efficiency of searching and identification of target endogenous metabolites, a new software tool for nontargeted metabolomics data processing called MS-IDF was developed based on the principle of a narrow mass defect filter. The developed tool provided two function modules, including IsoFinder and MDFinder. The IsoFinder function module applied a conventional peak extraction method by using a fixed mass differences between the heavy and light labels and by the alignment of chromatographic retention time (RT). On the other hand, MDFinder was designed to incorporate the accurate mass defect differences between or among stable isotopes in the peak extraction process. By setting an appropriate filter interval, the target metabolites can be efficiently screened out while eliminating interference. Notably, the present results showed that the efficiency in compound identification using the new MDFinder module was nearly doubled as compared to the conventional IsoFinder method (an increase from 259 to 423 compounds). The Matlab codes of the developed MS-IDF software are available from github at https://github.com/jydong2018/MS_IDF. Based on the MS-IDF software tool, a novel and effective approach from nontargeted to targeted metabolomics research was developed and applied to the exploration of potential primary amine biomarkers in patients with schizophrenia. With this approach, potential biomarkers, including N,N-dimethylglycine, S-adenosine-l-methionine, dl-homocysteine, and spermidine, were discovered.

Low-cost apparatus for cigarette smoke exposure in rats

BACKGROUND

The effects of tobacco smoke on the central nervous system are usually studied with isolated nicotine, ignoring other compounds present in cigarette smoke. The few studies that use in vivo whole-body cigarette smoke exposure are usually performed in expensive commercial apparatus.

NEW METHOD

We presented a feasible, safe, and low-cost apparatus for cigarette smoke exposure in rodents.

RESULTS

Rats exposed to cigarette smoke in this apparatus showed cotinine levels similar to human active smokers. Additional results showed that cigarette smoke exposure increased glutamate and aspartic acid levels and decreased leucine, isoleucine, ornithine, phenylalanine, and tryptophan levels in the cerebrospinal fluid of rats.

COMPARISON WITH EXISTING METHOD(S)

Our apparatus is feasible, safe, and costs 67-fold less than a commercial automatized smoking machine. Beyond the low cost, it does not require specialized knowledge for building or maintenance.

CONCLUSIONS

We concluded that our low-cost apparatus is reliable and reproduces cigarette smoke use in humans.

Evidence That Methylphenidate Treatment Evokes Anxiety-Like Behavior Through Glucose Hypometabolism and Disruption of the Orbitofrontal Cortex Metabolic Networks

Methylphenidate (MPH) has been widely misused by children and adolescents who do not meet all diagnostic criteria for attention-deficit/hyperactivity disorder without a consensus about the consequences. Here, we evaluate the effect of MPH treatment on glucose metabolism and metabolic network in the rat brain, as well as on performance in behavioral tests. Wistar male rats received intraperitoneal injections of MPH (2.0 mg/kg) or an equivalent volume of 0.9% saline solution (controls), once a day, from the 15th to the 44th postnatal day. Fluorodeoxyglucose-18 was used to investigate cerebral metabolism, and a cross-correlation matrix was used to examine the brain metabolic network in MPH-treated rats using micro-positron emission tomography imaging. Performance in the light-dark transition box, eating-related depression, and sucrose preference tests was also evaluated. While MPH provoked glucose hypermetabolism in the auditory, parietal, retrosplenial, somatosensory, and visual cortices, hypometabolism was identified in the left orbitofrontal cortex. MPH-treated rats show a brain metabolic network more efficient and connected, but careful analyses reveal that the MPH interrupts the communication of the orbitofrontal cortex with other brain areas. Anxiety-like behavior was also observed in MPH-treated rats. This study shows that glucose metabolism evaluated by micro-positron emission tomography in the brain can be affected by MPH in different ways according to the region of the brain studied. It may be related, at least in part, to a rewiring in the brain the metabolic network and behavioral changes observed, representing an important step in exploring the mechanisms and consequences of MPH treatment.

Mass spectrometry-based metabolomics in hair from current and former patients with methamphetamine use disorder

Drug use disorder, a chronic and relapsing mental disorder, is primarily diagnosed via self-reports of drug-seeking behavioral and psychological conditions, accompanied by psychiatric assessment. Therefore, the identification of peripheral biomarkers that reflect pathological changes caused by such disorders is essential for improving treatment monitoring. Hair possesses great potential as a metabolomic sample for monitoring chronic diseases. This study aimed to investigate metabolic alterations in hair to elucidate a suitable treatment modality for methamphetamine (MA) use disorder. Consequently, both targeted and untargeted metabolomics analyses were performed via mass spectrometry on hair samples obtained from current and former patients with MA use disorder. Healthy subjects (HS), current (CP), and former (FP) patients with this disorder were selected based on psychiatric diagnosis and screening the concentrations of MA in hair. The drug abuse screening questionnaire scores did not differentiate between CP and FP. Moreover, according to both targeted and untargeted metabolomics, clustering was not observed among all three groups. Nevertheless, a model of partial least squares-discriminant analysis was established between HS and CP based on seven metabolites derived from the targeted metabolomics results. Thus, this study demonstrates the promising potential of hair metabolomes for monitoring recovery from drug use disorders in clinical practice.

Relationship between amniotic fluid metabolic profile with fetal gender, maternal age, and gestational week

BACKGROUND

Amniotic fluid (AF) provides vital information on fetal development, which is also valuable in identifying fetal abnormalities during pregnancy. However, the relationship between the metabolic profile of AF in the second trimester of a normal pregnancy with several maternal-fetal parameters remains poorly understood, which therefore limits its application in clinical practice. The aim of this study was to explore the association between the metabolic profile of AF with fetal gender, maternal age, and gestational week using an untargeted metabolomics method.

METHODS

A total of 114 AF samples were analyzed in this study. Clinical data on fetal gender, maternal age, and gestational week of these samples were collected. Samples were analyzed by gas chromatography/time-of-flight-mass spectrometry (GC-TOF/MS). Principal component analysis(PCA), orthogonal partial least square discrimination analysis(OPLS-DA) or partial least square discrimination analysis (PLS-DA) were conducted to compare metabolic profiles, and differential metabolites were obtained by univariate analysis.

RESULTS

Both PCA and OPLS-DA demonstrated no significant separation trend between the metabolic profiles of male and female fetuses, and there were only 7 differential metabolites. When the association between the maternal age on AF metabolic profile was explored, both PCA and PLS-DA revealed that the maternal age in the range of 21 to 40 years had no significant effect on the metabolic profile of AF, and only four different metabolites were found. There was no significant difference in the metabolic profiles of AF from fetuses of 17-22 weeks, and 23 differential metabolites were found.

CONCLUSIONS

In the scope of our study, there was no significant correlation between the AF metabolic profile and the fetal gender, maternal age and gestational week of a small range. Nevertheless, few metabolites appeared differentially expressed.

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

Despite the progress in characterising the pharmacological profile of drugs of abuse, their precise biochemical impact remains unclear. The metabolome reflects the multifaceted biochemical processes occurring within a biological system. This includes those encoded in the genome but also those arising from environmental/exogenous exposures and interactions between the two. Using metabolomics, the biochemical derangements associated with substance abuse can be determined as the individual transitions from recreational drug to chronic use (dependence). By understanding the biomolecular perturbations along this time course and how they vary across individuals, metabolomics can elucidate biochemical mechanisms of the addiction cycle (dependence/withdrawal/relapse) and predict prognosis (recovery/relapse). In this review, we summarise human and animal metabolomic studies in the field of opioid and psychostimulant addiction. We highlight the importance of metabolomics as a powerful approach for biomarker discovery and its potential to guide personalised pharmacotherapeutic strategies for addiction targeted towards the individual's metabolome.

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

γ-Hydroxybutyric acid (GHB) is an endogenous short chain fatty acid that acts as a neurotransmitter and neuromodulator in the mammalian brain. It has often been illegally abused or misused due to its strong anesthetic effect, particularly in drug-facilitated crimes worldwide. However, proving its ingestion is not straightforward because of the difficulty in distinguishing between endogenous and exogenous GHB, as well as its rapid metabolism. Metabolomics and metabolism studies have recently been used to identify potential biomarkers of GHB exposure. This mini-review provides an overview of GHB-associated metabolic alterations and explores the potential of metabolites for application as biomarkers of GHB exposure. For this, we discuss the biosynthesis and metabolism of GHB, analytical issues of GHB in biological samples, alterations in metabolic pathways, and changes in the levels of GHB conjugates in biological samples from animal and human studies. Metabolic alterations in organic acids, amino acids, and polyamines in urine enable discrimination between GHB-ingested animals or humans and controls. The potential of GHB conjugates has been investigated in a variety of clinical settings. Despite the recent growth in the application of metabolomics and metabolism studies associated with GHB exposure, it remains challenging to distinguish between endogenous and exogenous GHB. This review highlights the significance of further metabolomics and metabolism studies for the discovery of practical peripheral biomarkers of GHB exposure.

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

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.