Plasma metabolites changes in male heroin addicts during acute and protracted withdrawal

Zhou, Yong; Xie, Zhenrong; Zhang, Zunyue; Yang, Jun; Chen, Ming‐Hui; Chen, Fengrong; Ma, Yuru; Chen, Cheng; Peng, Qingyan; Zou, Lei; Gao, Jianyuan; Xu, Yu; Kuang, Yi‐Qun; Zhu, Mei; You, Dingyun; Yu, Juehua; Wang, Kunhua

doi:10.18632/aging.203311

Cited by 8 publications

(8 citation statements)

References 66 publications

(71 reference statements)

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“…Potential heroin biomarkers were investigated in human plasma from 50 participants (20 heroin addicts with acute abstinence, 15 with prolonged abstinence and 15 controls) by Zhou et al using ultraperformance LC-MS/MS. 120 The major finding of the study was that alphaaminobutyric acid, alloisoleucine, ketoleucine and oxalic acid did not recover following the heroin administration. Plasma metabolites were found to experience severe change during the withdrawal period.…”

Section: Forensic Toxicologymentioning

confidence: 93%

“…Heroin and amphetamine‐type substances are potentially the most well‐known drugs in the wider community and therefore are a focus for forensic toxicology due to their recreational use. Potential heroin biomarkers were investigated in human plasma from 50 participants (20 heroin addicts with acute abstinence, 15 with prolonged abstinence and 15 controls) by Zhou et al using ultraperformance LC–MS/MS 120 . The major finding of the study was that alpha‐aminobutyric acid, alloisoleucine, ketoleucine and oxalic acid did not recover following the heroin administration.…”

Section: Applicationsmentioning

confidence: 99%

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Metabolomics in clinical and forensic toxicology, sports anti‐doping and veterinary residues

Keen

Cawley

Reedy

et al. 2022

Drug Testing and Analysis

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Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti‐doping and veterinary residues.

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Section: Forensic Toxicologymentioning

confidence: 93%

Section: Applicationsmentioning

confidence: 99%

Metabolomics in clinical and forensic toxicology, sports anti‐doping and veterinary residues

Keen

Cawley

Reedy

et al. 2022

Drug Testing and Analysis

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“…The DE miRNA has been previously identified in Chen et al (2021) and Yu et al (2021). The mRNAs targeted by miRNAs were predicted using miRanda algorithm according to the miRNA-mRNA binding data.…”

Section: Construction Of Lncrna-mirna-mrna Regulatory Network Associa...mentioning

confidence: 99%

“…In addition to those METH induced neuropsychiatric and neurodegenerative symptoms such as depression, anxiety, and/or cognitive impairments (Hall et al, 1996 ; McKetin et al, 2008 ), methamphetamine use disorders (MUDs) could also give rise to considerable problems associated with immunoglobins and cytokine/chemokine signaling pathways, which may contribute to spread of devastating infectious diseases (Potula et al, 2010 ; Harms et al, 2012 ) and thereafter influence the recovery from neurodegeneration defects (Loftis and Huckans, 2013 ). To date, despite the astounding efforts into the knowledge base of addiction medicine, the underlying molecular mechanism and functional interplay between immune response and neurodegeneration remain largely lacking (Zhang et al, 2021 ; Zhou et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Dynamic immune and exosome transcriptomic responses in patients undergoing psychostimulant methamphetamine withdrawal

Zhang²,

Ma³

et al. 2022

Front. Cell. Neurosci.

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Methamphetamine (METH) addiction and withdrawal cause serious harm to both the immune system and nervous system. However, the pathogenesis remains largely unknown. Herein, we investigated the peripheral cytokines and exosomal transcriptome regulatory networks in the patients with METH use disorders (MUDs) undergoing withdrawal. Twenty-seven cytokines were simultaneously assessed in 51 subjects, including 22 at the acute withdrawal (AW) stage and 29 at the protracted withdrawal (PW) stage, and 31 age and gender-matched healthy controls (HCs). Compared to the HCs, significantly decreased levels of interleukin (IL)-1β, IL-9, IL-15, Basic FGF, and MIP1a, increased levels of IL-1rα, IL-6, Eotaxin IP-10, VEGF, and RANTES were identified in AW. These disturbances were mostly or partly restored to the baseline in PW. However, the cytokines IL-6, IL-7, and IL-12p70 were consistently increased even after one year of withdrawal. Besides, a significant decrease in CD3+T and CD4+T cell numbers was observed in AW, and the diminishment was restored to baseline in PW. Comparatively, there were no statistically significant changes in CD8+T, NK, and B cells. Furthermore, the exosomal mRNAs and long non-coding RNAs (lncRNA) were profiled, and the lncRNA-miRNA-mRNA networks were constructed and associated with METH AW and PW stages. Notably, the chemokine signaling was remarkably upregulated during AW. By contrast, the differentially expressed mRNAs/lincRNAs were significantly enriched in neurodegeneration-related diseases. Taken together, a group of METH withdrawal-related cytokines and exosomal mRNA/lncRNA regulatory networks were obtained, which provides a useful experimental and theoretical basis for further understanding of the pathogenesis of the withdrawal symptoms in MUDs.

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“…These methods have limitations on personnel willingness, location, and instrument. In our previous studies [20], metabolomics could reflect changes in the metabolic state of metabolic disease in different periods, including morphine, heroin [21][22][23], and methamphetamine [13], which altered metabolic status during periods of exposure, withdrawal, and relapse in serum or urine. These studies suggest that metabolomics has the ability to reflect changes in the metabolic rate at different times of drug use through changes in the peripheral circulation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Plasma metabolites changes in male heroin addicts during acute and protracted withdrawal

Cited by 8 publications

References 66 publications

Metabolomics in clinical and forensic toxicology, sports anti‐doping and veterinary residues

Metabolomics in clinical and forensic toxicology, sports anti‐doping and veterinary residues

Dynamic immune and exosome transcriptomic responses in patients undergoing psychostimulant methamphetamine withdrawal

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

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