The Omnibus Satiety Metric (OmniSaM)
Phase: NA
Conditions: Satiation, Appetite, Behavior, Hunger
Sponsor: Derek Victor Byrne

Official Title of the study: The Omnibus Satiety Metric (OmniSaM) 
Date of the document: 1 March 2019
Full study protocol and statistical analysis plan 
Contents 
1. Participant Flow
2. Baseline Characteristics
3. Outcome Measures
4. Adverse Event Information
5. Limitations and Caveats
6. Certain Agreements
7. Results Point of Contact
1. Participant Flow 
Recruitment Details. Participants were recruited via convenience sample via the website 
www.forsøgsperson.dk . The recruitment started oct 2017 and lasted around approx. 
16months.  
Pre-assignment Details. Via a phone meeting we first assess if the volunteers fulfill the 
inclusion-exclusion criteria. Those who meet the initial criteria for inclusion will participate in 
an information meeting where information about procedures will be delivered and participants 
can ask questions. Those who agree to participate in the study will give written consent, 
deliver a blood sample to be analyze for: hemoglobin, liver and kidney function and 
inflammatory state, and weight, height and blood pressure will be measured, to ensure the 
recruited people meet all the inclusion and exclusion criteria. These criteria are listed in the 
main clinical trials document. 
Enrollment (total, anticipatory). Total whom started the study 28; Anticipated completion of 
all four test days 25; Fully completion of all four test days 23; Excluded from analyses 5  
Allocation (randomized). This is a single arm study in which all conditions of a 2x2 factorial 
design and allocated to the same group in a randomized order. 
Follow-up (immediately after intervention). There is no follow up. 
Arm/Group Information. There is 1 arm in the study. 
Arm/Group Description. All participants in this single arm study undergo a dietary 
intervention, comprising the 2x2 factorial design in which they are tested under four 
conditions in which only the dietary preload changes.  
1. high protein2carb high calorie 
2. high protein2carb low calorie 
3. low protein2carb high calorie
4. low protein2carb low calorie 
Period(s). There is only one stage in the study, comprising the four test days, one for each of 
the experimental conditions 
Period Title. Overall Study. 
Started. 28 started the study. 
Completed. 23 finished the study. 
Milestone Title. There are no follow ups so therefore no milestones. 
Reason Not Completed Type. The reasons for not completing the study included only the
following, and were fully recorded: Metal thread behind front teeth (1); Excluded by project 
staff due to frivolous attitude towards the study (1); Physiological hindrance of drawing blood 
from subject (1); Blood sampling not possible (2); Personal issues without the possibility of 
rescheduling (1) 
2. Baseline Characteristics 
Arm/Group Information. Baseline characteristics will be computed in the form of standard 
summary statistics (means, standard deviations, Bayesian credibility intervals)  
Baseline Measure Information. A group of demographic characteristics will be measured in 
the study, including: age; gender; weight; height; educational level; income;  
Baseline Measure Title. Age: Continuous (years); Gender: Categorical (female, male); Weight: 
Continuous (kg); Height Continuous (meter); Educational level: Categorical (lower secondary, 
higher secondary, higher secondary with trainee, short-length higher education, medium-
length higher education, long-length higher education); Income: Categorical (< 100.000 Dkkr, 
100.000-199.999 Dkkr, 200.000-299.999 Dkkr, 300.000-399.999 Dkkr, 400.000-599.999 Dkkr, 
600.000-799.999 Dkkr, ≥ 800.000 Dkkr, do not wish to inform) 
Measure of Dispersion. Standard Deviation, Interquartile ranges, Range 
3. Outcome Measures 
Outcome Measure Information. The primary outcome is the energy consumed in the ad 
libitum meal. 
Outcome Measure Type. Primary 
Outcome Measure Title. Energy consumed 
Outcome Measure unit. kJ 
Outcome Measure Time Frame. There will be four such measurements per participant, one for 
each condition. These are taken at the end of the experimental testing day. 
Overall Number of Participants Analyzed. There will be data analysed from 23 participants. 
Measure Type. Means 
Measure of Dispersion/Precision. Standard Deviation and Bayesian central credibility intervals 
Outcome Data. The measurement value(s) for each outcome measure cannot obtain right now 
since data collection has not finished yet. 
Comparison Group Selection. There is only 1 group. 
Statistical Testing.  
Statistical modelling of multimodal metric 
A Bayesian linear regression will be implemented in JASP software, with the primary outcome 
of energy consumption as the dependent variable. The regressor variables will be variables 
derived from all three data modalities (described below). This regression model computes all 
possible combinations of regressors, with each combination specified as a separate model and 
assigned an equal prior probability. The regressors to be tested are defined operationally as 
follows: we compute a Principal component analysis (PCA) to identify the components within 
the data necessary to explain 95% of the variance in the data, with the PCA performed 
independently for the three modalities of data, brain, blood and behavior. For all of these 
different models, Bayes Factors can be computed comparing each model against the best 
model, or alternatively against the null model. The null model is taken to include all covariates 
not included in the neuroimaging data, subjective report data, or endocrine and metabolite 
data. Inclusion probabilities will be computed for all regressors, which allows us to infer Bayes 
factors that indicate the evidence for models including each regressor against all models that 
do not. This indicates how much the data favors the inclusion of the regressor in the best 
model. Inclusion probabilities and their associated Bayes factors will be computed for all 
regressors and their groupings into the three data modalities. This affords inference into the 
how much the data favors the inclusion of a particular data modality. Bayes factors will also be 
computed between data modalities, to evaluate the relative importance of each modality in 
predicting energy consumption. The regressors can also be grouped according to timepoint, 
affording comparison of inclusion probabilities between distant and proximal times to the 
point of consumption.  
Statistical modelling of subjective measures 
In modelling the subjective reports of appetitive sensations we will perform the following 
procedures:  To address the question of how well subjective measures predict energy intake 
we will perform a Partial Least Squares analyses, to find the multidimensional direction in 
appetite sensation space, that explains the maximum variance in energy intake space. To 
isolate the subset of subjective factors that are best predictive of energy intake we will 
perform a Bayesian Linear regression with subjective reports and time as regressors, and with 
energy expenditure, weight and BMI as covariates. To address the question of between 
preload effects on subjective reports of hedonic- and appetite ratings and energy intake, and 
the discriminability of the different appetite ratings, we will perform a repeated measures 2-
way ANOVA, with both preload dimensions and time as independent/explanatory variables 
and hedonic, appetite and intake data as dependent/response variables. The repeated-
measure factor will be the within-subject factor. Post hoc pairwise multiple-comparisons will 
be used as follow-up tests to compare every group mean.  
Statistical modelling of neural measures 
The aim of neuroimaging analysis is to delineate the interoceptive neural systems 
underpinning satiety, and its interface with behavioral and endocrine systems. Broadly we 
hypothesise that tonic & food cue-evoked activity in viscerosensory & visceromotor networks, 
will be predictive of next-meal energy intake, specifically in Hypothalamus & Insula and more 
broadly in regions of interest: ACC, OFC, SGC, PBN, NTS, amygdala, striatum. We will engage in 
exploratory data analysis by mapping the neural modulations caused by post-prandial 
endocrine dynamics. Finally we will extract key features of data from regions of interest to 
assess how neural activity can predict energy intake.  For the functional neuroimaging data, a 
multiple linear regression model will be constructed via the software Statistical Parametric 
Mapping. The onset of food and object images will be modelled as stimulus onsets using 
standard methods, convolving stick functions representing stimulus onsets with hemodynamic 
response functions. Regressors of interest will include food images classified according to high 
or low energy density, and objects. Resting state data will be modelled according to 
standardized pipelines. We will compute the main effect of caloric density of food cues. We 
will compute the Parametric modulations of blood and subjective covariates on these caloric 
density responses. We will compute the main effect of endocrine and subjective covariates as 
slow varying modulators of resting activity. Multivariate Bayesian decoding will be performed 
on regions of interest to ask which brain regions decode onto endocrine responses, subjective 
responses, and ultimately energy intake.    
Statistical modelling of endocrine responses 
In modelling the endocrine responses we will focus on 2 physiological modalities; appetite 
sensation (outcome=energy intake) and nutrient sensing (outcome=hormone responses). 
1. Appetite sensation: To address the question of how well endocrine responses predict energy 
intake we will perform a linear regression models with energy intake as dependent variable 
and AUC of endocrine metabolic marker and energy expenditure (calculated from height and 
weight) as covariates (and subject id as random effect). A regression model will be performed 
for each of the different endocrine markers to find the marker that explains the maximum 
variance in energy intake. 
In addition, we will perform a Bayesian linear regression (as described in preceding section), 
with energy intake as dependent variable. The regressors of interest will be based on area 
under the curve metrics computed from all of the endocrine and metabolic markers obtained 
from the blood sampling including GLP1, Ghrelin, PYY, CCK, glucose, insulin. 
2. Nutrient sensing: To address the question of between preload effects on endocrine 
responses we will perform a repeated measures 2-way ANOVA with AUC of endocrine marker 
as dependent variable and both preload dimensions as independent fixed effects and 
individual subject id as an independent random effect. Post hoc pairwise multiple-comparisons
will be used as follow-up tests to compare every group mean. 
4. Adverse Event Information 
Time Frame. Possible adverse events can include fainting at the point of drawing blood. This is 
monitored by two researchers at all times, where the participant is vulnerable to fainting. 
Adverse Event Reporting Description. We will add relevant information about adverse event 
after finishing the study. 
5. Limitations and Caveats
There may be several limitations or caveats. First, it is possible that the dietary intervention 
does not impact on energy consumption hours later. The dietary intervention is primarily used 
as a manipulation to drive variation in consumption. As long as consumption has variance 
across and within participants, then the main goal of using the data modalities to predict 
satiety is still achievable. Second, it is possible participants do not comply with instructions 
prior to their experimental day. This will be evident in the blood measures, indicating for 
instance if they have not fasted. Non-compliance could be evident in their tracker activity, for 
instance if they have engaged in extreme activity prior to testing. However not all such cases 
can be inferred from these measures. 
6. Certain Agreements 
Are all PIs Employees of Sponsor? No. There is one sponsor-investigator and none of the other 
principal investigators are employed by the sponsor. 
Results Point of Contact 
Name. Dr Barbara Vad Andersen, PhD 
Position title: Post Doc 
Organization Name. University of Aarhus 
Phone. +45 30 70 78 60 
Email. barbarav.andersen@food.au.dk