Clinical Effectiveness of Body Fat Distribution Imaging in Real-World Practice: The BODY-REAL Study
Phase: NA
Conditions: Overweight and Obesity, PreDiabetes, Type 2 Diabetes, Cardiovascular Risk Factor
Sponsor: University Hospitals Cleveland Medical Center

 
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PROTOCOL TITLE: 
Clinical Effectiveness of Body Fat Distribution Imaging in Real-World Practice: The 
BODY-REAL Study (STUDY20201918) 
 
PRINCIPAL INVESTIGATOR: 
Ian J. Neeland, MD 
Department of Medicine 
216-844-5965 
Ian.Neeland@UHHospitals.org 
 
UH FACULTY ADVISOR: 
If the principal investigator’s primary role at UH is resident, fellow or student, identify a faculty 
advisor. 
☒ N/A 
 
OTHER DEPARTMENTS INVOLVED IN THIS STUDY (IF APPLICABLE):     
☒  N/A    
 
VERSION NUMBER: 
☐  2    
 
Objectives  
Our overall goal is to determine the real-world feasibility and utility of body fat imaging using 
rapid MRI to enhance risk perception, induce behavioral change, and improve clinical outcomes 
in overweight and obese individuals. Here, we will perform a pragmatic clinical effectiveness 
pilot trial using a 2x2 factorial design to test the hypothesis that provision of a detailed 
individualized visual report of body fat distribution directly to patients will translate into changes 
in patient risk perception, behavior, and improved clinical outcomes. We will accomplish this 
objective by completing the following two specific aims: 
 
Specific Aim 1: To compare the clinical effectiveness of communicating the body weight and 
BMI using a visual aid alone versus a detailed body fat distribution report including 
individualized images and values relative to normative data using a visual scale in a population 
of overweight and obese adults with prediabetes or type 2 diabetes and at least one additional 
cardiovascular disease risk factor. Hypothesis 1: Provision of a detailed body fat distribution 
report contextualized with information describing the relevance of each body fat parameter will 
be superior to provision of body weight/BMI information alone on risk perception, behavioral 
change (enhanced physical activity, dietary choices, and preventive provider practices and 
medication adherence), and clinical outcomes (reduction in weight and waist circumference, 
blood pressure, triglycerides, and glycosylated hemoglobin).     
 
Specific Aim 2: To compare the clinical effectiveness of communicating body fat information to 
the medical provider (with the intent that the provider interprets the data and translates it to the 
patient) versus communicating the body fat information directly to the patient. Hypothesis 2: 
Provision of body fat information directly to the patient will be superior to provision of the 

 
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information to the provider on risk perception, behavioral change, and clinical outcomes (as 
assessed in Aim 1).  
 
Background 
SIGNIFICANCE 
Scope and costs of the obesity epidemic.  Nearly 40% of American adults have obesity.1 In 
specific segments of the population, the prevalence is even higher; non-Hispanic black women, 
for example, have a prevalence of obesity of 55%. Obesity-related health expenses in the U.S. 
were estimated to be a staggering $190 billion a year in 2005.2 This figure does not account for 
additional indirect costs, such as loss of workplace productivity. The direct costs include both the 
cost of obesity treatment and the cost of treating obesity-related conditions such as diabetes, 
which are difficult to separate. It is known that a great deal is spent on obesity treatment. Outside 
of the health care system alone, Americans spend roughly $72 billion annually on weight loss 
products and services, including commercial weight loss programs, supplements, diet foods, etc.3 
Despite this enormous expenditure, rates of obesity continue to increase.  
 
Body fat distribution is fundamental to obesity-related disease. The marked increase in the 
prevalence of overweight and obesity4 has contributed to a doubling in type 2 diabetes incidence 
over the past three decades.5  This has counterbalanced reductions in other cardiovascular disease 
(CVD) risk factors, and is the primary factor contributing to a slowed decline in CVD event rates 
in the population.6  In turn, professional societies and stakeholders have renewed efforts 
targeting obesity in order to reduce the diabetes burden.7  Clinical trials aimed at lifestyle,8 
pharmacologic,9 and surgical10 approaches to diabetes consistently focus on reducing weight or 
body mass index (BMI).  However, emerging data suggests that obesity, simply characterized by 
increased BMI, is neither necessary nor sufficient to promote diabetes and its consequences.  
BMI does not adequately discriminate diabetes risk among obese individuals11 and many obese 
persons appear resistant to the development of metabolic disease.12  Rather, it appears that risk 
for diabetes varies substantially across different fat depots,13-15 and that an excess of visceral 
adipose tissue (VAT) may be central to the pathogenesis of insulin resistance and diabetes.16, 17   
 
Behavioral motivation programs for weight loss are impactful and less expensive. An 
effective in-person 6-month program described by Krukowski et al all in 2011 reported a cost per 
participant, for example, of $706.18 However, utilization of behavioral interventions for obesity, 
including those with high risk conditions such as diabetes, remains low.19 Overall, there is 
evidence that behavioral motivation programs for treatment of obesity can have a modest but 
significant positive impact (e.g. significant enough to improve cardiovascular risks) upon body 
weight and weight-related behaviors.20 This is especially true for visceral and ectopic fat depots 
where exercise/physical activity is highly impactful for reducing VAT and related risk.21   
 
Physicians lack the time, training, and priority to address obesity with patients. Most 
physicians are inadequately equipped to address obesity with their patients.22, 23 In a recent 
survey of 81 internal medicine residencies, ~50% of programs reported that obesity management 
education was covered to a “very little extent” or “not at all”.22 Furthermore, primary care 
physicians surveyed about their top priorities in delivery of preventive care did not often include 
diet, exercise, and weight loss although these were among the 3 preventive services most likely 
to improve life expectancy.23 Access to obesity treatment remains a significant challenge. Access 

 
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is especially poor in rural areas, where the obesity rate is high and the ability of residents to take 
advantage of evidence-based treatment programs in hospitals and academic centers is limited.24 
Expanding the reach of obesity treatment is an important priority. In this regard, many programs 
have been developed to deliver obesity treatment in primary care and community-based 
settings.25, 26 Rao and Kirley describe three main characteristics of successful new obesity 
treatment strategies, apart from their ability to help patients lose weight.27 First, programs should 
be convenient and accessible to those in need; second, such programs should be cost-effective; 
finally, and most importantly, participation should be sustainable. Obesity is a lifelong condition, 
and a successful program should be patient-centered, highly informative, transparent, and 
provide a perspective on risk that the patient can simply understand. We strongly believe a 
program of providing visual imaging evidence of body fat burden/distribution directly to the 
patient meets these criteria and will empower individuals to make serious lifestyle changes to 
improve health outcomes. 
 
High risk overweight and obese patients with prediabetes or diabetes are highly prevalent 
in our health system. The UH Wellness Dashboard is a comprehensive enterprise data 
warehouse aggregated from 10 sources 
encompassing all patients within the 
University Hospitals Health System (UHHS) 
with a UH medical record number attributed to 
the accountable care organization. As of 
December 24, 2020, there were 554,417 total 
patients accounting for 1.9 million ambulatory 
visits in the year 2020, of whom, 115,078 have 
a measurement of glycosylated hemoglobin 
(Hb A1c). Among this group, over 50% have a 
diagnosis of prediabetes or diabetes 
accounting for >65,000 patients in the UH 
network (Figure 1). Among those with 
diagnosed diabetes, ~6000 (or 19%) have 
severely uncontrolled diabetes with a mean Hb A1c 
of 10.9%. The UHHS serves a historically 
underserved and economically disadvantaged 
population (Figure 2), in whom behavioral 
interventions such as the one described in this 
proposal may have a significant impact. Five of the 
8 counties served by UHHSHS have >10% of 
individuals living below poverty levels; 7/8 counties 
have >30% with less than a college education; 6/8 
counties have patients with high levels of physical 
inactivity (>25% of population), and all 8/8 counties 
have diabetes prevalence above the US median.  
The significant health burden in our health system 

 
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speaks to both the high prevalence of patients who would qualify for our study, as well as the 
potential impact the outcomes of this project would provide to our high risk Northeast Ohio 
population.   
 
RATIONALE. Despite its availability for the last 30 years,36, 37 advanced body fat imaging has 
been, to date, relegated to the research domain only. Many experts in the field believe that since 
readily available routine risk indicators, such as blood pressure, blood lipids, blood glucose, 
waist circumference, and body weight, could be taken from the existing patient record and used, 
why would quantification and distribution of body fat and lean mass through graphic imaging be 
a superior motivational tool for patient behavior modification? The lack of a clear answer to this 
question is precisely the rationale for the proposed project. Without a dedicated study evaluating 
the real-world utility and feasibility of an advanced body fat imaging approach, the field will 
never answer this question and fail to evolve despite rapidly improving technology, increased 
accessibility, and lower cost of advanced imaging. The evolution of coronary artery calcium 
(CAC) scoring is a paradigm example of how advanced imaging can inform risk assessment and 
preventive/therapeutic decision making. Much in the same way that CAC provides important risk 
information beyond traditional risk factors, it is possible that advanced body fat imaging may 
add to and improve current obesity-related risk algorithms, something that BMI and WC have 
failed to do.38 Despite the general understanding in the lay public that obesity has long-term 
adverse health effects, in more recent years, due to the increase in the average weight/BMI of the 
population, many patients describe complacency regarding their weight and are often unprepared 
to take up provider advice.39 Behavioral science has long understood the importance of visual 
cues to modify behavior and motivation. It is well understood that habit learning is facilitated 
when the new behavior is consistently preceded by specific (e.g. visual) cues.40 A leading theory 
is the Health Belief Model, comprising several behavior constructs focusing on the individual’s 
perception of societal influence, one’s ability to overcome behavior barriers, and one’s reaction 
to cues. According to health behavior models, compliance with external (e.g. visual) cues will 
depend on one’s belief that the potential hazard actually carries a significant personal impact.41 
This is especially important for behavioral motivation to lose weight as other health issues are 
often felt to take precedence, and patients describe inconsistent provision of information and 
resources to assist them in tackling their weight problems.39 Therefore, there is strong rationale 
to implement highly effective visual cues that influence the patient’s understanding of the hazard 
of excess body fat and offer a personalized, visually interpretable representation of that hazard. It 
is on the basis of this rationale that this project is proposed and promises to be highly impactful 
and innovative, as detailed in the next section. 
 
INNOVATION 
Although the potential importance of VAT as a risk factor and therapeutic target for obesity and 
diabetes has been appreciated since Reaven’s description of Syndrome X in 1988,42 no approach 
to date has been effective in moving beyond the status quo of using simple anthropometric 
targets as surrogates for VAT.43, 44  Contemporary guidelines emphasize the importance of 
including waist circumference (WC) as a risk factor to guide recommendations for weight loss 
among those with overweight or obesity.19 WC is correlated with excess abdominal fat45, and 
within a given BMI, the addition of WC improves prediction of intra-abdominal (visceral) fat 
beyond the BMI alone46. However, only ~40% of the variance of visceral fat is explained by 
WC47-49 making precise estimation of excess abdominal fat without imaging impossible. Several 

 
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other anthropometric and/or laboratory markers for abdominal adiposity have been utilized 
clinically but have modest correlation with direct imaging-based assessments of body fat. 
Furthermore, surrogate markers do not differentiate between different body fat depots nor 
between fat and lean mass. Therefore, clinical implementation of more accurate and precise 
imaging measures of obesity, specifically fat amount and distribution, are vital to improve the 
diagnosis of obesity, guide decision-making for appropriate interventions, and monitor responses 
to treatment. The proposed research in this application is innovative, in our opinion, because it 
represents a new and substantive departure from the status quo, namely 1) clinical 
implementation of a rapid, non-contrast MRI protocol to accurately measure visceral and ectopic 
fat, and 2) use of a simple, pragmatic, clinical effectiveness design for the first time in humans to 
test the hypothesis that provision of direct imaging-based reports of body fat burden and 
distribution to patients will enhance risk perception, induce behavioral change, and improve 
clinical outcomes in overweight and obese individuals. Preliminary data (see Approach) strongly 
suggest that this approach will be feasible in obtaining findings that will lead to more definitive, 
hypothesis-driven trials to test the clinical effectiveness of our approach.  As a consequence, a 
more profound understanding of the utility and acceptability of direct body fat imaging for 
obesity management will be elucidated with highly significant potential impact on future 
implementation science interventions and clinical effectiveness studies in cardiometabolic care to 
improve the lives and health of millions of people.   
 
Preliminary Data. Important foundational work 
has been performed that establishes the feasibility 
and utility of this proposal. 
Body fat distribution imaging is feasible, 
accurate, and reproducible in our hands. MRI 
allows complete separation of adipose and lean 
tissues into fat-only (adipose) images and water-
only (lean) images using fat-sensitive imaging 
techniques, such as chemical-shift or Dixon 
imaging.50 Intravenous contrast is not necessary 
for the purpose of fat 
segmentation. The PI and his 
team have cumulatively 
performed >400 body fat 
MRI scans using the same 
protocol over the last 6 years 
in three separate clinical 
studies on three different 
scanners (Philips, GE, 
Siemens) (Figure 3).  To test 
intra- and inter-scanner 
reproducibility, eighteen 
healthy volunteers of varying 
BMI and adiposity were each 
scanned twice on five 

 
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different 1.5T and 3T scanners from three different vendors by members of the study team. Two-
point Dixon neck-to knee images and an additional liver scan were acquired with similar 
protocols. VAT volume, abdominal subcutaneous adipose tissue (ASAT) volume, thigh muscle 
volume, and muscle fat infiltration (MFI) in the thigh muscle were measured. Liver proton 
density fat fraction (PDFF) was assessed using a 6-point ROI method. Within-scanner test-retest 
repeatability and between-scanner reproducibility were calculated using analysis of variance and 
are presented in Table 1.  Overall there was with excellent accuracy and reproducibility for body 
fat distribution, with very low coefficient of variation seen in VAT, ASAT, and thigh muscle 
volume, and more variation seen in MFI and liver fat. These results speak to the excellent rigor 
and reproducibility of the imaging methods to be used in this study. 
 
University Hospitals has established behavioral motivation programs for weight loss with a 
successful track record. “Fitter Me” is a medical obesity treatment program established at UH 
in 2018. “Fitter” implies both fitness and individualization. Since inception, the program has had 
nearly 200 participants. The program is clinic based. Initial visits include a detailed medical 
evaluation, as well as identifying key habits contributing to obesity and motivations for weight 
loss. Participants then meet with a trained behaviorist who helps them implement behavioral 
goals, using a variety of techniques such as motivational interviewing. Patients are seen roughly 
monthly through clinic visits to monitor progress. The behaviorist is available between 
appointments by phone, email and text for support and problem solving.  Pharmacotherapy is 
prescribed by a provider depending upon progress and patient interest. We have follow-up 
outcomes data for 85 patients. The average weight loss has been 2.96kg across a number of 
different time frames. Mean weight loss is 1.5kg for patients with 2-3 total visits, compared to a 
weight loss of 44.45kg for one patient with 15 visits. Weight loss has averaged 2.5kg for women 
and 5.75kg for men. Weight loss has averaged 3.63kg for white participants compared to 2.28kg 
for black participants.  
 
Inclusion and Exclusion Criteria 
 
Inclusion Criteria 
1. 
Age ≥ 35 years 
2. 
Able to provide informed consent 
3. 
Overweight or Obese (BMI ≥25 kg/m2) 
4. 
Prediabetes or Type 2 Diabetes: 
• Fasting glucose >100 mg/dl, or 
• Hb A1c >5.7%, or 
• Medical (i.e. pharmacologic) treatment for type 2 diabetes 
5.  At least 1 additional cardiovascular risk factor (defined by ATP III criteria2) 
including: 
• Hypertension (BP>130/80 or on medical therapy for hypertension) 
• Low HDL-cholesterol (<40 mg/dL in men and <50 mg/dL in women) 
• High triglycerides (>150 mg/dL or on treatment for hypertriglyceridemia) 
• Obstructive sleep apnea (clinical diagnosis) 
• Coronary artery disease (clinical diagnosis) 
• Congestive heart failure (clinical diagnosis) 
• Atrial fibrillation (clinical diagnosis) 

 
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Exclusion Criteria 
1. 
Receipt of any anti-obesity drug or supplement within 1 month prior to screening 
for this trial or plan to initiate therapy during the trial. 
2. 
Self-reported or clinically documented history of significant fluctuations (>5% 
change) in weight within 1 month prior to screening for this trial. 
3. 
Current or history of treatment with medications that may cause significant weight 
gain, within 1 month prior to screening for this trial, including systemic 
corticosteroids (except for a short course of treatment, i.e., 7- 10 days), tri-cyclic 
antidepressants, atypical antipsychotic and mood stabilizers (e.g., imipramine, 
amitryptiline, mirtazapine, paroxetine, phenelzine, clorpromazine, thioridazine, 
clozapine, olanzapine, valproic acid and its derivatives, and lithium). 
4. 
Surgery scheduled for the trial duration period, except for minor surgical 
procedures, at the discretion of the Investigator. 
5. 
Language barrier, mental incapacity, unwillingness or inability to understand. 
6. 
Females of childbearing potential who are pregnant, breast-feeding or intend to 
become pregnant or are not using adequate contraceptive methods. These include 
abstinence and the following methods: diaphragm with spermacide, condom with 
spermacide (by male partner), intrauterine device, sponge, spermacide, Norplant®, 
Depo-Provera® or oral contraceptives.  
7. 
Unable to complete/tolerate magnetic resonance imaging (MRI) due to severe 
claustrophobia or metallic implants. 
8. 
≥2 no-shows to recruitment clinic within the 6 months prior to screening.  
 
Number of Research Participants 
Planned number of subjects to be screened:  200 
Planned number of subjects to be randomized:140 
Anticipated number of subjects to be analyzed: 126 (assumes 10% drop-out)  
Number of trial sites: Single center 
 
Recruitment Methods  
Participants for the proposed study will be recruited primarily from two sources at UH: the Fitter 
Me program (described above) and The Center for Integrated and Novel Approaches in 
Vascular-Metabolic Disease (CINEMA) program. We founded CINEMA in order to address the 
significant health burden in our Northeast Ohio population. CINEMA is an integrated, patient-
centered, team-based intervention for patients with type 2 diabetes at high risk for cardiovascular 
disease events (i.e. those with established atherosclerotic cardiovascular disease, high risk 
coronary calcium score >100, and/or chronic kidney disease stages 2-4). The program was 
launched in May 2020 by UH, a large health care network in Northeast Ohio, comprising 11 
hospitals and 18 regional medical centers with the intent of impacting the care of type 2 diabetes 
with complex cardiovascular and renal disease. The CINEMA team (including the PI who is Co-
Director of the program) consists of a program administrator; 5 cardiologists with a special 
interest focus on diabetes, prevention, cardiovascular imaging, and vascular medicine; a nurse 
coordinator; a certified diabetes care and education specialist; and a dedicated pharmacist who 
aim to target lifestyle and pharmacologic therapies to the cardiac, vascular, and renal 
manifestations of diabetes. Within the second half of  2020, despite the COVID-19 pandemic, we 

 
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have cumulatively enrolled 132 patients in CINEMA, with steady increases in monthly referrals 
since the start of the program. The CINEMA and Fitter Me programs will be the sources for 
participant enrollment for the trial proposed here.      
 
Setting 
Participants will be recruited from the CINEMA and Fitter Me clinics described above. As the 
directors of these clinics are members of the study team, staff will identify potential participants 
who attend these clinics and study coordinators will engage the patients for their interest in the 
study. If participants are interested in enrolling and initial inclusion/exclusion criteria are 
satisfied, written informed consent will be obtained and the participant will be enrolled in the 
study. Once enrolled, the surveys will be administered either after the clinic visit in-person or 
provided electronically for the participant to complete at home. Participants will then be 
randomized using a computer-generated code and those assigned to body fat imaging will be 
contacted by phone or email to set up an MRI imaging appointment. All MRI imaging will occur 
in the Lerner MRI (MR7) in the basement of Lerner Tower on the Cleveland Medical Center 
campus. All additional clinical data and outcomes will be obtained from the electronic health 
record (EHR).  
 
Consent Process 
All participants will undergo written, informed consent for this study. The study team will be 
requesting a partial HIPPA waiver in order to screen potential participants from the Fitter Me 
and CINEMA clinics using the EHR in order to identify potential eligibility.  
 
Sharing of Results with Research Participants 
By design of this study, the results of the MRI imaging will be shared with either the participant 
or the participant’s physician via a detailed body composition report. Other clinical and 
laboratory data are readily available in the EHR and can be accessed by the physician and patient 
as per clinical routine.  
 
Study Design 
The study design is a pragmatic, clinical effectiveness trial using a two-by-two factorial design to 
test the hypothesis that provision of a detailed individualized visual report of body fat 
distribution directly to patients will translate into changes in patient risk perception, behavior, 
and improved clinical outcomes. Participants will be randomized to i) receive body weight and 
body mass index information using a visual aid alone or a detailed body fat distribution report 
including individualized images and values relative to normative data using a visual scale, and ii) 
the report will be provided directly to the medical provider or the report will be provided directly 
to the participant (patient).   
 
Study Procedures 
Aim 1 – Compare the clinical effectiveness of communicating the body weight and BMI 
using a visual aid alone versus a detailed and visual body fat distribution profile report 

 
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Overall design. Our trial is designed to address the hypothesis that provision of a detailed 
individualized visual report of body fat distribution directly to patients will translate into changes 
in patient risk perception, behavior, and improved clinical outcomes. It is consistent with the 
“health belief model” of behavior change, whereby an individual is more likely to adopt a 
healthy behavior if they perceive increased personal risk and believe that specific behaviors will 
reduce risk (Figure 5).51, 52 In this 2-by-2 factorial design (Figure 6), we will randomly assign 
overweight/obese patients with high cardiovascular risk to a detailed body fat distribution report 
or provision of standard weight/BMI and to provision of the report directly to the patient or to 
the provider. Specific Aim 1 is dedicated to the randomized comparison of the body fat 
distribution report vs. provision of standard weight and BMI information.  
Study population. The 
study population will 
consist of overweight and 
obese adults with 
prediabetes or type 2 
diabetes and at least one 
additional cardiovascular 
disease risk factor (see 
Inclusion/Exclusion criteria 
in the Human Subjects and 
Clinical Trials information 
section of the Appendix). 
Participants will be 
recruited from clinical 
programs (described above) 
targeted to this population. 
Efforts will be made to 
recruit equally from both 
men and women and 
between race/ethnicity with 
a goal of 50% men/50% 

 
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women and 50% White/50% Black with at least 20% Hispanic participants. Sex- and 
race/ethnicity-stratified analyses (see Statistical section) will be performed to better understand 
the influence of sex and race/ethnicity on the intervention and outcomes.  
Randomization. After screening and written informed consent, participants  will be randomized 
to receive either the detailed body fat distribution report or standard weight/BMI information 
using a computerized randomization code generated by the UH Translational Science Unit. 
Given the pragmatic nature of the trial, neither providers nor patients will be blinded to the 
treatment assignment. However, outcomes adjudication (see below) will be obtained through the 
electronic medical record and chart abstractors will be blinded to the treatment assignment so as 
not to bias the collection of outcomes.  
Anthropometrics and body fat distribution imaging methods. All participants will undergo 
standard anthropometric measurements of height and weight using a standard scale (model HBF-
514C, Omron Healthcare, Lake Forest, IL). The scale will be calibrated on a weekly basis using 
a standard 10 kg weight to ensure accuracy and precision over the course of the study. BMI will 
be calculated as weight (kg)/height (m)2. Waist circumference will be measured with a standard 
tape 1 cm above the iliac crest and hip circumference will be measured at the widest 
circumference of 
the buttocks at the 
area of the greater 
trochanters. These 
measurements are 
routinely obtained 
in both the Fitter 
Me and CINEMA 
programs in a 
standardized 
manner. Those 
randomized to 
body fat 
distribution 
imaging will be 
scanned on a 1.5T 
Siemens Aera MRI scanner (Siemens Healthineers, Erlangen, Germany), located in the Center 
for Advanced Heart and Vascular Care using a 6-minute dual-echo Dixon Vibe protocol 
providing a water and fat separated volumetric data set covering neck to knees, and a multiecho 
Dixon acquisition for proton density fat fraction assessment in the liver. Images of the liver will 
be acquired using a 16-channel SENSE XL Torso coil and images from the rest of the body will 
be acquired using the body coil. Volumetric imaging datasets of the body derived by MRI will be 
generated using validated and readily-available protocols with rapid scan time to perform precise 
measurements of total and regional body composition and fat distribution.  Apart from 
quantifying adipose tissue depots, such as in the abdominal subcutaneous compartment (ASAT), 
visceral compartment (VAT), and hips and buttocks (lower body fat); we will employ more 
recently developed MRI techniques to measure the proton density fat fraction of the liver (i.e. 
hepatic steatosis) as well as the quality of lean (skeletal muscle) including muscle volume and 
degree of fat infiltration (Figure 7).53 Quantification of body fat distribution parameters will be 
performed using AMRA Profiler Research (AMRA Medical AB, Linköping, Sweden).50, 54-56 

 
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Briefly, the image analysis consists of (1) image calibration, (2) fusion of image stacks, (3) 
image segmentation, and (4) quantification of fat and muscle volumes and included manual 
quality control by an analysis engineer. Dr. Neeland has personally recruited >250 participants 
and supervised >400 MRI body fat scans utilizing this identical protocol. Drs. Linge and 
Dahlqvist Leinhard will supervise the measurement and interpretation of the acquired images and 
provide the dedicated reports.  
Provision of reports. Participants randomized to weight/BMI information alone will receive a 
simple informational report consisting of weight, BMI, and a visual representation of their BMI 
(www.bmivisualizer.com, last accessed December 30, 2020). This report also categorizes their 
BMI into underweight, normal weight, overweight, or obese categories according to the World 
Health Organization categorization schema.57 Those randomized to the body fat distribution 
report will receive a detailed body composition profile report that consists of the following 
elements: basic demographic data, percent body fat, weight to muscle ratio, visceral fat and 
abdominal subcutaneous fat volume, visceral fat ratio (the fraction of visceral divided by total 
abdominal fat), muscle fat infiltration and liver fat (%), and thigh muscle volumes (also 
separated into right and left, anterior and posterior compartments). Each parameter is presented 
on a visual scale in the context of the individual value, general population defined by reference 

 
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data (from UK Biobank population), a metabolic disease-free population (also from UK 
Biobank), low/high and very low/very high, corresponding to 15th and 5th percentiles, 
respectively. There are also descriptions of each biomarker and how they are derived to provide 
context for the recipient. A sample report is shown in Figure 8.  
Outcomes. The planned outcomes for this study combine both traditional participant data 
acquisition (validated survey instruments) as well as pragmatic data obtained from the electronic 
health record (EHR) (provider practice patterns and clinical outcomes). The rationale underlying 
this design is that our goal is to assess “real-world” impact of the intervention, in a pragmatic, 
cost-effective manner to provide feasibility/pilot data for this small R01 project. Physician 
practice data and clinical outcome variables will be collected from the EHR linked to the 
translational research warehouse (CLEARPATH, described in detail below, 4.5.5) which will 
provide extensive, albeit “clinically-driven”, data. One of the biggest strengths of the UHHS 
EHR system is its integration of 11 hospitals and 18 regional medical centers translating to ~2 
million ambulatory visits per year. The UHHS laboratory system is extensive and highly 
available throughout NE Ohio and also captures laboratory data obtained at other health systems 
in the Cleveland metro-area, allowing broad capture of clinical laboratory data with minor 
anticipated missingness, especially for the common clinical variables we are collecting. 
Outcomes will be assessed at as close as possible to the following time points: Baseline, 2 weeks 
(survey data only), 3 months, and 6 months. The rationale for the spacing of time points in this 
manner is to assess both “immediate” impact of the information on risk perception and behavior, 
as well as more intermediate/long term changes in risk perception, behavior, and clinical 
outcomes. Given that the biggest impact of weight reducing interventions occur between 3-6 
months (with plateau and regain usually seen thereafter), we are using this time interval to assess 
clinical outcomes.   
Risk perception. Cardiovascular risk perception will be measured using the Perception of Risk 
of Heart Disease Scale which has been shown to be reliable and well-validated for use in this 
population.58-63 Diabetes risk perception will be assessed using the validated Risk Perception for 
Developing Diabetes Risk (RPS-DD) instrument. The instrument consists of 4 sections with 
responses recorded on a Likert scale. These sections assess knowledge of diabetes risk factors, 
perceptions of personal control, perceptions of the benefits and barriers, and optimistic bias 
about developing diabetes.64, 65 Motivation to change diabetes risk behaviors will be measured 
using the Treatment Self-regulation Questionnaire (TSRQ) that consists of 15 self-report Likert 
scale items assessing motivation across sub-domains (e.g., exercise, diet).66 Each participant will 
be queried as to their perception of T2D and CVD risk at 4 time points during the study: i) after 
written informed consent but prior to randomized allocation of the intervention, ii) within 2 
weeks (±3 days) of randomized allocation of the intervention, iii) after 3 months (±3 days) post-
randomized allocation of the intervention, and iv) after 6 months (±3 days) post-randomized 
allocation of the intervention.  
Behavioral change. Outcomes of behavioral change will be assessed generally in the following 
categories: i) enhanced physical activity, ii) dietary choices, and iii) preventive medication 
prescription/adherence. 
Physical activity. Physical activity will be assed using the Global Physical Activity 
Questionnaire (GPAQ) available from the World Health Organization. The GPAQ uses 
metabolic equivalent (MET) levels to identify physical activity level. MET scores will be 
calculated for each domain and subdomain of the GPAQ. A categorical indicator will be created 
that accounts for the time spent on physical activity during a typical week, the number of days, 

 
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and the intensity of the activity. Although this is a self-report of physical activity, we believe 
that, in this pragmatic trial, the GPAQ, which is widely used in population-level studies, will 
provide a measure sufficient for our purposes. 
Dietary choices. All participants will undergo a standardized, validated dietary questionnaire 
(Automated Self-Administered 24-Hour Dietary Assessment Tool or “ASA24”, accessed online 
at https://epi.grants.cancer.gov/asa24/) to better understand their dietary choices. The ASA24 is a 
free, web-based tool that enables multiple, automatically coded, self-administered 24-hour diet 
recalls and/or single or multi-day food records, also known as food diaries, available from the 
National Cancer Institute. Since AS24 was released in 2009, >6000 studies have registered to use 
it and >521,000 record days have been collected as of January 2020. No dietary counseling will 
be mandated during the study but it is anticipated that participants will receive dietary counseling 
from their providers and/or outside sources during the course of the study and that the study 
intervention may influence seeking out dietary advice and instituting dietary changes. Each 
participant will complete a dietary questionnaire at 4 time points during the study: i) after written 
informed consent but 1 week prior to prior to randomized allocation of the intervention, ii) 
within 2 weeks (±3 days) of randomized allocation of the intervention, iii) after 3 months (±3 
days) post-randomized allocation of the intervention, and iv) after 6 months (±3 days) post-
randomized allocation of 
the intervention.  
Medication adherence 
and preventive provider 
practices. Medication 
adherence will be measured 
using the Medication 
Adherence Rating Scale 
(MARS), 4-item self-report 
instrument developed and 
tested in a wide range of 
populations.67 Although the 
MARS is not a precise 
measure of adherence to the 
medication prescription, we 
believe the domains 
assessed (attitudes and 
skills associated with 
medication taking) are appropriate for our purpose. Patient burden in completing all of the 
aforementioned measures is low in that the complete profile takes less than 30 minutes. 
Preventive provider practices will be accessed through the EHR (Table 2) and data warehouse, 
known as Clinical and Demographic Data Retrieval: Translational Research Data Warehouse 
(CLEARPATH). This is a unique Cleveland-based combination of robust electronic health 
record clinical data and bio-specimen annotation data from several health systems in Cleveland, 
collected into an aggregated, de-identified limited data set available to researchers to perform 
queries from across the entire database. Some of its unique capabilities are: Common Data 
Model:  Clinical data from many different sources harmonized, profiled, and mapped to a 
common data model to support aggregate analytic capabilities. The common data model utilizes 
several different mapped ontologies that enables the description of these complex data sets 
Table 2.  Preventive Provider Practice Outcomes Extracted from EHR Data 
Outcome over 12-months 
Query/RxNorm/CPT Definition 
Prescriptions 
New/additional prescription of 
lipid medication 
New prescription of any agent in LIPID 
MODIFYING AGENTS / id: C10 / class type: 
ATC1-4 
New prescription of aspirin 
Aspirin / id: N0000006582 / class type: CHEM  
New prescription of smoking 
cessation medication 
Varenicline / id: N0000179790 / class type: 
CHEM Or Bupropion / id: N0000006417 / class 
type: CHEM AND/or Nicotine  
New or additional antihypertensive  
ANTIHYPERTENSIVES / id: C02 / class type: 
ATC1-4  
New prescription of metformin 
Metformin / id: N0000006631 / class 
type:CHEM  
Counseling services 
Initial consultation with 
dietician/nutrition  
CPT code 97802 
Prevention counseling by physician 
99401 or 99402 
Smoking cessation counseling 
99406 or 99407 
Follow-up care 
Frequency of primary care visits 
# per 12-month follow-up period 

 
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across domains, information system vendors, and institutions.  Person De-duplication:  Utilizing 
a unique technology, each person in the CLEARPATH database is assigned a master person ID. 
This allows for constructing a single person’s medical information from across multiple health 
care systems. This will allow information on participants undergoing care at UHHS but being 
admitted to another hospital in NE Ohio allowing us unprecedented access to information. Even 
more impressive, this is accomplished without sharing Personal Health Information (PHI) 
information between various institutions using a state-of-the-art, trusted privacy preservation 
network enabled by a hash-based matching algorithm. It is implemented in accordance with 
HIPAA Privacy Rules 164.514 (b) (1) and 164.514(c) which enables the source institution to re-
identify patients from an identified group through approved IRB protocol. For the purposes of 
this study, we will use data from patients undergoing care through UHHS, but collect outcomes 
data from all CLEARPATH partner institutions. We acknowledge that outcomes data is likely to 
be missing from a significant number of patients. For example, a preventive service may be 
delivered to a patient from a non-CLEARPATH institution. Our primary purpose is to 
demonstrate a substantial relative impact of risk scoring on outcomes, rather than to measure that 
impact with great precision. We believe that missing data will be randomly distributed among 
the 4 groups of patients, and will therefore not influence our measure of relative impact. To 
control for changes that may occur unrelated to our pragmatic intervention of providing image 
based reports and how they affect physician behavior, and to possibly understand directionality 
of physician practices as being related to our imaging-report, we will include a non-
cardiometabolic health care outcome (influenza vaccine), to determine if any change in 
prevention practice is specifically related to cardiometabolic care or is more general in nature.  
Clinical outcomes. Given the pragmatic nature of our trial, we will obtain clinical outcomes 
parameters from the electronic health record as part of routine clinical care. Sitting systolic and 
diastolic blood pressure, body weight, BMI, and waist circumference will be recorded routinely 
at clinic visits. HbA1c, lipid profile, and blood glucose will be performed by UHHS Clinical 
Chemistry labs using standard clinical procedures. Blood pressure is typically measured through 
out UHHS using automated oscillometric devices as recommended by guidelines using trained 
clinical staff. A summary of all outcomes measures is provided in Table 3 below.  
 
Aim 2 – Compare the clinical effectiveness of communicating body fat information to the 
medical provider versus communicating the body fat information directly to the patient. 
Overall design. This aim is designed to address the hypothesis that provision of body mass/fat 
information directly to patients, compared with provision to the provider, will translate into 
changes in patient risk perception, behavior, and improved clinical outcomes. This hypothesis is 
consistent with the belief that many, if not most primary care providers (PCPs) or other health 
care providers, find it challenging to provide lay interpretations of obesity-related risk for T2D 
and CVD in the time available of a busy office visit for the scope of counseling needed. Most 
PCPs already know the risk of elevated BMI in combination with other clinical health risk 
indicators; the primary issues are lack of training in behavioral lifestyle counseling skills, lack of 
clinic time, lack of reimbursement for counseling, and lack of appropriate referral mechanisms to 
other allied health professionals with appropriate intensive behavioral lifestyle coaching skills 
which are already known to be efficacious. Moreover, patients do not always have the time or 
financial means to attend visits with these allied health professionals, many of whom are not 
covered by standard medical insurance plans. Furthermore, potentially complex explanations 
associated with elevated risk associated with subcutaneous vs. visceral and ectopic fat 

 
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distribution beyond body weight/BMI and other standard risk factors alone to enhance patient 
risk perception and induce behavioral change may not be feasible or readily accepted/understood 
by the patient. Therefore, it is imperative to assess whether direct provision of this information to 
the patient may be superior to relying on the provider to contextualize and interpret the 
information and relay it to the patient adequately. Given that we live in a technologically savvy 
and open-information world, it is possible that patients would prefer and benefit from having the 
direct information accessible in order to research and learn about its impact on their health risks. 
In this 2-by-2 factorial design (see Figure 6 above), we will randomly assign overweight/obese 
patients with high cardiovascular risk to provision of the report directly to the patient or to the 
provider. Specific Aim 2 is dedicated to the randomized comparison of the patient vs. the 
provider being the recipient of the body weight/fat distribution report. Given the 2x2 factorial 
design of this trial, the study population, randomization and allocation, data and outcomes 
assessments are identical to Aim 1 except for the different allocation assignment. 
 
Study Timeline 
 
Data to be Collected for your study  
(AFTER consent and HIPAA Authorization have been obtained) 
See Table 3 above and description of all data to be collected in the Study Procedures section. 
 
Data Analysis Plan 
We hypothesize that provision of a detailed body fat distribution report contextualized with 
information describing the relevance of each body fat parameter will be superior to provision of 
body weight/BMI information alone on the outcomes described above. Characteristics will be 
compared between participants randomized to a brief vs. detailed report using chi-square tests for 
dichotomous variables and Wilcoxon rank-sum tests for continuous variables. All analyses will 
be based on the intention-to-treat principle. For continuous endpoints, an analysis of covariance 
model with both fixed and mixed (time-updated) effects will be used to analyze mean changes. 
Table 3. Study specific outcomes and timeline 
Pre-
screening 
Baseline 
2 
wk 
3 
m 
6 
m 
Estimated time requirement of visit for participant 
0 hr 
1 hr 
1 hr 
1 
hr 
1 
hr 
Chart review for eligibility (inclusion/exclusion criteria) 
X 
 
 
 
 
DATA EXTRACTION FROM ELECTRONIC MEDICAL RECORDS 
Clinical Outcomes 
 
 
Clinically driven visits with assessment of weight, BMI, waist circumference 
and blood pressure 
 
X 
 
X 
X 
Lipid profile, HbA1c, fasting glucose 
 
X 
 
X 
X 
Physician Practices  
 
 
Medication prescriptions, referrals for weight loss counseling, and lifestyle 
programs  
 
<------------------------> 
Collected throughout the 
study 
DATA TO BE OBTAINED DIRECTLY FROM THE PARTICIPANT 
Patient Risk Perception and Behavior 
 
 
Perception of Risk for Diabetes (RPS-DD) 
 
X 
X 
X 
X 
Perception of Heart Disease (PRHDS) 
 
X 
X 
X 
X 
Motivation to change behaviors (TSRQ) 
 
X 
 
X 
 
Global Physical Activity Questionnaire (GPAQ) 
 
X 
X 
X 
X 
ASA24 Dietary instrument 
 
X 
X 
X 
X 
Medication Adherence (MARS) 
 
X 
X 
X 
X 

 
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For categorical endpoints, logistic regression will be used with the same fixed and mixed effects 
and covariates as the analysis of covariance model. The models will include randomized 
allocation, age, sex, obesity status at baseline, T2D status at baseline, interaction between obesity 
and T2D status strata as fixed effects, with the baseline and subsequent values of the relevant 
variable as a mixed effects covariate. Effects will be reported as mean ± standard deviation or 
proportion in each group with between group differences reported as control group-adjusted 
difference with 95% confidence interval. Given the inherent nature of the study 
(pilot/feasibility), we also will perform separate analyses between baseline and 2 weeks, baseline 
and 3 months, and baseline and 6 months without adjustment for multiple testing. Although 
pilot/feasibility trials are, by definition, not fully powered to answer the question, we provide a 
basic rationale for our planned inclusion of 126 participants. This trial is powered to detect a 5% 
control-corrected relative reduction in body weight. The FDA considers the 5% mark for weight 
loss clinically significant, and uses this benchmark for development of drugs and therapeutic 
biologics regulated within the Center for Drug Evaluation and Research (CDER) in the Food and 
Drug Administration (FDA).68 Assuming a mean 2% relative reduction in weight among control 
(standard weight/BMI) participants and a 7% relative reduction in weight among those provided 
a detailed report (with a standard deviation of 10%, based on prior data), we expect to require 
126 total participants (in a 1:1 randomization scheme) to achieve 80% power to detect an 5% 
difference between groups at an alpha level of 0.05. Assuming an estimated 10% of participants 
might not attend clinical visits within the planned 6 months time frame, we expect to enroll a 
total of 140 participants in order to achieve at least 126 participants with outcome measures. 
 
Study Timeline 
This is a three-year study. Table 5 shows the study timeline. The research team assembly is 
expected to start early on in the process in the third quarter of 2021. The third quarter of 2021 
will also be used to finalize study surveys/questionnaires, and establish an EHR extraction 
protocol with IT department. Recruitment is expected to start in the last quarter of 2021 and 
will continue for ~2 calendar years, through the third quarter of 2023. Clinical follow-up and 
follow-up questionnaire/survey administration will commence as early as recruitment of first 
patient and will last for 6 months after the last patient is recruited. EHR data extraction for 
clinical variables will also run in parallel with follow-up questionnaire administration. Data and 
safety monitoring (see plan below) will commence with first patient-first visit and continue 
through the end of study procedures, followed by an annual progress report. Analysis of data 
will commence in 2024, which will be followed by presentations and manuscripts.  
 
Table 5. Study Timeline 
Action Item 
2021 
2022 
2023 
2024 
 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Research Team Assembly 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruitment 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Follow-up 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EHR Data Extraction 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data & Safety Monitoring 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annual Progress Reports 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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Risks to Research Participants 
Loss of Confidentiality 
Although every effort will be made to keep information collected about patients confidential, 
there is a small risk that this information may be seen by unauthorized persons. Loss of 
confidentiality will be minimized by storing data in locked cabinets and electronic data will be 
password protected on the University Hospitals and CWRU computer network using secure 
research data collection software. Only de-identified and encrypted data will be sent 
electronically to study staff. All email with subject-identifiable information will be password 
protected.  Only key personnel will have access to the information in the study database on an as-
needed basis. Key personnel may not alter the data in the database or directly view all of it 
without specific cause and approval of the PI. 
 
Risks of Magnetic Resonance Imaging (MRI) 
There are no known risks from exposure to magnetic fields. Participants may experience 
nervousness and/or anxiety due to the loud banging made by the machine while it is taking 
pictures and from confinement in a tight space (claustrophobia). The study team will make all 
attempts to identify the potential risk for claustrophobia/anxiety prior to enrollment; however, 
occasionally, participants underestimate their reaction once in the scanner environment. If a 
participant becomes anxious, the procedure can be halted at any time. The MRI machine uses a 
strong magnet and radiofrequency magnetic fields to make images of the inside the body. A 
known risk is that the MRI scanner uses a very strong magnet that will attract some metals and 
affect some electronic devices. Patients with retained bullets, shrapnel, or MRI non-compatible 
ferromagnetic objects will be excluded from this study. Participants will be asked to remove any 
metal objects before entering the magnet room. University Hospitals Cleveland Medical Center 
standard screening guidelines for MRI safety will be followed. The participant may also feel 
temporary muscle stiffness and minor discomfort due to lying still during the study. 
 
MRI may not be appropriate if the participant is pregnant. MRI may not be appropriate if the 
participant has permanent eyeliner or eyebrows or any pieces of metal in their body, such as the 
following: 
• heart pacemaker, heart valve replacement, or aortic clips 
• metal fragments in the eyes, skin, or elsewhere in the body 
• brain clips or pieces of metal used in aneurysm surgery or intercranial bypass 
• venous umbrella 
• pieces of metal in the body resulting from work as a sheet-metal worker or welder 
• clips placed in an internal organ 
• prosthetic devices, such as middle ear, eye, joint, or penile implants 
• joint replacement. 
• hearing aid that cannot be removed 
• neurostimulator 
• insulin pump 
• intrauterine device (IUD) 
 
Analysis of Completed Data 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Presentations and Manuscripts 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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• shunts or stents 
• metal mesh or coil implants 
• metal plate, pin, screws, or wires, or any other metal implants 
 
Reproductive risks  
Being a part of this study while pregnant may expose an unborn child to serious risks. The reason 
for this is that the goal of the study is to lose weight. Women who are pregnant or lactating 
should not necessarily undergo a medical weight loss program unless it is under the supervision 
of their gynecologist and other treating physicians. The reason for this is that it may affect the 
fetus/newborn’s nutritional status and growth. Therefore, pregnant and lactating women will be 
excluded from this study as participation in a weight loss program may pose risk to the 
fetus/newborn. 
 
Psychological Stress from Survey Questions 
Although only health-related information will be obtained in this study, some of the surveys 
relate to the potentially sensitive subject matter of patient weight/obesity. There is a small risk 
that some of the questions we will ask as part of this study may make the participant feel 
uncomfortable.  The participant may refuse to answer any of the questions, take a break or stop 
participation in this study at any time. 
 
Provisions to Protect the Privacy Interests of Research Participants 
Loss of confidentiality will be minimized by storing data in locked cabinets and electronic data 
will be password protected on the University Hospitals and CWRU computer network using 
secure research data collection software. Only de-identified and encrypted data will be sent 
electronically to study staff. All email with subject-identifiable information will be password 
protected.  Only key personnel will have access to the information in the study database on an as-
needed basis. Key personnel may not alter the data in the database or directly view all of it 
without specific cause and approval of the PI. University Hospitals Cleveland Medical Center 
standard screening guidelines for MRI safety will be followed. Patients with retained bullets, 
shrapnel, or MRI non-compatible ferromagnetic objects will be excluded from this study. 
Participants will be asked to remove any metal objects before entering the magnet room. The 
study team will make all attempts to identify the potential risk for claustrophobia/anxiety prior to 
enrollment; however, occasionally, participants underestimate their reaction once in the scanner 
environment. If a participant becomes anxious, the procedure can be halted at any time. Pregnant 
and lactating women will be excluded from this study. 
 
Potential Benefit to Research Participants 
There may be direct benefit to the research participants in the form of knowledge (report) about 
their health status as it pertains to body weight/fatness, which may induce behavioral change to 
improve health. Otherwise, there is no direct benefit of the proposed research to study participants. 
Knowledge gained from this project will likely lead to better understanding of diabetes in the 
general population and across ethnic minorities and women as well as potentially lead to improved 
targets of therapies aimed at reducing diabetes and cardiovascular disease risk. The minimal risks 
outlined in this proposal are reasonable for this societal benefit as it may lead to improved care to 

 
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reduce diabetes and cardiovascular disease risk, leading causes of morbidity and mortality in the 
U.S.  
 
Withdrawal of Research Participants 
The study team may determine it is necessary to withdraw participants from the research without 
their consent if: 
i. The researchers believe that participation in the research is no longer safe. 
ii. The sponsor or the FDA stops the research for the safety of the participants. 
iii. The sponsor cancels the research. 
iv. The participant is unable to keep appointments or to follow the researcher’s instructions. 
 
Partial withdrawal from this study by the participant is highly unlikely since the intervention is a 
one-time provision of health information and the study endpoints are primarily survey based. 
However, in the event this does occur and the participant partially withdrawals from study 
procedures (e.g. surveys) but does not withdraw consent for inclusion in the study, the research 
team may still actively obtain EHR related health information for use in the study.  
 
Alternatives to Participation 
Since all treatment is part of routine clinical care and not mandated by the study, there is no 
specific alternative to participation other than electing not to participate in the research.  
 
Costs to Research Participants 
There will be no direct costs to research participants. All MRI acquisition, interpretation, and 
reporting will be funded by the study sponsor. All other study outcomes are part of routine 
clinical care.  
 
Research Participant Compensation 
There will be no reimbursement for study participation. 
 
Provisions to Monitor the Data to Ensure the Safety of Research Participants 
Type of Research Data or Events to be Monitored: 
All research activities described in the project summary will be monitored in addition to study data 
accrual, protocol deviations, protocol violations, unanticipated problems, and adverse events. 
 
Methods and Frequency of Analysis: 
All research staff will have appropriate training to ensure the safety of participants, the appropriate 
conduct of research, and the integrity of safety and/or efficacy of data.  This training includes 
completion of all University-required training in the policies and procedures to protect the rights 
and welfare of people who participate in research.   
 
Subjects will be fully informed about the study requirements throughout the conduct of the trial 
by the study personnel.  At the beginning of the study, it will be noted in the research record that 
the subject received complete information about the study requirements.  Study personnel will give 
participants information relevant to continued participation.   
 

 
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The PI will perform monitoring activities. Monitoring will be performed monthly by the PI. The 
conclusions of the monitoring will be reported to the IRB in writing if there is any change in the 
risk/benefit ratio for subjects or if there is any new information which may affect a subject’s 
willingness to continue participation. Monitoring will include consideration for (1) progress of the 
trial, (2) data quality, (3) timeliness of data collection, (4) recruitment, (5) accrual and retention, 
(6) risk vs. benefit for participants, (7) protocol violations, and (8) other factors that could affect 
the outcome of the study.  Monitoring will be documented in the research record. 
 
Person(s) Responsible for Data Monitoring: 
The persons responsible for data monitoring in this research are Ian J. Neeland, MD and the 
primary study coordinator, Ann Dever.  Dr. Neeland will oversee study operations. The study 
coordinator will be responsible for aiding in subject recruitment and retention, protocol execution, 
and general study coordination.  
 
Reporting Unanticipated Problems, Adverse Events, Protocol Deviations and Protocol Violations: 
The principal investigator and research coordinator will monitor subject involvement in the study 
on a monthly basis. An adverse event is defined as both an expected side effect that is of a serious 
nature, or an unexpected side effect/event regardless of severity. All events will be graded as to 
their attribution (unrelated to protocol, or possibly, probably, or definitely related to protocol), as 
well as to the severity of the event (adverse - not serious (adverse event, AE) and adverse-serious 
(serious adverse event, SAE)). Any event that is reported to either the PI or his designated research 
associates by the subject or medical staff caring for the subject and which meets the criteria will 
be documented as such. The PI will report all classes of unexpected adverse events and any serious 
adverse events within 48 hours of recognition to the Federal agencies and University and hospital 
offices as specified by the IRB.  The PI will submit a summary of adverse events in continuing 
review reports to the IRB at intervals specified by the IRB.  Subject identifiers will be excluded 
from all reports. 
 
Stopping Rules: 
Subjects will be informed of the voluntary nature of the research and their option to stop study 
procedures for any reason.  They will also be advised that the PI may stop their participation if it 
is deemed to be in the subject’s best interest, if new information becomes available during the 
research, or if a subject cannot follow study instructions or keep appointments.   
 
Drugs or Devices 
N/A 
 
Additional Information 
A partial waiver of HIPPA authorization for screening purposes will be requested.  This is a 
waiver used only for collection of initial screening data to determine eligibility and/or recruit 
potential research subjects. Authorization by the subject will be obtained at the time of consent.  
This is justified because it is not practical to obtain a signed HIPPA Authorization form prior to 
review of the records of potential participants because review of records is required to screen for 
a participant's eligibility for study participation. This is not practical and the study could not be 
effectively conducted in this manner.  Inclusion and exclusion criteria could not be determined 

 
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without access to the PHI.  This would also not be possible with the use of de-identified 
information. 
 
Community-Based Participatory Research 
N/A 
 
International information 
The images obtained from the study MRI will be de-identified and sent via secure, encrypted 
electronic transfer to AMRA Medical, a digital health company physically located in Linköping, 
Sweden, for interpretation and report generation. Prior to any study procedures, a formal contract 
and data transfer/data use agreement will be executed between University Hospitals and AMRA 
Medical to ensure confidential, secure transfer of data. The PI is already working with the UH 
Office of Research Compliance/Legal (Lynsay Carrigan) and Research IT (Donna Gattis) to 
ensure all requirements are satisfied for collaboration with this international company.   
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HRP-503BIO 
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Prior Version: 
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