Identification and Treatment Response Prediction of Antipsychotic-Related Metabolic Syndrome

Study Overview
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Brief Summary
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The investigators developed an easy identification model to identify metabolic syndrome in patients with schizophrenia or schizoaffective disorder who received treatment of clozapine, olanzapine, or risperidone. The accuracy of the investigators' models showed well. In the study, the investigators aim to (1) to examine whether the developed identification models can be generalized to patients taking other antipsychotics or patients with other diagnoses; (2) to develop an easy risk score and validate it; (3) to switch antipsychotics to amisulpride or aripiprazole for those with metabolic syndrome, and compare the changes of metabolic parameters including adiponectin, and analyze their association with genetic variants, demography, and clinical variables; (4) to establish models using artificial neural network and statistic method to predict metabolic response after a switch to amisulpride or aripiprazole; (5) to investigate the effect of antipsychotics on adiponectin gene expression and secretion during the differentiation process of 3T3L1 adipocytes.

Official Title
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Easy Identification, Treatment Response Prediction, and Molecular Mechanism Exploration of Antipsychotic-related Metabolic Syndrome

Conditions
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Metabolic Syndrome X

Intervention / Treatment
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* Drug: amisulpride
* Drug: aripiprazole


Participation Criteria
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Eligibility Criteria
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Stage I for identification of metabolic syndrome: Inclusion Criteria: A diagnosis of schizophrenia, schizoaffective disorder, delusional disorder, mood disorders, or anxiety disorders based on DSM-IV-TR criteria. Age at least 20 years old. The current antipsychotic drugs have been used for at least 3 months before evaluation. Psychiatrically stable with Clinical Global Impression of Severity scale (CGI-S) not greater than 5 Exclusion Criteria: Severe uncontrolled medical illnesses, including cardiovascular, hepatic, renal and metabolic diseases (eg. cancer, poor-control hypertension, diabetes mellitus, and other metabolic diseases). Organic mental or neurological disorder, substance abuse or dependence (alcohol, amphetamine, heroin). Pregnant or breast-feeding women. Patients from Yuli Veterans Hospital, who attended our previous study of identification model. Stage II for switch response: Inclusion Criteria: The same as Stage I criteria. Fulfill the metabolic syndrome criteria. Exclusion Criteria: The same as Stage I criteria except the 4th item. Treated with depot form of antipsychotics.

Ages Eligible for Study
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Minimum Age: 20 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Amisulpride<br>A slow plateau cross-titration method was used to switch original antipsychotics to Amisulpride. | Drug: amisulpride<br>* Amisulpride dosage was increased from 200 up to a maximum of 1000 mg/day and the dosage of their previous antipsychotics can be tapered gradually under stable clinical condition.<br>* Other names: Solian;|
| Experimental: Aripiprazole<br>A slow plateau cross-titration method was used to switch original antipsychotics to Aripiprazole. | Drug: aripiprazole<br>* Aripiprazole dosage was increased from 5-7.5 up to a maximum of 30 mg/day and the dosage of their previous antipsychotics can be tapered gradually under stable clinical condition.<br>* Other names: Abilify;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Metabolic profile |  | half/one year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Clinical efficacy |  | half/one year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Antipsychotic Agents, Metabolic Syndrome X, Identification, Prediction