Differences in clinical features and gut microbiota between individuals with methamphetamine casual use and methamphetamine use disorder

A nomogram prediction model for embryo implantation outcomes based on the cervical microbiota of the infertile patients during IVF-FET

The microbiota of the female genital tract is crucial for reproductive health. This study aims to investigate the impact of the lower genital tract microbiota on in vitro fertilization and frozen embryo transfer (IVF-FET) outcomes. This study included 131 women aged 20-35 years who underwent their first or second IVF-FET cycle with no obvious unfavorable factors for implantation. Cervical microbiota samples were collected on the embryo transfer day and analyzed using 16S rDNA full-length sequencing. Clinical outcomes were followed up for analysis. Clinical pregnancy (CP) was achieved in 84 patients, and 47 patients experienced non-pregnancy (NP). The cervical microbiota diversity between NP and CP groups showed no significant differences, but some genera such as Halomonas (P = 0.018), Klebsiella (P = 0.039), Atopobium (P = 0.016), and Ligilactobacillus (P = 0.021) were obviously different between the two groups. Notably, there was no significant difference in the abundance of Lactobacillus between the two groups. A nomogram prediction model was developed using the random forest algorithm and logistic regression, including the classification of Halomonas, Atopobium, and Veillonella, as well as the relative abundance of Lactobacillus, to identify high-risk patients with embryo implantation failure. Both internal (area under the curve [AUC] = 0.718, 95% confidence interval [CI]: 0.628-0.807, P < 0.001) and external validation (AUC = 0.654, 95% CI: 0.553-0.755, P = 0.037) of the model performed well. In conclusion, this study established a correlation between cervical microbiota and embryo implantation failure in infertile women undergoing IVF-FET and developed a prediction model that could help in early identification of patients at high risk of implantation failure.IMPORTANCEThis study investigated the potential role of abnormal cervical microbiota in the pathology of implantation failure after in vitro fertilization and frozen embryo transfer (IVF-FET) treatment. Despite nearly half a century of advancements in assisted reproductive technology (ART), the implantation rate of high-quality embryos still hovered around 50%. Moreover, unexplained recurrent implantation failure (RIF) remains a significant challenge in ART. To our knowledge, we first discovered a prediction model for embryo implantation failure, identifying Halomonas and Veillonella as significantly adverse factors for embryo implantation. Despite some limitations, the internal and external validation of the model could bode well for its clinical application prospect. The insights gained from this study pave the way for intervention in the genital tract microbiota prior to IVF-FET, particularly in patients with RIF and RSA.

Pharmacogenomic and Pharmacomicrobiomic Aspects of Drugs of Abuse

BACKGROUND/OBJECTIVES

This review examines the role of pharmacogenomics in individual responses to the pharmacotherapy of various drugs of abuse, including alcohol, cocaine, and opioids, to identify genetic variants that contribute to variability in substance use disorder treatment outcomes. In addition, it explores the pharmacomicrobiomic aspects of substance use, highlighting the impact of the gut microbiome on bioavailability, drug metabolism, pharmacodynamics, and pharmacokinetics.

RESULTS

Research on pharmacogenetics has identified several promising genetic variants that may contribute to the individual variability in responses to existing pharmacotherapies for substance addiction. However, the interpretation of these findings remains limited. It is estimated that genetic factors may account for 20-95% of the variability in individual drug responses. Therefore, genetic factors alone cannot fully explain the differences in drug responses, and factors such as gut microbiome diversity may also play a significant role. Drug microbial biotransformation is produced by microbial exoenzymes that convert low molecular weight organic compounds into analogous compounds by oxidation, reduction, hydrolysis, condensation, isomerization, unsaturation, or by the introduction of heteroatoms. Despite significant advances in pharmacomicrobiomics, challenges persist including the lack of standardized methodologies, inter-individual variability, limited understanding of drug biotransformation mechanisms, and the need for large-scale validation studies to develop microbiota-based biomarkers for clinical use.

CONCLUSIONS

Progress in the pharmacogenomics of substance use disorders has provided biological insights into the pharmacological needs associated with common genetic variants in drug-metabolizing enzymes. The gut microbiome and its metabolites play a pivotal role in various stages of drug addiction including seeking, reward, and biotransformation. Therefore, integrating pharmacogenomics with pharmacomicrobiomics will form a crucial foundation for significant advances in precision and personalized medicine.

Examination of optical coherence tomography findings in patients with pregabalin use disorder

Background

Pregabalin abuse is a rapidly growing health problem worldwide, and little is known about the effects of prolonged high-dose use in patients with pregabalin use disorder.

Objective

In this study, the effects of pregabalin abuse on retinal layers were investigated in patients with pregabalin use disorder (PGUD).

Methods

This study included 35 controls and 34 patients with PGUD, according to Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria. Optic coherence tomography (OCT) measurements including the retinal nerve fiber layer (RNFL), ganglion cell layer-inner plexiform layer (GCL-IPL) and ganglion cell complex (GCC) were performed. RNFL thickness was evaluated in four quadrants (inferior, superior, nasal, temporal). GCL-IPL and GCC thickness were evaluated in six sectors (superior, superonasal, inferonasal, inferior, inferotemporal, superotemporal).

Results

GCC inferonasal (p = 0.040, r = 0.354), GCC inferior (p = 0.018, r = 0.402) GCL-IPL inferior (p = 0.031, r = 0.370) and GCL-IPL inferotemporal (p = 0.029, r = 0.376) thickness were positively correlated with the duration of pregabalin use. There was no significant sector or quadrant-wise difference between groups (p > 0.05).

Conclusion

Our findings emphasized the drug's potential neuroprotective effect. It should be taken into consideration that neurodegenerative changes due to substance use disorder occur with long-term. Longitudinal prospective studies investigating dose-duration relationship are needed.

Associations between impulsivity and fecal microbiota in individuals abstaining from methamphetamine

INTRODUCTION

Methamphetamine (MA) abuse is a major public problem, and impulsivity is both a prominent risk factor and a consequence of addiction. Hence, clarifying the biological mechanism of impulsivity may facilitate the understanding of addiction to MA. The microbiota-gut-brain axis was suggested to underlie a biological mechanism of impulsivity induced by MA.

METHODS

We therefore recruited 62 MA addicts and 50 healthy controls (HCs) to investigate the alterations in impulsivity and fecal microbiota and the associations between them in the MA group. Thereafter, 25 MA abusers who abstained from MA for less than 3 months were followed up for 2 months to investigate the relationship between impulsivity and microbiota as abstinence became longer. 16S rRNA sequencing was conducted for microbiota identification.

RESULTS

Elevated impulsivity and dysbiosis characterized by an increase in opportunistic pathogens and a decrease in probiotics were identified in MA abusers, and both the increased impulsivity and disrupted microbiota tended to recover after longer abstinence from MA. Impulsivity was related to microbiota, and the effect of MA abuse on impulsivity was mediated by microbiota.

CONCLUSION

Our findings potentially highlighted the importance of abstention and implicated the significant role of the microbiota-gut-brain axis in the interrelationship between microbiota and behaviors, as well as the potential of microbiota as a target for intervention of impulsivity.

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

Background

Methamphetamine 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.

Method

Fecal 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.

Results

MUD 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.

Conclusion

In 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.