Conditions: Obesity
Keywords: weight loss body mass index oxytocin magnetic resonance imaging
Allocation: RANDOMIZED | Intervention model: PARALLEL
Enrollment: 61
Healthy volunteers: no

Brief summary:
This is a randomized, double blind, placebo-controlled study of the effects of intranasal oxytocin in obese adults, ages 18-45 years old. Subjects will be randomized to receive of intranasal oxytocin or placebo (3 sprays per nostril, 4 times per day) for 8 weeks. Study visits include screening to determine eligibility, a 2-part baseline visit, and visits every 2 weeks thereafter until week 14, with a safety follow-up visit 6 weeks after the last dose of study drug. Study procedures include behavioral, metabolic, neuroimaging, and endocrine assessments.

Detailed description:

Arms:
arm 1: oxytocin nasal spray (24 IU nasal spray, 4 times per day for 8 weeks) arm 2: placebo nasal spray (4 times per day for 8 weeks)

Interventions: intervention 1: oxytocin nasal spray intervention 2: Placebo
Intervention descriptions: intervention 1: oxytocin intranasal spray intervention 2: Placebo nasal spray

Protocol document:
Prot_SAP_000.pdf: 
Page 1 
 
The Effects of Oxytocin in Obese Adults 
NCT03043053 
07 September 2022 
 
 


Page 2 
Wronski ML, Plessow F, Kerem L, Asanza E, O'Donoghue ML, Stanford FC, Bredella MA, Torriani 
M, Soukas AA, Kheterpal A, Eddy KT, Holmes TM, Deckersbach T, Vangel M, Holsen LM, Lawson 
EA. A randomized, double-blind, placebo-controlled clinical trial of 8-week intranasal oxytocin 
administration in adults with obesity: Rationale, study design, and methods. Contemp Clin Trials. 
2022 Nov;122:106909. doi: 10.1016/j.cct.2022.106909. Epub 2022 Sep 7. PMID: 36087842; 
PMCID: PMC10329413. 
 
Title: A randomized, double-blind, placebo-controlled clinical trial of 8-week intranasal 
oxytocin administration in adults with obesity: Rationale, study design, and methods 
 
Authors: Marie-Louis Wronski1, Franziska Plessow1, Liya Kerem2, Elisa Asanza1, Michelle L. 
O’Donoghue3, Fatima C. Stanford1,4, Miriam A. Bredella5, Martin Torriani5, Alexander A. Soukas6, 
Arvin Kheterpal5, Kamryn T. Eddy7, Tara M. Holmes8, Thilo Deckersbach9, Mark Vangel10, Laura 
M. Holsen11, and Elizabeth A. Lawson1 
 
1 Neuroendocrine Unit, Department of Medicine, Massachusetts General Hospital and Harvard 
Medical School, Boston, MA, USA. 
2 Division of Pediatric Endocrinology, Department of Pediatrics, Hadassah-Hebrew University 
Medical Center, Jerusalem, Israel. 
3 TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital and 
Harvard Medical School, Boston, MA, USA. 
4 Division of Pediatric Endocrinology, Department of Pediatrics, Massachusetts General Hospital 
and Harvard Medical School, Boston, MA, USA. 
5 Division of Musculoskeletal Imaging and Intervention, Department of Radiology, 
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.  
6 Center for Genomic Medicine, Diabetes Unit, Department of Medicine, Massachusetts General 
Hospital and Harvard Medical School, Boston, MA, USA. 
7 Eating Disorders Clinical and Research Program, Department of Psychiatry, Massachusetts 
General Hospital and Harvard Medical School, Boston, MA, USA. 


Page 3 
8 Translational and Clinical Research Centers, Massachusetts General Hospital and Harvard 
Medical School, Boston, MA, USA 
9 Diploma Hochschule/University of Applied Sciences, Bad Sooden-Allendorf, Germany. 
10 Biostatistics Center, Massachusetts General Hospital and Harvard Medical School, Boston, 
MA, USA.  
11 Division of Women’s Health, Department of Medicine and Department of Psychiatry, Brigham 
and Women’s Hospital and Harvard Medical School, Boston, MA, USA. 
Abstract 
Background: Obesity affects more than one-third of adults in the U.S., and effective treatment 
options are urgently needed. Oxytocin administration induces weight loss in animal models of 
obesity via effects on caloric intake, energy expenditure, and fat metabolism. We study intranasal 
oxytocin, an investigational drug shown to reduce caloric intake in humans, as a potential novel 
treatment for obesity. 
Methods: We report the rationale, design, methods, and biostatistical analysis plan of a 
randomized, double-blind, placebo-controlled clinical trial of intranasal oxytocin for weight loss 
(primary endpoint) in adults with obesity. Participants (aged 18–45 years) were randomly 
allocated (1:1) to oxytocin (four times daily over eight weeks) versus placebo. Randomization was 
stratified by biological sex and BMI (≥30 to <35 kg/m2, ≥35 to <40 kg/m2, ≥40 kg/m2). We 
investigate the efficacy, safety, and mechanisms of oxytocin administration in reducing body 
weight. Secondary endpoints include changes in resting energy expenditure, body composition, 
caloric intake, metabolic profile, and brain activation via functional magnetic resonance imaging 
in response to food images and during an impulse control task. Safety and tolerability are 
assessed biweekly and at a 6-week follow-up after treatment completion. 
Results: Sixty-one male and female participants aged 18–45 years were randomized (mean age 
34 years, mean BMI 37 kg/m2). The study sample is diverse with 38% identifying as non-White 
and 20% Hispanic. 


Page 4 
Conclusion: Investigating intranasal oxytocin's efficacy, safety, and mechanisms as an anti-
obesity medication will advance the search for optimal treatment strategies for obesity and its 
associated severe sequelae. 
 
Keywords: Obesity; Oxytocin; Weight loss; Appetite regulation; Energy metabolism; 
Neuroimaging 
 
1. Introduction 
Obesity affects over 42% of the adult U.S. population [1] and is associated with increased risk of 
type 2 diabetes mellitus, hepatic steatosis, cardiovascular disease, cancer, and premature 
mortality [2]. More than 20% of U.S. health care costs are directly attributable to obesity [3]. 
Furthermore, obesity is a significant risk factor for severe COVID-19-associated illness, 
hospitalization, intensive care, and death [4]. Weight loss improves outcomes [5], but meaningful 
weight reduction is difficult to achieve and sustain. Available treatment options to supplement 
lifestyle modifications have long been limited to bariatric surgery, associated with peri-/post-
operative complications [6], and weight-loss medications with modest efficacy and bothersome 
side effects. The established anti-obesity interventions often fail to yield sustained weight 
reduction, partly due to a physiological decrease in resting energy expenditure (REE) 
accompanying weight loss [7]. Clinical trials have revealed promising new drugs for weight 
management, such as the recently FDA-approved glucagon-like peptide-1 (GLP-1) receptor 
agonist semaglutide [8]. Nonetheless, gastrointestinal side effects are common, and most GLP-1 
receptor agonists currently approved for weight loss can only be administrated as subcutaneous 
injections [9]. There is an imperative need for novel, effective, and tolerable therapeutic strategies 
for weight loss to reduce obesity-associated health risks and increase the life expectancy and 
wellbeing of individuals with obesity. 


Page 5 
Oxytocin is a nine amino acid peptide hormone produced by neurons in the paraventricular and 
supraoptic nuclei of the hypothalamus, secreted directly into brain regions involved in energy 
homeostasis and appetite regulation, and released into the bloodstream by the posterior pituitary 
gland [10–12]. Oxytocin neurons project to anorexigenic arcuate nucleus proopiomelanocortin 
neurons critical for regulation of energy balance, as well as the ventral tegmental area (VTA) and 
nucleus accumbens responsible for reward-related eating behavior. Further, oxytocin has been 
shown to mediate anorexigenic actions of leptin and cholecystokinin (CCK). See Figure 1 for a 
working model of obesity-related pathophysiology and proposed neural and metabolic oxytocin 
effects. Animal studies have shown that oxytocin may drive weight loss and result in sustained 
weight reduction by different mechanisms [13–15]. In rodents and non-human primates with diet-
induced obesity, oxytocin administration prevents the physiological decrease in REE occurring 
with weight loss [13,16–18]. Oxytocin induces lipolysis resulting in reduced adipocyte size and 
decreased subcutaneous, visceral, and liver fat while preserving lean mass [13,15,17,19,20]. 
Exogenous oxytocin increases fat utilization [21] and might even induce conversion of white 
adipose tissue into thermogenic “beige” adipocytes [15]. Acute oxytocin administration reduces 
caloric consumption [22,23], particularly of palatable foods [24,25], whereas oxytocin antagonists 
have the reverse effect [26]. Further, oxytocin reduces cortisol levels and inflammation, which 
may, in turn, reduce caloric intake and improve metabolism [10,12]. 
In humans, proof-of-concept investigations show that acute intranasal oxytocin administration 
reduces food intake in both healthy men and women and those with obesity [21,25,27,28], 
potentially through modulation of neural processing in brain regions related to reward, energy 
homeostasis, and cognitive control [29–31]. Perturbations in these neural pathways may underly 
the development of obesity [32,33]. Intranasal oxytocin is well-tolerated by humans with minimal 
adverse events [34]. A clinical trial in a small sample of non-diabetic individuals with obesity (n=9 
in the oxytocin arm) reported a significant BMI reduction after 8-week treatment with 24 
international units (IU) (4 times/day) of intranasal oxytocin compared to placebo [35]. While the 


Page 6 
findings are promising, this pilot study was small and did not assess mechanisms or safety. More 
extensive clinical trials are needed. 
 
2. Methods 
2.1. Study rationale, preliminary data, and aims 
Given promising preclinical and preliminary clinical findings indicating beneficial effects of 
oxytocin on eating behavior, metabolism, and weight loss [12,35], investigation of the efficacy and 
safety of prolonged intranasal oxytocin administration as a potential weight-loss therapeutic in 
humans is warranted. Further, exploring mechanisms underlying the metabolic actions of oxytocin 
is of high interest. Our clinical trial addresses these key research questions by determining 
whether 8-week intranasal oxytocin promotes weight reduction in adults with obesity and 
investigating the neural and metabolic pathways leading to the proposed oxytocin effects on body 
weight.  
We demonstrated that a single dose of 24 IU intranasal oxytocin administered to fasting men 
across the weight spectrum reduced caloric intake at a test meal, increased fat utilization, and 
improved insulin sensitivity [21]. In a crossover functional magnetic resonance imaging (fMRI) 
study of men with overweight or obesity, we found that 24 IU intranasal oxytocin (versus placebo) 
reduced brain activation to high-calorie food images in the VTA, the origin of the mesolimbic 
dopaminergic reward system, and other hedonic (e.g., orbitofrontal cortex [OFC] and anterior 
insula) and homeostatic (e.g., hypothalamus) areas [30], and reduced functional connectivity 
between the VTA and reward-related brain regions (e.g., amygdala and insula) [29]. In contrast, 
oxytocin increased activation in cognitive control-related brain regions (e.g., dorsal anterior 
cingulate cortex [dACC] and dorsolateral prefrontal cortex [dlPFC]) [30] and improved impulse 
control, as assessed using a behavioral task previously linked to eating behavior [36]. 
Thus, we hypothesize that 8-week intranasal oxytocin will reduce food intake (by decreasing 
reward-related and homeostatic drive to eat and increasing eating-related impulse control) and 


Page 7 
body fat and weight (via effects on eating behavior, REE, and fat metabolism) with minimal side 
effects. We propose intranasal oxytocin as a potential novel, effective, and safe therapy for weight 
loss in human obesity. 
Our study was designed to investigate the following:  
• Aim 1 – Determine oxytocin effects on weight, REE, and body composition: We hypothesize that 
8-week intranasal oxytocin compared to placebo (a) reduces body weight, (b) increases REE 
(adjusted for lean/fat free mass), and (c) reduces total body, visceral, and liver fat in adults with 
obesity. 
• Aim 2 – Determine oxytocin effects on caloric intake and underlying neural pathways (fMRI): We 
hypothesize that intranasal oxytocin compared to placebo reduces caloric intake at a test meal 
(independent of weight change), mediated by (a) reduced activation (in fasted and fed states) 
of reward-related food motivation (VTA, OFC, anterior insula) and homeostatic (hypothalamus) 
brain regions during a visual food stimuli paradigm and (b) increased impulse control indicated 
by increased activation (fasted state) of cognitive control-related brain regions (dlPFC, dACC, 
anterior portion of supplementary motor cortex/preSMA) during a validated task requiring the 
engagement of cognitive control to suppress impulsive responses.  
The study is conducted under an Investigational New Drug application cleared by the U.S. Food 
and Drug Administration at a single study site, and the institutional review board approved the 
study protocol. 
 
2.2. Study design, participants, and randomization 
We are conducting a randomized, double-blind, placebo-controlled study to investigate the effects 
of intranasal oxytocin (24 IU, 4 times/day, for 8 weeks) in adults with obesity (BMI 30 kg/m2), 
aged 18–45 years. We randomized 61 individuals to achieve our target evaluable population of 
at least 50 participants who complete the 8-week treatment period. Participants were stratified by 


Page 8 
biological sex (female/male) and obesity class (class 1: BMI 30.0–34.9 kg/m2, class 2: BMI 35.0–
39.9 kg/m2, class 3: BMI ≥40.0 kg/m2) and randomized 1:1 to receive oxytocin or placebo. The 
unblinded study pharmacist performed randomization. All other study staff and participants are 
blinded to treatment assignment. We recruited participants through Massachusetts General 
Hospital (MGH) and advertisements online/in the surrounding community. Table 1 summarizes 
the inclusion and exclusion criteria of the study.  
 
2.3. Study and safety endpoints 
The primary endpoint is percent change in body weight from baseline following 8-week intranasal 
oxytocin compared to placebo. Key secondary endpoints include change from baseline to end of 
treatment in (a) REE (adjusted for lean mass), (b) body composition (total body, visceral, and liver 
fat mass), and (c) caloric intake (adjusted for weight) following 8-week intranasal oxytocin 
compared to placebo.  
Other secondary endpoints include change in metabolic profile (e.g., insulin sensitivity, lipid panel, 
high-sensitivity C-reactive protein [hsCRP] at 8 weeks; fat metabolism at 2 weeks), behavior (e.g., 
eating behavior, reward-related food motivation, impulse control, social emotional functioning, 
symptoms of disordered eating, depression, and anxiety), and quality of life between baseline and 
8-week intranasal oxytocin treatment compared to placebo (Table 2).  
We systematically assess safety and treatment tolerability throughout the study (Table 2), with a 
final safety visit 6 weeks after treatment completion and additional visits if needed (e.g., early 
termination). We categorize severity of adverse events according to the Common Terminology 
Criteria for Adverse Events (CTCAE, v5.0) [37] and evaluate expectedness of adverse events 
and relationship to study drug and procedures.  
 
2.4. Study procedures and assessments 


Page 9 
Study time points comprise the screening visit to determine eligibility, two baseline visits (the 
second one includes randomization and treatment initiation after baseline assessments are 
completed), on-treatment study time points (week 2, 4, 6, and 8 visits), and a final week 14 follow-
up visit to assess sustained treatment effects and safety.  
Study visits take place at the MGH Translational and Clinical Research Center and the Athinoula 
A. Martinos Center for Biomedical Imaging. We obtain and manage study data using a web-based 
secure Research Electronic Data Capture (REDCap) system [38]. 
See Figure 2 for an overview of the study design, including visits, endpoints, and assessment 
methods. Table 2 provides a detailed list of study procedures/assessments at each study visit.  
 
2.4.1. Diagnostic and screening measures 
We ask potential participants to complete a pre-screening questionnaire to establish preliminary 
study eligibility (Table 1). Following informed consent, we confirm eligibility at a screening visit, 
including review of medical history and physical examination, anthropometric measurements, 
pregnancy test in females, urine drug screen, blood tests (Table 2), electrocardiogram, and the 
Mini-International Neuropsychiatric Interview (M.I.N.I.) [39] to evaluate for current/past psychiatric 
conditions.  
 
2.4.2. Anthropometric measurements 
Anthropometric measurements are assessed by trained personnel using calibrated instruments. 
They include measurements of height (in triplicate using a stadiometer, at screening/baseline 
visits), weight, and waist/hip circumference (smallest, umbilicus, midpoint, iliac, broadest hip). 
BMI, waist-to-hip ratio, and percent ideal body weight are calculated (see Table 2 for formulas). 
 
2.4.3. Resting energy expenditure (REE)  


Page 10
We 
perform 
indirect 
calorimetry 
(VMAX 
Encore 
29 
metabolic 
cart/Viasys 
Healthcare/Carefusion/San Diego/CA/USA) to assess fasting REE at baseline, week 6 and 8 
visits.  
 
2.4.4. Body composition 
We obtain body composition measurements at baseline and week 8 visits. We use dual-energy 
X-ray absorptiometry (Hologic Horizon A/Hologic Inc./Marlborough/MA/USA) to measure total fat 
mass (precision 3% [40]), total lean mass, estimated visceral adipose tissue mass, and trunk fat 
mass. On a 3T MRI system (Siemens Trio/Siemens Medical Systems/Erlangen/Germany), we 
perform single-axial slices through the abdomen at the level of L4 and left mid-thigh to determine 
fat depots and thigh muscle cross-sectional area. We determine abdominal visceral adipose 
tissue (VAT) and subcutaneous adipose tissue (SAT) cross-sectional areas (cm2) and their sum, 
total abdominal adipose tissue (TAT), mid-thigh SAT and muscle area (cm2) using VITRAK 
software (Merge/eFilm/Milwaukee/WI/USA). We assess liver fat via breath-hold single-voxel 1H-
MR spectroscopy (1H-MRS) using a PRESS pulse sequence without water suppression: TE = 30 
ms, 8 acquisitions, TR = 1500 ms, 1024 data points, receiver bandwidth 2000 Hz) [41] and placing 
a voxel of 20×20×20 mm (8 mL) within the right and left hepatic lobes avoiding vessels or artifact. 
We use LCModel software (v6.3 -0K/S. Provencher/Oakville/Ontario/Canada) to fit 1H-MRS data, 
expressed in lipid-water-ratio. 
 
2.4.5. Fat biopsy  
Interventional, musculoskeletal radiologists perform abdominal subcutaneous fat biopsies (2–3 
core biopsies) at baseline and week 2 visits under sterile conditions and local lidocaine anesthesia 
using a 14-gauge Temno biopsy needle (CareFusion/San Diego/CA/USA). Biopsies are 
immediately transferred to dry ice, ethanol, or formalin solution and stored appropriately until 


Page 11
immunohistochemical and molecular analyses to investigate oxytocin effects on fat metabolism 
pathways involving white adipocyte “browning”, fatty acid oxidation, and lipolysis. 
 
2.4.6. Caloric intake 
A validated breakfast test meal [21] takes place after a minimum 10-hour overnight fast at baseline 
and week 6 visits (oxytocin administration 20–30 minutes before test meal at week 6 visit). 
Participants select a meal from a menu created by research dietitians using ProNutraTM software 
(VioCare/Princeton/NJ/USA). Metabolism and Nutrition Research staff prepare double portions 
with known nutrient content, and participants have 30 minutes to eat as much as they would like. 
We quantify caloric intake at test meals (i.e., total calories, carbohydrate/fat/protein content) using 
ProNutraTM software. 
At neuroimaging visits (baseline 2 and week 4, study drug administration held until after study 
visit), we ask participants to eat a standardized meal (~500 kcal, 60% carbohydrates, 20% fat, 
20% protein) over 15 minutes in its entirety to allow for assessment of food motivation neural 
circuitry and appetite hormones in the fed state [42–44]. Caloric intake is quantified using 
ProNutraTM software. 
Self-report measures of caloric intake include 24-hour food recalls and 4-day food records 
collected from participants at baseline, on-treatment, and off-treatment follow-up visits. Records 
are analyzed for total calories, macro- and micronutrients by study dieticians using NDS-R 
software (Nutrition Coordinating Center/University of Minnesota/Minneapolis/MN/USA).  
 
2.4.7. Laboratory assessments 
Blood samples are obtained after a minimum 10-hour overnight fast at approximately 8–9 a.m. at 
all study visits. Additionally, postprandial blood samples are obtained at neuroimaging visits 
(baseline 2, week 4) at 30, 60, and 120 minutes after a standardized meal (see 2.4.6.). Whole 
blood samples are immediately processed as follows: addition of Pefabloc or aprotinin (for 


Page 12
unstable appetite-regulating hormones only), centrifugation (4 °C, 2800xg, 20 minutes) to 
separate plasma/serum, dispensed into aliquots (acidification with HCl for unstable appetite-
regulating hormones only), and storage at -80 °C until laboratory analysis or immediate 
measurement (e.g., for glucose and HbA1c). We will report assays and characteristics (detection 
limits, intra-/inter-assay coefficients of variation) in publications using data from this study. We 
plan to assess metabolic profile (e.g., insulin sensitivity, lipid panel, hsCRP, appetite-regulating 
hormones) and gonadal steroids. We collect 24-hour urine (e.g., cortisol) and stool (e.g., 
microbiome) samples, aliquoted and stored at -80 °C, and hair samples (e.g., cortisol; proximal 2 
cm segment of approximately 3 mm diameter taken from posterior vertex position). See Table 2 
for details. 
 
2.4.8. Neuroimaging 
MRI scanning takes place on an FDA-approved 3T Skyra scanner (Siemens/Erlangen/Germany). 
We perform whole-brain functional fMRI using a gradient-echo EPI pulse sequence (33 
contiguous oblique-axial slices angled 30° from the AC-PC axis, 4 mm thick, TR/TE = 2000/30 
ms, flip angle = 90°, FOV = 200x200 mm, 120 total images per run).  
• Food cue paradigm [42] – before (fasted state) and after (fed state) a standardized meal (see 
2.4.6.): Participants view 100 high-calorie food stimuli (50% sweet/50% savory), 100 low-calorie 
food stimuli, 100 non-food object stimuli, and 100 fixation stimuli in a block design (5 runs, 4 
minutes/run, 3 seconds/stimulus, 5 images/block, 16 blocks/run, images presented only once). 
Participants also rate a selection of food stimuli on valence and caloric content. The primary 
variable of interest is brain activation to high-calorie food stimuli compared to non-food stimuli. 
• Stop-Signal Task (SST) [45,46] – fasted state: Participants are instructed to manually respond 
to visual go signals (circles) as fast and accurately as possible (within 3 seconds) but withhold 
their response when a stop signal (X) appears (25% of all trials, randomly selected, stop-signal 
delay following staircase-tracking algorithm). Trials are presented in a block design [64 


Page 13
trials/block, 3 blocks] following task practice outside the scanner. The primary variable of interest 
is brain activation to successful compared to failed response inhibition. 
In addition to functional tasks, we acquire structural T1-weighted MRI images (MPRAGE 
sequence) for co-registration between structural and functional datasets. On a subset of 
participants (for whom scanning time allows), the Multi-Source Interference Task (an fMRI task 
reliably activating the cognitive control network involved in inhibiting task interference) [47], multi-
echo T2-weighted MRI images, diffusion-weighted images, and resting state fMRI EPI images are 
acquired for further exploratory investigation of cognitive control, hypothalamic gliosis, white 
matter tractography, and functional connectivity.  
 
2.4.9. Psychological, behavioral, and quality of life assessments 
We use self-report questionnaires and behavioral tasks to assess obesity-related psychological 
aspects and behaviors that might also be affected by oxytocin administration, including physical 
activity, appetite and eating behavior and attitudes [11], reward processing and food motivation 
[29,30,48], impulse control, socioemotional functioning [49,50], and mood and anxiety symptoms 
[51]. Quality of life will also be evaluated. See Table 2 for details. 
 
2.5. Biostatistical considerations 
2.5.1. Sample size and statistical power 
Sixty-one participants were randomized to obtain evaluable data for at least 50 participants 
completing the 8-week treatment period. We conducted power and sample size calculations using 
2-sample t-tests at a 2-tailed significance level of 𝝰 = 0.05. The primary endpoint of this study is 
percent change in body weight from baseline following 8-week intranasal oxytocin treatment 
compared to placebo; with 25 participants per arm, we have 80% power to detect a between-
group (oxytocin versus placebo) mean difference in weight loss of ≥3.2 kg, based on a reported 


Page 14
standard deviation for the between-group difference in weight loss of approximately 4 kg in a pilot 
study of 8-week intranasal oxytocin administration in adults with obesity [35]. 
 
2.5.2. Analysis of neuroimaging data 
FMRI data will be analyzed using Statistical Parametric Mapping software running under the 
MATLAB environment (SPM12) [52]. Preprocessing steps will include realignment and 
geometric unwarping using magnitude and phase images from the field map, slice-time correction, 
echo planar (EPI) co-registration to the participant’s segmented, skull-stripped, bias-corrected T1-
weighted image, normalization to Montreal Neurological Institute space with resampling to 2 mm 
isotropic using 4th degree B-spline interpolation, and smoothing with a 6 mm isotropic full width at 
half-maximum Gaussian kernel. Each run of EPI data will be evaluated for motion: (a) the degree 
of linear movement will be assessed and classified (threshold = 3 mm linear movement in 
orthogonal 
planes), 
and 
(b) 
the 
ART 
toolbox 
(Artifact 
Detection 
Tools; 
www.nitrc.org/projects/artifact_detect) will be used to detect outlier volumes based on image 
intensity relative to the global mean image (threshold = 3.5xSD) and motion (threshold = 0.8 mm, 
measured as scan-to-scan movement). Individual runs will be excluded if they show either (a) >3 
mm linear movement and >10% outliers, or (b) >20% outliers. 
For subject-level analyses, a general linear model (GLM) either with regressors of high-calorie 
foods, low-calorie foods, and objects (food cue paradigm) or with regressors of successful stop 
trials and failed stop trials (SST) will be specified. Regressors will be placed at the onset of each 
condition within a block, convolved with the canonical hemodynamic response function. Global 
mean signal and motion outliers will be entered as nuisance regressors. Following GLM 
estimation, the primary contrast (high-calorie foods versus objects, successful versus failed stop 
trials) will be computed at the single-subject level to estimate condition effects at every voxel, 
separately for each session (fasted/fed). For subsequent group-level analysis, we will use a 
factorial design with baseline versus 4-week contrasts to examine changes in brain activation 


Page 15
following oxytocin treatment in a-priori defined regions of interest (ROIs, food cue paradigm: VTA, 
OFC, anterior insula; SST: dlPFC, dACC, preSMA). For mediation analyses, beta values will be 
extracted from subject-level contrast images for clusters within a-priori ROIs that meet family-wise 
error rate correction (p < 0.05) using Marsbar software [53]. We will also analyze activation in 
additional reward-related food motivation and impulse control-related brain regions and the whole 
brain (applying 𝝰-error correction procedures). Moreover, we will examine activation in the 
hypothalamus as a brain region responsible for energy homeostasis. 
 
2.5.3. Statistical analysis plan 
Figure 3 shows our statistical analysis plan. We will perform statistical analyses using R statistical 
software, and appropriate R packages [54]. For continuous endpoints (see 2.3.), we will apply 
mixed model regression for repeated measurements (MMRM). We will consider the between-
group (oxytocin versus placebo) difference in the least-squares mean change of each individual 
endpoint variable from baseline to each follow-up study time point of interest as the response in 
MMRM; the change from baseline to week 8 study time point (end of treatment) will be of primary 
interest (see 2.3.). Treatment group (oxytocin versus placebo), study time point (visit), and 
treatment group*study time point interaction will be included as fixed factors in MMRM. The 
baseline value of the endpoint variable and stratification factors (biological sex, BMI) will be 
entered as covariates. Additional covariates may be considered as appropriate. The intercept and 
time slope of individual participants will constitute random effects. Parameters of MMRM will be 
estimated via restricted maximum likelihood (REML). Depending on the distributional 
characteristics of the data, we may apply logarithmic transformations and/or robust statistical 
methods. We will adjust for multiple comparisons where appropriate via the approach of Hothorn, 
Bretz, and Westfall based on single-step simultaneous inference using a joint multivariate t-
distribution [55]. We will use a modified intention-to-treat approach for efficacy analyses, including 
all randomized participants with available data for at least the second baseline visit 


Page 16
(randomization/initiation of treatment) and the first on-treatment study time point. We will apply a 
per-protocol approach for sensitivity analysis, including only randomized participants who 
complete the study and demonstrate ≥80% adherence to treatment. We will carefully examine 
patterns of any missing data regarding their relationships with the outcomes of interest and 
incorporate uncertainty due to missing data with multiple imputations where applicable.  
We will conduct regression-based mediation analyses to explore mechanisms underlying the 
proposed effects of intranasal oxytocin treatment: We will investigate whether (a) the change in 
brain activation in key reward-related food motivation (VTA, OFC, anterior insula) and 
homeostatic (hypothalamus) regions during fasted and fed states as well as in cognitive control-
related regions (dlPFC, dACC, preSMA) during the fasted state from baseline following 4-week 
oxytocin treatment mediates change in caloric intake at a test meal 6 weeks from baseline, (b) 
the change in caloric intake at 6 weeks from baseline mediates the percentage change in body 
weight at 8 weeks from baseline, and (c) the change in REE at 6 weeks from baseline mediates 
the percentage change in body weight at 8 weeks from baseline.  
Safety and compliance endpoints (assessed at each study time point) will be reported and tested 
for between-group (oxytocin versus placebo) differences. Endpoints regarding the off-treatment 
follow-up study time point (week 14 visit) will be reported and analyzed using MMRM in relation 
to baseline and end of treatment (week 8 visit) time points where appropriate (separately from on-
treatment analyses). 
 
2.6. Study drug, study drug administration, and treatment adherence 
The study drug, 24 IU of intranasal oxytocin (Syntocinon® Nasal Spray, Mylan, Switzerland) or 
placebo (containing the same inactive ingredients as Syntocinon®), is applied as 3 sprays per 
nostril, 4 times per day (20–30 minutes before main meals and at bedtime), for an 8-week 
treatment period. We used the same intranasal oxytocin dose (24 IU) in our prior studies of men 
showing significant effects on food reward, homeostatic, and cognitive control-related 


Page 17
neurocircuits, impulsivity, and caloric intake [21,29,30,56]. Further, this oxytocin regimen (versus 
placebo) significantly reduced weight in a small study of 20 adults with obesity [35]. In the latter 
pilot study, as well as in a review article of 38 randomized controlled trials (n = 926 participants) 
administering up to 40 IU of intranasal oxytocin (4 times/day) over up to 13 weeks [57], no serious 
adverse events were reported and there were no differences in adverse events between oxytocin 
and placebo groups. 
To assess treatment adherence, we ask participants to complete and return a drug diary at each 
visit during the 8-week treatment (Table 2). Adherence is determined per the drug diary reflecting 
≥80% study drug administration. Drug accountability is assessed by study staff throughout the 
study, and subjects are asked to return all used and unused study drug at each visit. 
 
2.7. Study monitoring, quality assurance, and protection of human subjects 
Source documents are monitored and reviewed by study staff throughout the study, and medical 
and research records are maintained appropriately per regulatory and institutional requirements 
for the protection of confidentiality of participants. The study is conducted in full conformity with 
federal regulations and institutional requirements (Declaration of Helsinki; regulations for the 
protection of human patients of research; Good Clinical Practice), including periodic and 
expedited safety reporting. An independent data and safety monitoring board consisting of a 
biostatistician and two endocrinologists met before study initiation and regularly thereafter to 
monitor progress and safety. Our study is registered on ClinicalTrials.gov (NCT03043053). 
 
3. Results 
Table 3 presents characteristics of the randomized study sample, which has almost equal 
biological sex distribution and racial (38% non-White) and ethnic (20% Hispanic/Latino) diversity. 
 
4. Discussion 


Page 18
We have carefully designed and powered this randomized controlled double-blind trial to 
determine the efficacy, safety, and underlying mechanisms of 8-week oxytocin administration 
versus placebo as a novel weight-loss therapeutic in a diverse population of adults with obesity. 
Our study builds on solid evidence from animal models [10–12] and a preliminary clinical trial [35]. 
The innovative approach uses metabolic, neuroimaging, and behavioral assessments to provide 
important information about oxytocin mechanisms and actions. 


Page 19
Disclosures 
 
Acknowledgments 
The authors would like to express their deepest gratitude to the entire study team including study 
physicians, psychologists, nurse practitioners, dietitians, fellows, clinical research coordinators, 
and student interns for managing and conducting the study. The authors would like to thank all 
study participants for their participation. 
 
Funding 
This work was supported by National Institutes of Health (NIH) Grants R01 DK109932 (E.A.L.) 
and K24 MH120568 (E.A.L.), core services from the Nutrition Obesity Research Center of Harvard 
P30 DK040561, a Boston Nutrition Obesity Research Center pilot grant P30 DK046200 (F.P.), 
and a Massachusetts General Hospital Department of Medicine Eleanor and Miles Shore 
Fellowship Award (F.P.). 
 
Conflict of Interest Statement 
E.A.L. served on the scientific advisory board and has a financial interest in OXT Therapeutics, 
Inc. E.A.L. also received funding for an investigator-initiated study by Tonix Pharmaceuticals. All 
other authors declare no conflicts of interest. 


Page 20
References 
 
[1] 
C. Fryar, M. Carroll, J. Afful, Prevalence of overweight, obesity, and severe obesity among 
adults aged 20 and over: United States, 1960–1962 through 2017–2018, NCHS Health E-
Stats. (2020). 
[2] 
P. González-Muniesa, M.-A. Mártinez-González, F.B. Hu, J.-P. Després, Y. Matsuzawa, 
R.J.F. Loos, L.A. Moreno, G.A. Bray, J.A. Martinez, Obesity, Nat Rev Dis Primers. 3 (2017) 
17034. https://doi.org/10.1038/nrdp.2017.34. 
[3] 
J. Cawley, A. Biener, C. Meyerhoefer, Y. Ding, T. Zvenyach, B.G. Smolarz, A. Ramasamy, 
Direct medical costs of obesity in the United States and the most populous states, JMCP. 
27 (2021) 354–366. https://doi.org/10.18553/jmcp.2021.20410. 
[4] 
L. Kompaniyets, A.B. Goodman, B. Belay, D.S. Freedman, M.S. Sucosky, S.J. Lange, A.V. 
Gundlapalli, T.K. Boehmer, H.M. Blanck, Body Mass Index and Risk for COVID-19–
Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, 
and Death — United States, March–December 2020, MMWR Morb. Mortal. Wkly. Rep. 70 
(2021) 355–361. https://doi.org/10.15585/mmwr.mm7010e4. 
[5] 
A.A. Tahrani, J. Morton, Benefits of weight loss of 10% or more in patients with overweight 
or obesity: A review, Obesity. 30 (2022) 802–840. https://doi.org/10.1002/oby.23371. 
[6] 
V.L. Gloy, M. Briel, D.L. Bhatt, S.R. Kashyap, P.R. Schauer, G. Mingrone, H.C. Bucher, 
A.J. Nordmann, Bariatric surgery versus non-surgical treatment for obesity: a systematic 
review and meta-analysis of randomised controlled trials, BMJ. 347 (2013) f5934–f5934. 
https://doi.org/10.1136/bmj.f5934. 
[7] 
R.L. Leibel, M. Rosenbaum, J. Hirsch, Changes in Energy Expenditure Resulting from 
Altered Body Weight, N Engl J Med. 332 (1995) 621–628. 
https://doi.org/10.1056/NEJM199503093321001. 


Page 21
[8] 
J.P.H. Wilding, R.L. Batterham, S. Calanna, M. Davies, L.F. Van Gaal, I. Lingvay, B.M. 
McGowan, J. Rosenstock, M.T.D. Tran, T.A. Wadden, S. Wharton, K. Yokote, N. Zeuthen, 
R.F. Kushner, Once-Weekly Semaglutide in Adults with Overweight or Obesity, N Engl J 
Med. 384 (2021) 989–1002. https://doi.org/10.1056/NEJMoa2032183. 
[9] 
T.D. Filippatos, T.V. Panagiotopoulou, M.S. Elisaf, Adverse Effects of GLP-1 Receptor 
Agonists, Rev Diabet Stud. 11 (2014) 202–230. https://doi.org/10.1900/RDS.2014.11.202. 
[10] L. Kerem, E.A. Lawson, The Effects of Oxytocin on Appetite Regulation, Food Intake and 
Metabolism in Humans, IJMS. 22 (2021) 7737. https://doi.org/10.3390/ijms22147737. 
[11] E.A. Lawson, The effects of oxytocin on eating behaviour and metabolism in humans, Nat 
Rev Endocrinol. 13 (2017) 700–709. https://doi.org/10.1038/nrendo.2017.115. 
[12] S.E. McCormack, J.E. Blevins, E.A. Lawson, Metabolic Effects of Oxytocin, Endocrine 
Reviews. 41 (2020) 121–145. https://doi.org/10.1210/endrev/bnz012. 
[13] J.E. Blevins, J.L. Graham, G.J. Morton, K.L. Bales, M.W. Schwartz, D.G. Baskin, P.J. 
Havel, Chronic oxytocin administration inhibits food intake, increases energy expenditure, 
and produces weight loss in fructose-fed obese rhesus monkeys, American Journal of 
Physiology-Regulatory, Integrative and Comparative Physiology. 308 (2015) R431–R438. 
https://doi.org/10.1152/ajpregu.00441.2014. 
[14] E.E. Noble, C.J. Billington, C.M. Kotz, C. Wang, Oxytocin in the ventromedial hypothalamic 
nucleus reduces feeding and acutely increases energy expenditure, American Journal of 
Physiology-Regulatory, Integrative and Comparative Physiology. 307 (2014) R737–R745. 
https://doi.org/10.1152/ajpregu.00118.2014. 
[15] J. Yuan, R. Zhang, R. Wu, Y. Gu, Y. Lu, The effects of oxytocin to rectify metabolic 
dysfunction in obese mice are associated with increased thermogenesis, Molecular and 
Cellular Endocrinology. 514 (2020) 110903. https://doi.org/10.1016/j.mce.2020.110903. 
[16] J.E. Blevins, B.W. Thompson, V.T. Anekonda, J.M. Ho, J.L. Graham, Z.S. Roberts, B.H. 
Hwang, K. Ogimoto, T. Wolden-Hanson, J. Nelson, K.J. Kaiyala, P.J. Havel, K.L. Bales, 


Page 22
G.J. Morton, M.W. Schwartz, D.G. Baskin, Chronic CNS oxytocin signaling preferentially 
induces fat loss in high-fat diet-fed rats by enhancing satiety responses and increasing lipid 
utilization, American Journal of Physiology-Regulatory, Integrative and Comparative 
Physiology. 310 (2016) R640–R658. https://doi.org/10.1152/ajpregu.00220.2015. 
[17] N. Deblon, C. Veyrat-Durebex, L. Bourgoin, A. Caillon, A.-L. Bussier, S. Petrosino, F. 
Piscitelli, J.-J. Legros, V. Geenen, M. Foti, W. Wahli, V. Di Marzo, F. Rohner-Jeanrenaud, 
Mechanisms of the Anti-Obesity Effects of Oxytocin in Diet-Induced Obese Rats, PLoS 
ONE. 6 (2011) e25565. https://doi.org/10.1371/journal.pone.0025565. 
[18] G.J. Morton, B.S. Thatcher, R.D. Reidelberger, K. Ogimoto, T. Wolden-Hanson, D.G. 
Baskin, M.W. Schwartz, J.E. Blevins, Peripheral oxytocin suppresses food intake and 
causes weight loss in diet-induced obese rats, American Journal of Physiology-
Endocrinology and Metabolism. 302 (2012) E134–E144. 
https://doi.org/10.1152/ajpendo.00296.2011. 
[19] J.E. Blevins, D.G. Baskin, Translational and therapeutic potential of oxytocin as an anti-
obesity strategy: Insights from rodents, nonhuman primates and humans, Physiology & 
Behavior. 152 (2015) 438–449. https://doi.org/10.1016/j.physbeh.2015.05.023. 
[20] Y. Maejima, Y. Iwasaki, Y. Yamahara, M. Kodaira, U. Sedbazar, T. Yada, Peripheral 
oxytocin treatment ameliorates obesity by reducing food intake and visceral fat mass, 
Aging. 3 (2011) 1169–1177. https://doi.org/10.18632/aging.100408. 
[21] E.A. Lawson, D.A. Marengi, R.L. DeSanti, T.M. Holmes, D.A. Schoenfeld, C.J. Tolley, 
Oxytocin reduces caloric intake in men: Oxytocin Reduces Caloric Intake, Obesity. 23 
(2015) 950–956. https://doi.org/10.1002/oby.21069. 
[22] R. Arletti, A. Benelli, A. Bertolini, Oxytocin inhibits food and fluid intake in rats, Physiology 
& Behavior. 48 (1990) 825–830. https://doi.org/10.1016/0031-9384(90)90234-U. 


Page 23
[23] B.R. Olson, M.D. Drutarosky, M.-S. Chow, V.J. Hruby, E.M. Stricker, J.G. Verbalis, 
Oxytocin and an oxytocin agonist administered centrally decrease food intake in rats, 
Peptides. 12 (1991) 113–118. https://doi.org/10.1016/0196-9781(91)90176-P. 
[24] V. Ott, G. Finlayson, H. Lehnert, B. Heitmann, M. Heinrichs, J. Born, M. Hallschmid, 
Oxytocin Reduces Reward-Driven Food Intake in Humans, Diabetes. 62 (2013) 3418–
3425. https://doi.org/10.2337/db13-0663. 
[25] M. Thienel, A. Fritsche, M. Heinrichs, A. Peter, M. Ewers, H. Lehnert, J. Born, M. 
Hallschmid, Oxytocin’s inhibitory effect on food intake is stronger in obese than normal-
weight men, Int J Obes. 40 (2016) 1707–1714. https://doi.org/10.1038/ijo.2016.149. 
[26] D.G. Baskin, F. Kim, R.W. Gelling, B.J. Russell, M.W. Schwartz, G.J. Morton, H.N. 
Simhan, D.H. Moralejo, J.E. Blevins, A New Oxytocin-Saporin Cytotoxin for Lesioning 
Oxytocin-Receptive Neurons in the Rat Hindbrain, Endocrinology. 151 (2010) 4207–4213. 
https://doi.org/10.1210/en.2010-0295. 
[27] M.S. Spetter, G.B. Feld, M. Thienel, H. Preissl, M.A. Hege, M. Hallschmid, Oxytocin curbs 
calorie intake via food-specific increases in the activity of brain areas that process reward 
and establish cognitive control, Sci Rep. 8 (2018) 2736. https://doi.org/10.1038/s41598-
018-20963-4. 
[28] V. Burmester, E.L. Gibson, G. Butler, A. Bailey, P. Terry, Oxytocin reduces post-stress 
sweet snack intake in women without attenuating salivary cortisol, Physiology & Behavior. 
212 (2019) 112704. https://doi.org/10.1016/j.physbeh.2019.112704. 
[29] L. Kerem, N. Hadjikhani, L. Holsen, E.A. Lawson, F. Plessow, Oxytocin reduces the 
functional connectivity between brain regions involved in eating behavior in men with 
overweight and obesity, Int J Obes. 44 (2020) 980–989. https://doi.org/10.1038/s41366-
019-0489-7. 
[30] F. Plessow, D.A. Marengi, S.K. Perry, J.M. Felicione, R. Franklin, T.M. Holmes, L.M. 
Holsen, N. Makris, T. Deckersbach, E.A. Lawson, Effects of Intranasal Oxytocin on the 


Page 24
Blood Oxygenation Level-Dependent Signal in Food Motivation and Cognitive Control 
Pathways in Overweight and Obese Men, Neuropsychopharmacol. 43 (2018) 638–645. 
https://doi.org/10.1038/npp.2017.226. 
[31] O.A. Klockars, J.R. Waas, A. Klockars, A.S. Levine, P.K. Olszewski, Neural Basis of 
Ventromedial Hypothalamic Oxytocin-Driven Decrease in Appetite, Neuroscience. 366 
(2017) 54–61. https://doi.org/10.1016/j.neuroscience.2017.10.008. 
[32] K.S. LaBar, D.R. Gitelman, T.B. Parrish, Y.H. Kim, A.C. Nobre, M.M. Mesulam, Hunger 
selectively modulates corticolimbic activation to food stimuli in humans, Behav Neurosci. 
115 (2001) 493–500. https://doi.org/10.1037/0735-7044.115.2.493. 
[33] L.E. Martin, L.M. Holsen, R.J. Chambers, A.S. Bruce, W.M. Brooks, J.R. Zarcone, M.G. 
Butler, C.R. Savage, Neural Mechanisms Associated With Food Motivation in Obese and 
Healthy Weight Adults, Obesity. 18 (2010) 254–260. https://doi.org/10.1038/oby.2009.220. 
[34] D.S. Quintana, A. Lischke, S. Grace, D. Scheele, Y. Ma, B. Becker, Advances in the field 
of intranasal oxytocin research: lessons learned and future directions for clinical research, 
Mol Psychiatry. 26 (2021) 80–91. https://doi.org/10.1038/s41380-020-00864-7. 
[35] H. Zhang, C. Wu, Q. Chen, X. Chen, Z. Xu, J. Wu, D. Cai, Treatment of Obesity and 
Diabetes Using Oxytocin or Analogs in Patients and Mouse Models, PLoS ONE. 8 (2013) 
e61477. https://doi.org/10.1371/journal.pone.0061477. 
[36] R. Guerrieri, C. Nederkoorn, K. Stankiewicz, H. Alberts, N. Geschwind, C. Martijn, A. 
Jansen, The influence of trait and induced state impulsivity on food intake in normal-weight 
healthy women, Appetite. 49 (2007) 66–73. https://doi.org/10.1016/j.appet.2006.11.008. 
[37] U.S. Department of health and human services, Common Terminology Criteria for Adverse 
Events (CTCAE). Version 5.0, 2017. 
[38] P.A. Harris, R. Taylor, R. Thielke, J. Payne, N. Gonzalez, J.G. Conde, Research electronic 
data capture (REDCap)—A metadata-driven methodology and workflow process for 


Page 25
providing translational research informatics support, Journal of Biomedical Informatics. 42 
(2009) 377–381. https://doi.org/10.1016/j.jbi.2008.08.010. 
[39] D.V. Sheehan, Y. Lecrubier, K.H. Sheehan, P. Amorim, J. Janavs, E. Weiller, T. Hergueta, 
R. Baker, G.C. Dunbar, The Mini-International Neuropsychiatric Interview (M.I.N.I.): the 
development and validation of a structured diagnostic psychiatric interview for DSM-IV and 
ICD-10, J Clin Psychiatry. 59 Suppl 20 (1998) 22-33;quiz 34-57. 
[40] R.B. Mazess, H.S. Barden, J.P. Bisek, J. Hanson, Dual-energy x-ray absorptiometry for 
total-body and regional bone-mineral and soft-tissue composition, Am J Clin Nutr. 51 
(1990) 1106–1112. https://doi.org/10.1093/ajcn/51.6.1106. 
[41] M.A. Bredella, R.H. Ghomi, B.J. Thomas, H.A. Ouellette, D.V. Sahani, K.K. Miller, M. 
Torriani, Breath-Hold 1H-Magnetic Resonance Spectroscopy for Intrahepatic Lipid 
Quantification at 3 Tesla, Journal of Computer Assisted Tomography. 34 (2010) 372–376. 
https://doi.org/10.1097/RCT.0b013e3181cefb89. 
[42] L. Holsen, E. Lawson, J. Blum, E. Ko, N. Makris, P. Fazeli, A. Klibanski, J. Goldstein, Food 
motivation circuitry hypoactivation related to hedonic and nonhedonic aspects of hunger 
and satiety in women with active anorexia nervosa and weight-restored women with 
anorexia nervosa, J Psychiatry Neurosci. 37 (2012) 322–332. 
https://doi.org/10.1503/jpn.110156. 
[43] L.M. Holsen, J.R. Zarcone, W.M. Brooks, M.G. Butler, T.I. Thompson, J.S. Ahluwalia, N.L. 
Nollen, C.R. Savage, Neural Mechanisms Underlying Hyperphagia in Prader-Willi 
Syndrome*, Obesity. 14 (2006) 1028–1037. https://doi.org/10.1038/oby.2006.118. 
[44] E.A. Lawson, L.M. Holsen, M. Santin, E. Meenaghan, K.T. Eddy, A.E. Becker, D.B. 
Herzog, J.M. Goldstein, A. Klibanski, Oxytocin Secretion Is Associated with Severity of 
Disordered Eating Psychopathology and Insular Cortex Hypoactivation in Anorexia 
Nervosa, The Journal of Clinical Endocrinology & Metabolism. 97 (2012) E1898–E1908. 
https://doi.org/10.1210/jc.2012-1702. 


Page 26
[45] O.M. Farr, S. Hu, D. Matuskey, S. Zhang, O. Abdelghany, C.R. Li, The effects of 
methylphenidate on cerebral activations to salient stimuli in healthy adults., Experimental 
and Clinical Psychopharmacology. 22 (2014) 154–165. https://doi.org/10.1037/a0034465. 
[46] G.D. Logan, T. Van Zandt, F. Verbruggen, E.-J. Wagenmakers, On the ability to inhibit 
thought and action: General and special theories of an act of control., Psychological 
Review. 121 (2014) 66–95. https://doi.org/10.1037/a0035230. 
[47] G. Bush, L.M. Shin, The Multi-Source Interference Task: an fMRI task that reliably 
activates the cingulo-frontal-parietal cognitive/attention network, Nat Protoc. 1 (2006) 308–
313. https://doi.org/10.1038/nprot.2006.48. 
[48] T.M. Love, Oxytocin, motivation and the role of dopamine, Pharmacology Biochemistry and 
Behavior. 119 (2014) 49–60. https://doi.org/10.1016/j.pbb.2013.06.011. 
[49] A. Campbell, Oxytocin and Human Social Behavior, Pers Soc Psychol Rev. 14 (2010) 
281–295. https://doi.org/10.1177/1088868310363594. 
[50] T.R. Insel, The Challenge of Translation in Social Neuroscience: A Review of Oxytocin, 
Vasopressin, and Affiliative Behavior, Neuron. 65 (2010) 768–779. 
https://doi.org/10.1016/j.neuron.2010.03.005. 
[51] D.M. Cochran, D. Fallon, M. Hill, J.A. Frazier, The Role of Oxytocin in Psychiatric 
Disorders: A Review of Biological and Therapeutic Research Findings, Harvard Review of 
Psychiatry. 21 (2013) 219–247. https://doi.org/10.1097/HRP.0b013e3182a75b7d. 
[52] G. Flandin, K.J. Friston, Analysis of family‐wise error rates in statistical parametric 
mapping using random field theory, Hum. Brain Mapp. 40 (2019) 2052–2054. 
https://doi.org/10.1002/hbm.23839. 
[53] M. Brett, J.-L. Anton, R. Valabregue, J.-B. Poline, Region of interest analysis using an 
SPM toolbox [abstract] Presented at the 8th International Conference on Functional 
Mapping of the Human Brain, Available on CD-ROM in NeuroImage, Vol 16, No 2. Sendai, 
Japan (2002). 


Page 27
[54] R Core Team, R version 4.1.1 (2021-08-10): A language and environment for statistical 
computing, R Foundation for Statistical Computing, Vienna, Austria, 2021. https://www.R-
project.org/. 
[55] T. Hothorn, F. Bretz, P. Westfall, Simultaneous Inference in General Parametric Models, 
Biom. J. 50 (2008) 346–363. https://doi.org/10.1002/bimj.200810425. 
[56] F. Plessow, D.A. Marengi, S.K. Perry, E.A. Lawson, Oxytocin Administration Increases 
Proactive Control in Men with Overweight or Obesity: A Randomized, Double‐Blind, 
Placebo‐Controlled Crossover Study, Obesity. 29 (2021) 56–61. 
https://doi.org/10.1002/oby.23010. 
[57] E. MacDonald, M.R. Dadds, J.L. Brennan, K. Williams, F. Levy, A.J. Cauchi, A review of 
safety, side-effects and subjective reactions to intranasal oxytocin in human research, 
Psychoneuroendocrinology. 36 (2011) 1114–1126. 
https://doi.org/10.1016/j.psyneuen.2011.02.015. 
[58] Metropolitan Life Insurance Company, New weight standards for men and women, 64:1–9 
(1983). 
[59] R.S. Paffenbarger, S.N. Blair, I.M. Lee, R.T. Hyde, Measurement of physical activity to 
assess health effects in free-living populations, Med Sci Sports Exerc. 25 (1993) 60–70. 
https://doi.org/10.1249/00005768-199301000-00010. 
[60] A. Flint, A. Raben, J.E. Blundell, A. Astrup, Reproducibility, power and validity of visual 
analogue scales in assessment of appetite sensations in single test meal studies, Int J 
Obes Relat Metab Disord. 24 (2000) 38–48. https://doi.org/10.1038/sj.ijo.0801083. 
[61] J.M. Mond, P.J. Hay, B. Rodgers, C. Owen, P.J.V. Beumont, Validity of the Eating Disorder 
Examination Questionnaire (EDE-Q) in screening for eating disorders in community 
samples, Behaviour Research and Therapy. 42 (2004) 551–567. 
https://doi.org/10.1016/S0005-7967(03)00161-X. 


Page 28
[62] T. van Strien, J.E.R. Frijters, G.P.A. Bergers, P.B. Defares, The Dutch Eating Behavior 
Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating 
behavior, Int. J. Eat. Disord. 5 (1986) 295–315. https://doi.org/10.1002/1098-
108X(198602)5:2<295::AID-EAT2260050209>3.0.CO;2-T. 
[63] A.D. Ozier, O.W. Kendrick, L.L. Knol, J.D. Leeper, M. Perko, J. Burnham, The Eating and 
Appraisal Due to Emotions and Stress (EADES) Questionnaire: Development and 
Validation, Journal of the American Dietetic Association. 107 (2007) 619–628. 
https://doi.org/10.1016/j.jada.2007.01.004. 
[64] A.J. Stunkard, S. Messick, The three-factor eating questionnaire to measure dietary 
restraint, disinhibition and hunger, Journal of Psychosomatic Research. 29 (1985) 71–83. 
https://doi.org/10.1016/0022-3999(85)90010-8. 
[65] C.S. Carver, T.L. White, Behavioral inhibition, behavioral activation, and affective 
responses to impending reward and punishment: The BIS/BAS Scales., Journal of 
Personality and Social Psychology. 67 (1994) 319–333. https://doi.org/10.1037/0022-
3514.67.2.319. 
[66] J. Polivy, C.P. Herman, T. McFarlane, Effects of anxiety on eating: does palatability 
moderate distress-induced overeating in dieters?, J Abnorm Psychol. 103 (1994) 505–510. 
https://doi.org/10.1037//0021-843x.103.3.505. 
[67] M.R. Lowe, M.L. Butryn, E.R. Didie, R.A. Annunziato, J.G. Thomas, C.E. Crerand, C.N. 
Ochner, M.C. Coletta, D. Bellace, M. Wallaert, J. Halford, The Power of Food Scale. A new 
measure of the psychological influence of the food environment, Appetite. 53 (2009) 114–
118. https://doi.org/10.1016/j.appet.2009.05.016. 
[68] D.E. Gard, M.G. Gard, A.M. Kring, O.P. John, Anticipatory and consummatory components 
of the experience of pleasure: A scale development study, Journal of Research in 
Personality. 40 (2006) 1086–1102. https://doi.org/10.1016/j.jrp.2005.11.001. 


Page 29
[69] J.H. Patton, M.S. Stanford, E.S. Barratt, Factor structure of the barratt impulsiveness 
scale, J. Clin. Psychol. 51 (1995) 768–774. https://doi.org/10.1002/1097-
4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1. 
[70] S. Bejerot, G. Edman, H. Anckarsäter, G. Berglund, C. Gillberg, B. Hofvander, M.B. 
Humble, E. Mörtberg, M. Råstam, O. Ståhlberg, L. Frisén, The Brief Obsessive–
Compulsive Scale (BOCS): A self-report scale for OCD and obsessive–compulsive related 
disorders, Nordic Journal of Psychiatry. 68 (2014) 549–559. 
https://doi.org/10.3109/08039488.2014.884631. 
[71] A. Luszczynska, M. Diehl, B. Gutiérrez-Doña, P. Kuusinen, R. Schwarzer, Measuring one 
component of dispositional self-regulation: attention control in goal pursuit, Personality and 
Individual Differences. 37 (2004) 555–566. https://doi.org/10.1016/j.paid.2003.09.026. 
[72] S.C. Kushner, L.C. Quilty, J.L. Tackett, R.M. Bagby, The Hierarchical Structure of the 
Dimensional Assessment of Personality Pathology (DAPP-BQ), Journal of Personality 
Disorders. 25 (2011) 504–516. https://doi.org/10.1521/pedi.2011.25.4.504. 
[73] S. Cohen, R. Mermelstein, T. Kamarck, H.M. Hoberman, Measuring the Functional 
Components of Social Support, in: I.G. Sarason, B.R. Sarason (Eds.), Social Support: 
Theory, Research and Applications, Springer Netherlands, Dordrecht, 1985: pp. 73–94. 
https://doi.org/10.1007/978-94-009-5115-0_5. 
[74] N.K. Rytwinski, D.M. Fresco, R.G. Heimberg, M.E. Coles, M.R. Liebowitz, S. Cissell, M.B. 
Stein, S.G. Hofmann, Screening for social anxiety disorder with the self-report version of 
the Liebowitz Social Anxiety Scale, Depress. Anxiety. 26 (2009) 34–38. 
https://doi.org/10.1002/da.20503. 
[75] S. Baron-Cohen, S. Wheelwright, J. Hill, Y. Raste, I. Plumb, The “Reading the Mind in the 
Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome 
or high-functioning autism, J Child Psychol Psychiatry. 42 (2001) 241–251. 


Page 30
[76] O. Gillath, J. Hart, E.E. Noftle, G.D. Stockdale, Development and validation of a state adult 
attachment measure (SAAM), Journal of Research in Personality. 43 (2009) 362–373. 
https://doi.org/10.1016/j.jrp.2008.12.009. 
[77] R.M. Bagby, G.J. Taylor, J.D.A. Parker, The twenty-item Toronto Alexithymia scale—II. 
Convergent, discriminant, and concurrent validity, Journal of Psychosomatic Research. 38 
(1994) 33–40. https://doi.org/10.1016/0022-3999(94)90006-X. 


Page 31
Tables 
 
Table 1. Inclusion and exclusion criteria. 
Inclusion criteria 
Exclusion criteria 
• 18–45 years old 
• BMI ≥30 kg/m2 
• Willingness to maintain 
current diet and lifestyle for 
the duration of study 
participation (e.g., agree not 
to start any special diets or 
lifestyle changes intended to 
modify weight) 
Weight/diet 
• Weight change >5 kg within 3 months prior to randomization 
• Nonstandard diet (e.g., Paleo, Atkins, raw diet, macrobiotic diet)  
• Significant change in physical activity or diet within 1 month of 
randomization 
Medical/psychiatric conditions 
• History of any of the following medical conditions:  
o Cardiovascular disease, prolonged QT on EKG at screening 
visit 
o Clinically significant gastrointestinal disorder 
o Bariatric surgery (except for participants with a history of 
laparoscopic adjustable gastric band surgery) 
o Bleeding disorder 
o Untreated thyroid disease 
o Malignancy within 5 years of screening visit (except for basal 
cell carcinoma or squamous cell carcinoma) 
o Epilepsy 
o Any significant illness or condition that the investigator 
determines could interfere with study participation and impact 
data collection or participant safety  
• History of an eating disorder or psychosis 
• Current psychiatric disorder (unless on stable dose of anti-depressant or 
anti-anxiety treatment for ≥6 months with no active symptoms that would 
qualify for an active psychiatric diagnosis as assessed by the Mini-
International Neuropsychiatric Interview)  
• Substance use disorder active within the last 6 months or clinical suspicion 
of ongoing substance use disorder at screening visit (based on history, 
Mini-International Neuropsychiatric Interview, and/or lab results) 
• Current smoking or tobacco use 
• Allergy to lidocaine 
Medications 
• Medication changes within 4 weeks of enrollment 
• Oral contraceptives 
• Use of prescription or over-the-counter drugs or dietary/herbal 
supplements affecting metabolism, blood glucose, or appetite (metformin 
allowed if on stable dose with stable weight for ≥3 months) 
Screening labs 
• Hematocrit >2% below normal 
• Fasting glucose >125 mg/dL or hemoglobin A1c ≥7% 
• Liver transaminases (ALT, AST) >2.5 times upper limit of normal 
• Creatinine >1.5 mg/dL 
• Hyponatremia 
Further exclusion criteria 
• Contraindications to MRI and/or DXA  
• Pregnancy or breastfeeding 
• Unwilling to use a medically acceptable form of contraception throughout 
the study period (female of child-bearing potential only) 
• Participation in any clinical study involving an investigational drug, device, 
or biologic within 1 month of randomization 


Page 32
Abbreviations: BMI, body mass index; EKG, electrocardiogram; ALT, alanine transaminase; AST, aspartate 
transaminase; MRI, magnetic resonance imaging; DXA, dual-energy X-ray absorptiometry.


Page 33
Table 2. Study procedures at individual study time points. 
Assessments 
Screen 
BL 
ON-T (oxytocin versus placebo) 
OFF-T 
BL1 
BL2 
W2 
W4 
W6 
W8 
W14 
Screening and safety  
• Medical history and physical exam, 
MMSE 
X 
X 
X 
X 
X 
X 
X 
X 
• Screening/safety labs (CBC, TSH, 
urine drug screen (at screen only); 
CMP, HbA1c) 
X 
 
X 
X 
X 
 
X 
X 
• EKG 
X 
 
 
X 
 
 
X 
 
• M.I.N.I.  
X 
 
 
 
 
 
 
 
• Adverse events monitoring, review 
of concomitant medication, 
pregnancy test (urine/serum hCG) 
in females 
X 
X 
X 
X 
X 
X 
X 
X 
Anthropometric measurements 
(height, weight, %IBWa, BMIb, WHRc) 
X 
X 
X 
X 
X 
X 
X 
X 
Energy expenditure, body composition, and fat metabolism  
• Indirect calorimetry (REE) 
 
X 
 
 
 
X 
X 
 
• DXA (total fat mass, lean body 
mass) 
 
X 
 
 
 
 
X 
 
• MRI (abdominal and thigh 
measures) 
 
X 
 
 
 
 
X 
 
• 1H-MRS (liver fat) 
 
X 
 
 
 
 
X 
 
• Fat biopsy 
 
X 
 
X 
 
 
 
 
Caloric intake  
• Breakfast test meal (experimental 
assessment of caloric intake) 
 
X 
 
 
 
X 
 
 
• 4-day food record (typical eating) 
 
X 
X 
 
X 
X 
X 
X 
• 24-hr food recall (caloric intake 
immediately preceding visit) 
 
X 
X 
 
X 
X 
 
 
Laboratory assessments  
• Blood for metabolic profile (e.g., 
insulin sensitivity, lipid panel, 
hsCRP, appetite-regulating 
hormones), gonadal steroids, 
leukocyte RNA, DNA (optional) 
 
 
X 
X 
X 
X 
X 
X 
• 24h urine (e.g., cortisol, creatinine) 
 
X 
 
 
 
 
X 
 
• Stool (e.g., microbiome; optional) 
 
 
X 
 
X 
 
X 
 
• Hair (e.g., cortisol; optional) 
 
 
X 
 
 
 
X 
 
Neuroimaging 
 
 
 
 
 
 
 
 
• Food cue task (fasting and post-
meal): VTA, OFC, anterior insula, 
hypothalamus 
 
 
X 
 
X 
 
 
 
• Impulse control tasks (Stop-Signal 
Task; Multi-Source Interference 
Task [optional]) (fasting): dlPFC, 
dACC, preSMA 
• Handedness questionnaire 
 
 
 
 
X 
 
X 
 
 
X 
 
 
 
Psychological, behavioral, and quality of life assessments 
• Physical activity (PPAQd) 
X 
 
 
 
X 
X 
 
X 
• Appetite (VASe), eating behavior 
and attitudes (EDEQf, DEBQg, 
EADESh, TFEQi) 
 
X 
X 
 
X 
X 
X 
X 
• Reward-related food motivation 
(BIS/BASj, CTTk, PFSl) and reward-
related behaviors and choices 
 
 
X 
 
X 
 
 
X 


Page 34
(Probabilistic Reward Task and 
Social Reward Task, TEPSm; 
optional) 
• Impulse control and compulsivity 
(Delay Discounting Task, Reversal 
Learning Task, BIS-11n, BOCSo, 
SRSp; optional) 
 
 
X 
 
X 
 
 
 
 
• Social-emotional functioning 
(DAPP-BQq, ISELr, LSASs, RMETt, 
SAAMu, TAS-20v) 
 
X 
 
 
 
 
X 
 
• Mood and anxiety symptoms (BDI-
II, STAI-trait) 
 
 
X 
 
X 
 
X 
X 
• Quality of life (SF-36) 
 
 
X 
 
X 
 
X 
X 
Adherence  
• Study drug diary 
 
 
 
X 
X 
X 
X 
 
 
Footnotes (including brief definition and reference) and abbreviations: 
aIdeal body weight is determined using elbow breadth and frame size and the 1983 Metropolitan Life Insurance 
Height/Weight Tables [58]. 
bBody mass index is calculated as weight in kg / (height in m)2. 
cWaist-to-hip ratio is Iliac circumference over broadest hip circumference. 
dPaffenbarger Physical Activity Questionnaire (interview conducted by trained research coordinators) measuring 
physical activity [59]. 
eVisual Analogue Scales (self-report) asking for subjective appetite [60]. 
fEating Disorder Examination Questionnaire (self-report) screening for eating disorders [61]. 
gDutch Eating Behavior Questionnaire (self-report) measuring restrained, emotional, and external eating [62]. 
hEating and Appraisal Due to Emotions and Stress (self-report) questionnaire assessing stress coping in relation to 
eating [63]. 
iThree Factor Eating Questionnaire (self-report) measuring cognitive restraint of eating, disinhibition, and hunger [64]. 
jBehavioral 
Inhibition 
System/Behavioral 
Activation 
System 
(self-report) 
assessing 
motivation 
regarding 
aversive/appetitive stimuli [65]. 
kCookie Taste Test (behavioral task, post standardized meal and snack) assessing reward-related food motivation 
beyond satiety [24,66]. 
lPower of Food Scale (self-report) measuring appetite for palatable foods [67]. 
mTemporal Experience of Pleasure Scale (self-report) measuring anticipatory and consummatory facets of pleasure 
[68]. 
nBarratt Impulsiveness Scale (self-report) assessing attentional, motor, and non-planning impulsiveness [69]. 
oBrief Obsessive-Compulsive Scale (self-report) measuring the presence of obsessive-compulsive symptoms [70]. 


Page 35
pSelf-Regulation Scale (self-report) capturing attentional control in goal pursuit [71]. 
qDimensional Assessment of Personality Pathology-Basic Questionnaire (self-report) assessing suspiciousness and 
secure attachment [72]. 
rInterpersonal Support Evaluation List (self-report) assessing perceived availability of social support [73]. 
sLiebowitz Social Adjustment Scale (self-report) measuring fear and avoidance of social situations [74]. 
tReading the Mind in the Eyes Test (behavioral task) assessing theory of mind [75]. 
uState Adult Attachment Measure (self-report) capturing attachment insecurity [76]. 
vToronto Alexithymia Scale (self-report) measuring the ability to describe and identify emotions [77]. 
 
BL, baseline visit; T, Treatment; W, week of visit; HR, heart rate; MMSE, Mini-Mental Status Exam; CBC, complete 
blood count; TSH, thyroid stimulating hormone; CMP, comprehensive metabolic panel (including liver 
[alanine/aspartate transaminase] and renal [creatinine] function tests, electrolytes [potassium, sodium], and glucose); 
HbA1c, hemoglobin A1c; EKG, electrocardiogram; M.I.N.I., Mini-International Neuropsychiatric Interview; hCG, human 
chorionic gonadotropin; REE, resting energy expenditure; DXA, dual-energy X-ray absorptiometry; MRI, magnetic 
resonance imaging; 1H-MRS, magnetic resonance spectroscopy; hsCRP, high-sensitivity C-reactive protein; VTA, 
ventral tegmental area; OFC, orbitofrontal cortex; dlPFC, dorsolateral prefrontal cortex; dACC, dorsal anterior cingulate 
cortex; preSMA, anterior portion of the supplementary motor cortex; BDI, Beck Depression Inventory; STAI, State Trait 
Anxiety Inventory; SF-36, 36-Item Short Form Survey. 
 


Page 36
Table 3. Characteristics of the randomized participants at baseline (BL1 visit). 
Characteristic 
Mean (SD) or N (%) 
Demographics 
 
N randomized 
61 
Biological sex: F | M 
33 (54.10%) | 28 (45.90%) 
Age (years) 
33.58 (6.22) 
Race 
• Asian 
6 (9.84%) 
• Black 
13 (21.31%) 
• White 
38 (62.30%) 
• Other/More than one race 
3 (4.92%) 
• Asked but unknown 
1 (1.64%) 
Ethnicity 
• Hispanic/Latino ethnic group 
12 (19.67%) 
Clinical characteristics 
BMI (kg/m2) [F | M] 
37.02 (4.93) [37.77 (4.92) | 36.13 (4.87)] 
BMI distribution (kg/m2) 
• ≥30 to <35 [F | M] 
24 (39.34%) [8 | 16] 
• ≥35 to <40 [F | M] 
21 (34.43%) [15 | 6] 
• ≥40 [F | M] 
16 (26.23%) [10 | 6] 
 
Abbreviations: F, female; M, male; BMI, body mass index.


Page 37
Figures 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


Page 38
Figure 1. Proposed model of oxytocin effects on eating behavior and metabolism leading to weight loss. 
Intranasal oxytocin may reduce body weight via multiple complementary mechanisms. Oxytocin may suppress caloric intake via reduced activation of reward and 
homeostatic brain regions and increased activation of cognitive control brain regions. Oxytocin reduces inflammation, which in turn may reduce appetite and increase 
energy expenditure. Oxytocin increases thermogenesis and energy expenditure. These effects lead to lipolysis, fat oxidation, and reduced fat mass, ultimately 
resulting in weight loss. 


Page 39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2. Study design. 
Abbreviations: IU, international unit; REE, resting energy expenditure; BL, baseline visit; W, week of visit.  
 
Primary endpoint: 
∆(%) Body weight ∆W8-BL
Mediators ∆W4-BL:
• Metabolic profile
• Brain activation of food 
reward, homeostatic, and 
cognitive control regions
BL Body weight
Screening
Intranasal Oxytocin (24 IU)
Placebo
8-Week Double Blind Treatment Period
Week 2 (W2)
Week 4 (W4)
Week 6 (W6)
Week 8 (W8), 
End of treatment
N≥50 completers
End of Study
Week 14 (W14)
Follow-up
Baseline
Randomization 
(1:1)
≤3 months
2 weeks
2 weeks
2 weeks
6 weeks
Baseline assessment (BL)
Eligibility
Mediators ∆W6-BL:
• REE
• Caloric intake
• REE 
• Body composition
• Caloric intake
• Metabolic profile
• Brain activation of food reward, 
homeostatic, and cognitive control 
regions
Secondary endpoints 
∆W8-BL:
• REE
• Body composition
• Caloric intake
• Metabolic profile
Separate analysis 
∆W14-BL and ∆W14-W8:
• Body weight
• Caloric intake
• Metabolic profile
∆(%) Body weight ∆W2-BL
Assessment of safety and adverse events at each study time point
2 weeks
Off-treatment
∆(%) Body weight ∆W4-BL
∆(%) Body weight ∆W6-BL


Page 40
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Variable
Use/Objective
Variable type
Statistical model
Methods for missing data
Change(%) in body weight (∆W8-BL)
Primary endpoint
Continuous
Mixed-model regression for 
repeated measurements 
(MMRM), adjustment for 
multiple comparisons as 
appropriate via approach of 
Hothorn, Bretz, and Westfall
Modified intention-to-treat 
approach (mITT), Multiple 
imputation (MI) where 
applicable
REE (adjusted for lean mass) (∆W8-BL)
Key secondary endpoint
Continuous
Total body, visceral, and liver fat mass (∆W8-BL)
Key secondary endpoint
Continuous
Caloric intake (∆W6-BL, ∆W8-BL)
Key secondary endpoint
Continuous
Metabolic profile (∆W8-BL)
Secondary endpoint
Continuous
Psychological, behavioral and quality of life assessments Secondary endpoint
Continuous
Vital signs, QTc on EKG, %IBW, BMI, waist-to-hip ratio
Vital signs, EKG, 
anthropometrics
Continuous
Descriptive statistics
Age, biological sex, race, ethnicity, coexisting conditions, 
medication
Demographic and clinical 
variables
Continuous/ 
Categorical
1 – Endpoints
Mediator variable
Dependent variable
Independent variable
Statistical model
Brain activation (∆W4-BL): food reward, homeostatic and 
cognitive control regions
Caloric intake (∆W6-BL)
Treatment (oxytocin vs. 
placebo)
Regression-based mediation 
analysis
Caloric intake (∆W6-BL)
Change(%) in body weight (∆W8-BL)
Treatment
REE (∆W6-BL)
Change(%) in body weight (∆W8-BL)
Treatment
2 – Mediators
3 – Safety and Compliance (all study time points)
Order of analyses
Randomized, double-blind, placebo-controlled clinical trial
Participants: F/M, 18-45 years, BMI ≥30kg/m2
Randomization at BL: 1 : 1 oxytocin : placebo
Stratification: Biological sex, BMI
Variable
Use/Objective
Variable type
Statistical model
N (%) of in-trial period treatment-emergent adverse 
events (AE), AE-related discontinuations
Safety endpoint
Categorical/
Count
χ2 test (oxytocin vs. placebo) for severity categories (mild, 
moderate, severe)
N (%) of total expected study drug doses administered 
per subject
Treatment adherence
Count
Descriptive statistics: N (%) with compliance ≥ 80%
Variable
Use/Objective
Variable type
Statistical model
Body weight, anthropometrics, caloric intake, metabolic 
profile, psychological, behavioral and quality of life 
assessments, safety
Follow-up (sustained 
effects) and safety
Continuous
Descriptive statistics, MMRM (separate analysis)
4 – Off-treatment follow-up (W14)


Page 41
Figure 3. Proposed statistical analysis plan. 
Abbreviations: F, female; M, male; BMI, body mass index; BL, baseline visit; W, week of visit; REE, resting energy expenditure; QTc, QT corrected; EKG, 
electrocardiogram; IBW, ideal body weight. 
 
For study endpoints, main contrasts are stated (e.g., ∆W8-BL); further contrasts may be investigated as appropriate and according to assessment time points of 
endpoint variables (Table 2). 
 
 


Page 42
Declaration of interests 
 
 
☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to 
influence the work reported in this paper. 
 
☒The authors declare the following financial interests/personal relationships which may be considered as potential competing 
interests:  
 
 
 
 
E.A.L. served on the scientific advisory board and has a financial interest in OXT Therapeutics, Inc. 
E.A.L. also received funding for an investigator-initiated study by Tonix Pharmaceuticals.  
 
All other authors declare no conflicts of interest.