Metabolic Effects of Antipsychotics in Children
Phase: PHASE4
Conditions: Aggression, Attention Deficit-Hyperactivity, Oppositional Defiant Disorder, Pervasive Development Disorders, Bipolar Disorder
Sponsor: Washington University School of Medicine

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Metabolic Effects of Antipsychotics in Children (RO1MH072912) 
 
NCT #: 00205699 
 
Date: 3/22/18 
 
Protocol Version: 1.9 
 
Revised Final for JAMA Psychiatry and clinicaltrials.gov submissions 
 
 

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Protocol Amendment Timeline 
 
 
 
Protocol 1.0 (6/22/06): 
First approved protocol post-fundingAlready had the single-patient inclusion for age under 7 & addition of 
genetic samples prior to this  
 
Protocol 1.1 (08/30/07): 
Extra safety monitoring (3 and 9 week fasting blood draws and height/weight/WC, home urine glucose 
monitoring kit) for participants who have impaired fasting glucose at baseline or who develop impaired fasting 
glucose during participation; elevated triglycerides (>250 mg/dL); rapid weight gain (>10% of baseline by 6 
weeks) 
Changed allowable stimulant dose to 2 mg/kg/day MTP equivalent 
Omitted 6 week fsOGTT for participants 55-93 lbs due to blood draw limitation (3 ml/kg per 2 months) 
Added 3-month post-study follow up visit 
 
Protocol 1.2 (7/11/08): 
Changed inclusion age from 7-18 to 6-18 
 
Protocol 1.3 (9/18/08): 
Established the max blood draw as 3 mg/kg per 2 months to allow decreased minimum allowable weight for 
children under 94 lbs 
 
Protocol 1.4 (7/16/09): 
Added diet/exercise & psychotherapy questionnaires, Family Environment Scale 
Additional data collection from schools for grades and suspension information 12 months prior to study 
inclusion 
 
Protocol 1.5 (8/24/09): 
Allowed concurrent treatment with atomoxetine 
Added risk of agranulocytosis with aripiprazole 
 
Protocol 1.6 (7/28/10): 
Increase number of subjects from 240 to 325 due to high screen-failure and dropout rate 
 
Protocol 1.7 (10/5/10): 
Changed additional safety monitoring to be at PI discretion instead of limited to 3 and 9 weeks 
 
Protocol 1.8 (6/16): 
Fine-tuning analysis section to reflect that we had less missing data than expected 
Eliminating items that were not tested/samples not run due to limited funding 
 
Protocol 1.9 (3/18): 
Clarification of primary versus secondary outcomes in the data analysis section 
 

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A. 
Specific Aims  
 
The prevalence of overweight and obesity, insulin resistance and type 2 diabetes mellitus (T2DM) are 
increasing, particularly in children, with the Centers for Disease Control warning of epidemic rates of these 
conditions in children in the United States (US). Increased adiposity and related reductions in insulin sensitivity, 
also referred to as insulin resistance, are major risk factors for the development of dyslipidemia, metabolic 
syndrome, T2DM, cardiovascular disease (CVD) – e.g., risk of myocardial infarction and stroke, other adverse 
health outcomes, and reduced psychosocial function. Reductions in lifespan attributable to obesity impact 
younger, at-risk individuals most measurably.  
 
Certain medications can increase regional adipose tissue mass and insulin resistance, contributing to 
both short-term and long-term metabolic risk. Antipsychotic medications are used extensively in children, with 
some agents producing larger increases in weight and adiposity than any other commonly used drugs in this 
age group. Recent studies also indicate that some antipsychotics may affect insulin sensitivity independent of 
adiposity, suggesting a potential additional mechanism for metabolic risk. The use of atypical antipsychotics in 
children is increasing, and has been stimulated by reported efficacy for aggression and irritability in a variety of 
childhood psychiatric disorders. However, no study in children has sensitively quantified the adverse metabolic 
effects of these agents despite reports of alarming levels of weight gain.  
 
The proposed randomized clinical trial aims to assess the metabolic safety of atypical 
antipsychotic agents in antipsychotic-naive children with aggression in the setting of various 
childhood psychiatric disorders during 12 weeks of prospective, randomized treatment with 
olanzapine, risperidone or aripiprazole.  
 
Primary Aim 1: To evaluate antipsychotic treatment effects on insulin action in skeletal muscle 
(glucose disposal), liver (glucose production) and adipose tissue (lipolysis). This study hypothesizes that 
treatments causing greater increases in adiposity (e.g., olanzapine) will be associated with reduced sensitivity 
to insulin effects on glucose disposal, glucose production, and glycerol/fatty acid release, in comparison to 
treatments producing less change in adiposity (e.g., aripiprazole). Hypotheses will be evaluated by measuring 
whole-body glucose and lipid kinetics with the use of stable isotope tracer methodology, using rate of 
disappearance of glucose (glucose Rd; primary), rate of appearance of glucose (glucose Ra), and rate of 
appearance of glycerol (glycerol Ra) as endpoints for the assessment of insulin sensitivity.  
 
Primary Aim 2: To evaluate antipsychotic treatment effects on abdominal fat mass and total 
body fat. This study hypothesizes that the selected antipsychotic medications have significantly different 
affects on direct measures of fat mass (e.g., olanzapine > risperidone, olanzapine > aripiprazole). These 
hypotheses will be evaluated by measuring body composition using whole body dual energy x-ray 
absorptiometry (DEXA) and abdominal magnetic resonance imaging (MRI), quantifying percent total body fat 
(primary) and subcutaneous and visceral abdominal fat as endpoints.  
 
The secondary aims of this study will be to evaluate the effects of selected antipsychotic treatments 
on 1) fasting plasma lipids and waist circumference, which are indirect or derivative surrogates for insulin 
sensitivity and abdominal fat, in order to assess the extent to which changes in the primary endpoints, 
measured directly with gold-standard tools, are also detectable using measures commonly available to 
clinicians, 2) effectiveness for treatment of symptoms of aggression and irritability, using the Clinical Global 
Impressions Scale (CGI) as the endpoint, and 3) (not for inclusion in the primary outcome paper) insulin 
secretion, using frequently sampled oral glucose tolerance tests (fsOGTT) to calculate post-load area-under-
the-curve insulin..  
 
Exploratory aims include the assessment of non-metabolic adverse events, and the assessment of 
metabolic effects with and without concomitant stimulant therapy. Children aged 6-18 will be studied, allowing 
exploration of age-related differences in vulnerability to treatment-induced adverse metabolic changes.  
 
Relevant data on the primary aims are critically needed to assess the risks of antipsychotic therapy in 
children, to identify targets for additional basic research, and to guide clinical decision-making.  
 

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B. Background 
 
Overview:  
 
Soon after the launch of the second-generation antipsychotic medications, case reports of treatment-
related severe hyperglycemia, hypertriglyceridemia, new-onset T2DM and diabetic complications like diabetic 
ketoacidosis entered the psychiatric literature. Based on early adverse event signals from FDA MedWatch 
post-marketing surveillance,1-4 the FDA began to formally request regular updates from manufacturers 
beginning in the summer of 2000. In January of 2001, an NIDDK/NIMH workgroup focused on the association 
of mental disorders and diabetes incorporated evidence of antipsychotic medication effects on the risk for 
T2DM.5 A recent comprehensive review of relevant reports cites three levels of evidence supporting the 
association between antipsychotic medications and metabolic risk: 1) case reports, case series and 
uncontrolled observational studies; 2) a series of retrospective database analyses using surrogate markers for 
incident T2DM (e.g., ICD codes, co-prescription of hypoglycemics); and 3) a small number of controlled 
analytic studies, including randomized clinical trials.6 The PI/applicant contributed to the American Diabetes 
Association (ADA) Consensus Development Conference on this topic, which led to a position paper jointly 
sponsored by the ADA, the American Psychiatric Association (APA), the American Association of Clinical 
Endocrinologists (AACE), and the North American Association for the Study of Obesity (NAASO).7 This review 
found “compelling evidence” that treatment with clozapine and olanzapine produces a risk of substantial weight 
gain, and a risk of T2DM and dyslipidemia. Risperidone and quetiapine were noted to produce less weight gain 
and discrepant results with respect to any association with T2DM or dyslipidemia, while ziprasidone and 
aripiprazole produced the least weight gain and no apparent association with T2DM and dyslipidemia based on 
reports to date. Unfortunately, this report and other reviews either fail to mention children or note only that the 
relative weight gain experienced by children during treatment with antipsychotics is much larger than that 
observed in adults, with no current evidence concerning the specific metabolic consequences associated with 
antipsychotic treatment.  
 
Careful studies of antipsychotic-induced metabolic changes (e.g., changes in fat versus lean muscle 
mass, and changes in insulin action versus secretion) have just begun in adult humans. It remains unclear why 
children are especially vulnerable to weight gain with these medications, and how such metabolic change is 
associated with specific antipsychotics. Measurement of drug-specific treatment-induced changes in whole-
body and regional fat mass, and tissue-specific changes in insulin actions relevant to glucose and lipid 
metabolism, are critical to predicting risk and planning interventions, particularly for children. 
 
Children, Obesity, and T2DM:  
 
The prevalence of obesity, insulin resistance and T2DM is increasing in the population in general and in 
children in particular, with the Centers for Disease Control (CDC) warning that one-third of U.S. children born in 
2000 will develop T2DM (but up to one-half of African American and Hispanic children born that year will 
develop T2DM--Associated Press June 14, 2003). It is estimated that at least one-third of T2DM may be 
undiagnosed (6 million individuals), out of a total prevalence of 17 million in the U.S.8 Prevalence increased by 
almost 50% between 1990 and 2000,9 and the number of individuals diagnosed with diabetes is predicted to 
rise from 11 million to 30 million by 2050.8 Currently, an additional 25 million individuals in the U.S. are 
estimated to have insulin resistance in the form of impaired glucose control below the diagnostic threshold for 
T2DM, typically related to increased adiposity and often accompanied by the metabolic syndrome. The 
metabolic syndrome can include increased abdominal adiposity, insulin resistance, hyperinsulinemia, 
hyperglycemia, dyslipidemia, hypertension, hypercoagulability, endothelial dysfunction and other features, 
sometimes alternatively defined under the terms insulin resistance syndrome or syndrome X. The National 
Cholesterol Education Program (NCEP) guidelines from the US Public Health Service provide the U.S. 
definition of the metabolic syndrome used for this project. Using these criteria, it is estimated that over 20% of 
the U.S. population, and approximately 60% of the obese U.S. population, has the metabolic syndrome.10-12 In 
addition to the well-known risk of T2DM, obesity also has other well-established adverse health consequences, 
such as increased risk for cardiovascular disease, osteoarthritis, and breast, prostate, and colon cancers, with 
annual attributable costs in the U.S. that exceed $100 billion.13 Annual costs related specifically to diabetes 
mellitus in the U.S. also currently exceed $100 billion.14,15 Exemplifying high-cost morbidity, impaired glucose 
control and T2DM are the major cause in the U.S. of non-traumatic amputations, blindness and end-stage 

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renal disease. With respect to mortality, reductions in lifespan attributable to obesity and its consequences 
most measurably impact younger, at-risk individuals, with severely obese 20 year-old African American males, 
for example, expected to lose 20 years of life.16  
 
NCEP recognizes the metabolic syndrome as a major risk factor and T2DM as a risk equivalent for 
cardiovascular disease.12 Substantial evidence indicate a progressive relationship between hyperglycemia and 
cardiovascular event risk (e.g., myocardial infarction, stroke), beginning at glucose levels well below diabetic 
thresholds.17-23 These reports indicate that treatment-induced hyperglycemia can have important clinical 
significance even when glucose levels do not reach "impaired" or diabetic thresholds. Hypertriglyceridemia, 
commonly associated with insulin resistance as detailed below, is also a strong predictor of cardiovascular 
events,24-26 and the combination of hyperglycemia, dyslipidemia and abdominal adiposity is strongly predictive 
of increased cardiovascular morbidity.24-27 One of the best-established effects of adiposity on cardiovascular 
risk occurs via perturbations of lipid metabolism. Increased adiposity, especially increased visceral abdominal 
adiposity, is associated with increased small, dense low-density lipoprotein (LDL) particles and decreased 
high-density lipoprotein (HDL) cholesterol.28-30 Since LDL cholesterol comprises 60-70% of the total cholesterol 
level, total cholesterol has been used as a marker for increased LDL level in population studies. The National 
Health and Nutrition Examination Study III (NHANES III) provided prevalence data indicating that 
hypercholesterolemia (total cholesterol >240) increased with increasing BMI.31 These increases in cholesterol 
are strongly associated with increased cardiovascular risk.32,33 Recent meta-analyses have reported that 
hypertriglyceridemia is an independent risk factor for cardiovascular disease,34,35 and current NCEP III 
recommendations state that greater emphasis needs to be placed on therapeutic lifestyle changes in people 
with hypertriglyceridemia.36 
 
More recently, investigators have begun to assess the prevalence of the metabolic syndrome in 
children and adolescents. As part of the NHANES III, 2430 male and female adolescents aged 12-19 were 
examined for presence of metabolic syndrome.37 The NCEP definition of metabolic syndrome was modified for 
age to include triglyceride level > 110, HDL < 40 and abdominal obesity was defined as a waist circumference 
> 90th percentile of the sample. High fasting glucose was defined as > 110 and high blood pressure was 
defined as > 90th percentile for the sample. They found an overall prevalence of metabolic syndrome in their 
adolescent sample of 4.2%. It was more common in males (6.1%) than females (2.1%). In addition, 41% of the 
subjects had at least 1 risk factor and 14% had 2 or more. Of those adolescents who had the metabolic 
syndrome, 73.9% were considered overweight by their BMI, defined as > 95th percentile. The authors 
concluded that up to 30% of overweight adolescents may meet criteria for metabolic syndrome, which would 
translate to approximately 1 million adolescents affected in the US. 
 
Adiposity and insulin resistance: 
 
A key risk factor for the development of the metabolic syndrome and T2DM is increased adiposity 
leading to decreased sensitivity to insulin actions, often referred to as insulin resistance. There is a well-
characterized progression from insulin resistance to frank T2DM. Insulin resistance can be associated with 
simple hyperglycemia or dyslipidemia, impaired glucose tolerance, or the full complement of changes called 
the metabolic syndrome. All of these conditions are characterized by decreased sensitivity to insulin actions on 
skeletal muscle (glucose disposal), liver (glucose production) and adipose tissue (lipolysis), with T2DM also 
characterized by progressive secondary disturbances in insulin secretion. Decreased insulin sensitivity at 
adipose tissue tends to occur later in the course of naturally occurring diabetes, but potentially early during 
antipsychotic treatment, characterized by an impaired ability to decrease lipolysis with corresponding increases 
in the release of free fatty acids (FFAs) into circulation.38 Increased plasma FFA (triglyceride + glycerol) is 
detected clinically as an elevation in plasma triglyceride. 
 
Using data from the National Health and Nutrition Examination Survey, Epidemiology Follow-up Study 
(1971-1992),39 race- and sex-specific odds ratios of diabetes in relation to baseline BMI were calculated. The 
risk of diabetes increases as a function of BMI in all study groups. Family history, race, and ethnicity also play 
a role, and these variables will be measured for this project. However, substantial evidence indicates that 
increased central or abdominal adiposity, in contrast to femoral-gluteal fat, is a key predictor of decreased 
insulin sensitivity and the risk of T2DM.40,41 For this project, visceral and subcutaneous abdominal fat will be 

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measured by MRI and total body % fat will be measured by DEXA, in order to sensitively characterize changes 
in fat mass and measure the effect of these changes on sensitive indicators of glucose and lipid metabolism. 
 
Insulin resistance can be caused by increases in adiposity, but insulin resistance can also contribute to 
subsequent increases in fat mass. The relationship between insulin and body weight regulation is in part under 
central nervous system control, via widely distributed central insulin receptors and insulin signaling proteins.42 
The close relationship between insulin, lipids and adiposity is mediated in the periphery in part by lipoprotein 
lipase (LPL). LPL is bound to glycosaminoglycans at capillary endothelial surfaces,43 with major LPL 
expression in adipose tissue, heart and skeletal muscle.44 LPL can be thought of as a "gate keeper" enzyme, 
activated in response to hormonal (e.g. insulin), nutritional (e.g. carbohydrates), or mechanical (e.g. exercise) 
stimuli, which regulates the flow of fatty acids either toward muscle for oxidation or toward fat for storage 
depending on physiological circumstances. Skeletal muscle is usually the principal site of triglyceride 
clearance.45-47 However, skeletal muscle LPL activity is strongly correlated with insulin sensitivity, i.e. those 
who are most sensitive to insulin (and have low circulating insulin levels) have the highest levels of skeletal 
muscle LPL activity.48,49 In insulin resistant subjects with obesity, LPL activity can be elevated in adipose tissue 
and decreased in skeletal muscle, so that adipose tissue LPL becomes quantitatively dominant in terms of 
whole-body LPL activity, potentially steering additional fatty acids into fat storage. An underlying hypothesis for 
this project is that treatment-induced insulin resistance in children contributes to increased fatty acid storage. 
 
Children and Antipsychotic Medication Use:  
 
The use of antipsychotic medications in children is increasing. In addition to use in children with 
schizophrenia, clinical practice and recent studies have suggested additional indications, currently not 
approved by the FDA or fully informed by detailed risk-benefit considerations. Similar to other states, 
approximately 25% of the children treated with any psychotropic medication within Missouri Medicaid and the 
Department of Mental Health receive an atypical antipsychotic. Case reports, case series, and open label trials 
have described successful use of atypical antipsychotics for the treatment of aggression in children with a 
variety of psychiatric diagnoses.50-55 Two recent double-blind, randomized, placebo-controlled trials have 
demonstrated the efficacy of risperidone in reducing aggression in children with a variety of diagnoses 
including autism, conduct disorder, oppositional defiant disorder, attention deficit disorder, disruptive behavior 
disorder, as well as subaverage intelligence.56,57 These results are likely to further stimulate atypical 
antipsychotic prescriptions in children with aggression in the setting of a variety of psychiatric disorders. The 
proposed randomized, open-label study would address clinically relevant questions concerning both efficacy 
and safety in aggressive children with a variety of psychiatric disorders (see D.2. Methods).  
 
Antipsychotic-induced changes in weight and adiposity: 
 
While multiple environmental factors can contribute to increasing adiposity, certain medications can 
potently increase regional adipose tissue mass and insulin resistance, contributing to short-term and long-term 
metabolic risk. For example, investigators from Washington University School of Medicine have made major 
contributions to the study of agents like protease inhibitors that can increase central adiposity with related 
adverse changes in glucose and lipid metabolism.58-60 However, these drugs receive limited use in children. In 
contrast, antipsychotic medications are extensively used in children and certain antipsychotics can produce 
greater increases in adiposity than any other commonly used drugs in this age group. 
 
Antipsychotic treatments can produce weight gain of varying magnitude,61-63 with more prominent 
effects linked to low-potency phenothiazines, clozapine,63-66 and olanzapine.66-68 Newer medications can have 
larger effects on weight gain than older agents, inducing excessive weight gain in up to 50% of adult patients,69 
with children and adolescents evidently susceptible to larger effects.70 In general, reported weight change over 
specific periods of time has tended to underestimate the extent of weight gain observed with any of the 
medications by using last-observation-carried-forward (LOCF) data, rather than “completer” analyses.71 FDA-
approved weight loss agents are generally poorly tolerated in neuropsychiatric patients (e.g., due to events like 
activation, insomnia, and hallucinations), and initial efforts to identify tolerable augmentation strategies to 
attenuate antipsychotic-induced weight gain have been disappointing.72,73 
 

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Olanzapine induces clinically significant increases in short- and long-term weight gain. A comparative 
study of antipsychotic weight gain at 10 weeks of treatment showed a mean weight gain of 4.15 kg (9.15 lbs) 
with olanzapine, vs. 4.45 kg (9.81 lbs) with clozapine.74 Within the first three months of treatment, weight 
increases approximate a mean of 4 kg (8.8 lbs).63 Mean increases during 6-12 months of treatment with 
olanzapine or clozapine have been reported in the range of 14-26.4 lbs, with a mean of over 10 kg (22 lbs) at 
12 months with olanzapine at 15mg/day, and individual patients experiencing 50-100 lb increases. U.S. 
package insert data show that 29% of patients taking olanzapine for 6 weeks (vs. 3% of placebo controls), and 
56% of patients taking olanzapine long-term, gain greater than 7% of their baseline weight. 
 
Although risperidone can produce more weight gain than some high-potency conventional agents and 
more than ziprasidone and aripiprazole, it produces relatively less short-term weight gain than olanzapine and 
clozapine,75 ranging from 3 to 5 lbs. over the first 10 weeks of treatment.63,76-78 In a 6-month comparison, Tran 
et al reported that mean weight gain on risperidone was 5 lbs versus 9 lbs with olanzapine treatment.79 
However, longer treatment with risperidone has been associated with greater gains (9 lbs) than those reported 
in the Tran study.80 Interestingly, Ganguli et al. showed mean weight loss of 2 lbs with risperidone at a mean 
treatment duration of 125.3 days in unpublished data.77 It may be critical to control for the previous treatment 
received, as switches from higher weight gain liability agents to lower liability agents can be associated with 
weight loss. Package insert data for risperidone indicate that 18% of patients gain 7% or more of their body 
weight over 6-8 weeks, vs. 9% of placebo controls. 
 
Short-term clinical trial data from the aripiprazole package labeling indicate that aripiprazole is 
associated with approximately 2 times the incidence of clinically significant (7% or more) weight gain compared 
to placebo. Pooled data from five short-term studies indicate that 4 weeks of aripiprazole treatment is 
associated with 0.71 kg (1.6 lbs) of weight gain, similar to haloperidol.81,82 Extrapolating this to 10 weeks for 
comparability with other agents, haloperidol is known to produce approximately 1 kg of weight increase over 10 
weeks,63 which may crudely approximate expected gain over the same period for aripiprazole. In a 26-week 
double-blind study, aripiprazole was associated with a 0.87 kg (1.9 lbs) decrease in weight.83 Analysis of data 
from a 1-year double-blind study showed that aripiprazole was associated with 1.1 kg (2.4 lbs) of weight gain.81 
While published data are currently sparse for this relatively new agent, results suggest that aripiprazole 
compares favorably to other atypical antipsychotics associated with minimal weight gain.84 
 
Special concerns regarding weight gain in children and adolescents 
 
Weight gain in children can be associated with normal growth-related increases in lean muscle mass, 
or with increases in adiposity. The few studies in children that have attempted to address this distinction have 
compared treatment-induced weight gain to standardized growth curves rather than directly measuring 
changes in lean muscle versus fat mass, or calculating change in % body fat. Child psychiatrists have clinically 
noted substantial weight gain in children during antipsychotic treatment, providing anecdotal reports and early 
observations that this effect in children is even more pronounced than that observed in adults. However, no 
data are available to describe how treatment-induced changes in adiposity impact glucose and lipid 
metabolism in children. 
 
 
The few studies that have reported changes in body weight in children treated with antipsychotics raise 
concern that weight gain may be significant in this treatment population. An open label study of olanzapine 
(mean dose 10.7 mg/day) in 25 children with pervasive developmental disorder demonstrated an average 
weight gain of 4.7 kg (10.3 lbs) after 12 weeks of treatment,85 corresponding to nearly a 10% increase in body 
weight. Olanzapine treatment (mean dose 17.5 mg/day) of 8 psychotic adolescents resulted in an average 
14% increase in body weight over an average of 9 weeks of treatment.86 A retrospective study with 50 patients 
under age 18 treated with olanzapine (mean dose 13.9mg) found an average weight gain of 3.8 kilograms after 
an average duration of treatment of 39 days. 87 A study of 8 weeks of randomized treatment with either 
risperidone (up to 3 mg) or placebo in 26 children with Tourette’s disorder resulted in an average weight gain of 
2.8 kg (6 lbs) compared to no weight change during placebo treatment.88 A multi-center double-blind study of 
118 children ages 5-12 years with mental retardation and disruptive behaviors found a mean weight increase 
of 2.2 kilograms after only 6 weeks of treatment with risperidone (mean dose 1.16mg) compared to 0.9 kg with 
placebo.56 A more recent study of 19 children aged 7-17 years with either Tourette’s disorder or chronic motor 
tic disorder had an average weight gain of 1.9 kilograms over the 4-week treatment period with risperidone 

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(mean dose 2.5mg).89 A similar open label study of risperidone treatment (mean dose of 1.26mg) in autistic 
children reported an average weight gain of 3.2 kg after 4 weeks of treatment.90 A multi-site randomized, 
double blind trial of risperidone (mean dose 1.8mg) compared with placebo among 101 children ages 5-17 with 
autism found an average weight gain of 2.7 kilograms after 8 weeks of treatment compared to 0.8kg with 
placebo.57 Sixty-three of these children treated openly for an additional 4 months had an average weight gain 
of 5.6 kilograms after a total 6 months, suggesting that, similar to adults, treatment effects on weight gain are 
not limited to the short-term.91 Finally, Sikich et al randomized 50 psychotic children and adolescents to 8 
weeks of treatment with haloperidol, olanzapine, or risperidone.92 While significant weight gain was seen in all 
groups, between-group differences in weight gain and BMI were statistically significant, with olanzapine > 
risperidone > haloperidol. Risperidone-treated subjects had an average weight gain of 4.9 kg (10.9 lbs) 
compared to a mean weight gain of 7.1 kg (15.7 lbs) in olanzapine-treated subjects. All of these studies were 
limited by uncertain prior treatment. 
 
 
Although the use of young adults rather than children might tend to minimize the effects actually observed 
in pediatric populations, treatment of antipsychotic-naïve first-episode schizophrenia patients may offer one of 
the best estimates of the potential weight gain in antipsychotic-naïve children. Recently analyzed data from an 
NIMH-funded study of first-episode schizophrenia patients (personal communication) suggests that olanzapine 
treatment is associated with a mean increase in body weight of 15.7% of baseline weight, while risperidone 
treatment is associated with a mean increase of 9.2% of baseline weight. Using recently analyzed data from a 
similar placebo-controlled first-episode schizophrenia study, aripiprazole induced a 2.7% increase in body 
weight from baseline (personal communication). In the absence of an available controlled antipsychotic-naïve 
treatment data in children, we used mean and S.D. data from these studies to estimate potential changes in 
percent body fat for this project. 
  
Antipsychotic treatment and abnormal glucose metabolism: 
 
Recent reports suggest that newer antipsychotic medications, perhaps even more than older agents, 
can contribute to the occurrence of clinically significant glucoregulatory abnormalities (e.g., new onset 
diabetes), dyslipidemia (e.g., increased plasma triglyceride) and increased weight and adiposity.93 While 
phenothiazine treatment can contribute to abnormalities in glucose regulation, this association is not 
consistently observed at the same frequency for all older antipsychotics.94 Hyperglycemia, exacerbation of 
existing T2DM, new onset T2DM, and diabetic ketoacidosis (DKA) have been associated with treatment using 
clozapine,2,95-116 and olanzapine.103,112,113,116-134 Fewer reports have been associated with quetiapine116,130,135-137 
risperidone,116,138-142 and ziprasidone.143 Random plasma glucose data from Pfizer’s clinical trial dataset 
suggest that ziprasidone may be similar to haloperidol in the rate of associated hyperglycemia.144 Aripiprazole 
has not been associated with published reports of impaired glucose metabolism at the time of protocol 
initiation, with fasting glucose and glycolated hemoglobin values comparable to placebo after 26 weeks of 
treatment in adults.81 Deaths have been attributed to the induction of DKA or other hyperglycemia-related 
events during treatment with certain atypical antipsychotics.2,125 The current published evidence for 
antipsychotic-associated hyperglycemia spans case reports and case series, FDA MedWatch data, case-
control comparisons, prospective observational studies, retrospective database analyses and industry-initiated 
prospective randomized trials.6 Glick et al.145 reported that 6 weeks of randomized treatment with olanzapine 
produced a significant increase in adiposity, weight, plasma glucose, insulin and lipid as well as homeostasis 
model assessment (HOMA) insulin resistance,146,147 in comparison to no significant change in these measures 
during ziprasidone treatment. Case reports suggest that children may be just as vulnerable to hyperglycemia 
during treatment with atypical antipsychotics.1,148-150 The limited data from children precluded any mention of 
this at-risk group in the recent report from the ADA’s Consensus Development Conference addressing second 
generation agents, co-sponsored by APA, AACE and NAASO.7 
 
Insulin resistance with and without weight gain during antipsychotic treatment:  
 
Weight gain producing increased visceral adiposity can increase insulin resistance and contribute to 
hyperglycemia,40,151 so that treatment-related changes in glucose regulation are expected secondary to 
changes in adiposity. However, approximately 20-25% of cases of new onset T2DM in schizophrenia patients 
may occur in the absence of weight gain, suggesting potential direct effects on insulin secretion or action in a 
smaller subset of patients.2,93,95,96,104,123,129 Recent studies also suggest the potential for drug effects on insulin 

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sensitivity independent of adiposity. Using fasting and post-load plasma measures from modified oral glucose 
tolerance tests (fsOGTTs), the P.I. and colleagues have reported that clozapine and olanzapine may be 
associated with disturbances in glucose regulation independent of differences in adiposity.152 Similarly, a 
recent euglycemic clamp study in Wistar rats demonstrated acute impairments in insulin sensitivity associated 
with olanzapine and clozapine administration.153 Henderson and colleagues154 reported similar results with a 
frequently sampled intravenous glucose tolerance test (fsIVGTT) using Bergman's Minimal Model (MINMOD) 
analysis in subject groups similarly matched for adiposity, age and race.155-160 MINMOD analyses have been 
used to assess insulin sensitivity and "glucose effectiveness" when gold-standard techniques are not 
possible.156,157,160-162 The perspective gained to date in adults suggests commonly occurring, treatment-induced 
and largely adiposity-related increases in insulin resistance, with less consistent changes in insulin resistance 
independent of adiposity, and smaller or more sporadic defects in insulin secretion. Reports of sporadic cases 
of deaths associated with DKA underscore that large defects in insulin secretion may occur in some patients. 
No data are available from children to inform a similar understanding of the pattern of metabolic change during 
antipsychotic therapy. It is not clear at this time whether children experience primarily adiposity-related 
changes in glucose and lipid metabolism, or to what extent adiposity-independent effects may play a role. The 
answer to these questions is directly relevant to future efforts to establish prevention and intervention 
strategies to lower long-term metabolic risk for T2DM and CVD in children.  
 
Antipsychotic treatment and abnormal lipid metabolism:  
 
In early studies, phenothiazine treatment was associated with increases in measured plasma lipids.163 
Recent reports have similarly identified clinically significant increases in plasma triglycerides in patients treated 
with clozapine,164-166 olanzapine,124,167-169 and quetiapine.167 Pfizer assessed fasting plasma triglycerides during 
brief randomized treatment with several newer and older antipsychotic agents144 with groups ranging in size 
from 27 to 34 subjects. They reported increases in fasting plasma triglycerides during olanzapine, quetiapine 
and thioridazine treatment, and decreases during ziprasidone, risperidone and haloperidol treatment. A similar 
pattern of minimal effect on plasma lipids and other elements of the metabolic syndrome has been observed 
for aripiprazole during 26 weeks of randomized treatment, in comparison to both placebo and olanzapine.170 
While case reports indicate that children may also be vulnerable to abnormalities in lipid metabolism during 
antipsychotic treatment, no controlled or detailed studies have been performed.171 
 
Interrelationship between lipid and glucose metabolism: The mechanism underlying treatment-related 
increases in circulating triglyceride levels remains unknown, but is hypothesized to involve changes in 
adiposity leading to decreased insulin sensitivity at adipocytes. Plasma lipoproteins are commonly classified as 
chylomicrons, very low density lipoproteins (VLDL), low density lipoprotein (LDL), and high density lipoprotein 
(HDL) based on their flotation in an ultracentrifuge. VLDL, produced by the liver, is the major endogenous 
triglyceride-rich lipoprotein. Triglycerides represent 65% of the lipids in VLDL particles and are formed by 
esterification of free fatty acids delivered to the liver from plasma, free fatty acids released from intrahepatic 
triglycerides, and fatty acids made de novo by the liver (de novo lipogenesis). Increased production of VLDL 
apoB and triglyceride is often causally related with hypertriglyceridemia associated with insulin resistance in 
other conditions.172-174  Interrelationships between insulin, glucose, and fatty acid metabolism in regulating 
VLDL production are complex. Important basic methodological issues remain to be addressed in this patient 
population. Changes in adiposity can be strongly related to differences in lipid metabolism and circulating 
plasma lipoproteins. The most sensitive measure of treatment effects on glycerol and fatty acid kinetics is not 
changes in circulating plasma triglyceride levels, a complex function of rate-limiting hepatic apoB production 
and VLDL export, but rather a direct measure of glycerol and fatty acid kinetics as proposed in this application.  
 
C. Significance:  
 
T2DM and CVD are important disease targets for the NIH and the U.S. Public Health Service, with 
overweight and obesity, hyperglycemia and dyslipidemia identified as major modifiable risk factors. These risk 
factors can be altered by adverse changes in nutritional status and activity level, and by adverse medication 
effects. The identification of factors that contribute to the increased prevalence of medical illnesses such as 
T2DM and CVD in patients with major mental disorders is similarly a high priority. The critical evaluation of 
adverse events, as well as efficacy, is required for new medications seeking new on- or off-label indications. To 
our knowledge, the proposed Metabolic Effects of Antipsychotics in Children (MEAC) study would be the only 

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NIH funded study concerning adverse medication effects on direct measures of key metabolic risk factors for 
cardiovascular disease and T2DM in children (i.e., insulin sensitivity and adiposity). The broad generalizability 
of the results will be enhanced by the study of children with aggressive behaviors in the setting of a variety of 
disorders, with and without concomitant stimulant treatment.  
 
Noted above, the recent ADA/APA Consensus Development Conference report concerning second generation 
antipsychotic medications makes no mention of use in children and no recommendations for risk management, 
due to the lack of relevant information from treated children. Relevant data are critically needed to assess the 
risks as well as benefits of antipsychotic therapy in children, identify needs for additional basic research, as 
well as guide administrative and regulatory decision-making.  
D. Research Design & Methods   
 
1. Study Site Resources and Timetable:  
 
The proposed experiments will be conducted using existing dedicated research space at Washington 
University School of Medicine, including the Adult and Pediatric General Clinical Research Centers (GCRC), 
Diabetes Research and Training Center (DRTC) and Clinical Nutrition Research Unit (CNRU) where all 
previously reported experiments have been performed.  
 
Year 1:  
a. Subject recruitment; 
b. Screen visit, (Study Visit 1) for 156 subjects; 
c. Baseline evaluations (Study Visits 2 and 3) for 33 subjects; 
d. 6-week post treatment evaluations (Study Visit 4) for 26 subjects; 
e. 12-week post treatment evaluations (Study Visits 5 and 6) for 19 subjects; 
f. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 
 
Year 2:  
a. Subject recruitment; 
b. Screen visit, (Study Visit 1) for 300 subjects; 
c. Baseline evaluations (Study Visits 2 and 3) for 66 subjects; 
d. 6-week post treatment evaluations (Study Visit 4) for 66 subjects; 
e. 12-week post treatment evaluations (Study Visits 5 and 6) for 66 subjects; 
f. 3-month post 12-week study evaluations for subset of subjects (Study Visit 7) estimated 22-42  
subjects. Estimate is based on needs of current subjects: 17 out of 57 enrolled subjects 
 (30%) vs 17 out of 27 subjects who have completed the study (63%); 
g. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry 
 
Year 3:  
a. Subject recruitment; 
b. Screen visit, (Study Visit 1) for 300 subjects; 
c. Baseline evaluations (Study Visits 2 and 3) for 66 subjects; 
d. 6-week post treatment evaluations (Study Visit 4) for 66 subjects; 
e. 12-week post treatment evaluations (Study Visits 5 and 6) for 66 subjects; 
h. 3-month post 12-week study evaluations for subset of subjects (Study Visit 7) for estimated 22-42 
subjects; 
h. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 
 
Year 4:  
a. Subject recruitment; 
b. Screen visit, (Study Visit 1) for 300 subjects; 
c. Baseline evaluations (Study Visits 2 and 3) for 66 subjects; 
d. 6-week post treatment evaluations (Study Visit 4) for 66 subjects; 
e. 12-week post treatment evaluations (Study Visits 5 and 6) for 66 subjects; 
f. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 

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g. 3-month post 12-week study evaluations for subset of subjects (Study Visit 7) for estimated 22-42 
subjects; 
h. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 
Year 5:  
a. No additional subject recruitment; 
b. No additional Screen visits (Study Visit 1); 
c. Baseline evaluations (Study Visits 2 and 3) for 33 subjects; 
d. 6-week post treatment evaluations (Study Visit 4) for 40 subjects; 
e. 12-week post treatment evaluations (Study Visits 5 and 6) for 47 subjects; 
f. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 
g. 3-month post 12-week study evaluations for subset of subjects (Study Visit 7) for estimated 22-42 
subjects; 
h. Plasma sample analyses for substrate concentration and isotopic enrichment; data entry. 
i. 
Complete all data coding and entry; complete all final data analysis, write up results, presentation(s). 
 
2. Subjects: 
 
We will enroll 325 eligible subjects with clinically significant aggression symptoms, as defined by a score of ≥18 
on the Irritability subscale of the Aberrant Behavior Checklist 205, 206 in the context of one or more Axis I DSM 
IV207 childhood psychiatric disorders, including conduct disorder, oppositional defiant disorder, disruptive 
behavior disorder, autism, pervasive developmental disorder, attention deficit disorder, bipolar affective 
disorder and schizophrenia, who assent to participate and whose guardian(s) give informed consent for 
participation in the study. Individuals with clinically significant suicidal ideation will not be included. We plan on 
screening up to four times the enrollment target number in order to achieve recruitment targets (Study Visit 1). 
All subjects will be 6-18 years of age and will include all races and ethnic groups and both genders, with 
targeted enrollment reflecting the overall gender distribution of males and females for “externalizing” disorders 
(i.e., 2.5:1, male:female).208.  By initially enrolling 108 subjects in each of 3 treatment groups described below, 
we estimate this will enable us to obtain a study population of 80 subjects per group for a total of 240 
analyzable subjects who complete the protocol (assuming 10% attrition). Recruitment will involve targeted 
community outreach to pediatricians, schools and family support groups, as well as screening potential 
subjects from the Child and Adolescent Psychiatry Clinic and the Barnes Jewish Christian 175 Behavioral Health 
System which have a combined annual total of 2,464 individual patients with a large number of potentially 
eligible subjects. 
 
The inclusion criteria are: i) age 6-18 years ii) generally healthy and a score of ≥18 on the Aberrant Behavior 
Checklist for irritability in the context of one or more Axis I DSM IV childhood psychiatric disorder, including 
conduct disorder, oppositional defiant disorder, disruptive behavior disorder, autism, pervasive developmental 
disorder, attention deficit disorder, bipolar affective disorder and schizophrenia; iii) Children’s Global 
Assessment Scale (CGAS) Score ≤ 60; iv) not previously treated with an antipsychotic; individual subjects with 
a remote (i.e., > 1 year), brief (i.e., < 1 week) prior antipsychotic exposure may be considered for enrollment on 
a case by case basis; v) patient assent and informed consent obtained from the parent or guardian; vi) no 
clinically significant (PI determination) changes in permitted medications (e.g., stimulants, SSRI’s) for 
approximately 1 month prior to Baseline Evaluations;  
  
The exclusion criteria are: i) active suicidality or a primary diagnosis of major depressive disorder; ii) any 
lifetime use of antipsychotics; individual subjects with a remote, brief remote prior antipsychotic exposure may 
be considered for enrollment as above; iii) the presence of any serious medical disorder that may confound the 
assessment of relevant biologic measures or diagnoses, including: significant organ system dysfunction; 
endocrine disease, including type 1 or type 2 diabetes mellitus; coagulopathy; anemia; or acute infection; all 
based on PI discretion; iv) subjects regularly taking within the last 3 months any glucose lowering agent, lipid 
lowering agent, exogenous testosterone, recombinant human growth hormone, or any other endocrine agent 
that might confound substrate metabolism, oral glucocorticoids (glucocorticoid nasal spray and inhalers are 
permitted), sedating antihistamines (non-sedating antihistamines such as but not limited to Claritin (loratadine) 
and Zyrtec (cetirizine) are permitted), and certain mood stabilizing agents, as some medications may 

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themselves worsen or otherwise alter weight gain, glucose and lipid regulation or otherwise make it difficult to 
assess the effects of the antipsychotic alone; (note that exposure to many psychotropic agents including 
stimulants and SSRI’s is permitted in order to maintain the generalizability of the sample); v) IQ < 70 (based on 
school records and/or evaluation by clinician); vi) current substance abuse; vii) past history of, or current 
dyskinesia; viii) stimulant dosage significantly higher (per PI) than the equivalent of approximately 2 mg/kg/day 
methylphenidate equivalent dose.  
  
Rationale for Inclusion and Exclusion Criteria: The CGAS score corresponds to definite impairment that 
requires clinical intervention. Intelligence quotient cut-off is to ensure that subjects can comprehend the 
assessment questions. In order to aid recruitment, multiple psychiatric disorders including attention deficit 
disorder, bipolar affective disorder, conduct disorder, oppositional defiant disorder, and disruptive behavior 
disorder will be included, as long as participants meet screening criteria for aggression and are not actively 
suicidal or primarily depressed. Substance use disorders are excluded to avoid confounding mental status 
examinations and/or measures of metabolic factors. Stimulant drugs at a stable dose are permitted for some of 
the research subjects as at least half of these subjects will have comorbid ADHD. Not allowing stimulants 
would therefore likely decrease recruitment and would provide ecologically valid data. Children cannot be on a 
stimulant dosage significantly higher than the clinical standard of no more than 2mg/kg/day equivalent of 
methylphenidate. We choose to also study subjects off stimulants, in order to explore the effect of stimulant 
therapy on adiposity gain and metabolic change. 
 
Subject recruitment and retention: Potential subjects will be recruited by Clinical Research Coordinator’s in 
our unit, under the direction of the PI, from the clinics listed above, with additional outreach to sources such as 
pediatrician’s offices and the school systems as needed. The names and contact numbers of these families will 
be sent to the PI and Clinical Research Coordinators, to do a telephone screen to rule out children who are 
unlikely to have the inclusion criteria or who have exclusion criteria. Those families not excluded by telephone 
screen will be given appointments for baseline assessments. Once subjects are enrolled, research staff will 
maintain at least weekly contact with subjects and relevant family members. The PI’s Clinical Research 
Coordinators and staff have extensive experience working with neuropsychiatric populations and their families 
and efficiently performing recruiting, screening, and follow-up to maintain subject flow and high follow-up rates. 
Retention in this project will be further enhanced by the use of highly experienced clinicians, providing 
medication adjustments and monitoring, as well as supportive psychotherapy, throughout the course of the 12-
week study (weekly visits for the first 4 weeks, followed by every other week visits for the final 8 weeks, with 
additional visits available as needed). 
 
Subjects with Attention-Deficit Hyperactivity Disorder (ADHD) Not Already on Stimulants: If, at 
screening or during baseline, children are suspected of or diagnosed with ADHD, but are not already on 
treatment, they will be referred back to their community physicians for treatment. If the treatment is with a 
stimulant, and if the treatment has not alleviated the aggressive behaviors, these children can be referred back 
for the metabolic study of antipsychotic therapy. Reasons for excluding non-stimulant ADHD treatments and for 
the maximum dose of stimulant are provided above. 
 
 
3. Study Outline and Randomization Plan:  
 
All 240 eligible, recruited subjects completing the Screen Visit (Study Visit 1) will be randomized to 
prospective open-label treatment with one of three atypical antipsychotic medications. Specifically, a) 80 
subjects will be randomized to open-label treatment with aripiprazole; b) 80 subjects will be randomized to 
open-label treatment with risperidone; c) 80 subjects will be randomized to open-label treatment with 
olanzapine. Recruitment and randomization will be further structured to target having half of the children in 
each treatment group aged 6-12, and half aged 13-18, using separate randomization schedules for each age 
group. In addition, within each of the 2 age groups in each of the 3 treatment conditions, recruitment and 
randomization will be further structured to target having half of the children on previously prescribed and 
ongoing stimulant therapy, and half not receiving stimulants. This plan targets the acquisition of 20 younger 
subjects on stimulants and 20 younger subjects off stimulants, as well as 20 older subjects on stimulants and 
20 older subjects off stimulants, within each of the antipsychotic treatment groups. Targeted group size is 

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based on power estimates, described below, which will permit hypothesis testing for treatment group 
interactions with age and with stimulant therapy. Group composition will otherwise be monitored and matched 
to the greatest extent possible for disease severity, specific age, and ethnicity, by over-recruiting any under-
represented groups identified in the active enrollment phase.  
 
Randomized subjects will have Baseline Evaluations (Study Visits 2 and 3), followed by assessments at 6 
weeks (Study Visit 4) and 3 months (Study Visits 5 and 6) as described below and summarized in Table 1 
below. The 6-week (Study Visit 4) follow-up evaluation after the treatment change is selected to provide 
sufficient time for only initial changes in adiposity, glucose and lipid metabolism, without larger changes in 
adiposity predicted at this point in children. The 3-month follow-up is selected to measure predicted changes in 
adiposity, along with metabolic parameters. DXA scans at 6 weeks (Study Visit 4) and 3 months (Study Visit 
5) will allow percent total body fat to be quantified for use as a covariate in the study analyses. The 3-month 
follow-up will include the MR scan (Study Visit 5) repeated from baseline, as sufficient time will have elapsed 
to permit larger changes in adiposity. Flexible antipsychotic dosing in the longitudinal follow-ups will enhance 
clinical outcomes and allow the possibility of exploring effects of dose, although there is currently no evidence 
for dose-dependent effects. Adverse events and aggression symptoms are rated, to assess status at baseline 
(Study Visit 2) and 12-weeks (Study Visit 6), during the isotope clamp protocol. In addition, the Modified 
Adverse Event Checklist will be used during weekly contacts with all subjects. In order to evaluate clinical 
response to post-study recommendations, there will be a 3-month post study follow-up visit (Study Visit 7 - 
approximately 3 months after the end of the 12-wk study) for a subset of subjects who have had increases in 
metabolic risk factors (e.g. 10% or greater increase from baseline body weight, fasting triglyceride levels > 250 
mg/dl at 6 or 12 weeks, and/or a fasting or post-load glucose > 100 or 140 mg/dl, respectively, at 6 or 12 
weeks). 
 
Quality of Care During the randomized treatment phase of the study, subjects will continue to receive care 
from their primary physician, with extensive supplemental contacts and monitoring as outlined above so that all 
subjects are monitored at a level that exceeds the standard of care. This site has extensive experience in the 
performance of clinical studies in children with psychiatric disorders, including interactions with children with 
acute and sometimes psychotic illness. This site also has extensive experience in performing complex 
metabolic assessments in pediatric populations, as well as in psychiatric patients with severe psychotic 
illnesses, maintaining an outstanding safety record and high retention rate in all of these endeavors. 
 
Choice of Specific Treatment and Experimental Conditions: The antipsychotic medications selected for 
this project were chosen in order to compare specific newer antipsychotic medications needed to address the 
study questions, where the use in child populations is either well supported by current literature and/or the drug 
is used extensively for the treatment of aggression based on national and state prescribing patterns or showing 
rapid growth for this indication. Olanzapine and risperidone are the most frequently prescribed antipsychotic 
medications for aggression associated with psychiatric illness, with aripiprazole use increasing. Olanzapine is 
associated with the greatest amount of weight gain and metabolic effects in reports to date, making it an ideal 
positive control in this study. Risperidone is widely used in this population, and is the best supported by 
published literature, with intermediate weight gain and metabolic effects among the newer medications. 
Aripiprazole, most recently approved by the FDA, appears to have a favorable side effect profile in children, 
especially with respect to weight gain and minimal metabolic effects in adults. Although we could have chosen 
ziprasidone, concerns about QTc prolongation may have complicated recruitment and currently in the state of 
Missouri, aripiprazole is used in children within the Department of Mental Health at approximately twice the 
rate of ziprasidone. It is predicted to see extensive use in children in the coming months and years. 
Aripiprazole’s favorable metabolic side effect profile in preclinical and early clinical trials make it a good 
candidate for a negative treatment control in this study.  

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4. Specific screening tests and procedures:  
 
Table 1:  Planned procedures 
 
MAGIC Medical 
Exam/ 
ECG 
Fasting 
Labs 
fsOGTT 
DXA 
Scan 
MRI Isotope 
Clamp 
Hours of 
Participation 
Study Visit 1 
(Screen) 
X 
 
 
 
 
 
 
4-5 
Study Visit 2 
(Baseline)  
 
X 
X 
X 
X 
X 
 
3.5 
Study Visit 3 
(Baseline  
 
 
 
 
 
 
X 
8 
Study visit 3A Extra 
safety visit at 3 wks 
 
 
X 
 
 
 
 
1 
Study Visit 4 
(6-weeks) 
 
 
X for 
subjects >93 
lbs 
X for 
subjects 
94+ lbs 
X 
 
 
2.5 
Study visit 4A Extra 
safety visit at 9 wks 
 
 
X 
 
 
 
 
1 
Study Visit 5 
(12-week end of 
study  
 
 
X 
X 
X 
X 
 
3.5 
Study Visit 6 
(12-week end of 
study.  
X 
 
 
 
 
 
X 
8 
 Visit 7, 3-mo. post 
study follow-up for 
subset of subjects.  
 
 
X 
 
 
 
 
1 
 
Medical examination and history: All subjects will be screened with a detailed history and physical 
examination performed by a study physician, routine blood tests including a glycated hemoglobin (A1C) level, 
and resting 12-lead electrocardiogram.  
 
Assessment of Clinically-Available Parameters: fasting labs and anthropomorphic measures: In 
addition to the exploratory aim of assessing non-metabolic adverse events, a secondary aim of the study is to 
assess the extent to which changes in the primary endpoints, measured directly with gold-standard tools, are 
also detectable using surrogate or derivative measures that are commonly available to clinicians. For example, 
an indirect surrogate indicator of insulin sensitivity is triglyceride/HDL ratio.176 Routine blood tests will be 
performed including A1C, complete blood count with differential, comprehensive metabolic panel,, and a 
fasting lipid panel. Blood pressure and a measure of waist circumference will also be obtained. Waist 
circumference is used to indirectly assess abdominal fat. Some studies have suggested the utility of 
anthropomorphic measures as proxies for abdominal fat as measured by abdominal computed tomography 
(CT), although concerns about reliability remain.177-179  
 
Body composition analyses: Body composition will be evaluated at baseline, 6 weeks, and 3 months. 
Percent total body fat and percent total fat-free mass will be determined by DXA (Hologic QDR 1000/w, 
Waltham, MA).180,181 The error of regional fat free mass determination by this technique, as compared with 
computerized tomography, is less than 5%.180,181 
 
Magnetic resonance images of the abdomen will be obtained at baseline and 3 months to directly quantify 
abdominal (subcutaneous and intra-abdominal) adipose tissue mass.182 Images will be acquired on a 1.5-T 
superconducting magnet (Siemens, Iselin, NJ) using a T1-weighted pulse sequence with a TR of 500 msec and 
TE of 12 msec. The imaging matrix will be 256x256, and section thickness will be 8 mm with a 2mm 
intersection gap. Consistent slice localization will be accomplished by using a rigid landmark (i.e., the iliac 
crest) to position the subject in the machine and by using coronal scouting images to identify the site for image 
acquisition (i.e., the L3-L4 interspace). Three cross-sectional images at the level of the umbilicus, one above, 

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and one below the umbilicus section, will be obtained. Twenty-three slices are analyzed by selecting the first 8 
sequential slices beginning at the inferior pole of the most superior kidney and continuing inferiorly. Visceral 
(VAT) and subcutaneous (SAT) adipose tissue surface area (cm2) are calculated for each slice and reported as 
a mean value over the 8 slices.  The image analysis will be performed by using NIH image software and a 
program constructed in the Mallinckrodt Institute of Radiology.  
 
Isotope infusion clamp: Participants will be instructed to begin fasting, except for water, at 2000 the night 
before the study assessment, following a standard meal provided by their parent or caregiver. The following 
morning at approximately 0600, participants will be admitted to the Pediatric Clinical Research Unit for the 
clamp procedure. At approximately 0700, a catheter will be inserted into an antecubital vein of one arm to 
infuse stable isotopically labeled glucose, dextrose and insulin. Another catheter will be inserted into a 
contralateral hand or forearm vein heated to 55 °C using a thermostatically controlled hand-warming box to 
obtain arterialized blood samples.183 At approximately 0800, prior to beginning the tracer infusion, blood 
samples will be taken to obtain baseline measurements of glucose and glycerol enrichments. Then, a 3-h 
primed-constant infusion of [6,6-2H2]glucose in 0.9% NaCl solution (22 µmol/kg prime and 0.25 µmol.kg-1.min-1 
infusion rate) will be used to determine basal glucose kinetics. After 120 min of tracer glucose infusion, a 1-h 
primed-constant infusion of [1,1,2,3,3-2H5]glycerol (1.2 µmol/kg prime and 0.08 µmol.kg-1.min-1 infusion rate) 
will be used to determine basal glycerol kinetics. After the 180 minute basal tracer infusion period, a 
euglycemic, hyperinsulinemic clamp will be initiated and continued for 180 minutes.161 During the clamp, insulin 
will be infused at a rate of 40 mU.m-2.min-1 for 180 minutes (initiated with a two-step priming dose of 160 mU.m-
2.min-1 for 5 min followed by 80 mU.m-2.min-1 for 5 min), to achieve plasma insulin concentrations of 
approximately 90 µU/ml. This plasma insulin concentration provides an optimal level for evaluating insulin’s 
effect on glucose production and lipolysis.184 Euglycemia will be achieved by infusing a variable rate of 20% 
dextrose enriched to approximately 2.5% with [6,6-2H2]glucose to minimize changes in plasma glucose tracer 
to trace ratio.185 The infusion of [6,6-2H2]glucose will be decreased by 50% of basal from 180-360 minutes to 
account for the expected decline in hepatic glucose production.186,187 The infusion of [1,1,2,3,3-2H5]glycerol will 
also be decreased by 50% of the basal rate during the clamp to account for the expected decline in whole-
body lipolytic rate.188 Blood glucose will be measured every 5-10 minutes during the clamp procedure to inform 
adjustments to the dextrose infusion rate, using either a glucose oxidase method via a glucose analyzer 
(Yellow Springs Instruments, Yellow Springs, Ohio 45387 USA) or a validated portable blood glucometer when 
necessary to decrease the total amount of blood drawn in smaller individuals. Blood samples will also be 
collected every 10 min during the last 30 min of both the basal period and the insulin clamp periods to 
determine glucose and glycerol isotopic enrichments and the determination of glucose and glycerol kinetics, 
{Korenblat, 2008 #19307} {Deivanayagam, 2008 #19305} {Magkos, 2009 #19308} as well as plasma hormone 
(insulin, C-peptide, glucagon, growth hormone, epinephrine, norepinephrine, free fatty acid and leptin) 
concentrations. For children < 77 lbs, collection of plasma catecholamines, growth hormone, free fatty acid and 
leptin will be restricted to limit total blood draws to no greater than 3 ml/kg body weight. 183,189 Insulin-stimulated 
percent change for glucose and glycerol kinetics (Glucose Ra and Rd, Glycerol Rd) will be calculated as the 
absolute value of the difference between the kinetic rate calculated during insulin-stimulated versus basal 
conditions.  The resulting value will be divided by the basal condition and multiplied by 100%. Primary Aim 1 
endpoints from the isotope clamp protocol are: 1) Insulin-stimulated % change in Glycerol Ra; 2) Insulin-
stimulated % change in Glucose Ra and 3) Insulin-stimulated % change in Glucose Rd.  
 
Frequently-sampled oral glucose tolerance test (fsOGTT)(Secondary analysis, not for inclusion in the 
primary outcome paper): At baseline, 6 weeks and 3 months, after an overnight (12 h) fast, blood samples 
for plasma glucose, insulin, C-peptide, epinephrine, norepinephrine, glucagon, cortisol, concentrations will be 
obtained immediately at 0 (baseline), 10, 20, 30, 60, 90, and 120 min after ingesting a 75 g oral glucose 
load.190 The new criteria of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus will 
be used to determine subject eligibility. Area under the curve insulin will be used as the endpoint in the 
secondary aim concerning treatment effects on insulin secretion. Subjects under 94 lbs will have fasting labs 
instead of a 6-week fsOGTT to decrease total blood volume drawn over 2-months. 
 
5. Psychiatric/Medical Diagnostic Assessment Methods: 

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General Considerations and Time Frames: Instruments were selected to be comprehensive and to permit 
comparability to those used in other studies in federally funded research projects in child psychiatry. At 
baseline, the time frame will be lifetime. At follow-up appointments, the time frames will be the three-month 
interval since the prior assessment.  
 
Need for Both Parent and Child Informants for Assessment of Child Psychopathology: In child 
psychiatry research, parent and child informants are both needed due to poor mother-child concordance.191-199 
Parent and child reports will be combined by using the most severe rating given by either, in accordance with 
the work of Bird et al.200 Second informants for child aged subjects are usually biological mothers. If the mother 
is incapacitated (e.g., hospitalized for suicide attempt), the father or other guardian who lives with the child is 
the second informant. Different raters are used for the mother and child within each family. Scientifically this is 
important to avoid bias from knowing what the other informant has reported. This procedure also aids 
recruitment and retention, because families greatly appreciate mother and child interviews conducted in 
parallel by two raters, as this saves them time in the research unit.  
 
Missouri Assessment of Genetics Interview for Children (MAGIC): The MAGIC interview is a semi-
structured standardized psychiatric diagnostic interview with six versions: Child, Adolescent, Young Adult and 
Adult are self-report versions, and the Parent and Parent of Young Adult are parent reports on siblings. The 
MAGIC is a revised version of the Diagnostic Interview for Children (5). All MAGIC versions are computerized 
for simultaneous administration and data entry. The MAGIC uses criteria of the DSM-III-R, DSM-IV and ICD-10 
to diagnose child, adolescent, young adult, and adult psychiatric disorders. The interview can be administered 
by telephone or in person. A current paper version exists for all interviews should one be needed.  The 
average length of time to administer the MAGIC is 2 hours for adults and 90 minutes for children or 
adolescents. The MAGIC has a lengthy companion specifications manual (Appendix 2) that is used to maintain 
consistency of administration across interviewers and across studies.  An in-depth, 6-week training course with 
practice sessions is required for all interviewers, followed by a qualifying certification by an experienced 
certified interviewer or by one of the MAGIC authors.  Diagnostic algorithms have been created, tested, and 
verified. The DSM-IV diagnoses include domain impairment and duration requirements.  The specific sections 
included in the MAGIC interviews are: Demographics, Attention Deficit/Hyperactivity Disorder, Oppositional 
Defiant Disorder, Conduct Disorder, Alcohol Abuse, Tobacco and Glue-Sniffing Abuse, Marijuana and Street 
Drug Abuse, Gambling, Depression, Mania, Dysthymia, Separation Anxiety, Panic Disorder, Phobias, 
Generalized Anxiety Disorder, Obsessive Compulsive Disorder, Posttraumatic Stress Disorder, Eating 
Disorders, Premenstrual Dysphoric Disorder, Somatization, Psychosis and Schizophrenia, Psychosocial 
History, Home Environment, Peer and Sibling Relationships, Perinatal History, and Health Services Usage.   
Reliability and prospective stability studies have been completed on the MAGIC; in particular, for diagnosis of 
ADHD, inter-rater reliability for the Child, Adolescent, and Parent versions was excellent, (kappa >.9 for DSM-
IV ADHD subtype diagnoses as well as for the endorsement of the 18 individual DSM-IV Criterion A ADHD 
symptoms). The 18-month prospective stability (done with raters blinded to the initial diagnosis) for a diagnosis 
of ADHD was also good (kappa =.78); kappas for population-defined ADHD subtypes including inattentive and 
combined subtypes were .76 and .67, respectively (6). 
 
Achenbach Child Behavior Checklist/Adult Behavior Checklist (CBCL/ABCL): The CBCL/ABCL serves as 
a general screening measure of behavior problems, competencies, and school functioning, and has well-
established norms. It obtains reports from parents, other close relatives, and/or guardians regarding children’s 
competencies and behavioral/emotional problems. A shorter version of the CBCL/ABCL was created for the 
large Missouri sibship study, which does not include 20 competence items covering the child's activities, social 
relations, and school performance. These shortened versions will be used for this project. The revised 
CBCL/ABCL has 113/126 items that describe specific behavioral and emotional problems, plus one open-
ended item for reporting additional problems. Parents rate their child for how true each item is now or within the 
past 6 months using the following scale: 0 = not true (as far as you know); 1 = somewhat or sometimes true; 2 
= very true or often true. The subscales scored from the CBCL are Aggressive Behavior; Anxious/Depressed; 
Attention Problems; Rule-Breaking Behavior; Social Problems; Somatic Complaints; Thought Problems; and 
Withdrawn/Depressed. The six DSM-oriented scales are: Affective Problems; Anxiety Problems; Somatic 
Problems; Attention Deficit/Hyperactivity Problems; Oppositional Defiant Problems; and Conduct Problems. 

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Wechsler Intelligence Scale for Children, Version 4 (WISC-IV): To confirm the exclusion of mental 
retardation and to give a general estimate of intellectual ability all participants will be administered the 
Vocabulary subtest of either the WISC-IV (children/adolescents) or Wechsler Adult Intelligence Scale (WAIS-
III) 201. Normal scoring protocols will be used to create a standardized score.  
 
Aberrant Behavior Checklist: 202,203 This instrument is a five-factor scale comprising 58 items under the 
categories of (I) Irritability, Agitation, Crying; (II) Lethargy, Social Withdrawal; 204 Stereotypic Behavior; (IV) 
Hyperactivity, Noncompliance; and (V) Inappropriate Speech. The 15-item Irritability subscale includes 
questions about aggression, self-injury, tantrums, agitation, and unstable mood on a scale of 0 to 45. It has 
been estimated that our cut-off of ≥ 18 on this subscale will identify individuals that are 1.3 to 1.5 SD above the 
mean.205,206 
 
Social Responsiveness Scale (SRS): The SRS is a 65-item questionnaire with a 4-point rating scale (“Not 
true” to “Almost always true”) designed for use by parents and/or teachers in which ratings of the frequency of 
typical autistic symptoms across all three DSM-IV symptom domains are obtained. It measures the severity of 
autism spectrum symptoms as they occur in natural social settings. The SRS provides a clear picture of a 
child’s social impairments, assessing social awareness, social information processing, capacity for reciprocal 
social communication, social anxiety/avoidance, and autistic preoccupations and traits. It is appropriate for use 
with children from 4 to 18 years of age. The SRS has a major advantage over other instruments used to 
assess autism spectrum conditions. Rather than providing a “yes or no” decision about the presence of 
symptoms of a given disorder, the SRS measures impairment on a quantitative scale across a wide range of 
severity, from sub-clinical to severe—which is consistent with recent research indicating that autism is best 
conceptualized as a spectrum condition rather than an all-or-nothing diagnosis. Unlike most questionnaire 
instruments, SRS scores are highly correlated between parents and teachers (0.75-0.80), have minimal age 
differences in distribution, and show high test-retest reliability for at least 17 months (0.7-0.85). In addition, 
there are minimal associations between intelligence and SRS scores (correlations on the order of 0.1 to 1.2 in 
both normal and autistic children). 
 
Children's Global Assessment Scale (CGAS): 207,208 This instrument is a global measure of severity based 
on psychiatric symptomatology and impairment of adaptation in family, social, school, and work areas. On this 
scale 0 is worst, 100 is best, and ≤ 60 is definite clinical impairment.  
 
Clinical Global Impressions Scale (CGI): The CGI scale209 is widely used in psychopharmacology research 
across the age span. It will be completed by a blinded rater. At baseline, the severity measure is used. At post-
baseline assessment times, severity and improvement scales are used. At all time points, a severity score of 1 
or 2 is little or no impairment, a score of 3 is mild and a score of  4 is moderate or greater psychopathology. 
At the three-month assessment, scores of 1or 2 are fully or almost fully recovered, score of 3 is minimal 
recovery and scores of  4 are almost no improvement or worsened from baseline. This is the endpoint for the 
secondary aim concerning effectiveness for symptoms of aggression. 
 
Pubertal Status Questionnaire (PSQ):210 This instrument is completed by subjects at least 10 years old. The 
PSQ has demonstrated high reliability with physical examination. Rather than a physical exam, the PSQ relies 
on participant self-report of Tanner Stage by endorsement of the appropriate cartoon representation of the 
respondent’s pubertal status. The PSQ has been accepted by the Washington University IRB for the evaluation 
of pubertal status. 
 
Modified Study Side Effects Scale (SE): This scale collects data on side effects such as sedation, 
galactorrhea and gynecomastia, and has been used in multiple NIH funded treatment studies.  
 
Abnormal Involuntary Movement Scale (AIMS): This instrument is administered to the child and has been 
modified to include instructions on how to examine for cogwheeling and tongue dyskinesias in children.  
 
Barnes Akathisia Scale (BAS): This instrument is administered to the child for the assessment of akathisia. 
 
Simpson-Angus Scale (SAS): This instrument is administered to the child for the assessment of 
pseudoparkinsonianism. 

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Medical Records (MR) and Medical History Form: This information is obtained by telephoning to obtain 
mailing or faxing preference and then mailing or faxing consents to the various physicians, other health care 
providers, and hospitals. Consents accompany mailing materials with prepaid return postage or with 
instructions for faxing materials back to the investigators. The Medical History Form is administered to the 
mother about the child. 
  
Locator Form: This form is completed at baseline to enhance the ability to locate subjects at the three-month 
follow-up. This form includes: (1) Social security numbers of the subject and each parent. (2) Work and home 
telephone numbers and addresses of both biological parents, step-parents and parents residing outside of the 
household. (3) Names, telephone numbers, and addresses of spouses, significant others, siblings, 
grandparents, close friends and neighbors of the participants. (4) The name, address and telephone number of 
the participant’s school, and their teacher’s name, as well as a check box option asking whether it is 
acceptable to contact the school.  In our experience families do not object to supplying this information and 
understand that it will only be used to locate them without -under any circumstance- revealing why the 
University is looking for them other than that they are study participants.  
 
Diet and Exercise Behaviors Questionnaire: 
This retrospective phone questionnaire will assess participants’ dietary habits and activity levels during the 
course of participation, and related changes.  Study participants and their guardians will be given a 17-item 
questionnaire adapted from diet and exercise questionnaires used in similar research studies. 211 212 213 214 215 
216 217 218 219 220 Parents of subjects age 12 and under will be asked to complete the questionnaire, and, for 
subjects age 13 and up, the parent will be the default recipient of the questionnaire as well.  Parents of 
subjects ages 13 and up will be asked at the beginning of the questionnaire whether their teenager has a 
significantly better idea of their own diet and exercise patterns, and, if they answer yes, their teenager will be 
contacted and asked verbal consent for the diet and exercise questionnaire at a later time.  For subjects who 
have already enrolled in the MEAC study, consent will be implied via continuation of the phone interview, and 
verbal consent will be asked at the beginning of the phone call as well.  All newly enrolled subjects will be 
formally consented for permission to be contacted during or after study participation. Subjects will be reminded 
that participation is voluntary, that their participation in the main MEAC study will be unaffected if they choose 
not to participate in this questionnaire, and that they may choose not to answer any question or end their 
participation in this segment at any time. 
 
Psychotherapy Attitudes and Perceptions Questionnaire: 
This retrospective phone questionnaire will assess parental attitudes and experiences with psychotherapy, and 
how these points of view might influence their decision to participate in optional family/individual psychotherapy 
offered to all study participants at the time of enrollment.  Only guardians will be asked the 22-item 
questionnaire, adapted from questionnaires assessing attitudes and perceptions towards psychotherapy in 
similar studies of children with psychiatric diagnoses.221 222 223 224 225 For subjects who have already completed 
the MEAC study, consent will be implied via continuation of the phone interview.  All newly enrolled subjects 
will be formally consented for permission to be contacted during or after study participation. Subjects will be 
reminded that participation is voluntary, that their participation in the main MEAC study will be unaffected if 
they choose not to participate in this questionnaire, and that they may choose not to answer any question or 
end their participation in this segment at any time. 
 
Family Environment Scale: 
To characterize the family’s perception of expressed emotion within the family during study 
participation, the Family Environment Scale (FES) will be included. FES form R, 226 which measures 
perceptions of family environment, is a 90 question True/False questionnaire with ten total scoring 
domains encompassing relationship (Cohesion, Expressiveness, Conflict), personal growth 
(Independence, Achievement Orientation, Intellectual-Cultural Orientation, Active-Recreational 
Orientation, Moral-Religious Emphasis), and system maintenance (Organization, Control). Caregivers 
of newly-enrolled MEAC subjects will be given the FES over the phone at baseline and in paper form 

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at the 3-month time point. Caregivers of patients who have already completed the MEAC study will be 
given the FES once as a retrospective phone questionnaire. 
 
Establishing DSM-IV Diagnoses: All research materials (assessment instruments, school reports, agency 
records, pediatrician/medical charts), are reviewed in consensus conferences to establish final diagnoses.  
 
STUDY ASSESSMENT INSTRUMENTS 
Instrument 
Second 
Informant 
Child 
Missouri Assessment of Genetics Interview for 
Children (MAGIC) 
X 
X 
Child Behavioral Checklist 
X 
 
WISC-IV 
 
X 
Aberrant Behavior Checklist 
X 
 
Social Responsiveness Scale 
X 
 
Children's Global Assessment Scale 
X 
X 
Clinical Global Impressions Scale 
X 
X 
Missouri Adolescent Female Twin Study (MOAFTS), 
trauma subscale 
X 
X 
Duke Pubertal Status Questionnaire 
 
X 
Modified Side Effects Scale 
X 
X 
Abnormal Involuntary Movements 
 
X 
Barnes Akathisia Scale 
 
X 
Simpson-Angus Scale 
 
X 
Medical History Form 
X 
 
Locator Form 
X 
 
Diet and Exercise Behaviors Questionnaire: 
 
X 
X 
Psychotherapy Attitudes and Perceptions 
Questionnaire: 
 
X 
X 
Family Environment Scale 
X 
 
 
 
Blinding, Training and Maintaining Interrater Reliability of Research Clinicians: Raters are blind to 
treatment group assignment. Families will be instructed not to reveal the drug that child has been on. This has 
worked well in multiple other studies. Research staff are trained to interrater reliability and recalibrated 
annually. Raters have virtual 100% agreement on diagnostic categories and symptom severity ratings five 
times in a row as both interviewer and observer.  
 
Appointment Times and Reimbursements for Effort and Inconvenience: Reimbursements will be paid at a 
level commensurate with similar studies in the St. Louis area. Free parking is provided in a safe underground 
area. Each family receives reminder phone calls in the days and weeks prior to the follow-up visit from the 
research study staff.  
 
6. Blood for Future Genetic Studies 
 

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Blood samples for future genetic studies will be obtained. These will only be obtained from families who 
specifically give permission on a separate genetic study consent form. These consent forms (child and 
parent/related guardian (e.g. grandparent)) will be separate from that of the metabolic study to reinforce that 
participation in the metabolic study does not mandate participation in the genetic study. Separate consents are 
also needed to accommodate the special language required in consent forms for genetic studies. For families 
consenting to future genetic studies, an RNA sample (2.5ml) will be obtained from the child at baseline and at 
12 weeks. DNA samples, 10 ml each, will be obtained from the child and both parents when possible.  All 
genetic samples will be stored at -80 C until processed or for up to 20 years. The samples will be labeled only 
with the subject study ID number. 
 
7. Plasma Assays:  
 
All analytical procedures are routinely performed in Washington University laboratories including the GCRC 
Core Laboratory. 
 
 
a. Plasma metabolites: Plasma glycerol and glucose will be recovered in the aqueous fraction following 
solvent extraction of plasma lipids and converted to the HFB derivative for GC/MS. Plasma glucose 
concentration will be determined by the glucose oxidase method. Plasma and microdialysis glycerol 
concentrations will be determined by an enzymatic assay (Sigma). Plasma insulin and C-peptide,227 
glucagon228 and cortisol229 concentrations will be measured by radioimmunoassay. Plasma catecholamine 
concentrations will be determined by a radioenzymatic method developed by our GCRC core laboratory.230 
Amino acids will be isolated from plasma by cation exchange chromatography and converted to HFBPr 
derivatives231 for GC/MS. Plasma fatty acids will be recovered by solid phase extraction as recently described, 
and analyzed by GC/MS and quantitative GC.232 VLDL will be isolated from 3 mL of fresh plasma by 
ultracentrifugation at d=1.006 g/mL and stored at –70°C for further processing. VLDL triglyceride 
concentrations will be determined by enzymatic assay (Sigma Chemical Co., St. Louis, MO). TG will be 
isolated from the lipid extract by TLC, and fatty acids recovered as methyl esters and glycerol recovered as the 
HFB derivative for GC/MS analysis as previously described.233,234 
 
 
b. GC/MS analyses of glycerol: Enrichment of HFB-[2H5]glycerol will be measured by electron impact 
(EI) by monitoring a fragment (M-O-HFB, m/z 467/472) which retains all 5 initial deuteriums in the [2H5]glycerol 
tracer. Washington University investigators have recently demonstrated that by incorporating concentration 
dependency into a standard curve approach one can decrease between-run variation in values to <1%.235 
HFB-glycerol and glucose will be analyzed by EI using ions at m/z 467/472 ([2H5]glycerol) and m/z 519/521 
([2H2]glucose). For all GC/MS analyses, instrument response will be calibrated using standards of known 
enrichment. 
 
 
 8. Calculations 
 
 
a. Whole-body substrate (glycerol and glucose) Ra and Rd: During isotopic and physiologic steady 
state, Steele’s steady state equation236 will be used to calculate substrate Ra and Rd. Steele’s non-steady-
state equation236 will be used to calculate substrate Ra during metabolic non-steady state conditions. 
 
 
 b. Whole body substrate oxidation: The rate of fat and glucose oxidation will be calculated using the 
stoichiometric equations described by Frayn.237 
  
9. Data Management and Analysis Techniques: 
 
Data management and quality control:  The data management and quality control process will begin with the 
initial design of data collection forms (when needed) and data capture forms in SPSS (IBM SPSS; Armonk, 
NY). Quality control measures will include the flagging of out-of-range and inappropriately missing data items, 
the identification of forms that have not been completed for a particular subject, and confirmation that enrolled 
subjects satisfy eligibility criteria.  To further ensure accurate and complete data, all data collection personnel 
will be asked to review forms as soon as they are filled out so that problems related to missing or illegible data 
items can be quickly corrected.  Because forms will include the initials of the data collection person who 
completed the form, it will be easy to contact the appropriate individual when problems are identified. 

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Data analysis: Primary and secondary analyses for adiposity and insulin sensitivity outcomes were conducted 
using Intent-to-Treat (ITT) analyses (IBM SPSS; Armonk, NY) including randomized subjects. Primary 
outcomes were change in DEXA-measured adiposity (DEXA % total fat) and clamp-derived insulin sensitivity 
at muscle (percent change in Glucose Rd), with secondary outcomes of change in MRI measured adiposity 
and percent change in Glucose Ra and Glycerol Ra. Primary analysis for change in DEXA % fat used a 
likelihood-based mixed-effects model using time (0, 6 and 12 weeks) and medication group as independent 
variables, with Toeplitz covariance structure specified, based on Bayesian information criteria (BIC). The 
primary outcome analysis for insulin sensitivity, as well as the secondary outcome analyses for adiposity and 
insulin sensitivity, used repeated-measures analyses of covariance (ANCOVA) with baseline values of the 
dependent variables as the covariate (to address potential baseline influence on outcomes) and time and 
treatment condition as independent factors, testing for time by treatment condition as well as covariate 
interactions. When time by treatment interactions were significant, contrasts were used to test comparisons of 
interest; when not significant, treatment condition was removed from the model to calculate the main effect of 
time and any interactions. MRI abdominal fat models were run with compartment (subcutaneous versus 
visceral) as an additional 2-level factor, to test for time by compartment and time by treatment by compartment 
interactions. Exploratory analyses tested whether the effects of age, stimulant use, gender and race altered 
primary results, re-running primary analyses with an additional two- or three-level factor (e.g., yes/no 
stimulant); these exploratory analyses were corrected for multiple tests (Bonferroni; 0.05/4= 0.0125). Other 
exploratory analyses used ANCOVA as above (week 0 and 12) to support interpretation of primary/secondary 
measures (e.g., DEXA lean, clamped insulin concentration) or for clinical context (e.g., BMI %ile, psychiatric 
symptoms), with ANOVA used to test the effect of time within individual treatment groups. Effect sizes 
(Cohen’s d) were calculated for primary and secondary outcomes. 
 
Exploratory analyses include correlating the 12 week change in measures of insulin sensitivity and the 
corresponding change in percent fat with a view towards a more precise understanding of the degree to which 
changes in insulin sensitivity can be explained by changes in fat mass. Analyses of covariance that employ 
changes in insulin sensitivity as the dependent variable and include changes in percent fat as a predictor will 
provide estimates of the percent of variability explained (R2) both by the change in fat mass alone and by the 
combination of this measure and the covariates already noted. These analyses will also provide information 
about the degree to which the covariates are independently predictive of changes in insulin sensitivity. Other 
exploratory analyses will involve the role of regulatory hormones such as cortisol, glucagon, and leptin. Since 
these hormones will be measured at baseline and at 6 and 12 weeks, mixed model repeated measures 
analysis of variance will evaluate the effect of treatment on these hormones and will provide information about 
the pattern of change during the 12 week follow up period. A final set of possible analyses will involve the use 
of logistic regression to determine factors that are associated with the presence of metabolic syndrome at 
baseline, to be performed only if baseline levels are meaningful. While we will also use logistic regression to 
explore factors that are predictive of the development of metabolic syndrome during follow-up and will evaluate 
between group differences in the incidence of syndrome development, we do not anticipate that large numbers 
of subjects who do not have this syndrome will develop it during the relatively brief 12-week follow-up. Thus, 
we expect that the statistical power associated with these latter analyses will be small and view them as 
exploratory hypothesis generating analyses. 
 
In all of the analyses we perform, we will give careful attention to the appropriateness of the statistical 
procedures that are employed. Thus, all analyses will be preceded by graphical assessments of the data to 
evaluate the distribution of particular variables and to determine whether outliers are present. When we 
perform analyses of covariance, we will routinely evaluate regression residuals to ensure that the model is 
appropriate. Similarly, we will evaluate the Hosmer-Lemeshow goodness of fit statistic to assess the 
appropriateness of logistic regression models. When variables are poorly distributed and assumptions for a 
given analytic procedure are violated, we will explore and implement remedial measures that include data 
transformations and semi-parametric approaches based on the ranks of the data if satisfactory data 
transformations cannot be found. We have defined only two primary specific aims with the primary comparison 
being baseline to 12 weeks both because of the importance of those aims and to minimize multiple 
comparisons concerns. Because of the multiple comparisons issue, p-values associated with the secondary 
and exploratory analyses we perform will be interpreted cautiously as hypothesis-generating rather than as 
hypothesis-confirming. 

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Sample size and statistical power: The target sample size is 80 subjects in each of the three study groups. 
Prior data from young adults (i.e., NIMH and industry funded first episode studies where change in % body fat 
may be crudely estimated from known change in body weight and the assumption that increases occur in body 
fat rather than lean muscle), which may underestimate changes in children, suggests that percent body fat 
increases of 10 + 4% may occur in the olanzapine group, 5 + 4% in the risperidone group and 0 + 4% in the 
aripiprazole group (e.g., 10 lbs fat increase in a 70 lb subject who starts at 20% body fat yields 30% body fat). 
Based on this, the power for a two-sided test at the 0.05 level of significance to compare any one group with 
any other group will be essentially 1. This substantial power will permit us to test hypotheses about changes in 
percent body fat in important subgroups that are defined by age, gender, race, and whether the subject is or is 
not taking stimulants (e.g., planned younger off-stimulant subgroup sample size is 20), as well as explore other 
predictors. Because we do not have reliable prior data on likely pooled baseline to endpoint or between-group 
differences in insulin sensitivity measures like the glucose disappearance rate, we base our power 
computations on the effect size that is detectable with 80 subjects per group. Using a two-sided test at the 0.05 
level of significance, the target sample size implies that we can detect a between group difference in the mean 
disappearance rate equal to half of a standard deviation with a power of 0.88.  
 
A secondary interest of the proposed research will be whether the younger prepubescent children between the 
age of 6 and 11 respond differently from children between the ages of 12 and 18. In our existing clinic system, 
a sample of 527 children who had the study eligible diagnostic codes indicated that 69.3% were in the younger 
age group. Even assuming a worst-case scenario that we fail in our planned efforts at over-recruitment to the 
older age groups and that these percentages continue as we recruit for the planned study, we anticipate that in 
each study arm, we will have about 56 subjects in the younger age group and 24 in the older age group. With 
these numbers and using within group analyses, we will have a power of 0.81 to detect a response rate 
difference in an outcome measure in younger as compared to older individuals that is 70% of a standard 
deviation, with the power increasing to 0.90 if the effect size is 80% of a standard deviation. If there are no 
statistical interactions between treatment group and age category, it will be reasonable to combine all three 
groups in asking about age-related differences so that worst case sample sizes will be 168 in the older group 
and 72 in the younger group. This dramatically increases statistical power to the extent that we will be able to 
detect an effect size equal to 40% of a standard deviation with probability of 0.81. The power increases to 0.94 
under this no interaction assumption if the effect size comparing age differences is 50% of a standard 
deviation. 
 
 
 
E. Human Subjects  
Protection of Human Subjects 
 
1. Risks to the subjects: 
 
a) Human Subjects Involvement and Characteristics: 
 
We will enroll 325 eligible subjects with clinically significant aggression symptoms, as defined by a score of ≥18 
on the Irritability subscale of the Aberrant Behavior Checklist 205, 206 in the context of one or more Axis I DSM 
IV207 childhood psychiatric disorders, including conduct disorder, oppositional defiant disorder, disruptive 
behavior disorder, autism, pervasive developmental disorder, attention deficit disorder, bipolar affective 
disorder and schizophrenia, who assent to participate and whose guardian(s) give informed consent for 
participation in the study. Individuals with clinically significant suicidal ideation will not be included. We plan on 
screening up to four times the enrollment target number in order to achieve recruitment targets (Study Visit 1). 
All subjects will be 6-18 years of age and will include all races and ethnic groups and both genders, with 
targeted enrollment reflecting the overall gender distribution of males and females for “externalizing” disorders 
(i.e., 2:1, male:female).208.  By initially enrolling 108 subjects in each of 3 treatment groups described below, 
we estimate this will enable us to obtain a study population of 80 subjects per group for a total of 240 
analyzable subjects who complete the protocol (assuming 10% attrition).  
 

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The inclusion criteria are: i) age 6-18 years ii) generally healthy and a score of ≥18 on the Aberrant Behavior 
Checklist for irritability in the context of one or more Axis I DSM IV childhood psychiatric disorder, including 
conduct disorder, oppositional defiant disorder, disruptive behavior disorder, autism, pervasive developmental 
disorder, attention deficit disorder, bipolar affective disorder and schizophrenia; iii) Children’s Global 
Assessment Scale (CGAS) Score ≤ 60; iv) not previously treated with an antipsychotic; individual subjects with 
a remote, brief prior antipsychotic exposure may be considered for enrollment by the PI on a case by case 
basis; v) patient assent and informed consent obtained from the parent or guardian; vi) no clinically significant 
(based on PI determination) changes in permitted medications (e.g., stimulants, SSRI’s) for approximately 1 
month prior to Baseline Evaluations based on PI discretion;  
  
The exclusion criteria are: i) active suicidality or a primary diagnosis of major depressive disorder; ii) any 
lifetime use of antipsychotics; individual subjects with a remote, brief prior antipsychotic exposure may be 
considered for enrollment on a case by case basis by the PI; iii) the presence of any serious medical disorder 
that may confound the assessment of relevant biologic measures or diagnoses, including: significant organ 
system dysfunction; endocrine disease, including type 1 or type 2 diabetes mellitus; coagulopathy; anemia; or 
acute infection; all based on PI discretion; iv) subjects regularly taking within the last 3 months any glucose 
lowering agent, lipid lowering agent, exogenous testosterone, recombinant human growth hormone, or any 
other endocrine agent that might confound substrate metabolism, oral glucocorticoids (glucocorticoid nasal 
spray and inhalers are permitted), sedating antihistamines (non-sedating antihistamines such as but not limited 
to Claritin (loratadine) and Zyrtec (cetirizine) are permitted), and certain mood stabilizing agents, as some 
medications may themselves worsen or otherwise alter weight gain, glucose and lipid regulation or otherwise 
make it difficult to assess the effects of the antipsychotic alone; (note that exposure to many psychotropic 
agents including stimulants and SSRI’s is permitted in order to maintain the generalizability of the sample); v) 
IQ < 70 (based on school records and/or evaluation by clinician); vi) current substance abuse; vii) past history 
of, or current dyskinesia; viii) stimulant dosage significantly higher (per PI judgment) than the equivalent of 
approximately 2 mg/kg/day methylphenidate equivalent dose.  
All recruited subjects meeting inclusion/exclusion criteria will have Baseline Evaluations, followed by 
assessments at 6 weeks and 12 weeks. Subjects will be randomized to prospective treatment with one of 3 
newer atypical antipsychotic medications that currently are either used extensively in children, or are showing 
rapid growth or growth potential in this population. Specifically: a) 40 subjects who have never been treated 
with an antipsychotic will be randomized to open-label treatment with risperidone (Risperdal). Of those 
subjects, 20 will be under the age of 11 years, 6 months and 20 subjects will be over the age 11 years, six 
months; b) 40 subjects who have never been treated with an antipsychotic but are receiving a stimulant will be 
randomized to open-label treatment with risperidone (Risperdal). Of those subjects, 20 will be under the age of 
11 years, 6 months and 20 subjects will be over the age 11 years, six months; c) 40 subjects never treated with 
an antipsychotic will be randomized to open-label treatment with aripiprazole (Abilify). Of those subjects, 20 will 
be under the age of 11 years, 6 months and 20 subjects will be over the age 11 years, six months; d) 40 
subjects who have never been treated with an antipsychotic but are receiving a stimulant will be randomized to 
open-label treatment with aripiprazole (Abilify). Of those subjects, 20 will be under the age of 11 years, 6 
months and 20 subjects will be over the age 11 years, six months; e) 40 subjects never treated with an 
antipsychotic will be randomized to open-label treatment with olanzapine (Zyprexa). Of those subjects, 20 will 
be under the age of 11 years, 6 months and 20 subjects will be over the age 11 years, six months; f) 40 
subjects receiving a stimulant but who have never treated with an antipsychotic will be randomized to open-
label treatment with olanzapine (Zyprexa). Of those subjects, 20 will be under the age of 11 years, 6 months 
and 20 subjects will be over the age 11 years, six months. 
  
 
b) Sources of Materials: 
 
Research materials will be obtained by psychiatric interview, physical exam, medical record review, 
observation, indirect calorimetry by using a metabolic cart and a ventilated hood system (Vmax 29, 
SensorMedics, Yorba Linda, CA), DXA and MR imaging of body tissue, and collection of whole blood, as 
described in the Experimental Design Methods section of this application. The specimens will be obtained 
solely for research purposes. Although these generally will be obtained specifically for the purposes of the 
study, use will be made, where appropriate, of existing records and data obtained as part of routine clinical 
care. Study samples and data sheets will be coded with an identification number for each subject. All of the 

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data from individual subjects will be maintained confidentially and their names and identities will not be 
disclosed in any published document. 
 
c) Potential Risks: 
 
There are no physical risks associated with the psychiatric diagnostic and assessment procedures or clinical 
ratings. Some subjects may find the questions to be boring or difficult to answer and thus mildly distressing. 
Minimal risks exist for the collection of sensitive information on subjects. Although we will not ask about sexual 
function in this study, subjects will be asked by a physician or nurse to complete a Pubertal Status 
Questionnaire (PSQ). This is an IRB approved, age-appropriate questionnaire used to assess pubertal status. 
The potential physical risks of this study are small. The risks of blood drawing and catheter insertion include 
discomfort, minimal bleeding or infection at the site of insertion. However, sterile technique will be used and 
subjects with coagulopathy will be excluded. Prior to phlebotomy or placement of an intravenous catheter, a 
topical anesthesia will be offered to participants to minimize pain at insertion site and decrease the 
psychological affect associated with any invasive phlebotomy or intravenous catheter placements. The 
associated side effects of the topical anesthesia are very rare. The most common side effects are irritation, 
redness, itching, or rash. These side effects usually do not require medical attention. Regardless of the 
severity, a physician will evaluate all side effects. The risks of infusing stable isotope tracers include the 
possibility of pyrogen response or infection. However, all solutions are tested for pyrogens and sterility before 
infusion and are administered under strict aseptic conditions. There are no known short- or long-term risks 
associated with the infusion of the isotopes themselves. The risks of euglycemic clamping are transient hyper- 
or hypoglycemia that might lead to nausea, headache or feeling sweaty or shaky, however, glucose levels are 
very closely monitored and kept within + 10 mg/dl of a target of 95 mg/dl, so that such adverse events have not 
occurred in any of the approximately 300 clamps conducted by the P.I. to date. The risks of DXA scanning 
include discomfort while lying on the scanning table, and low-dose x-ray exposure (less than a standard chest 
film). There are no known direct risks of MR scanning in the absence of having some metal in the body, a basis 
for exclusion from this aspect of the study. Some people who feel uncomfortable in small or confined spaces 
may feel similarly when they are positioned in the MR machine or under the indirect calorimetry ventilated 
hood. The hood may also cause some feelings of discomfort such as being too warm and/or facial sweating. 
The patients who are randomized to an antipsychotic medication will not do this exclusively for the purpose of 
this study, but for some clinical indication, and thus any risk associated with this switch is part of their routine 
clinical care, rather than research. Thus, they may be at risk for clinical adverse events associated with the 
newly started treatment, including the risk that their symptoms do not respond as well or temporarily worsen. 
The potential risk that subject reimbursement could be experienced as coercive has been addressed and 
minimized per our IRB. The cost for reimbursing subjects is based on our experiences recruiting patients into 
studies of similar complexity and inconvenience to subjects, with this level of reimbursement approved by our 
IRB and consistent with that of other similar projects in children at this institution. In addition, this project will be 
conducted on our GCRC, where a further layer of protection for human subjects is in place in the form of the 
Research Subject Advocate (RSA) Program, directed by NIH (NCRR) guidelines. The GCRC RSA will closely 
monitor this project, including the obtaining and maintenance of informed consent, through routine meetings 
with subjects and parents and attendance at some of the planned study visits. 
 
2)  Adequacy of Protection Against Risks 
 
a) Recruitment and Informed Consent:  
 
All key personnel involved in the design and conduct of the research involving human subjects will 
receive the required education on the protection of human research participants prior to funding of this project. 
Procedures to recruit subjects for the protocol and obtain their informed consent are conducted and supervised 
by the P.I. Targeted educational and recruitment in-services will be conducted at all appropriate facilities in 
order to make providers and administrators aware of this project and to assist in identifying eligible subjects. 
Clinicians and administrators at these sites are informed about the purpose, procedures, risks, and benefits of 
the protocol, as well as the inclusion and exclusion criteria, so that they can best discuss the research project 
with individual patients/guardians that might be eligible and interested, and make appropriate referrals for study 

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screening. The P.I. or Collaborators will discuss the study, including the risks and benefits of participation, with 
relevant potential subjects, their guardians, and relevant family members to obtain informed consent from 
interested individuals. Informed consent will be obtained from the patient/guardian whenever possible, or with 
assent from the patient and consent from the guardian. Guardians will be included in all informed consent 
processes. The consent form, which incorporates HIPAA authorization, contains a description of the purpose 
and procedures, risks, procedures to minimize them, and possible benefits. Subjects (and their guardians) are 
assured that they are free to withdraw consent and discontinue participation without prejudice to their current 
or future medical care. The objectives of the project, all of the requirements for participation, and any possible 
discomforts and risks will be clearly explained to the subjects orally and in writing in lay terms which they are 
able to comprehend. The subject/guardian must sign an informed consent form, approved by the Washington 
University School of Medicine Institutional Review Board, before they can participate in the study. 
 
b) Protection Against Risk:  
 
The risks of breaching confidentiality will be strictly limited by the use of locked and restricted access to 
data as well as numbers rather than names in the database that will be created for this project. No identifiers 
will be included in any computer files or reports generated by this study. All key personnel involved in the 
design or conduct of research involving the human subjects will receive the required education on the 
protection of human research participants prior to funding of this project.  
Metabolic Measures-The discomfort associated with blood drawing or catheter placement is usually 
mild and brief; should it persist, participation will be discontinued. All blood will be drawn on the General 
Clinical Research Center (GCRC), a well-staffed medical inpatient and outpatient facility within the Washington 
University Medical Center. The risk of adverse events during the blood drawing and IV access procedures will 
be monitored by the nurse, who is in attendance at all times; treatment is facilitated by the extensive medical 
resources available on the GCRC. The risks of blood drawing and catheter insertion will be minimized by use 
of sterile technique and the exclusion of subjects with coagulopathy. The risks of infusing stable isotope tracers 
will be minimized by testing all solutions for pyrogens and sterility before infusion and will be administered 
under strict aseptic conditions. The risks of euglycemic clamping will be minimized by monitoring glucose 
levels very closely and keeping them within + 10 mg/dl of a target of 95 mg/dl; no such adverse events have 
occurred in any of the approximately 300 clamps conducted by the P.I. to date. Should the need for medical 
attention arise, all the resources of a large teaching hospital are available for subject evaluation and treatment. 
Complete physical examinations will be performed to screen out subjects who may be at potential risk. The 
infusion studies will be conducted under the supervision of a licensed physician. The GCRC is equipped with a 
defibrillator and all appropriate emergency medications. Subjects with known or suspected heart disease will 
not be accepted into the study. A resting ECG will be obtained and read by a physician prior to the start of the 
studies. Any significant ECG abnormality at rest will lead to automatic exclusion of the subject from the project. 
Prior to participation in the MR scan, subjects are carefully screened for the presence of any metal objects in 
their body; the presence of metal objects are a basis for exclusion from this part of the study. Any physical or 
emotional discomfort with any procedures will be handled by allowing patients to stop and rest, or discontinue 
the procedure whenever they desire. Subjects enrolled into the randomized open-label treatment with 
risperidone, olanzapine, or aripiprazole will be chosen carefully in collaboration with treating physicians and 
psychiatrists, so that antipsychotics are added with adequate titration schedules and careful dose selection. 
These subjects will be closely monitored by their primary treating physician and supervised by Drs. Newcomer, 
Nicol, and Haupt, in order to minimize any adverse effects of adding antipsychotic therapy. Clinical care will be 
supplemented to a level above what is routine in clinical practice, using weekly safety visits either in person or 
via phone by the research staff in collaboration with Ms. Schweiger. Close follow-up will enhance compliance 
with prescribed medications and rapid clinical response to difficulties or need for medication adjustment. 
Subjects with increased risk factors at baseline or who have developed increased risk factors during the 12-
weeks of study medication will have added study visits at 3-weeks and/or 9-weeks and will be given a home 
monitoring kit for weekly testing. (See Data and Safety Monitoring Plan).  A Data and Safety Monitoring 
Committee has been developed for this protocol.  
 
 
3-month post study follow-up for a subset of subjects completing the 12-week study 
To assess end of study recommendations to parents and treating clinicians, a subset of subjects will be 
evaluated who have had increases in metabolic risk factors (e.g. 10% or greater increase from baseline body 
weight, fasting triglyceride levels greater than or equal to 250mg/dL at 6 or 12-weeks and /or a fasting or post-

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load glucose of greater than or equal to 100 mg/dL or 140 mg/dL, respectively, at 6 or 12-weeks). Each subject 
will be evaluated individually for these criteria at the end of the 12-week study to determine which subjects 
would meet criteria for an additional safety visit. Those subjects meeting criteria and consenting/assenting 
would be scheduled for an interim medical history and measurement of weight, height, waist circumference, 
fasting glucose and fasting lipid profile. Clinically significant abnormal findings would be discussed with the 
parent(s) and relevant clinicians.  
 
Behavioral Assessments- Only highly trained research staff or physicians will be utilized to collect data 
and these individuals will be experts in confidential and professional interaction with study participants. 
Participants will be informed in the informed consent document that any suicidal or homicidal information 
obtained from a child/adolescent will be shared with parent(s) to protect the life of the child/adolescent. If a 
child/adolescent is found to be suicidal or homicidal during any evaluation, the individual performing the 
evaluation will immediately notify the parent or guardian and will contact one of the study physicians. The 
research staff will provide the family with appropriate mental health care referrals, if the family does not already 
have a mental health caretaker. If the suicidal or homicidal participants are 18 years old, precautions and 
referrals will be given directly to the participant and to the participants’ adult household members. If the 
participant is the sole adult household member and suicide/homicide is not deemed to be imminent, 
precautions and referrals, including emergency room contacts, will be provided. If any participant is deemed to 
be imminently suicidal and/or homicidal, 911 will be contacted as soon as possible. Participants will also be 
informed in the informed consent document that a request for a referral for professional care will be provided 
by the research staff. To protect against any misuse of knowledge about study participation, participants will be 
informed in the informed consent document that employers or insurers could act negatively if they learned of 
the study participation. Furthermore, participants will be informed that they may choose not to tell their health 
care providers or insurers about their study participation. Participants will also be told that the study will be 
covered by a federal certificate of confidentiality, which protects against subpoenas of the research materials. 
 
3) Potential Benefits of the Proposed Research to the Subjects and Others:  
 
There are minimal risks associated with participation in this protocol. Even though most subjects will 
derive little immediate benefit from participating in this study, they will receive both glucose and lipid 
evaluations, physical exams, and routine medical screenings that may supplement their usual level of clinical 
care, as well as compensation for their time and inconvenience. The potential benefits include gaining a better 
understanding of the effects of antipsychotic medications, glucose and lipid metabolism and adiposity in CD 
that may ultimately improve our ability to prevent and/or manage the adverse effects of newer, and otherwise 
beneficial, antipsychotic treatments as well as benefits from the knowledge that they have contributed to 
learning about childhood onset emotional illness. Such benefits far outweigh the relatively small risks involved 
in this protocol.  
 
4) Importance of the Knowledge to be Gained:  
 
This study will provide a much-needed understanding of the long-term effects of antipsychotic 
medications on glucose regulation, and ways to address the risk of diabetes and other cardiovascular disease 
in young patients treated with antipsychotics. Results of this investigation may also help to inform doctors 
about the risk of treatment-related changes in glucose regulation and cardiovascular risk associated with 
certain antipsychotic agents. These benefits to society as well as the benefits to the subject of gaining 
important information about his/her health status from the results of these tests far outweigh the minimal risks 
involved in this protocol. 
 
Females and Minority Inclusion in Clinical Research 
Inclusion of Females:  
 
Females will be included. 

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Inclusion of Minorities:  
All races/ethnicities represented in the St. Louis region will be recruited, and groups will be balanced for 
ethnicity based on the ethnic composition of the St. Louis area. 
Patients will be recruited from clinics and private psychiatrists in the St. Louis area. In past studies at 
this university, 50% of our subjects have been recruited from St. Louis City (43.8% White, 51.2% Black or 
African American, 2.0% Asian, and 0.3% American Indian or Alaskan Native, 0.8% other race, 2.0% Hispanic 
or Latino origin; 2000 Census of Population and Housing, U.S. Census Bureau); and 50% have been recruited 
from St. Louis County (76.8% White, 19.0% Black or African American, 2.2% Asian and 0.2% American Indian 
or Alaskan Native; 1.4% Hispanic or Latino origin; 2000 Census of Population and Housing, U.S. Census 
Bureau). This 50/50 recruitment represents an over sampling of the less populous city population in the St. 
Louis area, but maintains the generalizability of our samples to most national metropolitan areas. Based on 
these percentages, the proposed enrollment is as follows: 60.3% White; 35.1% Black or African American; 
2.1% Asian; 0.2% American Indian or Alaskan Native, and 1.7% Hispanic or Latino origin. Based on the 
gender and minority enrollment of past studies in this laboratory, we do not anticipate any difficulty with 
obtaining a subject sample of this composition using our proposed recruitment techniques. See 
"Targeted/Planned Enrollment Table" below. 
 
 
 
Targeted/Planned Enrollment Table 
 
Study Title: Metabolic Effects of Antipsychotics in Children 
 
Total Planned Enrollment: 325 
 
TARGETED/PLANNED ENROLLMENT: Number of Subjects 
Ethnic Category 
Sex/Gender 
Females 
Males 
Total 
 Hispanic or Latino 
1 
4 
5 
 Not Hispanic or Latino 
109 
211 
320 
 Ethnic Category Total of All 
Subjects* 
110 
215 
325 
Racial Categories 
  
 American Indian/Alaska Native 
0 
 1 
1 
 Asian 
3 
5 
8 
 Native Hawaiian or Other Pacific 
Islander  
0 
0 
0 
 Black or African American  
39 
77 
116 
 White 
68 
132 
200 
 Racial Categories: Total of All 
Subjects * 
110 
215 
325 

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*The “Ethnic Category Total of All Subjects” must be equal to the “Racial Categories Total of  
All Subjects.” 
 
Participation of children  
 
The proposed study is for children and young teenagers. Separate study of this age group is warranted 
because clinical, developmental, and neurobiological factors are sufficiently different in the pediatric age group.  
 
Data and Safety Monitoring Plan 
 
Although this proposal does not carry significant risk and is not a Phase III clinical intervention study 
requiring a Data and Safety Monitoring Committee (DMC), we have elected to appoint a committee as an 
added safeguard for our minor participants.  
 
 During the study, we have added extra safety measures and testing for those subjects who have at 
baseline or who have developed by 12 weeks on study medication the following: 
a) Fasting blood glucose greater than or equal to 100 mg/dL or post-load glucose of greater than or 
equal to 140 mg/dL by SureStep Pro, Yellow Springs Instrument (YSI) or laboratory plasma glucose 
assay. 
b) Rapid weight gain of greater than 10% of baseline weight by 6-weeks on study medication. 
c) Increased fasting triglyceride levels of greater than or equal to 250 mg/dL. 
 
Safety monitoring will be enhanced for these subjects by: 
a) Adding fasting blood glucose, fasting blood lipid, TPR & B/P and weight/height/waist circumference 
measurements at 3–weeks, 9-weeks, or PRN at physician discretion, and 
b) Providing a home study kit to measure urine sugar/ketones plus assess for adverse events that 
might require immediate treatment or a decision to discontinue study medication.  
 
 
  
Subjects who are given the home study kits will be asked to check and document urine sugar and urine 
ketone measurements weekly or more often as directed. Although urine measurements will not provide the 
quantitative information provided by home blood glucose monitoring, it will potentially alert the investigators to 
increases in blood glucose levels above 180mg/dL and the presence of urine ketones. Urine measurements 
are less invasive for children and families who already facing added challenges of aggressive behavioral 
issues. 
 
3-month safety visit post study for a subset of subjects completing the 12-week study 
To assess end of study recommendations to parents and treating clinicians, a subset of subjects will be 
evaluated who have had increases in metabolic risk factors (e.g. 10% or greater increase from baseline body 
weight, fasting triglyceride levels greater than or equal to 250mg/dL at 6 or 12 weeks and /or a fasting or post-
load glucose of greater than or equal to 100 mg/dL or 140 mg/dL, respectively, at 6 or 12 weeks). Each subject 
will be evaluated individually for these criteria at the end of the 12-week study to determine which subjects 
would meet criteria for an additional safety visit. Those meeting criteria and consenting/assenting would be 
scheduled for an interim medical history and measurement of weight, height, waist circumference, fasting 
glucose and fasting lipid profile. Clinically significant abnormal findings would be discussed with the parent(s) 
and relevant clinicians.  
 
 
Role of the Committee- The DSMC’s principal goal will be to ensure that participant risk-benefit ratio 
and study integrity are maintained at the highest possible standards. The following definitions and procedures 
will be used to monitor and follow any adverse events. 
 
Adverse events (AEs) definition 
 
 
AEs are defined conventionally as any untoward medical occurrence during the planned observation 
period in a research participant that develops during participation in the study. The AE and study participation 
do not have to be causally related. 
 

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Serious adverse events (SAEs) definition 
 
 
SAEs are also defined conventionally, as any medical occurrence that results in death, is life 
threatening, requires inpatient hospitalization, results in persistent or significant disability, is a congenital 
anomaly or birth defect, or is an event requiring medical intervention to prevent any of these examples of an 
SAE. 
 
 
SAEs may be mild (transient, easily tolerated by the participant), moderate (causes discomfort or 
interrupts the study or the participant’s usual activities), or severe (causes considerable interference with usual 
activities) in severity. 
 
Causality of adverse events 
 
 
The causality of each AE in terms of relationship to administration of test drug will be assessed as 
definite (reasonable temporal relationship, with or without supporting laboratory data), probable (reasonable 
temporal relationship and other possible causes can be reasonably excluded), possible (reasonable temporal 
relationship and other possible causes are at least as or more likely), and unrelated (temporal relationship is 
not reasonable or other causes are reasonably more likely). 
 
Collecting/reporting of adverse events 
 
 
In the study, all adverse experiences, whether expected or unexpected, will be reported to the DSMC. 
Any adverse experience that occurs to a greater severity than expected will be reported to the Washington 
University Institutional Review Board (IRB) and the General Clinical Research Center (GCRC), and to the 
National Institutes of Health (NIH). 
 
 
The IRB, GCRC, and the GCRC Research Subject Advocate (RSA), will be notified of any serious 
adverse experience within ten working days of occurrence. 
 
 
If the event is fatal or life threatening, the DSMC, IRB, and RSA will be notified immediately, but not 
more than 24 hours after occurrence. The GCRC and NIH will be notified by telephone no later than three days 
after occurrence. 
 
 
The IRB, GCRC, and NIH will receive annual reports regarding all adverse experiences. 
 
 
Follow-up of serious adverse events 
All SAEs will be followed until resolution or permanent outcome of the event or until stabilization. Some 
SAEs will require study discontinuation. For example, individual participants will discontinue the study in the 
rare case they develop diabetes mellitus, or experience triglyceride elevation of 400 mg/dl or greater. These 
participants will continue to undergo follow-up evaluation by the investigators, direct consultation between the 
investigators and the treating physicians and establishment of ongoing care plans. For subjects experiencing 
an SAE that does not require study discontinuation, continuing study participation will require discussions of 
the potential risks and benefits of continuing participation, involving the participant and their caregivers, the 
investigators, and the participant’s treating physicians.  
 
 
Follow-up of adverse events 
 
Appropriate steps will be taken to minimize any AE. Specific examples include: 
▪ 
Fasting blood sugar elevation above 99 mg/dl, but less than 126 mg/dl. In this case, participants will be 
monitored on a weekly, biweekly, or PRN basis for further increases in fasting blood sugar. 
▪ 
Fasting triglyceride elevations above 250 mg/dl, but less than 400 mg/dl. In this case, participants will 
be monitored on a weekly, biweekly, or PRN basis for further increases in fasting triglycerides. 
▪ 
Increase from baseline body weight of 10% or more. In this case, participants will be monitored on a 
weekly, biweekly, or PRN basis for other AEs that may occur in the setting of weight gain, especially 
hyperglycemia and hyperlipidemia. 
 
 
DSMC Activities 

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The DSMC will include senior clinicians and investigators with expertise in child psychiatry, pediatrics, 
and statistics (see Membership below). The committee will make recommendations to ensure data integrity 
and the safety of the volunteers. DSMC activities will include: 
 
▪ 
Review the research protocol and approve plans for data and safety monitoring. 
▪ 
Evaluate the progress of the study, including periodic assessments of data quality and timeliness, 
participant recruitment, accrual and retention, participant risk versus benefit, and other factors that can 
affect study outcome. Monitoring will also consider factors external to the study when interpreting the 
data, such as scientific or therapeutic developments that may have an impact on the safety of the 
participants or the ethics of the study. The principal investigator will make available to the DSMC any 
newly published information related to the use of atypical antipsychotic medications or the performance 
of the study procedures in adults or children that may affect patient safety. 
▪ 
Issues regarding feasibility of the study will be evaluated by the DSMC who will make 
recommendations to NIH as needed regarding continuation of the study. 
▪ 
Make recommendations to Washington University School of Medicine, its IRB, and investigators 
concerning continuation or conclusion of the study. 
▪ 
Submission of an annual report to the IRB. 
▪ 
Protect the confidentiality of the trial data and the results of monitoring. 
▪ 
Review of data and safety issues (including any and all adverse events).  
▪ 
The DSMC will be empowered to stop the study, at their discretion, for safety reasons. 
 
DSMC Meeting Frequency 
 
 
The DSMC will meet to review above listed information quarterly, with frequency to be adjusted as needed 
by the DSMC. 
 
Stopping Rules: 
▪ 
The study may be discontinued at any time during the study at the recommendation of the DSMC, IRB 
or NIH.  
▪ 
If the study is terminated for any reason, the investigators will promptly inform the participants and 
provide appropriate therapy and follow-up. 
▪ 
The study will be stopped for any individual participant in the case of any SAE which the PI or DSMC 
determines would be a contraindication to study participation. Noted above, if a participant is withdrawn 
from the study because of an adverse event, the participant will be followed and treated by the 
investigators until the abnormal parameter or symptom has resolved or stabilized, and care 
appropriately transitioned to the primary treating physician. 
▪ 
New recruitment/enrollment will be discontinued after any SAE until satisfactorily reviewed by the 
DSMC, with recommendations, if any, for changes to recruitment practices, the informed consent 
document, or safety/monitoring practices. All such recommendations will be communicated to the 
GCRC and IRB. 
 
 
 
Membership 
 
The DSMC membership has been restricted to individuals free of apparent significant conflicts of interest. 
The membership for this DSMC is comprised of: 
 
▪ 
George Vogler, PhD, Director, Center for Developmental and Health Genetics, Professor of 
Biobehavioral Health, Pennsylvania State University, with expertise in statistical analysis. 
▪ 
Gordon Bloomberg, MD, Associate Professor of Pediatrics, Division of Allergy and Pulmonary 
Medicine, Washington University School of Medicine. 
▪ 
TENTATIVE NEW MEMBER: Robert Berkowitz, MD, Chair, Department of Child and Adolescent 
Psychiatry, Children’s Hospital of Pennsylvania; Associate Professor of Psychiatry; Attending 
Psychiatry, Weight and Eating Disorders Program. 
 
 

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F. Vertebrate Animals: N/A 

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G: Literature Cited: 
 
1. 
Koller E, Malozowski S, Doraiswamy PM. Atypical antipsychotic drugs and hyperglycemia in 
adolescents. JAMA. 2001 Nov 28;286(20):2547-2548. 
2. 
Koller E, Schneider B, Bennett K, Dubitsky G. Clozapine-associated diabetes. Am J Med. 2001 Dec 
15;111(9):716-723. 
3. 
Koller EA, Cross JT, Doraiswamy PM, Schneider BS. Risperidone-associated diabetes mellitus: a 
pharmacovigilance study. Pharmacotherapy. 2003;23(6):735-744. 
4. 
Koller EA, Doraiswamy PM. Olanzapine-associated diabetes mellitus. Pharmacotherapy. 
2002;22(7):841-852. 
5. 
Haupt DW, Newcomer JW. Abnormalities in glucose regulation associated with mental illness and 
treatment. J Psychosom Res. 2002;53(4):925-933. 
6. 
Casey DE, Haupt DW, Newcomer JW, et al. Antipsychotic-induced weight gain and metabolic 
abnormalities: implications for increased mortality in patients with schizophrenia. J Clin Psych. 2004:In 
Press. 
7. 
Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes. Diabetes 
Care. 2004;27(2):596-558. 
8. 
Centers for Disease Control and Prevention. National diabetes fact sheet:  general information and 
national estimates on diabetes in the United States, 2000. US Department of Health and Human 
Services. 2002. 
9. 
Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of 
obesity and diabetes in the United States. JAMA. 2001;286(10):1195-1200. 
10. 
Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from 
the third National Health and Nutrition Examination Survey. JAMA. 2002;287(3):356-359. 
11. 
Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The Metabolic Syndrome: 
Prevalence and Associated Risk Factor Findings in the US Population From the Third National Health 
and Nutrition Examination Survey, 1988-1994. Arch Intern Med. 2003;163(4):427-436. 
12. 
National Cholesterol Education Program. Executive Summary of The Third Report of The National 
Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High 
Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-2497. 
13. 
National Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of 
overweight and obesity in adults: the evidence report. Obes Res. 1998;6(suppl 2):51S-209S. 
14. 
Hodgson TA, Cohen AJ. Medical care expenditures for diabetes, its chronic complications, and its 
comorbidities. Prev Med. 1999;29(3):173-186. 
15. 
American Diabetes Association. Economic consequences of diabetes mellitus in the U.S. in 1997. 
Diabetes Care. 1998;21:296-306. 
16. 
Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity. JAMA. 
2003;289(2):187-193. 
17. 
Harris MI, Flegal KM, Cowie CC, et al. Prevalence of diabetes, impaired fasting glucose, and impaired 
glucose tolerance in U.S. adults. The Third National Health and Nutrition Examination Survey, 1988-
1994. Diabetes Care. 1998 Apr;21(4):518-524. 
18. 
Scheidt-Nave C, Barrett-Connor E, Wingard DL, Cohn BA, Edelstein SL. Sex differences in fasting 
glycemia as a risk factor for ischemic heart disease death. Am J Epidemiol. 1991 Mar 15;133(6):565-
576. 
19. 
Park S, Barrett-Connor E, Wingard DL, Shan J, Edelstein S. GHb is a better predictor of cardiovascular 
disease than fasting or postchallenge plasma glucose in women without diabetes. The Rancho 
Bernardo Study. Diabetes Care. 1996 May;19(5):450-456. 
20. 
Fuller JH, Shipley MJ, Rose G, Jarrett RJ, Keen H. Coronary-heart-disease risk and impaired glucose 
tolerance. The Whitehall study. Lancet. 1980 Jun 28;1(8183):1373-1376. 
21. 
Fuller JH, Shipley MJ, Rose G, Jarrett RJ, Keen H. Mortality from coronary heart disease and stroke in 
relation to degree of glycaemia: the Whitehall study. Br Med J. 1983 Sep 24 (Clin Res Ed) 1983 Sep 
24;287(6396):867-870. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
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22. 
Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident 
cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 
individuals followed for 12.4 years. Diabetes Care. 1999 Feb;2(2):233-240. 
23. 
Gerstein HC. Is glucose a continuous risk factor for cardiovascular mortality? Diabetes Care. 1999 
May;22(5):659-660. 
24. 
Despres JP, Marette A. Relation of components of insulin resistance syndrome to coronary disease 
risk. Curr Opin Lipidol. 1994 Aug;5(4):274-289. 
25. 
Austin MA. Plasma triglyceride and coronary heart disease. Arterioscler Thromb. 1991 Jan-Feb;11(1):2-
14. 
26. 
Semenkovich CF. Hypertriglyceridemia and combined hyperlipidemia. In: Callow AD, B EC, eds. 
Vascular surgery: theory and practice. Stamford, Connecticut: Appleton & Lange; 1995:105-117. 
27. 
Kaplan NM. The deadly quartet. Upper-body obesity, glucose intolerance, hypertriglyceridemia, and 
hypertension. Arch Intern Med. 1989 Jul;149(7):1514-1520. 
28. 
Albrink MJ, Krauss RM, Lindgrem FT, von der Groeben J, Pan S, Wood PD. Intercorrelations among 
plasma high density lipoprotein, obesity and triglycerides in a normal population. Lipids. 
1980;15(9):668-676. 
29. 
Terry RB, Wood PD, Haskell WL, Stefanick ML, Krauss RM. Regional adiposity patterns in relation to 
lipids, lipoprotein cholesterol, and lipoprotein subfraction mass in men. J Clin Endocrinol Metab. 
1989;68(1):191-199. 
30. 
Reaven GM, Chen YD, Jeppesen J, Maheux P, Krauss RM. Insulin resistance and hyperinsulinemia in 
individuals with small, dense low density lipoprotein particles. J Clin Invest. 1993;92(1):141-146. 
31. 
Brown CD, Higgins M, Donato KA, et al. Body mass index and the prevalence of hypertension and 
dyslipidemia. Obes Res. 2000;8(9):605-619. 
32. 
Assmann G, Schulte H. Relation of high-density lipoprotein cholesterol and triglycerides to incidence of 
atherosclerotic coronary artery disease (the PROCAM experience). Prospective Cardiovascular 
Munster study. Am J Cardiol. 1992;70(7):733-737. 
33. 
Lamarche B, Lemieux I, Despres JP. The small, dense LDL phenotype and the risk of coronary heart 
disease: epidemiology, patho-physiology and therapeutic aspects. Diabetes Metab. 1999;25(3):199-
211. 
34. 
Assmann G, Schulte H, Funke H, von Eckardstein A. The emergence of triglycerides as a significant 
independent risk factor in coronary artery disease. Eur Heart J. 1998;19 Suppl M:M8-14. 
35. 
Austin MA. Epidemiology of hypertriglyceridemia and cardiovascular disease. Am J Cardiol. 
1999;83(9B):13F-16F. 
36. 
Eidelman RS, Lamas GA, Hennekens CH. The new National Cholesterol Education Program 
guidelines: clinical challenges for more widespread therapy of lipids to treat and prevent coronary heart 
disease. Arch Intern Med. 2002;162(18):2033-2036. 
37. 
Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome 
phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 
1988-1994. Arch Pediatr Adolesc Med. 2003;157(8):821-827. 
38. 
Lebovitz HE. Type 2 diabetes: an overview. Clin Chem. 1999;45(8 Pt 2):1339-1345. 
39. 
Resnick HE, Valsania P, Halter JB, Lin X. Differential effects of BMI on diabetes risk among black and 
white Americans. Diabetes Care. 1998 Nov;21(11):1828-1835. 
40. 
Banerji MA, Lebowitz J, Chaiken RL, Gordon D, Kral JG, Lebovitz HE. Relationship of visceral adipose 
tissue and glucose disposal is independent of sex in black NIDDM subjects. Am J Physiol. 1997 
Aug;273(2 Pt 1):E425-432. 
41. 
Ohlson LO, Larsson B, Svardsudd K, et al. The influence of body fat distribution on the incidence of 
diabetes mellitus. 13.5 years of follow-up of the participants in the study of men born in 1913. Diabetes. 
1985;34(10):1055-1058. 
42. 
Bruning JC, Gautam D, Burks DJ, et al. Role of Brain Insulin Receptor in Control of Body Weight and 
Reproduction. Science. 2000 Sep 22;289(5487):2122-2125. 
43. 
Eckel RH. Lipoprotein lipase. A multifunctional enzyme relevant to common metabolic diseases. N Engl 
J Med. 1989 Apr 20;320(16):1060-1068. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
34
44. 
Semenkovich CF, Chen SH, Wims M, Luo CC, Li WH, Chan L. Lipoprotein lipase and hepatic lipase 
mRNA tissue specific expression, developmental regulation, and evolution. J Lipid Res. 1989 
Mar;30(3):423-431. 
45. 
Vessby B, Selinus I, Lithell H. Serum lipoprotein and lipoprotein lipase in overweight, type II diabetics 
during and after supplemented fasting. Arteriosclerosis. 1985 Jan-Feb;5(1):93-100. 
46. 
Lithell H, Lindgarde F, Hellsing K, et al. Body weight, skeletal muscle morphology, and enzyme 
activities in relation to fasting serum insulin concentration and glucose tolerance in 48-year-old men. 
Diabetes. 1981 Jan;30(1):19-25. 
47. 
Rossner S. Studies on an intravenous fat tolerance test. Methodological, experimental and clinical 
experiences with Intralipid. Acta Med Scand Suppl. 1974;564:1-24. 
48. 
Knudsen P, Eriksson J, Lahdenpera S, Kahri J, Groop L, Taskinen MR. Changes of lipolytic enzymes 
cluster with insulin resistance syndrome. Botnia Study Group. Diabetologia. 1995 Mar;38(3):344-350. 
49. 
Pollare T, Vessby B, Lithell H. Lipoprotein lipase activity in skeletal muscle is related to insulin 
sensitivity. Arterioscler Thromb. 1991 Sep-Oct;11(5):1192-1203. 
50. 
Buitelaar JK. Open-label treatment with risperidone of 26 psychiatrically-hospitalized children and 
adolescents with mixed diagnoses and aggressive behavior. J Child Adolesc Psychopharmacol. 2000 
Spring;10. 10(1. 1):19-26. 
51. 
Buitelaar JK, Montgomery SA, van Zwieten-Boot BJ. Conduct disorder: guidelines for investigating 
efficacy of pharmacological intervention. Eur Neuropsychopharmacol. 2003;13(4):305-311. 
52. 
McDougle CJ, Holmes JP, Carlson DC, Pelton GH, Cohen DJ, Price LH. A double-blind, placebo-
controlled study of risperidone in adults with autistic disorder and other pervasive developmental 
disorders [see comments]. Arch Gen Psychiatry. 1998 Jul;55. 55(7. 7):633-641. 
53. 
Soderstrom H, Rastam M, Gillberg C. A clinical case series of six extremely aggressive youths treated 
with olanzapine. Eur Child Adolesc Psychiatry. 2002;11(3):138-141. 
54. 
Snyder R, Turgay A, Aman M, Binder C, Fisman S, Carroll A. Effects of risperidone on conduct and 
disruptive behavior disorders in children with subaverage IQs. J Am Acad Child Adolesc Psychiatry. 
2002;41(9):1026-1036. 
55. 
Gerardin P, Cohen D, Mazet P, Flament MF. Drug treatment of conduct disorder in young people. Eur 
Neuropsychopharmacol. 2002;12(5):361-370. 
56. 
Aman MG, De Smedt G, Derivan A, Lyons B, Findling RL. Double-blind, placebo-controlled study of 
risperidone for the treatment of disruptive behaviors in children with subaverage intelligence. Am J 
Psychiatry. 2002;159(8):1337-1346. 
57. 
McCracken JT, McGough J, Shah B, et al. Risperidone in children with autism and serious behavioral 
problems. N Engl J Med. 2002;347(5):314-321. 
58. 
Yarasheski KE, Tebas P, Sigmund C, et al. Insulin resistance in HIV protease inhibitor-associated 
diabetes. J Acquir Immune Defic Syndr. 1999;21(3):209-216. 
59. 
Nolte LA, Yarasheski KE, Kawanaka K, Fisher J, Le N, Holloszy JO. The HIV protease inhibitor 
indinavir decreases insulin- and contraction-stimulated glucose transport in skeletal muscle. Diabetes. 
2001;50(6):1397-1401. 
60. 
Yarasheski KE, Tebas P, Claxton S, et al. Visceral adiposity, C-peptide levels, and low lipase activities 
predict HIV-dyslipidemia. Am J Physiol Endocrinol Metab. 2003;285(4):E899-905. 
61. 
Pijl H, Meinders AE. Bodyweight change as an adverse effect of drug treatment. Mechanisms and 
management. Drug Saf. 1996 May;14(5):329-342. 
62. 
Casey DE. The relationship of pharmacology to side effects. J Clin Psychiatry. 1997;58 Suppl 10:55-62. 
63. 
Allison DB, Mentore JL, Heo M, et al. Antipsychotic-induced weight gain: a comprehensive research 
synthesis. Am J Psychiatry. 1999 Nov;156(11):1686-1696. 
64. 
Leadbetter R, Shutty M, Pavalonis D, Vieweg V, Higgins P, Downs M. Clozapine-induced weight gain: 
prevalence and clinical relevance. Am J Psychiatry. 1992 Jan;149(1):68-72. 
65. 
Lamberti JS, Bellnier T, Schwarzkopf SB. Weight gain among schizophrenic patients treated with 
clozapine. Am J Psychiatry. 1992 May;149(5):689-690. 
66. 
Collaborative Working Group on Clinical Trial Evaluations. Adverse effects of the atypical 
antipsychotics. J Clin Psychiatry. 1998;59 Suppl 12:17-22. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
35
67. 
Kraus T, Haack M, Schuld A, et al. Body weight and leptin plasma levels during treatment with 
antipsychotic drugs. Am J Psychiatry. 1999 Feb;156(2):312-314. 
68. 
Gupta S, Droney T, Al-Samarrai S, Keller P, Frank B. Olanzapine-induced weight gain. Ann Clin 
Psychiatry. 1998 Mar;10(1):39. 
69. 
Baptista T. Body weight gain induced by antipsychotic drugs: mechanisms and management. Acta 
Psychiatr Scand. 1999 Jul;100(1):3-16. 
70. 
Kelly DL, Conley RR, Love RC, Horn DS, Ushchak CM. Weight gain in adolescents treated with 
risperidone and conventional antipsychotics over six months. J Child Adolesc Psychopharmacol. 
1998;8(3):151-159. 
71. 
Zipursky R, Gu H, Green AI, et al. Clinical correlates of weight gain in first episode psychosis patients 
treated with olanzapine. Schizophrenia Research. 2003;60(1):372. 
72. 
Poyurovsky M, Pashinian A, Gil-Ad I, et al. Olanzapine-induced weight gain in patients with first-
episode schizophrenia: a double-blind, placebo-controlled study of fluoxetine addition. Am J Psychiatry. 
2002 Jun;159(6):1058-1060. 
73. 
Bustillo JR, Lauriello J, Parker K, et al. Treatment of weight gain with fluoxetine in olanzapine-treated 
schizophrenic outpatients. Neuropsychopharmacology. 2003;28(3):527-529. 
74. 
Blin O. A comparative review of new antipsychotics. Can J Psychiatry. 1999;44(3):235-244. 
75. 
Penn JV, Martini J, Radka D. Weight gain associated with risperidone [letter]. J Clin Psychopharmacol. 
1996 Jun;16. 16(3. 3):259-260. 
76. 
Marder SR, Meibach RC. Risperidone in the treatment of schizophrenia. Am J Psychiatry. 1994 
Jun;151(6):825-835. 
77. 
Ganguli R. Weight gain associated with antipsychotic drugs. J Clin Psychiatry. 1999;60 Suppl 21:20-24. 
78. 
Masand PS. Weight gain associated with atypical antipsychotics. J Psychotic Disord. 1998;2:4-6. 
79. 
Tran PV, Hamilton SH, Kuntz AJ, et al. Double-blind comparison of olanzapine versus risperidone in 
the treatment of schizophrenia and other psychotic disorders. J Clin Psychopharmacol. 1997 
Oct;17(5):407-418. 
80. 
Wirshing DA, Wirshing WC, Kysar L, et al. Novel antipsychotics: comparison of weight gain liabilities. J 
Clin Psychiatry. 1999 Jun;60(6):358-363. 
81. 
Jody D, Saha AR, Iwamoto T. Meta-analysis of weight effects with aripiprazole. Int J 
Neuropsychopharmacol. 2002;5(suppl 1):S186. 
82. 
Stock E, Marder SR, Saha AR. Safety and tolerability meta-analysis of aripiprazole in schizophrenia. Int 
J Neuropsychopharmacol. 2002;5(suppl 1):S185. 
83. 
Carson WH, Pigott TA, Saha AR. Aripiprazole vs. placebo in the treatment of chronic schizophrenia. Int 
J Neuropsychopharmacol. 2002;5(suppl 1):S187. 
84. 
Kane JM, Carson WH, Saha AR, et al. Efficacy and safety of aripiprazole and haloperidol versus 
placebo in patients with schizophrenia and schizoaffective disorder. J Clin Psychiatry. 2002;63(9):763-
771. 
85. 
Kemner C, Willemsen-Swinkels SH, de Jonge M, Tuynman-Qua H, van Engeland H. Open-label study 
of olanzapine in children with pervasive developmental disorder. J Clin Psychopharmacol. 
2002;22(5):455-460. 
86. 
Ercan ES, Kutlu A, Varan A, Cikoglu S, Coskunol H, Bayraktar E. Olanzapine treatment of eight 
adolescent patients with psychosis. Hum Psychopharmacol. 2004;19(1):53-56. 
87. 
Patel NC, Kistler JS, James EB, Crismon ML. A retrospective analysis of the short-term effects of 
olanzapine and quetiapine on weight and body mass index in children and adolescents. 
Pharmacotherapy. 2004;24(7):824-830. 
88. 
Scahill L, Leckman JF, Schultz RT, Katsovich L, Peterson BS. A placebo-controlled trial of risperidone 
in Tourette syndrome. Neurology. 2003;60(7):1130-1135. 
89. 
Gilbert DL, Batterson JR, Sethuraman G, Sallee FR. Tic reduction with risperidone versus pimozide in a 
randomized, double-blind, crossover trial. J Am Acad Child Adolesc Psychiatry. 2004;43(2):206-214. 
90. 
Gagliano A, Germano E, Pustorino G, et al. Risperidone treatment of children with autistic disorder: 
effectiveness, tolerability, and pharmacokinetic implications. J Child Adolesc Psychopharmacol. 
2004;14(1):39-47. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
36
91. 
Martin A, Scahill L, Anderson GM, et al. Weight and leptin changes among risperidone-treated youths 
with autism: 6-month prospective data. Am J Psychiatry. 2004;161(6):1125-1127. 
92. 
Sikich L, Hamer RM, Bashford RA, Sheitman BB, Lieberman JA. A pilot study of risperidone, 
olanzapine, and haloperidol in psychotic youth: a double-blind, randomized, 8-week trial. 
Neuropsychopharmacology. 2004;29(1):133-145. 
93. 
Haupt DW, Newcomer JW. Hyperglycemia and antipsychotic medications. J Clin Psychiatry. 2001 
Dec;62(Supplement 27):15-26. 
94. 
Keskiner A. A long-term follow-up of fluphenazine enanthate treatment. Curr Ther Res Clin Exp. 1973 
Jun;15(6):305-313. 
95. 
Hagg S, Joelsson L, Mjorndal T, Spigset O, Oja G, Dahlqvist R. Prevalence of diabetes and impaired 
glucose tolerance in patients treated with clozapine compared with patients treated with conventional 
depot neuroleptic medications. J Clin Psychiatry. 1998 Jun;59(6):294-299. 
96. 
Popli AP, Konicki PE, Jurjus GJ, Fuller MA, Jaskiw GE. Clozapine and associated diabetes mellitus. J 
Clin Psychiatry. 1997 Mar;58(3):108-111. 
97. 
Peterson GA, Byrd SL. Diabetic ketoacidosis from clozapine and lithium cotreatment [letter]. Am J 
Psychiatry. 1996 May;153(5):737-738. 
98. 
Ai D, Roper TA, Riley JA. Diabetic ketoacidosis and clozapine. Postgrad Med J. 1998 Aug;74(874):493-
494. 
99. 
Kamran A, Doraiswamy PM, Jane JL, Hammett EB, Dunn L. Severe hyperglycemia associated with 
high doses of clozapine [letter]. Am J Psychiatry. 1994 Sep;151(9):1395. 
100. 
Kostakoglu AE, Yazici KM, Erbas T, Guvener N. Ketoacidosis as a side-effect of clozapine: a case 
report. Acta Psychiatr Scand. 1996 Mar;93(3):217-218. 
101. 
Koval MS, Rames LJ, Christie S. Diabetic ketoacidosis associated with clozapine treatment [letter]. Am 
J Psychiatry. 1994 Oct;151(10):1520-1521. 
102. 
Smith H, Kenney-Herbert J, Knowles L. Clozapine-induced diabetic ketoacidosis [letter]. Aust N Z J 
Psychiatry. 1999 Feb;33(1):120-121. 
103. 
Wirshing DA, Spellberg BJ, Erhart SM, Marder SR, Wirshing WC. Novel antipsychotics and new onset 
diabetes. Biol Psychiatry. 1998 Oct 15;44(8):778-783. 
104. 
Yazici KM, Erbas T, Yazici AH. The effect of clozapine on glucose metabolism. Exp Clin Endocrinol 
Diabetes. 1998;106(6):475-477. 
105. 
McDonnell ME, Ruderman NB. Cutting the Gordian knot. Addition of metformin to insulin therapy in a 
patient with uncontrolled diabetes and schizophrenia [letter]. Diabetes Care. 1999 Nov;22(11):1912-
1913. 
106. 
Mohan D, Gordon H, Hindley N, Barker A. Schizophrenia and diabetes mellitus [letter]. Br J Psychiatry. 
1999 Feb;174:180-181. 
107. 
Colli A, Cocciolo M, Francobandiera F, Rogantin F, Cattalini N. Diabetic ketoacidosis associated with 
clozapine treatment [letter]. Diabetes Care. 1999 Jan;2(1):176-177. 
108. 
Henderson DC, Cagliero E, Gray C, et al. Clozapine, diabetes mellitus, weight gain, and lipid 
abnormalities: A five-year naturalistic study. Am J Psychiatry. 2000 Jun;157(6):975-981. 
109. 
Wehring H, Alexander B, Perry PJ. Diabetes mellitus associated with clozapine therapy. 
Pharmacotherapy. 2000 Jul;20(7):844-847. 
110. 
Melson AK, Selke G, Fucetola R, Schweiger JA, Newcomer JW. Clozapine can change glucose 
regulation in schizophrenia independent of body mass index (BMI). Society for Neuroscience Abstracts. 
1999;25(2):2074. 
111. 
Maule S, Giannella R, Lanzio M, Villari V. Diabetic ketoacidosis with clozapine treatment [letter]. 
Diabetes Nutr Metab. 1999 Apr;12(2):187-188. 
112. 
Rigalleau V, Gatta B, Bonnaud S, et al. Diabetes as a result of atypical anti-psychotic drugs--a report of 
three cases. Diabet Med. 2000 Jun;17(6):484-486. 
113. 
Selke GJ, Newcomer JW, Fucetola R, Cooper BP, Schweiger JA. Atypical antipsychotic-induced 
differences in glucose regulation in schizophrenia independent of differences in adiposity. Soc Neurosci 
Abstr. 2000;26(1):275. 
114. 
Avram AM, Patel V, Taylor HC, Kirwan JP, Kalhan S. Euglycemic clamp study in clozapine-induced 
diabetic ketoacidosis. Ann Pharmacother. 2001 Nov;35(11):1381-1387. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
37
115. 
Pierides M. Clozapine monotherapy and ketoacidosis. Br J Psychiatry. 1997 Jul;171:90-91. 
116. 
Griffiths J, Springuel P. Atypical antipsychotics: impaired glucose metabolism. CMAJ. 2001 Oct 
2;165(7):943-945, 947-949. 
117. 
Dervaux A, Mascarenhas N, Lambolez T. [Importance of blood glucose level when starting 
antipsychotic treatment]. Presse Med. 2001 Sep 22;30(26):1298. 
118. 
Ananth J, Gunatilake S, Aquino S, Bach V, Costa J. Are African American patients at a higher risk for 
olanzapine-induced glucose intolerance? Psychopharmacology (Berl). 2001 Sep;157(3):324-325. 
119. 
Van Meter SA, Seaburg H, McLendon B, Doraiswamy PM. Olanzapine, new-onset diabetes mellitus, 
and risk for insulin overdose. J Clin Psychiatry. 2001 Dec;62(12):993-994. 
120. 
Johnson RP, Al-Taher MT, Madlock LE, Guo M, Nasdahl CS. Increasing Insulin Dose for Olanzapine-
Related Diabetes. Am J Psychiatry. 2002 Jan 1;159(1):150-151. 
121. 
Bechara CI, Goldman-Levine JD. Dramatic worsening of type 2 diabetes mellitus due to olanzapine 
after 3 years of therapy. Pharmacotherapy. 2001 Nov;21(11):1444-1447. 
122. 
Seaburg HL, McLendon BM, Doraiswamy PM. Olanzapine-associated severe hyperglycemia, 
ketonuria, and acidosis: case report and review of literature. Pharmacotherapy. 2001;21(11):1448-
1454. 
123. 
Paizis M, Cavaleri S, Schwarz ME, Levin Z. Acute-onset ketoacidosis during olanzapine treatment in a 
patient without pretreatment obesity or treatment-associated weight gain. Primary Psychiatry. 
1999;6(12):37-38. 
124. 
Lindenmayer JP, Patel R. Olanzapine-induced ketoacidosis with diabetes mellitus [letter]. Am J 
Psychiatry. 1999 Sep;156(9):1471. 
125. 
Von Hayek D, Huttl V, Reiss J, Schweiger HD, Fuessl HS. [Hyperglycemia and ketoacidosis associated 
with olanzapine]. Nervenarzt. 1999 Sep;70(9):836-837. 
126. 
Fertig MK, Brooks VG, Shelton PS, English CW. Hyperglycemia associated with olanzapine [letter]. J 
Clin Psychiatry. 1998 Dec;59(12):687-689. 
127. 
Ober SK, Hudak R, Rusterholtz A. Hyperglycemia and olanzapine [letter]. Am J Psychiatry. 1999 
Jun;156(6):970. 
128. 
Goldstein LE, Sporn J, Brown S, et al. New-onset diabetes mellitus and diabetic ketoacidosis 
associated with olanzapine treatment. Psychosomatics. 1999 Sep-Oct;40(5):438-443. 
129. 
Gatta B, Rigalleau V, Gin H. Diabetic ketoacidosis with olanzapine treatment [letter]. Diabetes Care. 
1999 Jun;22(6):1002-1003. 
130. 
Bettinger TL, Mendelson SC, Dorson PG, Crismon ML. Olanzapine-induced glucose dysregulation. Ann 
Pharmacother. 2000 Jul-Aug;34(7-8):865-867. 
131. 
Muench J, Carey M. Diabetes mellitus associated with atypical antipsychotic medications: new case 
report and review of the literature. J Am Board Fam Pract. 2001;14(4):278-282. 
132. 
Selva KA, Scott SM. Diabetic ketoacidosis associated with olanzapine in an adolescent patient. J 
Pediatr. 2001 Jun;138(6):936-938. 
133. 
Kropp S, Emrich HM, Bleich S, Degner D. Olanzapine-related hyperglycemia in a nondiabetic woman. 
Can J Psychiatry. 2001 Jun;46(5):457. 
134. 
Bonanno DG, Davydov L, Botts SR. Olanzapine-induced diabetes mellitus. Ann Pharmacother. 2001 
May;35(5):563-565. 
135. 
Sobel M, Jaggers ED, Franz MA. New-onset diabetes mellitus associated with the initiation of 
quetiapine treatment [letter]. J Clin Psychiatry. 1999 Aug;60(8):556-557. 
136. 
Wilson D, Hammond C, D'Souza L, Sarkar N. Glucose intolerance with atypical antipsychotics. 
Schizophr Res. 2001 Apr;49(Supplement):290. 
137. 
Procyshyn RM, Pande S, Tse G. New-onset diabetes mellitus associated with quetiapine [letter]. Can J 
Psychiatry. 2000 Sep;45(7):668-669. 
138. 
Wirshing DA, Erhart SM, Pierre JM, Boyd JA. Nonextrapyramidal side effects of novel antipsychotics. 
Curr Opin Psychiatry. 2000;13:45-50. 
139. 
Wirshing DA, Pierre JM, Eyeler J, Weinbach J, Wirshing WC. Risperidone-associated new-onset 
diabetes. Biol Psychiatry. 2001 Jul 15;50(2):148-149. 
140. 
Mallya A, Chawla P, Boyer SK, DeRosear L. Resolution of hyperglycemia on risperidone 
discontinuation: a case report. J Clin Psychiatry. 2002;63(5):453-454. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
38
141. 
Croarkin PE, Jacobs KM, Bain BK. Diabetic ketoacidosis associated with risperidone treatment? [letter]. 
Psychosomatics. 2000 Jul-Aug;41(4):369-370. 
142. 
Haupt DW, Newcomer JW. Risperidone-associated diabetic ketoacidosis. Psychosomatics. 2001 
Jun;42:279-280. 
143. 
Yang SH, McNeely MJ. Rhabdomyolysis, pancreatitis, and hyperglycemia with ziprasidone. Am J 
Psychiatry. 2002;159(8):1435. 
144. 
Pfizer. Study 054. FDA Website. 
145. 
Glick ID, Fryburg D, O'Sullivan RL, Siu C, Simpson GM. Ziprasidone's benefits versus olanzapine on 
weight gain and insulin resistance. American Psychiatric Association 154th Annual Meeting, May 2001, 
New Orleans, LA, Abstract #NR261. 2001. 
146. 
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model 
assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin 
concentrations in man. Diabetologia. 1985 Jul;28(7):412-419. 
147. 
Haffner SM, Miettinen H, Stern MP. The homeostasis model in the San Antonio Heart Study. Diabetes 
Care. 1997 Jul;20(7):1087-1092. 
148. 
Domon SE, Webber JC. Hyperglycemia and hypertriglyceridemia secondary to olanzapine. J Child 
Adolesc Psychopharmacol. 2001;11(3):285-288. 
149. 
Domon SE, Cargile CS. Quetiapine-associated hyperglycemia and hypertriglyceridemia. J Am Acad 
Child Adolesc Psychiatry. 2002;41(5):495-496. 
150. 
Bloch Y, Vardi O, Mendlovic S, Levkovitz Y, Gothelf D, Ratzoni G. Hyperglycemia from olanzapine 
treatment in adolescents. J Child Adolesc Psychopharmacol. 2003;13(1):97-102. 
151. 
Goodpaster BH, Kelley DE, Wing RR, Meier A, Thaete FL. Effects of weight loss on regional fat 
distribution and insulin sensitivity in obesity. Diabetes. 1999 Apr;48(4):839-847. 
152. 
Newcomer JW, Haupt DW, Fucetola R, et al. Abnormalities in glucose regulation during antipsychotic 
treatment of schizophrenia. Arch Gen Psychiatry. 2002;59:337-345. 
153. 
Houseknecht KLR, A. S. 
Johnson, D. E. 
Rollema, H. Clozapine and olanzapine, but not ziprasidone, cause acute insulin resistance in normal rats. Biol 
Psychiatry. 2005;57(8 (Supplement 1)):128S. 
154. 
Henderson DC, Cagliero E, Copeland PM, et al. Glucose metabolism in patients with schizophrenia 
treated with atypical antipsychotic agents: a frequently sampled intravenous glucose tolerance test and 
minimal model analysis. Arch Gen Psychiatry. 2005;62(1):19-28. 
155. 
Ader M, Pacini G, Yang YJ, Bergman RN. Importance of glucose per se to intravenous glucose 
tolerance. Comparison of the minimal-model prediction with direct measurements. Diabetes. 1985 
Nov;34(11):1092-1103. 
156. 
Beard JC, Bergman RN, Ward WK, Porte D. The insulin sensitivity index in nondiabetic man. 
Correlation between clamp-derived and IVGTT-derived values. Diabetes. 1986;35(3):362-369. 
157. 
Bergman RN, Prager R, Volund A, Olefsky JM. Equivalence of the insulin sensitivity index in man 
derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest. 
1987;79(3):790-800. 
158. 
Bergman RN. Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-
model approach. Diabetes. 1989 Dec;38(12):1512-1527. 
159. 
Welch S, Gebhart SS, Bergman RN, Phillips LS. Minimal model analysis of intravenous glucose 
tolerance test-derived insulin sensitivity in diabetic subjects. J Clin Endocrinol Metab. 1990;71(6):1508-
1518. 
160. 
Steil GM, Volund A, Kahn SE, Bergman RN. Reduced sample number for calculation of insulin 
sensitivity and glucose effectiveness from the minimal model. Suitability for use in population studies. 
Diabetes. 1993;42(2):250-256. 
161. 
DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin 
secretion and resistance. Am J Physiol. 1979 Sep;237(3):E214-223. 
162. 
Saad MF, Anderson RL, Laws A, et al. A comparison between the minimal model and the glucose 
clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin 
Resistance Atherosclerosis Study. Diabetes. 1994 Sep;43(9):1114-1121. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
39
163. 
Clark M, Dubowski K, Colmore J. The effect of chlorpromazine on serum cholesterol in chronic 
schizophrenic patients. Clin Pharmacol Ther. 1970 Nov-Dec;11(6):883-889. 
164. 
Ghaeli P, Dufresne RL. Serum triglyceride levels in patients treated with clozapine. Am J Health Syst 
Pharm. 1996 Sep 1;53(17):2079-2081. 
165. 
Gaulin BD, Markowitz JS, Caley CF, Nesbitt LA, Dufresne RL. Clozapine-associated elevation in serum 
triglycerides. Am J Psychiatry. 1999 Aug;156(8):1270-1272. 
166. 
Spivak B, Roitman S, Vered Y, et al. Diminished suicidal and aggressive behavior, high plasma 
norepinephrine levels, and serum triglyceride levels in chronic neuroleptic-resistant schizophrenic 
patients maintained on clozapine. Clin Neuropharmacol. 1998 Jul-Aug;21(4):245-250. 
167. 
Meyer JM. Novel antipsychotics and severe hyperlipidemia. American College of 
Neuropsychopharmacology 38th Annual Meeting Scientific Abstracts. 1999 Dec:211. 
168. 
Osser DN, Najarian DM, Dufresne RL. Olanzapine increases weight and serum triglyceride levels. J 
Clin Psychiatry. 1999 Nov;60(11):767-770. 
169. 
Sheitman BB, Bird PM, Binz W, Akinli L, Sanchez C. Olanzapine-induced elevation of plasma 
triglyceride levels [letter]. Am J Psychiatry. 1999 Sep;156(9):1471-1472. 
170. 
L'Italien GJ. Pharmacoeconomic impact of antipsychotic-induced metabolic events. Am J Manag Care. 
2003;3(2):S38-S42. 
171. 
Nguyen M, Murphy T. Olanzapine and hypertriglyceridemia. J Am Acad Child Adolesc Psychiatry. 
2001;40(2):133. 
172. 
Egusa G, Beltz WF, Grundy SM, Howard BV. Influence of obesity on the metabolism of apolipoprotein 
B in humans. J Clin Invest. 1985 Aug;76(2):596-603. 
173. 
Tobey TA, Greenfield M, Kraemer F, Reaven GM. Relationship between insulin resistance, insulin 
secretion, very low density lipoprotein kinetics, and plasma triglyceride levels in normotriglyceridemic 
man. Metabolism. 1981 Feb;30(2):165-171. 
174. 
Howard BV. Lipoprotein metabolism in diabetes mellitus. J Lipid Res. 1987 Jun;28(6):613-628. 
175. 
BJC Health System. Drug Formulary, 200-2001. Hudson, OH: Lexi-Comp, Inc.; 2000. 
176. 
McLaughlin T, Abbasi F, Cheal K, Chu J, Lamendola C, Reaven G. Use of metabolic markers to identify 
overweight individuals who are insulin resistant. Ann Intern Med. 2003;139(10):802-809. 
177. 
Pouliot MC, Despres JP, Lemieux S, et al. Waist circumference and abdominal sagittal diameter: best 
simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related 
cardiovascular risk in men and women. Am J Cardiol. 1994 Mar 1;73(7):460-468. 
178. 
Kamel EG, McNeill G, Van Wijk MC. Usefulness of anthropometry and DXA in predicting intra-
abdominal fat in obese men and women. Obes Res. 2000 Jan;8(1):36-42. 
179. 
van der Kooy K, Leenen R, Seidell JC, Deurenberg P, Visser M. Abdominal diameters as indicators of 
visceral fat: comparison between magnetic resonance imaging and anthropometry. Br J Nutr. 1993 
Jul;70(1):47-58. 
180. 
Jensen MD, Kanaley JA, Roust LR, et al. Assessment of body composition with use of dual-energy x-
ray absorptiometry: evaluation and comparison with other methods. Mayo Clin Proc. 1993;68(9):867-
873. 
181. 
Heymsfield SB, Smith R, Aulet M, et al. Appendicular skeletal muscle mass: measurement by dual-
photon absorptiometry. Am J Clin Nutr. 1990;52(2):214-218. 
182. 
Abate N, Burns D, Peshock RM, Garg A, Grundy SM. Estimation of adipose tissue mass by magnetic 
resonance imaging: validation against dissection in human cadavers. J Lipid Res. 1994 
Aug;35(8):1490-1496. 
183. 
McGuire EA, Helderman JH, Tobin JD, Andres R, Berman M. Effects of arterial versus venous 
sampling on analysis of glucose kinetics in man. J Appl Physiol. 1976;41(4):565-573. 
184. 
Jensen MD, Nielsen S. Insulin dose response analysis of free fatty acid kinetics. Metabolism. 
2007;56(1):68-76. 
185. 
Finegood DT, Bergman RN, Vranic M. Estimation of endogenous glucose production during 
hyperinsulinemic-euglycemic glucose clamps. Comparison of unlabeled and labeled exogenous 
glucose infusates. Diabetes. 1987 Aug;36(8):914-924. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
40
186. 
Korenblat KM, Fabbrini E, Mohammed BS, Klein S. Liver, muscle, and adipose tissue insulin action is 
directly related to intrahepatic triglyceride content in obese subjects. Gastroenterology. 
2008;134(5):1369-1375. 
187. 
Deivanayagam S, Mohammed BS, Vitola BE, et al. Nonalcoholic fatty liver disease is associated with 
hepatic and skeletal muscle insulin resistance in overweight adolescents. Am J Clin Nutr. 
2008;88(2):257-262. 
188. 
Magkos F, Mittendorfer B. Stable isotope-labeled tracers for the investigation of fatty acid and 
triglyceride metabolism in humans in vivo. Clin Lipidol. 2009;4(2):215-230. 
189. 
Howie SR. Blood sample volumes in child health research: review of safe limits. Bull World Health 
Organ. 2011;89(1):46-53. 
190. 
Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ. A model-based method for assessing insulin sensitivity 
from the oral glucose tolerance test. Diabetes Care. 2001;24(3):539-548. 
191. 
Tillman R, Geller B, Craney JL, Bolhofner K, Williams M, Zimerman B. Relationships of parent and child 
informants to prevalence of mania symmptoms in children with a prepubertal and early adolescent 
bipolar disorder phenotype. Am J Psychiatry. In press. 
192. 
Herjanic B, Reich W. Development of a structured psychiatric interview for children: agreement 
between child and parent on individual symptoms. J Abnorm Child Psychol. 1982;10(3):307-324. 
193. 
Weissman MM, Wickramaratne P, Warner V, et al. Assessing psychiatric disorders in children. 
Discrepancies between mothers' and children's reports. Arch Gen Psychiatry. 1987;44(8):747-753. 
194. 
Angold A, Weissman MM, John K, et al. Parent and child reports of depressive symptoms in children at 
low and high risk of depression. J Child Psychol Psychiatry. 1987;28(6):901-915. 
195. 
Jensen PS, Rubio-Stipec M, Canino G, et al. Parent and child contributions to diagnosis of mental 
disorder: are both informants always necessary? J Am Acad Child Adolesc Psychiatry. 
1999;38(12):1569-1579. 
196. 
Grills AE, Ollendick TH. Multiple informant agreement and the anxiety disorders interview schedule for 
parents and children. J Am Acad Child Adolesc Psychiatry. 2003;42(1):30-40. 
197. 
Ivens C, Rehm LP. Assessment of childhood depression: correspondence between reports by child, 
mother, and father. J Am Acad Child Adolesc Psychiatry. 1988;27(6):738-747. 
198. 
Mokros HB, Poznanski E, Grossman JA, Freeman LN. A comparison of child and parent ratings of 
depression for normal and clinically referred children. J Child Psychol Psychiatry. 1987;28(4):613-624. 
199. 
Nguyen N, Whittlesey S, Scimeca K, et al. Parent-child agreement in prepubertal depression: findings 
with a modified assessment method. J Am Acad Child Adolesc Psychiatry. 1994;33(9):1275-1283. 
200. 
Bird HR, Gould MS, Staghezza B. Aggregating data from multiple informants in child psychiatry 
epidemiological research. J Am Acad Child Adolesc Psychiatry. 1992;31(1):78-85. 
201. 
Expert Panel on Detection E, and Treatment of High Blood Cholesterol in Adults,. Expert Panel of the 
Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, 
Evaluation, And Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Text in box 
on page 2489, fifth bullet point, concerning drugs that increase LDL cholesterol and decrease HDL 
cholesterol. JAMA. 2001;285(19):2489. 
202. 
Aman M, Singh N. Aberrant Behavior Checklist (ABC) - Community Supplementary Manual (1994). 
1994. 
203. 
Aman MG, Singh NN, Stewart AW, Field CJ. The aberrant behavior checklist: a behavior rating scale 
for the assessment of treatment effects. Am J Ment Defic. 1985;89(5):485-491. 
204. 
Gogtay N, Ordonez A, Herman DH, et al. Dynamic mapping of cortical development before and after 
the onset of pediatric bipolar illness. J Child Psychol Psychiatry. 2007;48(9):852-862. 
205. 
Marshburn EC, Aman MG. Factor validity and norms for the aberrant behavior checklist in a community 
sample of children with mental retardation. J Autism Dev Disord. 1992;22(3):357-373. 
206. 
Brown EC, Aman MG, Havercamp SM. Factor analysis and norms for parent ratings on the Aberrant 
Behavior Checklist-Community for young people in special education. Res Dev Disabil. 2002;23(1):45-
60. 
207. 
Shaffer D, Gould MS, Brasic J, et al. A children's global assessment scale (CGAS). Arch Gen 
Psychiatry. 1983;40(11):1228-1231. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
41
208. 
Bird HR, Canino G, Rubio-Stipec M, Ribera JC. Further measures of the psychometric properties of the 
Children's Global Assessment Scale. Arch Gen Psychiatry. 1987;44(9):821-824. 
209. 
Guy W. ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: US Department of 
Health, Education and Welfare; 1976. 
210. 
Duke PM, Litt IF, Gross RT. Adolescents' self-assessment of sexual maturation. Pediatrics. 
1980;66(6):918-920. 
211. 
O'Loughlin J, Paradis G, Renaud L, Meshefedjian G, Gray-Donald K. Prevalence and correlates of 
overweight among elementary schoolchildren in multiethnic, low income, inner-city neighbourhoods in 
Montreal, Canada. Ann Epidemiol. 1998;8(7):422-432. 
212. 
Stevens J, Cornell CE, Story M, et al. Development of a questionnaire to assess knowledge, attitudes, 
and behaviors in American Indian children. Am J Clin Nutr. 1999;69(4 Suppl):773S-781S. 
213. 
Economos CD, Sacheck JM, Kwan Ho Chui K, et al. School-based behavioral assessment tools are 
reliable and valid for measurement of fruit and vegetable intake, physical activity, and television viewing 
in young children. J Am Diet Assoc. 2008;108(4):695-701. 
214. 
Sallis JF, Condon SA, Goggin KJ, Roby JJ, Kolody B, Alcaraz JE. The development of self-
administered physical activity surveys for 4th grade students. Res Q Exerc Sport. 1993;64(1):25-31. 
215. 
Paradis G, Levesque L, Macaulay AC, et al. Impact of a diabetes prevention program on body size, 
physical activity, and diet among Kanien'keha:ka (Mohawk) children 6 to 11 years old: 8-year results 
from the Kahnawake Schools Diabetes Prevention Project. Pediatrics. 2005;115(2):333-339. 
216. 
Nelson MC, Lytle LA. Development and evaluation of a brief screener to estimate fast-food and 
beverage consumption among adolescents. J Am Diet Assoc. 2009;109(4):730-734. 
217. 
Nelson MC, Neumark-Sztainer D, Hannan PJ, Story M. Five-year longitudinal and secular shifts in 
adolescent beverage intake: findings from project EAT (Eating Among Teens)-II. J Am Diet Assoc. 
2009;109(2):308-312. 
218. 
Veugelers PJ, Fitzgerald AL. Prevalence of and risk factors for childhood overweight and obesity. 
CMAJ. 2005;173(6):607-613. 
219. 
Borradaile KE, Foster GD, May H, et al. Associations between the Youth/Adolescent Questionnaire, the 
Youth/Adolescent Activity Questionnaire, and body mass index z score in low-income inner-city fourth 
through sixth grade children. Am J Clin Nutr. 2008;87(6):1650-1655. 
220. 
Levesque L, Guilbault G, Delormier T, Potvin L. Unpacking the black box: a deconstruction of the 
programming approach and physical activity interventions implemented in the Kahnawake Schools 
Diabetes Prevention Project. Health Promot Pract. 2005;6(1):64-71. 
221. 
Bennett DS, Power TJ, Rostain AL, Carr DE. Parent acceptability and feasibility of ADHD interventions: 
assessment, correlates, and predictive validity. J Pediatr Psychol. 1996;21(5):643-657. 
222. 
Post RM, Leverich GS, Fergus E, Miller R, Luckenbaugh D. Parental attitudes towards early 
intervention in children at high risk for affective disorders. J Affect Disord. 2002;70(2):117-124. 
223. 
Soley G, Marshall R, Chambliss C. Training therapists about client expectations of psychotherapy. 
Research report. Ursinus College. 2000. 
224. 
Johnston C, Seipp C, Hommersen P, Hoza B, Fine S. Treatment choices and experiences in attention 
deficit and hyperactivity disorder: relations to parents' beliefs and attributions. Child Care Health Dev. 
2005;31(6):669-677. 
225. 
Jorm AF, Morgan AJ, Wright A. A comparison of clinician, youth, and parent beliefs about helpfulness 
of interventions for early psychosis. Psychiatr Serv. 2008;59(10):1115-1120. 
226. 
Moos RH, Moos BS. Family Environment Scale Manual: Development, Applications, Research. A social 
climate scale. Fourth edition. Center for Health Care Evaluation. Department of Psychiatry and 
Behavioral Sciences. Stanford University School of Medicine. Available at: 
http://www.mindgarden.com/. . Palo Alto, California2009. 
227. 
Kuzuya H, Blix PM, Horwitz DL, Steiner DF, Rubenstein AH. Determination of free and total insulin and 
C-peptide in insulin-treated diabetics. Diabetes. 1977 Jan;26(1):22-29. 
228. 
Ensinck J. Immunoassays for glucagon. In: Lefebrve P, ed. Handbook of Experimental Pharmacology. 
Vol 66. New York: Springer Verlag; 1983:203-221. 
229. 
Farmer RW, Pierce CE. Plasma cortisol determination: radioimmunoassay and competitive protein 
binding compared. Clin Chem. 1974 Apr;20(4):411-414. 

Principal Investigator/Program Director (Last, First, Middle): Newcomer, John W.     
PHS 398/2590 (Rev. 10/10)                                                                 Page   
Continuation Format Page 
 
42
230. 
Shah SD, Clutter WE, Cryer PE. External and internal standards in the single-isotope derivative 
(radioenzymatic) measurement of plasma norepinephrine and epinephrine. J Lab Clin Med. 1985 
Dec;106(6):624-629. 
231. 
Patterson BW, Hachey DL, Cook GL, Amann JM, Klein PD. Incorporation of a stable isotopically 
labeled amino acid into multiple human apolipoproteins. J Lipid Res. 1991 Jul;32(7):1063-1072. 
232. 
Patterson BW, Zhao G, Elias N, Hachey DL, Klein S. Validation of a new procedure to determine 
plasma fatty acid concentration and isotopic enrichment. J Lipid Res. 1999 Nov;40(11):2118-2124. 
233. 
Elias N, Patterson BW, Schonfeld G. Decreased production rates of VLDL triglycerides and ApoB-100 
in subjects heterozygous for familial hypobetalipoproteinemia. Arterioscler Thromb Vasc Biol. 1999 
Nov;19(11):2714-2721. 
234. 
Elias N, Patterson BW, Schonfeld G. In vivo metabolism of ApoB, ApoA-I, and VLDL triglycerides in a 
form of hypobetalipoproteinemia not linked to the ApoB gene. Arterioscler Thromb Vasc Biol. 2000 
May;20(5):1309-1315. 
235. 
Patterson BW, Zhao G, Klein S. Improved accuracy and precision of gas chromatography/mass 
spectrometry measurements for metabolic tracers. Metabolism. 1998 Jun;47(6):706-712. 
236. 
Steele R. Influences of glucose loading and of injected insulin on hepatic glucose output. Ann NY Acad 
Sci.82:420-430. 
237. 
Frayn KN. Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol. 1983 
Aug;55(2):628-634.