Energy Intake Response to Short-Term Alterations of Energy Expenditure in Humans
Phase: NA
Conditions: Healthy Volunteers
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Abbreviated Title: EIR
Version Date: 03/23/2023
1
NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES
       
Abbreviated Title: EIR
NIH IRB #: 17DKN004. 
Version Date: March 23, 2023
Title: Energy intake response to short-term alterations of energy expenditure in humans
Principal Investigator:
Douglas Chang, MD
ODCRS, PECRB, NIDDK
4212 N 16th St
Phoenix, AZ 85016
Tel: 602-200-5312
douglas.chang2@nih.gov
NCT02939404  

2
Table of Contents
STATEMENT OF COMPLIANCE .................................................................................................................................5
PRÉCIS......................................................................................................................................................................6
INTRODUCTION .......................................................................................................................................................1
Energy Expenditure and Energy Intake Link ........................................................................................................1
Table 1. Determinants of ad libitum food intake using data from a previous protocol. .....................................3
Cold Exposure Increases Energy Expenditure......................................................................................................4
Possible Mediators of the Energy Expenditure to Energy Intake Link.................................................................6
Behavioral Aspects ..............................................................................................................................................7
Summary..............................................................................................................................................................8
AIMS OF THE STUDY ................................................................................................................................................8
Primary Aims........................................................................................................................................................9
Secondary Aims ...................................................................................................................................................9
HYPOTHESES..........................................................................................................................................................10
Primary Hypotheses...........................................................................................................................................10
Secondary Hypotheses ......................................................................................................................................10
STUDY DESIGN AND METHODS .............................................................................................................................11
Study Design......................................................................................................................................................11
Subjects .............................................................................................................................................................11
Inclusion Criteria................................................................................................................................................12
Exclusion Criteria ...............................................................................................................................................13
Monitoring Subjects and Criteria for Withdrawal of Subjects from the Study..................................................14
Experimental Design..........................................................................................................................................15
Figure 1. Study diagram of the clinical trial. ......................................................................................................16
Randomization...................................................................................................................................................18
Admission ..........................................................................................................................................................18
Baseline Phase...............................................................................................................................................19
Intervention Phase ........................................................................................................................................29
Outpatient Visits................................................................................................................................................31
Blood Withdrawal..............................................................................................................................................32
Sample Size Calculation .....................................................................................................................................32
Table 2. Sample size calculations achieving a statistical power ≥0.8.................................................................34
Table 3. Power calculations based on different values of alpha (α), correlation value (r) and sample size (n).35

3
Data Analysis .....................................................................................................................................................35
Informed Consent Process.....................................................................................................................................40
BENEFITS................................................................................................................................................................40
HUMAN SUBJECT PRECAUTIONS...........................................................................................................................41
Rationale for Subject Selection..........................................................................................................................41
Risks and Discomforts........................................................................................................................................41
Genetic Studies..................................................................................................................................................45
Disclosure of Medical Conditions ......................................................................................................................46
Clinical Monitoring ............................................................................................................................................46
Data and Safety Monitoring ..............................................................................................................................47
Event Reporting.................................................................................................................................................47
Research Use and Disposition of Human Samples and Data and Data Sharing.................................................47
Remuneration....................................................................................................................................................49
Table 4. Study Variables. ...................................................................................................................................51
REFERENCES...........................................................................................................................................................54

4
Abbreviations
DXA:
dual-energy x-ray absorptiometry
EE: 
energy expenditure
FGF21:
fibroblast growth factor 21
GLP-1:
glucagon-like peptide 1
IGF-1:
insulin-like growth factor 1
IL-6:
interleukin-6
OGTT:
oral glucose tolerance test
PYY:
peptide YY
PP:
pancreatic polypeptide
RMR:
resting metabolic rate
RQ:
respiratory quotient
SPA:
spontaneous physical activity
T3:
triiodothyronine
T4:
thyroxine
TSH:
thyroid stimulating hormone
WMEN:
weight maintaining energy needs

5
STATEMENT OF COMPLIANCE
The protocol will be carried out in accordance with International Conference on Harmonisation Good Clinical 
Practice (ICH GCP) and the following: 
United States (US) Code of Federal Regulations (CFR) applicable to clinical studies (45 CFR Part 46, 21 
CFR Part 50, 21 CFR Part 56, 21 CFR Part 312, and/or 21 CFR Part 812) 
National Institutes of Health (NIH)-funded investigators and trial site staff who are responsible for the conduct, 
management, or oversight of NIH-funded trials have completed Human Subjects Protection and ICH GCP 
Training.
The protocol, informed consent form(s), recruitment materials, and all participant materials will be submitted 
to the Institutional Review Board (IRB) for review and approval.  Approval of both the protocol and the consent 
form must be obtained before any participant is enrolled.  Any amendment to the protocol will require review 
and approval by the IRB before the changes are implemented to the study.  In addition, all changes to the 
consent form will be IRB-approved; an IRB determination will be made regarding whether a new consent needs 
to be obtained from participants who provided consent, using a previously approved consent form.

6
PRÉCIS
More than 30% of adults are considered overweight. In general, lifestyle changes (diet and 
exercise) or current weight loss drugs only lead to about 5 to 10% weight loss. This may be because a 
person’s energy expenditure, aka the number of calories the body uses, leads to hunger and may 
increase the amount of food a person eats. Cold exposure is known to increase metabolism but it may 
not lead to weight loss if appetite and the desire for food are also increased. The primary goal of this 
study is to evaluate whether changing energy expenditure by cool temperature exposure results in 
changes in food intake. This study will involve a stay on our clinical research unit where we will 
determine the energy requirements (at 24°C) of 68 healthy, adult volunteers without evidence of 
diabetes. Exposure to cool temperatures (19°C) will be used to increase the number of calories a 
person’s body uses in a day. Participants will spend 24 hours in a room that measures energy 
expenditure while the temperature in the room is turned down, once with a fixed diet and once with 
a buffet of food choices. After the fixed diet, volunteers will self-select how much food they wish to 
eat for one day from a vending machine. Volunteers will also spend one day fasting followed by a day 
self-selecting their food from the vending machine. Findings from this study will provide knowledge 
about a possible causal link between energy expenditure and eating behavior. This information may 
shed light on why many weight loss interventions that increase energy expenditure do not work as 
well as expected, and may eventually lead to new weight loss approaches and therapies. 

Abbreviated Title: EIR
Version Date: 03/23/2023
1
1
INTRODUCTION
2
Energy Expenditure and Energy Intake Link
3
The fundamental principle of energy balance states that energy intake and energy expenditure 
4
(EE) are the counterbalancing factors that determine body weight change. Both components are likely 
5
influenced by environmental and genetic factors; however, while energy intake can vary largely from 
6
day to day due to psychological and social influences, daily sedentary EE is largely determined by body 
7
size and composition. An increased energy intake leads to a small increase (~10% on average) in EE 
8
known as the diet-related energy expenditure [1]. In contrast, we have found in a previous study that 
9
larger individuals have both a higher absolute EE due to a larger amount of fat-free mass and also, eat 
10
a greater amount of food in an ad libitum setting both as total kilocalories and as a percentage of 
11
weight maintaining energy needs [2]. As the 24-h EE measures done in this study were done during a 
12
eucaloric diet (achieved energy balance within 20%), this correlation is likely because body energy 
13
requirements modulate a physiological signal for hunger that results in increased food intake [2, 3]. Of 
14
note, during the ad libitum portion of the study, participants ate more than their weight maintaining 
15
energy requirements on average, suggesting that relatively higher energy demands may lead some 
16
people to overeat to a greater degree in a food rich environment. A previous study by Caudwell et al. 
17
also found a direct correlation between resting EE (RMR) and food intake as well as hunger sensations 
18
measured during a 3-month trial [3]. Interestingly, after accounting for fat free mass, fat mass is 
19
negatively associated with subsequent food intake [2].  The effects of EE on food intake may act, in 
20
part, through central pathways that modulate appetite as fat free mass, the primary determinant of 
21
resting EE, has been associated with food intake [4] and reduced gray matter volume in regions 
22
implicated in energy homeostasis [5]. 

2
23
From an evolutionary viewpoint, a link between energy expenditure, i.e. energy needs, and 
24
food intake would ensure that an organism would experience a physiologic drive to find enough food 
25
to maintain both life and reproduction. Although some studies have found an inverse association 
26
between lower-than-predicted EE and future body weight change [6, 7], others have found that a 
27
greater-than-predicted EE predicts future weight gain [8], including preliminary results from one of 
28
our own studies with an ethnically diverse study population. The former results may be limited to 
29
certain ethnicities (Native American) or may reflect differences between a population with a high 
30
prevalence of obesity versus not.  It is possible that a greater-than-predicted EE may increase hunger 
31
and food-seeking behavior in most individuals, often leading to a greater-than-necessary intake, and 
32
eventual weight gain.  What is unknown is whether an increase in daily EE in humans would promote 
33
a compensatory caloric intake either because of the creation of a negative energy balance or because 
34
there is a homeostatic mechanism directly linking energy expended with hunger and food intake. It is 
35
known that intermittent cold exposure, which leads to increases in basal metabolic rate in rodents, 
36
also increases food intake, [9] resulting in weight changes similar to controls. These findings in 
37
rodents lend support to the hypothesis of a homeostatic link between EE and energy intake, 
38
potentially due to a direct signal reflecting the energy requirements of the organism. More detailed 
39
and specific studies are warranted to elucidate whether directly increasing EE will alter dietary intake. 
40
In our previous study, the 24-h RQ, as an estimate of carbohydrate-to-fat oxidation, and 
41
particularly the carbohydrate oxidation rate during energy balance were both positively associated 
42
with ad libitum food intake and weight change during three days when subjects self-selected their 
43
food using the vending machine system [10]. These results imply that a negative carbohydrate 
44
balance due to higher oxidation may trigger hunger sensations, possibly due to a depletion of 

3
45
glycogen stores [11, 12]. In a recent reanalysis of the same EE-intake data [13], both 24-h RQ and 24-h 
46
EE were independent determinants of ad libitum food intake (Table 1). This finding indicates that 
47
substrate oxidation rates may influence dietary intake independently from the total amount of 
48
calories expended. Nevertheless, it is not clear whether an altered EE and RQ would independently 
49
influence food intake.
50
Table 1. Determinants of ad libitum food intake using data from a previous protocol.
Predictors
Total energy intake
(kcal/day)
Total energy intake
(% WMEN)
Age (years)
−21.0 ± 11.6 (p=0.073)
−0.6 ± 0.5 (p=0.195)
Gender (female)
949.1 ± 505.2 (p=0.064)
44.6 ± 19 (p=0.021)*
Ethnicity (Nat. Am.)
27.0 ± 212.6 (p=0.899)
2.6 ± 8.2 (p=0.752)
Fat mass (kg)
−71.0 ± 20.1 (p=0.001)*
−2.9 ± 0.8 (p<0.001)*
Fat free mass (kg)
10.6 ± 23.2 (p=0.649)
−0.1 ± 0.9 (p=0.926)
Energy balance (kcal)
1.01 ± 1.27 (p=0.426)
0.03 ± 0.05 (p=0.470)
SPA (%)
30.7 ± 35.4 (p=0.389)
1.0 ± 1.3 (p=0.460)
24-h EE (100•kcal/day)
316.7 ± 123.5 (p=0.012)*
10.5 ± 4.6 (p=0.026)*
24-h RQ (%)
183.7 ± 44.3 (p<0.001)*
6.7 ± 1.7 (p<0.001)*
Intercept
−16,076 ± 3,989
−525 ± 150
Total R2
54%*
42%*
51
Abbreviations: EE, energy expenditure; RQ, respiratory quotient; SPA: spontaneous physical activity; WMEN, 
52
weight maintaining energy needs.
53
Total energy intake and macronutrients intake during a 3-day ad libitum period on the vending machine is 
54
expressed both as the average kcal/day and as percentage of WMEN on the metabolic ward. 
55
Beta coefficients in each cell are reported as mean ± SE. Beta coefficients for 24-h EE and 24-h RQ are 
56
expressed per 100 kcal-increase and per 0.01-unit increase, respectively. *: P<0.05
57

4
58
We now propose to clarify the cause-and-effect relationship between EE and energy intake 
59
with a proof-of-concept study wherein we alter EE and assess the subsequent effect on energy intake. 
60
We will also ask participants to return at 6 months and 1 year to assess whether any observed EE-
61
energy intake link is associated with change in weight over time. An increased understanding of the 
62
ways in which changes in EE may alter energy intake may provide insight into the limited efficacy of 
63
obesity treatments directed solely at increasing EE.
64
Cold Exposure Increases Energy Expenditure
65
Thermoregulation to maintain core body temperature is a fundamental homeostatic drive in 
66
homeotherms. In previous studies, cold exposure increased EE by approximately 5-15% depending on 
67
the temperature exposure and body weight of the study population [14-17]. The individual maximum 
68
increase in EE due to cold exposure can already be detected after one day, as shown in a previous 4-
69
month study assessing cold-induced thermogenesis over 24 hours in a whole-room calorimeter after 
70
one-month overnight acclimation at both 24°C and 19°C [18]. In this study [18], no difference in the 
71
magnitude of 24-h cold-induced thermogenesis was observed after one month spent at 
72
thermoneutrality (24°C) versus after one month with overnight cold-exposure at 19°C, suggesting that 
73
the full increase in EE due to cold exposure can already be detected after 24 hours. This increase in EE 
74
is due to activation of adaptive thermogenesis mechanisms to maintain core body temperature, and 
75
include both activation of brown adipose tissue [15] and non-shivering thermogenesis in skeletal 
76
muscle. However, in at least one study, lean subjects had a greater increase in 24-h EE and a greater 
77
decrease in distal skin temperature (measured by iButtons®) in response to cool temperature 
78
exposure than obese subjects [17]. Cold exposure is known to increase food intake in rodents [9]  and 
79
even with prolonged exposures to cold temperatures, rodents maintain body weight [19]. Weight loss 
80
due to prolonged cold temperature exposure does not begin to occur until about −8°C [19]. This 

5
81
increase in both EE and energy intake with cold exposure has also been observed in other animal 
82
species including pigeons [20], which lack the UCP1 gene responsible for heat generation in the brown 
83
adipose tissue. This finding suggests that the mechanisms underlying the positive feedback between 
84
increased EE and energy intake may involve pathways beyond the activation of brown adipose tissue. 
85
Nevertheless, the effects of cold exposure on food intake have not been adequately assessed in 
86
humans, including a potential correlation between the degree of increase in EE with cold exposure 
87
and any change in subsequent food intake. In a previous pilot study of 9 subjects residing for 24-h in a 
88
whole-room calorimeter once at 22°C and once at 16°C with free access to food [16, 21], subjects 
89
overate approximately 30% of energy requirements in both conditions , but there was no difference 
90
in the degree of overeating at 22°C (+32%) and at 16°C (+34%) in this small study group [16]. 
91
Nevertheless, this study included only 9 lean White males, a study population not representative of 
92
the current US population in terms of body size, ethnicity and gender. Therefore, studies in a larger 
93
and more diverse cohort employing an objective method to measure ad libitum food intake are 
94
warranted to clearly establish whether or not an association exists between increased EE and food 
95
intake. Furthermore, it is unknown how long any effect of increased EE due to cold exposure on 
96
energy intake would persist. For example, the energy deficit created by the cold-induced 
97
thermogenesis may lead to a hunger signal that persists even with a return to thermoneutral 
98
temperatures. Alternatively, the increase in EE from the cold-induced thermogenesis may directly 
99
stimulate food-seeking behavior. If there is a direct effect of an EE increase, it is unclear if it would 
100
exist only during cold exposure or if it would persist even after a return to more comfortable 
101
temperatures.    

6
102
Possible Mediators of the Energy Expenditure to Energy Intake Link
103
The signals from lean tissue that might indicate energy needs to the orexigenic centers of the 
104
brain are largely unknown. One possibility is hormonal signals. Hormones that may be potential 
105
transmitters of energy needs include myokines released from skeletal muscle or other factors 
106
released from non-fat tissue. Interleukin-6 (IL-6) is a multifunctional cytokine secreted by a number of 
107
tissues including skeletal muscle. IL-6 deficient mice develop obesity late in life but also, many of the 
108
beneficial effects of brown fat are not present if the brown adipose tissue is missing the IL-6 gene. 
109
Interestingly, when the calcium release channel of the sarcoplasmic reticulum (RYR1), a protein 
110
implicated in non-shivering thermogenesis of skeletal muscle, is activated, the myotubes secrete IL-6 
111
[22]. In addition to local effects, IL-6 can also bind to a circulating, soluble form of the IL-6 receptor 
112
(IL-6R) and activate cells that do not express the membrane form of the receptor. The soluble form of 
113
IL-6R has been reported to be correlated with EE and both IL-6R and IL-6 are reported to increase with 
114
exercise [23] but any effect of the IL-6 pathway on food intake is not clear. Studies have identified 
115
fibroblast growth factor 21 (FGF21) as a hormone involved in energy homeostasis, which increases in 
116
response to fasting, low protein diets, and intense exercise, and stimulates appetite. As noted above, 
117
thyroid hormone is known to act through independent pathways to increase both EE and appetite 
118
and, is therefore, also a potential candidate to explain any EE-energy intake link.
119
More traditional anorexigenic and orexigenic hormones may also be important if the creation 
120
of an energy deficit is the cause of increased energy intake after increased EE. These include 
121
adiponectin and leptin, hormones secreted from adipocytes to indicate the current status of energy 
122
stores in the body [24-26]. A fall in leptin with calorie restriction or weight loss activates the appetite-
123
stimulating pathways in the body, including the melanocortin system. Leptin concentrations might 
124
also moderate any EE-energy intake link, i.e. by blunting the link if the body already has adequate 

7
125
energy stores or by providing the signal that leads to increased food intake. Cold exposure is known to 
126
induce a decrease in plasma leptin up to 20% [27, 28], suggesting that leptin may play a role in the EE 
127
response to cold by increasing appetite and, subsequently, energy intake. Concentrations of 
128
gastrointestinal hormones known to stimulate hunger, i.e. ghrelin, or act as satiety agents, including 
129
glucagon-like peptide 1 (GLP-1), glucagon, insulin, peptide YY (PYY) and pancreatic polypeptide (PP) 
130
are other potential mediators. In a previous study, acute cold exposure induced a rise in glucagon 
131
concentrations in a setting of unchanged glucose and insulin levels as well as increased 
132
concentrations of free fatty acids and β-hydroxybutyrate [29], indicating increasing needs for glucose 
133
and ketone bodies production by the liver. The thyroid hormone, triiodothyronine (T3), has been 
134
associated with EE [30], increases and decreases with weight gain and weight loss,  and changes in 
135
free T3 related to weight changes are associated with changes in urinary norepinephrine, resting EE, 
136
and 24-h EE [31]. Variations in free T3, free thyroxine (T4) and in the thyroid stimulating hormone 
137
(TSH) with cold exposure may partly explain the EE-energy intake link. In a previous study, exposure 
138
to cold temperatures resulted in an increase in urinary norepinephrine, cortisol and serum TSH 
139
without significant changes in serum free T4, T3, or cortisol levels [15], while another study reported 
140
an increase in T4 [14]. Conversely, other studies report no significant changes in thyroid hormones 
141
and TSH compared to thermoneutral conditions [18, 32, 33]. This current study will help to increase 
142
understanding of the role these hormones may have in signaling energy requirements to the body.
143
Behavioral Aspects
144
There are many cognitive and behavioral components of food intake that may mediate or 
145
moderate the effects of EE on energy intake. Of note, although EE was correlated with energy intake 
146
in our past studies [2], individuals still tended to eat more than their energy requirements. Behavioral 
147
factors and executive function measures may also independently influence food intake in this study. 

8
148
Dietary restraint, disinhibition, sensations of hunger, stress and emotional related eating behaviors, 
149
impulsivity, perseveration, cognitive flexibility and decision making processes may all influence eating 
150
behavior. For example, a prior study in lean weight-stable women found that dietary restraint was 
151
greater in those with lower relative EE (despite similar BMIs between restrained and unrestrained 
152
individuals), potentially indicating that the dietary restraint was a cognitive response to reduced 
153
calorie requirements [34]. Assessing how such characteristics may affect or moderate any link 
154
between EE and energy intake, as well as understanding the independent effects of these factors will 
155
increase knowledge of determinants of eating behavior.   
156
Summary
157
Although there is data indicating that 24-h EE is associated with food intake, a direct link 
158
between EE and energy intake has not been clearly established. In this study, we propose to explore 
159
whether increases in 24-h EE lead to increases in ad libitum food intake. We will explore this potential 
160
relationship by increasing 24-h EE in humans using cold exposure and secondarily comparison also 
161
with fasting, and then assess whether concomitant and/or subsequent 24-h food intake also 
162
increases. Given the dynamic nature of human physiology, it is highly likely that these acute increases 
163
in EE will exert an almost immediate effect on ad libitum food intake to insure that the body has 
164
enough fuel to meet daily energy needs. We will also explore potential physiologic mechanisms 
165
underlying an EE-energy intake link including assessment of behavioral mediators and hormones.  
166
AIMS OF THE STUDY
167
The overarching goal of this study is to better understand the short-term association between 
168
EE and energy intake by altering EE through environmental means (cold exposure) and assessing the 
169
degree to which subsequent and concomitant ad libitum energy intake changes. The specific aims of 
170
the study are as follows: 

9
171
Primary Aims
172
1. To investigate whether increases in 24-h EE by cold exposure lead to increased ad libitum food 
173
intake, as measured using a vending machine paradigm. 
174
2. To determine whether the effects of increased EE due to cool temperature exposure on 
175
energy intake are only concomitant with the increase in EE or persist until the following day. 
176
This will be explored by repeating the cool temperature exposure intervention twice: i) once 
177
inside the whole-room calorimeter with an ad libitum diet; and, ii) once inside the whole-room 
178
calorimeter during a fixed diet, followed by one day of ad libitum food intake on the vending 
179
machines. 
180
Secondary Aims
181
1. To determine whether 36 hrs of fasting will result in a change in energy intake on the following 
182
day, and to determine whether the decrease in 24-h EE during fasting will correlate with 
183
amount of food consumed the following day.
184
2. To determine if cold exposure will alter the 24-h respiratory quotient (RQ, an approximation 
185
for the ratio of carbohydrate-to-fat oxidation rates), and test whether the induced RQ changes 
186
independently contribute to the altered energy intake.
187
3. To determine if the 24-h EE responses to cool temperature exposure correlate with core body 
188
temperature or changes in peripheral skin temperatures, and to determine if these changes 
189
are related to body adiposity or associated with food intake.
190
4. To assess if behavioral testing addressing dietary restraint, stress related eating behaviors, and 
191
executive function or the responses to visual analogue scales assessing hunger modify any 
192
observed EE to energy intake association.

10
193
5. To assess whether the above behavioral testing as measured by previously validated 
194
questionnaires and tasks are associated with measured food intake or hormone 
195
concentrations.
196
6. To assess if concentrations of appetitive or exercise-induced hormones will correlate with the 
197
observed changes in EE and energy intake. 
198
7. To determine if any observed association between EE and energy intake predicts future weight 
199
change at 6 months and 1 year. 
200
HYPOTHESES
201
Primary Hypotheses
202
1. An increase in 24-h EE, as induced by 24 hours of exposure to 19°C, will be associated with an 
203
increase in ad libitum food intake, which will exceed the increase in 24-h EE, on the day of the 
204
intervention and the effect will persist for at least 24 hours.
205
2. The effect of increasing EE by cool temperature exposure on ad libitum food intake will be 
206
evident both on the day of the intervention and the following day.
207
Secondary Hypotheses
208
1. Thirty-six hours of fasting will induce compensatory overeating when the subject is re-
209
introduced to food, and the decrease in 24-h EE during fasting will positively predict the 
210
degree of energy consumption.
211
2. The creation of an energy deficit will lead to a decrease in RQ, but the observed changes in 
212
energy intake due to changes in EE will be independent of any change in RQ.
213
3. Subjects with less body adiposity will have a greater increase in 24-h EE with cool temperature 
214
exposure and a greater decrease in distal skin temperatures while more obese subjects will 
215
have a smaller increase in 24-h EE with cool temperature exposure and a greater decrease in 

11
216
proximal skin temperatures indicating greater reliance on insulating mechanisms rather than 
217
adaptive thermogenesis. 
218
4. Measures of executive function and results of questionnaires related to eating behaviors will 
219
be associated with the amount of ad libitum caloric intake, but the effects of EE will be 
220
independent of these relationships. 
221
5. Induced increases in EE will be associated with changes in hormone concentrations, including 
222
cold-induced increases in adiponectin, insulin, free T4, TSH, FGF21, glucagon, ghrelin, urinary 
223
free cortisol, urinary catecholamines and decreases in leptin, GLP-1, PP and PYY, and at least 
224
some of these changes will be associated with the change in food intake.
225
6. The amount of energy intake increase after an increase in EE or the ratio of Δenergy 
226
intake/ΔEE will be directly associated with weight change at 6 months and 1 year.  
227
STUDY DESIGN AND METHODS
228
Study Design
229
            This is a inpatient natural history, observational cohort study of healthy human research 
230
volunteers with research interventions designed to understand the relationship between the 
231
physiologic sensing of EE and food intake behavior. This is primarily a cross-sectional study but with a 
232
longitudinal component to assess the association of baseline measurements with future weight 
233
change. 
234
Subjects
235
Sixty-eight healthy participants of all body sizes representing both genders, without evidence 
236
of diabetes mellitus [35], aged 18-55 years will be recruited from the greater Phoenix area for 
237
assessment of EE and ad libitum food intake before and after environmental intervention. All subjects 
238
will be asked to reside for an approximately 23-day stay on the clinical research unit. 

12
239
Pre-Screening
240
Prior to the official screening appointment, prospective subjects will be interviewed in person 
241
or by phone. Both general and protocol-specific recruitment scripting will be used to guide pre-
242
screening interviews. Limited identifiable information pertaining to demographics, general health 
243
status, and contact information will be collected. We are requesting a waiver of consent for the pre-
244
screening interviews since, per 45 CFR 46.116(d) (pre-2018 requirements):
245
1.
The collection of limited identifiable information for pre-screening   purposes involves 
246
no more than minimal risk to the subjects.
247
2.
The waiver will not adversely affect subjects’ rights or welfare. Pre-screening 
248
interviews will not impact subjects’ patient care or ability to decline participation before or during the 
249
study.
250
3.
Pre-screening interviews could not practicably be carried out without a waiver of 
251
consent as recruiting services, utilized in the ODCRS (Obesity & Diabetes Clinical Research Section), 
252
perform pre-screening activities for multiple studies and obtaining consent for each is beyond their 
253
resources.
254
4.
It is unlikely that any pertinent information about the pre-screening process will be 
255
discovered after subjects have completed it, but should that occur, we will provide subjects with that 
256
new information as appropriate.
257
Following pre-screening interviews, all prospective subjects will be fully informed of the aims, 
258
nature, and risks of the study and written informed consent will be obtained.  Informed consent will 
259
be obtained by either the principal investigator or one of the associate investigators who will be well 
260
informed of the nature, risks and procedures of the study. 

13
261
Inclusion Criteria
262
1. Premenopausal women and men <55 years of age
263
2. Body weight <204 kg (<450 pounds) and ≥36 kg (≥80 pounds)
264
3. Stable weight (±5% within past 6 months) as determined by volunteer report
265
4. Healthy, as determined by medical history, physical examination, and laboratory tests
266
Exclusion Criteria
267
1. Age <18 years
268
2. Weight ≥204 kg (≥450 pounds, maximum weight of the iDXA machine as per 
269
manufacturer’s manual), or weight <36 kg (<80 pounds, minimum weight allowed based 
270
on the NIH guidelines of blood drawing for research purposes)
271
3. Use of medications affecting metabolism and appetite in the last three months
272
4. Expresses unwillingness to consume all food given during the weight maintaining diet 
273
portions of the study (e.g., due to strict dietary restrictions including allergies or vegetarian 
274
or kosher diet)
275
5. Current use of tobacco products, marijuana, amphetamines, cocaine, or intravenous drug 
276
use
277
6. Current pregnancy, pregnancy within the past 6 months or lactation
278
7. History or clinical manifestation of:
279

Type 1 and Type 2 diabetes mellitus
280

History of surgery for the treatment of obesity
281

Endocrine disorders, such as Cushing’s disease, pituitary disorders, and hypo and 
282
hyperthyroidism
283

Pulmonary disorders, including chronic obstructive pulmonary disease

14
284

Cardiovascular diseases, including coronary heart disease, heart failure, arrhythmias, 
285
and peripheral artery disease
286

High blood pressure by sitting blood pressure measurement using an appropriate cuff 
287
higher than 140/90 mmHg on two or more occasions, or current antihypertensive 
288
therapy
289

Liver disease, including cirrhosis, active hepatitis B or C, and AST or ALT ≥2x normal.
290

Gastrointestinal disease including Crohn’s disease, ulcerative colitis, celiac disease or 
291
other malabsorptive disorders
292

Abnormal kidney function (eGFR <60 mL/min/1.73m2)
293

Central nervous system disease, including previous history of cerebrovascular 
294
accidents, dementia, neurodegenerative disorders or history of severe head trauma
295

Cancer requiring treatment in the past five years, except for nonmelanoma skin 
296
cancers or cancers that have clearly been cured 
297

Infectious disease such as active tuberculosis, HIV (by self report), chronic 
298
coccidiomycoses or other chronic infections that might influence EE and weight
299

Diagnosis of binge eating disorder, anorexia and major psychiatric disorders based 
300
upon the DSM-IV including depression, schizophrenia and psychosis, which may impact 
301
the ability of the participant to be in the respiratory chamber for 24 hour time periods
302

Chronic ethanol use (more than 3 drinks/day)
303
Strategies for Recruitment and Retention
304
Subject recruitment for this study will be through IRB-approved advertisements in local 
305
newspapers and through postings of approved flyers and pamphlets in various public places such as, 

15
306
but not limited to, libraries, public universities, hospitals, health clubs, and local health fairs. 
307
Additionally, advertisements may be placed in some local and national newspapers or websites (e.g. 
308
Craigslist) and distributed via ResearchMatch and OPR and NIH Listservs to reach a wider audience. 
309
The study will also be listed on the webpage created by the NIH Clinical Center Office of 
310
Communications; this webpage will offer a brief synopsis of studies recruiting at NIDDK in Phoenix, 
311
AZ. In addition, volunteers who had previous contact with the Recruitment Office of NIDDK in Phoenix 
312
and had agreed to be contacted about possible participation in future studies may be contacted. 
313
Monitoring Subjects and Criteria for Withdrawal of Subjects from the Study
314
Volunteers who are unable to follow study and unit policies and procedures, are discovered to 
315
have a diagnosis consistent with the exclusion criteria following admission, or who develop an illness 
316
unrelated to the study will be withdrawn from the study. Volunteers who are withdrawn may be 
317
considered for re-enrollment. 
318
Experimental Design
319
The study consists of two phases: a baseline and an intervention phase. The baseline phase 
320
will be for assessment of EE during energy balance as well as ad libitum food intake following energy 
321
balance. We will also determine 24-h EE with ad libitum food intake in the respiratory chamber during 
322
this phase. The intervention phase will include two 24-h EE measurements during 19°C exposure, as 
323
well as any associated changes in concomitant and subsequent food intake. Finally, we will determine 
324
24-h EE during fasting as well as ad libitum food intake the following day. The fasting assessment will 
325
occur last to prevent any residual effects of fasting on other interventions. Subjects will undergo the 
326
cool temperature exposure assessments in random order. A summary study diagram of the protocol is 
327
shown in Figure 1.

Abbreviated Title: EIR
Version Date: 03/23/2023
16
328
Figure 1. Study diagram of the clinical trial.  
329
330
Quest.
BASELINE 
PERIOD
INTERVENTION 
PERIOD
Chamber
Vending
machine
DISCHARGE
Oral Glucose
Tolerance Test
ADMISSION
Vending
machine
Chamber
Chamber*
DXA
Exercise Test
Questionnaires
Vending machine days
Chamber
Fridge 
with food
Chamber*
*: These two chamber sessions will be done in a random order
Blood 
draw
Blood 
draw
Blood 
draw
Blood 
draw
Fasting

17
331
 
: 24 hours on the floor (weight-maintaining diet)
: 24 hours inside the metabolic chamber (chamber diet) 
: 24 hours on the floor using the vending machines (ad libitum diet) 
: 24 hours inside the metabolic chamber (ad libitum diet) 
Quest.: self-administered questionnaires and computerized tasks
OGTT: oral glucose tolerance test
DXA: dual energy X-ray absorptiometry for body composition assessment
VO2 max: graded-cycle ergometry
Chamber: 24-h measure of EE in the metabolic chamber
Vending: computerized vending machines for ad libitum food intake measurement

Abbreviated Title: EIR
Version Date: 03/23/2023
18
332
Volunteers will provide informed consent prior to entering the study. Subsequently, they will 
333
be screened and interviewed for eligibility. During screening, a medical history will be obtained as 
334
well as a physical exam and resting 12 lead electrocardiogram. Blood will be drawn for screening tests 
335
including complete blood cell count, platelet count, glucose, electrolytes, urea nitrogen, creatinine, 
336
liver function tests (total protein, albumin, bilirubin, AST, ALT, gamma-GT, alkaline phosphatase),  
337
thyroxine stimulating hormone (TSH), calcium and phosphorus. Urine will be collected for complete 
338
urinalysis, urinary hCG (pregnancy test for females only) and drug testing.  All subjects who meet 
339
inclusion and exclusion criteria and agree to participate will be admitted to the clinical research unit 
340
for about 23 days to complete the study. Initial demographic information will be collected on all 
341
subjects including comorbidities, race, gender, weight, height and waist, thigh and neck 
342
circumferences. There is nursing staff available 24 hours a day.
343
Randomization
344
Randomization will be done using a permuted block randomization design with a block size of 
345
6 stratified by sex to account for potential order effects of cold interventions (described below). 
346
Specifically, half the subjects will undergo first the assessment of ad libitum food intake on the 
347
vending machines the day following the cold intervention inside the metabolic chamber (as shown in 
348
Figure 1,), with the other half undergoing first the ad libitum food intake assessment the day of the 
349
cold exposure inside the metabolic chamber (Figure 1). The random allocation sequence will be 
350
created by a statistician who does not have subject contact.
351
Admission
352
A description of the inpatient phase of the study is provided below. The specific day numbers on which 
353
procedures occur represent a common scenario. The precise day may vary due to normal subject scheduling 
354
variations or technical issues associated with sampling that will not impact the health of the subject or the 

19
355
interpretation of the data. Specifically, all tasks scheduled prior to the first chamber session can occur in any 
356
order, so long as the revised order does not compromise the scientific integrity of those procedures or 
357
compromise participant safety. Additional weight-maintaining days or washout days may be inserted before or 
358
between chamber sessions throughout the inpatient stay.  
359
Baseline Phase
360
Day 1: Upon admission, subjects will be given a standard weight maintaining diet (50% carbohydrates, 
361
30% fats and 20% proteins) for three days. The weight maintaining energy needs of the floor diet will 
362
be calculated on the basis of weight and gender [10] using the following unit specific equations 
363
(kcal/day): Female volunteers = weight (kg) × 9.5 + 1745; Male volunteers = weight (kg) × 9.5 + 1973. 
364
Morning weight will be checked daily and the weight maintaining diet will be adjusted as necessary 
365
throughout the stay to maintain a stable weight within ±1%.
366
Subjects will be asked to complete a food-selection questionnaire (FSQ) to rank 80 different 
367
food items presented in random order on a 9-point Likert scale with the following anchors: never 
368
tasted; dislike extremely (=1); neutral (=5); like extremely (=9). These breakfast, lunch, dinner, and 
369
snack food items are based on the model developed by Geiselman et al. [36] but with modifications 
370
which reflect intake patterns common to the Southwestern United States.  These 80 food items are 
371
categorized as high in fat (>45%) or low in fat (<20% kcal), and then further subdivided as high in 
372
simple sugar ( 30% kcal), complex carbohydrate ( 30% kcal), or protein ( 13% kcal). In addition, 
373
subjects are asked to indicate how often they eat an item, and how often they would prefer to eat an 
374
item given a lack of financial and other constraints.
375
Volunteers will also be asked to complete the self-administered international physical activity 
376
questionnaire (IPAQ) [37], which consists of 27 questions to assess the degree of physical activity 
377
during the subject’s everyday life. Questions address job-related physical activity, methods of 

20
378
transportation, home-related physical activity, leisure-time physical activity and time spent sitting. 
379
This questionnaire has been validated across more than 12 countries to estimate metabolic 
380
equivalents (MET) per day. 
381
Day 2: Body composition will be determined in the morning before breakfast using dual-
382
energy x-ray absorptiometry (DXA) for the estimation of percent body fat, fat mass and fat free mass 
383
by using the General Electric Lunar iDXA machine (Madison, WI). DXA is an X-ray device which non-
384
invasively assesses both skeletal density and regional soft tissue composition with a precision of <1% 
385
for bone and 4-5% for soft tissue densities. The total amount of x-ray exposure is a fraction of that 
386
received during a standard chest x-ray. Total body scan requires 5-10 minutes depending on the 
387
stature and thickness of the subject. The Lunar iDXA machine allows patients up to 204 kg (450 
388
pounds) to be scanned. The software calculates body composition in grams of fat tissue and lean 
389
tissue and percentage of body fat, along with volume estimates of subcutaneous and visceral adipose 
390
tissue in the abdomen (CoreScan, GE [38]). The operator remains in the room with the subject during 
391
the entire scan. 
392
Cycle ergometry will be used to determine subject’s aerobic capacity during a submaximal 
393
graded exercise test. Subjects will sit comfortably on the cycle ergometer (Monark 839 E), which has a 
394
maximum user weight of 250 kg, and the mouthpiece for indirect calorimetry (Parvo Medics’ 
395
TrueOne® 2400) will be placed on the subject. Heart rate and rhythm will be continuously monitored 
396
throughout. Measurement of oxygen consumption and CO2 production will be measured for 2 
397
minutes while the subject cycles at a comfortable pace (about 50 to 60 rpm) with no resistance to 
398
become familiarized with the machine. The power output will then be set to 25W for a 2-minute 
399
warm up. The workload will progressively be increased by 25W every minute until the heart rate will 

21
400
reach 85% of the age-predicted value (=220 bpm−age [years]). The ergometer provides a constant 
401
workload during each interval that is independent of pedal speed. Upon completion, the power will 
402
be decreased to 25W and the subjects will continue to pedal for 10 minutes or until their heart rate is 
403
less than 100 bpm. The aerobic capacity measured during the submaximal graded exercise test will 
404
provide a measure of physical fitness and exercise capacity that may be an important confounder in 
405
understanding the 24-h EE relationship with energy intake.
406
Eating-related psychological constructs will be assessed using validated self-administered 
407
questionnaires. In addition, executive functioning will be assessed using validated computerized tests. 
408
Questionnaires include the following:
409
1. Stunkard’s three-factor eating questionnaire (TFEQ) [39]: a self-administered questionnaire 
410
comprised of three subscales measuring dietary restraint, i.e. the cognitive control of 
411
eating, disinhibition, i.e. the tendency to eat food without inhibition, and perceived 
412
hunger, i.e. the susceptibility of eating in response to subjective feelings of hunger.
413
2. Power of Food (PFS) [40]: a 15 item self-administered questionnaire to assess feelings of 
414
being controlled by food and susceptibility to food-abundant environments, independent 
415
from feelings of hunger.
416
3. Emotional Appetite Questionnaire (EAQ) [41]: a self-administered questionnaire to 
417
examine eating response to both positive and negative emotions and situations.
418
4. Food frequency questionnaire (FFQ) [42, 43]: a measure of habitual dietary intake over the 
419
past 6 months.
420
5. Perceived Stress Scale (PSS) [44]: a questionnaire to assess stress and an individual’s 
421
perceived ability to cope with such stress over the past month.

22
422
6. Household Food Security Scale: Six-Item Short Form (FS) [45]: a 6 item assessment of food 
423
insecurity over the past year. It classifies households into 3 categories of food security 
424
status: food secure, food insecure without hunger, or food insecure with hunger.
425
7. Physical Anhedonia Scale (PAS) [46]: a scale to assess sensitivity to reward. It is designed to 
426
reflect the degree to which individuals take pleasure from and are motivated to engage in 
427
rewarding behaviors including food consumption.
428
8. Barratt Impulsivity Scale-11 (BIS-11) [47]: a self-administered, 30 item scale that 
429
categorizes impulsivity into 3 main aspects: motor (acting without thinking), cognitive 
430
(quick decisions), and present orientation (non-planning).
431
9. Reward-Based Eating Drive Scale (RED) [48]: 9-item scale to assess excessive drive to eat 
432
due to feelings of lack of control, diminished satiety and preoccupation with food. It is 
433
used to assess the more normative reward-based eating in non-clinical samples and on a 
434
non-pathological continuum. 
435
10. MacArthur Scale of Subjective Social Status [49]: contains 2 ladders measuring subjective 
436
socio-economic status (SES).  Studies have indicated that ladder rankings are more 
437
powerful determinants of health-related outcomes than traditional measures of SES. 
438
11. The Social Problem-Solving Inventory - Revised (SPSI-R) [50] is a 52-item self-report 
439
measure that uses a 5-point Likert scale to assess problem solving abilities. It has 5 
440
subscales: 1) Positive Problem Orientation; 2) Negative Problem Orientation; 3) Rational 
441
Problem Solving; 4) Impulsivity/Carelessness Style; and 5) Avoidance Style.
442
12. The Alexithymia Scale (TAS-20) [51]: is a 20-item instrument that classifies people who 
443
have trouble identifying and describing emotions and who tend to minimize emotional 

23
444
experience and focus attention externally. It has 3 subscales: 1) Difficulty Describing 
445
Feelings; 2) Difficulty Identifying Feelings; and 3) Externally-Oriented Thinking.
446
13. Hollingshead Four Factor Index of Social Status [52]: The Hollingshead scale was selected 
447
due to its wide scale use in public health measures of socioeconomic status. The 
448
Hollingshead index provides a score based on a combination of an adult’s marital status, 
449
education, and occupation. Education level is assigned a value of 1 to 7 and occupation title 
450
is assigned a value from 1 to 9. The total score is determined by weighting and summing the 
451
scaled education and occupation title values. Total scores range from 8 to 66, with higher 
452
scores indicating higher socioeconomic status. Based on the total score individuals fall into 
453
one (score 8-19, unskilled laborer) of five (score 55-66, higher executive and professional) 
454
social strata categories where higher groups represent higher ranking in social position. For 
455
homes with multiple sources of income (i.e. marital status) scores for an individual are 
456
averaged to obtain a single score.
457
14.
458
459
The computerized tasks include: 
460
1. Iowa Gambling Task (IGT) [53]: A computerized test to evaluate decision-making. Subjects 
461
are instructed to gain as much fake money as possible by drawing 100 selections from a 
462
choice of 4 decks of cards. The decisions to choose from the decks should be motivated by 
463
gain and loss schedules inherent in the task.  
464
2. Stroop Color Word test and Food-related Stroop Word Test [54]: Tests used to assess 
465
cognitive flexibility, selective attention and, for the Food Stroop, food specific 
466
distractibility. Names of colors are presented in either the same color as the word or in a 

24
467
different color than the color named. The subject has to either read the words or name the 
468
ink colors as quickly as possible within a time limit. The food-related Stroop test presents 
469
food words and neutral words in varying colors. The subject has to name the color of the 
470
word, rather than read the word.
471
3. Wisconsin Card Sorting Test [55]: A computerized test to examine executive functioning 
472
related to flexibility and perseveration. Subjects are presented with 4 stimulus cards and 
473
128 response cards. They are instructed to respond with response cards to match each of 
474
the 4 stimulus cards (based on color, number or shape) and told if they are right or wrong. 
475
Once a number of correct answers are made, the sorting principle is changed without 
476
warning and the subject must deduce the new sorting strategy.
477
4. Go/No-go Test [56]: A task assessing the executive functions of sustained attention and 
478
self-control. Participants are asked to make a binary decision for each stimulus. They must 
479
either press a button in response to one stimuli or inhibit that action under a different set 
480
of stimuli. Accuracy and reaction time are measured. 
481
Day 3 and 4: Subjects will be fed a weight maintaining diet.
482
Day 5: Glucose tolerance and confirmation of non-diabetic status will be assessed by a 75g oral 
483
glucose tolerance test (OGTT) after at least two full days on the weight maintaining diet. After an 
484
overnight fast, an intravenous catheter will be placed in the forearm vein for blood withdrawal. 
485
Volunteers will then ingest 75 g of glucose over 2 minutes. Blood will be drawn at −15, 0, 30, 60, 120, 
486
and 180 minutes for plasma glucose and insulin. Fasting samples for DNA will be drawn at time −15 
487
minutes to screen for known and future discoveries of genetic changes associated with human 
488
obesity, EE, and food intake, as described in the “Genetic Studies” section.

25
489
To measure subjects’ activity during the inpatient stay as a potential covariate for EE and 
490
energy intake changes, subjects will be asked to wear an omnidirectional accelerometer (Actigraph®). 
491
An Actigraph monitor will be placed on both wrists, both ankles and at the waist and thigh to monitor 
492
physical activity. Subjects will be asked to wear the monitors from day 5 to day 23 (last day of 
493
inpatient). The Actigraph is designed to be worn for about one week at a time so the monitors will be 
494
changed twice on about day 15 and day 17.
495
Day 6: Participants will spend approximately 23 hours and 15 minutes in the respiratory chamber for 
496
the assessment of energy metabolism during eucaloric conditions at an ambient temperature of 24°C, 
497
which is maintained by an adjustable air conditioning system. The respiratory chamber is a 
498
comfortable, air-tight room (10 feet x 8 feet x 7 feet, net volume ~20,000 liters) constructed as a large 
499
open-circuit whole-room indirect calorimeter. It is furnished with a toilet, sink, bed, desk, chair, color 
500
television, DVD player, and radio. Visual contact with the subject is possible through one of the 
501
windows and verbal contact is possible via the telephone. An emergency call bell is within reach of 
502
the bed and toilet. Food is provided into the chamber through an airtight interlock. Spontaneous 
503
physical activity (SPA) is continuously monitored by a radar system based on the Doppler Effect. The 
504
flow rate through the chamber and the CO2 and O2 concentrations of the out-flowing air are 
505
continuously computed, and calculations of oxygen consumption, carbon dioxide production, RQ [57], 
506
and SPA are made every minute. Volunteers will be asked to only engage in sedentary activities while 
507
in this chamber. Breakfast will be given immediately before volunteers enter the chamber. Total food 
508
intake for the day (four meals total) will be equivalent to 80% of the weight maintaining diet to 
509
account for the restricted physical activity in the confined environment of the chamber. 

26
510
Prior to entering the respiratory chamber, fasting blood will be collected for measurement of 
511
insulin, glucagon, leptin, adiponectin, ghrelin, insulin-like growth factor 1 (IGF-1), active GLP-1, PP, 
512
PYY, IL-6, free T3, free T4, TSH, FGF21, estradiol (women only) and progesterone (women only), beta-
513
hydroxybutyrate, lactate, pyruvate. A 24 hour urine specimen will be collected for measurement of 
514
nitrogen, urinary catecholamines, and urinary free cortisol. Carbohydrate and fat oxidation rates will 
515
also be calculated after accounting for protein oxidation, estimated from measurement of 24-h 
516
urinary nitrogen excretion [58]. 
517
During the approximately 23.25-h stay in the respiratory chamber, the subjects will complete a 
518
Visual Analogue Scale (VAS) for measurement of appetite sensations, mood and body temperature 
519
including hunger, anticipated food consumption, and desire to eat something fatty, salty, or sweet 
520
[59, 60]. Subjects will complete this VAS at 3 time points: 1) prior to entering the chamber; 2) at 
521
approximately 6:30 pm, prior to their evening snack; and, 3) immediately upon exiting the chamber.
522
We will use the CorTemp™ core body temperature monitoring system to assess body 
523
temperature during this eucaloric feeding chamber. The system uses an ingestible, wireless sensor to 
524
transmit a signal to a data recorder worn on the hip. The data recorder will be worn by volunteers for 
525
the duration of the EE assessment to provide a continuous measurement of body temperature 
526
change. 
527
We will also ask the subjects to wear iButtons® to measure their skin temperature [61]. 
528
iButtons® are digital thermometers with a built-in real-time clock measuring 1.7 cm in circumference 
529
and 0.6 cm in depth. They record more than 2000 consecutive temperature measurements to a 0.5°C 
530
resolution which can then be downloaded to a computer. iButtons® are intended for monitoring of 
531
the body temperature of humans and have been used in other studies to monitor peripheral skin 

27
532
temperature [61, 62]. Because they are lightweight, they can be attached to the skin simply with tape. 
533
Four iButtons will be used to measure distal skin temperature: two iButtons will be placed on the 
534
wrist of both arms, and two iButtons will be placed at the ankles of both legs. Five iButtons will be 
535
also used to measure proximal skin temperature: one cm below the navel (1°), near the left 
536
paravertebral on the upper back (2°), two iButtons on the left and right supraclavicular fossa (3° and 
537
4°), and one iButton near the right paravertebral at the lower back (5°).  
538
Day 7: Upon exiting the respiratory chamber, the subjects will complete the VAS. Fasting blood will 
539
then be collected for measurement of insulin, glucagon, leptin, adiponectin, ghrelin, IGF-1, active GLP-
540
1, PP, PYY, IL-6, free T3, free T4, TSH, FGF21, beta-hydroxybutyrate, lactate, pyruvate, to assess for 
541
stability after a day of energy balance spent inside the metabolic chamber.
542
The participants will then be introduced to the vending machine system which they will use to 
543
self-select all their food for three consecutive days. Based on the FSQ results, their individualized, 
544
refrigerated vending machine will be stocked with 40 different foods that the subject has given an 
545
intermediate hedonic rating. In addition, a core group of condiments including butter, cream cheese, 
546
salad dressing, bread, water, milk, juices and a drink of the subject’s choosing will be provided. The 
547
vending machine will require the use of a security code to access the shelves, and this allows us to 
548
track the time of day the shelves are accessed. The shelves can be accessed only twice per meal, once 
549
to retrieve an item and a second time to return the wrappings and leftovers for weighing and exact 
550
determination of caloric intake by the metabolic kitchen. This also provides information on the 
551
duration of the meal. Each volunteer will be asked to follow his typical eating pattern as closely as 
552
possible. Volunteers may eat as little or as much as they wish; however, they will be asked to eat all 
553
meals in the vending machine room, which is equipped with a table, chair, a microwave oven, and a 

28
554
toaster. Volunteers will not be permitted to watch television or read during meals.  Using the actual 
555
weights of the foods, beverages, and condiments consumed, total energy intake and specific 
556
macronutrient intake will be calculated using the CBORD Professional Diet Analyzer Program (version 
557
4.1.11, CBORD Group Inc., Ithaca, NY, USA) based on the Food Processor SQL Edition database 
558
(version 10.0.0, ESHA Research, Salem, OR, USA). On our unit, repeat measurements of food intake in 
559
the same individual using this vending machine paradigm are very reproducible with an intraclass 
560
correlation coefficient of 0.9 (p<0.0001) [63]. Upon completion of the vending day, subjects will be 
561
asked to complete a vending machine exit questionnaire designed to assess the motivation to eat 
562
while using the vending machines. It includes 12 items related to mood, food enjoyment and timing of 
563
meals. 
564
Days 8 and 9: Subjects will self-select all their food on the vending machines as described above.
565
Day 10: Subjects will again reside for approximately 23.25 hrs in the respiratory chamber at an 
566
ambient temperature of 24°C, this time with ad libitum access to food choices similar to those 
567
provided during the vending machine assessment on the floor. Snacks, drinks, condiments and food 
568
items will be stocked in a refrigerator within the respiratory chamber. A small microwave oven will be 
569
available inside the chamber, just as in the vending room, for preparation of hot food items. The 
570
participant will be asked to keep a record of the timing of all food consumption, and to place all waste 
571
and leftover food in the air lock for weighing by the metabolic kitchen. Total energy intake will be 
572
calculated as for day 7. A 24-h urine collection will again be done for nitrogen, cortisol and 
573
catecholamines. Subjects will complete the VAS at 3 time points during the approximately 23.25 hours 
574
inside the calorimeter.  The results of this assessment will provide a measure of ad libitum food intake 
575
inside the confined environment of the metabolic chamber at 24°C. This assessment will be used as 

29
576
the baseline comparator for the energy intake assessment at 19°C when subjects will again be 
577
confined within the metabolic chamber. 
578
Days 11, 12 and and 13: After exiting the chamber, there will be a washout phase for any residual 
579
effects of the excess food consumption expected to occur with ad libitum food intake to wane. 
580
Subjects will be fed a weight maintaining diet.
581
Intervention Phase 
582
Procedures and interventions described on days 14-15 and 18 will be conducted in random order.
583
Day 14: Subjects will again spend approximately 23.25 hrs inside the respiratory chamber as above, 
584
but this time the temperature will be set at 19°C. Prior to entering the respiratory chamber, fasting 
585
blood will be collected for measurement of plasma glucose and insulin. The energy intake provided 
586
during this chamber will equal the EE expended during the chamber assessment done on Day 6. 
587
Standardized clothing, including shorts, a t-shirt and flip-flops will be provided for this 24-h EE 
588
assessment. For sleep (from approximately 11 pm to 6 am), 3 blankets will be provided so that 
589
volunteers can create their own microenvironment to achieve a level of comfort conducive to sleep. If 
590
the subjects begin to shiver, they will be instructed to notify the nurse at the nurse’s station and to 
591
use the provided blankets until the shivering ceases. 
592
The cold-induced thermogenesis (CIT, kcal/day) will be calculated as the difference between 
593
the 24-h EE at 19°C and 24-h EE at 24°C measured on Day 6. The CorTemp™ core body temperature 
594
monitoring system will be used to assess changes in body temperature during this 24-h period. 
595
Subjects will again be asked to wear 9 iButtons® to measure skin temperature changes during cool 
596
temperature exposure.  A 24-h urine collection will again be done for nitrogen, cortisol and 
597
catecholamines. Subjects will complete the VAS at 3 time points during the approximately 23.25 hours 
598
inside the calorimeter.

30
599
Day 15: Upon exiting the chamber, the subjects will complete the VAS. Fasting blood will be then 
600
collected for measurement of insulin, glucagon, leptin, adiponectin, ghrelin, IGF-1, active GLP-1, PP, 
601
PYY, IL-6, free T3, free T4, TSH, FGF21, estradiol and progesterone, beta-hydroxybutyrate, lactate, 
602
pyruvate. Subjects will again spend 24 hrs using the vending machine paradigm, as above, for the 
603
assessment of ad libitum food intake following the increased EE resulting from the cold-induced 
604
thermogenesis during Day 14. 
605
Day 16 and 17: This will be a wash-out period for any residual effects of the overeating expected to 
606
occur during ad libitum food intake to wane [10]. Subjects will be fed a weight maintaining diet.
607
Day 18: Subjects will again reside for approximately 23.25 hrs in the respiratory chamber in a cool 
608
environment (19°C), this time with access to food choices similar to those provided during the 
609
assessment on Day 10. A 24-h urine collection will again be done for nitrogen, cortisol and 
610
catecholamines. Subjects will complete the VAS at 3 time points during the approximately 23.25 hours 
611
inside the calorimeter.
612
The amount of food intake on Day 18, as well as on Day 15, will help to determine the length 
613
of time in which the cold-induced increase in EE might affect food intake, i.e. whether any EE-effect 
614
on energy intake only occurs during cooler temperature exposure or whether the effect persists until 
615
the following day. The amount of food intake consumed inside the metabolic chamber on Day 18 at 
616
19° C will be compared to that obtained on Day 10 at 24°C in the same setting to evaluate the 
617
difference in energy intake during the cool temperature exposure. 
618
Days 19 and 20: This will be a  washout period for any residual effects of the overeating expected to 
619
occur with ad libitum food consumption to wane. Subjects will be fed a weight maintaining diet.

31
620
Day 21: Subjects will spend approximately 23.25 hrs inside the respiratory chamber at 24°C but with 
621
no food intake, for assessment of 24-h EE while fasting. Water will be provided and volunteers will be 
622
encouraged to stay well-hydrated. Prior to entering the respiratory chamber, fasting blood will be 
623
collected for measurement of plasma glucose and insulin. A 24-h urine collection will again be done 
624
for nitrogen, cortisol and catecholamines. The CorTemp™ core body temperature monitoring system 
625
will be used to assess changes in body temperature during fasting. Subjects will again be asked to 
626
wear 9 iButtons® to measure skin temperature changes during fasting.  Subjects will complete the 
627
VAS at 3 time points during the 24 hours inside the calorimeter.
628
Day 22: Upon exiting the chamber, the subjects will complete the VAS. Fasting blood will be then 
629
collected for measurement of insulin, glucagon, leptin, adiponectin, ghrelin, IGF-1, active GLP-1, PP, 
630
PYY, IL-6, free T3, free T4, TSH, FGF21, estradiol and progesterone, beta-hydroxybutyrate, lactate, 
631
pyruvate. Subjects will spend 24-h using the vending machine paradigm on the floor, as above. The 
632
results of this vending machine assessment will be used to ascertain the amount of intake following a 
633
maximal energy deficit, as a baseline comparison.
634
Day 23: Participants will complete the vending machine exit questionnaire, an 11-item questionnaire 
635
designed by our staff to assess motivation to eat while using the vending machines. This ad hoc 
636
questionnaire includes items related to mood, liking of food, and timing of meals.  Subjects will then 
637
be discharged home. 
638
Outpatient Visits
639
Volunteers will be invited to return after 6 months and 1 year from the date of discharge. On 
640
these visits, vital signs and weight data will be obtained. Volunteers will also have an assessment of 
641
body composition by DXA and will be administered the IPAQ and FFQ questionnaires to have 
642
measures of physical activity and dietary intake, respectively, during the follow-up period. Fasting 

32
643
blood collection will be done for measurement of complete blood cell count, platelet count, glucose, 
644
electrolytes, plasma urea nitrogen, creatinine, liver function tests, thyroid profile as well as plasma 
645
estradiol and progesterone (women only). Urine pregnancy test (β-hcg) will be done in females only. 
646
The DXA scan will not be performed if the pregnancy test is positive.
647
Blood Withdrawal
648
Blood withdrawal will remain within the NIH guideline of <10.5 mL/kg or <550 mL (whichever 
649
is smaller) every 8 weeks for research purposes. The total amount of blood drawn is detailed below. 
650
651
Tests
Amount
Screening lab work
32 mL
OGTT
151 mL
Fasting blood measurements
EBL chamber (pre- and post-)
CIT chamber (pre- and post-)
FST chamber (pre- and post-)
76 mL
60 mL
60 mL
Total (baseline admission):
379 mL
Outpatient visits
6 months
1 year
32 mL
32 mL
Total:
443 mL
652

33
653
Sample Size Calculation
654
The sample size calculation to answer the primary research question is based on the number 
655
of patients needed to demonstrate an association between any change in EE due to interventions and 
656
subsequent change in energy intake. In preliminary analyses using data from our clinical research unit 
657
[13], the cross-sectional Pearson correlation between unadjusted 24-h EE during energy balance and 
658
energy intake is 0.46 (p<0.0001). A target sample size of 46 participants will provide a power of 
659
greater than 0.80 with an alpha of 0.05 to detect a 0.40 correlation (r2=0.16) using simple linear 
660
regression assuming that the association between change in EE and change in energy intake will be 
661
the same as that between EE and energy intake. However, any finding will subsequently be adjusted 
662
in multivariate linear regression analysis for age, sex, race, fat mass and fat free mass (5 covariates), 
663
which requires a minimum of 50 volunteers. In addition, given the number of analyses that will be 
664
done, an alpha of 0.01 is likely more appropriate. Therefore, to detect a correlation of 0.4 with a 
665
power >0.80 at an alpha = 0.01, a total of 68 subjects will be enrolled for the core study (Table 2). 
666

34
667
Table 2. Sample size calculations achieving a statistical power ≥0.8.
α=0.05
α=0.01
α=0.005
α=0.001
r=0.50
n=29
n=41
n=47
n=59
r=0.40
n=46
n=68*
n=77
n=98
r=0.30
n=84
n=124
n=142
n=181
668
r: Pearson’s unadjusted correlation between change in EE and change in energy intake
669
α: significance level (2-sided)
670
*: Sample size for the final analysis
671
672
An interim analysis will be conducted when a target sample size of 47 subjects complete the 
673
baseline admission. This sample size will provide 80% power (alpha=0.005) to detect a simple 
674
correlation of 0.5 between change in EE due to any intervention and change in energy intake. The 
675
alpha level for this interim analysis is set to 0.005 based on the alpha spending function approach 
676
[[64, 65]]. The alpha level for the final analysis will remain at 0.01. More importantly, the interim 
677
analysis will allow us to assess whether there is likely to be a significant correlation once the full 
678
sample size is reached or whether the null hypothesis is highly likely to be accepted. If the correlation 
679
within the first 47 subjects is <0.2, the study will be stopped as the null hypothesis will be highly likely 
680
to be accepted. However, if the correlation is much stronger than anticipated, the study will continue 
681
to obtain adequate sample size for adjusting for covariates and answering the secondary research 
682
questions. If the interim correlation is between 0.2 and 0.4, the final sample size will be increased 
683
from 68 to 124 subjects to provide 80% power at an alpha of 0.01 to detect a more moderate 
684
association. Achieved power as a function of final sample size, alpha and correlation value between 
685
change in EE and energy intake is reported in Table 3. Assuming a 30% dropout rate, we will ask for a 
686
total enrollment of 160 subjects.
687

35
688
Table 3. Power calculations based on different values of alpha (α), correlation value (r) and 
689
sample size (n).
α
r
n
Power
47
0.103
68
0.208
0.30
124
0.544
47
0.308
68
0.547
0.40
124
0.915
47
0.635
68
0.873
0.001
0.50
124
0.997
47
0.223
68
0.378
0.30
124
0.727
47
0.503
68
0.732
0.40
124
0.969
47
0.803*
68
0.949
0.005
0.50
124
0.999
47
0.301
68
0.470
0.30
124
0.799
47
0.598
68
0.804**
0.40
124
0.982
47
0.863
68
0.969
0.01
0.50
124
1.000
690
*: interim analysis
691
**: final analysis
692
693
Data Analysis
694
Our primary outcome will be the association between the change in EE and the change in 
695
energy intake induced by the interventions (cool temperature exposure and fasting). As the purpose 
696
of this protocol is to evaluate the effects of altered EE on food intake regardless of the type of 
697
intervention, only within-subject analyses rather than between-interventions analyses will be carried 

36
698
out to evaluate the EE response to each intervention compared to energy balance. For descriptive 
699
statistics, normally distributed variables will be described with means and standard deviations, 
700
skewed variables will be described with medians and interquartile range.  Baseline differences in 
701
demographics between sexes and races will be assessed using Student’s t-test or one-way ANOVA, 
702
respectively. Determinants of the EE response to interventions will be determined using mixed 
703
models to account for repeated measures and including the variables age, sex, race, percentage body 
704
fat, aerobic capacity and type of intervention as fixed effects. The diagonal (DIAG), the compound-
705
symmetry (CS) and the first-order autoregressive (AR1) covariance structures will be considered for 
706
estimation of the random effect of the within-subject repeated measures. The model fit of all 
707
calculated models will then be compared using the Bayesian information criterion (BIC) statistic to 
708
select the best model in terms of covariance structure for random effects and the number of 
709
predictors (i.e., lowest BIC).  Post-hoc tests comparing interventions to 24-h EE during energy balance 
710
will be conducted using the Dunnett’s test to correct for multiple comparisons.  
711
The change in 24-h EE during each intervention will be calculated as the absolute change in 
712
kcal as well as percent change compared to energy balance. Similarly, change in 24-h RQ, 
713
carbohydrate and fat oxidation rates, sleeping EE and sleeping RQ will be also calculated and 
714
considered in the analyses. Changes in ad libitum food intake due to interventions will be calculated 
715
as absolute kcal change as well as percent change in weight maintaining energy needs. Associations 
716
between continuous variables will be quantified by Pearson’s r for data that are normally distributed 
717
and Spearman’s ρ for data not normally distributed. These analyses will include correlations among 
718
changes in 24-h EE during fasting and cool temperature exposure to uncover those EE responses that 
719
share common underlying mechanisms, and the association between the EE responses and 

37
720
concomitant and subsequent ad libitum food intake. Multivariate regression models will be calculated 
721
to control for potential confounders including age, sex, race, aerobic capacity, fat mass and fat free 
722
mass. In evaluating the relationship between altered EE by cool exposure/fasting and ad libitum food 
723
intake, the concomitant RQ will also be included as further covariate in the multivariate model. Lastly, 
724
sensitivity analyses accounting for the SPA inside the metabolic chamber will be done. Although we 
725
anticipate that the wash-out period will remove any carry-over effect, potential order effects arising 
726
from the cross-over design of the study will be assessed by including a dichotomous variable for the 
727
sequence effect of interventions (e.g., 0 = “cold exposure & subsequent ad libitum food intake”; 1 = 
728
cold & concomitant ad libitum food intake) in the multivariate models assessing the association 
729
between change in EE and change in energy intake. Student t-tests will be also calculated to 
730
determine if the change in EE or the change in energy intake differ between these two groups. If no 
731
difference exists in the outcome variables between these 2 groups and the dichotomous variable is 
732
not significant in the multivariate model, we will determine that no evidence for an order effect exists 
733
and leave the dichotomous variable out of the final multivariate model. If there will be evidence of an 
734
order effect, we will include the dichotomous variable in the multivariate model to ‘adjust’ for the 
735
order effect. 
736
To follow-up the regression analyses and assess potential causality linking EE to energy intake, 
737
mediation analysis based on hierarchical multiple regression models as outlined in [66] will be used to 
738
evaluate whether the effect of increased EE on energy intake is exerted through the influence of 
739
mediators, including hormonal changes, RQ appetite sensations, behavioral and neurocognitive 
740
scores. The Sobel test [67] will be used to test the significance of the mediation effect.

38
741
For all hormonal measures, the change in concentrations induced by the interventions will be 
742
compared to the baseline fasting concentrations drawn prior to the energy balanced 24-h EE 
743
assessment using paired t-tests for normally distributed data or the Wilcoxon nonparametric test for 
744
skewed data. A sample size of 68 subjects will provide greater than 80% power with an alpha of 0.05 
745
to detect a minimum mean expected decrease in fasting leptin, as a representative hormone, of 
746
−1.8±6.0 ng/mL as observed in prior studies measuring leptin concentrations during 24-h fasting [1]. 
747
This sample size provides a similar power to detect a correlation of −0.30 at an alpha of 0.05 between 
748
the decrease in any of the hormones and cold induced thermogenesis or the expected increase in 
749
energy intake.  Hormonal changes will be evaluated as possible contributors to the altered 24-h EE 
750
and subsequent energy intake first in correlation analyses stratified by type of intervention (i.e., cold 
751
exposure and 24-h fasting) and then using similar mixed models as described above. Additionally, 
752
behavioral and cognitive test scores will be evaluated as possible predictors of the EE-energy intake 
753
link, again using correlations and mixed models. The minimum expected correlation detectable in 68 
754
subjects for any of the scores with changes in energy intake will equal 0.30, as reported above. 
755
Differences in core body temperature and skin temperatures from measures during energy balance 
756
will be calculated for fasting and cold exposure. The significance of these changes will be assessed 
757
using one-sample t-tests. The association between these changes and percent body fat as well as EE 
758
responses will be assessed using correlation coefficients as above, and again, the minimum detectable 
759
correlation with 68 subjects equals 0.30. Significant associations will be evaluated further with mixed 
760
models to account for repeated measures, sex, race and age and other potential confounders.  
761
Associations between EE responses to each intervention and absolute weight change at each 
762
follow-up visit will be quantified by regression models accounting for age, sex, race, and baseline 

39
763
weight. All longitudinal analyses for future weight change will be confirmed using percent change in 
764
weight as the dependent variable instead of absolute weight change. Similar models will be calculated 
765
for the absolute changes in fat mass and fat free mass including baseline measures as covariates. In 
766
case of significant associations between the EE responses to different intervention and weight 
767
change, full regression models will be calculated to determine independence of the identified 
768
predictors. Mixed model analysis will be used to evaluate the predictive effect of baseline EE 
769
measures in relation to the body weight trajectory including all the follow-up visits and accounting for 
770
repeated measures. The Compound Symmetry (CR) and the AR(1) covariance structures will be 
771
evaluated and compared by the BIC statistic to identify the best model for weight data over time. 
772
Changes in EE after 24-h of fasting and its association with changes in body weight at 6 months and 1 
773
year will be combined with data from prior studies (using the exact same methodology) to increase 
774
the statistical power of the weight change analysis (currently we have >60 subjects with complete 
775
data available at baseline).
776
Statistical analysis will be performed using SAS with E-guide (SAS Institute, Cary, NC). All tests 
777
of primary outcome measures will have a 2-sided Type 1 error of p=0.01. For the secondary outcome 
778
analyses, we will control for Type 1 error at the level of the comparison with p=0.05 as each 
779
hypothesis is substantively important and stated a priori.

40
780
Informed Consent Process
781
Written informed consent will be obtained from the participant prior to any screening visits, 
782
study procedures or treatments. The person obtaining consent will explain the study in language 
783
understandable to the subject. Sufficient time and opportunity will be given for discussion of the 
784
research as well as to answer any questions they may have, taking care to minimize or eliminate the 
785
perception of coercion or undue influence. The participant and the investigator will sign the current 
786
IRB-approved informed consent document. A copy of the consent will be given to the subject for 
787
future reference. The signed documents will be sent to the Medical Records Department for 
788
placement in the subject's permanent record.
789
Participants will have the opportunity to carefully review the written consent form, either as a 
790
physical or electronic document, and ask questions prior to signing. Participants will be informed that 
791
participation is voluntary and that they may withdraw from the study at any time, without prejudice. 
792
The consenting process may be performed using the NIH iMedConsent platform (which is 21 
793
CFR Part 11 compliant). If the iMedConsent platform is used, then the participant and consenting 
794
investigator will review and sign the current IRB-approved electronic informed consent document 
795
using a hand signature (e.g., signing using a finger, stylus, or mouse). Electronic signatures (i.e., the 
796
“signature” and a timestamp are digitally generated) will not be used. If a paper copy of the IRB-
797
approved consent document is used, the participant and consenting investigator will both sign using 
798
an ink hand signature. A copy of the fully signed consent will be given to the subject for future 
799
reference.
800

41
801
We do not plan or anticipate the enrollment of non-English speaking subjects. This is due to 
802
the use of questionnaires which are important for the study. These questionnaires have not been 
803
translated or validated other languages.  
804
BENEFITS
805
 
There are no direct benefits to the individuals participating in this study other than identifying 
806
their current state of health, but this study is likely to yield generalizable knowledge to further 
807
society’s understanding of the relationship of energy requirements and food intake, which will 
808
provide important knowledge about the development and prevention of obesity. Volunteers will 
809
receive information about some aspects of their health including the results of screening laboratory 
810
tests, a thorough physical examination, measures of body fat and EE and assessment of their glucose 
811
tolerance status. The results of these tests will be available to the participants and will be provided to 
812
their personal physician, if requested.  
813
HUMAN SUBJECT PRECAUTIONS
814
Rationale for Subject Selection
815
The main purpose is to increase understanding of the normal physiology and regulation of 
816
energy balance in adult humans. Subjects will be recruited from the general Phoenix area via 
817
advertisement. All races and genders will be recruited. Due to changes in EE and hormone 
818
measurements that may occur with menopause, only premenopausal women will be admitted to the 
819
study. Men will be limited to age less than 55 years to prevent discrepancies in age range between 
820
men and women in the study population. Children are excluded as: 1) this study requires an inpatient 
821
stay and may interfere with school; and, 2) the energy expenditure and associated food intake during 
822
the active growth period of childhood is likely to be different than that in adults who have completed 

42
823
linear growth. In addition, pregnant women are also excluded, again, because of changes in EE and 
824
hormones that occur with pregnancy.
825
Risks and Discomforts 
826
1.
Urine drug screening:  If the results of the urine drug screen are positive, the volunteer will be 
827
ineligible for the study as per the inclusion/exclusion criteria. The volunteer will be informed 
828
confidentially of their results and referred to their primary care provider for further treatment. 
829
The results of the urine drug screen will be part of the volunteer’s medical records. This fact will 
830
be emphasized during the consent procedure. 
831
2.
Executive Function computer tests and questionnaires including food preferences, eating 
832
behavior, and psychological questionnaires: There are no known risks associated with completing 
833
these questionnaires. Subjects will be informed that they may opt not to complete the 
834
questionnaires if they feel uncomfortable answering questions about appetite and their 
835
emotional relationship with food.  
836
3.
Oral glucose tolerance test: The risks of this test are those associated with a four hour indwelling 
837
catheter including hematoma, ecchymoses, and infection. These will be treated if they occur.
838
4.
ActiGraph GT9X Link activity monitor: There are no risks associated with this procedure. The 
839
Actigraph GT9X Link activity monitor is 3.5 x 3.5 x 1 cm, weighs 14 grams, and can be worn on the 
840
wrist (comparable to a watch), ankle, waist or thigh. It is water resistant up to 1 meter. Per the 
841
manufacturer, “the Actigraph Link is an FDA cleared Class II medical device within the United 
842
States.”
843
5.
Radiation exposure due to DXA: The radiation exposure for a scan may be as much as 1 mrem, as 
844
indicated by a January 2014 internal radiation exposure review. For subjects who move 
845
excessively during the scan, a complete or partial repeat of the scan procedure may be necessary. 

43
846
Thus, subjects could receive a maximum of 2 mrem per procedure, which is equivalent to 2.5 
847
days of exposure from natural background sources, such as the sun or radioactive materials 
848
found naturally in the earth’s air and soil. In our study, three DXA scans will be performed over 
849
the duration of the study (including follow-up visits) resulting in a possible maximum exposure of 
850
6 mrem (equivalent to 7.5 days of exposure from natural sources). This is well within the NIH-
851
Phoenix Radiation Safety Committee’s Guidelines for research subjects of 3,000 mrem to any 
852
organ or tissue in a 13-week period and 5,000 mrem per year. The radiation exposure from each 
853
DXA scan increases the lifetime cancer risk from 25% to 25.00008%. With the 3 DXA scans over 
854
the duration of this study, the increase in lifetime cancer risk will be from 25% to 25.00024%. 
855
Pregnancy will be excluded using measurement of hCG in urine prior to the DXA scan. 
856
6. Cycle Ergometry to determine aerobic capacity: Because we will carefully screen the subjects with 
857
an EKG, history and physical, and cardiovascular disease is an exclusion criterion, we anticipate 
858
that there will be minimal risk associated with this measurement. Blood pressure and heart 
859
rate/rhythm will also be monitored before, during, and after the cycle ergometer test. If there is 
860
significant abnormality on blood pressure or cardiac monitoring, we will stop the cycle ergometer 
861
test.  If subjects develop extreme shortness of breath or chest pain, we will stop the cycle 
862
ergometer test immediately and do a 12-lead EKG. If the subject feels claustrophobic from the 
863
mouthpiece for indirect calorimetry, we will not do the measurement. Nursing staff present 
864
during the procedure will be certified in basic life support.  A medical provider trained in advanced 
865
cardiac life support will directly supervise the cycle ergometer test.  A hospital code cart is readily 
866
available on the clinical research unit if needed.

44
867
7.
Respiratory chamber: There is minimal risk associated with this procedure. There is a telephone in 
868
the room so subjects can be in contact with the nurses or their local friends and family at any 
869
time. The nurses also monitor the room by checking the temperature as well as the different 
870
parameters listed on our computer screen every 4 hours. In addition, sound alarms are set for 
871
CO2 concentration and air flow. The CO2 alarm is triggered if the CO2 concentration rises above 
872
1%. The air flow alarm alerts if air flow drops below 45 L/min. These limits, which are far from 
873
being harmful to the subject, were chosen because measurements are not reliable when these 
874
limits are exceeded. In case of discomfort, the volunteer can ask to be taken out of the chamber 
875
at any time. In the event of an emergency, the subject can immediately leave the chamber of 
876
his/her own accord through the airtight interlock.
877
8.
Twenty-four hour urinary collection for nitrogen, free cortisol, and catecholamines: There are no 
878
risks related to this procedure.
879
9.
Fasting blood collection: Topical lidocaine cream will be used to minimize the pain associated 
880
with the procedure. The blood volume withdrawn will remain within the NIH guideline of <10.5 
881
mL/kg or 550 mL (whichever is smaller) every 8 weeks, as previously described. 
882
10. CorTemp™ core body temperature monitoring system: The system is intended for the 
883
measurement of continuous core body temperature. Contraindications for use include weight 
884
less than 80 lbs (36.4 kg); the presence of obstructive GI disease, previous GI surgery, felinization 
885
of the esophagus, or GI hypomotility; a history of gag reflex impairment; MRI scanning while the 
886
sensor is in the body (which will not occur in this study); a cardiac pacemaker or other implanted 
887
electromedical device. Prior to administration of the CorTemp sensor pill, the contra-indications 
888
will be reviewed to make sure the volunteer is an appropriate candidate. Per the device manual, 

45
889
the CorTemp sensor is “registered and cleared by the FDA as a Class II Non-Invasive Medical 
890
Device under 510(k) number (880639)”.
891
11. iButtons®: The only risk associated with wearing the iButtons would be a small risk of local skin 
892
reaction from the stainless steel case or the tape. If the subject reports a rash or pruritis, they will 
893
be free to remove the iButtons.
894
12. Vending machine food intake assessment: There are no known risks to performing this test.
895
13. Exposure to 19°C (66°F) for 1 day: There are no known risks to the exposure to cool temperature. 
896
Other studies have used similar temperature exposures within a respiratory chamber for as long 
897
as 48 hours [17]. We will provide blankets as needed to prevent shivering and sleep deprivation.
898
899
Genetic Studies
900
The genetic studies are designed to screen for monogenic forms of obesity and to identify 
901
novel genes involved in the metabolic pathways regulating food intake. The types of genetic analyses 
902
for which DNA may be used are as follows:
903
1.
Investigation of specific candidate genes selected by their known biology or association 
904
with diabetes, obesity, and related medical problems.
905
2.
Genome-wide association studies (GWAS).
906
3.
Sequencing of all known exons or entire genome.
907
4.
Epigenetic studies.
908
5.
Other technologies that will be developed before this research is completed.
909
Results from genetic studies could theoretically lead to as yet unknown discrimination, for 
910
example, difficulty obtaining medical insurance. All information on genetic markers in individuals is 
911
maintained with the same safeguards that apply to medical records and other personal information. 

46
912
Any information provided to other investigators who may receive DNA will be coded so that 
913
identifying information can be linked to specific individuals only by the investigators named in this 
914
protocol or employees working directly under their supervision. All participants will be informed that 
915
genetic information relating to them will not be divulged unless genetic variations are identified that 
916
could be important to their health care. Clinically actionable findings for the purpose of this study are 
917
defined as disorders appearing in the American College of Medical Genetics and Genomics 
918
recommendations for the return of incidental findings that is current at the time of primary analysis. If 
919
this is the case, participants will be asked to provide an additional sample for validation in a CLIA-
920
certified laboratory and then informed if the finding is confirmed. Depending on the nature of the 
921
findings, which currently cannot be predicted with certainty, the recommendation may be to seek 
922
appropriate clinical care relevant to the finding or to receive professional genetic counseling. The 
923
investigators of this protocol and other senior investigators and clinicians at NIDDK will meet as 
924
needed to discuss potentially actionable findings (i.e. a finding of a genetic variant, the knowledge of 
925
which could help in prevention or treatment of a health condition). When appropriate, we will involve 
926
other clinicians, clinical geneticists, or ethicists in these discussions. Potentially actionable findings will 
927
also be discussed with others at NIH such as the Bioethics Core at the National Human Genome 
928
Research Institute.
929
930
Disclosure of Medical Conditions
931
The participants will be informed of any medical conditions uncovered by the screening 
932
process and referred to their primary care provider. The information will also be entered into their 
933
medical record. All information from the study will be made available to patients and their physicians 
934
at the patient’s request. Individuals will not be directly informed of results from non-CLIA certified 

47
935
laboratories, and such results will not be entered into the medical record. Study information will be 
936
treated with the same protection and confidentiality as all other medical records. If requested by an 
937
insurance company and the subject has signed a release of information, data from the study may be 
938
made available to insurance companies. This could theoretically affect future insurability and 
939
employment opportunities. These facts will be emphasized during the consent process.
940
941
Clinical Monitoring
942
Study procedures will be subject to audits and/or monitoring visits to ensure compliance with 
943
the protocol and applicable regulatory requirements consistent with the NIDDK quality assurance 
944
program plan.  Audit and/or monitoring visits results will be reported to the Principal Investigator for 
945
further reporting as appropriate.  Study documents and hospital records will be reviewed to verify 
946
that the conduct of the study is consistent with the protocol plan.
947
948
Data and Safety Monitoring
949
Since this is not a medical intervention trial, the principal investigator will act as the data and 
950
safety monitor. Data collection is the responsibility of the clinical trial staff at the site under the 
951
supervision of the site manager. The investigator is responsible for ensuring the accuracy, 
952
completeness, legibility, and timeliness of the data reported. The PI and study team will review 
953
participant data on a monthly basis to review the data for safety and efficacy. Events meeting 
954
requirements for expedited reporting as described in HRPP Policy 801 will be submitted within the 
955
required timelines.
956

48
957
Event Reporting
958
Adverse events, non-compliance both serious or continuing, protocol deviations both major and 
959
minor, as well as unanticipated problems are defined & described by the NIH Office of Human 
960
Subjects Research Protection policy #801, and will be reported in accordance with this policy.
961
 
962
Research Use and Disposition of Human Samples and Data and Data Sharing
963
Blood (stored as plasma or serum) and urine samples not immediately used for study purposes will be 
964
stored for future measurements. All samples will be initially stored in freezers located on the 
965
premises at the NIH in Phoenix, AZ. Stored samples and specimens will be used only to measure 
966
factors that relate to diabetes, obesity, and their complications. All stored samples, specimens, and 
967
data will be coded so that when sent for measurements the identity of the volunteer remains 
968
confidential. Identification of coded samples will be kept in secure, password-protected database 
969
accessible only to investigators, but will be identifiable in case specific tests yield clinical information 
970
of importance to a particular volunteer or samples can be destroyed per volunteer request (see 
971
below). Samples will be used only for research and not for commercial purposes. Research volunteers 
972
will not be informed of individual results from analyses performed specifically for research purposes, 
973
unless there is clear evidence accepted by the medical community that these results would impact 
974
the volunteer’s individual medical care or future health. Reports of samples lost due to technical 
975
issues or destroyed secondary to volunteer request will be included in the annual renewal report. 
976
Samples collected for DNA will be screened as described but then will be stored. 
977
As part of the consent process, participants will be informed that their samples may made 
978
available collaborating investigators upon request and review of the research proposal to assure that 
979
it is within the research goals for the study under which the samples were originally collected.  If 

49
980
samples are sent to collaborators and participants request that stored samples be destroyed, every 
981
attempt will be made to assure this is done by collaborators as well, if they have stored samples but 
982
we will make it clear to participants that this may be difficult to verify.  
983
As part of the consent process, participants will also be informed that their data may be 
984
shared through publicly accessible databases. For genomic data, this will involve de-identified data 
985
from this study will being made available by submission to dbGAP or a similar publicly available 
986
database, for which user registration is required. Data will be made available proximately following 
987
publication of findings that address the main and secondary aims of the protocol, and will include the 
988
relevant genomic data and corresponding phenotypic data. Users must agree to the conditions of use 
989
governing access to the public release data, including limitation of research to investigations 
990
consistent with the participants’ consent, restrictions against attempting to identify study 
991
participants, destruction of the data after analyses are completed, reporting responsibilities, 
992
restrictions on redistribution of the data to third parties, and proper acknowledgement of the data 
993
resource. To protect privacy, the tribal affiliations of the participants will not be included in the 
994
submitted data.  Some study participants may be members or residents of a small, well-defined 
995
Native American population, including the Gila River Indian Community and Navajo Nation.  On 
996
several occasions, these community members and leaders have expressed that study data not be 
997
made available other than to the research team because of concerns with individual privacy and 
998
stigmatization of the Community. Individual privacy would be seriously threatened by data sharing for 
999
these study participants because of the extensive data collected on these individuals as well as data 
1000
on their relatives. Therefore any submitted data will not include specific tribal affiliations and would 
1001
only list race as Native American. 

50
1002
1003
Remuneration
1004
For those study participants who complete their stay on the clinical research unit, the 
1005
payment will be a total of $3, 645.00. This includes $3,385.00 for the inpatient days and testing 
1006
procedures; plus, $130.00 each for the two outpatient visits (at +6 months and 1 year).  
1007
1008
If a volunteer is recruited and makes a good-faith effort to attend the screening, but at the 
1009
time of the examination is found to be ineligible, the $50.00 payment for time and inconvenience will 
1010
still be made. In such cases, any clinically relevant data that was obtained prior to determination of 
1011
ineligibility will be reported to the participant, as indicated. A record will be kept that the volunteer 
1012
was seen, along with any clinically relevant data that might have been obtained.
1013
If a volunteer is deemed ineligible for reasons not disclosed during pre-screening/recruitment, 
1014
such as positive for drug use or nicotine use, payment will not be provided. The urine drug and 
1015
nicotine screen are performed upfront prior to the more thorough health exam. 
1016
1017
Per local Ethics Committee guidelines, if early discharge occurs prior to completion of the planned 
1018
inpatient stay, reimbursement will be at the rate of $50.00 per day, including the day of discharge, 
1019
plus any procedures that have been completed. This reimbursement rate goes into effect if the study 
1020
is not completed for any reason. Because we conduct these studies under highly controlled 
1021
conditions, the scientific validity of our studies may be affected if participants leave the clinical 
1022
research unit unsupervised. Thus, participants will generally not be allowed to return to complete the 
1023
study if they need to leave the unit. However, this will be evaluated by the investigators on a case by 
1024
case basis.

Abbreviated Title: EIR
Version Date: 03/23/2023
51
Table 4. Study Variables.
Variable
Time of 
Assessment*
Method of Ascertainment
Purpose
Demographics (age, gender, ethnicity)
Day 1
Patient Interview / Medical Record
Descriptive
Comorbidities, prior medication use
Day 1
Patient Interview / Medical Record
Descriptive
Physical Exam (blood pressure, heart rate)
Day 1, FU visits
Screening Exam
Descriptive
Weight
Days 1-23, FU visits
Measured by RN
Secondary Outcome
Height
Day 1
Measured by RN
Descriptive
BMI
Days 1-23, FU visits
Calculated
Descriptive
Waist Circumference
Day 1
Measured by RN
Descriptive
Thigh Circumference
Day 1
Measured by RN
Descriptive
Neck Circumference
Day 1
Measured by RN
Descriptive
Percentage of body fat, fat mass and fat free mass
Day 2, FU visits
iDXA
Potential Confounders /Effect Modifier
Glucose tolerance
Day 5
75g OGTT
Descriptive
DNA collection
Day 5
Fasting blood draw
Potential Mediator
Psychological Assessment
       Food-selection questionnaire (FSQ)
Day 1
Self-administered
To set-up Vend
       Physical activity (IPAQ)
Day 1, FU visits
Self-administered
Descriptive
       Dietary restraint (TFEQ)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Power of food (PFS)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Emotional overeating (EAQ)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Food frequency (FFQ)
Day 2, FU visits
Self-administered
Potential Confounder

52
Variable
Time of 
Assessment*
Method of Ascertainment
Purpose
       Perceived stress (PSS)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Household Food Security (FS)
Day 2
Self-administered
Potential Confounder
       Physical anhedonia (PAS)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Impulsivity (BIS)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Reward-Based Eating Drive (RED)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       MacArthur Scale of Subjective Social Status
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Social Problem-Solving Inventory - Revised (SPSI-R)
Day 2
Self-administered
Effect Modifier/Potential Confounder
       Alexithymia Scale (TAS-20)
       Hollingshead Four Factor Index of Social Status
Day 2
Day 2
Self-administered
Self-administered
Effect Modifier/Potential Confounder
Effect Modifier/Potential Confounder
Cognitive Assessment
       Iowa Gambling Task (IGT)
Day 2
Computerized test
Effect Modifier/Potential Confounder
       Stroop tests
Day 2
Computerized test
Effect Modifier/Potential Confounder
       Wisconsin test
Day 2
Computerized test
Effect Modifier/Potential Confounder
       Go/No-go Test
Day 2
Computerized test
Effect Modifier/Potential Confounder
Physiologic and Metabolic Measures       
Aerobic capacity
Day 2
Cycle ergometer
Potential confounder / Effect modifier
Physical activity levels
Days 5-23
Actigraph®
Potential confounder / mediator
Core body temperature
Days 6, 14, 21
CorTempTM
Secondary Outcome
Skin temperature
Days 6, 14, 21
iButtons®
Secondary Outcome
24-h energy expenditure (EE)
Days 6, 10, 14, 18, 21
Metabolic Chamber
Primary Outcome
Respiration quotient (RQ) 
Days 6, 10, 14, 18, 21
Metabolic Chamber
Secondary Outcome

53
Variable
Time of 
Assessment*
Method of Ascertainment
Purpose
Laboratory tests
Hormones (insulin, glucagon, leptin, adiponectin, ghrelin, IGF-1, 
active GLP-1, PP, PYY, IL-6, FT3, FT4, TSH, FGF21)
Days 6, 7, 14, 15, 21, 
22
Fasting blood draw
Secondary Outcome / Potential Mediator
Estrogens (estradiol and progesterone, women only)
Days 6, 14, 22, FU visits
Fasting blood draw
Descriptive/ Potential Confounder
Ketones (beta-hydroxybutyrate, lactate, pyruvate)
Days 6, 7, 15, 22
Fasting blood draw
Secondary Outcome / Potential Mediator
Urinary measures (catecholamines and urinary free cortisol)
Days 6, 7, 10, 14, 18, 
21
24-h urine collection
Secondary Outcome / Potential Mediator
Food intake measures
Ad libitum food intake
Days 7-10, 15, 18, 22
Vending machines
Primary Outcome
Appetite sensations
Day 6, 10, 15, 18, 21
Visual analog scale (VAS)
Descriptive / Potential Mediator
Vending machine questionnaire
Day 23
Self-administered
Descriptive
FU: follow-up
* All tasks scheduled prior to the first chamber session can occur in any order, so long as the revised order does not compromise the scientific integrity of those 
procedures or compromise participant safety. Additional weight-maintaining days or washout days may be inserted before or between chamber sessions 
throughout the inpatient stay. 

54
REFERENCES
1.
Thearle MS, Pannacciulli N, Bonfiglio S, Pacak K, Krakoff J. Extent and determinants of thermogenic responses to 24 hours of fasting, energy balance, 
and five different overfeeding diets in humans. The Journal of clinical endocrinology and metabolism. 2013;98(7):2791-9. Epub 2013/05/15. doi: 
10.1210/jc.2013-1289. PubMed PMID: 23666976; PubMed Central PMCID: PMC3701281.
2.
Weise CM, Hohenadel MG, Krakoff J, Votruba SB. Body composition and energy expenditure predict ad-libitum food and macronutrient intake in 
humans. International journal of obesity (2005). 2013. doi: 10.1038/ijo.2013.85. PubMed PMID: 23736368; PubMed Central PMCID: PMC3909024.
3.
Caudwell P, Finlayson G, Gibbons C, Hopkins M, King N, Naslund E, et al. Resting metabolic rate is associated with hunger, self-determined meal 
size, and daily energy intake and may represent a marker for appetite. The American journal of clinical nutrition. 2013;97(1):7-14. Epub 2012/11/30. doi: 
10.3945/ajcn.111.029975. PubMed PMID: 23193010.
4.
Blundell JE, Caudwell P, Gibbons C, Hopkins M, Naslund E, King NA, et al. Body composition and appetite: fat-free mass (but not fat mass or BMI) 
is positively associated with self-determined meal size and daily energy intake in humans. The British journal of nutrition. 2012;107(3):445-9. Epub 
2011/07/08. doi: 10.1017/S0007114511003138. PubMed PMID: 21733267.
5.
Weise CM, Thiyyagura P, Reiman EM, Chen K, Krakoff J. Fat-free body mass but not fat mass is associated with reduced gray matter volume of 
cortical brain regions implicated in autonomic and homeostatic regulation. NeuroImage. 2013;64:712-21. doi: 10.1016/j.neuroimage.2012.09.005. PubMed 
PMID: 22974975; PubMed Central PMCID: PMC4178061.
6.
Piaggi P, Thearle MS, Bogardus C, Krakoff J. Lower energy expenditure predicts long-term increases in weight and fat mass. The Journal of clinical 
endocrinology and metabolism. 2013;98(4):E703-7. Epub 2013/02/19. doi: 10.1210/jc.2012-3529. PubMed PMID: 23418317; PubMed Central PMCID: 
PMCPMC3615206.
7.
Ravussin E, Lillioja S, Knowler WC, Christin L, Freymond D, Abbott WG, et al. Reduced rate of energy expenditure as a risk factor for body-weight 
gain. The New England journal of medicine. 1988;318(8):467-72. Epub 1988/02/25. doi: 10.1056/NEJM198802253180802. PubMed PMID: 3340128.
8.
Luke A, Durazo-Arvizu R, Cao G, Adeyemo A, Tayo B, Cooper R. Positive association between resting energy expenditure and weight gain in a lean 
adult population. The American journal of clinical nutrition. 2006;83(5):1076-81. Epub 2006/05/11. PubMed PMID: 16685049.
9.
Ravussin Y, Xiao C, Gavrilova O, Reitman ML. Effect of intermittent cold exposure on brown fat activation, obesity, and energy homeostasis in mice. 
PloS one. 2014;9(1):e85876. Epub 2014/01/28. doi: 10.1371/journal.pone.0085876. PubMed PMID: 24465761; PubMed Central PMCID: PMC3895006.
10.
Pannacciulli N, Salbe AD, Ortega E, Venti CA, Bogardus C, Krakoff J. The 24-h carbohydrate oxidation rate in a human respiratory chamber predicts 
ad libitum food intake. The American journal of clinical nutrition. 2007;86(3):625-32. Epub 2007/09/08. PubMed PMID: 17823426; PubMed Central PMCID: 
PMC2128058.
11.
Flatt JP. Use and storage of carbohydrate and fat. The American journal of clinical nutrition. 1995;61(4 Suppl):952S-9S. Epub 1995/04/01. PubMed 
PMID: 7900694.
12.
Flatt JP. Carbohydrate balance and body-weight regulation. The Proceedings of the Nutrition Society. 1996;55(1B):449-65. Epub 1996/03/01. 
PubMed PMID: 8832812.
13.
Piaggi P, Thearle MS, Krakoff J, Votruba SB. Higher Daily Energy Expenditure and Respiratory Quotient, Rather Than Fat-Free Mass, Independently 
Determine Greater ad Libitum Overeating. The Journal of clinical endocrinology and metabolism. 2015;100(8):3011-20. Epub 2015/06/19. doi: 
10.1210/jc.2015-2164. PubMed PMID: 26086330; PubMed Central PMCID: PMCPMC4524995.

55
14.
Celi FS, Brychta RJ, Linderman JD, Butler PW, Alberobello AT, Smith S, et al. Minimal changes in environmental temperature result in a significant 
increase in energy expenditure and changes in the hormonal homeostasis in healthy adults. European journal of endocrinology / European Federation of 
Endocrine Societies. 2010;163(6):863-72. Epub 2010/09/10. doi: 10.1530/EJE-10-0627. PubMed PMID: 20826525; PubMed Central PMCID: PMC3113548.
15.
Chen KY, Brychta RJ, Linderman JD, Smith S, Courville A, Dieckmann W, et al. Brown fat activation mediates cold-induced thermogenesis in adult 
humans in response to a mild decrease in ambient temperature. The Journal of clinical endocrinology and metabolism. 2013;98(7):E1218-23. Epub 
2013/06/20. doi: 10.1210/jc.2012-4213. PubMed PMID: 23780370; PubMed Central PMCID: PMC3701264.
16.
Westerterp-Plantenga MS, van Marken Lichtenbelt WD, Strobbe H, Schrauwen P. Energy metabolism in humans at a lowered ambient temperature. 
Eur J Clin Nutr. 2002;56(4):288-96. Epub 2002/04/20. doi: 10.1038/sj.ejcn.1601308. PubMed PMID: 11965504.
17.
Wijers SL, Saris WH, van Marken Lichtenbelt WD. Cold-induced adaptive thermogenesis in lean and obese. Obesity (Silver Spring). 2010;18(6):1092-
9. Epub 2010/04/03. doi: 10.1038/oby.2010.74. PubMed PMID: 20360754.
18.
Lee P, Smith S, Linderman J, Courville AB, Brychta RJ, Dieckmann W, et al. Temperature-acclimated brown adipose tissue modulates insulin 
sensitivity in humans. Diabetes. 2014;63(11):3686-98. doi: 10.2337/db14-0513. PubMed PMID: 24954193; PubMed Central PMCID: PMC4207391.
19.
Zhao ZJ, Chi QS, Cao J, Han YD. The energy budget, thermogenic capacity and behavior in Swiss mice exposed to a consecutive decrease in 
temperatures. The Journal of experimental biology. 2010;213(Pt 23):3988-97. Epub 2010/11/16. doi: 10.1242/jeb.046821. PubMed PMID: 21075940.
20.
Henderson D, Fort MM, Rashotte ME, Henderson RP. Ingestive behavior and body temperature of pigeons during long-term cold exposure. 
Physiology & behavior. 1992;52(3):455-69. Epub 1992/09/01. PubMed PMID: 1409905.
21.
van Marken Lichtenbelt WD, Schrauwen P, van De Kerckhove S, Westerterp-Plantenga MS. Individual variation in body temperature and energy 
expenditure in response to mild cold. American journal of physiology Endocrinology and metabolism. 2002;282(5):E1077-83. Epub 2002/04/06. doi: 
10.1152/ajpendo.00020.2001. PubMed PMID: 11934673.
22.
Ducreux S, Zorzato F, Muller C, Sewry C, Muntoni F, Quinlivan R, et al. Effect of ryanodine receptor mutations on interleukin-6 release and 
intracellular calcium homeostasis in human myotubes from malignant hyperthermia-susceptible individuals and patients affected by central core disease. 
J Biol Chem. 2004;279(42):43838-46. Epub 2004/08/10. doi: 10.1074/jbc.M403612200. PubMed PMID: 15299003.
23.
Almada C, Cataldo LR, Smalley SV, Diaz E, Serrano A, Hodgson MI, et al. Plasma levels of interleukin-6 and interleukin-18 after an acute physical 
exercise: relation with post-exercise energy intake in twins. Journal of physiology and biochemistry. 2013;69(1):85-95. Epub 2012/07/20. doi: 
10.1007/s13105-012-0191-x. PubMed PMID: 22810957.
24.
Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue. Nature. 
1994;372(6505):425-32.
25.
Maffei M, Halaas J, Ravussin E, Pratley RE, Lee GH, Zhang Y, et al. Leptin levels in human and rodent: measurement of plasma leptin and ob RNA in 
obese and weight-reduced subjects. Nature medicine. 1995;1(11):1155-61. Epub 1995/11/01. PubMed PMID: 7584987.
26.
Rosenbaum M, Leibel RL. Leptin: a molecule integrating somatic energy stores, energy expenditure and fertility. Trends in endocrinology and 
metabolism: TEM. 1998;9(3):117-24. Epub 2008/04/15. PubMed PMID: 18406252.
27.
Ricci MR, Fried SK, Mittleman KD. Acute cold exposure decreases plasma leptin in women. Metabolism: clinical and experimental. 2000;49(4):421-3. 
PubMed PMID: 10778861.
28.
Lee P, Linderman JD, Smith S, Brychta RJ, Wang J, Idelson C, et al. Irisin and FGF21 are cold-induced endocrine activators of brown fat function in 
humans. Cell metabolism. 2014;19(2):302-9. Epub 2014/02/11. doi: 10.1016/j.cmet.2013.12.017. PubMed PMID: 24506871.

56
29.
Seitz HJ, Krone W, Wilke H, Tarnowski W, Carsten D, Dunkelmann B, et al. Rapid rise in plasma glucagon induced by acute cold exposure in man 
and rat. Pflügers Archiv European Journal of Physiology. 1981;389(2):115-20.
30.
Ortega E, Pannacciulli N, Bogardus C, Krakoff J. Plasma concentrations of free triiodothyronine predict weight change in euthyroid persons. The 
American journal of clinical nutrition. 2007;85(2):440-5. Epub 2007/02/08. PubMed PMID: 17284741; PubMed Central PMCID: PMC1857361.
31.
Rosenbaum M, Hirsch J, Murphy E, Leibel RL. Effects of changes in body weight on carbohydrate metabolism, catecholamine excretion, and thyroid 
function. The American journal of clinical nutrition. 2000;71(6):1421-32. Epub 2000/06/06. PubMed PMID: 10837281.
32.
Ouellet V, Labbe SM, Blondin DP, Phoenix S, Guerin B, Haman F, et al. Brown adipose tissue oxidative metabolism contributes to energy expenditure 
during acute cold exposure in humans. The Journal of clinical investigation. 2012;122(2):545-52. doi: 10.1172/JCI60433. PubMed PMID: 22269323; PubMed 
Central PMCID: PMC3266793.
33.
van der Lans AA, Hoeks J, Brans B, Vijgen GH, Visser MG, Vosselman MJ, et al. Cold acclimation recruits human brown fat and increases nonshivering 
thermogenesis. The Journal of clinical investigation. 2013;123(8):3395-403. doi: 10.1172/JCI68993. PubMed PMID: 23867626; PubMed Central PMCID: 
PMC3726172.
34.
Tuschl RJ, Platte P, Laessle RG, Stichler W, Pirke KM. Energy expenditure and everyday eating behavior in healthy young women. The American 
journal of clinical nutrition. 1990;52(1):81-6. Epub 1990/07/01. PubMed PMID: 2360553.
35.
Mellitus ECotDaCoD. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Guideline
Practice Guideline. 2003 Jan. Report No.: 0149-5992 (Print)
0149-5992 (Linking).
36.
Geiselman PJ, Anderson AM, Dowdy ML, West DB, Redmann SM, Smith SR. Reliability and validity of a macronutrient self-selection paradigm and 
a food preference questionnaire. Physiology & behavior. 1998;63(5):919-28. PubMed PMID: 9618017.
37.
Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public 
health nutrition. 2006;9(6):755-62. Epub 2006/08/24. PubMed PMID: 16925881.
38.
CoreScan, GE 2015. Available from: http://www3.gehealthcare.com/en/Products/Categories/Bone_Health/CoreScan.
39.
Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of psychosomatic 
research. 1985;29(1):71-83. Epub 1985/01/01. PubMed PMID: 3981480.
40.
Lowe MR, Butryn ML, Didie ER, Annunziato RA, Thomas JG, Crerand CE, et al. The Power of Food Scale. A new measure of the psychological influence 
of the food environment. Appetite. 2009;53(1):114-8. Epub 2009/06/09. doi: 10.1016/j.appet.2009.05.016. PubMed PMID: 19500623.
41.
Geliebter A, Aversa A. Emotional eating in overweight, normal weight, and underweight individuals. Eating behaviors. 2003;3(4):341-7. PubMed 
PMID: 15000995.
42.
Block G, Coyle LM, Hartman AM, Scoppa SM. Revision of dietary analysis software for the Health Habits and History Questionnaire. Am J Epidemiol. 
1994;139(12):1190-6. Epub 1994/06/15. PubMed PMID: 8209877.
43.
Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J 
Epidemiol. 1986;124(3):453-69. Epub 1986/09/01. PubMed PMID: 3740045.
44.
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. Journal of health and social behavior. 1983:385-96.
45.
Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the Household Food Security Scale. American journal of 
public health. 1999;89(8):1231-4. Epub 1999/08/05. PubMed PMID: 10432912; PubMed Central PMCID: PMCPmc1508674.

57
46.
Chapman LJ, Chapman JP, Raulin ML. Scales for physical and social anhedonia. Journal of abnormal psychology. 1976;85(4):374.
47.
Barratt ES. Barratt Impulsiveness Scale: Barratt-Psychiatry Medical Branch, University of Texas; 1975.
48.
Epel ES, Tomiyama AJ, Mason AE, Laraia BA, Hartman W, Ready K, et al. The reward-based eating drive scale: a self-report index of reward-based 
eating. PloS one. 2014;9(6):e101350. Epub 2014/07/01. doi: 10.1371/journal.pone.0101350. PubMed PMID: 24979216; PubMed Central PMCID: 
PMC4076308.
49.
Adler NE, Epel ES, Castellazzo G, Ickovics JR. Relationship of subjective and objective social status with psychological and physiological functioning: 
preliminary data in healthy white women. Health psychology : official journal of the Division of Health Psychology, American Psychological Association. 
2000;19(6):586-92. Epub 2000/12/29. PubMed PMID: 11129362.
50.
Shewchuk RM, Johnson MO, Elliott TR. Self-appraised social problem solving abilities, emotional reactions and actual problem solving performance. 
Behaviour research and therapy. 2000;38(7):727-40. Epub 2000/06/30. PubMed PMID: 10875194.
51.
Bagby RM, Taylor GJ, Parker JD. The Twenty-item Toronto Alexithymia Scale--II. Convergent, discriminant, and concurrent validity. Journal of 
psychosomatic research. 1994;38(1):33-40. Epub 1994/01/01. PubMed PMID: 8126688.
52.
Hollingshead AB. Four factor index of social status. In: Yale University NH, CT, editor. Unpublished manuscript1975.
53.
Bechara A, Damasio H, Tranel D, Damasio AR. Deciding advantageously before knowing the advantageous strategy. Science (New York, NY). 
1997;275(5304):1293-5. Epub 1997/02/28. PubMed PMID: 9036851.
54.
Stroop JR. Studies of interference in serial verbal reactions. Journal of experimental psychology. 1935;18(6):643.
55.
Kongs SK, Thompson LL, Iverson GL, Heaton RK. Wisconsin card sorting test-64 card version (WCST-64). Odessa, FL: Psychological Assessment 
Resources. 2000.
56.
Langenecker SA, Zubieta JK, Young EA, Akil H, Nielson KA. A task to manipulate attentional load, set-shifting, and inhibitory control: convergent 
validity and test-retest reliability of the Parametric Go/No-Go Test. Journal of clinical and experimental neuropsychology. 2007;29(8):842-53. Epub 
2007/09/14. doi: 10.1080/13803390601147611. PubMed PMID: 17852593.
57.
Lusk G. Animal calorimetry: analysis of oxidation of mixtures of carbohydrates and fat. J Biol Chem. 1924;59:41-2.
58.
Abbott WG, Howard BV, Christin L, Freymond D, Lillioja S, Boyce VL, et al. Short-term energy balance: relationship with protein, carbohydrate, and 
fat balances. The American journal of physiology. 1988;255(3 Pt 1):E332-7. Epub 1988/09/01. PubMed PMID: 3421330.
59.
Flint A, Raben A, Blundell JE, Astrup A. Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single 
test meal studies. Int J Obes Relat Metab Disord. 2000;24(1):38-48. Epub 2000/03/07. PubMed PMID: 10702749.
60.
Stubbs RJ, Hughes DA, Johnstone AM, Rowley E, Reid C, Elia M, et al. The use of visual analogue scales to assess motivation to eat in human subjects: 
a review of their reliability and validity with an evaluation of new hand-held computerized systems for temporal tracking of appetite ratings. Br J Nutr. 
2000;84(4):405-15. Epub 2000/12/05. doi: S0007114500001719 [pii]. PubMed PMID: 11103211.
61.
van Marken Lichtenbelt WD, Vanhommerig JW, Smulders NM, Drossaerts JM, Kemerink GJ, Bouvy ND, et al. Cold-activated brown adipose tissue 
in healthy men. N Engl J Med. 2009;360(15):1500-8. Epub 2009/04/10. doi: 360/15/1500 [pii]
10.1056/NEJMoa0808718. PubMed PMID: 19357405.
62.
Wijers SL, Saris WH, van Marken Lichtenbelt WD. Cold-induced adaptive thermogenesis in lean and obese. Obesity (Silver Spring). 18(6):1092-9. 
Epub 2010/04/03. doi: oby201074 [pii]
10.1038/oby.2010.74. PubMed PMID: 20360754.

58
63.
Venti CA, Votruba SB, Franks PW, Krakoff J, Salbe AD. Reproducibility of ad libitum energy intake with the use of a computerized vending machine 
system. The American journal of clinical nutrition. 2010;91(2):343-8. Epub 2009/11/20. doi: 10.3945/ajcn.2009.28315. PubMed PMID: 19923376; PubMed 
Central PMCID: PMC2806891.
64.
O'Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979:549-56.
65.
Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64(2):191-9.
66.
MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.
67.
Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. Sociological methodology. 1982;13(1982):290-312.