Effects of Ultra Processed Food on Intestinal Energy Harvest
Phase: NA
Conditions: Obesity and Overweight
Sponsor: Columbia University

Protocol Title: Effects of ultra processed food on intestinal energy harvest
NCT Number:  NCT06853288
Date of Document: December 19, 2024
December 19, 2024 
v1.0 
Design overview: 
The proposed study will employ a randomized crossover-controlled feeding trial. Each 
participant will undergo two phases separated by a washout period. Each phase will represent 
one of two conditions: 80% ultraprocessed foods (UPFs) vs. 20% UPFs. Participants will 
consume isocaloric diets differing only in the level of processing, and the diets will be designed 
to match the participants’ individual energy needs to maintain baseline body weight. 
Figure 1. Research design 
Procedures: 
Recruitment 
Healthy adults with obesity (30-40 kg/m2) will be recruited by flyers posted in high traffic areas 
in the catchment areas of CUIMC/NYP and Columbia University’s RecruitMe website. 
Additional recruitment methods involve in-person presentations to campus and community 
groups. Interested individuals will contact study staff for initial screening and review of 
eligibility criteria. Participants will be screened for any medical history contraindicated to 
metabolic studies (i.e., current or recent history of AOMs, other medications/supplements known 
to affect metabolism with no intention to change current medication/supplements for duration of 
study, recent antibiotic use, currently pregnant or intending to become pregnant), not actively 
trying to lose or gain weight, no known food allergies, and willing to commit to a 9 weeks  study. 
Participants will also have to verbalize that they do their own grocery shopping. Participants who 

Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
December 19, 2024  
 
v1.0 
 
successfully screen in will receive remuneration for their time and provided food for the two diet 
phases. 
Baseline (Day 0) 
At the start of their first study phase, accrued participants will complete a 5-day baseline period. 
On the first day of the baseline period (Day 0), participants will come to the laboratory to 
provide consent and receive a tutorial of the Healthie app, a HIPAA compliant dietetic practice 
app, that will be used as a compliance assessment tool and a communication tool. Healthie is a 
cloud-based practice management platform designed for nutrition and wellness professionals. 
The Healthie platform can be accessed from the web browser or through iOS or Android app. 
Participants will receive an assigned one time log in credential to access Healthie; this will be 
provided as Gmail alias accounts that will be administered through a main study Gmail account 
that will only be used for one time log-ins to the HIPAA compliant app at the Day 0 in person 
visit. There is no exchange of PHI/PII through the Gmail account.  Participants will be provided 
with an email address and password to use that is appended to the main email account and 
follows the format of “email address+ParticipantIDnumber”, which looks like an unique address 
but this way participants will not have to use their own personal email accounts, further 
protecting their anonymity with Healthie. Participants will be given an overview of the Healthie 
app, the NIH’s Automated Self-Administered 24-h Diet Record tool and provided stool 
collection kits and instructions for use and shipping to the investigator. Additionally, pregnancy 
will be assessed by urine dipstick. 
Baseline (Days 1-3): 
Over the next 3 days, participants will electronically record all foods and beverages consumed 
each day using the NIH’s Automated Self-Administered 24-h Diet Record tool. Participants will 
also be asked to collect and provide the date and time of at least 3 consecutive stool samples with 
date and time. The participants will ship the samples to CUIMC researchers.  These samples will 
be deidentified and sent to UNC for microbiome analysis. 
Baseline (Day 4) 
On the fourth day of the of the baseline period (Day 4), participants will return to the clinic to 
have body composition, energy expenditure and cardiometabolic measures will be performed 
before randomization to their diet assignment order. 
• Body Composition will be measured by quantitative magnetic resonance (QMR). QMR 
(Echo-MRI; Echo Medical Systems, Houston TX) is a non-imaging technique that uses a 
magnetic field to detect the hydrogen atoms of fat, lean tissue and water4. These protons 
have different spin characteristics according to their environment or tissue that are 
attached to that can be detected. The processed signal is obtained from the whole body at 
once. Participants will be scanned a total of three times for a total measurement time of 
no more than 10 minutes. 
Resting energy expenditure (REE) will be measured using whole room indirect calorimetry5. Data 
collection will occur using a whole-room calorimeter consisting of an air-tight temperature-
controlled room with pre-specified flow rate connected to a pull through calorimetry system 
(Sable Systems Intl, Las Vegas NV). By flowing a known amount of fresh air through the room, 

Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
December 19, 2024  
 
v1.0 
 
respiratory gases from the study participant are sampled on the exhaust side of the system for 
measurement of oxygen and carbon dioxide concentrations. The respiratory gases are analyzed 
using fuel cell oxygen and near infrared carbon dioxide sensors (Model GA-3m2, Sable Systems 
International, Las Vegas, NV). All acquisition and analysis software for a SW-Promethium 
System is installed on a Sable Systems approved computer, with a Sable Systems Promethium 
Interface Module (Model IM-2). Data are recorded and processed on-line by the Sablesoftware 
programs Caloscreen and Expedata (Sable Systems International, Las Vegas, NV). During the 60-
minute data collection period, the participant will be asked to remain motionless, awake and 
relaxed. The first 10 minutes of data collection and the final 10 minutes of data collection are 
discarded REE is estimated from the expiratory gases Participants will be asked to come in 
fasting (12 hours) and required not to exercise during the 24hr period before the measure. 
Participants will be monitored to ensure they remain awake during the measure. 
• Cardiometabolic markers: Plasma will be collected after the REE measure. This includes 
fasting plasma glucose, insulin, c-peptide, serum lipid and lipoproteins, and inflammatory 
markers and metabolomics related to bacterial metabolism (e.g. LPS ). 
Diet Randomization Assignment 
On the final day of the baseline period, participants will meet with a registered dietitian 
nutritionist (RDN) to receive their diet prescription for the phase. The RDN will provide 
participants with detailed daily menus for each day of the phase along with a complete list of 
foods and beverages and amounts needed. Participants will be provided with a stipend and 
guidance on where to purchase food. They will be told to consume all foods and beverages listed 
on the respective day’s menu and the specific times of day at which to consume meals. 
Participants will also be instructed not to consume any foods beyond those listed on the menus 
and to record any deviations from the protocol, including photos of any additional foods 
consumed and nutrition information. Compliance will further be assessed through 1) collection 
of receipts to verify food purchases, 2) participant provision of time-stamped photos of the 
empty food packages following consumption. 
These procedures will be repeated for the second phase following a minimum 14-d washout 
period. Participants will be counseled to return to their usual diet as documented during the 
baseline phase (Days 1-3). 
Diet conditions (Days 4-18 and 33-46): 
Each diet period will be isocaloric and have similar energy densities, fiber content, micronutrient 
content, palatability, and CODEX Alimentarius annotated food additives to best isolate the effect 
of processing on intestinal energy harvest. Each diet period will mirror the other with the only 
difference being the level of UPF or unprocessed food. During each diet period, participants will 
be asked to photograph all their foods and upload to the HIPAA compliant app, Healthie, along 
with receipts, to help the investigator assess compliance to diet. 
Measures during diet periods 1 and 2: During each diet period, participants will be asked to 
provide 3 consecutive stool samples in the same fashion as the baseline period. At the end of 
each diet phase, participants will be asked to return to the clinic for a repeated measure of QMR, 
REE and cardiometabolic markers. 
Outcome and process assessments: All energy intake, blood, energy expenditure and body 

Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
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composition data will be collected and analyzed at the Human Phenotyping Core (HPC), 
NYNORC, along with dietary counseling. All microbiome data will be shipped to and analyzed 
at the Microbiome Laboratory, UNC NORC. Fecal samples will be saved in sterile plastic 
containers and frozen within 24 hours of collection until final processing and analysis. 

Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
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v1.0 
 
Stool sample collection and storage: Samples will be collected according to UNC published 
protocols1,3,10,7 . Briefly, stool samples will be collected by participants at home using stool 
colletion kits. The participants will be provided supplies to record date and time and 
immediately same day ship each sample with cold packs to the investigator. Recorded data will 
be checked by the investigator for completeness and samples will be sent on ice to the UNC 
Microbiome Laboratory where the sample will be weighed and then mechanically homogenized 
and partitioned into as many 2 mL cryotubes as possible. Aliquots of fecal samples will be stored 
in a -80ºC freezer. 
Bomb calorimetry: Frozen fecal samples will be used to assess stool calorie loss (a proxy for the 
degree of energy harvest from the intestine). An aliquot of stool will be mixed with distilled 
water equal to total weight of the fecal sample. This mixture will then be homogenized and 
dehydrated (incubated at 65˚C for 48 hours)2. Next, the dried fecal pellets will be used to 
determine the energy content of stool samples via bomb calorimetry using the Parr 6200 
Calorimeter with a Parr 6510 water handling system (PARR Instrument Co). Weighed Samples 
will be run in duplicate, and results averaged. If duplicate samples are >40 kcal apart, 
calorimetry will be repeated until duplicate samples are <40 kcal apart or the mean of four 
consecutive readings will be used. Benzoic acid standards will be used following 10 samples for 
standardization. The calorie loss from stool will be calculated as 
(i) 
kcal per g of sample (weight of the entire stool sample is recorded at the time of 
homogenization), and 
(ii) 
relative energy content (# of kcal in stool sample divided by the number of kcal 
consumed the previous day). 
16S rRNA gene sequencing: Genomic microbial DNA from fecal samples will be isolated11. 
Fecal microbiotas will be characterized by creating sequencing libraries from the variable 4 (V4) 
region of the 16S rRNA gene (515 bp-806 bp) using PCR and sequencing on the Illumina MiSeq 
platform at the High-Throughput Sequencing Facility in the Carolina Center for Genome 
Sciences at UNC6,11. The DADA2 pipeline will generate SVs at a 100% identity threshold. Read 
counts will be normalized prior to any statistical analysis9. Taxonomic classification will be 
performed using DADA2-formatted reference databases (such as silva) with assignTaxonom and 
addSpecies functions in DADA2. This approach will generate taxonomic abundances (phylum- 
genus level) and diversity measures (α- and β-diversity) of gut microbiotas in mice. Gut 
microbiotas will also be characterized by 2 × 150 bp paired-end whole genome sequencing of 
fecal microbial DNA using the Illumina NovaSeq 6000 platform at the same high throughput 
sequencing facility. Sequences will be taxonomically classified using the Kraken2 pipeline 20. 
Metabolic pathways will be profiled using the HUMAnN2 pipeline and the MetaCyc database 
4,8. This approach will generate taxonomic abundances (phylum-species level), α- and β- 
diversity measures, and metabolic pathway abundances. 
 
 
Statistical analysis 
The primary outcomes are fecal energy harvest differences in the two diet periods compared to 
baseline and changes in microbial taxa between baseline and diet period 1 and baseline and diet 

Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
December 19, 2024  
 
v1.0 
 
period 2. Statistics associated with diet treatment will be performed using linear models 
implemented in Stata. A mixed effect model will be used to include a random intercept term. 
Exploratory path analysis with multiple mediators will be utilized to understand mechanisms of 
change and estimate effect sizes. Covariates will be included to control for differences, if any. 
Links between additive exposures and study outcomes will be explored. 
Anticipated participation 
We anticipate screening n=40 to obtain our n=20 participants for enrollment. We have budgeted 
for n=15 study completers. 
Additional Information 
All measurements collected have strict manuals in place and will be conducted by individuals 
certified to perform them. Measures of weight and height and body composition by QMR, and 
REE will be performed by certified staff in the respective HPC labs. Rigorous quality control 
procedures are in place for instrument calibration, collection of measures, analysis of data, data 
storage and transfer to PI. All data are deidentified at time of collection and transmitted via 
secured channels This proposal will be carried out at the NYNORC by Dr. Whyte and the UNC- 
CH NORC by Dr. Carroll. 
References: 
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Marine Oil Supplements Increase Peripheral Blood Specialized Pro-Resolving Mediators 
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Protocol Title: Effects of ultra processed food on intestinal energy harvest 
 
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10. Zhang HJ, Gao X, Guo XF, Li KL, Li S, Sinclair AJ, et al. Effects of dietary 
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