Atypical Antipsychotic Exposure May Not Differentiate Metabolic Phenotypes of Patients with Schizophrenia

Tolerability and safety outcomes of first-line oral second-generation antipsychotics in patients with schizophrenia

INTRODUCTION

Antipsychotics are the foundation of pharmacologic treatment for schizophrenia. There are many oral antipsychotics available and given that these medications are generally considered comparably efficacious when titrated to an adequate dose, their varied tolerability, and safety profiles become critically important for medication selection.

AREAS COVERED

This paper reviews tolerability and safety considerations for first-line second-generation oral antipsychotics currently approved for the treatment of schizophrenia in the USA. Excluded from consideration are clozapine and non-oral formulations.

EXPERT OPINION

Among antipsychotics, there are many differences in adverse reactions observed in clinical trials, such as variable likelihood to cause sedation vs insomnia, weight gain and abnormalities in glucose/lipid metabolism, hyperprolactinemia, potential for impact on the QT interval, and motoric adverse effects. Additional safety data that can help with medication selection include safety in pregnancy and lactation, and potential for drug-drug interactions. Ultimately, working with patients to personalize treatment by focusing on safety and individual tolerability considerations for various adverse effects can help in building a therapeutic alliance and improving patients' outcomes.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

Sex differences in the prevalence and clinical correlates of diabetes in Chinese patients with chronic schizophrenia

Sex differences have been noted in schizophrenia (SCZ) and diabetes mellitus (DM); however, the effect of sex on SCZ patients with DM remains unknown. We aimed to investigate sex differences in the prevalence, demographic and clinical correlates of DM in Chinese patients with chronic SCZ. A total of 988 Han Chinese SCZ patients (male/female: 638/350) were recruited from two psychiatric hospitals in China. We used the Positive and Negative Syndrome Scale (PANSS) to evaluate the psychopathological symptoms of the patients. In addition, serum glucose and lipid levels were assayed. The prevalence of DM in female patients (57/350, 16.29%) was higher than that in male patients (79/638, 12.38%). Binary logistic regression analyses confirmed that the prevalence of DM in female patients was higher than that in male patients (P < 0.001, OR = 4.62, 95% CI = 2.11-10.11). Moreover, female patients had significantly higher positive symptoms than male patients (P = 0.003, OR = 1.08, 95% CI = 1.03-1.14). Further, higher body mass index (BMI) and higher triglyceride (TG) were significantly associated with DM in men (both P < 0.05). Decreased high density lipoprotein (HDL) was significantly associated with DM in both male and female patients (both P < 0.01). Comorbid DM is more common in female SCZ patients, and there are sex-specific correlates of DM in SCZ.

[Schizophrenia and cardiometabolic disorders]

The aim of this review is to analyze the basic biological mechanisms of comorbidity of schizophrenia and metabolic, cardiovascular diseases, which are not directly associated with external risk factors. The study of the general pathophysiological mechanisms of schizophrenia and metabolic disorders can provide a significant basis not only for the fundamentally novel therapeutic, preventive and diagnostic measures, but also for a better understanding of the etiopathogenesis of these diseases. It seems likely that schizophrenia represents a heterogeneous group with a varying genetic basis for both mental symptoms and neuroendocrine, inflammatory processes that form concomitant somatic disorders. Thus, the new integrated approaches to the study of this problem with the latest methods of genetic and molecular research are relevant.

Effects of Psychostimulants and Antipsychotics on Serum Lipids in an Animal Model for Schizophrenia

Schizophrenia (SCZ) treatment is essentially limited to the use of typical or atypical antipsychotic drugs, which suppress the main symptoms of this mental disorder. Metabolic syndrome is often reported in patients with SCZ under long-term drug treatment, but little is known about the alteration of lipid metabolism induced by antipsychotic use. In this study, we evaluated the blood serum lipids of a validated animal model for SCZ (Spontaneously Hypertensive Rat, SHR), and a normal control rat strain (Normotensive Wistar Rat, NWR), after long-term treatment (30 days) with typical haloperidol (HAL) or atypical clozapine (CLZ) antipsychotics. Moreover, psychostimulants, amphetamine (AMPH) or lisdexamfetamine (LSDX), were administered to NWR animals aiming to mimic the human first episode of psychosis, and the effects on serum lipids were also evaluated. Discrepancies in lipids between SHR and NWR animals, which included increased total lipids and decreased phospholipids in SHR compared with NWR, were similar to the differences previously reported for SCZ patients relative to healthy controls. Administration of psychostimulants in NWR decreased omega-3, which was also decreased in the first episode of psychosis of SCZ. Moreover, choline glycerophospholipids allowed us to distinguish the effects of CLZ in SHR. Thus, changes in the lipid metabolism in SHR seem to be reversed by the long-term treatment with the atypical antipsychotic CLZ, which was under the same condition described to reverse the SCZ-like endophenotypes of this validated animal model for SCZ. These data open new insights for understanding the potential influence of the treatment with typical or atypical antipsychotics on circulating lipids. This may represent an outcome effect from metabolic pathways that regulate lipids synthesis and breakdown, which may be reflecting a cell lipids dysfunction in SCZ.

Genetic and Metabolite Variability in One-Carbon Metabolism Applied to an Insulin Resistance Model in Patients With Schizophrenia Receiving Atypical Antipsychotics

Background: Patients with schizophrenia are at high risk of pre-mature mortality due to cardiovascular disease (CVD). Our group has completed studies in pharmacogenomics and metabolomics that have independently identified perturbations in one-carbon metabolism as associated with risk factors for CVD in this patient population. Therefore, this study aimed to use genetic and metabolomic data to determine the relationship between folate pharmacogenomics, one-carbon metabolites, and insulin resistance as measured using the homeostatic model assessment for insulin resistance (HOMA-IR) as a marker of CVD. Methods: Participants in this pilot analysis were on a stable atypical antipsychotic regimen for at least 6 months, with no diabetes diagnosis or use of antidiabetic medications. Participant samples were genotyped for MTHFR variants rs1801131 (MTHFR A1298C) and rs1801133 (MTHFR C677T). Serum metabolite concentrations were obtained with NMR. A least squares regression model was used to predict log(HOMA-IR) values based on the following independent variables: serum glutamate, glycine, betaine, serine, and threonine concentrations, and carrier status of the variant alleles for the selected genotypes. Results: A total of 67 participants were included, with a median age of 47 years old (IQR 42-52), 39% were female, and the median BMI was 30.3 (IQR 26.3-37.1). Overall, the model demonstrated an ability to predict log(HOMA-IR) values with an adjusted R ² of 0.44 and a p-value of < 0.001. Glutamate, threonine, and carrier status of the MTHFR 1298 C or MTHFR 677 T allele were positively correlated with log(HOMA-IR), whereas glycine, serine, and betaine concentrations trended inversely with log(HOMA-IR). All factors included in this final model were considered as having a possible effect on predicting log(HOMA-IR) as measured with a p-value < 0.1. Conclusions: Presence of pharmacogenomic variants that decrease the functional capacity of the MTHFR enzyme are associated with increased risk for cardiovascular disease, as measured in this instance by log(HOMA-IR). Furthermore, serine, glycine, and betaine concentrations trended inversely with HOMA-IR, suggesting that increased presence of methyl-donating groups is associated with lower measures of insulin resistance. Ultimately, these results will need to be replicated in a significantly larger population.

Metabolomic profiles associated with a mouse model of antipsychotic-induced food intake and weight gain

Antipsychotic drugs (AP) are used to treat a multitude of psychiatric conditions including schizophrenia and bipolar disorder. However, APs also have metabolic side effects including increased food intake and body weight, but the underlying mechanisms remain unknown. We previously reported that minocycline (MINO) co-treatment abrogates olanzapine (OLZ)-induced hyperphagia and weight gain in mice. Using this model, we investigated the changes in the pharmacometabolome in the plasma and hypothalamus associated with OLZ-induced hyperphagia and weight gain. Female C57BL/6 mice were divided into groups and fed either i) control, CON (45% fat diet) ii) CON + MINO, iii) OLZ (45% fat diet with OLZ), iv) OLZ + MINO. We identified one hypothalamic metabolite indoxylsulfuric acid and 389 plasma metabolites (including 19 known metabolites) that were specifically associated with AP-induced hyperphagia and weight gain in mice. We found that plasma citrulline, tricosenoic acid, docosadienoic acid and palmitoleic acid were increased while serine, asparagine and arachidonic acid and its derivatives were decreased in response to OLZ. These changes were specifically blocked by co-treatment with MINO. These pharmacometabolomic profiles associated with AP-induced hyperphagia and weight gain provide candidate biomarkers and mechanistic insights related to the metabolic side effects of these widely used drugs.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Atypical Antipsychotics and the Human Skeletal Muscle Lipidome

Atypical antipsychotics (AAPs) are a class of medications associated with significant metabolic side effects, including insulin resistance. The aim of this study was to analyze the skeletal muscle lipidome of patients on AAPs, compared to mood stabilizers, to further understand the molecular changes underlying AAP treatment and side effects. Bipolar patients on AAPs or mood stabilizers underwent a fasting muscle biopsy and assessment of insulin sensitivity. A lipidomic analysis of total fatty acids (TFAs), phosphatidylcholines (PCs) and ceramides (CERs) was performed on the muscle biopsies, then lipid species were compared between treatment groups, and correlation analyses were performed with insulin sensitivity. TFAs and PCs were decreased and CERs were increased in the AAP group relative to those in the mood stabilizer group (FDR q-value <0.05). A larger number of TFAs and PCs were positively correlated with insulin sensitivity in the AAP group compared to those in the mood stabilizer group. In contrast, a larger number of CERs were negatively correlated with insulin sensitivity in the AAP group compared to that in the mood stabilizer group. The findings here suggest that AAPs are associated with changes in the lipid profiles of human skeletal muscle when compared to mood stabilizers and that these changes correlate with insulin sensitivity.