Investigation of molecular serum profiles associated with predisposition to antipsychotic-induced weight gain

Roles of inflammation in intrinsic pathophysiology and antipsychotic drug-induced metabolic disturbances of schizophrenia

Schizophrenia is a debilitating psychiatric illness that remains poorly understood. While the bulk of symptomatology has classically been associated with disrupted brain functioning, accumulating evidence demonstrates that schizophrenia is characterized by systemic inflammation and disturbances in metabolism. Indeed, metabolic disease is a major determinant of the high mortality rate associated with schizophrenia. Antipsychotic drugs (APDs) have revolutionized management of psychosis, making it possible to rapidly control psychotic symptoms. This has ultimately reduced relapse rates of psychotic episodes and improved overall quality of life for people with schizophrenia. However, long-term APD use has also been associated with significant metabolic disturbances including weight gain, dysglycemia, and worsening of the underlying cardiometabolic disease intrinsic to schizophrenia. While the mechanisms for these intrinsic and medication-induced metabolic effects remain unclear, inflammation appears to play a key role. Here, we review the evidence for roles of inflammatory mechanisms in the disease features of schizophrenia and how these mechanisms interact with APD treatment. We also discuss the effects of common inflammatory mediators on metabolic disease. Then, we review the evidence of intrinsic and APD-mediated effects on systemic inflammation in schizophrenia. Finally, we speculate about possible treatment strategies. Developing an improved understanding of inflammatory processes in schizophrenia may therefore introduce new, more effective options for treating not only schizophrenia but also primary metabolic disorders.

Exploring cellular markers of metabolic syndrome in peripheral blood mononuclear cells across the neuropsychiatric spectrum

Recent evidence suggests that comorbidities between neuropsychiatric conditions and metabolic syndrome may precede and even exacerbate long-term side-effects of psychiatric medication, such as a higher risk of type 2 diabetes and cardiovascular disease, which result in increased mortality. In the present study we compare the expression of key metabolic proteins, including the insulin receptor (CD220), glucose transporter 1 (GLUT1) and fatty acid translocase (CD36), on peripheral blood mononuclear cell subtypes from patients across the neuropsychiatric spectrum, including schizophrenia, bipolar disorder, major depression and autism spectrum conditions (n = 25/condition), relative to typical controls (n = 100). This revealed alterations in the expression of these proteins that were specific to schizophrenia. Further characterization of metabolic alterations in an extended cohort of first-onset antipsychotic drug-naïve schizophrenia patients (n = 58) and controls (n = 63) revealed that the relationship between insulin receptor expression in monocytes and physiological insulin sensitivity was disrupted in schizophrenia and that altered expression of the insulin receptor was associated with whole genome polygenic risk scores for schizophrenia. Finally, longitudinal follow-up of the schizophrenia patients over the course of antipsychotic drug treatment revealed that peripheral metabolic markers predicted changes in psychopathology and the principal side effect of weight gain at clinically relevant time points. These findings suggest that peripheral blood cells can provide an accessible surrogate model for metabolic alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic complications following antipsychotic therapy.

Insulin Resistance in Schizophrenia

Schizophrenia and diabetes have been known to be linked disorders for decades. One reason is due to the fact that a major side effect of antipsychotic medication treatment is metabolic syndrome, which increases the risk of the patients developing type 2 diabetes and cardiovascular disorders. However, signs of metabolic syndrome in schizophrenia patients were identified more than 100 years ago, even before the development of antipsychotic drugs. This suggests that schizophrenia itself predisposes towards diabetes and, in turn, insulin resistance may be a risk factor for the development of schizophrenia. This review summarizes the findings surrounding this issue and places them into context with regards to increasing our understanding of the aetiology of schizophrenia and in support of biomarker and drug discovery efforts.

T4 and waist:hip ratio as biomarkers of antipsychotic-induced weight gain in Han Chinese inpatients with schizophrenia

Second-generation antipsychotic agents (SGAs) cause serious metabolic side effects, including weight gain, dyslipidemia, and glucose metabolism abnormalities, which occur by unknown mechanisms. Therefore, the search for prospective markers for antipsychotic-induced weight gain (AIWG) has been of major interest. So far, predictive factors predisposing patients to the develop obesity and related metabolic disturbances induced by SGAs have been relatively less studied among large samples of Chinese schizophrenic patients. In this study, 264 Han Chinese inpatients diagnosed with schizophrenia or schizoaffective disorder initiated treatment with olanzapine (n=131) or risperidone (n=133) and were followed for 12weeks. Anthropometric measurements and laboratory analyses of thyroid hormone, fasting plasma glucose (FPG), and lipid levels were conducted as part of routine medical care. The results showed baseline thyroxine (T₄) and waist:hip ratio (WHR)were negatively correlated to AIWG (T₄: r_s=-0.154, P=0.014; WHR: r_s=-0.199, P=0.008). Correlations remained significant after multiple regression analyses. The two treatment groups statistically differed for changes in body mass index, WHR, LDL cholesterol, and FPG; in both groups FPG decreased at first and then increased. Our findings suggest basal T₄ and WHR may serve as early biomarkers for weight gain as a side effect of single-SGA treatment.

Blood-Based Lipidomics Approach to Evaluate Biomarkers Associated With Response to Olanzapine, Risperidone, and Quetiapine Treatment in Schizophrenia Patients

This is the first study to identify lipidomic markers in plasma associated with response of acutely ill schizophrenia patients in response to specific antipsychotic treatments. The study population included 54 schizophrenia patients treated with antipsychotics for 6 weeks. Treatment led to significant improvement in positive and negative symptoms for 34 patients with little or no improvement for 20 patients. In addition, 37 patients showed an increase in body mass index after the 6 week treatment period, consistent with effects on metabolism and the association of such effects with symptom improvement. Profiling of plasma samples taken prior to therapy using liquid chromatography tandem mass spectrometry (LC-MS/MS) resulted in identification of 38, 10, and 52 compounds associated with the olanzapine, risperidone, and quetiapine treatment groups, which could be used to distinguish responders from non-responders. Limitations include the retroactive active nature of the study and the small sample size. Further investigations with larger sample sets could lead to the development of a molecular test that could be used to help psychiatrists determine the best treatment options for each patient.

A Clinical Study Protocol to Identify Serum Biomarkers Predictive of Response to Antipsychotics in Schizophrenia Patients

An increasing number of studies are now exploring the potential of using blood-based biomarkers for prediction of antipsychotic treatment response in studies of schizophrenia patients. Here we describe the detailed setup of a clinical study to identify biomarker candidates for prediction of response of patients prior to receiving antipsychotics over a 6-week treatment period. The main emphasis is on study design, patient recruitment, sampling and outcome measures.

Application of Proteomic Techniques for Improved Stratification and Treatment of Schizophrenia Patients

For major psychiatric disorders such as schizophrenia, there have been shortcomings in the translation of scientific findings into new treatments and this has led to diminished interest for large pharmaceutical companies. This chapter describes how incorporation of proteomic approaches into the clinical pipeline can lead to identification and implementation of biomarker tests for improved patient characterization, prediction of treatment response and monitoring treatment effects to help revitalize efforts in this important area. In addition, the construction of specific biomarker tests for disease prediction should smooth the progress of early intervention strategies which, in turn, may help to slow disease onset or progression. Finally, the development of purpose-built biomarker tests using lab-on-a-chip platforms with smartphone readouts will help to shift the diagnosis and treatment of this major psychiatric disorder into point-of-care settings for increased effectiveness and improved patient outcomes.

Proteomic Differences in Blood Plasma Associated with Antidepressant Treatment Response

The current inability of clinical psychiatry to objectively select the most appropriate treatment is a major factor contributing to the severity and clinical burden of major depressive disorder (MDD). Here, we have attempted to identify plasma protein signatures in 39 MDD patients to predict response over a 6-week treatment period with antidepressants. LC-MS/MS analysis showed that differences in the levels of 29 proteins at baseline were found in the group with a favorable treatment outcome. Most of these proteins were components of metabolism or immune response pathways as well as multiple components of the coagulation cascade. After 6 weeks of treatment, 43 proteins were altered in responders of which 2 (alpha-actinin and nardilysin) had been identified at baseline. In addition, 46 proteins were altered in non-responders and 9 of these (alpha-actinin, alpha-2-macroglobulin, apolipoprotein B-100, attractin, C-reactive protein, fibrinogen alpha chain, fibrinogen beta chain, nardilysin and serine/threonine-protein kinase Chk1) had been identified at baseline. However, it should be stressed that the small sample size precludes generalization of the main results. Further studies to validate these as potential biomarkers of antidepressant treatment response are warranted considering the potential importance to the field of psychiatric disorders. This study provides the groundwork for development of novel objective clinical tests that can help psychiatrists in the clinical management of MDD through improved prediction and monitoring of patient responses to antidepressant treatments.

The Application of Multiplex Biomarker Techniques for Improved Stratification and Treatment of Schizophrenia Patients

In the case of major psychiatric disorders such as schizophrenia, shortcomings in the conversion of scientific discoveries into newer and safer treatment options has led to a loss of confidence and precipitated a crisis for large pharmaceutical companies. This chapter describes how incorporation of multiplex biomarker approaches into the clinical pipeline can lead to better patient characterization, delivery of novel treatment approaches and help to renew efforts in this important area. The development of specific biomarker test panels for disease prediction should facilitate early intervention strategies, which may help to slow disease development or progression. Furthermore, the development of such tests using lab-on-a-chip and smartphone platforms will help to shift diagnosis and treatment of this major disorder into a point-of-care setting for improved patient outcomes.

Evidence for impaired glucose metabolism in the striatum, obtained postmortem, from some subjects with schizophrenia

Studies using central nervous system tissue obtained postmortem suggest pathways involved in energy and metabolism contribute to the pathophysiology of schizophrenia; neuroimaging studies suggesting glucose metabolism is particularly affected in the striatum. To gain information on the status of pathways involved in glucose metabolism in the striatum, we measured levels of glucose, pyruvate, acetyl-CoA and lactate as well as the β subunit of pyruvate dehydrogenase, a rate limiting enzyme, in the postmortem tissue from subjects with schizophrenia and age/sex-matched controls. The subjects with schizophrenia were made up of two subgroups, which could be divided because they either had (muscarinic receptor deficit schizophrenia (MRDS)), or did not have (non-MRDS), a marked deficit in cortical muscarinic receptors. Compared to controls, levels of β subunit of pyruvate dehydrogenase were lower (Δ mean=-20%) and levels of pyruvate (Δ mean=+47%) and lactate (Δ mean=+15%) were significantly higher in the striatum from subjects with schizophrenia. Notably, in subjects with non-MRDS, striatal levels of β subunit of pyruvate dehydrogenase were lower (Δ mean=-29%), whereas levels of pyruvate (Δ mean=-66%), acetyl-CoA (Δ mean=-28%) and glucose (Δ mean=-27%) were higher, whereas levels of lactate (Δ mean=+17%) were higher in MRDS. Finally, discriminate analyses using levels the β subunit of pyruvate dehydrogenase and glucose, or better still, β subunit of pyruvate dehydrogenase and glucose in combination with pyruvate, lactate or acetyl-CoA could separate subjects with non-MRDS from controls with high levels of specificity (up to 93%) and selectivity (up to 91%). Our data show the benefit of being able to study defined subgroups within the syndrome of schizophrenia as such an approach has revealed that changes in glucose metabolism may be a significant contributor to the pathophysiology of non-MRDS.

Employing proteomics to unravel the molecular effects of antipsychotics and their role in schizophrenia

Schizophrenia is an incurable neuropsychiatric disorder managed mostly by treatment of the patients with antipsychotics. However, the efficacy of these drugs has remained only low to moderate despite intensive research efforts since the early 1950s when chlorpromazine, the first antipsychotic, was synthesized. In addition, antipsychotic treatment can produce often undesired severe side effects in the patients and addressing these remains a large unmet clinical need. One of the reasons for the low effectiveness of these drugs is the limited knowledge about the molecular mechanisms of schizophrenia, which impairs the development of new and more effective treatments. Recently, proteomic studies of clinical and preclinical samples have identified changes in the levels of specific proteins in response to antipsychotic treatment, which have converged on molecular pathways such as cell communication and signaling, inflammation and cellular growth, and maintenance. The findings of these studies are summarized and discussed in this review and we suggest that this provides validation of proteomics as a useful tool for mining drug mechanisms of action and potentially for pinpointing novel molecular targets that may enable development of more effective medications.

Antipsychotic induced weight gain: genetics, epigenetics, and biomarkers reviewed

Antipsychotic-induced weight gain (AIWG) is a prevalent side effect of antipsychotic treatment, particularly with second generation antipsychotics, such as clozapine and olanzapine. At this point, there is virtually nothing that can be done to predict who will be affected by AIWG. However, hope for the future of prediction lies with genetic risk factors. Many genes have been studied for their association with AIWG with a variety of promising findings. This review will focus on genetic findings in the last year and will discuss the first epigenetic and biomarker findings as well. Although there are significant findings in many other genes, the most consistently replicated findings are in the melanocortin 4 receptor (MC4R), the serotonin 2C receptor (HTR2C), the leptin, the neuropeptide Y (NPY) and the cannabinoid receptor 1 (CNR1) genes. The study of genetic risk variants poses great promise in creating predictive tools for side effects such as AIWG.

Applications of blood-based protein biomarker strategies in the study of psychiatric disorders

Major psychiatric disorders such as schizophrenia, major depressive and bipolar disorders are severe, chronic and debilitating, and are associated with high disease burden and healthcare costs. Currently, diagnoses of these disorders rely on interview-based assessments of subjective self-reported symptoms. Early diagnosis is difficult, misdiagnosis is a frequent occurrence and there are no objective tests that aid in the prediction of individual responses to treatment. Consequently, validated biomarkers are urgently needed to help address these unmet clinical needs. Historically, psychiatric disorders are viewed as brain disorders and consequently only a few researchers have as yet evaluated systemic changes in psychiatric patients. However, promising research has begun to challenge this concept and there is an increasing awareness that disease-related changes can be traced in the peripheral system which may even be involved in the precipitation of disease onset and course. Converging evidence from molecular profiling analysis of blood serum/plasma have revealed robust molecular changes in psychiatric patients, suggesting that these disorders may be detectable in other systems of the body such as the circulating blood. In this review, we discuss the current clinical needs in psychiatry, highlight the importance of biomarkers in the field, and review a representative selection of biomarker studies to highlight opportunities for the implementation of personalized medicine approaches in the field of psychiatry. It is anticipated that the implementation of validated biomarker tests will not only improve the diagnosis and more effective treatment of psychiatric patients, but also improve prognosis and disease outcome.