Methamphetamine Withdrawal Questionnaire

Zorick, Todd; Nestor, Liam; Miotto, Karen; Sugar, Catherine A.; Hellemann, Gerhard; Scanlon, G.; Rawson, Richard A.; London, Edythe D.

doi:10.1037/t30330-000

Cited by 2 publications

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“…Positron emission tomography has revealed that (compared with measures of control group participants) methamphetamine addicts have lower levels of dopamine transporters in the striatum and prefrontal cortex and show differences in regional cerebral glucose metabolism. [14][15][16][17][18][19][20][21][22][23] Another unpleasant and also one of the most visible consequence effects that is associated with methamphetamine abuse is extreme tooth decay; called meth mouth. Researchers argue that relevant tooth decay is due to methamphetamine's restriction of salivary flow, which leads to xerostomia (dry mouth).…”

Section: Clinical Effectsmentioning

confidence: 99%

Methamphetamine Addiction

İVECEN¹,

Gökdemir

2022

DAHUDER Medical Journal

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A person can develop addiction to many kinds of substances. Methamphetamine is the second most widely abused drug worldwide. Long-term methamphetamine usage may cause social, psychological, and physical problems. Long-term effects (such as weight loss, memory loss, tremors, convulsion, psychosis, paranoia, hallucinations, Parkinson's-like symptoms, and cardiovascular collapse) and short-term effects (such as euphoria, dilated pupils, insomnia, reduced appetite, increased blood pressure ) are listed and, brain damage and mental problems due to methamphetamine abuse are briefly mentioned in this text. The severity of withdrawal symptoms varies from person to person. Unfortunately, there is no exact cure for methamphetamine abuse. This is not a systematic review.

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Section: Clinical Effectsmentioning

confidence: 99%

Methamphetamine Addiction

İVECEN¹,

Gökdemir

2022

DAHUDER Medical Journal

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“…The withdrawal period plays a pivotal role in MUD, as it is critical for determining a patient's prognosis regarding whether they will achieve sustained recovery or experience a relapse (Zorick et al, 2010). Specifically, the initial three months after withdrawal are of utmost importance, as MUD patients tend to experience worse outcomes, including elevated levels of anxiety, depression, impulsivity, and cravings for methamphetamine, during this period (Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

Liu,

Deng,

Liu

et al. 2023

Front. Cell. Infect. Microbiol.

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BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.

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Methamphetamine Withdrawal Questionnaire

Cited by 2 publications

References 0 publications

Methamphetamine Addiction

Methamphetamine Addiction

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

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