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      ["post_title"]=>
      string(179) "NCT00074477 - A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of 50 and 100 Mg-eq of Paliperidone Palmitate in Patients With Schizophrenia"
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  ["property_scientific_abstract"]=>
  string(1607) "Background: Patients with schizophrenia carry higher risk of weight gain and metabolic syndrome (MetS) compared to the general population. Separately, studies have documented racial/ethnic differences in obesity and prevalence of MetS with certain races/ethnicities having higher risk of these health problems. It is unknown if racial/ethnic differences exist in the risk of weight and MetS in patients with schizophrenia/schizoaffective disorder.

Objective: To investigate racial/ethnic differences in body mass index (BMI) and MetS, factors contributing to the risk of MetS, and MetS-related health outcomes in patients with schizophrenia/schizoaffective disorder enrolled in studies conducted at sites in the US.

Study Design: Retrospective, cross-sectional review of records of adult patients with schizophrenia/schizoaffective disorder enrolled in studies conducted at the US sites.

Participants: Records (screening and baseline) of all adult patients with schizophrenia/schizoaffective disorder enrolled in the studies conducted at US sites will be eligible for the proposed analyses.

Outcome measures: The primary outcome measures include the BMI and MetS components. The secondary outcome measures include MetS-related health consequences.

Statistical analyses:   One-way ANOVA will be used to test the main racial/ethnic effect in BMI and all 5 components of MetS among schizophrenia/schizoaffective disorder patients. Generalized linear models will be used to investigate racial/ethnic differences in factors contributing to MetS and MetS-related health outcomes"
  ["project_brief_bg"]=>
  string(3046) "Metabolic syndrome (MetS), a significant public health problem, is associated with high risk of diabetes and other adverse metabolic and cardiovascular outcomes [1]. Patients with schizophrenia carry higher risk of MetS compared to the general population [2]. As a result, cardiovascular deaths are significant cause of mortality in patients with schizophrenia [3]. Numerous factors contribute to risk of MetS in patients with schizophrenia. These factors include genetic factors, lifestyle factors and as well as use of certain antipsychotic (APs) medications [2]. Evidence suggests that certain genes that increase the risk of serious mental illness might also heighten the risk of cardiometabolic disorders [4]. Additionally, patients with Schizophrenia have a high incidence of lifestyle factors contributing to MetS and related cardiometabolic risks, such as poor diet, smoking, stress and lack of exercise [5]. It is also well documented that certain APs are associated with significant weight gain and add substantial cardiometabolic risks in these patients [2].

According to a survey conducted by the United States (US) census Bureau in 2008, approximately 33%, or more than 100 million people, identified themselves as belonging to a racial or ethnic minority population [6]. Evidence suggests that there are race/ethnic differences in obesity and prevalence of MetS, with higher rates among African American and Hispanic populations [7]. It is unknown if such differential risk also exists in patients with schizophrenia based on their race/ethnicity. It is known that certain factors such as access to better nutrition, access to health care, and stress are further compromised in racial/ethnic minorities due to diverse economic and psychosocial stressors. These factors may exacerbate risk of weight gain and MetS in patients with schizophrenia of certain race/ethnic background. It is also possible that in patients with schizophrenia of certain race/ethnicity, specific genetic factors may have a differential contribution, increasing their risk of weight gain and MetS.  Other psychosocial factors may play a role. For example, a recent study reported racial/ethnic disparities in monitoring of MetS in patients with schizophrenia in Veterans Health Administration [8].

It is important to investigate prevalence of MetS in patients with schizophrenia based on their race/ethnicity and evaluate any differential impact of factors contributing to MetS in these patients. This will improve and personalize management of this serious health problem affecting the lives of patients with schizophrenia.  The purpose of the proposed project is to investigate racial/ethnic differences in MetS in patients with schizophrenia enrolled in schizophrenia/schizoaffective disorder studies conducted in the United States (US). The proposed study will also investigate racial/ethnic differences in factors contributing to MetS risk in these patients and differences in MetS related health outcomes such as cardiometabolic outcomes."
  ["project_specific_aims"]=>
  string(892) "Hypotheses: Racial/ethnic differences have an impact on risk of MetS in patients with schizophrenia and schizoaffective disorder. Patients with schizophrenia/schizoaffective disorder of certain races/ethnicities carry higher risk of MetS compared to other races/ethnicities.

Specific Aims: In a cross-sectional, retrospective review of records of patients enrolled in schizophrenia/schizoaffective disorder studies at the US study sites-

1.	Investigate racial/ethnic differences in body-mass index (BMI) and other MetS components in patients with schizophrenia and schizoaffective disorder.

2.	Investigate racial/ethnic differences in factors contributing to the risk of MetS in patients with schizophrenia and schizoaffective disorder.

3.	Assess racial/ethnic differences in MetS-related health outcomes in patients with schizophrenia and schizoaffective disorder"
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  ["project_research_methods"]=>
  string(1217) "Records of patients enrolled in the schizophrenia/schizoaffective disorder studies at the US sites (available in the YODA project) will be eligible for the study. In the multi-site studies such as the ones available in the YODA project, comprehensive assessments of all patients enrolled in the study are conducted at screening and baseline (prior to initiation of any study intervention). Such assessments include assessment of demographic/clinical characteristics, lifestyle factors, medical/psychiatric comorbidities and comprehensive laboratory investigations.  The proposed analyses will utilize data available from screening/baseline assessments of patients enrolled in these studies.

Inclusion/Exclusion Criteria:

Inclusion criteria- Records of adult (age 18 and above) patients enrolled in schizophrenia/schizoaffective disorder studies at any US site

Exclusion criteria- Records of child/adolescent (less than 18 years of age) patients or patients enrolled at non-US sites 

Based on brief information available from the YODA Project Trials webpage, over 4,000 adult study participants from six Paliperidone trials and six Paliperidone palmitate trials satisfied the above criteria"
  ["project_main_outcome_measure"]=>
  string(531) "Primary Outcome measures (from screening/baseline evaluations data source):

1.	Body mass index

2.	Five Components of MetS: Fasting glucose, high-density-lipoprotein (HDL) cholesterol, triglycerides, blood pressure, and waistline

Secondary Outcome measures (from screening/baseline evaluations data source):

1.	Other health related data such as heart rate, QTc interval gathered

2.	Health outcomes including diabetes, cardiovascular illness, cerebrovascular illness and other medical comorbidities"
  ["project_main_predictor_indep"]=>
  string(194) "Self-reported racial/ethnic from screening or baseline evaluations will be used to define the main predictor, a four-level categorical variable: Caucasian, African-American, Hispanic, and Other."
  ["project_other_variables_interest"]=>
  string(608) "Other variables of interest and potential confounders (if available from screening/baseline evaluations data):

1.	Demographic variables such as age, gender, socioeconomic status, geographic  location, and other  psycho-social factors

2.	Lifestyle factors such as smoking, other substance use, diet, exercise

3.	Psychiatric comorbidities and relevant psychiatric symptomatology at baseline

4.	Family medical history (e.g. history of diabetes, cardiovascular/cerebrovascular illness)

5.	Any available data on health care utilization, access to health care and quality of life"
  ["project_stat_analysis_plan"]=>
  string(994) "Specific Aim 1: One-way ANOVA will be used to test the main racial/ethnic effect in BMI and all 5 components of MetS among schizophrenia/schizoaffective disorder patients. A four-level racial/ethnic categorical variable will be considered: Caucasian, African-American, Hispanic, and Other.

Specific Aim 2: General linear models with racial/ethnic main effect, MetS risk factor main effect, and racial/ethnic by MetS risk factor interaction will be used to determine if there are racial/ethnic-specific MetS risk factor. MetS risk factors will include age, gender, socioeconomic status, living environment, past and current substance use, diet and exercise habit, comorbid medical and psychiatric problems and family history.

Specific Aim 3: Generalized linear models will be used to examine how MetS-related outcomes such as QTc interval, diabetes, cardiovascular and cerebrovascular illness differ by racial/ethnic categories after adjusting for potential confounding covariates."
  ["project_timeline"]=>
  string(257) "Data analyses will start as soon as study data is available. . It will require approximately 4-6 months from the point of data access. Approximately 4-6 months will then be required for dissemination including presentation and publication of study outcomes."
  ["project_dissemination_plan"]=>
  string(175) "Study outcomes will be presented at national or international meetings (e.g. ASCO or ACNP meeting) and published in psychiatry journals (e.g. Journal of Psychiatric Research)."
  ["project_bibliography"]=>
  string(1375) "
[1] Rutter MK, Meigs JB, Wilson PW. Cardiovascular risk and the metabolic syndrome. Metab Syndr Relat Disord. 2006; 4(4):252-60.

[2] Meyer JM, Stahl SM. The metabolic syndrome and schizophrenia. Acta Psychiatr Scand. 2009; 119(1):4-14.

[3] Newcomer JW. Antipsychotic medications: metabolic and cardiovascular risk. J Clin Psychiatry. 2007; 68 Suppl 4:8-13.

[4] Gough SC and O?Donovan MC. Clustering of metabolic comorbidity in schizophrenia: A genetic contribution? Journal of Psychopharmacology. 2005; 19(suppl 6), 47-55.

[5] Meyer J, Koro CE and L?Italien GJ. The metabolic syndrome and schizophrenia: A review. International Review of Psychiatry. 2005; 17(3), 173-180.

[6] U.S. Census Bureau, American FactFinder. American Community Survey. 2008 American Community Survey 1-year estimates [Internet]. ACS demographic and housing estimates: 2008. Available from: http://factfinder.census.gov.

[7] Beltrn-Snchez H, Harhay MO, Harhay MM, McElligott S. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010. J Am Coll Cardiol. 2013; 62(8):697-703.

[8] Phillips KL, Copeland LA, Zeber JE, Stock EM, Tsan JY, MacCarthy AA. Racial/Ethnic disparities in monitoring metabolic parameters for patients with schizophrenia receiving antipsychotic medications. Am J Geriatr Psychiatry. 2015; 23(6):596-606.
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