Dietary Composition and Energy Expenditure During Weight-Loss Maintenance
Phase: NA
Conditions: Obesity, Diabetes, Cardiovascular Disease
Sponsor: Boston Children's Hospital

 
June 14, 2017 
STUDY PROTOCOL  
 
Dietary Composition and Energy Expenditure during Weight-Loss Maintenance 
 
 
 
Date – June 14, 2017  
 
Principal Investigators – David S. Ludwig, MD, PhD – Cara B. Ebbeling, PhD 

 
June 14, 2017 
TABLE OF CONTENTS 
 
 
Page 
 
 
Project Summary .........................................................................................................................................  
ii 
 
Tables and Figures ......................................................................................................................................  iii 
 
1. SPECIFIC AIMS ......................................................................................................................................  1 
 
2. RESEARCH PROTOCOL .......................................................................................................................  2 
 
2.A. Overview .........................................................................................................................................  2 
2.B.  Timeline ..........................................................................................................................................  3 
2.C.  Diets................................................................................................................................................  3 
2.D.  Subjects ..........................................................................................................................................  5 
2.E.  Randomization ................................................................................................................................  6 
2.F.  Assessment of Outcomes ...............................................................................................................  7 
2.G. Data Management ...........................................................................................................................  10 
2.H.  Statistical Methods ..........................................................................................................................  10 
 
3. PROTECTION OF HUMAN SUBJECTS .................................................................................................  11 
 
3.A.  Risks To The Subjects ....................................................................................................................  11 
 3.A.1. Human Subjects Involvement, Characteristics, and Design ..................................................  11 
 3.A.2. Sources of Materials .............................................................................................................  12 
 3.A.3. Potential Risks ......................................................................................................................  12 
 
3.B.  Adequacy of Protection Against Risks .............................................................................................  12 
 3.B.1. Recruitment and Informed Consent ......................................................................................  12 
 3.B.2. Protection Against Risk .........................................................................................................  12 
 
3.C. Potential Benefits of the Proposed Research to the Subject and Others .........................................  13 
3.D. Importance of the Knowledge to be Gained.....................................................................................  13 
3.E. Data and Safety Monitoring (DSM) Plan ..........................................................................................  13 
3.F. Trial Registration (ClinicalTrials.gov Requirements) ........................................................................  14 
3.G. Inclusion of Women and Minorities ..................................................................................................  14 
 
4. BIBLIOGRAPHY AND REFERENCES CITED ..........................................................................................  15 
 
 
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June 14, 2017 
PROJECT SUMMARY  
Many overweight and obese people can lose weight for a few months, but most have difficulty maintaining 
weight loss over the long term. One explanation for the poor long-term outcome of weight loss diets relates to 
behavior, in that motivation to adhere to restrictive regimens typically diminishes with time. An alternative 
explanation is that weight loss elicits biological adaptations – specifically a decline in energy expenditure and 
an increase in hunger – that promote weight regain. We recently examined this question in a cross-over 
feeding study with 21 overweight or obese young adults and found that total energy expenditure (TEE) during 
weight-loss maintenance was 325 kcal/d greater on a low-carbohydrate diet compared to a conventional low-
fat (high-carbohydrate) diet (Ebbeling et al. JAMA 2012). A moderate-carbohydrate diet elicited intermediate 
effects on TEE. We also found potentially important dietary effects on insulin resistance, cortisol excretion, and 
other chronic disease risk factors. The purpose of the proposed study is to follow-up our initial findings using a 
parallel design, so that each of three test diets can be examined for 20 weeks, substantially extending duration 
compared to the 4-week periods in our cross-over study. Following 12±2% weight loss on a standard run-in 
diet, 150 adults (aged 18 to 65 years) will be randomly assigned to one of three weight-loss maintenance diets 
controlled for protein content (20% of energy) and varying widely in dietary carbohydrate-to-fat ratio: High-
carbohydrate (HI, 60% of energy from carbohydrate, 20% fat), Moderate- carbohydrate (MOD, 40% 
carbohydrate, 40% fat), Low-carbohydrate (LO, 20% carbohydrate, 60% fat). During the weight-loss 
maintenance phase, energy intake will be adjusted to prevent changes in body weight. The primary outcome 
will be change in total energy expenditure (indirect calorimetry using stable isotopes) through 20 weeks. 
Secondary outcomes will include resting energy expenditure (indirect calorimetry using respiratory gas 
exchange), physical activity (accelerometry), measures of insulin resistance and skeletal muscle work 
efficiency, components of the metabolic syndrome, and hormonal and metabolic measures that might inform 
an understanding of physiological mechanisms. In addition, we will test for effect modification by key baseline 
covariates, including insulin secretion. We also will assess weight change during a 2-week ad libitum feeding 
phase, as an objective measure of dietary effects on hunger. The study will be performed in collaboration with 
a local university and high school, providing a novel and feasible method for feeding subjects in dining halls 
and monitoring compliance.  
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June 14, 2017 
TABLES AND FIGURES 
 
 
Page 
 
Tables 
 
1. Study timeline ..........................................................................................................................................  3 
2. Dietary energy and macronutrient composition ........................................................................................  4 
3.  Inclusion and exclusion criteria ................................................................................................................  5 
4. Compensation schedule ..........................................................................................................................  6 
5.  Study outcomes and covariates ...............................................................................................................  7 
6.  Detectable difference for primary and secondary outcomes .....................................................................  11 
7. Frequency of data review .........................................................................................................................  13 
 
 
Figures 
 
1. Study design ............................................................................................................................................  2 
2.   Power of one-way ANOVA to detect deviation of mean ΔTEE under one diet from the other two ............  11 
 
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June 14, 2017 
1. SPECIFIC AIMS 
 
We recently examined the effects of dietary composition on energy metabolism in a cross-over feeding study 
with 21 overweight or obese young adults and found that total energy expenditure (TEE) during weight-loss 
maintenance was 325 kcal per day greater with a low-carbohydrate diet compared to a conventional low-fat 
(high-carbohydrate) diet.1 A moderate-carbohydrate diet elicited intermediate effects on TEE. We also found 
potentially important dietary effects on insulin resistance, leptin sensitivity, cortisol excretion, and 
cardiovascular disease (CVD) risk factors. However, the short duration of each test diet, only 4 weeks, limits 
conclusions regarding long-term effects. Here we propose a follow-up study using a parallel design, so that 
each of three test diets can be examined for 20 weeks, and a sample size of N=150 adults, ensuring robust 
power.  
 
Specific Aim #1: To evaluate the effects of 3 diets varying widely in carbohydrate-to-fat ratio (high-
carbohydrate, moderate-carbohydrate, low-carbohydrate) on energy expenditure during weight-loss 
maintenance. 
Hypotheses  
1a Total energy expenditure during weight-loss maintenance will differ among test diets through 20 weeks. 
1b Resting energy expenditure during weight-loss maintenance will differ among test diets through 20 
weeks. 
Primary outcome: total energy expenditure (assessed by indirect calorimetry using stable isotopes). 
Secondary outcomes: resting energy expenditure (assessed by indirect calorimetry using respiratory gas 
exchange), physical activity (assessed by accelerometry). 
Potential effect modification: including insulin secretion (insulin at 30 minutes after standard 75-g oral glucose 
load).  
 
Specific Aim #2: To evaluate the effects of 3 diets varying widely in carbohydrate-to-fat ratio on chronic 
disease risk factors during weight-loss maintenance. 
Hypothesis 
2 Chronic disease risk factors during weight-loss maintenance will differ among test diets through 20 
weeks. 
Secondary outcomes: insulin sensitivity and insulin secretion (assessed by frequently-sampled oral glucose 
tolerance test (OGTT), urine C-peptide, glycemic control (HgA1c, 1,5-anhydroglucitol), lipid profiles (total 
cholesterol, HDL-cholesterol, LDL-cholesterol, non-HDL cholesterol, triglycerides), lipoprotein particle 
subfraction distribution, coagulopathy (PAI-1, fibrinogen), inflammatory mediators (hsCRP, IL-6), blood 
pressure, sleep (assessed by accelerometry). 
 
Specific Aim #3: To evaluate physiological mechanisms potentially relating dietary carbohydrate-to-fat 
ratio to metabolism and risk for chronic disease – including CVD, type 2 diabetes, and cancer.  
Hypothesis 
3 Differences among diets in measures of skeletal muscle work efficiency, body composition, insulin 
sensitivity and secretion, anabolic and catabolic hormones, gut microbiome, and metabolomics profiles 
will provide additional physiological insights into the effects of dietary composition on health outcomes 
during weight-loss maintenance. 
Secondary outcomes: skeletal muscle work efficiency (assessed by cycle ergometry), body composition 
(assessed by a multi-component model), insulin sensitivity and secretion, urine C-peptide, thyroid functions 
(thyroxine [T4], free T4, rT3, TSH), growth hormone action (IGF-1, IGF binding proteins), reproductive 
hormones (LH, FSH, testosterone [total and free], estradiol), stress hormones (24-hour urinary cortisol and 
catecholamines), leptin, adiponectin (total, high-molecular weight), ghrelin, gut microbiome (saved stool 
samples), metabolomics profiles (saved serum samples). 
 
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June 14, 2017 
Specific Aim #4: To evaluate the effects of 3 diets varying widely in carbohydrate-to-fat ratio on 
voluntary food intake and body weight during an ad libitum feeding phase.  
Hypothesis 
4 Body weight will differ among diets during the 2-week ad libitum feeding phase (implemented after the 
test phase of weight-loss maintenance).  
Secondary outcome: body weight. 
 
2. RESEARCH STRATEGY  
2.A. Overview. This RCT will comprise three phases, as shown in Figure 1, using a feeding protocol. The 
purpose of the run-in phase is to obtain baseline measurements and restrict energy intake to achieve a 12±2% 
decrease in body weight, and then stabilize body weight. The purpose of the test phase is to compare the 
metabolic effects of high−, moderate−, and low−carbohydrate diets during weight-loss maintenance (SA#1, 
SA#2) and the physiological mechanisms underlying these effects (SA#3). The purpose of the ad libitum 
feeding phase is to evaluate the effects of these diets on body weight (SA#4). Food will be prepared in 
kitchens at FSU and Assabet Valley Regional Technical High School (AV) by chefs hired to work on the study 
in collaboration with study dietitians and food service personnel. Subjects (n=150) will be FSU students, 
faculty, and staff who are regularly on campus (FSU or AV), along with members of the greater MetroWest 
community who are able to come to campus for meals. We anticipate that many of the enrolled students will 
live in residence halls. We will ask all subjects, regardless of residence status, to consume meals under 
observation by study staff in the dining hall. Compliance with this request for campus-based participants will be 
defined as consumption of no fewer than two meals per day (Monday – Friday) in the dining hall to 
accommodate school, work, and family schedules. Compliance with this request for community-based 
participants will be defined as consumption of no less than one meal per day (Monday – Friday) in the dining 
hall to accommodate work and family schedules. Other meals and the evening snack will be packaged for 
take-out. The primary outcome is TEE through 20 weeks. 
 
 
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June 14, 2017 
 
2.B. Timeline. The proposed 4-year study consists of an initial start-up period; recruitment, preparation, and 
implementation periods for each of 3 cohorts; and a data management and analysis period (Table 1). During 
the start-up period, we will develop the diets, establish protocols, prepare a manual of operations, hire and 
train staff, and begin recruitment. For each cohort, recruitment will occur during the spring semester prior to 
study participation. We will prepare for the cohort during the summer so that participation can begin in the fall 
semester and continue through the next spring semester. The data management and analysis period will be 
devoted to preparation of data sets and programs for statistical analysis.  
Table 1. Study timeline. 
 
Jul 
Aug 
Sep 
Oct 
Nov 
Dec 
Jan 
Feb 
Mar 
Apr 
May 
Jun 
Year 1 (2013-2014) 
 
 
 
 
 
 
 
 
 
 
 
 
Study Start-up 
 
 
 
 
 
 
 
 
 
 
 
 
Recruitment, Cohort 1 
  
  
  
  
  
  
  
n=25 
 
  
  
  
Year 2 (2014-2015) 
  
  
  
  
  
  
  
  
  
  
  
  
 Preparation, Cohort 1 
 
 
 
 
 
 
 
 
 
 
 
 
 Implementation, Cohort 1 
  
  
  
  
 
 
 
 
 
  
  
  
Recruitment, Cohort 2 
  
  
  
  
 
 
 
n=65 
 
  
  
  
Year 3 (2015-2016) 
 
 
 
 
 
 
 
 
 
 
 
 
Preparation, Cohort 2 
 
 
 
 
 
 
 
 
 
 
 
 
Implementation, Cohort 2 
  
  
  
  
 
 
 
 
 
  
  
  
Recruitment, Cohort 3 
 
 
 
 
 
 
 
n=60 
 
 
 
 
Year 4 (2016-2017) 
 
 
 
 
 
 
 
 
 
 
 
 
Preparation, Cohort 3 
 
 
 
 
 
 
 
 
 
 
 
 
Implementation, Cohort 3 
 
 
 
 
 
 
 
 
 
 
 
 
Data Management and Analysis 
  
  
  
  
  
  
  
  
  
  
  
  
We carefully considered the academic schedule when developing the timeline. Subjects will begin participation 
upon their return to campus for the fall semester. They will complete the 12-week run-in phase at the end of 
November and the first few weeks of the test phase in December, before winter break. In January, subjects will 
return to campus for the spring semester. They will complete the 10-week assessments at the end of January 
and beginning of February. Finally, they will complete the 20-week assessments in April and ad libitum feeding 
period in May, at the end of the academic year. We will provide subjects with packaged food during breaks. In 
addition, we will give subjects Wi-Fi scales (Withings Inc., Cambridge, MA). Subjects will weigh themselves 
daily during the study. The scales will be linked to a patient monitoring Website called SetPoint Health (SPH). 
We will use the SPH Website (https://fs2.sphpro.com) to remotely monitor subject body weight and to track 
self-reported food intake (amount of provided food consumed). We will contact subjects by telephone, if 
necessary, to promote compliance.  
2.C. Diets. Energy content and macronutrient composition of the run-in and test diets are summarized in Table 
2. For all diets, total energy intake will be distributed across meals and snacks throughout the day: 22.5% for 
breakfast, 32.5% for lunch, 32.5% for dinner, and 12.5% for an evening snack. The macronutrient composition 
of every meal will reflect the composition of each respective diet. We will instruct subjects to consume their 
meals at regularly scheduled times and not skip meals. Concerning ad libitum consumption of non-caloric 
beverages and artificial sweeteners, we will advise participants to have no more than 3 servings per day of 
each of the following items: beverages containing artificial sweeteners, caffeinated beverages, packets of 
artificial sweeteners, and gum and mints containing artificial sweeteners. We will ask subjects not to consume 
alcoholic beverages (and exclude heavy consumers and binge drinkers from the study, 2.D). To ensure 
micronutrient adequacy and minimize the influence of micronutrient differences among test diets, we will give 
each subject a daily multi-vitamin and mineral supplement throughout the study.  
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June 14, 2017 
 
Table 2. Dietary energy and macronutrient composition. 
Dietary Variable 
Run-In 
Phase 
Test Phase 
Energy 
Restriction 
High (HI) 
Carbohydrate  
Diet 
Moderate (MOD) 
Carbohydrate 
Diet 
Low (LO) 
Carbohydrate 
Diet 
Energy  
(% of weight maintenance needs) 
60 
100 
100 
100 
Carbohydrate  
(% of total energy) 
45 
60 
40 
20 
Fat  
(% of total energy) 
30 
20 
40 
60 
Protein  
(% of total energy) 
25 
20  
20 
20 
Sodium 
(mg) 
− 
3000 
3000 
3000 
Added Sugar  
(% of total carbohydrate) 
− 
15 
15 
15 
 
(% of total energy) 
− 
9 
6 
3 
Saturated Fat 
(% of total fat) 
− 
35 
35 
35 
 
(% of total energy) 
− 
7 
14 
21 
Fiber 
(g) 
− 
35 
30 
25 
 
Run-in Diet. The run-in diet will be consistent with recommendations specified by the Institute of 
Medicine,2 with protein intake at the upper end of an acceptable range to enhance satiety during weight loss.3 
Consistent with the methodology used in our preliminary study,1 we will calculate individual energy needs as 
the arithmetic product of REE, estimated using a regression equation,4,5 and a physical activity factor of 1.5.6 
To avoid overestimation of energy needs, we will use measured body weight adjusted for ideal body weight 
(IBW) as the weight variable (([measured weight – IBW] × 0.25) + IBW) in the regression equation. To estimate 
IBW, we will determine the sex- and height-specific midpoints of weight ranges for medium frames, using 
Metropolitan Life Insurance Company Height-Weight Tables. Energy intake will be restricted to 60% of 
calculated needs to promote weight loss, without feeding any subject <1200 kcal/d. The goal is to reduce body 
weight to a level that is 12±2% below baseline weight. The amount of food provided will be adjusted, if 
necessary, to achieve the target weight loss over 9 to 10 weeks.   
Test Diets. We will compare 3 diets varying in carbohydrate and fat, as specified in Table 2, while 
controlling for protein. We will stabilize body weight at the end of the run-in phase, prior to the start of the test 
phase. Energy needs for weight stabilization will be estimated based on recent rate of weight loss (energy 
intake during weight loss [kcal/day] + (rate of weight loss [kg/day] × 7700 kcal/kg]). Although the conversion 
factor of 7700 kcal/kg is not appropriate for calculations of long-term, energy balance,7,8 this calculation is 
useful for estimating energy needs over the short term. Added sugars will contribute no more than 15% of total 
carbohydrate. Saturated fat will comprise approximately 35% of total fat, with the remainder distributed 
between mono- and polyunsaturated fat. Targets for sodium, added sugar, fat, and fiber will be achieved based 
on daily intakes averaged over one week. We will use many of the same foods, in differing amounts, across 
diets and systematically replace foods when necessary to achieve the specified macronutrient targets. As 
such, the diets will reflect gradients in amounts of foods rich in carbohydrate and fat. The quantity of non-
starchy vegetables will be approximately the same across diets. High-Carbohydrate Diet. This diet will contain 
60% of total energy from carbohydrate and 20% from fat, based on contemporary public health 
recommendations9 that emphasize sources of carbohydrate such as whole grains, vegetables, fruits, legumes, 
and low-fat dairy products. Moderate-Carbohydrate Diet. This diet will contain 40% of total energy from 
carbohydrate and 40% from fat. These targets will be achieved by decreasing the quantity of grains relative to 
the high-carbohydrate, decreasing fruits, adding foods containing fat (e.g., nuts, seeds, sauces, spreads, 
toppings), decreasing amounts of legumes when necessary, and including some higher fat dairy products. 
Low-Carbohydrate Diet. This diet will contain 20% of total energy from carbohydrate and 60% from fat. 
Relative to the moderate-carbohydrate diet, these targets will be achieved by eliminating all grains, removing 
some fruits, adding more foods containing fat, further decreasing amounts of legumes, and increasing some 
higher fat dairy products.  
Adjusting Energy Intake to Achieve Weight Stability. During the test phase of the study, we will monitor 
body weight using the SetPoint Health Website and adjust energy intake to achieve weight stability, defined as 
weight change of no more than ±2 kg. We will measure weight every day and regress weight (g) on time 
(days). A slope of ≥15 g per day over 14 days will indicate the need to adjust energy intake to achieve stability. 
Adjustments will be made, based on deviation from the post-weight loss anchor weight and the slope of the 
regression line, no more frequently than every 2 weeks.  
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June 14, 2017 
2.D. Subjects. We will recruit adults who meet the criteria outlined in Table 3. These criteria will be assessed 
during visits with potential subjects and in communication with primary care providers.  
Table 3. Inclusion and exclusion criteria. 
Inclusion criteria 
Exclusion criteria 
• Aged 18 to 65 years (FSU students, faculty, 
staff, community members). 
• BMI ≥ 25 kg/m2. 
• Weight ≤ 350 lbs (159 kg). 
• Medical clearance from a primary care provider.  
• Plans to matriculate as a student at FSU or work 
on campus throughout the academic year of 
enrollment in the study. 
• Willingness and ability to come to campus 
throughout the academic year of enrollment in 
the study.  
• Willingness to eat and drink only the foods and 
beverages on the study menus during 
participation, with no food allergies or aversions. 
• Willingness to eat in the dining hall. 
• Willingness to abstain from consuming alcohol 
during participation. 
• Academic and social clearance from the FSU 
Office of Enrollment and Student Development 
(student subjects) or Criminal Offender Record 
Information (CORI) check and Sex Offender 
Registry Information (SORI) check (community-
based subjects). 
• Change in body weight exceeding ±10% during prior year. 
• Recent adherence to a special diet. 
• Recent adherence to a vigorous physical activity regimen (as 
indicated by participation in a varsity sport). 
• Chronic use of any medication or dietary supplement that could 
affect study outcomes. 
• Current smoking (1 cigarette in the last week). 
• Heavy baseline alcohol consumption (> 10 drinks/week) or history 
of binge drinking (≥ 5 drinks in 1 day, anytime in past 6 months). 
• Physician diagnosis of a major medical illness or eating disorder. 
• Abnormal laboratory screening tests (hemoglobin A1c, TSH, 
hematocrit<30%, BUN, creatinine, ALT>200% of normal upper 
limit). 
• Plans for a vacation during the study that would preclude 
adherence to prescribed diets. 
Additional exclusion criteria for females 
• Irregular menstrual cycles. 
• Any change in birth control medication during the 3 months prior to enrollment.  
• Pregnancy during the 6 months prior to enrollment. 
Lactation during the 3 months prior to enrollment. 
 
Rationale for Criteria. Students (FSU), faculty, staff, and community members aged 18 to 65 who are 
regularly on campus (FSU and AV), and members of the greater MetroWest community who are willing and 
able to come to campus will be eligible to participate in the study. We specify a BMI inclusion criterion starting 
at 25 kg/m2, which corresponds to the conventional definition of overweight. We will not enroll anybody who 
weighs more than 350 lbs (159 kg), corresponding to the upper limit for some assessment equipment. Recent 
and substantial weight change, adherence to a special diet or vigorous physical activity regimen, use of 
medications or dietary supplements, smoking, or excessive alcohol consumption will be exclusionary due to 
possible confounding of study outcomes. Abnormal laboratory tests, indicative of unrecognized illness, also will 
be exclusionary. We will use a cut-point corresponding to 200% of the normal upper limit for ALT given the 
high prevalence of fatty liver among obese individuals and recognizing that the first line of treatment for fatty 
liver typically is weight loss.10 Vacation plans over winter or spring break that involve international travel or that 
do not permit adequate and safe food storage and food preparation are exclusionary given the high likelihood 
of noncompliance for over a week. Additional exclusion criteria for females are specified to diminish 
confounding from unpredictable changes in reproductive hormone cycles.  
Recruitment and Screening. We will recruit ethnically and racially diverse subjects by posting flyers, 
newspaper advertisements, and Internet announcements at FSU and AV during the spring semester. When an 
individual responds, we will implement a multi-step screening and enrollment process: 1) telephone 
conversation, 2) informational visit, 3) medical clearance from a primary care provider, 4) screening visit, and 
5) informed consent visit. During the telephone conversation, we will provide an overview of the study and ask 
preliminary screening questions. If the individual is provisionally eligible based on the telephone conversation, 
we will invite him/her to an informational visit during which we will explain the study protocol as outlined in the 
consent form and further assess provisional eligibility. If the individual remains provisionally eligible, we will 
request medical clearance in writing from his/her primary care provider (with verbal and written permission 
from the provisionally eligible subject to make this request). We will request academic and social clearance in 
writing from the FSU Office of Enrollment and Student Development for all provisionally eligible student 
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June 14, 2017 
subjects. We will obtain permission to do CORI/SORI background checks for provisionally eligible subjects for 
community-based participants, in compliance with University and School District policies. Any potential 
subjects for whom we cannot obtain medical, academic, or social clearance will be ineligible for the study. At 
the screening visit, we will obtain a blood sample for analysis of hemoglobin A1c, TSH, hematocrit, BUN, 
creatinine, ALT. Subjects will be recruited in three cohorts at the rate presented in Table 1. 
Retention. We designed the study to promote retention as follows. 1) We will bring research operations to 
the FSU campus where adults spanning a wide age range attend classes or work during the academic year. 
We also will recruit community-based participants who work or live in metropolitan and residential areas in 
close proximity to FSU or AV. 2) The study protocol includes a run-in phase to promote weight loss, prior to 
randomization. Individuals who lose weight at the expected rate will have demonstrated their capacity to 
comply with arguably the most rigorous phase of our study. 3) We will collaborate with professional chefs to 
develop a cycle menu of palatable meals and snacks corresponding to specifications of the run-in and test 
diets (Table 2). 4) We carefully considered logistical feasibility when selecting outcomes and determining 
assessment schedules. 5) The study provides benefits to subjects, including weight loss.  
Compensation. We will offer compensation for time and any inconvenience imposed by study participation 
and to cover the cost of the meal plan. All enrolled participants will receive a study stipend totaling $3,280. 
Resident student participants, who are required to purchase a full meal plan according to FSU policies, will 
also receive up to $3,220 as reimbursement for the cost of the meal plan. Non-resident students, faculty, staff, 
and community-based participants will receive study meals and snacks valued at up to $3,220. Thus, the 
maximum total value of compensation is $6,500, dispersed at eight specific time points during the study, as 
presented in Table 4. This plan is not considered coercive given the rigors of study participation, but 
encourages study completion. 
Table 4.  Reimbursement and Compensation Schedule. 
  
  
Resident FSU Students 
Non-Resident FSU Students, 
Faculty, Staff, Community 
members 
Week 
Phase or Event 
Monthly 
Meal Plan * 
Monthly 
Stipend * 
Additional 
Stipend † 
Monthly  
Stipend * 
Additional  
Stipend † 
 
 
Reimbursement 
Compensation 
Compensation 
-9 
Run-In  
$400 
$100 
$200 
$100 
$200 
-4 
Run-In  
$400 
$120 
  
$120 
  
1 
Test  
$400 
$120 
$300 
$120 
$300 
6 
Test  
$400 
$130 
  
$130 
  
11 
Test  
$400 
$140 
$400 
$140 
$400 
16 
Test  
$400 
$150 
  
$150 
  
21 
Ad libitum 
$400 
$170 
$400 
$170 
$400 
  
Completion 
$420 
$550 
$500 
$550 
$500 
* 
Monthly payments for participating in the intervention. Each payment includes reimbursement for the meal plan (resident students 
only) and a stipend as compensation for time and effort to adhere to the study diets. 
† 
Additional stipend as compensation for time and effort required for measurement of study outcomes at specified time points. 
2.E. Randomization. A blocked randomization design will be employed to ensure close balance among the 
three diet arms at any point in the study. The randomization will be stratified by feeding site (FSU, AV), sex, 
ethnicity-race (non-Hispanic white, other), age (18–39.9 years and 40.0–65.9 years), and BMI (overweight: 
25.0−29.9 kg/m2, obese: ≥30.0 kg/m2) to ensure balance at the completion of enrollment within every 
subcategory, regardless of size. Enrollment logs, one for each stratum, will be prepared with a numerical 
sequence of identifiers. Diet assignment lists, identical to the enrollment logs except for the addition of a 
randomly chosen diet, will be prepared under supervision of the study biostatistician, using specialized 
software developed for that purpose. The diet assignments will be randomly permuted within blocks of 3, 6, 
and 9, and the blocks themselves will be randomly permuted. Each upcoming assignment will thus be 
unpredictable, preventing any deliberate or inadvertent bias on the part of those conducting enrollment. The 
assignment list will be kept in the private custody of a clinical research specialist who, confirming adequate 
weight loss during the run-in phase, will relay the diet assignment of each subject to intervention staff.  All staff 
involved in assessing study outcomes and conducting analyses of biospecimens will be masked to diet 
assignments. 
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June 14, 2017 
2.F. Assessment of Outcomes. We will assess study outcomes under free-living conditions during visits to 
on-campus research facilities, as listed in Table 5 and described below for each Specific Aim. 
Table 5. Study outcomes, covariates, and effect modifiers. 
 
Run-In Phase 
Test Phase 
Ad libitum  
Baseline 
Post-Weight Loss 
Midpoint 
End 
Feeding Phase 
-14 to -12 weeks 
-2 to 0 weeks 
8 to 10 weeks 
18 to 20 weeks 
21 to 22 weeks 
Study Outcomes Corresponding to each SA 
 
 
 
 
 
SA#1 
 
 
 
 
 
TEE  
X 
X 
X 
X 
 
REE  
X 
X 
X 
X 
 
Physical activity 
X 
X 
X 
X 
 
SA#2 
 
 
 
 
 
Insulin sensitivity and secretion (OGTT) * 
X 
X 
X 
X 
 
Urine C-peptide 
X 
X 
 
X 
 
Glycemic control (HgA1c, 1,5-anhydroglucitol) 
X 
X 
X 
X 
 
Lipid profiles (TC, HDL-C, LDL-C, non-HDL-C, TG) 
X 
X 
X 
X 
 
Lipoprotein particle subfraction distribution 
X 
X 
X 
X 
 
Coagulopathy (PAI-1, Fibrinogen) 
X 
X 
X 
X 
 
Inflammatory mediators (C-reactive protein, IL-6) 
X 
X 
X 
X 
 
 
 
 
 
 
 
Blood pressure 
X 
X 
X 
X 
 
Sleep 
X 
X 
X 
X 
 
SA#3 
 
 
 
 
 
Skeletal muscle work efficiency (cycle ergometry) 
X 
X 
 
X 
 
Body composition (multi-component model) * 
X 
X 
 
X 
 
Insulin sensitivity and secretion (OGTT) * 
X 
X 
X 
X 
 
Urine C-peptide 
X 
X 
 
X 
 
Thyroid functions (T4, Free T4, rT3, TSH) 
X 
X 
 
X 
 
Growth hormone action (IGF-1, IGF-BPs)  
X 
X 
 
X 
 
Reproductive hormones (LH, FSH, E2, total and free 
TST) 
X 
X 
 
X 
 
Stress 
hormones 
(urine 
cortisol, 
urine 
catecholamines) 
X 
X 
 
X 
 
Leptin, adiponectin (total, high-molecular weight), 
ghrelin 
X 
X 
X 
X 
 
Gut microbiome 
X 
X 
 
X 
 
Serum metabolomics profile 
X 
X 
X 
X 
 
SA#4 
 
 
 
 
 
Body weight 
 
 
 
 
X 
Covariates and Effect Modifiers 
 
 
 
 
 
Sex 
X 
 
 
 
 
Race 
X 
 
 
 
 
Ethnicity 
X 
 
 
 
 
Body weight, BMI * 
X 
X 
X 
X 
 
Body composition (multi-component model) * 
X 
X 
 
X 
 
Insulin sensitivity and secretion (OGTT) * 
X 
X 
X 
X 
 
Obesity-related genes 
X 
 
 
 
 
Palatability of Test Diet 
 
 
 
X 
 
Abbreviations. OGTT, oral glucose tolerance test; HgA1c, Hemoglobin A1c; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, 
low-density lipoprotein cholesterol; non-HDL-C, non-high-density lipoprotein cholesterol; TG, triglycerides; PAI-1, plasminogen activator inhibitor-1; IL-
6, interleukin-6; T4, thyroxine; rT3, Reverse triiodothyronine; TSH, thyroid stimulating hormone; LH, luteinizing hormone; FSH, follicle stimulating 
hormone; TST, testosterone; E2, estradiol.  
 
* Variables derived from the OGTT and body composition assessment will be assessed as outcomes and as covariates. 
 
Energy Expenditure (SA #1). Macronutrient composition could affect energy expenditure directly because 
metabolic pathways vary in energetic efficiency or indirectly through hormonal responses that regulate 
metabolic pathways.11,12 Dietary carbohydrate, in particular, may control disposition of macronutrients via 
effects on the rise in blood glucose after eating and secretion of insulin and other hormones.13 In our 
preliminary study, we observed a significant inverse trend by dietary glycemic load for TEE and REE. While we 
did not see any differences in physical activity across diets in our preliminary study, here we hypothesize that 
dietary composition may influence level of physical activity due to differences in availability of metabolic fuels 
with a longer intervention, consistent with animal data,14 or intrinsic skeletal muscle work efficiency as 
regulated by leptin.15-18 We will measure TEE under free-living conditions over 21-day periods at baseline, at 
post-weight loss (week 0), and during intervals ending at weeks 10 and 20 of the test phase. We will assess 
REE and physical activity at the same time points.  
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June 14, 2017 
Total energy expenditure (with doubly labeled water, DLW). Following oral administration of the stable 
isotopes of hydrogen (2H) and oxygen-18 (18O) in the form of 2H2
18O, the 18O is eliminated from the body as 
both carbon dioxide and water, and the 2H is excreted exclusively as water. The difference between the urinary 
elimination rates of 18O and 2H provides a measure of carbon dioxide production (rCO2) used to estimate TEE. 
The dose of DLW will be a mixed cocktail containing 0.086 g of 2H2O at 99.98 atom % 2H and 1.38 g of 10% 
18O per kg body weight. Urine samples will be collected before each dose and at regular intervals over 14 days 
after each dose. Isotopic enrichment data will be obtained by gas-isotope-ratio mass spectrometry,19 converted 
to  atom percent, and used to model rCO2. TEE will be calculated from rCO2 using the equation of Ravussin et 
al,20 with the food quotient (FQ) as an estimate of respiratory quotient (RQ).21 Resting energy expenditure. We 
will measure REE by indirect calorimetry using respiratory gas exchange after a 12-hour overnight fast. The 
calorimetry system will be calibrated according to the manufacturer’s specifications (TrueOne 2400, Parvo 
Medics, Sandy, UT). Room temperature will be maintained at a constant level, and lighting and noise will be 
minimized to limit variability in measurements. The subject will be awake and reclining. Oxygen consumption 
and carbon dioxide production will be measured for 30 minutes, and REE will be calculated by the Weir 
equation22 using data averaged over the last 20 minutes. We will assess REE on two separate mornings.  If the 
two measurements are not within 10%, we will obtain a third measurement. We will take the average of the two 
closest measurements as the best estimate of REE. Physical activity. While measures of TEE and REE can be 
used in combination to obtain an estimate of physical activity level,23 this approach does not provide 
information regarding quality of physical activity. Thus, we will assess physical activity with an accelerometer 
(ASP-BTLE, Actigraph LLC, Pensacola, FL). The device measures and sums the magnitude of accelerations, 
and data are expressed as intensity counts per minute. We will ask each subject to wear the accelerometer on 
the right hip for 7 days per assessment, except when sleeping, bathing, or participating in water activities. Daily 
physical activity will be quantified as total counts and minutes of moderate- to vigorous-intensity physical 
activity, consistent with published methodology.24 We also will ask participants to complete daily physical 
activity diaries, recording each activity performed during 15-minute time blocks throughout each day the 
monitor was worn 25. We will use information from the diaries to confirm the times that the physical activity 
monitors were worn during waking hours 26. 
Chronic Disease Risk Factors (SA#2). The effect of macronutrient composition on conventional CVD risk 
remains a topic of controversy, confounded in part by diet-induced differences in body weight. The proposed 
study offers a special opportunity to examine the effects of different macronutrient diets on novel, as well as 
conventional, CVD risk factors, independent of body weight and taking into account diet-related effects on 
relative adiposity. We will assess chronic disease risk factors following an overnight fast at baseline, post-
weight loss (week 0), and at weeks 10 and 20 of the test phase. Insulin sensitivity and insulin secretion. We will 
conduct an OGTT, using a standard 75-gram dose of dextrose (TrutolTM, ThermoFisher Scientific, Waltham, 
MA). Blood for determination of plasma glucose and serum insulin will be obtained by indwelling venous 
catheter at −10, −5, 0, 10, 20, 30, 60, 90, and 120 minutes relative to the start time of dextrose consumption. 
The hand and forearm will be placed in a warming box set at 50-55°C (~120-130°F) to arterialize venous blood 
samples. Using plasma glucose and serum insulin data, we will calculate indexes of peripheral and hepatic 
insulin sensitivity as described by Abdul-Ghani et al.27 Insulin levels during the first 30 minutes following the 
dose of dextrose will be used to assess insulin secretion. We will measure C-peptide in a 24-hour urine sample 
as an indicator of daily insulin secretion 28. We will assess HbA1c in a fasting blood sample as an integrated 
measure of mean glycemia over approximately three months 29 and 1,5-anhydroglucitol (1,5-AG) as marker of 
diet-induced glucose excursions 30. Lipid profiles, coagulopathy, inflammation, leptin, and adiponectin. Serum 
total cholesterol, HDL-cholesterol, and triglycerides will be measured using standardized assays. Serum LDL 
cholesterol will be measured using direct enzymatic/spectrophotometric methodology. Non-HDL-cholesterol 
will be calculated as an indicator of atherogenic particle concentration.31,32 Lipoprotein particle subfraction 
distribution will be assessed by nuclear magnetic resonance (NMR) spectroscopy (Liposcience Inc., Raleigh, 
NC) to further evaluate metabolic risk. Plasma PAI-1 and fibrinogen will be measured as indicators of 
coagulopathy, and levels of high-sensitivity C-reactive protein and IL-6 will be measured as markers of chronic 
inflammation. Blood pressure. Consistent with current guidelines,33 we will measure blood pressure by 
auscultation at the right arm using a sphygmomanometer (System 5, American Diagnostic Corporation, 
Hauppauge, New York), 3 times at each assessment visit. Sleep. We will measure sleep using the same 
accelerometer used to assess physical activity (ASP-BTLE, Actigraph LLC, Pensacola, FL). We will ask each 
subject to wear the accelerometer on the non-dominant wrist for 7 nights per assessment. We will quantify total 
sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency. We also will ask subjects to 
complete a daily sleep diary. 
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June 14, 2017 
Physiological Mechanisms (SA#3). We will measure skeletal muscle work efficiency and other outcomes 
at baseline, post-weight loss (week 0), and week 20 of the test phase. Skeletal muscle work efficiency. We will 
measure work efficiency during graded cycle ergometry according to published methods.15,16 In brief, following 
a 10-minute warm-up period, subjects will pedal at 60 rpm against graded resistance to generate power 
corresponding to grades of 10W, 25W, and 50W in 4-minute stages. We will measure oxygen uptake and 
carbon dioxide production by indirect calorimetry and convert oxygen consumption to energy expenditure 
based on respiratory exchange ratio. We will define skeletal muscle work efficiency at each grade as power 
generated per increase in energy expenditure above resting. Other hormonal axes that affect or respond to 
energy balance. We will assess thyroid functions (Thyroxine [T4], free T4, rT3, TSH), growth hormone action 
(IGF-1, IGF-BP3), reproductive hormones (LH, FSH, testosterone, estradiol), stress hormones (24-hour urinary 
cortisol, 24-hour urinary catecholamines), leptin, adiponectin (total, high molecular weight), and ghrelin using 
standard procedures. Body composition. We will assess body composition using a multi-component model with 
measures of total body volume from air displacement plethysmography (ADP, BodPod, Cosmed USA Inc., 
Concord, CA), total body water by isotope dilution, and bone mineral content by dual-energy x-ray 
absorptiometry (DXA, Horizon A, Hologic Inc., Bedford, MA). We will instruct participants to fast for at least 5 
hours prior to the DXA and ADP measurements. Gut microbiome. We will save stool samples to assess 
changes in gut flora previously associated with metabolic changes and obesity in humans (e.g.,relative 
abundance of Firmicutes and Bacteroidetes species) following published methods.34-36 This assessment will be 
done only for subjects who “opt in” to collect stool samples. Metabolomics profiles. We will save serum 
samples and use liquid chromatography tandem mass spectrometry for metabolomics profiling. Such a 
platform has proven to be highly informative of pathophysiologic status, providing for example quantitative 
data regarding disease development before the appearance of conventional disease markers.37-39  
Weight Change (SA#4). We will allow voluntary food intake during the 2-week ad libitum feeding phase at 
the conclusion of the study, providing a complementary approach to the preceding 20-week test phase of 
weight-loss maintenance. Subjects will continue to consume test diets with specified proportions of 
macronutrients, according to random assignment. We will provide the same amount of food relative to that 
provided for weight stabilization and instruct subjects as follows: “Have your meals according to your usual 
schedule. Eat as much or as little of each meal as you like until you are satisfied. If you finish your meal and 
are hungry before the next meal, eat something of your own choosing until you are satisfied. If you do not finish 
your meal, do not eat anything before the next meal. We ask that you eat until satisfied, but avoid overeating to 
the point of feeling too full. Do not drink alcohol during the free feeding phase.” Body weight. Subjects will 
weigh themselves daily, using Wi-Fi scales linked to the SetPoint Health Website, throughout the ad libitum 
feeding phase so that we can assess weight change. 
Covariates and Effect Modifiers. Variables that will be included as covariates or effect modifiers in 
statistical analyses are listed in Table 6. Demographic data. At baseline, we will collect self-report data with 
regard to sex, race, and ethnicity. Body weight and composition. We will measure body weight at the time of 
each study assessment using an electronic scale and body composition using a multi-component model, as 
described above at the time points denoted in Table 6. Insulin sensitivity and secretion. We will conduct an 
OGTT, as described above at the time points denoted in Table 6. Obesity-related genes. The effects of diet on 
study outcomes may be modified by obesity-related genes. For participants who “opt in,” we will isolate and 
save buffy coat from blood samples for extracting DNA. Genetic studies may include, but not be limited to, 
candidate gene analysis and whole genome/exome sequencing. We have particular interest in amylase gene 
copy number 40.  Palatability of Test Diet. We will measure perceived palatability (tastiness) of the test diets 
using a 10-cm Visual Analog Scale (VAS).  
2.G. Data Management. We will follow clinical data management best practices. Essential data management 
activities will include design and development of case report forms (CRFs); design, development, and 
maintenance of the data management system; and preparation of datasets. 
Case Report Forms. We will develop customized CRFs to capture data required to test our hypotheses, 
with particular attention to minimizing data entry errors and ensuring efficient, consistent, and unambiguous 
data abstraction from source documents. The design will include simple introductions for each section, obvious 
skip patterns, and standard coding and formatting conventions. We will write a standard operating procedure 
describing how to complete each CRF (question-by-question guide). A clinical research specialist will complete 
a critical review of all CRFs prior to pre-testing to evaluate the form for programmability. Staff then will pre-test 
CRFs to evaluate the logic, wording, and general construction. Pre-testing also will include evaluation of overall 
feasibility of the data collection plan and procedures. The principal investigators and the study biostatistician 
will approve the final set of CRFs prior to database programming. 
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June 14, 2017 
Data Management System. We will use the Research Electronic Data Capture (REDCap) software 
application to collect and manage data. REDCap is designed to comply with HIPAA regulations. The research 
team will work collaboratively with personnel in the Clinical Research Center at Boston Children’s Hospital to 
build a study-specific data dictionary for use in implementing the REDCap system.  The web-based system 
offers an intuitive interface for data entry, with real time validation rules (automated data type and range 
checks). Data entered in REDCap will be stored in a password-protected database hosted on a secure, 
firewall-protected server. Only authorized users will have access to data in the REDCap system. All users will 
undergo REDCap training by the Harvard Catalyst resource specialist and will follow standard operating 
procedures for data entry into REDCap. The system is supported by the Clinical Research Information 
Technology and Research Computing Departments, with nightly back-up of data.  
Preparation of Datasets. Data for primary and secondary outcomes, process measures, and covariates 
will be recorded on CRFs and entered into the REDCap data management system. The study biostatistician 
will oversee transfer of data to statistical analysis programs. 
 
2.H. Statistical Methods. We will follow the a priori analysis plan described below, with a sample size of 
N=150 adults ensuring robust power.  
 
Analysis Plan. The primary outcome measure of the trial is total energy expenditure (TEE) per kg body 
weight, measured at four time points: baseline (pre-weight loss), week 0 (post-weight loss, pre-randomization), 
week 10 (midway through the test phase), and week 20 (end of test phase). The primary null hypothesis is that 
the time course of TEE between week 0, week 10, and week 20 will be the same for all three diets:  high (HI), 
moderate (MOD), or low (LO) carbohydrate-to-fat ratio. An alternative hypothesis is detailed below. 
The analytic framework for addressing both primary and secondary hypotheses will be repeated-measures 
analysis of variance (ANOVA), with the outcomes of interest as dependent variables and study arm (diet: HI, 
MOD, LO) as a three-level independent variable.  Although covariates are theoretically balanced by the 
randomization and thus should have little influence, we will adjust the ANOVA for a number of baseline and 
time-varying covariates in order to reduce residual variance and improve our power to detect diet differences. 
These include the outcome of interest at baseline (pre-weight loss); change in body weight over the test phase 
(weeks 0–20); demographic characteristics (sex, race, ethnicity, age); baseline anthropometric measures 
(body-mass index, percentage lean mass, percentage weight lost pre-randomization); and design variables 
(study site, cohort, enrollment wave).  We will employ an autoregressive covariance structure to account for 
potentially diminishing within-subject correlation over time.  To minimize the influence of extreme values on the 
fitted model, we will employ an outlier-deletion algorithm equivalent to robust regression with iterative 
reweighting.41 A single subject who developed a disqualifying medical condition (hypothyroidism, as 
documented by 2 elevated TSH values) post-randomization will be excluded from the primary analysis. 
To test the primary hypothesis, we will construct appropriate contrasts from parameters of the fitted 
repeated-measures model (namely, adjusted mean TEE at week 10 and week 20 – adjusted mean TEE 
atweek 0, HI vs. MOD vs. LO), and test their significance with a 2-df F-test and critical p-value 0.05.  If the 
overall null hypothesis is rejected, pairwise contrasts between study arms will be constructed and compared to 
zero. The principle of closed testing42 dictates that in this special situation of three groups, compared pairwise 
only if the overall null hypothesis is rejected, we may make each pairwise comparison with a critical p-value of 
0.05 and still preserve the Type I error rate for the family of four comparisons at 5%. 
In secondary analyses, we will test each covariate for effect modification (covariate × diet interaction) and, 
if significant effects are found, construct separate estimates for the diet effects by covariate stratum. Additional 
secondary analyses will be conducted with insulin-30 (from baseline OGTT) as a covariate. The use of time-
varying covariates will allow us to test hypotheses of mediation by diet-related behavior and other process 
measures, secondary to the primary hypothesis of intervention efficacy. We will also perform a “per protocol” 
analysis, excluding any subject whose weight was out of target range at the 20-week time point (defined as 
weight change of no more than ±2 kg relative to the PWL anchor weight), or who began taking an exclusionary 
medication. 
Secondary outcomes will be assessed similarly to TEE. Measures with skewed distribution will be log-
transformed for analysis and re-transformed to natural units for reporting, with changes and differences (∆log) 
expressed as ratios (exp(∆log)) or percentages (100% × (ratio–1)). 
In all analyses, except “per protocol,” we will follow the intention-to-treat principle, ascribing the randomly 
assigned diet to each subject regardless of degree of compliance. To test for biased dropout, we will compare 
baseline characteristics of completers with those of non-completers, using standard procedures (Student t, 
Wilcoxon rank-sum, Fisher exact test). We will use inverse probability weighting to compensate for missing 
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June 14, 2017 
Table 6.  Detectable difference for primary and secondary 
outcomes. Under minimal-impact alternative hypothesis that 
one diet will differ from the other two in mean 20-week change.  
Derived from non-central F distribution assuming 45 
completers per diet arm, 5% Type I error, and standard 
deviation as observed in pilot study.1 
Outcome 
Detectable effect 
Total energy expenditure (TEE)  
237 kcal/d 
Resting energy expenditure (REE) 
89 kcal/d 
Moderate-vigorous physical activity 
8 min/d 
Leptin* 
19% 
Urinary cortisol* 
31% 
C-reactive protein* 
68% 
Peripheral insulin sensitivity index† 
0.57 
* Log-transformed for analysis, detectable effect expressed as 
percentage difference. 
† Derived from oral glucose tolerance test:  rate of decline of 
serum glucose between 60 and 120 minutes (mg/dL/hr) 
divided by time-weighted mean serum insulin (μIU/mL) 
between baseline and 120 minutes. 
data, constructing a logistic model for missingness based on the baseline characteristics that differed between 
completers and non-completers. SAS software (SAS Institute Inc., Cary, NC) will be used for all computations. 
Sample Size, Power, and Detectable Effects. For comparability with pilot data from prior studies, our 
sample-size calculation is formulated in terms of changes in TEE uncorrected for body weight between week 0 
and week 20.  The primary null hypothesis is that the mean value of ΔTEE, defined as change in total energy 
expenditure at week 20 of the test phase compared to week 0 (post-weight loss), will be the same in all three 
diet arms. To estimate our power to detect deviations from this null hypothesis, we use the power 
characteristics of a simple one-factor, three-level ANOVA using covariate-adjusted residual variance estimates 
taken from our pilot data. As a conservative, minimal-impact alternative, pictured in the inset panel of Figure 2, 
we hypothesize that one of the three diets (high carbohydrate-to-fat ratio, or HI) will differ from the other two. 
The power of the ANOVA test to reject the null hypothesis increases with the magnitude of difference, as 
detailed in Figure 2 for three illustrative sample sizes. These curves were derived from the non-central F 
distribution under a range of alternatives from 0 to 400 kcal/d, taking account of the parallel-group design (as 
contrasted with our pilot study, a 3-arm crossover design) and assuming a standard deviation of 412 kcal/d for 
ΔTEE among subjects as observed in the pilot data. Our proposed sample size, 45 completers per diet 
(allowing for 10% attrition from recruited sample of 50), provides 80% power to detect a difference of 237 
kcal/d, as indicated by the point on the middle curve. This is a smaller effect than was discerned in the pilot, 
where mean ΔTEE under a high-carbohydrate (low-fat) diet differed from the average under moderate- and 
low-carbohydrate diets by 263 kcal/d. The smaller sample size, 25 per arm, would provide only 50% power for 
that magnitude of effect, while the larger sample size, 75 per arm, would be much more costly and would 
reduce the effect detectable with 80% power only to 175 kcal/d. We thus believe our design choice to be 
optimal in terms of feasibility and statistical power. 
Detectable-effect figures for a variety of secondary outcomes are tabulated in Table 6. Based on the same 
design, alternative hypothesis, and analytic strategy, each figure represents 0.575 standard deviations of the 
outcome, a moderate effect size by conventional benchmarks. 
  
3. PROTECTION OF HUMAN SUBJECTS 
3.A. Risks To The Subjects 
3.A.1. Human Subjects Involvement, Characteristics, and Design. The proposed procedures involving 
human subjects are described in detail above with regard to study design (2.A), inclusion and exclusion criteria 
(2.D), recruitment and retention strategies (2.D), randomization (2.E), dietary intervention (2.C), and sample 
size (2.H). Key personnel have completed training in the protection of human subjects. 
We will send de-identified urine samples to the Baylor College of Medicine via Federal Express for gas-
isotope-ratio mass spectrometry, as part of the protocol utilizing doubly-labeled water to assess total energy 
 
Figure 2.  Power of one-way ANOVA to detect deviation of 
mean ΔTEE under one diet from the other two (inset). Each 
curve represents a fixed sample size. Dot indicates 
detectable effect, 237 kcal/d at 80% power, with proposed 
45/arm. Curves based on non-central F distribution, 5% Type 
I error, between-subject standard deviation 412 kcal/d as in 
preliminary data. 
11 

 
June 14, 2017 
expenditure (2.F). Each sample will contain only a study-specific subject identification (ID) number and 
information regarding the date and time of sample collection to ensure integrity of the data collection process. 
Serum, plasma, and other urine samples collected for assessment of chronic disease risk factors and 
physiological mechanisms will be labeled in the same manner and then triaged to appropriate laboratories or a 
biorepository. 
We will use the Web-based platform of SPH to track subject weight and food intake. SPH will require 
access to the following Protected Health Information (PHI): study ID number, subject name, telephone number, 
and email address. Subjects will interact with the SPH Website via computer or through a free smartphone 
application or “app;” therefore subject IP addresses and other electronic identifiers may be known and stored. 
A Business Associate Agreement and a Web Site Agreement will be executed between SPH and the Clinical 
Trials Business Office at BCH. A copy of SPH’s HIPAA-compliant IT Data Security Standards will be filed with 
the Institutional Review Board. 
  
3.A.2. Sources of Materials. Data will be obtained directly from subjects for research purposes only, as 
described in 2.F. Daily weight and food intake data will be collected from subjects through the SPH Website or 
smartphone “app.” During the screening process, we will obtain permission from subjects, in writing, to request 
medical clearance from their primary care providers. We will also obtain permission from subjects, in writing, to 
register them with SPH, and to obtain their preference for receiving study reminders (email or text message). 
Laboratory testing will be performed on blood, stool, and urine samples obtained at the time of the study. SPH 
will be used to collect data on subject weight and food intake. Data will not be obtained from other sources.  
All data will be held in strict confidence such that only personnel listed in the protocol on file with the IRB 
will have access to individually identifiable private information about the subjects. Any publication or report 
resulting from this work will maintain the anonymity of study subjects. A study identification (ID) assignment log 
will be the only link between subject ID codes and identifiable information. The log will be kept in a password 
protected electronic file, accessible only to study staff for the purposes of enrolling and tracking subjects. 
Tracking information will be stored in a separate database that is not linked to the data management system 
(2.G). All specimens and case report forms will be labeled with study ID codes, without individually identifiable 
private information. 
3.A.3. Potential Risks. The project has been designed to keep risks to the lowest level possible. All subjects 
will be fully screened prior to enrollment to rule-out the presence of pre-existing or complicating medical 
conditions. The dietary treatments will satisfy all nutritional requirements, and diets similar to these have been 
consumed for extended periods (i.e., > 1 year) with safety. Our low-carbohydrate diet, with ~60% of energy 
from fat, is similar to the Atkins Diet consumed by millions of Americans for months to years, and less 
restrictive than the classic ketogenic diet, with 80% of energy from fat, recommended for chronic consumption 
in some clinical settings (e.g., epilepsy management). Some inconvenience may be caused by the frequency 
of assessment visits and the requirement to consume only study foods. Slight pain and small bruises may be 
expected from insertion of intravenous lines for blood draws. The subject may feel lightheaded or nauseated 
with fasting prior to assessment visits and during the oral glucose tolerance test (OGTT). Total radiation 
exposure from DXA (about 4 millirem per scan) is considered small in comparison to estimated annual 
background radiation dose for the average person living in the United States (300 millirem). The SPH Web site 
satisfies HIPAA regulations for data security; therefore the risk of loss of confidentiality is minimized to the 
fullest-possible extent. 
3.B. Adequacy of Protection Against Risks 
3.B.1. Recruitment and Informed Consent. Recruitment procedures are described in detail above (2.D). A 
full explanation of the study will be provided to each subject by verbatim reading of the consent form approved 
by the IRB at BCH. Written informed consent will be obtained from each subject by study personnel, under the 
supervision of Drs. Ebbeling and Ludwig, who have been appropriately trained in the protection of human 
subjects. Study personnel will provide as much time as needed for each subject to review protocols and ask 
questions. The consent process will be performed in a private room at Framingham State University (FSU). 
Signed consent forms will be filed in a locked cabinet at BCH, and a copy will be given to the subject for future 
reference. 
3.B.2. Protection Against Risk. We will take several steps to protect against possible risk. A physician will be 
on call 24-hour per day should any problems arise. A member of the BCH research team will be available to 
assist all subjects with questions or issues around the use and security of the SPH Website. The OGTT and all 
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June 14, 2017 
blood draws will be performed by a nurse.  Adverse events will be reviewed and action will be taken as 
described below (3.E). Abnormal serum electrolytes have rarely occurred on carbohydrate-restricted diets, and 
we will check serum electrolytes (sodium, potassium, chloride, bicarbonate, calcium, magnesium and 
phosphate) in any subject assigned to the low-carbohydrate diet who reports potentially-related symptoms 
(e.g., unexplained muscle aches or fatigue).   
3.C.  Potential Benefits of the Proposed Research to the Subject and Others 
Benefits to the subjects include medical assessment that might identify a treatable pre-existing illness and 
substantial weight loss resulting from energy-restriction during the run-in phase. The results of DXA scans and 
blood tests will be sent to the PCP at the end of the study, when all participants have completed all of the study 
measurements. In addition, the study will demonstrate the effects of different macronutrient diets on 
metabolism and chronic disease risk factors, a topic of great significance to society in general. These benefits 
are believed to considerably outweigh the risks and possible inconvenience of participating in this study. 
3.D. Importance of the Knowledge to be Gained 
The study will provide important information regarding the effects of different macronutrient diets, a topic of 
great potential significance to society in general. We believe that the benefits (3.C) and knowledge to be 
gained considerably outweigh the risks and inconvenience to study subjects. 
3.E.  Data and Safety Monitoring (DSM) Plan 
The dietary interventions are modeled after popular dietary patterns, and the study does not employ 
pharmacological agents or notably invasive procedures. Thus, the DSM Plan will focus on enrollment and 
drop-out rates. However, subjects will be closely monitored by an Endocrinology Fellow and Dr. Ludwig, in 
close communication with the safety officer (Dr. Agus, see below). Serious adverse events will be promptly 
reported to the IRB at BCH. 
The frequency of data review is summarized in the Table 7. The Study Director (Ms. Klein) will be 
responsible for assembling the data and producing summary reports for data review. Reports will be sent to the 
PIs (Drs. Ludwig and Ebbeling), biostatistician (Dr. Feldman), and safety officer. 
Subject Accrual, Drop-out Rate. Subject accrual rate, with careful attention to inclusion/exclusion criteria, 
will be reviewed monthly during the recruitment phases. This review will also assess targeted/planned 
enrollment of ethnic-racial groups to ensure diversity.  Drop-out rate will be monitored on a monthly basis. 
Data collection. A clinical research specialist will review all data collection forms on a monthly basis for 
completeness, accuracy, and compliance with standardized protocols. A statement reflecting the results of the 
review will be sent to the IRB as part of the annual continuing review.  
Adverse events. All serious, unexpected, and study-related adverse events (hospitalization, serious illness 
or disability) will be evaluated by the Endocrinology Fellow, under the supervision of Dr. Ludwig, and the safety 
officer within 24 hours of recognition by study personnel and promptly reported to the IRB at BCH.  All non-
serious events will be reviewed by the Endocrinology Fellow within 1 week of recognition. If an adverse event 
occurs, the Endocrinology Fellow, under the direction of Dr. Ludwig, will communicate with the subject’s 
primary care physician to ensure necessary medical or professional intervention. A case report form will be 
used to monitor adverse events. All events will be reported to the IRB and safety officer as part of the annual 
continuing review.  
 
Table 7. Frequency of data review. 
Data type 
Frequency of review 
Subject accrual 
Monthly during recruitment 
Drop-out rate 
Monthly 
Data collection 
Quarterly 
Adverse events 
Upon occurrence 
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Qualifications and Responsibilities of the Safety Officer. The safety officer for this trial will be Michael 
Agus, MD, an Assistant in Critical Care Medicine, with appointments to the Division of Endocrinology and 
Medicine Critical Care Program at BCH.  Dr. Agus is a practicing physician licensed in the state of 
Massachusetts, with experience in the fields of critical care medicine, endocrinology, and clinical research. As 
safety officer, Dr. Agus will be available as needed for consultation regarding serious and non-serious adverse 
events. 
3.F. Trial Registration (ClinicalTrials.gov Requirements) 
Drs. Ebbeling and Ludwig will register the proposed study in ClinicalTrials.gov prior to screening or enrolling 
any subjects. 
3.G. Inclusion of Women and Minorities 
We will enroll both males and females. The distribution of ethnic-racial groups will reflect the diversity of the 
community at FSU. We will reach out to students, faculty, and staff using a variety of venues (2.D). 
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4. BIBLIOGRAPHY AND REFERENCES CITED 
 
1. 
Ebbeling CB, Swain JF, Feldman HA, et al. Effects of dietary composition on energy expenditure during 
weight-loss maintenance. JAMA. 2012;307:2627-2634. PMID: 22735432. 
2. 
Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, 
Cholesterol, Protein, and Amino Acids. Washington (DC): The National Academies Press; 2002.  
3. 
Halton TL, Hu FB. The effects of high protein diets on thermogenesis, satiety and weight loss: a critical 
review. J Am Coll Nutr. 2004;23:373-385. PMID: 15466943. 
4. 
Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting 
energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241-247. PMID: 2305711. 
5. 
Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic 
rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005;105:775-789. 
PMID: 15883556. 
6. 
Pereira MA, FitzerGerald SJ, Gregg EW, et al. A collection of Physical Activity Questionnaires for health-
related research. Med Sci Sports Exerc. 1997;29:S1-205. PMID: 9243481. 
7. 
Katan MB, Ludwig DS. Extra calories cause weight gain--but how much? JAMA. 2010;303:65-66. PMID: 
20051571. 
8. 
Hall KD, Heymsfield SB, Kemnitz JW, Klein S, Schoeller DA, Speakman JR. Energy balance and its 
components: implications for body weight regulation. Am J Clin Nutr. 2012;95:989-994. PMID: 22434603. 
9. 
U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines 
for Americans, 2010. 7th Edition, Washington, DC: U.S. Government Printing Office, December 2010.  
10. York LW, Puthalapattu S, Wu GY. Nonalcoholic fatty liver disease and low-carbohydrate diets. Annu Rev 
Nutr. 2009;29:365-379. PMID: 19575599. 
11. Feinman RD, Fine EJ. Thermodynamics and metabolic advantage of weight loss diets. Metab Syndr 
Relat Disord. 2003;1:209-219. PMID: 18370664. 
12. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and 
cardiovascular disease. JAMA. 2002;287:2414-2423. PMID: 11988062. 
13. Feinman RD, Fine EJ. Nonequilibrium thermodynamics and energy efficiency in weight loss diets. Theor 
Biol Med Model. 2007;4:27. PMID: 17663761. 
14. Scribner KB, Pawlak DB, Aubin CM, Majzoub JA, Ludwig DS. Long-term effects of dietary glycemic index 
on adiposity, energy metabolism, and physical activity in mice. Am J Physiol Endocrinol Metab. 
2008;295:E1126-1131. PMID: 18780772. 
15. Baldwin KM, Joanisse DR, Haddad F, et al. Effects of weight loss and leptin on skeletal muscle in human 
subjects. Am J Physiol Regul Integr Comp Physiol. 2011;301:R1259-1266. PMID: 21917907. 
16. Goldsmith R, Joanisse DR, Gallagher D, et al. Effects of experimental weight perturbation on skeletal 
muscle work efficiency, fuel utilization, and biochemistry in human subjects. Am J Physiol Regul Integr 
Comp Physiol. 2010;298:R79-88. PMID: 19889869. 
17. Rosenbaum M, Goldsmith R, Bloomfield D, et al. Low-dose leptin reverses skeletal muscle, autonomic, 
and neuroendocrine adaptations to maintenance of reduced weight. J Clin Invest. 2005;115:3579-3586. 
PMID: 16322796. 
18. Rosenbaum M, Vandenborne K, Goldsmith R, et al. Effects of experimental weight perturbation on 
skeletal muscle work efficiency in human subjects. Am J Physiol Regul Integr Comp Physiol. 
2003;285:R183-192. PMID: 12609816. 
19. Wong WW, Lee LS, Klein PD. Deuterium and oxygen-18 measurements on microliter samples of urine, 
plasma, saliva, and human milk. Am J Clin Nutr. 1987;45:905-913. PMID: 3578092. 
20. Ravussin E, Harper IT, Rising R, Bogardus C. Energy expenditure by doubly labeled water: validation in 
lean and obese subjects. Am J Physiol. 1991;261:E402-409. PMID: 1909495. 
21. Black AE, Prentice AM, Coward WA. Use of food quotients to predict respiratory quotients for the doubly-
labelled water method of measuring energy expenditure. Hum Nutr Clin Nutr. 1986;40:381-391. PMID: 
3771290. 
22. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J 
Physiol. 1949;109:1-9. PMID: 15394301. 
23. Martin CK, Das SK, Lindblad L, et al. Effect of calorie restriction on the free-living physical activity levels 
of nonobese humans: results of three randomized trials. J Appl Physiol. 2011;110:956-963. PMID: 
21292847. 
15 

 
June 14, 2017 
24. Metzger JS, Catellier DJ, Evenson KR, Treuth MS, Rosamond WD, Siega-Riz AM. Patterns of objectively 
measured physical activity in the United States. Med Sci Sports Exerc. 2008;40:630-638. PMID: 
18317384. 
25. Bouchard C, Tremblay A, Leblanc C, Lortie G, Savard R, Theriault G. A method to assess energy 
expenditure in children and adults. Am J Clin Nutr. 1983;37:461-467. PMID: 6829488. 
26. Keadle SK, Shiroma EJ, Freedson PS, Lee IM. Impact of accelerometer data processing decisions on the 
sample size, wear time and physical activity level of a large cohort study. BMC Public Health. 
2014;14:1210. PMID: 25421941. 
27. Abdul-Ghani MA, Matsuda M, Balas B, DeFronzo RA. Muscle and liver insulin resistance indexes derived 
from the oral glucose tolerance test. Diabetes Care. 2007;30:89-94. PMID: 17192339. 
28. Hall KD, Chen KY, Guo J, et al. Energy expenditure and body composition changes after an isocaloric 
ketogenic diet in overweight and obese men. Am J Clin Nutr. 2016;104:324-333. PMID: 27385608. 
29. American Diabetes Association. Glycemic Targets. Diabetes Care. 2017;40:S48-S56. PMID: 27979893. 
30. Juraschek SP, Miller ER, 3rd, Appel LJ, Christenson RH, Sacks FM, Selvin E. Effects of dietary 
carbohydrate on 1,5-anhydroglucitol in a population without diabetes: results from the OmniCarb trial. 
Diabet Med. 2017. PMID: 28574153. 
31. Miller M, Ginsberg HN, Schaefer EJ. Relative atherogenicity and predictive value of non-high-density 
lipoprotein cholesterol for coronary heart disease. Am J Cardiol. 2008;101:1003-1008. PMID: 18359322. 
32. Di Angelantonio E, Sarwar N, Perry P, et al. Major lipids, apolipoproteins, and risk of vascular disease. 
JAMA. 2009;302:1993-2000. PMID: 19903920. 
33. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans 
and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals 
from the Subcommittee of Professional and Public Education of the American Heart Association Council 
on High Blood Pressure Research. Hypertension. 2005;45:142-161. PMID: 15611362. 
34. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with 
obesity. Nature. 2006;444:1022-1023. PMID: 17183309. 
35. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut 
microbiome with increased capacity for energy harvest. Nature. 2006;444:1027-1031. PMID: 17183312. 
36. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial 
ecology. Proc Natl Acad Sci U S A. 2005;102:11070-11075. PMID: 16033867. 
37. Christofk HR, Vander Heiden MG, Harris MH, et al. The M2 splice isoform of pyruvate kinase is important 
for cancer metabolism and tumour growth. Nature. 2008;452:230-233. PMID: 18337823. 
38. Lewis GD, Wei R, Liu E, et al. Metabolite profiling of blood from individuals undergoing planned 
myocardial infarction reveals early markers of myocardial injury. J Clin Invest. 2008;118:3503-3512. 
PMID: 18769631. 
39. Sabatine MS, Liu E, Morrow DA, et al. Metabolomic identification of novel biomarkers of myocardial 
ischemia. Circulation. 2005;112:3868-3875. PMID: 16344383. 
40. Rukh G, Ericson U, Andersson-Assarsson J, Orho-Melander M, Sonestedt E. Dietary starch intake 
modifies the relation between copy number variation in the salivary amylase gene and BMI. Am J Clin 
Nutr. 2017. PMID: 28539377. 
41. Rousseuw PJ, Leroy AM. Robust Regression and Outlier Detection. New York, NY: John Wiley & Sons; 
1987. 
42. Bauer P. Multiple testing in clinical trials. Stat Med. 1991;10:871-889; discussion 889-890. PMID: 
1831562. 
 
 
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