MOBILITY- Metformin for Overweight & OBese ChILdren and Adolescents With BIpolar Spectrum Disorders Treated With Second-Generation AntipsYchotics
Phase: PHASE4
Conditions: Bipolar Disorder
Sponsor: Melissa Delbello

 
 
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TITLE:  
MOBILITY- METFORMIN FOR OVERWEIGHT & OBESE CHILDREN AND 
ADOLESCENTS WITH BIPOLAR SPECTRUM DISORDERS TREATED WITH 
SECOND-GENERATION ANTIPSYCHOTICS. 
FUNDER: 
PCORI  
 
 
 
A.  SPECIFIC AIMS  
The specific aim of this study is to address the significant gap in the evidence base for metformin (MET) 
use in overweight and obese youth with Bipolar Spectrum Disorders (BSD).  
 
The overall objective of this prospective, large, PRAGMATIC randomized trial is to study the impact of 
MET and healthy lifestyle intervention (LIFE) vs. LIFE alone on patient-centered outcomes of body 
weight, Second Generation Antipsychotics (SGA) adherence and satisfaction, psychiatric symptom 
burden (e.g. mood/anxiety), and quality of life (QoL). We will also examine metabolic outcomes. 
 
Specific Aim 1:  Assess the overall and subgroup-specific impact of MET+LIFE versus LIFE alone on 
short- (6mo) and long-term (24mo) weight and metabolic health in overweight and obese youth with 
BSD. 
Hypothesis (H)1.1: Overall, assignment to treatment with MET will abrogate more weight gain and lead to 
fewer adverse metabolic outcomes than LIFE alone at 6 and 24 months. 
H1.2 (Heterogeneity of Treatment Effects [HTE]): MET’s effectiveness at abrogating weight gain and 
components of metabolic syndrome will vary with respect to the following factors (see Patient Population 
and Background for rationale and prior data): 1) Change in BMI z-score from SGA initiation to baseline 
(in patients already receiving SGA treatment); 2) Prior exposure to and duration of SGA treatment; 3) 
Baseline BMI z- score; 4) Baseline psychotropic weight burden (in SGA non-naïve patients; as defined in 
Measures below); 5) BSD as diagnosed by a structured interview; 6) Age; 7) Type of insurance 
(public/private); 8) Ethnicity (Hispanic/Non); 9) Baseline insulin resistance (HOMA-IR); 10) Race 
(African-American (AA) vs non-AA). 
 
Specific Aim 2:  Assess the overall and subgroup-specific impact of MET+LIFE vs. LIFE alone on short-
(6mo) and long-term (24mo) SGA adherence and treatment satisfaction, including assessment of how 
these effects may be mediated by MET effects on weight gain. 
H2.1: Overall, assignment to treatment with MET will lead to better SGA adherence and greater treatment 
satisfaction than LIFE alone after both 6 and 24 months. 
H2.2: (HTE): MET effectiveness with regard to SGA adherence will vary with respect to: 1) Baseline 
weight acceptability; 2) Baseline psychotropic medication treatment acceptability; 3) Type of insurance 
(public/private).   
H2.3: The overall effects of MET treatment on SGA adherence will be mediated by degree of weight 
abrogation, and this relationship will be stronger among girls than boys.  We anticipate a possibly 
complex relationship between abrogation of weight gain (associated with MET) and SGA adherence, 
because weight gain may also be avoided via SGA non-adherence (at the probable cost of sub-optimal 
psychiatric outcomes; see Specific Aim 3).  A statistical framework (marginal structural models) to 
accommodate this possibility is described in the Statistical Analysis Plan.  
 
Specific Aim 3: Assess the overall and subgroup-specific impact of MET+LIFE vs. LIFE alone on 
psychiatric symptom burden, psychotropic treatment changes, and overall and weight-related QoL, 
including assessment of how these effects are mediated by MET effects on weight gain and SGA 
adherence. 
H3.1: Overall, assignment to treatment with MET will be associated with lower psychiatric symptom 
burden, decreased risk for symptom-driven treatment changes, and greater improvements in overall and 
weight-related QoL than LIFE alone at both 6 and 24 months, post-randomization. 
H3.2: (HTE): MET effects on psychiatric symptoms will be smaller in patients who meet full DSM criteria 
for a BSD.  
 
 

 
 
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B.  BACKGROUND AND SIGNIFICANCE  
Overweight and Obese Youth with Bipolar Spectrum Disorders (BSD):  1 out of 3 youth in the 
United States is overweight or obese. The rising prevalence of obesity and associated metabolic problems 
has led to elevated healthcare costs, marked morbidity, and high mortality. Compared with the general 
population, individuals with BSD have higher rates of obesity, poor access to medical care and premature 
mortality, largely due to the greater incidence of cardiovascular disease.1-3 BSD are common psychiatric 
illnesses characterized by periods of mania, hypomania, and depression. Epidemiological studies suggest 
that BSD most commonly begin during adolescence and have a prevalence rate of up to 5% in youth.4-7 
Youth with BSD have a relapsing and remitting illness course, with greater functional impairment, poorer 
quality of life (QoL), and greater economic burden  than those with later-onset BSD as well as youth with 
chronic medical and behavioral illnesses (e.g., asthma, heart conditions, and depression).11 A major factor 
contributing to poor outcomes is the high rate of overweight or obesity in youth with BSD. 12 Rates of 
overweight/obesity in youth with BSD vary in studies between 45-80%, more than double that of the 
general population.12-14 Overweight or obese youth with BSD have a more severe illness course, with a 
greater number of hospitalizations and higher rates of substance use disorders and physical abuse than 
youth with BSD who are not overweight or obese.15  Moreover, these youth typically become overweight 
or obese adults with more medical problems and higher rates of psychiatric symptom recurrence, 
hospitalizations, and suicidality16-18 as well as elevated morbidity and mortality.15,19 Therefore, prevention 
and intervention for weight gain in youth with BSD is of significant public health interest and clinical 
concern. 
 
Treatment with Second Generation Antipsychotics (SGAs) is on the Rise for Youth with BSD: 
Maximizing mood stability and minimizing medication side effects are the primary treatment goals for 
improving overall outcome and QoL of youth with BSD.20-22 Over the past decade, there has been an 
increase in evidence-based treatments for youth with BSD. There have been at least 10 double-blind 
randomized controlled trials demonstrating that SGAs are effective for treating mania and depression in 
youth with bipolar disorder.20,23-25 These studies have led to the United States Food and Drug 
Administration’s (USFDA) approval of four SGAs (olanzapine, risperidone, aripiprazole, and quetiapine) 
for the treatment of mania in youth and of olanzapine/fluoxetine combination as the only evidence-based 
treatment for depression associated with bipolar disorder in youth. Furthermore, in contrast to adults with 
bipolar disorder, several studies have recently demonstrated that youth with BSD have a significantly 
greater response to SGAs than to other mood stabilizing medications (e.g., lithium, valproic acid).23,29-31 
In fact, in the only double-blind placebo-controlled study of lithium for mania in youth, only 38% had a > 
50% reduction in symptoms of mania, as compared to most studies of SGAs, which report response rates 
of 55-80%.23 Without equally efficacious evidence-based options for treating BSD in youth, SGAs use 
will likely continue to increase. Although treatment with SGAs offers advantages over conventional 
antipsychotics (e.g., haloperidol), such as lower rates of extrapyramidal syndromes, there is significant 
concern about the dramatic increase in SGA prescriptions for youth, because younger patients are more 
susceptible to SGA-related side effects than adults23 and discontinuing SGAs without an effective 
alternative medication is not a treatment option for many youth with BSD. 
 
Treatment with SGAs often leads to considerable symptom improvement, but is very commonly 
associated with substantial weight gain early in treatment. A primary etiology of overweight/obesity 
in youth with BSD is SGA exposure.32 SGA-induced weight gain increases their already elevated long-
term risk of cardiovascular disease and premature mortality.33,34 Furthermore, metabolic syndrome is a 
common, more immediate consequence of SGA treatment in youth.35,36 The risk for SGA-induced weight 
gain is much greater in youth than in adults.23,32,37 The weight gain associated with SGAs is often 
dramatic and places these youth at risk for dyslipidemias and insulin resistance, which may progress to 
diabetes. Weight gain typically begins immediately following SGA initiation. In a recent study, over 50% 
of 272 treatment-naïve youth gained >7% of their baseline weight within 12 weeks.38,39 These data may 
underestimate the problem; recently, we determined that 80% of SGA-treated youth gained > 7% of their 
baseline weight over two months while hospitalized at our Academic Health Center. The so-called 
“appetite aggression” induced by SGAs40,41 may lead to violent behaviors and medication refusal 
following food restraint.  

 
 
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Although all SGAs cause some weight gain in youth, the amount of weight gain varies by specific SGA, 
prior exposure to SGAs, and the individual.1,42 Treatment-naïve youth may be more susceptible to SGA-
related weight gain.1,38,43,44 Nonetheless, weight gain typically continues over the course of treatment. 
Indeed, we found 1 lb/week weight gain over 16 weeks in youth prescribed olanzapine, risperidone, or 
quetiapine for up to one year.36,45 While it is unclear whether SGA-related weight gain is dose-dependent, 
1,38,46,47 SGA polypharmacy may be associated with greater risk of metabolic syndrome compared to SGA 
monotherapy.43 
 
SGA-related weight gain in this already overweight/obese population is particularly problematic, as most 
obese youth become obese adults.48 In fact, studies report that approximately 70% of youth treated with 
SGAs are overweight or obese.13,49 Although relatively few youth develop diabetes because of their large 
insulin reserves, 12-50% of youth treated with SGAs experience hyperinsulinemia and dyslipidemia,50 
which are related to the development of metabolic syndrome and cardiovascular morbidity and 
mortality.51 Additionally, obesity during youth influences later morbidity and mortality to a greater extent 
than obesity present only during adult life 52,53 and the relationship of overweight, obesity, and metabolic 
abnormalities to poor cardiovascular outcomes is accelerated when age-inappropriate weight gain and 
metabolic abnormalities begin during development.54 The immediate and dramatic nature of the 
anthropometric and metabolic effects is a common reason for SGA discontinuation,55,56 is predictive of 
medication non-adherence in youth,57 and has a negative impact on overall satisfaction with care and 
illness outcome.58 Moreover, poor adherence in youth  with BSD increases the likelihood of relapse59,60 
and is associated with substance use.59 
 
Evidence-Based and Accessible Strategies for Managing SGA-related Weight Gain and Obesity in 
Youth with BSD are Urgently Needed. The mechanisms of SGA-related weight gain are likely multi-
factorial, involving effects on central and peripheral hormonal, neuronal, and metabolic regulation of 
energy intake and consumption.1,61-63 Regardless, the widespread and devastating impact of SGA-related 
weight gain on psychiatric symptoms, other health outcomes, and health care costs have led to an 
immediate need for standardized, evidence-based approaches to manage this common and serious side 
effect.64 
 
Despite SGA-related medical concerns, these medications are highly effective for treating BSD23 and 
thus, given a choice, patients and their families would prefer to effectively manage this side effect rather 
than change medications and risk relapse (see Significance). Nonetheless, discontinuation of SGAs or 
switching to a lower-risk SGA is a potential strategy to manage weight gain. Unfortunately, switching 
SGAs produces limited or inconsistent improvements in weight and metabolic outcomes, along with some 
psychiatric risk.65 Indeed, in the recently completed IMPACT study,66 youth treated with SGAs who were 
randomized to switching to an SGA with lower weight burden experienced significantly greater rates of 
psychiatric symptom exacerbation than those maintained on their original SGA with lifestyle intervention 
or metformin added.    
 
Healthy Lifestyle Interventions promote physical activity and dietary lifestyle changes. Healthy lifestyle 
programs designed for youth, such as the “Healthy Eating Plan” associated with Healthworks!, developed 
and tested by Dr. Siegel (Co-Investigator), may lead to weight loss in obese youth.67,68 However, some 
studies  show only limited benefits and involve programs that are not easily accessible.69,70 Studies of 
older adolescents and adults treated with SGAs found that healthy lifestyle interventions are effective for 
preventing weight gain and for weight loss, respectively, as well as for improving QoL, particularly when 
used early in the course of SGA treatment.71-75 In general, 2-5% reduction in weight or 1-1.5 body mass 
index (BMI) units is reported in studies of healthy lifestyle interventions for patients with mental health 
disorders. However, these studies suffer from high dropout rates, small sample sizes, and short 
duration.65,71-73,75  

 
 
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Furthermore, the effects on weight and other metabolic parameters are small compared with 
pharmacological interventions.65. Despite these limitations, The American Medical Association (AMA) 
200776 provides guidelines on interventions for youth ages 2-19 years old who meet CDC criteria for “at 
risk for overweight” and “overweight” (BMI% >85%ile) that involve simple education regarding diet and 
physical activity, i.e., healthy lifestyle intervention. Although these guidelines were not specifically 
developed for mentally ill youth, the approach has advantages for this population, including simple 
dietary guidelines and inexpensive strategies to reduce sedentary behavior. This approach is similar to the 
standardized Healthy Eating Plan and Physical Activity education (LIFE) that will be provided to each 
participant of the proposed trial.67,76  
 
Metformin (MET) for Obesity in Youth. Pharmacological therapies, such as MET, supplement the 
effects of lifestyle interventions for weight loss. MET targets a molecular mechanism for weight gain, i.e., 
hypothalamic neuronal AMP kinase, a metabolic “fuel sensor”. SGAs directly affect hypothalamic AMP 
kinase through histamine H1 receptors, decreasing leptin and insulin sensitivity.77 MET is an oral anti-
hyperglycemic, insulin- sensitizing agent that reduces hepatic gluconeogenesis and is noteworthy among 
diabetes treatments for not promoting weight gain.78 MET is approved by the USFDA for treatment of 
type II diabetes in youth >10 years. MET decreases food intake in obese, non-diabetic individuals.79 A 
recent meta-analysis80 of 14 randomized clinical trials (N>1000) examining the effectiveness of MET for 
obesity in youth found an average BMI reduction of -1.38 (95% CI, -1.93 to -0.82) over 6 months 
compared with controls. Although a greater effect was seen for youth with a BMI > 35 at baseline, the 
authors concluded that a large trial is needed to determine the benefits to subgroups. Smaller meta-
analyses report BMI reductions of -1.1 to -2.7 in obese youth compared with placebo or lifestyle 
intervention alone, in trials ranging from 6-12 months with MET dosages of 1000-2000mg/day.81,82 
Fasting insulin resistance, glucose homeostasis, and dyslipidemia improved after MET. In these studies of 
youth from 8-18 years old, MET was well-tolerated at up to 2000mg/day.83-85  
 
MET is extremely safe in youth. Lactic acidosis is extremely rare, especially in the non-elderly and those 
with creatinine levels < 1.4, and the risk of neuropathy from chronic vitamin deficiency is low.85 MET for 
Weight Gain in Patients Treated with SGAs. We chose MET over other evidence-based medications for 
weight loss (e.g., orlistat, sibutramine, or topiramate) because MET is the only medication that has been 
systematically studied for reducing weight in adults and youth treated with SGAs and because of its 
superior tolerability and safety profile when prescribed in conjunction with SGAs. Indeed, we first 
reported that MET is safe and effective for minimizing weight gain in youth taking SGAs.36,86 
Furthermore, in our randomized, placebo-controlled study, MET was associated with weight loss and 
improved insulin sensitivity, minimal discontinuation and no significant side effects other than transient 
gastrointestinal symptoms.36 In contrast, 1 in 6 patients discontinued orlistat and 1 in 2 patients 
discontinued sibutramine because of side effects. Additionally, although topiramate is effective for weight 
loss, cognitive “slowing” commonly leads to discontinuation.87 Subsequent to our studies, the utility of 
MET for weight loss in patients treated with SGAs has been confirmed in several RCTs and meta-
analyses.39,65,88-93 In fact, there are now seven meta-analyses of treatments for weight gain and/or 
cardiovascular risk factors in patients taking SGAs.  
 
Most recently, Gierisch et al,65 conducted a meta-analysis of studies of patients taking SGAs who were 
treated for weight gain and reported that adding MET was the only intervention studied to date that 
improves metabolic parameters, although, “comparative effectiveness studies are needed to test multi-
modal strategies.”65 In another review of 11 MET studies (N=685) lasting on average 15.5 weeks, we 
determined that MET: 1) Produces statistically   and clinically significant weight loss (-3.1 kg; 95% CI -
4.5 to -1.6); 2) Improves lipid and glucose levels; 3) Is well tolerated; and 4) That weight gain resumes 
after stopping MET.39 We also concluded that MET’s effects might continue beyond 16 weeks, but noted 
there is a paucity of long-term studies.94   Although MET may be even more effective for prevention (i.e., 

 
 
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when MET is started concurrently with an SGA) rather than  intervention (i.e., participants were started 
on MET when on a stable dose of SGA), there have been only 2 prevention studies. While these studies 
were both from China and are therefore difficult to compare to studies conducted in the U.S., their 
findings are highly consistent with our preliminary data. Therefore, based on our patient surveys and 
these data, we opted to include both treatment-naïve and non-naive patients in our proposed pragmatic 
trial. This would be the first study to directly examine differences in MET’s effectiveness for prevention 
vs. intervention for weight gain in youth taking SGAs. Only one trial has compared MET alone, placebo 
plus healthy lifestyle intervention, and MET plus healthy lifestyle intervention: Wu et al.95 reported 
significant weight reduction and improved glucose and insulin levels for combined MET plus healthy 
lifestyle intervention compared with either alone.95 Based on these studies, the following 
recommendations were made:   1) The duration of future MET studies must be longer than 16 weeks to 
provide long-term efficacy data; 2) Larger studies are necessary to identify mediators and moderators; 3) 
Studies of MET in comparison to or combined with other strategies is much needed; 4) Because not all 
patients respond to MET, it is necessary to study heterogeneity of treatment effects (i.e., who is the best 
candidate for MET).85,96,97 
 
In summary, meta-analyses confirm that treatment with MET is the strategy for which there is the most 
evidence for weight loss in patients taking SGAs.65,90 However, each of the meta-analyses conclude that 
large- scale studies of MET in community settings and for long duration are urgently needed. We propose 
to conduct a pragmatic trial that addresses each of these gaps in evidence by investigating the long-term 
effectiveness of MET in obese and overweight youth with BSD, treated with SGAs in a community 
setting. 
 
C.  PRELIMINARY STUDIES 
Pilot naturalistic study: 47 youth with a BMI > 85%ile and prescribed an SGA were treated with MET 
at CCHMC’s residential treatment facility, using a MET dose titrated to 850 mg 2x/day over 2-4 weeks, 
with a further increase to 1000 mg 2x/day by 2 months if weight gain continued. BMI z score was the 
primary outcome measure. Electronic medical record data from two groups of these MET-treated patients 
were analyzed (MS submitted for publication): Group 1 (SGA non-naïve) was taking SGAs for at least 2 
months (mean ± SD = 9.2 ± 7.2 months, range= 2.3 - 24.9) prior to MET initiation (n=29, 47% girls, 
age=13.9 years ± 2.5); Group 2 (SGA naïve) started on MET and SGAs simultaneously (n=18; 59% girls, 
age=14.2 years + 2.8). The groups were similar, and, as in our previous report, 40% received > 1 
psychotropic medications. 80% of the SGA non-naïve patients had weight gain > 7% over the 2 months 
prior to MET initiation and exhibited a 0.45 sd increase in their BMI z score (Figure 1). Surprisingly, 
more of the SGA naïve (94%) than the SGA non-naïve (78%) patients had a BMI > 95%ile at MET 
initiation. BMI Z score was stabilized by MET in both groups, but there was a significant group by month 
interaction (p=0.003, generalized linear model), even after controlling for the higher baseline BMI z score 
(p=0.002). 
  
 
 

 
 
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Figure 1: MET prevention and intervention effects SGA-induced weight gain in youth 
Group1=SGA-non-naïve Group 2= SGA-naïve 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This group difference resulted in part from the immediate decrease in BMI z score in the SGA naïve 
group (BMI z score decreased by 0.23). BMI z score of the SGA non- naïve group decreased by 0.27+0.5 
during the 6 months on MET. SGA naïve patients responded at lower MET doses than SGA non-naïve 
patients (764 ± 225 mg/d vs. 834 ± 285). Additional determinates of MET response were: 1) Higher 
baseline BMI z score (p<0.0001); 2) Longer length of SGA treatment prior to MET initiation (p=0.049); 
and, 3) A > 7% increase in weight over the 2 months prior to starting MET (p=0.0007). These data 
confirm our previous results that MET abrogates SGA-induced abnormal weight gain in youth and also 
adds concerning information that a larger proportion of youth on SGAs are affected by weight gain than 
previously reported. 
 
IMPACT Study: Most recently, Dr. Correll completed a large rigorously controlled NIMH-supported 
study of youth with psychiatric disorders and BMI > 85%ile and SGA-related weight gain. Participants 
were randomized to 1 of 3 arms: 1) continuation of current SGA (control arm) (N=44); 2) switch from 
current SGA to lower-risk agent (LRA) (N=30) or 3) co-treatment with MET + continuation of current 
SGA (MET) (N=46). 
 
Sample characteristics were as follows:  Age: 13.7 (7.6-19.9; <18=88%) Male=65% Hispanic:12% Black: 
28%. At 12 and 24 weeks there was a significant decrease in BMI Z-scores in the MET (-0.034, -0.088, 
respectively) and LRA (-0.051, -0.112) groups compared with the control group (+0.026, +0.040). 
Additionally, at 24 months weight gain in the control group was 8.5lbs and 0.3lbs in the LRA group. In 
contrast, the MET group had an average decrease of -0.4lbs. 14.9% in the MET lost >5% of weight 
compared to 2.3% in the control group and 10.0% in the LRA group (Fisher’s exact, p=0.059). 
Importantly, dropout rates in the LRA (39%) and control (28%) groups were higher than in the MET 
(24%) group. The most common (58%) reason for discontinuation in the LRA group was “inadequate 
therapeutic effect.” 
 
These findings confirm those of prior studies, including our own.36,90 However, these, along with previous 
studies, have been conducted in academic settings, typically with many rigorous study criteria. Findings 
from such studies may not be generalizable to BSD youth treated in community settings where access to 
these interventions (e.g. healthy lifestyle strategies and medications) is often limited. Moreover, patient-
centered outcomes were not assessed in these studies. 
 

 
 
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D.  INVESTIGATOR EXPERIENCE 
Principal investigator Dr. Melissa P. DelBello and lead Co-investigator Dr. Christoph Correll are 
international experts in BSD research and treatment. Both have been involved in numerous clinical trials 
of SGAs in youth with BSD and have ardently studied and warned about the metabolic side effects of 
SGAs in youth. These efforts led investigators to seek out effective methods to minimize these side 
effects, including the initial studies of SGAs and MET. This proposal is the culmination of these 
endeavors, seeking to collaboratively expand from clinical efficacy to field effectiveness studies with the 
ultimate goal of providing relevant and immediate information to patients, their family members, 
clinicians and policy makers to enable them to make informed and patient-centered decisions regarding 
SGA and MET use. Both investigators have exceptional track records for study oversight, completion and 
budget management of large projects supported by the National Institutes of Health, industry, and 
foundations.  
 
The project team also includes: 1) Scientific experts in the field of community child and adolescent 
mental health (Michael Sorter, MD; Brian Kurtz, MD; Victor Fornari, MD), pharmacoepidemiology 
(Stephen Crystal, PhD) pediatric endocrinology (Nancy Crimmins, MD, Phyllis Speiser, MD, Steven 
Chernausek, MD) bipolar disorder and psychopharmacology (John Kane, MD; Stephen Strakowski, MS; 
Rodrigo Patino, MD), pediatric healthy lifestyle  management (Robert Siegel, MD), complex database 
management (Jeffrey Welge, PhD;Yuanfang Xu, PhD; Thomas Blom, MS), regulatory oversight (Jeremy 
Corsmo, MPH), psychometrics and patient-centered outcomes (Adam Carle, PhD), treatment adherence 
and QoL (Avani Modi, PhD; James Varni, PhD) and biostatistics (Jeffrey Welge, PhD;Bing Huang, PhD) 
and 2) Key stakeholders representatives from parents and patients with BSD (Avraham Goldstein, Elaine 
Nowery, Dan Nowery, Adam Schwarber, Bev Schwarber, Sharde Scott-Manning, Barbara Portugal, 
Cindy Starr, Barbara Valerius), advocacy organizations (NAMI-Heather Turner; DBSA-Ingrid Deetz), 
professional organizations (AACAP-Carmen Head; Endocrine Society-Steven Chernausek) and payers 
(Medicaid-Mary Appelgate, MD and Humana-Nick Patel, PharmD, Vinit Nair, B-Pharm, MS). This 
research team was assembled with the ultimate goal of ensuring the project 1) Answers key clinical 
questions that are important to patients and families; 2) Is completed on schedule, within budget; and 3) 
Holds to the principles of good clinical practice, human subject protection and scientific rigor. 
 
E. EXPRIMENTAL DESIGN & METHODS 
a. Methods and Procedures 
 
Study Overview: We propose a prospective, large, PRAGMATIC, randomized trial to study the impact 
of MET and healthy lifestyle intervention (LIFE) vs. LIFE alone on patient-centered outcomes of body 
weight, SGA-adherence and satisfaction, psychiatric symptom burden (e.g. mood/anxiety), and QoL. We 
will also examine metabolic outcomes.  
 
The focus of the pragmatic trial is to determine which intervention is most effective through observation 
of care as usual conditions or in “real-world clinical settings” rather than under controlled experimental 
circumstances.  Information should be collected when available as part of routine clinical practice rather 
than through study-specific procedures, with minimal interference to clinical practice. Adherence to 
protocol recommendations from practitioners and patients are observed and documented throughout the 
study.   
 
 
 

 
 
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Subjects: We propose to recruit approximately 1800 overweight/obese youth with BSD who are 
prescribed SGAs from at least 40 public and private mental health practices (approximately 900 each 
from sites under the coordination of University of Cincinnati, Midwestern center and from within the sites 
under the Northwell Health East Coast coordinating center), to participate in the proposed patient-
centered large pragmatic trial examining the effectiveness of MET and LIFE vs. LIFE alone. Patients will 
be eligible for study participation if they are: 
   
1. Inpatient or outpatient age 8-19 years inclusive; participants must live with a parent, guardian, or 
caregiver; 
2. Fluent in English; 
3. Diagnosed or told by a clinician that they have any of the following bipolar spectrum disorders 
(BSD): bipolar I, bipolar II, unspecified bipolar and related disorders, Disruptive Mood 
Dysregulation Disorder (DMDD), cyclothymic disorder, other specified bipolar and related 
disorders, as well as mood disorder not otherwise specified (if diagnosed in the past as per DSM-
IV);  
4. Body mass index >85%ile for age and sex by standard growth charts; 
5. Received a new or ongoing prescription for at least one SGA (i.e., olanzapine, clozapine, 
risperidone, quetiapine, aripiprazole, ziprasidone, iloperidone, lurasidone, paliperidone, 
brexpiprazole or cariprazine) that is not prescribed as a PRN medication 
 
Additionally, patients will be excluded by the following:  
 
1. Patients will be excluded if they have had exposure to a total daily dose of MET 1000 mg bid for 
at least 2 weeks in the past 3 months; 
2. Patients will be excluded if they could not tolerate MET during the recommended titration 
schedule outlined in the protocol; 
3. Major neurological or medical illnesses that affect weight gain (e.g., unstable thyroid disease) or 
require a systemic medication that might impact weight or glucose regulation (e.g., diabetes 
mellitus [insulin], chronic renal failure [steroids]);  
4. If lab results are available in the last 6 months, then a fasting serum glucose ≥ 126 mg/dL on two 
occasions prior to or during screening, indicating need for prompt treatment. 
5. If lab results are available in the last 6 months, then a serum creatinine ≥1.3 mg/dL on two 
occasions during screening and/or follow-up, indicating potential impairment of renal functioning 
6. Pregnant or breast feeding; 
7. Based on their clinician’s assessment, children and caregivers who are unable to complete 
screening/baseline assessments for any reason; 
 
The enrollment rate will be approximately 1-2 patients/month/site for a recruitment time of 57 months. 
 
Randomization: We will randomize 1800 patients (1:1 ratio) to either MET + LIFE or LIFE alone. All 
patients will receive a lifestyle intervention (LIFE) of brief standardized education. Once a patient is 
determined to meet the above criteria and has provided appropriate informed consent/assent, site staff will 
assign the participant a patient ID number. The site staff will then log on to the study’s REDCap site and 
enter the Patient ID, and the following stratification factors: site, overweight or obese, previous SGA 
exposure, and sex of patient (transgendered patients will be randomized according to sex assigned at 
birth). REDCap has a built in mechanism that prevents duplication of subject ID numbers. 
 
Once all stratification factors have been entered, the REDCap randomization module will return the next 
assignment for the appropriate strata.   
 
 
 

 
 
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OUTCOME MEASURES: 
Due to MOBILITY being a pragmatic and largely observational study, any participant who is deemed 
eligible by the above criteria will be enrolled in MOBILITY. These criteria, along with providing 
appropriate informed consent/assent, are the necessary criteria for participation in MOBILITY. We will 
continue to collect data in a naturalistic manner from all patients who have met these criteria regardless of 
whether they begin the intervention to which they have been randomized or have not attended all of their 
clinical follow up appointments. Patients will have follow-up study visits and data collected concurrent 
with their existing CLINICAL visit schedule.   
 
Primary Outcome Measure: BMI z-score will be computed from measurement of height and weight. 
Raw BMI is calculated as (weight (kg)/ height (m)2). Normalized BMI (z-score, adjusted for age and sex), 
will be calculated using the program provided by the USDA/ARS Children’s Nutrition Research Center at 
Baylor College of Medicine (http://www.bcm.edu/cnrc/bodycomp/bmiz2.html). At each site weight will be 
measured with a Seca scale, model 813, calibrated to the nearest 0.2 kg per manufacturer instructions 
annually using standard weights. A standard operating procedure on weight and height measurement will 
be made available to each site, and staff will be trained in its application. Scales will be zeroed before each 
individual measurement and the patient will be instructed to remove their outer clothing, shoes, backpack, 
coat, and any other extra heavy clothing. Weight will be recorded to the nearest 0.1 kg. Height will be 
measured with a wall-secured stadiometer. The stadiometer is not required to be calibrated as stated by 
manufacturer instructions. Height will be measured without shoes while standing on a flat surface with 
chin parallel to the floor. The patient’s height will be recorded to the nearest 0.1 cm. 
 
 
Secondary Outcome Measures: 
Metabolic Health and Nutrition: As per clinical standards, blood pressure will be measured after 5 
minutes of sitting at each patient visit. A minimum of 8-hour fasting total cholesterol, low-density 
lipoprotein (LDL)-cholesterol, high density lipoprotein (HDL)-cholesterol, triglycerides, glucose, insulin, 
and glycosylated hemoglobin (HbA1c) will be recommended for collection (see Table 2). For HTE 
analyses the homeostatic model assessment for insulin-resistance (HOMA-IR) will be computed as: 
(Insulin [IU/mL] x Glucose [mg/dL] /405). Following the modified ATPIII guidelines,
130-132 metabolic 
syndrome is defined when >3 of the following: 1) abdominal obesity (BMI >90%ile, as in NHANES study 
of metabolic syndrome in youth
133
; 2) blood pressure >90%ile for height, age, sex; 3) fasting triglycerides 
>150 mg/dL; 4) low HDL cholesterol (males <40 mg/dL and for females <50 mg/dL); 5) fasting glucose 
>100 mg/dL Fulfillment of individual criteria and metabolic syndrome will be outcome measures. 
 
Adherence and Treatment Satisfaction Outcomes: 
Self-Reported Adherence and Barriers: Caregivers and children will answer standardized questions related 
to adherence and adherence barriers to taking SGAs, MET (if applicable), and LIFE. Information on 
missed and partial doses of each medication in the past week will be ascertained. Questions prefaced by 
non-judgmental language that normalizes non-adherence to treatment will be used. A time period of one 
week will be used to maximize respondent recall and minimize memory decay. 
 
MEMS®6 TrackCap/SimpleMed PillBox: The Medication Event Monitoring Systems (MEMS) [AARDEX 
Corporation] and the SimpleMEd PillBox are electronic monitoring systems that measure the dosing 
histories of oral medications. They will be used to monitor adherence to SGAs or MET in a sub-sample of 
300 patients at a subset of sites. They consist of a standard plastic vial or box with a threaded opening and 
a closure for the vial that contains a micro-electronic circuit to register the dates and times the 
bottle/pillbox is opened and closed. Data from the MEMS TrackCap will be downloaded at each study 
visit. Patients randomized at an inpatient site to MEMS monitoring will only receive this monitoring if 
they are scheduled to be followed at an outpatient site that was selected for MEMS monitoring; otherwise, 
the MEMS aspect of the randomization should be disregarded.  

 
 
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Treatment Acceptability: A single-item 5-category Likert-type patient report of satisfaction with 
psychotropic medication regimen (0=not at all satisfied to 4=very satisfied). 
 
International Physical Activity Questionnaires (IPAQ):
140 The IPAQ is an instrument for cross-national 
monitoring of physical activity and inactivity with good reliability and validity for monitoring population 
levels of physical activity in diverse settings to assess adherence to LIFE physical activity 
recommendations. 
 
We will also assess food choices and eating habits using questions selected from the Healthworks Nutrition 
Screener, Satiety and Food Responsiveness using items from the Child Eating Behavior Questionnaire, and 
Uncontrolled Eating and Cognitive Restraint using items from the 3-factor Eating Questionnaire.167-168  
 
Psychiatric Outcome Measures:  
PROMIS (Patient-Reported Outcomes Measurement Information System) measures undergo a 
standardized, extensive measure development process which includes literature review, expert meetings, 
qualitative interviews with families, item development, cognitive testing of the item content, confirmatory 
factor analyses to establish construct validity, and item response theory to establish psychometric 
properties. We will use items from the validated short-form assessments of Anxiety, Depression and Anger 
as well as Global Health and Peer Relationships (the latter two are HRQoL instruments; see next section). 
 
PROMIS Anxiety Short Form: focuses on fear (e.g., fearfulness), anxious misery (e.g., worry), and 
hyperarousal (e.g., nervousness). Marginal Cronbach's α > 0.85 
 
PROMIS Depression Short Form: focuses on negative mood (e.g., sadness), anhedonia (e.g., loss of 
interest), negative views of the self (e.g., worthlessness, low self-esteem), and negative social cognition 
(e.g., loneliness, interpersonal alienation). Marginal Cronbach's α > 0.85 
 
PROMIS Anger Short Form: assesses angry moods (e.g., irritability and reactivity) and aggression (verbal 
and physical). Marginal Cronbach's α > 0.80 
 
Clinical Global Impressions Scale-Severity (CGI-S): Single-item assessments of symptom severity. 
Severity is rated from 1 (not at all ill) to 7 (among the most severely ill patients).  
 
Young Mania Rating Scale (YMRS):   11-item clinician-rated instrument that assesses symptoms of 
mania. 
 
Children's Depression Rating Scale (CDRS)-Revised selected items: a clinician-rated scale used for 
assessing severity of depression and change in depressive symptoms. 
 
Quality of Life (QoL) and Functional Outcomes: We will ask patients and caregivers the number of 
days that they missed from school and work, respectively, in the last month and will use items from the 
following scales:  
 
PROMIS Global Health: assesses physical, mental, and social health. Cronbach's α=0.88. 
 
PROMIS Peer Relationships: measures understanding and communication; companionship; and quality, 
reciprocity, and size of patients’ social network Cronbach's α> 0.80. 
 
Children's Global Assessment Scale (CGAS): A single-item index of overall functional capacity. Scores 
range from 1 (indicating severe impairment) to 100 (indicating superior functioning). 
 
Sizing Me Up & Sizing Them Up: self-report and parent-proxy measures of obesity-specific HRQoL for 
youth, with subscales measuring Emotional, Physical and School Functioning, Teasing/ Marginalization, 
Positive Social Attributes, Mealtime Challenges, and Social Distress/Avoidance, and a Total QoL score. 
Excellent documented internal consistency, test-retest reliability and convergent validity. 

 
 
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Body Weight, Image and Self-esteem Evaluation (B-WISE®): scale designed to capture and quantify 
weight satisfaction, ability to control food intake, self-esteem and other the psychosocial consequences of 
weight gain, scored on a 3-point Likert scale from 1 (never)-3 (all the time), Cronbach’s α=0.79.   
 
Caregiver Strain Questionnaire-Short Form 7 (CGSQ-SF7): Routine assessment of objective and 
subjective internalized caregiver strain, scored on a 5-point Likert scale from 1 (not at all)-5 (very much a 
problem), with good validity and reliability. 
 
Laboratory Measures: 
Site staff will collect and report any lab results that are obtained as part of the patient’s clinical care.  
Laboratory tests will be suggested according to clinical practice guidelines, but will not be required for 
patients enrolled in MOBILITY, with the exception of a pregnancy test for all girls who have reached 
menarche and who are randomized to metformin.  Additionally, since patients receive their treatment at 
mental health treatment facilities throughout the country and MOBILITY is a pragmatic study, patients 
may have laboratory tests performed at any laboratory facility that they typically would as part of their 
routine clinical care. We will collect the results of the laboratory tests once they are available in medical 
records. Therefore, we will not require CLIA certificates or other regulatory documents for each 
laboratory facility. Table 2 (at intervals that reflect standard clinical care) indicates the recommended lab 
measures according to practice guidelines.   
 
Other outcome measures:   
 
Patient Health Questionaire-9 (Item 9):152 Assesses current suicidality; 0 (not at all), 3 (nearly every day) 
scale.  
Zucker Hillside Side Effects Form:119,153 Assesses intensity (1=not present, 4=severe) of adverse events in 
five domains (behavioral, neurological, cardiovascular, autonomic, GI side effects). Open ended AE 
(adverse event) questions at every visit. 
 
STUDY SCHEDULE AND TREATMENT 
Since MOBILITY is a pragmatic study, we will collect the above information, when possible, and in 
conjunction with patients’ clinical visits. We will allow for data collection approximately monthly for the 
first three months, followed by every three months thereafter, for a total duration of 24 months. However, 
these visits and timeline are just guidelines. Data will be collected through information acquired during 
clinical visits. There is no minimum number of visits required for patients to continue to participate in 
MOBILITY and have their data collected. However, sites should not schedule patient appointments 
exclusively to conduct study specific visits or procedures. Therefore, it is anticipated that not all study 
visits or procedures will be completed for every patient.  However, we will emphasize the 
importance of collecting Month 6 and 24 data since those are primary outcomes.  Labs tests that are 
recommended in the Schedule of Events are based upon standard clinical practice guidelines, but will not 
be required for continuation in the study.  
 
Each site will have a copy of the MOBILITY Projected Visit Dates Spreadsheet (also found on the 
website:  MOBILITYstudy.org). The site coordinator will enter the patient’s baseline visit date where 
indicated, and the recommended visit schedules will auto-populate.  However, once patients are 
randomized at baseline, MOBILITY is primarily an observational pragmatic study, therefore, data will be 
collected only at clinical appointments.  
 
Screening/Baseline Procedures: At screening and following informed consent/assent and review of initial 
study criteria, we will obtain demographic and clinical information as follows: 
 
Demographic and Clinical Characteristics: Site staff will record patients’ date of birth, sex, type of 
insurance, contact information, and prior type and duration of psychiatric treatments. Patients will report 
current daily nicotine intake (packs/day) and whether they currently use other drugs (checking all that 
apply from a list of common drugs). Data on treatments and substance use will also be collected at six-

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
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month intervals. 
 
Screening laboratory tests recommended include renal and hepatic function tests, electrolytes, CBC and 
thyroid stimulating hormone (TSH), as per Table 2. However, we will collect any results of labs performed 
within the last six months, if available. The only mandatory test for screening will be a pregnancy test in 
girls of menarche.  
 
Every attempt will be made to encourage clinicians to obtain laboratory testing prior to starting 
metformin, however, patients who have not had baseline labs will continue to be followed in MOBILITY.    
 
Kiddie-SADS-Present and Lifetime (K-SADS-PL 2013):  After discussion with our patient/parent partners 
and site clinicians, we determined that the appropriate study population is all patients who receive a 
clinical diagnosis of a BSD, as these patients will typically receive SGA treatment regardless of whether 
they meet full DSM criteria. However, it was decided that using a structured research interview would 
provide an opportunity to examine concordance of clinical and research diagnoses and to assess HTE (i.e., 
do youth with a research diagnosis respond to MET differently than those with only a clinical diagnosis 
because risk for overweight/obesity and metabolic disorder may be higher for those with a research BSD 
diagnosis). Therefore, BSD diagnoses will be confirmed with the mood modules of the K-SADS-PL, a 
semi-structured diagnostic interview with established test-retest reliability.
128 
(Interviewers will be trained 
by qualified personnel. Trained staff at the respective coordinating centers who are not associated with the 
clinical sites will administer the interview separately to guardians and patients, if > 12 years old). Based 
on feedback from the clinical sites, we determined that having their staff administer the K-SADS-PL 
might create diagnostic confusion, therefore, we will develop a mechanism that clinicians will be provided 
with the K-SADS-PL diagnoses after participants have reached Month 6 of study participation. 
 
 
Type and duration of current/prior SGA treatment and weight 
gain during SGA treatment: Prior data suggest that the effect 
of MET may depend on the extent of weight gain during SGA 
treatment prior to study entry.  Site personnel will document 
weight and height prior to any SGA treatment, specific SGAs 
received and duration of treatment with each (i.e., by 
obtaining medical records). For a priori analyses of potential 
heterogeneity of treatment effect (HTE), we will then 
distinguish SGA-naïve patients (no current or prior SGA 
treatment as of baseline visit) versus those with any current or 
prior SGA treatment. Within the latter group, we will 
compute change in BMI z-score from the start of SGA 
treatment until baseline and perform HTE analyses to 
determine whether MET effectiveness varies based on the 
duration of SGA treatment and/or amount of excess (relative 
to normal development) weight gained during treatment. 
 
Classification system for psychotropic treatments: We anticipate substantial variation in the specific 
psychotropic medication regimens prescribed at baseline, and that these treatments will vary over time. 
SGAs may be prescribed singly or in combination at varying doses, and concomitant treatment with 
antidepressants, non-SGA mood stabilizers (e.g., divalproex and lithium) and/or stimulants (e.g., for the 
subset of patients with co-morbid ADHD) will also be common. For our a priori analyses of potential 
HTE depending on pharmacologic treatments other than MET, we have reduced this large set of options to 
three groups based on available data regarding their weight effects. Our data will provide considerable 
additional observational evidence about the impact of specific regimens, and we will conduct detailed 
analyses that may suggest useful post-hoc alternative classifications.
1,129 
Our survey data indicate that 
Table 1. Metformin (MET) Titration 
<50kg AM/PM 
>50kg AM/PM 
Week 1 0/500mg 
0/500mg 
Week 2 0/500mg 
0/500mg 
Week 3 500/500mg 
500/500mg 
Week 4 500/500mg 
500/500mg 
Week 5 500/1000mg 
500/1000mg 
Week 6 500/1000mg 
500/1000mg 
Week 7 500/1000mg 
1000/1000mg 
Week 8 500/1000mg 
1000/1000mg 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 13 
 
 
risperidone, aripiprazole, and quetiapine will be the three most common SGAs prescribed. 
 
(1) Mild to Moderate Risk of Weight Gain: Monotherapy with lurasidone, ziprasidone, or aripiprazole. 
 
(2) High Risk of Weight Gain: Monotherapy with asenapine, iloperidone, paliperidone, quetiapine or 
risperidone. SGA-naïve patients receiving monotherapy with a group (1) SGA and patients prescribed 
divalproex or lithium plus a group (1) SGA will be assigned to this group. 
 
(3) Severe Risk of Weight Gain: Monotherapy with clozapine or olanzapine. SGA-naïve patients 
receiving monotherapy with a group (2) SGA and patients prescribed divalproex or lithium plus a group 
(2) SGA will be assigned to this group.  
 
Note that we have not incorporated dose effects because dose-dependent weight gain has not been 
consistently demonstrated for most SGAs. We will examine association of dose and dose adjustments with 
weight change and other outcomes, though exploratory analyses of such observational relationships will 
require careful consideration of confounding factors. 
 
(See Next Page) 
 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 14 
 
 
Table 2. Schedule of Events. 
The Table of Events is an approximation for when routine clinic visits might occur. In order to adhere to 
the pragmatic nature of the study, data will only be collected during the patient’s regularly scheduled 
clinical visits and a participant should not be brought in exclusively to conduct study specific visits.  No 
specific study labs will be mandated for study participation, with the exception noted above (i.e., 
pregnancy test)  The lab tests noted in the Schedule of Events are suggested in accordance with clinical 
practice guidelines for patients receiving SGAs.    
  
Measures 
Visit (Approximate Months) 
SCREEN/ 
BASELINE 
1 
2 
3 
6 
9 
12 
15 18 21  24 
Demographics & Clinical 
Characteristics (P, Y) 
X 
Structured Diagnostic Assessment (K-
SADS)a  (C) 
X 
Medication List (C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Weight/Metabolic 
Body Mass Index [height and weight] 
(C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Recommended Metabolic Labs [insulin, 
lipid profile, glucose, hemoglobin A1C] 
(C) 
Xb,d, e 
X 
X 
X 
X 
Treatment Satisfaction 
Treatment Acceptability Questionnaire 
(Y) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Adherence 
Adapted Adherence Measure (P & Y) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
International Physical Activity 
Questionnaire (P & Y) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Nutrition, Satiety, and Appetite 
Measures (P & Y) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
MEMs Cap/ SimpleMed Pillbox 
 
Upload [300 patient subset] (C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Mood/Anxiety 
PROMISc Anxiety Short Form (P & 
Y)
X 
X 
X 
PROMISc Depression Short Form (P 
& Y) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
PROMISc Anger Short Form (P & Y) 
X 
X 
X 
Children’s Depression- 
Revised/Selected Items (C)  
X 
X 
X 
Young Mania Rating Scale (C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Clinical Global Impression-Severity 
(C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Quality of Life (Weight & General) 
Body Weight, Image & Self-Esteem 
Evaluation, B-WISE (Y) 
X 
X 
X 

 
 
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"Sizing Me Up"/"Sizing Them Up" (P 
& Y) 
X 
X 
X 
Height Weight Self for Viewpoint-
Items 5 & 6 (wt. acceptability) (Y) 
X 
X 
X 
X 
X 
X 
X X 
X X 
X 
PROMISc Pediatric Peer 
Relationships- Short Form (P & Y) 
X 
X 
X 
PROMISc Global Health Scale (P & 
X 
X 
X 
X 
X 
X 
X X 
X X 
X 
Clinical Global Assessment Scale (C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Number of School/Work Days Missed 
[past 30 days] (Y & P) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Caregiver Strain Questionnaire (P) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Other recommended measures 
Vital Signs [blood pressure, heart rate] 
(C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Other recommended laboratory 
measures:d,e thyroid, renal and liver 
panels, pregnancy test, CBC, and 
Vitamin B12 (C) 
X 
 
 
X 
 
 
 
 
 
X 
Suicidality, Patient Health 
Questionnaire (PHQ)-Item 9 (C) 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Zucker Hillside Side Effects Form (C) 
X 
 
 
 
X 
 
 
 
 
 
X 
Total Time/Visit for Youth 
Questionsf (minutes) 
15-20 
5 
5 
5 
15-20 5 
7 
5 
7 
5 
15-
20 
Total Time/Visit for Caregiver 
Questionsf (minutes) 
15-20 
5 
5 
5 
15-20 5 
7 
5 
7 
5 
15-
20 
P= Caregiver; Y= Youth, C= Clinician. Kiddie-SADS-Present and Lifetime-PL 2013;  
aKSADS will be performed at any time point after enrollment.  
b Baseline fasting serum glucose is highly recommended, however, results within the past six months will be 
acceptable.  
cItems from the PROMIS=Patient Reported Outcome Measurement Information System;  
dAny labs completed within the past 6 months are acceptable except for pregnancy test;  
At baseline a negative serum or urine pregnancy test is required in girls who have reached menarche prior to 
prescribing metformin.   
eLabs are recommended, however these are not required to start or continue in the study. 
fClinician and other assessments (e.g. labs, vitals) are part of a routine clinical visit and therefore, do not add time.  
 
Our population will be > 85%ile BMI (weight of such 8- and 9 year-olds will likely be comparable to an 
average 10-year-old child); 2) MET has a favorable side effect profile; and 3) prior reports as well as the 
investigators’ previous experiences with the safe and well-tolerated use of MET in patients > 8 years 
old.125 All patients will have a renal panel prior to starting MET to evaluate for renal impairment that 
would preclude use of MET.  
 
Participants randomized to treatment with MET will start at a recommended dose of 500 mg orally at 
night and slowly titrated in 2-week intervals to ensure that each patient achieves maximum insulin-
sensitizing effects of the drug while minimizing the chance of side effects (see Table 1). We selected 50kg 
as the weight at which we felt comfortable increasing to a maximum total daily dose of 2000mg (1000mg 
twice daily). These youth will be at a low-end adult-comparable weight. We anticipate that most 
participants >10 years old will be >50kg, which is the 95%ile for weight for 10-year-olds of both sexes. 
Children <50kg, likely those patients <10 years, will have a reduced maximum daily dose of 1500mg. If 

 
 
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patients exhibit untoward gastrointestinal side effects while taking MET, clinicians will have the option to 
switch to an equivalent dose of the once-daily extended-release form of MET, which may alleviate side 
effects.126 If a patient does not tolerate a specific dose, the clinician will also have the option of reducing 
that dose by 500mg/day to a minimum recommended daily dose of 500mg twice daily. At Week 8, the 
dose for the <50kg group may be titrated to the maximum of 2000mg/day (1000mg twice daily) if tolerated 
and strategy, in general, MET will be at steady-state target levels well before key outcomes are assessed at 
Month 6. We will also recommend that MET be taken with food to minimize side effects. If a participant’s 
BMI percentile <5% (=underweight) his/her treatment with MET will be discontinued. Although the risk of 
low vitamin B12 while taking MET is associated with age > 50 years and having type II diabetes, we will 
monitor B12 levels and a CBC throughout study participation.127 
 
Clinicians and patients must be willing to be randomized to either treatment arm. However, the clinician 
may choose to initiate or continue (for those on a subtherapeutic dose) metformin at their clinical 
discretion regardless of randomization following a recommended initial 3 month period of treatment. 
These patients’ data will be considered in a different analytic group regardless of randomization.   
 
Comparators: Healthy Lifestyle Intervention (LIFE): This healthy lifestyle intervention (LIFE) consists 
of introducing participants and families to a healthy eating plan and physical activity. Prior to study 
initiation, study site staff will participate in a live (or taped) training session from a dietician to become 
educated with LIFE. At baseline visit patients and caregivers will watch a standardized video about LIFE 
that was developed by Dr. Siegel and his group.  The video explains the concept of a Traffic Light Plan 
(TLP) that is a classification system for food and activity (GREEN, YELLOW, RED). This plan 
encourages participants to limit RED foods (high-calorie, low-nutrient) and RED activity (sedentary), 
replacing them with GREEN foods (low-calorie, high-nutrient) and GREEN activity (moderate-to-high 
intensity). Participants will also receive a Food Reference Guide and Activity Reference Guide, 
categorizing food and activities in the TLP framework that was adapted from the Healthworks! Youth 
Fitness 101 program developed specifically for overweight and obese children by Dr. Robert Siegel. The 
baseline session will include a brief discussion of diet and physical activity instruction. At least 150 
minutes/week of physical activity will be recommended.  A link to the LIFE video and all handouts will 
be available on the study websites at www.mobilitystudy.org  and offers a standardized, home-based 
exercise component for patients. At follow-up visits, patients and caregivers will have the option to watch 
the video again, and study staff will briefly reinforce nutritional guidance, remind participants about the 
need for 150 minutes of physical activity/week, and be available to answer any general questions on 
healthy lifestyle habits. Prior studies indicate that 33% of youth will decrease BMI with this LIFE 
intervention.67    
 
b. Data Analysis and Data Monitoring:  
Drs. Welge and Huang will conduct and/or supervise and have final responsibility for all analyses. Drs. 
Modi and Carle will conduct and/or supervise portions of the analysis that fall under their specific areas 
of expertise. Under the direction of the senior statistical team, Dr. Mara, Mr. Blom and Ms. Liu will carry 
out portions of the programming and analysis, which will begin within 6 months of the start of the trial 
(see Milestones). Analysis of short-term data will be complete by month  68 (when the last patient will 
have been followed for six months), and analysis of the long-term data will be completed by month 69 
(when all 24- month follow-up will be complete), which will allow graphical and tabular summaries and 
narrative interpretation of the results to be completed by Month 72. No interim efficacy analyses or 
adaptive features are planned. Model fitting performed before all outcome data are available will be 
provisional, for the purpose of verifying code and making limited modeling decisions (e.g., determining 
from early data whether parameters, such as residual variance or scale reliability, may vary among 
subgroups). The Data Management and Biostatistics Committee will receive regular updates on progress 
in completing the statistical analysis plan, discuss any additional or modified analyses, and report to the 
Steering Committee as necessary.  
 
Data Analyses: MOBILITY will use REDCap154 for patient-reported outcomes. REDCap is a software 
toolset and workflow methodology for collection and management of clinical research data developed by 
Vanderbilt University, in collaboration with institutional partners including the University of Cincinnati 
Academic Health Center. This secure, flexible, web-based application provides: 1) an intuitive interface 

 
 
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for data entry and real time validation (e.g., automated data type and range checks); 2) HIPAA-compliant 
and 21 CFR Part 11- ready audit trails for tracking data manipulation and exports; 3) record locking and 
electronic signature functions; 4) control of view/edit rights; 5) a tool for reporting, monitoring and 
querying patient records; and 6) automated export to common statistical packages. REDCap is hosted on a 
network specially designed to support the rigorous security and compliance requirements of clinical 
research projects.  Administered by the CCHMC Division of Biomedical Informatics, this network 
features multiple firewalls and a central facility for managing hosted systems and users, creating another 
layer of access control and audit capability on top of what REDCap already provides (e.g., user access can 
be monitored and controlled at the network level without making any changes within REDCap itself). 
These capabilities are available to authorized network administrators and REDCap study owners only, 
and user access changes are documented by an audit trail. The data management team will monitor the 
database throughout the study to assure that data are complete and accurate. 
 
 
Analyses of Specific Aims: Each specific aim involves assessment of domains that are hypothesized to 
be affected by MET either directly or indirectly through other domains. Our approach to potential HTE 
distinguishes effect moderators from mediators of distal outcomes. Moderators include fixed patient 
characteristics and pre-treatment covariates values. Mediators include variables that are hypothesized to 
be affected by MET treatment and that may in turn affect other outcomes. We will utilize generalized 
linear mixed models (GLMM), which allow for outcomes from a range of distributions and will include 
random effects to account for unspecified causes of variation among sites and patients, and subgroup-
specific coefficients to assess HTE.  
 
The basic model for outcomes will be a GLMM with change from baseline to either 6- or 24- months as 
the dependent variable. Independent variables will include fixed-effect terms for randomized treatment 
group (MET), baseline value of the outcome, and random site effects. For HTE analyses, a baseline value-
by-MET interaction (to assess HTE due to baseline status) will be included, and for particular domains 
additional continuous or categorical covariates and treatment-by-covariate interaction terms will be added 
to assess HTE. Outcomes within each domain will share the same set of HTE terms, specified in the 
Specific Aims.  Because the subgroups are in general correlated (e.g., duration of SGA treatment, weight 
gain history and psychotropic weight burden will be correlated with baseline BMI), we will include 
multiple first-order HTE terms simultaneously (to assess heterogeneity independently attributable to each 
moderator), but will also fit separate models for each term to gauge the impact of this correlation on our 
conclusions. Higher-order treatment-by-covariate interactions will not be included a priori, but will be 
explored with rigorous adjustment for multiple testing, using step-down testing (removing highest-order 
interaction terms first) or information criteria based on penalized likelihoods to guide model selection.  
 
Marginal Structural Modeling for Causal Inference: SA1 concerns the overall and sub-group specific 
effects of MET on abrogation of weight gain (with changes in BMI z-score from baseline as outcomes) 
and metabolic health, particularly metabolic syndrome and its component criteria. BMI and adherence 
measures are obtained at all study visits, so our models for these measures will use all available 
observations by treating the longitudinal series of changes in these variables and SGA treatment as 
dynamically dependent on each other. We will use marginal structural models156 to address this bi-
directional dependence, computing the probability of SGA treatment regimen change at each visit within 
weight gain treatment regimen groups and using appropriately normalized versions of the inverse of the 
probability as the stabilized weights. 
 
Adjustment of Self-Reported Adherence: Adherence rates for self-report and MEMS (where available) 
will be calculated as number of treatments/day divided by number of prescribed daily treatments. 
Truncated MEMs adherence rates (maximum of 100%) will be used in analyses to reduce inflation as a 
result of overuse or extra openings due to prescription refills. This method has been used successfully in 
studies assessing electronic monitoring (EM) adherence.157,158 Self-reported adherence vastly 
underestimates adherence as measured by EM.159 While it is not feasible to use EM for all patients and 
medications, we will use the statistical relationship between EM and self-reported adherence in a 
subsample of 300 patients (equal numbers per treatment group, i.e., 100 for SGA adherence who are also 
taking MET, 100 for SGA adherence who are not taking MET, and 100 for MET adherence, who are 

 
 
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randomly selected the 8 randomization strata) to estimate EM adherence from self-report in the remaining 
patients. We will regress EM adherence on self-reports, using standard diagnostics to ensure that non-
linearity, heterogeneity of variance across the range of adherence, or subgroup-specific regression 
relationships are included as needed. Predictions that accurately reflect the residual uncertainty can then 
be generated by multiple imputation, and the resulting complete-sample datasets will be used in primary 
analyses involving SGA adherence, with sensitivity assessed by parallel analyses using self-report 
directly.  
Data and Safety Monitoring plan: The Cincinnati site will provide an independent certified study data 
monitor who will visit each site prior to study initiation and approximately every 6 months thereafter. The 
monitor will follow a monitoring plan that includes source data verifying and ensuring compliance with 
good clinical practices (GCPs), the IRB-approved protocol and other applicable regulatory guidelines, and 
will report any problems to the site PI and the other clinical staff members.  Additionally, adherence to 
inclusion/exclusion criteria, completion of scheduled assessments, withdrawal/termination procedures, 
and maintenance of training and certification for all research personnel will be continuously monitored. 
The monitor will provide written recommendations following each visit to each site. All reports will be 
reviewed by members of the Steering Committee. 
 
Safety and tolerability data will be monitored by an independent Data Safety Monitoring Board (DSMB), 
the study investigators, and the CCHMC and NS/LIJ IRBs. The function of the DSMB is to protect the 
safety of the study participants. The DSMB members are: Tobias Gerhard, PhD, pharmacoepidemiologist 
with focus on SGA risks; Avraham Goldstein, adult diagnosed with BSD as an adolescent; Robert Parker, 
PhD, biostatistician; Boris Birmaher, MD, child psychiatrist; David Repaske, MD, pediatric 
endocrinologist; and Robert Gardiner, parent of youth with BSD. Members will disclose and resolve any 
potential conflicts of interest prior to each meeting. 
 
At their initial meeting (prior to study initiation), the DSMB members will select a Chair, who will 
provide written meeting minutes and recommendations and will review the study protocol and consent 
forms for safety and validity. Biannually, they will evaluate whether recruitment goals are being met and 
monitor the occurrence of adverse events and early withdrawals or terminations. All serious adverse 
events will be reported to the site’s IRB and the DSMB within 7 days of knowledge of occurrence and 
will be followed until resolution, permanent outcome of the event, or stabilization. The DSMB will 
provide recommendations based on this review (e.g., to terminate the study or recommend modifications, 
including additional safety or tolerability measures). Dr. DelBello, in conjunction with the DSMB, will be 
responsible for ensuring that DSMB reports are filed with the IRBs and PCORI. The DSMB will also 
provide the investigator with a summary report that includes their recommendations. Adverse event 
reports will be collected systematically at patient visits, any rated as severe will be documented and 
reported. All adverse events will be reviewed by the DSMB biannually and reports will be sent to the 
FAC and Steering Committees. 
 
c. Data Storage and Confidentiality:  
Information will be obtained from clinical assessments and confidential HIPAA compliant online 
questionnaires that will be completed on the tablet. Access to activate tablets will be limited to study staff 
and password protected. Patient and their caregivers will confirm their identity by answering a personal 
security question at the end of each session. Specific materials obtained in the study will include patient 
and caregiver reports from behavioral, adherence, treatment satisfaction and QOL assessment tools. 
 
Other information, such as medical history and clinician symptom ratings also will be collected by site 
staff from clinician notes and/or medical records or directly entered into password protected online 
databases by the clinicians or patients/caregivers. The following samples will be collected: urine in girls 
for a pregnancy test, if they confirm menarche. As recommended in standard clinical practice an 8-hour 
fasting blood draw for serum glucose, insulin, liver function tests, renal panel (including electrolytes), 

 
 
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TSH, HbA1c, and lipid profiles. The results of pregnancy test will be given to parents of minors at the 
discretion of the clinician. Blood pressure, and anthropometrics including height, weight, BMI, and BMI 
%ile will be recorded. 
 
As with any study, there is a potential risk of loss of confidentiality. Consistent with the Federal 
guidelines, all patients and guardians will be informed of the federally mandated reporting laws for child 
abuse and neglect, verbally and in the written consent form. Therefore, if abuse or neglect is suspected the 
Department of Human Services will be called. Risks associated with this include embarrassment, legal 
consequences, and removal of the child from the parents’/legal guardians’ home.  
 
d. Setting: Assessments will be conducted at the participating community-based private and public 
mental health treatment centers in the East Coast and Midwest Regions.  The participating site personnel 
will be made up of a real-world clinical trial team that includes members with varied training and 
experience with appropriate support from a qualified physician and centralized study trial team.  Site staff 
who will be obtaining informed consent, reviewing medication lists, and collecting and assessing adverse 
events must complete Human Participants Protection and Good Clinical Practice (HPP and GCP) training 
prior to involvement in the study.  These essential study staff will be documented via a site specific 
delegation of authority (DOA) log. DOA logs will be reviewed by the study monitor at each site visit to 
ensure that all new essential study staff have been added.  
 
e. Laboratory methods and facilities: Participants will be sent for blood work, at the clinician’s 
discretion, to any laboratory facility.  No specific laboratory test will be mandated, as it is expected that 
laboratory procedures will take place in settings where everyday care happens, such as community clinics, 
hospitals and health systems.  It is recommended that clinicians adhere to practice guidelines and labs are 
ordered through the patient's medical practice by their clinicians.    
 
f. Estimated Period of Time to Complete the Study:  The following is a timeline for the 72-month 
period of the proposed study: Months 1-2: Train study personnel; Months 3-57: Recruit an average of 
approximately 1-2 patients/site/month from 40 sites over 54 months for a total sample size of 1800 
patients, we expect 25% of the sample recruited by M10-11; 50% recruited by M30; 75% by M45 and the 
final patient recruited by M57; Months 34-68: Complete subject participation, data entry, and analyses. 
Final analyses will be completed M70- after the last patients have completed 6 and 24 months of follow-
up. 
 
F. HUMAN SUBJECTS 
a. Description of Subjects 
We will recruit a total of 1800 youth, (approximately 900 each from ~20 Midwest sites and ~20 East 
Coast  mental health treatment sites) to participate in the proposed patient-centered large pragmatic trial 
examining the effectiveness of MET and LIFE vs. LIFE alone. Subjects will be eligible to enroll and 
randomize if they are ages 8-19 years old (inclusive) at the time of screening, overweight or obese (BMI 
> 85%), continuing or starting treatment with at least one SGA (i.e., olanzapine, clozapine, risperidone, 
quetiapine, aripiprazole, ziprasidone, iloperidone, lurasidone, paliperidone, or asenapine), have a clinical 
diagnosis of BSD (bipolar I or II disorder, cyclothymia, or bipolar or mood not otherwise specified, or 
other specified bipolar or mood disorder) and are living at home with their caregivers. 
 
Based on feedback from our advisory board and our consultants including children and adolescents who 
are in state custody is essential so that our sample is not biased as SGA exposure in this population is 
extremely high, Therefore, we will allow children and adolescents who are under the custody of DHS  to 
enroll. We will seek the approval of a legally authorized individual from the county (e.g. Hamilton or 
Butler). This may be the county caseworker or it may be their supervisor. It will NOT be the case 
manager from the private agency that is handling the placement. But, the private agency case manager 

 
 
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will know who the appointed county JFS social worker. The foster parent(s) will NOT have the authority 
to authorize enrollment into a research study. We will need the foster parent(s)’ approval to ensure that 
study visits will be attended and compliance with study requirements is manageable. Consents may be 
faxed for signature to the legally authorized individual as specified from each county. A fully signed copy 
of the consents will be offered back to JFS after the consenting process occurs.       
 
For youth who reside in Northern KY and are in the custody of the Cabinet for the Commonwealth of 
Kentucky, we will obtain the permission of the assigned Cabinet Social Worker who is assigned to the 
individual. That person will determine if they have the authority to grant permission for participation or 
whether they need to seek higher approval.  
 
For youth in the custody of New York City foster care, the investigator will submit a proposal along with 
our IRB approval and relevant consent forms to the Administration for Children’s Services (ACS).  ACS 
will review, process, and approve the proposal. Once approved the proposal will go to the State Office of 
Children and Family Services (OCFS) for their review and approval. 
 
b. Sample Size  
Eighteen hundred (1800) children will participate in the study. Each site will enroll up to 90 participant’s 
total or 1-2 participants/month for a total study enrollment period of 57 months (some sites completed 
regulatory requirements well into the recruitment period) 
 
c. Inclusion Criteria 
1. Inpatient or outpatient age 8-19 years inclusive; participants must live with a parent, guardian, or 
caregiver; 
2. Fluent in English; 
3. Diagnosed or told by a clinician that they have any of the following bipolar spectrum disorders 
(BSD): bipolar I, bipolar II, unspecified bipolar and related disorders, Disruptive Mood 
Dysregulation Disorder (DMDD), cyclothymic disorder, other specified bipolar and related 
disorders, as well as mood disorder not otherwise specified (if diagnosed in the past as per DSM-
IV);  
4. Body mass index >85%ile for age and sex by standard growth charts; 
5. Received a new or ongoing prescription for at least one SGA (i.e., olanzapine, clozapine, 
risperidone, quetiapine, aripiprazole, ziprasidone, iloperidone, lurasidone, paliperidone, 
brexpiprazole or cariprazine) that is not prescribed as a PRN medication;   
 
Exclusion Criteria: 
1. Patients will be excluded if they have had exposure to a total daily dose of MET 1000 mg bid for 
at least 2 weeks in the past 3 months; 
2. Patients will be excluded if they could not tolerate MET during the recommended titration 
schedule outlined in the protocol; 
3. Major neurological or medical illnesses that affect weight gain (e.g., unstable thyroid disease) or 
require a systemic medication that might impact weight or glucose regulation (e.g., diabetes 
mellitus [insulin], chronic renal failure [steroids]);  
4. If lab results are available in the last 6 months, then a fasting serum glucose ≥ 126 mg/dL on 2 
occasions prior to or during screening, indicating need for prompt treatment. 
5. If lab results are available in the last 6 months, then a serum creatinine ≥1.3 mg/dL on 2 
occasions during screening and/or follow-up, indicating potential impairment of renal functioning 
6. Pregnant or breast feeding; 
7. Based on their clinician’s assessment, children and caregivers who are unable to complete 
screening/baseline assessments for any reason; 
 

 
 
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If a patient does not have fasting glucose or renal lab results within 6 months prior to their baseline visit, 
then every attempt will be made to obtain labs prior to starting metformin. However, patients will not be 
excluded from the study if they are unable to get serum creatinine or glucose. Renal dysfunction is 
extremely rare in otherwise healthy children and adolescents, and in a clinical setting renal panels are not 
typically done prior to starting metformin, unless there is a reason to suspect kidney disease.  
 
The initial rationale for requiring normal glucose prior to starting metformin was that patients who are 
hyperglycemic may need to receive metformin + LIFE rather than LIFE alone. However, if a patient does 
not have a normal glucose available prior to randomization, but meets all other study criteria, we will 
continue to collect data from the patient and administer the treatment to which they have been 
randomized, so that at a minimum they will receive LIFE. We will also strongly encourage their clinician 
to emphasize the importance of obtaining fasting glucose levels. If, however, lab results are available in 
the last 6 months, patient with a fasting glucose ≥ 126 mg/dL on 2 occasions will be excluded and 
referred to their primary care clinician for prompt evaluation for Type II diabetes. Including these 
patients, rather than waiting until they get their glucose level checked, will ensure that at least the patient 
will receive LIFE while we encourage the clinician to engage the patient to get fasting metabolic labs. At 
any point in the study of a patient has a fasting glucose > 126 mg/dL on 2 occasions, they will be 
immediately referred to their primary care physicians and possibly endocrinology.  
 
d. Intended Demographics 
We propose to recruit 1800 inpatient or outpatient youth (ages 8-19 years, inclusive) who have a BMI ≥ 
85%ile (i.e., overweight or obese) and are prescribed SGAs as either ongoing or new treatment. There 
will be no barriers to inclusion based on sex, race, ethnicity or clinical status with the exception that 
otherwise eligible patients with current or prior therapeutic MET exposure will be excluded. We 
anticipate equal recruitment by sex, and based on US census reports from the recruitment regions, 
approximately 25% non-White (20% African American/Black and 5% Asian) and 15% Hispanic youth. 
Because the diagnosis of BSD and the use of SGAs is more controversial in younger patients and there 
are little safety data regarding the use of MET in patients younger than 8 years old, we restrict our sample 
to 8-19 year olds.  
 
Process for consenting Spanish speaking caregivers: If it is determined that a caregiver cannot be properly 
consented in English, the caregiver will receive a consent document translated into Spanish. A qualified 
translator will be present to answer any questions the caregiver may have about the study. A qualified 
translator can be a bilingual study staff member, or a certified translator. A progress note will be written 
indicating a qualified translator was present at time of consent. The progress note will remain attached to 
the signed consent form.   
 
e. Study Population Source 
Participants will be recruited from approximately 40 community-based mental health treatment centers, 
some of which have multiple locations.  
 
f. Recruitment Plans 
Because our study is a large pragmatic patient-centered trial, we have minimal eligibility requirements, 
which should facilitate the feasibility of recruitment. Each selected clinical site is a large mental health 
practice that has more than 100 youth eligible for study participation. Therefore, we should have no 
difficulty reaching our proposed rate of recruitment of 1-2 patients/month/site at 40 sites and over the 
course of 57 months. In general, study visits will coincide with clinical visits, so we anticipate that 
retention will be at least as good as in our prior naturalistic outcome studies (~90%) over 2 years. Each 
week study sites will send their screening logs to the lead site that identify potential patients and will 
collect ethnicity information.   

 
 
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We will create a study website that will have the address of www.mobilitystudy.org. This website will be 
used as a communication tool to post important study related material and information to 
committee/stakeholder members, study sites, and patients enrolled in the study. A Login username and 
password will be required to view study specific material related to interventions, meeting material, and 
videos. There will be information about the study that will be available for the public to view, including 
information about the study, metformin, and bipolar spectrum disorders.  
 
We will have a study Twitter account for the MOBILITY study that will be used to disseminate 
information about the study. This will not be used as a means of recruitment. We will use the handle 
name @MOBILITY_Trial, to communicate information about the study to likely followers, including 
clinicians, researchers, members of the media and study participants and their families. The account will 
not provide medical or health advice or recommendations. It will provide background information about 
the study; background information about Metformin; and information about any publications or webinars 
related to the study. It could provide links to other research that is published during the next 5 years 
related to Metformin, SGAs and bipolar disorder in adolescents. We anticipate posting one tweet per 
week. Tweets will be reviewed prior to publication by Melissa DelBello, MD, or a study-related 
personnel designated by her.  
 
G.  RISK TO SUBJECTS  
a. Level of Risk Description  
Potential risks from study participation include possible risks from MET treatments, and other risks 
associated with any study participation (e.g. loss of confidentiality and fatigue and frustration from 
repeated evaluations): 
 
Confidentiality: As with any study, there is a potential risk of loss of confidentiality. Consistent with the 
Federal guidelines, all patients and guardians will be informed of the federally mandated reporting laws 
for child abuse and neglect, verbally and in the written consent form. Therefore, if abuse or neglect is 
suspected the Department of Human Services will be called. Risks associated with this include 
embarrassment, legal consequences, and removal of the child from the parents’/legal guardians’ home.  
 
Healthy Lifestyle Intervention (LIFE): There is little to no risk associated with LIFE, although patients 
may become bored, fatigued or develop anxiety by the weight monitoring and recommendations. The 
exercises recommended may result in muscle soreness or strains, as there may be an increase in activity 
levels.  
 
MET: All patients, guardians and clinicians will be made aware that MET is not approved by the USFDA 
for the treatment of overweight or obese youth or for SGA-related weight gain. Risks associated with 
MET include: Common side effects (≥5%): diarrhea, nausea, and vomiting. Less likely (<5%): abdominal 
pain/abdominal distension, constipation, dizziness, dysguesia, dyspepsia, flatus, headache, and upper 
respiratory infection. Rare: Lactic acidosis, 47 cases out of 1 million patients taking MET. Very rare: 
Vitamin B12 deficiency. Clinicians will be educated that youth who develop symptoms of lactic acidosis, 
such as hyperventilation, myalgia, malaise, and somnolence should stop taking MET and notify their 
treating clinician immediately. Additionally, we will educate all study-related clinicians about the very 
rare side effect of Vitamin B12 deficiency and associated potential clinical manifestations of vitamin B12 
deficiency (i.e., peripheral neuropathy and pernicious anemia).  
 
Blood Draws: Although blood draws are not required for participation in MOBILITY, they are highly 
encouraged in accordance with clinical practice guidelines. No study specific blood draws will be 
performed.  Risks of blood draws include discomfort and/or bruising at the blood draw site. Less 
commonly, fainting, the formation of a small blood clot, swelling of the vein and surrounding tissue, 

 
 
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bleeding from the puncture site or infection may occur. Other potential risks include possible fatigue and 
frustration from repeated evaluations and the potential for additional weight gain, worsening of lipids, and 
increased insulin resistance. Since only patients prescribed SGAs as part of their clinical care will be 
approached for participation and no patient will be started on an SGA for the purpose of the study, we do 
not consider other risks associated with SGA treatment as study specific risks. Additionally, patients may 
experience exacerbation of BSD symptoms during study participation. 
 
Protection Against Risks. The final protocol will be approved by the Steering and Family Advisory 
Committees, the CCHMC and NS/LIJ IRBs, and the study data and safety monitoring board (DSMB) 
prior to recruiting. Patients/parents will be clearly informed they are free to terminate participation at any 
point during study participation. Furthermore, to minimize risks associated with the study, all personnel 
will be thoroughly trained as previously described prior to having any contact with patients or data.  
 
Breach of confidentiality: All data will be entered into electronic case report forms (eCRF) or directly into 
databases (e.g. patient/parent questionnaires) that are 21 CFR Part 11 and/or HIPAA compliant. We will 
train all study-related personnel to follow HIPAA regulations for research to ensure confidentiality of all 
data and that the rights of the patients/guardians are protected. All data will reside on password-protected 
computers, with only the investigators and key members of the research team having access. A variety of 
other measures will be taken to protect confidentiality, including: We will 1) assign and use a unique ID 
number to each patient to label all components of the protocol, instead of patient names, 2) restrict access 
to the key linking names and ID numbers to key staff and the PI at each site, 3) store of any paper with 
data at each site in a locked file cabinet, 4) if necessary, only send de-identified from clinical sites to the 
data coordinating site. Patients and caregivers will directly enter information regarding adherence, 
treatment satisfaction, mood, anxiety, QoL, and drug and nicotine use, into the tablet where it will go 
directly to the study database. Therefore, there is little information that site staff will collect and most of it 
is part of a standard clinical visit, so that the risk of loss of confidentiality is minimal. 
 
Subjects will be told that agents of the Academic Health Center’s IRB, study monitors, representatives 
from the USFDA or the study coordinating sites (UC/CCHMC or LIJ/NS), and members of the DSMB 
will be allowed to inspect sections of their medical and research records related to this study, if requested. 
Site clinicians are federally mandated to report suspected abuse or neglect. However, we will encourage 
any such reports to be made with extreme confidentially to the Department of Human Services. Healthy 
Lifestyle Intervention (LIFE): All patients and families will receive LIFE education from a clinical site 
staff member. Dr. Seigel will ensure that all clinical site staff are adequately trained prior to initiating 
LIFE and will be available on an ongoing basis to all site staff that have questions about LIFE. The site 
staff will suggest that physical activity is spread over the course of 5 days (30 minutes/day) to achieve the 
recommended 150 hours/week, and not performed all at once to avoid muscle or other discomfort. The 
information that will be provided to the families outweighs the risks associated with LIFE. MET: 
Metformin is USFDA approved to treat youth with diabetes type 2, > age 10. Additionally, there are 
significant safety data for MET down to age 8 years. However, since MET is not approved for the 
attenuation of weight gain in youth, we will submit a research IND to propose studying MET for this 
purpose and wait the required 30 days before initiating the study. We will follow all appropriate FDA 
guidelines for IND submissions. Additionally, Dr. DelBello will be responsible for sending serious 
adverse events (SAEs) to the FDA, IRBs, and DSMB in a timely fashion in compliance of IND and FDA 
guidelines.  
 
To further protect against the risks associated with metformin, patient, parent, and endocrinologist 
research team members will train clinical site staff prior to study initiation regarding the recommended 
MET dosing for the study, how to discuss MET with patients and families, common and less common 
side effects, and strategies to mitigate side effects (e.g. decrease dose and how to switch to MET-XR or 
take it with meals). This training will be taped and available online for any new clinician during the 

 
 
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course of the study. Our study endocrinologists (Drs. Crimmins and Speiser) will also be available 
throughout the study should any site clinicians have questions regarding MET use. We will also educate 
site clinicians that it is part of standard of care to obtain labs prior to initiating MET. In particular, since 
MET is excreted by the kidneys, if a patient has creatinine ≥1.3 mg/dL on 2 occasions, they will be 
excluded from study participation and referred to their primary care clinician. Since lactic acidosis is a 
very rare side effect of MET and primarily occurs in elderly diabetic patients with significant renal 
insufficiency or congestive heart failure,165 we will inform clinicians of the risk and associated symptoms, 
but explain that we are excluding those most at risk and to our knowledge, that there have been no reports 
of lactic acidosis in youth. Additionally, we will encourage site clinicians to inform patients that 
excessive use of alcohol needs to be avoided as alcohol potentiates the effects of metformin on lactate 
metabolism (patients may be users of alcohol) and metformin should be discontinued prior to any IV 
radiocontrast study or surgical procedure. Additionally, to guard against risk, if a patient has two 8-hour 
fasting glucose levels ≥ 126 mg/dL, they will be referred to their primary care clinician (and if at 
screening they will not be included in the study), so that they may be evaluated for type II diabetes.  
 
Blood Draws: We will recommend that only trained and certified phlebotomists draw blood at a certified 
laboratory facility.  
 
Other potential risks: To guard against frustration and fatigue, questions and surveys have been carefully 
chosen by patients, parents, and stakeholders. However, when necessary, assessments can be divided to 
decrease the frustration and fatigue. In addition, research staff will be hired based on their research 
experience and work with children and adolescents. Lastly, patients and their parent(s)/legal guardian(s) 
will be advised that they do not have to participate in the study and that their participation is voluntary 
and refusal to participate will not impact their clinical care in any way. Their alternative to study 
participation is to continue to receive standard clinical care. Additional weight gain or metabolic 
abnormalities: Patients in the study will be closely monitored for weight gain and metabolic 
abnormalities. Clinicians, parents, or patients can discontinue assigned study treatment at any point, but 
will still be monitored for up to 24 month. If a patient continues to have substantial weight gain (> 10%) 
in the LIFE group, a clinician might decide to add MET to the treatment if clinically indicated. Likewise, 
if the patient is randomized to the MET + LIFE group and continues to have substantial weight (>10%) 
they will have the option to continue MET and/or add another treatment or switch the patient’s treatment 
for BSD, whichever is clinically indicated.  If a participant’s BMI percentile 5% (= underweight) his/her 
treatment with MET will be discontinued.  
 
Although the risk of low vitamin B12 while taking MET is associated with age > 50 years and having type II 
diabetes, we will encourage clinicians to monitor B12 levels and a CBC throughout study participation.127 We 
will educate all study-related clinicians about this potential side effect of metformin and the very rare 
clinical manifestations of MET-related vitamin B12 deficiency (i.e., peripheral neuropathy and pernicious 
anemia).  
 
All changes will be noted, however since this is a pragmatic trial we will not mandate specific study 
discontinuation criteria, even if data collection is missed at recommended times for study visits. The 
intervals between study data collection are just guidelines for the most common intervals between clinical 
visits. However, clinical visits may occur more or less frequently. Regardless, data will be collected from 
participants at clinical visits, when feasible, at a frequency of no more than recommended in the Schedule 
of Events (Table 2).  We will encourage clinicians to follow recommended guidelines for referrals and 
treat patients as clinically indicated. We will review guidelines at study initiation. For example, referrals 
to endocrine will be recommended for fasting glucose ≥126 mg/dL x 2, HbA1C ≥5.7%, or fasting insulin 
≥50 mcU/mL. Patients with LDL > 140 or triglycerides > 300 might be referred to a lipid clinic, or those 
with elevated liver enzymes ALT > 45, for a liver clinic for evaluation of non-alcoholic steatohepatitis 
(NASH). After a review of the literature and consulting with experts in the NASH field, we selected the 

 
 
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threshold for referral to liver clinic.  Abnormal ALT will be defined as approximately 2x the upper limit 
of normal of the NHANES-derived population-based ULN (>=22.1for girls, >=25.8 for males).166 
Although the cutoff would be higher for boys, for consistency we will use a single cut-off of ALT>=45.  
Referral for hypertension will be recommended if blood pressure is > 95th % at 3 times or > 130/90. Drs. 
Crimmins and Speiser will facilitate referrals as clinically necessary to ensure patients safety.  
 
We will monitor all patient records and train all clinicians to ensure that if they note a laboratory value as 
clinically significant, they will need to specify the recommended follow-up and outcome.  
 
Exacerbation of BSD is also a risk, however patients will be treated for BSD as clinical indicated and 
there are no restrictions on psychotropic medications use. Patients will be monitored closely by site 
clinicians who will use the YMRS to systematically assess manic symptoms and the PHQ-9 item #9, to 
assess current suicidality. Patients will remain in the study even if they require a change in treatment or 
psychiatric hospitalization. These events will be recorded as psychiatric symptom burden outcomes. 
Patients who become pregnant during their study participation will continue to be followed. We will 
encourage treating clinicians to discontinue metformin if it is being prescribed for study participation and 
to consult with the prescribing clinician prior to discontinuation, if it is prescribed by another clinician 
and for reasons unrelated to the study.   
 
b. Anticipated Benefit Justifies the Risk - We believe the risks associated with study participation are 
minimal. Despite patients being randomized to LIFE + MET or LIFE along, this is a pragmatic 
observational study and there are minimal study specific procedures associated with participation. 
Additionally, patients will remain in the study and data will be collected regardless of whether they 
switch treatment arms or miss visits, although all of this information will be recorded. Youth with BSD 
taking SGAs receive low rates of basic metabolic monitoring and even lower rates of intervention for 
being overweight or obese, even though weight gain is the side effect of SGAs that causes them the most 
concern and is a common reason for poor adherence. Participation may increase their likelihood of 
receiving state-of-the art monitoring and intervention/prevention for weight gain, obesity, and metabolic 
disorders. Potential benefits also include the chance to contribute to a scientific investigation which may 
benefit other patients like themselves in the future. The study will benefit society as a whole by 
potentially providing clinicians with a new means to optimize efficacy, minimize risk for adverse 
reactions, and ultimately achieve better quality of life for patients treated with SGAs. We may also gain a 
better understanding of MET’s effects on anthropomorphic measurements as well as glucose and lipid 
metabolism, which may ultimately improve our ability to prevent and/or manage the longer-term effects 
of SGA. Overall, the risks associated with this study are minimal and subjects may receive direct benefit 
from participation. Therefore, in the opinion of the research team, including patient, parent, and other 
stakeholders, the benefits of this study outweigh the risks. 
 
In addition, the benefits of this pragmatic trial to society, as well as to individual patients, such as gaining 
important information about his/her mental and physical health status and receiving healthy lifestyle 
intervention and MET, far outweigh its minimal risks involved. This patient-centered study will enhance 
our understanding of the effectiveness in a community setting of LIFE and MET to abrogate weight gain 
and its patient-reported complications, such as poor adherence, treatment satisfaction, QoL and 
psychiatric symptoms burden in obese or overweight youth with BSD who are treated with SGAs. In 
summary, this study will provide critical information to address an important public health concern in a 
highly vulnerable population. 
 
c. Anticipated Benefit as Favorable as Alternative Approaches – An alternative to study participation 
is not participating and receiving standard of care treatment as directed by a physician of that participant’s 
choosing.  Participation may increase their likelihood of receiving state-of-the art monitoring and 
intervention/prevention for weight gain, obesity, and metabolic disorders associated with SGAs. 

 
 
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H. PAYMENT 
Patients and their caregiver will each be paid $25 for visits at Screening, month 6, and month 24 for their 
time and effort. Also, they each will each be paid $10 for visits at month 1, 2, 3, 9, 12, 15, 18, and 21. 
This totals to $155 for the patient and $155 for the caregiver.  
 
I. SUBJECT COSTS 
Patients are expected to provide for the costs of clinical care (e.g., hospitalization costs) through standard 
health care payment procedures. Subjects will have no costs associated with research participation. 
 
K. LITERATURE CITED 
1. 
De Hert M, Detraux J, van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse 
effects associated with antipsychotic drugs. Nat Rev Endocrinol. 2012;8(2):114-126. 
2. 
De Hert M, Vancampfort D, Correll CU, Mercken V, Peuskens J, Sweers K, van Winkel R, 
Mitchell AJ. Guidelines for screening and monitoring of cardiometabolic risk in schizophrenia: 
systematic evaluation. Br J Psychiatry. 2011;199(2):99-105. 
3. 
Fagiolini A, Chengappa KN, Soreca I, Chang J. Bipolar disorder and the metabolic syndrome: 
causal factors, psychiatric outcomes and economic burden. CNS Drugs. 2008;22(8):655-669. 
4. 
Perlis RH, Dennehy EB, Miklowitz DJ, DelBello MP, Ostacher M, Calabrese JR, Ametrano RM, 
Wisniewski SR, Bowden CL, Thase ME, Nierenberg AA, Sachs G. Retrospective age at onset of 
bipolar disorder and outcome during two-year follow-up: results from the STEP-BD study. 
Bipolar Disord. 2009;11(4):391-400. 
5. 
Perlis RH, Miyahara S, Marangell LB, Wisniewski SR, Ostacher M, DelBello MP, Bowden CL, 
Sachs GS, Nierenberg AA, Investigators S-B. Long-term implications of early onset in bipolar 
disorder: data from the first 1000 participants in the systematic treatment enhancement program 
for bipolar disorder (STEP-BD). Biol Psychiatry. 2004;55(9):875-881. 
6. 
Merikangas KR, Cui L, Kattan G, Carlson GA, Youngstrom EA, Angst J. Mania with and without 
depression in a community sample of US adolescents. Arch Gen Psychiatry. 2012;69(9):943-951. 
7. 
Lewinsohn PM, Klein DN, Seeley JR. Bipolar disorders in a community sample of older 
adolescents: prevalence, phenomenology, comorbidity, and course. J Am Acad Child Adolesc 
Psychiatry. 1995;34(4):454-463. 
8. 
Van Meter AR, Henry DB, West AE. What goes up must come down: the burden of bipolar 
depression in youth. J Affect Disord. 2013;150(3):1048-1054. 
9. 
Berry EA, Heaton PT, Kelton CM. National estimates of the inpatient burden of pediatric bipolar 
disorder in the United States. J Ment Health Policy Econ. 2011;14(3):115-123. 
10. 
Birmaher B, Axelson D, Goldstein B, Strober M, Gill MK, Hunt J, Houck P, Ha W, Iyengar S, 
Kim E, Yen S, Hower H, Esposito-Smythers C, Goldstein T, Ryan N, Keller M. Four-year 
longitudinal course of children and adolescents with bipolar spectrum disorders: the Course and 
Outcome of Bipolar Youth (COBY) study. Am J Psychiatry. 2009;166(7):795-804. 
11. 
Freeman AJ, Youngstrom EA, Michalak E, Siegel R, Meyers OI, Findling RL. Quality of life in 
pediatric bipolar disorder. Pediatrics. 2009;123(3):e446-452. 
12. 
Goldstein BI, Birmaher B, Axelson DA, Goldstein TR, Esposito-Smythers C, Strober MA, Hunt J, 
Leonard H, Gill MK, Iyengar S, Grimm C, Yang M, Ryan ND, Keller MB. Preliminary findings 
regarding overweight and obesity in pediatric bipolar disorder. J Clin Psychiatry. 
2008;69(12):1953-1959. 
13. 
Patel NC, Hariparsad M, Matias-Akthar M, Sorter MT, Barzman DH, Morrison JA, Stanford KE, 
Strakowski SM, DelBello MP. Body mass indexes and lipid profiles in hospitalized children and 
adolescents exposed to atypical antipsychotics. J Child Adolesc Psychopharmacol. 
2007;17(3):303-311. 
14. 
Jerrell JM, McIntyre RS, Tripathi A. Childhood treatment with psychotropic medication and 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 27 
 
 
development of comorbid medical conditions in adolescent-onset bipolar disorder. Hum 
Psychopharmacol. 2011;26(7):451-459. 
15. 
Goldstein BI, Kemp DE, Soczynska JK, McIntyre RS. Inflammation and the phenomenology, 
pathophysiology, comorbidity, and treatment of bipolar disorder: a systematic review of the 
literature. J Clin Psychiatry. 2009;70(8):1078-1090. 
16. 
Fagiolini A, Frank E, Scott JA, Turkin S, Kupfer DJ. Metabolic syndrome in bipolar disorder: 
findings from the Bipolar Disorder Center for Pennsylvanians. Bipolar Disord. 2005;7(5):424-
430. 
17. 
Wang PW, Sachs GS, Zarate CA, Marangell LB, Calabrese JR, Goldberg JF, Sagduyu K, 
Miyahara S, Ketter TA. Overweight and obesity in bipolar disorders. J Psychiatr Res. 
2006;40(8):762-764. 
18. 
Calkin C, van de Velde C, Ruzickova M, Slaney C, Garnham J, Hajek T, O'Donovan C, Alda M. 
Can body mass index help predict outcome in patients with bipolar disorder? Bipolar Disord. 
2009;11(6):650-656. 
19. 
Goldstein BI, Liu SM, Schaffer A, Sala R, Blanco C. Obesity and the three-year longitudinal 
course of bipolar disorder. Bipolar Disord. 2013;15(3):284-293. 
20. 
Pfeifer JC, Kowatch RA, DelBello MP. Pharmacotherapy of bipolar disorder in children and 
adolescents: recent progress. CNS Drugs. 2010;24(7):575-593. 
21. 
Olsen BT, Ganocy SJ, Bitter SM, Findling RL, Case M, Chang K, Tohen M, DelBello MP. 
Health-related quality of life as measured by the child health questionnaire in adolescents with 
bipolar disorder treated with olanzapine. Compr Psychiatry. 2012;53(7):1000-1005. 
22. 
Stewart M, DelBello MP, Versavel M, Keller D. Psychosocial functioning and health-related 
quality of life in children and adolescents treated with open-label ziprasidone for bipolar mania, 
schizophrenia, or schizoaffective disorder. J Child Adolesc Psychopharmacol. 2009;19(6):635-
640. 
23. 
Correll CU, Sheridan EM, DelBello MP. Antipsychotic and mood stabilizer efficacy and 
tolerability in pediatric and adult patients with bipolar I mania: a comparative analysis of acute, 
randomized, placebo- controlled trials. Bipolar Disord. 2010;12(2):116-141. 
24. 
Dove D, Warren Z, McPheeters ML, Taylor JL, Sathe NA, Veenstra-VanderWeele J. Medications 
for adolescents and young adults with autism spectrum disorders: a systematic review. Pediatrics. 
2012;130(4):717-726. 
25. 
Schimmelmann BG, Schmidt SJ, Carbon M, Correll CU. Treatment of adolescents with early-
onset schizophrenia spectrum disorders: in search of a rational, evidence-informed approach. Curr 
Opin Psychiatry. 2013;26(2):219-230. 
26. 
Olfson M, Crystal S, Huang C, Gerhard T. Trends in antipsychotic drug use by very young, 
privately insured children. J Am Acad Child Adolesc Psychiatry. 2010;49(1):13-23. 
27. 
Olfson M, Blanco C, Liu L, Moreno C, Laje G. National trends in the outpatient treatment of 
children and adolescents with antipsychotic drugs. Arch Gen Psychiatry. 2006;63(6):679-685. 
28. 
Olfson M, Blanco C, Liu SM, Wang S, Correll CU. National trends in the office-based treatment 
of children, adolescents, and adults with antipsychotics. Arch Gen Psychiatry. 2012;69(12):1247-
1256. 
29. 
Pavuluri MN, Henry DB, Findling RL, Parnes S, Carbray JA, Mohammed T, Janicak PG, 
Sweeney JA. Double-blind randomized trial of risperidone versus divalproex in pediatric bipolar 
disorder. Bipolar Disord. 2010;12(6):593-605. 
30. 
DelBello MP, Kowatch RA, Adler CM, Stanford KE, Welge JA, Barzman DH, Nelson E, 
Strakowski SM. A double-blind randomized pilot study comparing quetiapine and divalproex for 
adolescent mania. J Am Acad Child Adolesc Psychiatry. 2006;45(3):305-313. 
31. 
Geller B, Luby JL, Joshi P, Wagner KD, Emslie G, Walkup JT, Axelson DA, Bolhofner K, Robb 
A, Wolf DV, Riddle MA, Birmaher B, Nusrat N, Ryan ND, Vitiello B, Tillman R, Lavori P. A 
randomized controlled trial of risperidone, lithium, or divalproex sodium for initial treatment of 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 28 
 
 
bipolar I disorder, manic or mixed phase, in children and adolescents. Arch Gen Psychiatry. 
2012;69(5):515-528. 
32. 
Correll CU. Weight gain and metabolic effects of mood stabilizers and antipsychotics in pediatric 
bipolar disorder: a systematic review and pooled analysis of short-term trials. J Am Acad Child 
Adolesc Psychiatry. 2007;46(6):687-700. 
33. 
Newcomer JW. Comparing the safety and efficacy of atypical antipsychotics in psychiatric 
patients with comorbid medical illnesses. J Clin Psychiatry. 2009;70 Suppl 3:30-36. 
34. 
Newcomer JW, Hennekens CH. Severe mental illness and risk of cardiovascular disease. JAMA. 
2007;298(15):1794-1796. 
35. 
Correll CU, Frederickson AM, Kane JM, Manu P. Equally increased risk for metabolic syndrome 
in patients with bipolar disorder and schizophrenia treated with second-generation antipsychotics. 
Bipolar Disord. 2008;10(7):788-797. 
36. 
Klein DJ, Cottingham EM, Sorter M, Barton BA, Morrison JA. A randomized, double-blind, 
placebo- controlled trial of metformin treatment of weight gain associated with initiation of 
atypical antipsychotic therapy in children and adolescents. Am J Psychiatry. 2006;163(12):2072-
2079. 
37. 
Correll CU, Penzner JB, Parikh UH, Mughal T, Javed T, Carbon M, Malhotra AK. Recognizing 
and monitoring adverse events of second-generation antipsychotics in children and adolescents. 
Child Adolesc Psychiatr Clin N Am. 2006;15(1):177-206. 
38. 
Correll CU, Manu P, Olshanskiy V, Napolitano B, Kane JM, Malhotra AK. Cardiometabolic risk 
of second-generation antipsychotic medications during first-time use in children and adolescents. 
JAMA. 2009;302(16):1765-1773. 
39. 
Correll CU, Sikich L, Reeves G, Riddle M. Metformin for antipsychotic-related weight gain and 
metabolic abnormalities: when, for whom, and for how long? Am J Psychiatry. 2013;170(9):947-
952. 
40. 
Benoit SC, McQuade JA, Clegg DJ, Xu M, Rushing PA, Woods SC, Seeley RJ. Altered feeding 
responses in mice with targeted disruption of the dopamine-3 receptor gene. Behav Neurosci. 
2003;117(1):46-54. 
41. 
Davidson TL, Kanoski SE, Chan K, Clegg DJ, Benoit SC, Jarrard LE. Hippocampal lesions impair 
retention of discriminative responding based on energy state cues. Behav Neurosci. 
2010;124(1):97- 105. 
42. 
Allison DB, Mentore JL, Heo M, Chandler LP, Cappelleri JC, Infante MC, Weiden PJ. 
Antipsychotic- induced weight gain: a comprehensive research synthesis. Am J Psychiatry. 
1999;156(11):1686-1696. 
43. 
Correll CU, Frederickson AM, Kane JM, Manu P. Does antipsychotic polypharmacy increase the 
risk for metabolic syndrome? Schizophr Res. 2007;89(1-3):91-100. 
44. 
Martinez-Ortega JM, Funes-Godoy S, Diaz-Atienza F, Gutierrez-Rojas L, Perez-Costillas L, 
Gurpegui M. Weight gain and increase of body mass index among children and adolescents 
treated with antipsychotics: a critical review. Eur Child Adolesc Psychiatry. 2013;22(8):457-479. 
45. 
Klein DJ, Saldana SN. Review: metformin is effective at attenuating the metabolic risks associated 
with use of atypical antipsychotics. Evid Based Ment Health. 2011;14(2):55. 
46. 
Shin L, Bregman H, Frazier J, Noyes N. An overview of obesity in children with psychiatric 
disorders taking atypical antipsychotics. Harv Rev Psychiatry. 2008;16(2):69-79. 
47. 
Simon V, van Winkel R, De Hert M. Are weight gain and metabolic side effects of atypical 
antipsychotics dose dependent? A literature review. J Clin Psychiatry. 2009;70(7):1041-1050. 
48. 
Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood 
from childhood and parental obesity. N Engl J Med. 1997;337(13):869-873. 
49. 
Keeshin BR, Luebbe AM, Strawn JR, Saldana SN, Wehry AM, DelBello MP. Sexual abuse is 
associated with obese children and adolescents admitted for psychiatric hospitalization. J Pediatr. 
2013;163(1):154-159 e151. 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 29 
 
 
50. 
Correll CU. Novel Antipsychotic-Induced Weight Gain and Metabolic Abnormalities in Youth. 
American Academy of Child and Adolescent Psychiatry Scientific Proceedings. 2005;32:32. 
51. 
Barnes TR, Paton C, Cavanagh MR, Hancock E, Taylor DM, Health UKPOfM. A UK audit of 
screening for the metabolic side effects of antipsychotics in community patients. Schizophr Bull. 
2007;33(6):1397- 1403. 
52. 
Must A, Anderson SE. Effects of obesity on morbidity in children and adolescents. Nutr Clin 
Care. 2003;6(1):4-12. 
53. 
Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat 
Metab Disord. 1999;23 Suppl 2:S2-11. 
54. 
Nathan BM, Moran A. Metabolic complications of obesity in childhood and adolescence: more 
than just diabetes. Curr Opin Endocrinol Diabetes Obes. 2008;15(1):21-29. 
55. 
Lieberman JA, Stroup TS, McEvoy JP, Swartz MS, Rosenheck RA, Perkins DO, Keefe RS, Davis 
SM, Davis CE, Lebowitz BD, Severe J, Hsiao JK, Clinical Antipsychotic Trials of Intervention 
Effectiveness I. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N 
Engl J Med. 2005;353(12):1209-1223. 
56. 
Naber D, Lambert M. The CATIE and CUtLASS studies in schizophrenia: results and implications 
for clinicians. CNS Drugs. 2009;23(8):649-659. 
57. 
Pogge DL, Singer MB, Harvey PD. Rates and predictors of adherence with atypical antipsychotic 
medication: a follow-up study of adolescent inpatients. J Child Adolesc Psychopharmacol. 
2005;15(6):901-912. 
58. 
Chiang YL, Klainin-Yobas P, Ignacio J, Chng CM. The impact of antipsychotic side effects on 
attitudes towards medication in people with schizophrenia and related disorders. J Clin Nurs. 
2011;20(15- 16):2172-2182. 
59. 
DelBello MP, Hanseman D, Adler CM, Fleck DE, Strakowski SM. Twelve-month outcome of 
adolescents with bipolar disorder following first hospitalization for a manic or mixed episode. Am 
J Psychiatry. 2007;164(4):582-590. 
60. 
Kafantaris V. Treatment of bipolar disorder in children and adolescents. J Am Acad Child Adolesc 
Psychiatry. 1995;34(6):732-741. 
61. 
Sporn AL, Bobb AJ, Gogtay N, Stevens H, Greenstein DK, Clasen LS, Tossell JW, Nugent T, 
Gochman PA, Sharp WS, Mattai A, Lenane MC, Yanovski JA, Rapoport JL. Hormonal correlates 
of clozapine-induced weight gain in psychotic children: an exploratory study. J Am Acad Child 
Adolesc Psychiatry. 2005;44(9):925-933. 
62. 
Goldstein TR, Goldstein BI, Mantz MB, Bailey B, Douaihy A. A brief motivational intervention 
for preventing medication-associated weight gain among youth with bipolar disorder: treatment 
development and case report. J Child Adolesc Psychopharmacol. 2011;21(3):275-280. 
63. 
Virk S, Schwartz TL, Jindal S, Nihalani N, Jones N. Psychiatric medication induced obesity: an 
aetiologic review. Obes Rev. 2004;5(3):167-170. 
64. 
Maayan L, Correll CU. Management of antipsychotic-related weight gain. Expert Rev Neurother. 
2010;10(7):1175-1200. 
65. 
Gierisch JM, Nieuwsma JA, Bradford DW, Wilder CM, Mann-Wrobel MC, McBroom AJ, 
Hasselblad V, Williams JW, Jr. Pharmacologic and behavioral interventions to improve 
cardiovascular risk factors in adults with serious mental illness: a systematic review and meta-
analysis. J Clin Psychiatry. 2014;75(5):e424-440. 
66. 
Reeves GM, Keeton C, Correll CU, Johnson JL, Hamer RM, Sikich L, Hazzard L, Alderman C, 
Scheer A, Mabe M, Kapoor S, Sheridan E, Borner I, Bussell K, Pirmohamed S, Bethea TC, 
Chekuri R, Gottfried R, Reinblatt SP, Santana E, Riddle MA. Improving metabolic parameters of 
antipsychotic child  treatment (IMPACT) study: rationale, design, and methods. Child Adolesc 
Psychiatry Ment Health. 2013;7(1):31. 
67. 
Siegel RM, Neidhard MS, Kirk S. A comparison of low glycemic index and staged portion-
controlled diets in improving BMI of obese children in a pediatric weight management program. 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 30 
 
 
Clin Pediatr (Phila). 2011;50(5):459-461. 
68. 
Wilfley DE, Kass AE, Kolko RP. Counseling and behavior change in pediatric obesity. Pediatr 
Clin North Am. 2011;58(6):1403-1424, x. 
69. 
Flodmark CE, Marcus C, Britton M. Interventions to prevent obesity in children and adolescents: a 
systematic literature review. Int J Obes (Lond). 2006;30(4):579-589. 
70. 
Summerbell CD, Waters E, Edmunds LD, Kelly S, Brown T, Campbell KJ. Interventions for 
preventing obesity in children. Cochrane Database Syst Rev. 2005(3):CD001871. 
71. 
Alvarez-Jimenez M, Hetrick SE, Gonzalez-Blanch C, Gleeson JF, McGorry PD. Non-
pharmacological management of antipsychotic-induced weight gain: systematic review and meta-
analysis of randomised controlled trials. Br J Psychiatry. 2008;193(2):101-107. 
72. 
Alvarez-Jimenez M, Martinez-Garcia O, Perez-Iglesias R, Ramirez ML, Vazquez-Barquero JL, 
Crespo- Facorro B. Prevention of Antipsychotic-Induced Weight Gain with Early Behavioural 
Intervention in First-Episode Psychosis: 2-Year Results of a Randomized Controlled Trial. 
Schizophr Res. 2010;116(1):16-19. 
73. 
Lowe T, Lubos E. Effectiveness of weight management interventions for people with serious 
mental illness who receive treatment with atypical antipsychotic medications. A literature review. 
J Psychiatr Ment Health Nurs. 2008;15(10):857-863. 
74. 
Faulkner G, Soundy AA, Lloyd K. Schizophrenia and weight management: a systematic review of 
interventions to control weight. Acta Psychiatr Scand. 2003;108(5):324-332. 
75. 
Alvarez-Jimenez M, Gonzalez-Blanch C, Vazquez-Barquero JL, Perez-Iglesias R, Martinez-
Garcia O, Perez-Pardal T, Ramirez-Bonilla ML, Crespo-Facorro B. Attenuation of antipsychotic-
induced weight gain with early behavioral intervention in drug-naive first-episode psychosis 
patients: A randomized controlled trial. J Clin Psychiatry. 2006;67(8):1253-1260. 
76. 
AMA. Expert Committee Recommendations on the Assessment, Prevention and Treatment of 
Child and Adolescent Overweight and Obesity. 2007; 
http://wwwamaassnorg/ama1/pub/upload/mm/433/ped_obesity_recspdf. 
77. 
Kim SF, Huang AS, Snowman AM, Teuscher C, Snyder SH. From the Cover: Antipsychotic drug- 
induced weight gain mediated by histamine H1 receptor-linked activation of hypothalamic AMP-
kinase. Proc Natl Acad Sci U S A. 2007;104(9):3456-3459. 
78. 
Aviles-Santa L, Sinding J, Raskin P. Effects of metformin in patients with poorly controlled, 
insulin- treated type 2 diabetes mellitus. A randomized, double-blind, placebo-controlled trial. 
Ann Intern Med. 1999;131(3):182-188. 
79. 
Paolisso G, Amato L, Eccellente R, Gambardella A, Tagliamonte MR, Varricchio G, Carella C, 
Giugliano D, D'Onofrio F. Effect of metformin on food intake in obese subjects. Eur J Clin Invest. 
1998;28(6):441-446. 
80. 
McDonagh MS, Selph S, Ozpinar A, Foley C. Systematic review of the benefits and risks of 
metformin in treating obesity in children aged 18 years and younger. JAMA Pediatr. 
2014;168(2):178-184. 
81. 
Brufani C, Crino A, Fintini D, Patera PI, Cappa M, Manco M. Systematic review of metformin use 
in obese nondiabetic children and adolescents. Horm Res Paediatr. 2013;80(2):78-85. 
82. 
Park MH, Kinra S, Ward KJ, White B, Viner RM. Metformin for obesity in children and 
adolescents: a systematic review. Diabetes Care. 2009;32(9):1743-1745. 
83. 
Kay JP, Alemzadeh R, Langley G, D'Angelo L, Smith P, Holshouser S. Beneficial effects of 
metformin in normoglycemic morbidly obese adolescents. Metabolism. 2001;50(12):1457-1461. 
84. 
Srinivasan S, Ambler GR, Baur LA, Garnett SP, Tepsa M, Yap F, Ward GM, Cowell CT. 
Randomized, controlled trial of metformin for obesity and insulin resistance in children and 
adolescents: improvement in body composition and fasting insulin. J Clin Endocrinol Metab. 
2006;91(6):2074-2080. 
85. 
Freemark M, Bursey D. The effects of metformin on body mass index and glucose tolerance in 
obese adolescents with fasting hyperinsulinemia and a family history of type 2 diabetes. 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 31 
 
 
Pediatrics. 2001;107(4):E55. 
86. 
Morrison JA, Cottingham EM, Barton BA. Metformin for weight loss in pediatric patients taking 
psychotropic drugs. Am J Psychiatry. 2002;159(4):655-657. 
87. 
Ijff DM, Aldenkamp AP. Cognitive side-effects of antiepileptic drugs in children. Handb Clin 
Neurol. 2013;111:707-718. 
88. 
Fiedorowicz JG, Miller DD, Bishop JR, Calarge CA, Ellingrod VL, Haynes WG. Systematic 
Review and Meta-analysis of Pharmacological Interventions for Weight Gain from 
Antipsychotics and Mood Stabilizers. Curr Psychiatry Rev. 2012;8(1):25-36. 
89. 
Ehret M, Goethe J, Lanosa M, Coleman CI. The effect of metformin on anthropometrics and 
insulin resistance in patients receiving atypical antipsychotic agents: a meta-analysis. J Clin 
Psychiatry. 2010;71(10):1286-1292. 
90. 
Maayan L, Vakhrusheva J, Correll CU. Effectiveness of medications used to attenuate 
antipsychotic- related weight gain and metabolic abnormalities: a systematic review and meta-
analysis. Neuropsychopharmacology. 2010;35(7):1520-1530. 
91. 
Bjorkhem-Bergman L, Asplund AB, Lindh JD. Metformin for weight reduction in non-diabetic 
patients on antipsychotic drugs: a systematic review and meta-analysis. J Psychopharmacol. 
2011;25(3):299-305. 
92. 
Das C, Mendez G, Jagasia S, Labbate LA. Second-generation antipsychotic use in schizophrenia 
and associated weight gain: a critical review and meta-analysis of behavioral and pharmacologic 
treatments. Ann Clin Psychiatry. 2012;24(3):225-239. 
93. 
Mizuno Y, Suzuki T, Nakagawa A, Yoshida K, Mimura M, Fleischhacker WW, Uchida H. 
Pharmacological Strategies to Counteract Antipsychotic-Induced Weight Gain and Metabolic 
Adverse Effects in Schizophrenia: A Systematic Review and Meta-analysis. Schizophr Bull. 
2014;40(6):1385- 1403. 
94. 
Jarskog LF, Hamer RM, Catellier DJ, Stewart DD, Lavange L, Ray N, Golden LH, Lieberman JA, 
Stroup TS, Investigators M. Metformin for weight loss and metabolic control in overweight 
outpatients with schizophrenia and schizoaffective disorder. Am J Psychiatry. 2013;170(9):1032-
1040. 
95. 
Wu RR, Zhao JP, Jin H, Shao P, Fang MS, Guo XF, He YQ, Liu YJ, Chen JD, Li LH. Lifestyle 
intervention and metformin for treatment of antipsychotic-induced weight gain: a randomized 
controlled trial. JAMA. 2008;299(2):185-193. 
96. 
Wang M, Tong JH, Zhu G, Liang GM, Yan HF, Wang XZ. Metformin for treatment of 
antipsychotic- induced weight gain: a randomized, placebo-controlled study. Schizophr Res. 
2012;138(1):54-57. 
97. 
Wu RR, Zhao JP, Guo XF, He YQ, Fang MS, Guo WB, Chen JD, Li LH. Metformin addition 
attenuates olanzapine-induced weight gain in drug-naive first-episode schizophrenia patients: a 
double-blind, placebo-controlled study. Am J Psychiatry. 2008;165(3):352-358. 
98. 
Findling RL, Cavus I, Pappadopulos E, Vanderburg DG, Schwartz JH, Gundapaneni BK, DelBello 
MP. Efficacy, long-term safety, and tolerability of ziprasidone in children and adolescents with 
bipolar disorder. J Child Adolesc Psychopharmacol. 2013;23(8):545-557. 
99. 
Findling RL, Correll CU, Nyilas M, Forbes RA, McQuade RD, Jin N, Ivanova S, Mankoski R, 
Carson WH, Carlson GA. Aripiprazole for the treatment of pediatric bipolar I disorder: a 30-week, 
randomized, placebo-controlled study. Bipolar Disord. 2013;15(2):138-149. 
100. Findling RL, Pathak S, Earley WR, Liu S, DelBello MP. Efficacy and safety of extended-release 
quetiapine fumarate in youth with bipolar depression: an 8 week, double-blind, placebo-controlled 
trial. J Child Adolesc Psychopharmacol. 2014;24(6):325-335. 
101. Miklowitz DJ, Schneck CD, George EL, Taylor DO, Sugar CA, Birmaher B, Kowatch RA, 
DelBello MP, Axelson DA. Pharmacotherapy and family-focused treatment for adolescents with 
bipolar I and II disorders: a 2-year randomized trial. Am J Psychiatry. 2014;171(6):658-667. 
102. Pathak S, Findling RL, Earley WR, Acevedo LD, Stankowski J, Delbello MP. Efficacy and safety 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 32 
 
 
of quetiapine in children and adolescents with mania associated with bipolar I disorder: a 3-week, 
double- blind, placebo-controlled trial. J Clin Psychiatry. 2013;74(1):e100-109. 
103. Chang K, Delbello M, Chu WJ, Garrett A, Kelley R, Mills N, Howe M, Bryan H, Adler C, 
Eliassen J, Spielman D, Strakowski SM. Neurometabolite effects of response to quetiapine and 
placebo in adolescents with bipolar depression. J Child Adolesc Psychopharmacol. 
2012;22(4):261-268. 
104. Haas M, Delbello MP, Pandina G, Kushner S, Van Hove I, Augustyns I, Quiroz J, Kusumakar V. 
Risperidone for the treatment of acute mania in children and adolescents with bipolar disorder: a 
randomized, double-blind, placebo-controlled study. Bipolar Disord. 2009;11(7):687-700. 
105. DelBello MP, Chang K, Welge JA, Adler CM, Rana M, Howe M, Bryan H, Vogel D, Sampang S, 
Delgado SV, Sorter M, Strakowski SM. A double-blind, placebo-controlled pilot study of 
quetiapine for depressed adolescents with bipolar disorder. Bipolar Disord. 2009;11(5):483-493. 
106. DelBello MP, Versavel M, Ice K, Keller D, Miceli J. Tolerability of oral ziprasidone in children 
and adolescents with bipolar mania, schizophrenia, or schizoaffective disorder. J Child Adolesc 
Psychopharmacol. 2008;18(5):491-499. 
107. Delbello MP, Schwiers ML, Rosenberg HL, Strakowski SM. A double-blind, randomized, 
placebo- controlled study of quetiapine as adjunctive treatment for adolescent mania. J Am Acad 
Child Adolesc Psychiatry. 2002;41(10):1216-1223. 
108. Tohen M, Kryzhanovskaya L, Carlson G, Delbello M, Wozniak J, Kowatch R, Wagner K, 
Findling R, Lin D, Robertson-Plouch C, Xu W, Dittmann RW, Biederman J. Olanzapine versus 
placebo in the treatment of adolescents with bipolar mania. Am J Psychiatry. 2007;164(10):1547-
1556. 
109. Rademacher J, DelBello MP, Adler C, Stanford K, Strakowski SM. Health-related quality of life in 
adolescents with bipolar I disorder. J Child Adolesc Psychopharmacol. 2007;17(1):97-103. 
110. Barzman DH, DelBello MP, Adler CM, Stanford KE, Strakowski SM. The efficacy and 
tolerability of quetiapine versus divalproex for the treatment of impulsivity and reactive 
aggression in adolescents with co-occurring bipolar disorder and disruptive behavior disorder(s). J 
Child Adolesc Psychopharmacol. 2006;16(6):665-670. 
111. Barzman DH, DelBello MP, Kowatch RA, Gernert B, Fleck DE, Pathak S, Rappaport K, Delgado 
SV, Campbell P, Strakowski SM. The effectiveness and tolerability of aripiprazole for pediatric 
bipolar disorders: a retrospective chart review. J Child Adolesc Psychopharmacol. 
2004;14(4):593-600. 
112. Strakowski SM, Johnson JL, Delbello MP, Hamer RM, Green AI, Tohen M, Lieberman JA, Glick 
I, Patel JK, Group HR. Quality of life during treatment with haloperidol or olanzapine in the year 
following a first psychotic episode. Schizophr Res. 2005;78(2-3):161-169. 
113. Toteja N, Gallego JA, Saito E, Gerhard T, Winterstein A, Olfson M, Correll CU. Prevalence and 
correlates of antipsychotic polypharmacy in children and adolescents receiving antipsychotic 
treatment. Int J Neuropsychopharmacol. 2014;17(7):1095-1105. 
114. Nielsen J, Kane JM, Correll CU. Real-world effectiveness of clozapine in patients with bipolar 
disorder: results from a 2-year mirror-image study. Bipolar Disord. 2012;14(8):863-869. 
115. Raebel MA, Penfold R, McMahon AW, Reichman M, Shetterly S, Goodrich G, Andrade S, 
Correll CU, Gerhard T. Adherence to Guidelines for Glucose Assessment in Starting Second-
Generation Antipsychotics. Pediatrics. 2014. 
116. Centorrino F, Mark TL, Talamo A, Oh K, Chang J. Health and economic burden of metabolic 
comorbidity among individuals with bipolar disorder. J Clin Psychopharmacol. 2009;29(6):595-
600. 
117. Christian R, Saavedra L, Gaynes BN, Sheitman B, Wines RCM, Jonas DE, Viswanathan M, Ellis 
AR, Woodell C, Carey TS. Future Research Needs for First- and Second-Generation 
Antipsychotics for Children and Young Adults. Rockville (MD)2012. 
118. Crowley MJ, McCrory DC, Chatterjee R, Gierisch JM, Myers ER, Schmit KM, Coeytaux RR, 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 33 
 
 
Correll CU, Kendrick AS, Sanders GD. Prioritization of research addressing antipsychotics for 
adolescents and young adults with bipolar disorder. Ann Intern Med. 2014;160(7):492-498. 
119. Penzner JB, Dudas M, Saito E, Olshanskiy V, Parikh UH, Kapoor S, Chekuri R, Gadaleta D, 
Avedon J, Sheridan EM, Randell J, Malhotra AK, Kane JM, Correll CU. Lack of effect of 
stimulant combination with second-generation antipsychotics on weight gain, metabolic changes, 
prolactin levels, and sedation in youth with clinically relevant aggression or oppositionality. J 
Child Adolesc Psychopharmacol. 2009;19(5):563-573. 
120. Stephens JR, Heffner JL, Adler CM, Blom TJ, Anthenelli RM, Fleck DE, Welge JA, Strakowski 
SM, DelBello MP. Risk and protective factors associated with substance use disorders in 
adolescents with first-episode mania. J Am Acad Child Adolesc Psychiatry. 2014;53(7):771-779. 
121. Herranz Barbero A, Lopez de Mesa MR, Azcona San Julian C. Influence of Overweight on the 
Health- Related Quality of Life in Adolescents. An Pediatr (Barc). 2014. 
122. Thorpe KE, Zwarenstein M, Oxman AD, Treweek S, Furberg CD, Altman DG, Tunis S, Bergel E, 
Harvey I, Magid DJ, Chalkidou K. A pragmatic-explanatory continuum indicator summary 
(PRECIS): a tool to help trial designers. CMAJ. 2009;180(10):E47-57. 
123. Barlow SE, Expert C. Expert committee recommendations regarding the prevention, assessment, 
and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 
2007;120 Suppl 4:S164-192. 
124. Epstein L, Squires S. The Stoplight Diet for Children: An Eight-Week Program for Parents and 
Children. 1st ed: Little Brown & Co; 1988. 
125. Lavine JE, Schwimmer JB, Van Natta ML, Molleston JP, Murray KF, Rosenthal P, Abrams SH, 
Scheimann AO, Sanyal AJ, Chalasani N, Tonascia J, Unalp A, Clark JM, Brunt EM, Kleiner DE, 
Hoofnagle JH, Robuck PR, Nonalcoholic Steatohepatitis Clinical Research N. Effect of vitamin E 
or metformin for treatment of nonalcoholic fatty liver disease in children and adolescents: the 
TONIC randomized controlled trial. JAMA. 2011;305(16):1659-1668. 
126. Blonde L, Dailey GE, Jabbour SA, Reasner CA, Mills DJ. Gastrointestinal tolerability of 
extended- release metformin tablets compared to immediate-release metformin tablets: results of a 
retrospective cohort study. Curr Med Res Opin. 2004;20(4):565-572. 
127. Haeusler S, Parry-Strong A, Krebs JD. The prevalence of low vitamin B12 status in people with 
type 2 diabetes receiving metformin therapy in New Zealand--a clinical audit. N Z Med J. 
2014;127(1404):8-16. 
128. Shaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME. NIMH Diagnostic Interview 
Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous 
versions, and reliability of some common diagnoses. J Am Acad Child Adolesc Psychiatry. 
2000;39(1):28-38. 
129. De Hert M, Dobbelaere M, Sheridan EM, Cohen D, Correll CU. Metabolic and endocrine adverse 
effects of second-generation antipsychotics in children and adolescents: A systematic review of 
randomized, placebo controlled trials and guidelines for clinical practice. Eur Psychiatry. 
2011;26(3):144-158. 
130. 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. 
131. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss 
RM, Savage PJ, Smith SC, Jr., Spertus JA, Costa F, American Heart A, National Heart L, Blood I. 
Diagnosis and management of the metabolic syndrome: an American Heart Association/National 
Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735-2752. 
132. Correll CU, Robinson DG, Schooler NR, Brunette MF, Mueser KT, Rosenheck RA, Marcy P, 
Addington J, Estroff SE, Robinson J, Penn DL, Azrin S, Goldstein A, Severe J, Heinssen R, Kane 
JM. Cardiometabolic Risk in Patients With First-Episode Schizophrenia Spectrum Disorders: 
Baseline Results From the RAISE-ETP Study. JAMA Psychiatry. 2014. 
133. Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 34 
 
 
phenotype in adolescents: findings from the third National Health and Nutrition Examination 
Survey, 1988-1994. Arch Pediatr Adolesc Med. 2003;157(8): 821-827. 
134. Modi AC, Rausch JR, Glauser TA. Patterns of nonadherence to antiepileptic drug therapy in 
children with newly diagnosed epilepsy. JAMA. 2011;305(16):1669-1676. 
135. Modi AC, Wu YP, Rausch JR, Peaugh JL, Glauser TA. Antiepileptic Drug Non-Adherence 
Predicts Pediatric Epilepsy Seizure Outcomes. Neurology. in press. 
136. Modi AC, Zeller MH, Xanthakos SA, Jenkins TM, Inge TH. Adherence to vitamin 
supplementation following adolescent bariatric surgery. Obesity (Silver Spring). 2013;21(3):E190-
195. 
137. Quittner AL, Espelage DL, Ievers-Landis CE, Drotar D. Measuring Adherence to Medical 
Treatments in Childhood Chronic Illness: Considering Multiple Methods and Sources of 
Information. Journal of Clinical Psychology in Medical Settings. 2000;7:41-54. 
138. Rand CS. "I Took the Medicine Like You Told Me Doctor": Self Report of Adherence with 
Medical Regimens. In: Stone A, Tukkan JS, Bachrael CM, Jobe JB, Kurtzman HJ, Cain VS, eds. 
The Science of Self-Report. Mahway, NJ: Lawrence Erlbaum Associates; 2000:257-276. 
139. Rapoff MA. Adherence to Pediatric Medical Regimens. New York, NY: Kluwer 
Academic/Plenum Publishers; 1999. 
140. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, 
Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and 
validity. Med Sci Sports Exerc. 2003;35(8):1381-1395. 
141. Irwin DE, Stucky B, Langer MM, Thissen D, Dewitt EM, Lai JS, Varni JW, Yeatts K, DeWalt 
DA. An item response analysis of the pediatric PROMIS anxiety and depressive symptoms scales. 
Qual Life Res. 2010;19(4):595-607. 
142. Irwin DE, Stucky BD, Langer MM, Thissen D, DeWitt EM, Lai JS, Yeatts KB, Varni JW, DeWalt 
DA. PROMIS Pediatric Anger Scale: an item response theory analysis. Qual Life Res. 
2012;21(4):697-706. 
143. Berk M, Ng F, Dodd S, Callaly T, Campbell S, Bernardo M, Trauer T. The validity of the CGI 
severity and improvement scales as measures of clinical effectiveness suitable for routine clinical 
use. J Eval Clin Pract. 2008;14(6):979-983. 
144. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and 
sensitivity. Br J Psychiatry. 1978;133:429-435. 
145. Forrest CB, Bevans KB, Pratiwadi R, Moon J, Teneralli RE, Minton JM, Tucker CA. 
Development of the PROMIS (R) pediatric global health (PGH-7) measure. Qual Life Res. 
2014;23(4):1221-1231. 
146. Dewalt DA, Thissen D, Stucky BD, Langer MM, Morgan Dewitt E, Irwin DE, Lai JS, Yeatts KB, 
Gross HE, Taylor O, Varni JW. PROMIS Pediatric Peer Relationships Scale: development of a 
peer relationships item bank as part of social health measurement. Health Psychol. 
2013;32(10):1093-1103. 
147. Zeller MH, Modi AC. Development and initial validation of an obesity-specific quality-of-life 
measure for children: sizing me up. Obesity (Silver Spring). 2009;17(6):1171-1177. 
148. Modi AC, Zeller MH. Validation of a parent-proxy, obesity-specific quality-of-life measure: sizing 
them up. Obesity (Silver Spring). 2008;16(12):2624-2633. 
149. Awad AG, Voruganti LN. Body weight, image and self-esteem evaluation questionnaire: 
development and validation of a new scale. Schizophr Res. 2004;70(1):63-67. 
150. Brannan AM, Athay MM, de Andrade AR. Measurement Quality of the Caregiver Strain 
Questionnaire- Short Form 7 (CGSQ-SF7). Adm Policy Ment Health. 2012;39(1-2):51-59. 
151. Brannan AM, Heflinger CA, Bickman L. The Caregiver Strain Questionnaire: Measuring the 
Impact on the Family of Living with a Child with Serious Emotional Disturbance. Journal of 
Emotional and Behavioral Disorders. 1997;5:212-222. 
152. Lowe B, Kroenke K, Herzog W, Grafe K. Measuring depression outcome with a brief self-report 

 
 
Protocol Version#9.0 Dated: AUGUST 14, 2018 
Page 35 
 
 
instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). J Affect Disord. 
2004;81(1):61-66. 
153. Guy W. Guy W. ECDEU Assessment Manual for Psychopharmacology. Rockville, MD1976. 
154. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture 
(REDCap)--a metadata-driven methodology and workflow process for providing translational 
research informatics support. J Biomed Inform. 2009;42(2):377-381. 
155. Rubin DB. Inference and Missing Data. Biometrika. 1976;63(3):581-592. 
156. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in 
epidemiology. Epidemiology. 2000;11(5):550-560. 
157. Cramer JA, Glassman M, Rienzi V. The relationship between poor medication       compliance 
and seizures.  Epilepsy Behav. 200;3(4): 338-342. 
158. Jones RM, Butler JA, Thomas VA, Peveler RC, Prevett M. Adherence to treatment in patients with 
epilepsy: associations with seizure control and illness beliefs. Seizure. 2006;15(7):504-508. 
159. Byerly MJ, Thompson A, Carmody T, Bugno R, Erwin T, Kashner M, Rush AJ. Validity of 
electronically monitored medication adherence and conventional adherence measures in 
schizophrenia. Psychiatr Serv. 2007;58:844-847. 
160. Rogers EM. Diffusion of Innovations. Glencoe: Free Press; 1962. 
161. Ajzen I. The Theory of Planned Behavior. Organizational Behavior and Human Decision 
Processes.1991;50:179-211. 
162. Parrinello MC. Prevention of metabolic syndrome from atypical antipsychotic medications: 
applying Rogers' diffusion of innovations model in clinical practice. J Psychosoc Nurs Ment 
Health Serv. 2012;50(12):36-44. 
163. Bouza C, Lopez-Cuadrado T, Gutierrez-Torres LF, Amate J. Efficacy and safety of metformin for 
treatment of overweight and obesity in adolescents: an updated systematic review and meta-
analysis. Obes Facts. 2012;5:753-765. 
164. Kendall D, Vail A, Amin R, Barrett T, Dimitri P, Ivison F, Kibirige M, Mathew V, Matyka K, 
McGovern A, Stirling H, Tetlow L, Wales J, Wright N, Clayton P, Hall C. Metformin in obese 
children and adolescents: the MOCA trial. J Clin Endocrinol Metab. 2013;98(1):322-329. 
165. Scheen AJ, Paquot N. Metformin revisited: a critical review of the benefit-risk balance in at-risk 
patients with type 2 diabetes. Diabetes Metab. 2013;39:179-190. 
166. Schwimmer et al. SAFETY study: alanine aminotransferase cutoff values are set too high for 
reliable detection of pediatric chronic liver disease.  Gastroenterology. April 2010. 
167. Carnell S, Wardle J. Measuring behavioural susceptibility to obesity: validation of the child 
eating behaviour questionnaire. Appetite. 48:104-113, 2007. 
168. de Lauzon B1, Romon M, Deschamps V, Lafay L, Borys JM, Karlsson J, Ducimetière P, Charles 
MA; Fleurbaix Laventie Ville Sante Study Group. The Three-Factor Eating Questionnaire-R18 is 
able to distinguish among different eating patterns in a general population. J Nutr. 
2004;134:2372-2380.