The Influence of CYP2D6 and CYP2C19 Genetic Variation on Diabetes Mellitus Risk in People Taking Antidepressants and Antipsychotics

Unidentified CYP2D6 genotype does not affect pharmacological treatment for patients with first episode psychosis

BACKGROUND

Research on the pharmacogenetic influence of hepatic CYP450 enzyme 2D6 (CYP2D6) on metabolism of drugs for psychosis and associated outcome has been inconclusive. Some results suggest increased risk of adverse reactions in poor and intermediate metabolizers, while others find no relationship. However, retrospective designs may fail to account for the long-term pharmacological treatment of patients. Previous studies found that clinicians adapted risperidone dose successfully without knowledge of patient CYP2D6 phenotype.

AIM

Here, we aimed to replicate the results of those studies in a Dutch cohort of patients with psychosis (N = 418) on pharmacological treatment.

METHOD

We compared chlorpromazine-equivalent dose between CYP2D6 metabolizer phenotypes and investigated which factors were associated with dosage. This was repeated in two smaller subsets; patients prescribed pharmacogenetics-actionable drugs according to published guidelines, and risperidone-only as done previously.

RESULTS

We found no relationship between chlorpromazine-equivalent dose and phenotype in any sample (complete sample: p = 0.3, actionable-subset: p = 0.82, risperidone-only: p = 0.34). Only clozapine dose was weakly associated with CYP2D6 phenotype (p = 0.03).

CONCLUSION

Clinicians were thus not intuitively adapting dose to CYP2D6 activity in this sample, nor was CYP2D6 activity associated with prescribed dose. Although the previous studies could not be replicated, this study may provide support for existing and future pharmacogenetic research.

Smoking may increase the usage of antidepressant: evidence from genomic perspective analysis

This study uses the two-sample Mendelian randomization (TSMR) method to explore the causal relationships between smoking initiation (SMKI), never smoking (NSMK), past tobacco smoking (PTSMK), and the usage of antidepressants (ATD). Single-nucleotide polymorphisms (SNPs) with genome-wide significance (P < 5E-08) related to SMKI, NSMK, and PTSMK were selected from the genome-wide association study (GWAS) database as instrumental variables (IVs). The main method, inverse variance weighted (IVW), was utilized to investigate the causal relationship. The results demonstrated a positive causal relationship between SMKI and ATD use, where SMKI leads to an increase in ATD use. Conversely, NSMK and PTSMK showed a negative causal relationship with ATD use, meaning that NSMK and PTSMK lead to a reduction in ATD use. Additionally, sensitivity analysis showed that the results of this study were robust and reliable. Using the TSMR method and from a genetic perspective, this study found that SMKI leads to an increase in ATD use, while NSMK and PTSMK reduce ATD use.

Associations of antidepressants and antipsychotics with lipid parameters: Do CYP2C19/CYP2D6 genes play a role? A UK population-based study

BACKGROUND

Dyslipidaemia is an important cardiovascular risk factor for people with severe mental illness, contributing to premature mortality. The link between antipsychotics and dyslipidaemia is well established, while evidence on antidepressants is mixed.

AIMS

To investigate if antidepressant/antipsychotic use was associated with lipid parameters in UK Biobank participants and if CYP2C19 and CYP2D6 genetic variation plays a role.

METHODS

Review of self-reported prescription medications identified participants taking antidepressants/antipsychotics. Total, low-, and high-density lipoprotein cholesterol (L/HDL-C) and triglycerides derived from blood samples. CYP2C19 and CYP2D6 metabolic phenotypes were assigned from genetic data. Linear regression investigated aims, adjusted for key covariates.

RESULTS

Of 469,739 participants, 36,043 took antidepressants (53% female, median age 58, 17% taking cholesterol-lowering medications) and 3255 took antipsychotics (58% female, median age 57, 27% taking cholesterol-lowering medications). Significant associations were found between use of each amitriptyline, fluoxetine, citalopram/escitalopram, sertraline, paroxetine and venlafaxine with higher total cholesterol, LDL-C, and triglycerides and lower HDL-C, compared to participants not taking each medication. Venlafaxine was associated with the worst lipid profile (total cholesterol, adjusted mean difference: 0.21 mmol/L, 95% confidence interval (CI): 0.17 to 0.26, p < 0.001). Antipsychotic use was significantly associated with lower HDL-C and higher triglycerides. In participants taking sertraline, CYP2C19 intermediate metabolisers had higher HDL-C (0.05 mmol/L, 95% CI: 0.01 to 0.09, p = 0.007) and lower triglycerides (-0.17 mmol/L, 95% CI: -0.29 to -0.05, p = 0.007), compared to normal metabolisers.

CONCLUSIONS

Antidepressants were significantly associated with adverse lipid profiles, potentially warranting baseline and regular monitoring. Further research should investigate the mechanistic pathways underlying the protective effects of the CYP2C19 intermediate metaboliser phenotype on HDL-C and triglycerides in people taking sertraline.

Insights on the pathogenesis of type 2 diabetes as revealed by signature genomic classifiers in an African American population in the Washington, DC area

AIMS

African Americans (AA) in the United States have a high risk of type 2 diabetes mellitus (T2DM) and suffer from disparities in the prevalence, mortality, and comorbidities of the disease compared to other Americans. The present study aimed to shed light on the molecular mechanisms of disease pathogenesis of T2DM among AA in the Washington, DC region.

METHODS

We performed TaqMan Low Density Arrays (TLDA) on 24 genes of interest that belong to three categories: metabolic disease and disorders, cancer-related genes, and neurobehavioural disorders genes. The 18 genes, viz. ARNT, CYP2D6, IL6, INSR, RRAD, SLC2A2 (metabolic disease and disorders), APC, BCL2, CSNK1D, MYC, SOD2, TP53 (Cancer-related), APBA1, APBB2, APOC1, APOE, GSK3B, and NAE1 (neurobehavioural disorders), were differentially expressed in T2DM participants compared to controls.

RESULTS

Our results suggest that factors including gender, smoking habits, and the severity or lack of control of T2DM (as indicated by HbA1c levels) were significantly associated with differential gene expression. APBA1 was significantly (p-value <0.05) downregulated in all diabetes participants. Upregulation of APOE and CYP2D6 genes and downregulation of the INSR gene were observed in the majority of diabetes patients.

CONCLUSIONS

Tobacco smoking and gender were significantly associated with case-control differences in expression of the APBA1 and APOE genes (connected with Alzheimer's disease) and the INSR and CYP2D6 (associated with metabolic disorders). The results highlight the need for more effective management of T2DM and for tobacco smoking cessation interventions in this community, and further research on the associations of T2DM with other disease processes, including cancer and neurobehavioral pathways.

Influence of Chromatographic Conditions on LOD and LOQ of Fluoxetine and Sertraline Analyzed by TLC-Densitometric Method

This research introduces the analysis of fluoxetine and sertraline by means of the TLC-densitometric method. They provide information on LOD and LOQ under various chromatographic conditions. The study used adsorption (NPTLC) and partition (RPTLC) thin-layer chromatography in combination with a densitometric analysis. Four types of chromatographic plates precoated with: silica gel 60 F254, silica gel 60, silanized silica gel 60 F254 (RP-2), and a mixture of silica gel 60 and kieselguhr F254, as well as three mobile phases: chloroform + methanol + ammonia (9:1:0.4, v/v/v), chloroform + methanol + glacial acetic acid (5:4:1, v/v/v), and acetone + toluene + ammonia (10:9:1, v/v/v), were used in NPTLC. RP-18F254 and silanized silica gel 60 F254 (RP-2) plates and four mobile phases: methanol + water (10:0 and 9:1, v/v), acetone + water (10:0 and 9:1, v/v), were used in RPTLC. The lowest LOD and LOQ values for fluoxetine were obtained using a silanized silica gel 60 F254 (RP-2) with acetone + toluene + ammonia (10:9:1, v/v/v) in NPTLC, and with a silanized silica gel 60 F254 (RP-2) in combination with methanol + water (10:0, v/v) in RPTLC. The lowest LOD and LOQ values of sertraline were obtained using a silica gel 60 with acetone + toluene + ammonia (10:9:1; v/v/v) in NPTLC. The smallest amount of sertraline was detected on the silanized silica gel 60 F254 plate in combination with methanol + water (9:1, v/v) in RPTLC. The obtained results provide important information that can give a good basis and set the direction for further, more detailed research; the results can also benefit other researchers who analyze fluoxetine and sertraline with the TLC technique in model systems (testing standards) as well as in drug and biological samples.

The Promise of Nanotechnology in Personalized Medicine

Both personalized medicine and nanomedicine are new to medical practice. Nanomedicine is an application of the advances of nanotechnology in medicine and is being integrated into diagnostic and therapeutic tools to manage an array of medical conditions. On the other hand, personalized medicine, which is also referred to as precision medicine, is a novel concept that aims to individualize/customize therapeutic management based on the personal attributes of the patient to overcome blanket treatment that is only efficient in a subset of patients, leaving others with either ineffective treatment or treatment that results in significant toxicity. Novel nanomedicines have been employed in the treatment of several diseases, which can be adapted to each patient-specific case according to their genetic profiles. In this review, we discuss both areas and the intersection between the two emerging scientific domains. The review focuses on the current situation in personalized medicine, the advantages that can be offered by nanomedicine to personalized medicine, and the application of nanoconstructs in the diagnosis of genetic variability that can identify the right drug for the right patient. Finally, we touch upon the challenges in both fields towards the translation of nano-personalized medicine.