Red and Processed Meat Effects on the Metabolome and Microbiome
Phase: NA
Conditions: Controlled Feeding Trial, Healthy Eating Index, Red Meat, Meat
Sponsor: Fred Hutchinson Cancer Center

NCT05724906
Red and Processed Meat Effects on the Metabolome and Microbiome
(Participant facing study title:  EAT-WELL:  Effects on metabolism of Altering Protein Sources in WELL 
balanced meals)
Protocol Document.  Version 3, dated 1.28.22 (final)

EAT-WELL Study Protocol_V3 
1 
Clinical Research Protocol 
EAT-WELL: Effects on metabolism of AlTering protein sources in WELL balanced meals 
Protocol Number: 
RG1121543 
Version Number: 
3 
Version Date: 
January 28, 2022 
Development Phase: 
NA 
Indication 
Healthy Eating Index Diet (2015) ± red and processed meats, 
healthy adult volunteers 
Study sites 
Fred Hutchinson Cancer Research Center 
Funding Organization: 
National Institute of Diabetes, Digestive, and Kidney Diseases, 
1 R21 DK128754-01  
Principal Investigator: 
Marian L. Neuhouser, PhD 
IRO received 02/08/22
FHCRC IRB Approval  
02/17/2022 
Document Released Date

EAT-WELL Study Protocol_V3 
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TABLE OF CONTENTS 
1.
BACKGROUND
5 
2.
STUDY DESIGN
6 
3.
ELIGIBILITY CRITERIA
7 
3.1 
INCLUSION CRITERIA 
7 
3.2 
EXCLUSION CRITERIA 
7 
4.
STUDY PROCEDURES
7 
4.1 
RECRUITMENT 
7 
4.2 
ORIENTATION/CONSENT VISIT 
7 
4.3 
CLINIC VISIT 1 (FEEDING PERIOD 1, DAY 1) 
8 
4.5 
CLINIC VISIT 2 (FEEDING PERIOD 1, DAY 21) 
8 
4.6 
CLINIC VISIT 3 (FEEDING PERIOD 2, DAY 1) 
8 
4.7 
FEEDING PERIOD 2 (FEEDING PERIOD 2, DAYS 1 – 20) 
8 
4.8 
CLINIC VISIT 4 AND END OF STUDY VISIT (FEEDING PERIOD 2, DAY 21) 
8 
4.9 
ONGOING STUDY PROCEDURES 
9 
4.10 
CONSENTS 
9 
4.11 
DESIGN OF FEEDING STUDY MEALS 
9 
4.12 
COMPLIANCE. 
9 
5.
DATA COLLECTION
9 
5.1 
OUTCOMES
10 
6.
PARTICIPANT RISKS AND BENEFITS
10 
6.1 
POTENTIAL RISKS 
10 
6.2 
PROTECTION AGAINST RISKS 
11 
6.3 
BENEFITS 
11 
6.4 
COMPENSATION 
11 
6.5 
ALTERNATIVES 
11 
7.
DATA ANALYSIS PLAN
12 
7.1 
SAMPLE SIZE FOR THE PRIMARY AIM 
12 
8.
DATA SAFETY MONITORING PLAN
12 
8.1 
ADVERSE EVENT REPORTING 
12 
8.2 
PLAN FOR SAFETY REVIEW 
13 
8.3 
PLAN FOR ANNUAL REPORTING 
13 
8.4 
MONITORING FOR DATA INTEGRITY AND SAFETY 
13 
9.
REFERENCES
14 

 
EAT-WELL Study Protocol_V3  
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Protocol Synopsis 
TITLE 
EAT-WELL: Effects on metabolism of AlTering protein sources in WELL balanced 
meals 
SPONSOR 
1 R21 DK128754-01 
FUNDING 
ORGANIZATION 
National Institute of Diabetes, Digestive, and Kidney Diseases 
NUMBER OF SITES 
1: Fred Hutchinson Cancer Research Center 
RATIONALE 
Observational cohorts have found that those who self-report frequent red and 
processed meat intake have higher risk of several diet-related chronic diseases 
such as cardiovascular disease, type II diabetes and cancer compared to those 
with low or no intake of these foods. These findings are valuable, but self-
reported diet without biological markers does not advance science towards 
understanding biological mechanisms. Some cohort studies have reported links 
between red and processed meat consumption and health related biomarkers 
such as lipids or food preparation-generated biomarkers such as polycyclic 
aromatic hydrocarbons (PAHs) and heterocyclic amines (HAs). However, many of 
these studies examined the associations of red and processed meat with blood or 
urine biomarkers in cross-sectional analyses, leaving the possibility for residual 
confounding and an inability to draw causal inference. Further, often only one 
pathway or one compound was measured in these studies, limiting insight into 
multiple pathways affected by red and processed meat consumption. And no 
studies have tested red and processed meat on systems-wide biomarkers using 
the metabolome and microbiome.  
STUDY DESIGN 
This controlled feeding trial will enroll 20 heathy adult females and males. 
Participants will consume two diets, in random order: 1) Diet A is based on the 
Healthy Eating Index-2015 (HEI-2015) and includes no red or processed meat; 2) 
Diet B is based on the Healthy Eating Index-2015 and includes red and 
processed meat (HEI-2015-M) as some of the protein sources. Both diet periods 
last 21 days and an approximately 21-day washout period occurs between diets. 
PRIMARY OBJECTIVE 
The study has the following primary specific aim: 
1. Identify the extent and nature of shifts in the metabolome when 
consuming a standard HEI-2015 diet compared to a HEI-2015-M diet. 
SECONDARY 
OBJECTIVES 
The study has the following secondary aim: 
1. Identify and characterize modulation of the gut microbial community when 
consuming a standard HEI-2015 diet compared to a HEI-2015-M diet. 
NUMBER OF 
SUBJECTS 
20 
SUBJECT SELECTION 
CRITERIA:  
Inclusion Criteria 
Inclusion Criteria: 
1. Healthy adults 
2. Age 18-50 years 
3. Able to read, speak and understand English 
4. Willing to come to the Fred Hutch campus twice weekly during the study 
SUBJECT SELECTION 
CRITERIA:  
Exclusion Criteria 
Exclusion Criteria: 
1. Known allergy to red or processed meat 
2. Vegetarian or vegan 
3. Religious or personal reason(s) to avoid red or processed meat 
4. Pregnant and/or exclusively breastfeeding  
5. Alcohol or recreational drug abuse 

 
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DURATION OF 
SUBJECT 
PARTICIPATION AND 
DURATION OF STUDY 
Subjects will participate in the study for 11 weeks. 
The total duration of the study is expected to be 1.5 years 
PRIMARY ENPOINTS 
 
Primary Aim 1:  Compare intervention effect of each feeding period on shifts in 
the metabolome as detected in blood and urine samples relative to baseline for 
each participant. 
 
SECONDARY 
ENDPOINTS 
Secondary Aim:  Compare intervention effect of each feeding period on shifts in 
the gut microbial community as detected by 16S rRNA gene sequencing and 
digital droplet PCR relative to baseline for each participant. 
STATISTICS 
Primary Analysis Plan 
Statistical analysis for primary Aim 1 (metabolome). Aim 1 identifies 
metabolomic markers that differ between HEI-2015 and HEI-2015-M. Metabolites 
from NMR and LC/MS-MS platforms will be combined to allow greater coverage 
of the metabolome. Metabolite levels are normalized by dividing by the batch’s 
median value followed by removing metabolites with >25% CVs in the embedded 
blinded duplicates as well as those with >20% missing values (expected at 5-10% 
of total). Next, we use unsupervised principal components analysis (PCA) to 
visually examine the data patterns and variability. We then apply supervised 
orthogonal partial least squares discriminant analysis (OPLS-DA) to identify 
candidate biomarkers that may differ by diet arm. VIP (variable importance in 
projection) scores >2 will be retained. For each identified metabolite, separate 
linear mixed models are fit to examine fold-changes in end of diet period 
metabolite levels by HEI-2015 and HEI-2015-M, adjusting for baseline. In the 
cross-over design each participant is their own control, minimizing noise in intra-
individual variation in endogenous metabolites. The cross-over design also 
minimizes confounding by age, BMI, and diet order, but models are run with and 
without these adjustments. The Benjamini-Hochberg approach controls for false 
discovery rate (FDR) <0.10. 
Rationale for Number 
of Subjects 
We have set a sample size of 20 and each participant will complete both arms  
for this pilot study, which will provide preliminary data to support an R01. The 
sample size is based on the number of participants needed to observe an 
intervention effect while staying within the fixed budget of an R21. 
 

 
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1. BACKGROUND 
Scientific premise and importance of the problem. The significance of this application is that mechanistic 
evidence by which red and processed meat affects overall systemic human metabolism is lacking. In 2019, a 
series of systematic reviews and meta analyses published by an ad hoc group21 concluded that, contrary to 
US2 and global10, 11 dietary guidance, red and processed meat could be consumed with no harm to human 
health.16-19, 21 In contrast, evidence from well-conducted observational cohorts finds that red and processed 
meat consumption is associated with higher risk of cardiovascular disease, type 2 diabetes, certain cancers 
and total mortality.7, 26-30 The authors of the 2019 reports based some of their conclusions on the fact that US 
dietary guidelines rely primarily on observational cohort data instead of randomized controlled trials, a study 
design they viewed as ‘weak’.21 Further, the authors cited a lack of robust data connecting red and processed 
meat consumption to biological mechanisms linked to health outcomes. The observational data juxtaposed 
with the ad hoc group’s recommendations that red and processed meat may be consumed with no ill effects, 
indicate that knowledge and evidence gaps remain.31 These gaps need to be filled to inform diet-related health 
promotion and disease prevention recommendations. This application does not diminish observational findings 
of red and processed meat and disease outcomes; rather the intent is to add to the body of evidence as the 
totality of evidence is needed to generate robust population dietary guidance.  
The problem of understanding the biological mechanisms by which red and processed meat affect human 
health will be explored in this project in a randomized cross-over feeding trial testing a Healthy Eating Index-
2015 (HEI-2015) diet with no red and processed meat compared to a HEI-2015 diet with red and processed 
meat (HEI-2015-M). A controlled feeding trial is a study design optimally suited for investigating mechanisms 
linking food intake with metabolic response due to the ability to carefully control food intake.32, 33 Two types of 
mechanistic biomarkers are outcomes for this study, both reflecting systems-wide readouts: (1) metabolomics; 
and (2) gut microbiota. The 2020-2030 NIH Strategic Plan for Nutrition Research34 calls for research into food 
effects on both the metabolome and the microbiome, making this project a timely fit.34, 35  
Biological effects of red and processed meat intake. Red meat is mammalian, “red pigmented flesh or 
organ meats including beef, pork, venison and game meats”.36 Processed meats are “any meats that have 
undergone salting, curing, fermentation and other methods of preservation and may include red meat, poultry 
and fish”.36 Red and processed meat are often grouped together as a single exposure in many investigations29, 
37, 38 and thus will be examined together in this study. A key question is: do red and processed meat generate a 
different metabolic and gut microbial community response than fish, poultry or plant-based proteins (e.g., nuts, 
beans)? Red meat has important nutrients including essential amino acids, iron, zinc and vitamin B12.39 Both 
red and processed meat are also high in nutrients that should be consumed in moderation such as saturated 
fat and sodium.1, 38 Other red and processed meat-derived non-nutrient compounds linked to health risks 
include advanced glycation-end products,40, 41 nitrosamines42-44 and potential endocrine disruptors derived from 
livestock feed or from that used in processed meat preservation and packaging.45, 46 Red meat cooking 
methods may also induce the formation of compounds linked to poor health including heterocyclic amines 
(HAs)47, 48 such as amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhiP) 24, 49 and polycyclic aromatic 
hydrocarbons (PAHs).3, 48 The evidence for HAs and PAHs supported the International Agency for Research 
on Cancer (IARC) declarations that red and processed meats are Group 2A and Group 1 carcinogens, 
respectively.15 The presence of these cooking-derived compounds are not observed with fresh poultry and fish, 
but are seen in all types of processed meat, including that derived from poultry and fish.15  
Prior interventions and controlled feeding studies testing red and processed meat. Recent interventions, 
including controlled feeding studies, demonstrated that red meat can be a component of a healthy diet with 
neutral effects or slight improvements in blood lipids, glucose tolerance and cardiometabolic risk factors.50-52 
These studies were rigorous and well-conducted, but were focused on specific health-related biomarkers, not 
whole-body mechanisms, leaving a gap in identifying systems-wide mechanisms. Other feeding studies 
identified blood and urine-based metabolomics and microbiome features that differed between lean seafood 
and non-seafood diets, but did not specifically test red and processed meat.53, 54 In still another, there was a 
rapid microbiome change in response to 5-day controlled diets where both a plant-based diet and an animal 
foods-based diet were administered.55 The plant-based diet induced bacterial diversity for key genera overall56, 
57 as well as increases in bacterial genes involved in plant polysaccharide decomposition, such as butyrate 
kinase (buk). In the animal foods-based diet bacteria involved in proteolysis and nitrogen metabolism 
increased, and overall bacterial diversity decreased.55 These results support the premise that diet change and 
specific food components induce rapid shifts in gut microbiota.55 However, still lacking are controlled trials 
explicitly testing red and processed meat while all other aspects of diet are held constant.  

 
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Rationale for metabolome and microbiome as endpoints. The 2020-2030 NIH Strategic Plan for Nutrition 
Research34 calls for mechanistic studies on the relationship between foods and both the microbiome and the 
metabolome to allow a systems approach to nutrition research, a strategy recognized as valuable by the NIH 
and nutrition research community.32, 33 Both the metabolome and microbiome are sensitive to short-term 
changes in diet, as shown by us and others.53-55, 58-62 Further, both outcomes are needed for a full complement 
of systems-wide nutritional effects. Evidence suggests that dietary patterns,63-68 individual foods, nutrients or 
food groups62, 69-76 may display a unique metabolome or microbiome signature, including for meat and fish 
consumption.75 However, many of these analyses were cross-sectional, associating baseline diet with baseline 
biospecimens, leaving the potential for confounding and the inability to draw causal inference. Further rationale 
for the microbiome endpoint is evidence that meat may expand certain bacterial groups in the colon and 
evidence that specific microbial metabolites are generated from the hydrolysis of meat-derived aromatic and 
sulfur containing amino acids.55, 77 Moreover, red meat derived heme induces genotoxic microbial H2S and 
nitrite production using narG, the first step in genotoxic reactions leading to DNA adducts.77 Finally, poor diet 
quality (lower HEI scores) in an observational study was linked to enrichment of two microbial species, 
Enterobacter and Escherichia-Shigella, that reduce nitrate to nitrite using nitrate reductase (narG),78, 79 findings 
relevant for Aim 2 as we will do 16S rRNA sequencing and identify microbial functional genes, including narG. 
 
This study has the following primary specific aim: 
1. Identify the extent and nature of shifts in the metabolome when consuming a standard HEI-2015 diet 
compared to a HEI-2015-M diet.  
Hypothesis 1: Daily intake of red and processed meat will induce shifts in the metabolome detected 
in blood and urine samples using various metabolomics platforms. 
This study has the following secondary aim 
2. Identify and characterize modulation of the gut microbial community when consuming a standard HEI-2015 
diet compared to a HEI-2015-M diet.  
Hypothesis 2: Daily intake of red and processed meat will shift the gut microbiome such that the two 
diet arms will show differences in the gut microbial community structure using16S rRNA gene 
sequencing and digital droplet PCR for assessing microbial functional genes. 
2. STUDY DESIGN 
Overview. This is a randomized cross-over feeding trial to test whether red and processed meat consumption, 
in the context of a controlled diet based on HEI-2015, will cause shifts in the metabolome and the microbiome 
compared to a controlled HEI-2015 diet with no red or processed meat. Twenty healthy adults will complete 
two feeding periods, in random order of 21 days each with an approximately 21-day washout between feeding 
periods (Figure 1). Study meals are prepared at the Fred  
Figure 1. Study design 
 
Hutch Human Nutrition Laboratory (HNL). Participants attend seven clinic visits at the Fred Hutch Prevention 
Center Clinic: an orientation visit at which consent and randomization will occur; three visits to complete fasting 
blood draws and to deliver 24-hour urine and stool specimens collected at home; one visit to initiate feeding 
period 2 and two mid-point visits to check for weight stability. The collected biospecimens are used for study 
outcomes. 

 
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Healthy volunteers will be recruited for this study.  Healthy volunteers are well-suited for this study so results 
will be generalizable.  
The study is supported by NIH’s R21 mechanism, which is designed to collect preliminary data to support 
larger trials. 
3. 
ELIGIBILITY CRITERIA 
Healthy adult volunteers who meet all of the inclusion and none of the exclusion criteria will be eligible for 
participation in this study.  All races/ethnicities are eligible to participate.   
 
3.1 Inclusion Criteria 
1. Healthy adults 
2. Age 18-50 years 
3. Able to read, speak and understand English 
4. Willing to come to the Fred Hutch campus twice weekly during the study 
 
3.2 Exclusion Criteria 
1. Known allergy to red or processed meat 
2. Vegetarian or vegan 
3. Religious or personal reason(s) to avoid red or processed meat 
4. Pregnant and/or exclusively breastfeeding 
5. Alcohol or recreational drug abuse 
 
Participants who routinely use dietary supplements may continue to do so while on study, but they will be 
asked not to start new supplements. Participants may consume alcohol but will need to provide their own and 
report consumption. Participants will be asked to maintain their usual level of physical activity but not start new 
exercise routines while on study. Any over the counter medications used while on study will be recorded. 
 
4. STUDY PROCEDURES  
4.1 
Recruitment 
Study staff will recruit participants by traditional media (print,), social media, community organizations and local 
colleges/universities. Additionally, the Fred Hutch Recruitment and Retention Core may assist with recruitment 
of minority participants. We aim to have minority enrollment that reflects the Seattle population (2019 Seattle 
census estimates are 35.5% minority). . Initial recruitment and eligibility activities take place via an online 
screening questionnaire in response to media and flyer postings. If an individual is determined to be eligible or 
possibly eligible via the online screener, staff will confirm interest and eligibility by phone or email, schedule the 
orientation visit and provide directions to the Fred Hutch Prevention Center Clinic. 
4.2 
Orientation/Consent Visit 
Study staff review the “study at a glance” document, the study consent and provide individuals an opportunity 
ask questions.  Participants will be shown the study menus and asked whether they are willing and able to 
consume the majority of study foods. If needed, some substitutions may be possible (for example, substitute 
an orange for grapefruit); however, some substitutions may not be possible. Individuals who are unwilling or 
unable to eat the majority of study foods will be asked to not enroll in the study. Individuals will be asked to 
provide written informed consent for study activities. After participants sign the consent, study staff will 
measure height and weight and participants will complete the “Lifestyle Questionnaire” on demographic 
characteristics, usual dietary supplement use, medical history and usual physical activity. Information provided 
in this questionnaire will be used to estimate energy requirements for study diet planning. Participants will be 
given a four-day food record and instructions and asked to complete at home in preparation for Clinic Visit 1. 
Participants will be given a stool collection and a 24-hour urine collection kit as well as detailed instructions for 
collecting these specimens at home in preparation for Clinic Visit 1. Study staff will schedule Clinic Visit 1 and 
review instructions to prepare for Clinic Visit 1. Participants will be randomized (using a computerized program) 
to diet order (Diet A then Diet B or Diet B then Diet A). 

 
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4.3 
Clinic Visit 1 (Feeding Period 1, Day 1) 
At clinic visit 1, study staff will receive and process urine and stool specimens collected by participants at 
home. Staff will also receive the completed food record. Clinic staff will next measure weight and waist 
circumference and draw fasting blood. After the blood draw, participants will eat their first study meal of feeding 
period 1.  Study staff will review instructions for the next three weeks (Feeding Period 1), meal pick-up/delivery, 
proper food storage and reheating, schedule Clinic Visits 2-4 and the check-in visits, provide a study calendar 
and answer any participant questions. Clinic visit 1 concludes with the participant being provided the first three 
days of study meals and daily meal check-off forms. 
4.4  
Feeding Period 1 (Feeding Period 1, Days 1 – 21)   
A 3- to 4-day supply of study meals will be picked by the participant on site at Fred Hutch.. Participants will be 
asked to visit the clinic 2 times per week during each Feeding Period. Each study meal is accompanied by a 
check-off list for participants to indicate foods consumed and amount (if any) remaining. Participants are asked 
to consume only study provided foods but may use and report their own coffee and tea, (record type, 
amount;sweetener and cream/milk will be provided by the study). Halfway through feeding period 1, 
participants will attend the Check-in Visit 1 when they pick up their next supply of study meals.  At this visit, 
participants will be weighed and any new medication/supplements will be reported.  Participants experiencing 
hunger or weight change of ≥±3% of clinic visit 1 weight will be placed on the next higher or lower kcal level as 
appropriate. A stool collection kit and 24-hour urine collection kit will be provided for use on the penultimate 
day of Feeding Period 1.  Participants will complete urine and stool collections at home in preparation for Clinic 
Visit 2.   
4.5 
Clinic Visit 2 (Feeding Period 1, Day 22 
Participants return to the clinic on day 22 of Feeding Period 1 for the second fasting blood draw and measure 
weight. Study staff also receive and process urine and stool specimens that were collected at-home on the 
penultimate day of Feeding Period 1. Study staff review instructions for the washout period (approximately the 
next three weeks and answer any participant questions. Staff will provide participants with $25.00 as 
compensation. 
4.6 
Clinic Visit 3 (Feeding Period 2, Day 1) 
After concluding an approximately three-week washout period, participants return to the clinic on Day 1 of 
Feeding Period 2 for repeat anthropometric measurements (weight and waist circumference).  To conserve 
resources, blood, urine and stool are not collected again at this visit; end of study diet samples are adjusted for 
baseline samples as we have done in other feeding studies.59, 62  Using the study calendar, study staff review 
instructions for the next three weeks (Feeding Period 2), meal pick-up/delivery, proper food storage and 
reheating and answer any participant questions. Clinic visit 3 concludes with participants being provided the 
first three days of Feeding Period 2 meals and corresponding daily meal check-off forms. 
4.7 
Feeding Period 2 (Feeding Period 2, Days 1 – 221)   
Similar to Feeding Period 1, a 3- to 4-day supply of study meals and daily meal check-off lists will be picked 
up.). Halfway through Feeding Period 2, participants will attend check-in Visit 2 when they pick up their next 
supply of study meals.  At this visit, participants will be weighed and any new medications/supplements will be 
reported. Participants experiencing hunger or weight changes of ≥±3% from Clinic Visit 3 are placed on the 
next higher or lower kcal level as appropriate. A stool collection kit and 24-hour urine collection kit will be 
provided for use on the penultimate day of Feeding Period 2.  Participants will complete urine and stool 
collections at home in preparation for Clinic Visit 4. 
4.8 
Clinic Visit 4 and End of Study Visit (Feeding Period 2, Day 22) 
Participants return for a final time to the clinic on Day 22 of Feeding Period 2 for the end-of-study fasting blood 
draw and repeat anthropometric measurements (weight and waist circumference). Study staff will receive and 
process the urine and stool specimens, which the participants completed at-home in preparation for Clinic Visit 
4.  Staff answers all participant questions and provides the End-of-Study Thank-you Letter and $75.00 as 
compensation. 

 
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4.9 
Ongoing Study Procedures 
Blood, urine and stool samples are processed at clinic visits using standard procedures and are stored at -
80°C until analysis. Throughout the study, participants maintain usual physical activity, but are asked to refrain 
from starting new activities that may alter total energy expenditure and energy intake needs. Participants are 
advised to continue to take prescription medications (if needed) but should check with study staff if new 
prescription or over-the-counter medications are used as this can affect the metabolome. Similarly, participants 
may continue usual dietary supplements (if any) but should not start new supplements. Participants may 
consume alcohol but must provide their own alcohol and will report type and amounts. Study staff will be 
available by phone, or email throughout the study should questions arise and to assist with retention.  
4.10 Consents 
All informed consent procedures and documentation is done in compliance with Fred Hutchinson Institutional 
Review Office Policy. 
 
Consent for screening. Individuals who visit the Online screening questionnaire will be explicitly asked to 
provide consent (via checkbox) to complete the screening questionnaire.  
 
Study Informed Consent. At the Orientation visit, the informed consent discussion will occur and participants 
will be asked to sign the Fred Hutch Study Informed Consent and each participant will be provided a copy of 
the consent.    
4.11 Design of Feeding Study Meals 
Feeding study menus are developed by the Fred Hutch Human Nutrition Lab (HNL) staff using Pronutra® 
(Viocare Technologies, Inc., Princeton, NJ), which is a metabolic diet management system specifically 
designed for conducting feeding studies. Two seven-day, eucaloric repeating menu cycles are created: (1) 
HEI-2015 with no red and processed meat and (2) HEI-2015-M with red and processed meat as some of the  
protein sources. The USDA defines red meat as “any red pigmented meat and includes beef, lamb, pork, veal 
and game meats”.36 This study will primarily utilize beef and pork due to budgetary and logistic restraints to 
obtain veal and game meats. A variety of types and cuts of red meat will be used (e.g., round, chuck, top 
sirloin) and cooking methods will include broiling, braising and roasting. No meats will be cooked by open 
flame or undergo charring. The USDA defines processed meat as “any meat that has undergone 
transformation through salting, curing, fermentation, or other processes to enhance flavor or improve 
preservation”.36 A variety of processed meat will be used for HEI-2015-M. The HEI-2015-M menus will include 
at least one daily serving of red or processed meat; other protein sources (eggs, poultry, fish, legumes,) may 
be used if needed to meet the daily protein goals. The HEI-2015 menus will include no red or processed meat 
and protein sources will include eggs, poultry, fish and beans/legumes. The seven-day cycle menu will repeat 
three times over each 21-day feeding period. Diets will be created at 7 different kcal levels, 1800 kcal to 3000 
kcal per day, in 200 kcal increments. Each day is matched within diet group for energy and for the cup and 
ounce equivalents for each food group, and food groups in each meal are identical across arms excluding the 
protein foods. Individual energy requirements for participants are estimated using the Mifflin equation.92 
Participants reporting hunger or weight changes are placed on the next higher or lower kcal level as 
appropriate. Weight should remain stable (+ 3% of clinic visit 1 and clinic visit 3 weights) throughout the study.  
All foods consumed by participants throughout the study will be prepared by HNL staff who have extensive 
experience with metabolic studies. All food items are individually weighed or portioned out for each participant 
and clearly labeled by meal. Study foods are precooked and frozen and are picked up  by participants on site 
at Fred Hutch. Safe food handling, storage and reheating instructions are included.   
4.12 Compliance.  
Compliance to the intervention will be evaluated by daily meal check-off forms, the mid-feeding period check-in 
with the study dietitian.   
5. 
DATA COLLECTION  
This study involves a total of seven participant visits. Visits will occur as follows:  1) Orientation Visit (pre-
study). 2) Clinic Visit 1 (Baseline; Day 1 of Feeding Period 1), 3) Midpoint Visit 1 (~Day 10 of Feeding Period 

 
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1), 4) Clinic Visit 2 (Day 22, the day after the end of Feeding Period 1), 5) Clinic Visit 3 (Day 1 of Feeding 
Period 2), 6) Midpoint Visit 2 (~Day 10 of Feeding Period 2), and 7) Clinic Visit 4 (end of study; Day 22 the day 
after the end of Feeding Period 2).  At the Orientation Visit, the study Lifestyle Questionnaire will be completed.  
At all clinic visits, participants will undergo assessments of anthropometrics, and on clinic visits 1, 2 and 4 they 
will bring stool and urine samples collected at home and will provide fasting blood.  
   
Twelve-hour fasting blood. Fasting blood samples will be collected on Day 1 of the first Feeding period, and on 
Day 22 of the first feeding period and Day 22 of the second feeding period.  Vacutainers will be labeled with 
participants’ study ID and date, processed per standard protocol and stored at -80ºC.   
 
Stool sample: Stool samples will be collected by participants at home prior to Clinic Visits the beginning (Clinic 
Visit 1) and end of Feeding Period 1 (Clinic Visit 2) and the end of Feeding Period 2 (end of study, Clinic Visit 
4). 
 
Urine sample:  24-hour urine samples will be collected by participants at home prior to Clinic Visits at the 
beginning (Clinic Visit 1) and end of Feeding Period 1 (Clinic Visit 2) and the end of Feeding Period 2 (end of 
study, Clinic Visit 4). 
 
Questionnaires:  
 
Lifestyle Questionnaire.  Date of birth, self-identified race/ethnicity, gender identity, education, smoking, 
alcohol use, recreational drug use, over-the-counter and prescription drug use, dietary supplement use, 
physical activity levels and general health history will be collected at the Visit 1 (self-administered 
questionnaire). 
 
Daily meal check-off forms.  All participants will complete study provided meal check-off forms to record 
and quantify daily meals consumed each day during both feeding periods.   
5.1 
Outcomes 
5.1.1 Metabolome 
Fasting blood, 24-hour urine, and fecal samples collected at baseline and after each diet period for each 
participant.  Shifts in the metabolome will be characterized by conducting metabolomics assays on the blood 
urine, and fecal samples and comparing the readouts between the HEI-2015 and HEI-2015-M diets. 
5.1.2 Microbiome 
Stool DNA will be extracted from the fecal samples collected at baseline and after each diet period for each 
participant. Modulation of the gut microbial community will be characterized by 16S rRNA gene sequencing of 
the extracted genomic DNA and comparing the readouts between the HEI-2015 and HEI-2015-M diets. 
In addition, digital droplet PCR assays will be conducted to measure changes in the genes for butyrate kinase 
(buk) and nitrate reductase (narG), and archival samples will be collected to conduct microbial growth 
experiments.   
5.1.3 Anthropometrics 
Height and waist circumference will be measured at the orientation visit only, and weight will be measured at 
the beginning, midpoint and end of each feeding period for each participant.   
6. 
PARTICIPANT RISKS AND BENEFITS 
6.1 Potential Risks 
1. Participants may experience a little discomfort or have a temporary bruise from having blood drawn.  
Occasionally a participant may feel lightheaded or feel faint when having blood drawn since blood is 
drawn after a 12 hour fast. The amount of blood drawn for this study does not exceed 50 ml total over the 
11 week study and does not occur more than twice weekly.   

 
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2. Some participants may feel that coming to study visits and consuming only study-provided foods for two 
3-week periods is burdensome.  
3. Some participants may find it inconvenient or uncomfortable to collect their urine and stool. 
 
6.2 Protection Against Risks  
In the event that this research activity results in an injury, medical treatment will be available, including first aid, 
emergency treatment and follow up care, as needed.  All procedures involving blood collection and 
anthropometry will be done at the Fred Hutch Prevention Center Clinic.  Participants will collect urine and stool 
samples at home. Food is carefully and safely prepared under highly controlled conditions. Trained staff will be 
available to assist should a medical emergency occur.  An MD licensed in Washington State is the Prevention 
Center Director. Participants will be informed in the written consent form that payment for any such treatment 
must be provided by the individual and/or the individual’s insurance company.   
1. Participants may experience a little discomfort, bruising, fainting or phlebitis from having blood drawn.  We 
will minimize these risks by making sure that all blood draws are conducted by well-trained nurses, medical 
assistants, or phlebotomists at the Fred Hutch Prevention Center.  Participants will be asked before the 
blood draw if they have experienced problems with blood draws in the past, and they will be offered an 
opportunity to lie down during the procedure if they wish. After the blood draw is complete, participants will 
remain seated, applying pressure to the site of venipuncture. If a participant feels faint, they will be 
instructed to lie down until the feeling goes away. We provide a small snack and fluids after the blood draw. 
In our experience, blood draws performed in this manner are well tolerated, and any side effects are 
minimal. 
2. We will make every effort to schedule participants to come to the Fred Hutch at times that are convenient for 
them; most participants find that morning appointments work well for the fasting blood draws.  During the 
approximately 3-week “washout” period, participants will be allowed to eat whatever foods they like.  
3. Participants will be provided all necessary supplies as well as instructions to help in the collection of stool 
and urine specimens. In addition, they will be provided with opaque bags to discretely carry specimens to 
their study visit. Study staff will be available by telephone should the participant have any questions or 
complications when collection these specimens. 
 
6.3 Benefits 
The data generated from this proposed clinical trial will provide essential information for participants regarding 
the effects of red and processed meat in the context of an overall healthy dietary pattern on the metabolome 
and microbiome of healthy adults.   
 
The research we propose here is important because if a specific nutritional intervention can be shown to 
improve measures of the metabolome and the microbiome, then it will provide compelling preliminary data to 
then launch a larger clinical trial which could, potentially, have far reaching implications for thousands healthy 
adult members of the general population. Thus, with minimal risk to study participants, we will generate data 
that will be beneficial to general dietary guidance related to health promotion and disease prevention – 
including cancer prevention.   
 
Overall, using this rigorous study design (randomized cross-over feeding trial), we have attempted to minimize 
any potential risks to study participants, whilst maximizing the amount of important scientific data that can be 
generated and ultimately shared with the scientific community and the general public.  
6.4 
Compensation 
Participants will be paid $100 total, $25 at the end of the first feeding period and $75 at the end of the second 
feeding period to help compensate for time and travel.  All food will be provided for the two feeding periods. 
6.5 
Alternatives 
Alternatives to participating in this research study include not participating. 

 
EAT-WELL Study Protocol_V3  
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7. 
DATA ANALYSIS PLAN 
This trial identifies (1) the extent and nature of metabolomic shifts and (2) as a secondary aim, modulation of 
the gut microbiome when consuming a standard HEI-2015 diet compared to a HEI-2015-M diet.  We expect 
that daily intake of red and processed meat will induce shifts in the metabolome detected in blood and urine 
samples, and secondarily in the gut microbiome detected in extracted fecal DNA. Under the NIH R21 
mechanism, it is considered a preliminary trial. 
 
Statistical Analysis for the Primary Aim.  Aim 1 identifies metabolomic markers that differ between HEI-2015 
and HEI-2015-M. Metabolites from NMR and LC/MS-MS platforms will be used to allow greater coverage of 
the metabolome and as shown feasible by Dr. Raftery97 and others.98 Metabolite levels are normalized by 
dividing by the batch’s median value followed by removing metabolites with >25% CVs in the embedded 
blinded duplicates as well as those with >20% missing values (expected at 5-10% of total). Next, we will use 
unsupervised principal components analysis (PCA) to visually examine the data patterns and variability. We 
then apply supervised orthogonal partial least squares discriminant analysis (OPLS-DA) to identify candidate 
biomarkers that may differ by diet arm. VIP (variable importance in projection) scores >2 will be retained. For 
each identified metabolite, separate linear mixed models will be fit to examine fold-changes in end of diet 
period metabolite levels by HEI-2015 and HEI-2015-M, adjusting for baseline. In the cross-over design each 
participant is their own control, minimizing noise in intra-individual variation in endogenous metabolites. The 
cross-over design also minimizes confounding by age, BMI, and diet order, but models are run with and 
without these adjustments. The Benjamini-Hochberg approach controls for false discovery rate (FDR) <0.10.99  
Statistical Analysis for the Secondary Aim.  To account for different sequencing depths, we will initially apply 
the centered-log ratio (CLR) transformation to each sample after replacing any zero counts by 1 or a small 
imputed value.104 We will first gauge the overall strength of microbial signal by applying a microbiome-wide 
kernel-association test fusing a GLMM-MiRKAT105 with respect to the weighted UniFrac distance.106, 107 
Analyses will account for the crossover study design using linear mixed models with outcome of either genera, 
alpha diversity, or genes for butyrate kinase (buk) or nitrate reductase (narG), adjusting for baseline. As with 
Aim 1, we will conduct analyses with and without adjustment for age, BMI, diet order and other covariates. We 
will use Benjamini-Hochberg approach to account for FDR <0.10.99 Based on our studies62 and that of others58 
we expect to have 80% power to detect β coefficients in the linear mixed models in the range of 0.3- 04 for 
differences between diets at p<0.05. Primary analysis focuses on three genera associated with butyrate 
production and three genera associated with nitrate reduction. All genera will be examined in exploratory 
analysis.108  
7.1 Sample Size for the Primary Aim  
The sample size is based on the primary aim. For this pilot trial with a fixed sample size of 20 participants, we 
estimate that we expect to have 80% power to detect fold changes between diets of + 0.20 for blood and urine 
metabolites. Power calculations used MetSizeR.110 Tests are 2-sided α=5%.   
8. DATA SAFETY MONITORING PLAN 
Oversight for this study will be provided by the Principal Investigator, Dr. Marian Neuhouser with delegation of 
responsibilities to designated Co-Investigators and study personnel. Dr. Neuhouser will ensue all entry criteria 
are met prior to the initiation of the protocol, and all study procedures and reporting of adverse events are 
performed according to the IRB-approved protocol. Study participants are healthy adult volunteers, in whom 
the study intervention is non-therapeutic.  
8.1 
Adverse Event Reporting 
All adverse events related to the study procedures will be fully documented on the appropriate case report 
form(s) and entered in a study database. For each adverse event, the investigator will provide the onset, 
duration, intensity, treatment required, and outcome, including documentation of need for premature 
termination of any study procedures. Anticipated adverse events for this study are expected to be very minimal 
since the diet activities have been safely used in many previous intervention studies without incident. Despite 
the low risk for adverse events, all study staff and investigators will carefully monitor and document any 
adverse events, which could include the following: bruising or fainting during the blood draws, and possible risk 

 
EAT-WELL Study Protocol_V3  
13 
 
of food borne illness if the participant does not follow the instructions provided for safe food handling, storage, 
and re-heating of the study foods 
 
Serious Adverse Events will be reported within to the Principal Investigator within 24 hours. Adverse event 
reporting to the IRO will occur within the required period of time depending on the nature and severity of the 
event. 
8.2 
Plan for safety review  
The PIs will perform a cumulative review of all adverse events and premature terminations review every 6 
months after study initiation.  
8.3 
Plan for annual reporting 
A summary of the investigation including all adverse events and how they were handled, enrollment, drop-outs 
and reason for discontinuation and any protocol modifications will be provided to the IRB on an annual basis.  
Annual Reports will contain:  
a. The number of adverse events and an explanation of how each event was handled  
b. The number of complaints and how each complaint was handled  
c. The number of subject withdrawals and an explanation of why the subject withdrew or was withdrawn  
d. The number of protocol deviations and how each was handled 
8.4 
Monitoring for data integrity and safety  
Monitoring for data integrity and safety will be the responsibility of the investigators. Investigators will include 
the following in routine monitoring review: A) Validity and integrity of data: Data are checked for missing, 
unusual, or impossible values when they are entered into the study’s computer database. B) Enrollment rate 
relative to expectation: Early lags in recruitment will be rectified with increased recruitment efforts so that 
recruitment will be completed on time. The investigators will monitor this closely to ensure full enrollment of 
appropriate participants. C) Retention of participants and adherence to protocol: The investigators will monitor 
retention and adherence to study protocol. Adherence to the intervention will be monitored via daily meal 
check-off forms and by monitoring amounts of study foods consumed/not consumed.  
 
 

 
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9. REFERENCES  
1. 
Millen BE, Abrams S, Adams-Campbell L, Anderson CA, Brenna JT, Campbell WW, Clinton S, Hu F, Nelson 
 
M, Neuhouser ML, Perez-Escamilla R, Siega-Riz AM, Story M, Lichtenstein AH. The 2015 Dietary 
 
Guidelines Advisory Committee Scientific Report: Development and major conclusions. Adv Nutr.  2016;7(3):438-
 
44. doi: 10.3945/an.116.012120. PubMed PMID: 27184271; PMCID: PMC4863277. 
 
2. 
United States Department of Agriculture (USDA) US Department of Health and Human Services  (DHHS). 2015-
 
2020 Dietary Guidelines for Americans, 8th Edition. United States Department of  Agriculture, United States 
 
Department of Health and Human Services, 2015. 
 
3. 
Kruger C, Zhou Y. Red meat and colon cancer: A review of mechanistic evidence for heme in the  context of risk 
 
assessment methodology. Food Chem Toxicol. 2018;118:131-53. Epub 2018/04/25. doi: 
 
10.1016/j.fct.2018.04.048. PubMed PMID: 29689357. 
 
4. 
Martin OCB, Naud N, Tache S, Debrauwer L, Chevolleau S, Dupuy J, Chantelauze C, Durand D,  Pujos-Guillot E, 
 
Blas YEF, Urbano C, Kuhnle GGC, Sante-Lhoutellier V, Sayd T, Viala D, Blot A,  Meunier N, Schlich P, Attaix D, 
 
Gueraud F, Scislowski V, Corpet DE, Pierre FHF. Targeting Colon Luminal Lipid Peroxidation Limits Colon 
 
Carcinogenesis Associated with Red Meat Consumption. Cancer Prev Res (Phila). 2018;11(9):569-80. Epub 
 
2018/06/30. doi: 10.1158/1940-6207.capr-17-0361. PubMed PMID: 29954759. 
 
5. 
Micha R, Shulkin ML, Penalvo JL, Khatibzadeh S, Singh GM, Rao M, Fahimi S, Powles J, Mozaffarian D. 
 
Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and 
 
diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert  Group 
 
(NutriCoDE). PLoS One. 2017;12(4):e0175149. Epub 2017/04/28. doi: 10.1371/journal.pone.0175149. PubMed 
 
PMID: 28448503; PMCID: PMC5407851. 
 
6. 
Ibsen DB, Warberg CK, Würtz AML, Overvad K, Dahm CC. Substitution of red meat with poultry or fish and risk 
 
of type 2 diabetes: a Danish cohort study. Eur J Nutr. 2019;58(7):2705-12. Epub 2018/09/19. doi: 
 
10.1007/s00394-018-1820-0. PubMed PMID: 30225630. 
 
7. 
Pan A, Sun Q, Bernstein AM, Manson JE, Willett WC, Hu FB. Changes in red meat consumption and 
 
subsequent risk of type 2 diabetes mellitus: three cohorts of US men and women. JAMA Intern Med. 
 
2013;173(14):1328-35. Epub 2013/06/20. doi: 10.1001/jamainternmed.2013.6633. PubMed PMID: 
 
23779232; PMCID: PMC3847817. 
 
8. 
Guenther PM, Reedy J, Krebs-Smith SM. Development of the Healthy Eating Index-2005. J Am Diet Assoc. 
 
2008;108(11):1896-901. Epub 2008/10/29. doi: 10.1016/j.jada.2008.08.016. PubMed PMID: 18954580. 
 
9. 
Reedy J, Lerman JL, Krebs-Smith SM, Kirkpatrick SI, Pannucci TE, Wilson MM, Subar AF, Kahle LL, Tooze 
 
JA. Evaluation of the Healthy Eating Index-2015. J Acad Nutr Diet. 2018;118(9):1622-33. Epub 2018/08/28. doi: 
 
10.1016/j.jand.2018.05.019. PubMed PMID: 30146073. 
 
10. 
Pérez-Escamilla R. The Mexican Dietary and Physical Activity Guidelines: Moving Public Nutrition 
 
Forward in a Globalized World. J Nutr. 2016;146(9):1924s-7s. Epub 2016/08/12. doi: 10.3945/jn.115.218784. 
 
PubMed PMID: 27511930. 
 
11. 
Monteiro CA, Cannon G, Moubarac JC, Martins AP, Martins CA, Garzillo J, Canella DS, Baraldi LG, 
 
Barciotte M, Louzada ML, Levy RB, Claro RM, Jaime PC. Dietary guidelines to nourish humanity and the planet in 
 
the twenty-first century. A blueprint from Brazil. Public Health Nutr. 2015;18(13):2311-22.  Epub 2015/07/25. doi: 
 
10.1017/s1368980015002165. PubMed PMID: 26205679. 
 
12. 
Cocking C, Walton J, Kehoe L, Cashman KD, Flynn A. The role of meat in the European diet: current state of 
 
knowledge on dietary recommendations, intakes and contribution to energy and nutrient intakes  and status. Nutr 
 
Res Rev. 2020:1-9. Epub 2020/01/11. doi: 10.1017/s0954422419000295. PubMed PMID: 31918784. 
 
13. 
Van Horn L, Carson JA, Appel LJ, Burke LE, Economos C, Karmally W, Lancaster K, Lichtenstein AH, 
 
Johnson RK, Thomas RJ, Vos M, Wylie-Rosett J, Kris-Etherton P. Recommended dietary pattern to achieve 
 
adherence to the American Heart Association/American College of Cardiology (AHA/ACC) Guidelines: A scientific 
 
statement from the American Heart Association. Circulation. 2016;134(22):e505-e29. Epub 2016/10/30. doi: 
 
10.1161/cir.0000000000000462. PubMed PMID: 27789558. 
 

 
EAT-WELL Study Protocol_V3  
15 
 
14. 
World Cancer Research Fund/American Institute of Cancer Research. Diet, Nutrition, Physical Activity and 
 
Cancer: a Global Perspective. Third Expert Report. London: World Cancer Research Fund; 2018. 
 
15. 
World Health Organization, International Agency for Research on Cancer (IARC). Red and Processed Meat. 
 
IARC Monographs on the Evaluation of Carconogenic Risks to Humans: IARC; 2015. 
 
16. 
Zeraatkar D, Johnston BC, Bartoszko J, Cheung K, Bala MM, Valli C, Rabassa M, Sit D, Milio K,  Sadeghirad B, 
 
Agarwal A, Zea AM, Lee Y, Han MA, Vernooij RWM, Alonso-Coello P, Guyatt GH, El Dib  R. Effect of Lower 
 
Versus Higher Red Meat Intake on Cardiometabolic and Cancer Outcomes: A Systematic Review of 
 
Randomized Trials. Ann Intern Med. 2019;171(10):721-31. Epub 2019/10/01. doi: 10.7326/m19-0622. 
 
PubMed PMID: 31569236. 
 
17. 
Zeraatkar D, Han MA, Guyatt GH, Vernooij RWM, El Dib R, Cheung K, Milio K, Zworth M, Bartoszko JJ,  Valli C, 
 
Rabassa M, Lee Y, Zajac J, Prokop-Dorner A, Lo C, Bala MM, Alonso-Coello P, Hanna SE, Johnston BC. 
 
Red and Processed Meat Consumption and Risk for All-Cause Mortality and Cardiometabolic Outcomes: A 
 
Systematic Review and Meta-analysis of Cohort Studies. Ann Intern Med. 2019;171(10):703-10. Epub 
 
2019/10/01. doi: 10.7326/m19-0655. PubMed PMID: 31569213. 
 
18. 
Vernooij RWM, Zeraatkar D, Han MA, El Dib R, Zworth M, Milio K, Sit D, Lee Y, Gomaa H, Valli C, Swierz 
 
MJ, Chang Y, Hanna SE, Brauer PM, Sievenpiper J, de Souza R, Alonso-Coello P, Bala MM, Guyatt GH, 
 
Johnston BC. Patterns of Red and Processed Meat Consumption and Risk for  
Cardiometabolic and Cancer 
 
Outcomes: A Systematic Review and Meta-analysis of Cohort Studies.  Ann Intern Med. 2019;171(10):732-41. 
 
Epub 2019/10/01. doi: 10.7326/m19-1583. PubMed PMID: 31569217. 
 
19. 
Han MA, Zeraatkar D, Guyatt GH, Vernooij RWM, El Dib R, Zhang Y, Algarni A, Leung G, Storman D, Valli C, 
 
Rabassa M, Rehman N, Parvizian MK, Zworth M, Bartoszko JJ, Lopes LC, Sit D, Bala MM, Alonso-Coello 
 
P, Johnston BC. Reduction of Red and Processed Meat Intake and Cancer Mortality and  Incidence: A Systematic 
 
Review and Meta-analysis of Cohort Studies. Ann Intern Med. 2019;171(10):711-20. Epub 2019/10/01. doi: 
 
10.7326/m19-0699. PubMed PMID: 31569214. 
 
20. 
Li SA, Alexander PE, Reljic T, Cuker A, Nieuwlaat R, Wiercioch W, Guyatt G, Schünemann HJ,  Djulbegovic B. 
 
Evidence to Decision framework provides a structured "roadmap" for making GRADE guidelines 
 
recommendations. J Clin Epidemiol. 2018;104:103-12. Epub 2018/09/27. doi: 10.1016/j.jclinepi.2018.09.007. 
 
PubMed PMID: 30253221. 
 
21. 
Johnston BC, Zeraatkar D, Han MA, Vernooij RWM, Valli C, El Dib R, Marshall C, Stover PJ, Fairweather-Taitt S, 
 
Wójcik G, Bhatia F, de Souza R, Brotons C, Meerpohl JJ, Patel CJ, Djulbegovic B, Alonso-Coello  P, Bala MM, 
 
Guyatt GH. Unprocessed Red Meat and Processed Meat Consumption: Dietary Guideline Recommendations 
 
From the Nutritional Recommendations (NutriRECS) Consortium. Ann Intern Med. 2019;171(10):756-64. Epub 
 
2019/10/01. doi: 10.7326/m19-1621. PubMed PMID: 31569235. 
 
22. 
Valli C, Rabassa M, Johnston BC, Kuijpers R, Prokop-Dorner A, Zajac J, Storman D, Storman M, Bala MM, 
 
Solà I, Zeraatkar D, Han MA, Vernooij RWM, Guyatt GH, Alonso-Coello P. Health-Related Values and 
 
Preferences Regarding Meat Consumption: A Mixed-Methods Systematic Review. Ann Intern Med. 
 
2019;171(10):742-55. Epub 2019/10/01. doi: 10.7326/m19-1326. PubMed PMID: 31569219. 
 
23. 
Guertin KA, Li XS, Graubard BI, Albanes D, Weinstein SJ, Goedert JJ, Wang Z, Hazen SL, Sinha R. Serum 
 
Trimethylamine N-oxide, Carnitine, Choline, and Betaine in Relation to Colorectal Cancer Risk in the Alpha 
 
Tocopherol, Beta Carotene Cancer Prevention Study. Cancer Epidemiol Biomarkers Prev. 2017;26(6):945-
 
52. Epub 2017/01/13. doi: 10.1158/1055-9965.epi-16-0948. PubMed PMID: 28077427;  PMCID: PMC5608021. 
 
24. 
Jian SH, Yeh PJ, Wang CH, Chen HC, Chen SF. Analysis of heterocyclic amines in meat products by liquid 
 
chromatography - Tandem mass spectrometry. J Food Drug Anal. 2019;27(2):595-602. Epub 2019/04/17. doi: 
 
10.1016/j.jfda.2018.10.002. PubMed PMID: 30987731. 
 
25. 
Mosley D, Su T, Murff HJ, Smalley WE, Ness RM, Zheng W, Shrubsole MJ. Meat intake, meat cooking 
 
methods, and meat-derived mutagen exposure and risk of sessile serrated lesions. Am J Clin Nutr. 
 
2020;111(6):1244-51. Epub 2020/02/23. doi: 10.1093/ajcn/nqaa030. PubMed PMID: 32077920;  PMCID: 
 
PMC7266682. 
 
26. 
Zheng Y, Li Y, Satija A, Pan A, Sotos-Prieto M, Rimm E, Willett WC, Hu FB. Association of changes in red 
 
meat consumption with total and cause specific mortality among US women and men: two prospective cohort 

 
EAT-WELL Study Protocol_V3  
16 
 
 
studies. BMJ. 2019;365:l2110. Epub 2019/06/14. doi: 10.1136/bmj.l2110. PubMed PMID: 31189526; PMCID: 
 
PMC6559336  
 
27. 
Guasch-Ferré M, Satija A, Blondin SA, Janiszewski M, Emlen E, O'Connor LE, Campbell WW, Hu FB, Willett 
 
WC, Stampfer MJ. Meta-Analysis of Randomized Controlled Trials of Red Meat Consumption in  Comparison 
 
With Various Comparison Diets on Cardiovascular Risk Factors. Circulation. 2019;139(15):1828-45. Epub 
 
2019/04/09. doi: 10.1161/circulationaha.118.035225. PubMed PMID: 30958719. 
 
28. 
Mattiuzzi C, Lippi G. Epidemiologic Burden of Red and Processed Meat Intake on Colorectal Cancer 
 
Mortality. Nutr Cancer. 2020:1-6. Epub 2020/05/14. doi: 10.1080/01635581.2020.1765259. PubMed PMID: 
 
32400201. 
 
29. 
Mehta SS, Arroyave WD, Lunn RM, Park YM, Boyd WA, Sandler DP. A Prospective Analysis of Red and 
 
Processed Meat Consumption and Risk of Colorectal Cancer in Women. Cancer Epidemiol Biomarkers  
 
Prev. 2020;29(1):141-50. Epub 2019/10/03. doi: 10.1158/1055-9965.epi-19-0459. PubMed PMID: 
 
31575555; PMCID: PMC6954313. 
 
30. 
Liao LM, Loftfield E, Etemadi A, Graubard BI, Sinha R. Substitution of dietary protein sources in relation  to 
 
colorectal cancer risk in the NIH-AARP cohort study. Cancer Causes Control. 2019;30(10):1127-35. Epub 
 
2019/07/22. doi: 10.1007/s10552-019-01210-1. PubMed PMID: 31327110; PMCID: PMC6736766. 
 
31. 
Neuhouser ML. Red and processed meat: more with less? Am J Clin Nutr. 2020;111(2):252-5. Epub 
 
2019/11/20. doi: 10.1093/ajcn/nqz294. PubMed PMID: 31740926. 
 
32. 
Maruvada P, Lampe JW, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, 
 
Dragsted LO, Draper J, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, F BH, Karp RW, Klurfeld 
 
DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O'Brien DM, Ordovas JM, Osganian SK, 
 
Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, Zeisel 
 
SH. Perspective: dietary biomarkers of intake and exposure-exploration with omics approaches. Adv Nutr. 
 
2019. Epub 2019/08/07. doi: 10.1093/advances/nmz075. PubMed PMID: 31386148. 
 
33. 
Mahabir S, Willett WC, Friedenreich CM, Lai GY, Boushey CJ, Matthews CE, Sinha R, Colditz GA, 
 
Rothwell JA, Reedy J, Patel AV, Leitzmann MF, Fraser GE, Ross S, Hursting SD, Abnet CC, Kushi LH, Taylor 
 
PR, Prentice RL. Research strategies for nutritional and physical activity epidemiology and cancer 
 
prevention. Cancer Epidemiol Biomarkers Prev. 2018;27(3):233-44. Epub 2017/12/20. doi: 10.1158/1055-
 
9965.epi-17-0509. PubMed PMID: 29254934. 
 
34. 
National Institutes of Health (NIH). 2020-2030 Strategic Plan for NIH Nutrition Research. A Report of  
the NIH 
 
Nutrition Research Task Force2020:1-24. 
 
35. 
Fleischhacker SE, Ballard RM, Starke-Reed PE, Galuska DA, Neuhouser ML. Developmental Process and 
 
Early Phases of Implementation for the US Interagency Committee on Human Nutrition Research 
 
National Nutrition Research Roadmap 2016-2021. J Nutr. 2017;147(10):1833-8. doi: 1 
0.3945/jn.117.255943. 
 
PubMed PMID: 28814532; PMCID: PMC5610552. 
 
36. 
https://www.fsis.usda.gov/wps/portal/fsis/topics/food-safety-education/get-answers/food-safety-fact-
 
sheets/food-labeling/meat-and-poultry-labeling-terms/meat-and-poultry-labeling-terms. (accessed Oct  
2019). 
 
37. 
Wu K, Spiegelman D, Hou T, Albanes D, Allen NE, Berndt SI, van den Brandt PA, Giles GG, Giovannucci E, 
 
Goldbohm RA, Goodman GG, Goodman PJ, Hakansson N, Inoue M, Key TJ, Kolonel LN, Mannisto S, 
 
McCullough ML, Neuhouser ML, Park Y, Platz EA, Schenk JM, Sinha R, Stampfer MJ, Stevens VL, Tsugane S, 
 
Visvanathan K, Wilkens LR, Wolk A, Ziegler RG, Smith-Warner SA. Associations between unprocessed red 
 
and processed meat, poultry, seafood and egg intake and the risk of prostate cancer: A pooled analysis of 15 
 
prospective cohort studies. Int J Cancer. 2016;138(10):2368-82. doi: 10.1002/ijc.29973. PubMed PMID: 
 
WOS:000372257300007; PMCID: PMC4837898. 
 
38. 
Zeng L, Ruan M, Liu J, Wilde P, Naumova EN, Mozaffarian D, Zhang FF. Trends in Processed Meat, 
 
Unprocessed Red Meat, Poultry, and Fish Consumption in the United States, 1999-2016. J Acad Nutr Diet.  
 
2019;119(7):1085-98.e12. Epub 2019/06/27. doi: 10.1016/j.jand.2019.04.004. PubMed PMID: 31234969;  PMCID: 
 
PMC6689198. 
 

 
EAT-WELL Study Protocol_V3  
17 
 
39. 
Klurfeld DM. What is the role of meat in a healthy diet? Anim Front. 2018;8(3):5-10. Epub 2018/07/07. doi: 
 
10.1093/af/vfy009. PubMed PMID: 32071794; PMCID: PMC7015455. 
 
40. 
Jiao L, Stolzenberg-Solomon R, Zimmerman TP, Duan Z, Chen L, Kahle L, Risch A, Subar AF, Cross AJ, 
 
Hollenbeck A, Vlassara H, Striker G, Sinha R. Dietary consumption of advanced glycation end products and 
 
pancreatic cancer in the prospective NIH-AARP Diet and Health Study. Am J Clin Nutr. 2015;101(1):126-34. 
 
Epub 2014/12/21. doi: 10.3945/ajcn.114.098061. PubMed PMID: 25527756; PMCID: PMC4266882. 
 
41. 
White DL, Collinson A. Red meat, dietary heme iron, and risk of type 2 diabetes: the involvement of 
 
advanced lipoxidation endproducts. Adv Nutr. 2013;4(4):403-11. Epub 2013/07/17. doi: 
 
10.3945/an.113.003681. PubMed PMID: 23858089; PMCID: PMC3941820. 
 
42. 
van Breda SG, Mathijs K, Sági-Kiss V, Kuhnle GG, van der Veer B, Jones RR, Sinha R, Ward MH, de  
Kok 
 
TM. Impact of high drinking water nitrate levels on the endogenous formation of apparent N-nitroso 
 
compounds in combination with meat intake in healthy volunteers. Environ Health. 2019;18(1):87. Epub 
 
2019/10/19. doi: 10.1186/s12940-019-0525-z. PubMed PMID: 31623611; PMCID: PMC6796425. 
 
43. 
Etemadi A, Sinha R, Ward MH, Graubard BI, Inoue-Choi M, Dawsey SM, Abnet CC. Mortality from 
 
different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and  Health Study: 
 
population based cohort study. BMJ. 2017;357:j1957. Epub 2017/05/11. doi: 10.1136/bmj.j1957. PubMed  PMID: 
 
28487287; PMCID: PMC5423547  
 
 
44. 
Inoue-Choi M, Sinha R, Gierach GL, Ward MH. Red and processed meat, nitrite, and heme iron intakes  and 
 
postmenopausal breast cancer risk in the NIH-AARP Diet and Health Study. Int J Cancer. 
 
2016;138(7):1609-18. Epub 2015/10/28. doi: 10.1002/ijc.29901. PubMed PMID: 26505173; PMCID: 
 
PMC4724256. 
 
45. 
Pietron W, Pajurek M, Mikolajczyk S, Maszewski S, Warenik-Bany M, Piskorska-Pliszczynska J.  Exposure to 
 
PBDEs associated with farm animal meat consumption. Chemosphere. 2019;224:58-64.  Epub 2019/02/27. doi: 
 
10.1016/j.chemosphere.2019.02.067. PubMed PMID: 30807914. 
 
46. 
Sakhi AK, Lillegaard IT, Voorspoels S, Carlsen MH, Løken EB, Brantsæter AL, Haugen M, Meltzer HM, 
 
Thomsen C. Concentrations of phthalates and bisphenol A in Norwegian foods and beverages and 
 
estimated dietary exposure in adults. Environ Int. 2014;73:259-69. Epub 2014/09/01. doi: 
 
10.1016/j.envint.2014.08.005. PubMed PMID: 25173060. 
 
47. 
Le NT, Michels FA, Song M, Zhang X, Bernstein AM, Giovannucci EL, Fuchs CS, Ogino S, Chan AT, Sinha 
 
R, Willett WC, Wu K. A Prospective Analysis of Meat Mutagens and Colorectal Cancer in the  
Nurses' Health 
 
Study and Health Professionals Follow-up Study. Environ Health Perspect. 2016;124(10):1529-36. Epub 
 
2016/04/23. doi: 10.1289/ehp238. PubMed PMID: 27105317; PMCID:  
PMC5047780. 
 
48. 
John EM, Stern MC, Sinha R, Koo J. Meat consumption, cooking practices, meat mutagens, and risk of 
 
prostate cancer. Nutr Cancer. 2011;63(4):525-37. Epub 2011/04/29. doi:  10.1080/01635581.2011.539311. 
 
PubMed PMID: 21526454; PMCID: PMC3516139. 
 
49. 
Ren X, Wang W, Bao Y, Zhu Y, Zhang Y, Lu Y, Peng Z, Zhou G. Isorhamnetin and Hispidulin from 
 
Tamarix ramosissima Inhibit 2-Amino-1-Methyl-6-Phenylimidazo[4,5-b]Pyridine (PhIP) Formation by 
 
Trapping Phenylacetaldehyde as a Key Mechanism. Foods. 2020;9(4). Epub 2020/04/09. doi: 
 
10.3390/foods9040420. PubMed PMID: 32260060; PMCID: PMC7230572. 
 
50. 
Roussell MA, Hill AM, Gaugler TL, West SG, Heuvel JP, Alaupovic P, Gillies PJ, Kris-Etherton PM. Beef in 
 
an Optimal Lean Diet study: effects on lipids, lipoproteins, and apolipoproteins. Am J Clin Nutr.  
012;95(1):9-
 
16. Epub 2011/12/16. doi: 10.3945/ajcn.111.016261. PubMed PMID: 22170364; PMCID:  PMC3238465. 
 
51. 
O'Connor LE, Paddon-Jones D, Wright AJ, Campbell WW. A Mediterranean-style eating pattern with lean, 
 
unprocessed red meat has cardiometabolic benefits for adults who are overweight or obese in a  randomized, 
 
crossover, controlled feeding trial. Am J Clin Nutr. 2018;108(1):33-40. Epub 2018/06/15.  doi: 
 
10.1093/ajcn/nqy075. PubMed PMID: 29901710; PMCID: PMC6600057. 
 
52. 
Maki KC, Wilcox ML, Dicklin MR, Buggia M, Palacios OM, Maki CE, Kramer M. Substituting Lean Beef for 
 
Carbohydrate in a Healthy Dietary Pattern Does Not Adversely Affect the Cardiometabolic Risk  Factor Profile in 

 
EAT-WELL Study Protocol_V3  
18 
 
 
Men and Women at Risk for Type 2 Diabetes. J Nutr. 2020. Epub 2020/05/03. doi: 10.1093/jn/nxaa116. 
 
PubMed PMID: 32359153. 
 
53. 
Schmedes M, Aadland EK, Sundekilde UK, Jacques H, Lavigne C, Graff IE, Eng Ø, Holthe A, Mellgren G, Young 
 
JF, Bertram HC, Liaset B, Clausen MR. Lean-seafood intake decreases urinary markers of mitochondrial lipid and 
 
energy metabolism in healthy subjects: Metabolomics results from a randomized crossover intervention study. 
 
Mol Nutr Food Res. 2016;60(7):1661-72. Epub 2016/02/14. doi: 10.1002/mnfr.201500785. PubMed PMID: 
 
26873789. 
 
54. 
Schmedes M, Balderas C, Aadland EK, Jacques H, Lavigne C, Graff IE, Eng Ø, Holthe A, Mellgren G, Young JF, 
 
Sundekilde UK, Liaset B, Bertram HC. The Effect of Lean-Seafood and Non-Seafood Diets on Fasting and 
 
Postprandial Serum Metabolites and Lipid Species: Results from a Randomized Crossover Intervention Study in 
 
Healthy Adults. Nutrients. 2018;10(5). Epub 2018/05/13. doi: 10.3390/nu10050598. PubMed PMID: 29751643; 
 
PMCID: PMC5986478. 
 
55. 
David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma  Y, 
 
Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ. Diet rapidly and reproducibly alters the human gut 
 
microbiome. Nature. 2014;505(7484):559-63. Epub 2013/12/18. doi: 10.1038/nature12820.  
PubMed PMID: 
 
24336217; PMCID: PMC3957428. 
 
56. 
Rios-Covian D, Gueimonde M, Duncan SH, Flint HJ, de los Reyes-Gavilan CG. Enhanced butyrate 
 
formation by cross-feeding between Faecalibacterium prausnitzii and Bifidobacterium adolescentis. FEMS 
 
Microbiol Lett. 2015;362(21). Epub 2015/10/01. doi: 10.1093/femsle/fnv176. PubMed PMID: 26420851. 
 
57. 
Flint HJ, Duncan SH, Scott KP, Louis P. Links between diet, gut microbiota composition and gut metabolism. 
 
Proc Nutr Soc. 2015;74(1):13-22. Epub 2014/10/01. doi: 10.1017/s0029665114001463.  PubMed PMID: 
 
25268552. 
 
58. 
Schmedes M, Brejnrod AD, Aadland EK, Kiilerich P, Kristiansen K, Jacques H, Lavigne C, Graff IE, Eng  Ø, 
 
Holthe A, Mellgren G, Young JF, Sundekilde UK, Liaset B, Bertram HC. The Effect of Lean-Seafood and Non-
 
Seafood Diets on Fecal Metabolites and Gut Microbiome: Results from a Randomized Crossover Intervention 
 
Study. Mol Nutr Food Res. 2019;63(1):e1700976. Epub 2018/03/07. doi:  10.1002/mnfr.201700976. PubMed 
 
PMID: 29509315. 
 
59. 
Navarro SL, Tarkhan A, Shojaie A, Randolph TW, Gu H, Djukovic D, Osterbauer KJ, Hullar MA, Kratz M, 
 
Neuhouser ML, Lampe PD, Raftery D, Lampe JW. Plasma metabolomics profiles suggest beneficial effects of a 
 
low-glycemic load dietary pattern on inflammation and energy metabolism. Am J Clin Nutr. 2019;110(4):984-92. 
 
Epub 2019/08/23. doi: 10.1093/ajcn/nqz169. PubMed PMID: 31432072; PMCID: PMC6766441. 
 
60. 
Ginos BNR, Navarro SL, Schwarz Y, Gu H, Wang D, Randolph TW, Shojaie A, Hullar MAJ, Lampe PD,  Kratz 
 
M, Neuhouser ML, Raftery D, Lampe JW. Circulating bile acids in healthy adults respond  differently to a dietary 
 
pattern characterized by whole grains, legumes and fruits and vegetables compared to a diet high in refined 
 
grains and added sugars: A randomized, controlled, crossover feeding study. Metabolism. 2018;83:197-204. 
 
Epub 2018/02/20. doi: 10.1016/j.metabol.2018.02.006.  PubMed PMID: 29458053; PMCID: PMC5960615. 
 
61. 
Miles FL, Navarro SL, Schwarz Y, Gu H, Djukovic D, Randolph TW, Shojaie A, Kratz M, Hullar MAJ, Lampe PD, 
 
Neuhouser ML, Raftery D, Lampe JW. Plasma metabolite abundances are associated with urinary enterolactone 
 
excretion in healthy participants on controlled diets. Food Funct. 2017;8(9):3209- 18. doi: 10.1039/c7fo00684e. 
 
PubMed PMID: 28808723; PMCID: PMC5607107. 
 
62. 
Pan S, Hullar MAJ, Lai LA, Peng H, May DH, Noble WS, Raftery D, Navarro SL, Neuhouser ML, Lampe  PD, 
 
Lampe JW, Chen R. Gut Microbial Protein Expression in Response to Dietary Patterns in a Controlled 
 
Feeding Study: A Metaproteomic Approach. Microorganisms. 2020;8(3). Epub 2020/03/12. doi: 
 
10.3390/microorganisms8030379. PubMed PMID: 32156071; PMCID: PMC7143255. 
 
63. 
Maskarinec G, Hullar MAJ, Monroe KR, Shepherd JA, Hunt J, Randolph TW, Wilkens LR, Boushey CJ,  Le 
 
Marchand L, Lim U, Lampe JW. Fecal Microbial Diversity and Structure Are Associated with Diet  Quality in the 
 
Multiethnic Cohort Adiposity Phenotype Study. J Nutr. 2019;149(9):1575-84. Epub 2019/06/13. doi: 
 
10.1093/jn/nxz065. PubMed PMID: 31187868; PMCID: PMC6862930. 
 
64. 
Khakimov B, Poulsen SK, Savorani F, Acar E, Gürdeniz G, Larsen TM, Astrup A, Dragsted LO,  Engelsen SB. 
 
New Nordic Diet versus Average Danish Diet: A Randomized Controlled Trial Revealed  Healthy Long-Term 

 
EAT-WELL Study Protocol_V3  
19 
 
 
Effects of the New Nordic Diet by GC-MS Blood Plasma Metabolomics. J Proteome Res. 2016;15(6):1939-54. 
 
Epub 2016/05/06. doi: 10.1021/acs.jproteome.6b00109. PubMed PMID: 27146725. 
 
65. 
Landberg R, Hanhineva K. Biomarkers of a Healthy Nordic Diet-From Dietary Exposure Biomarkers to 
 
Microbiota Signatures in the Metabolome. Nutrients. 2019;12(1). Epub 2019/12/28. doi: 10.3390/nu12010027. 
 
PubMed PMID: 31877633; PMCID: PMC7019922. 
 
66. 
Martínez-González M, Ruiz-Canela M, Hruby A, Liang L, Trichopoulou A, Hu FB. Intervention Trials with the 
 
Mediterranean Diet in Cardiovascular Prevention: Understanding Potential Mechanisms  through Metabolomic 
 
Profiling. J Nutr. 2015;146(4):913s-9s. Epub 2016/03/11. doi: 10.3945/jn.115.219147. PubMed PMID: 26962184; 
 
PMCID: PMC4807639. 
 
67. 
Playdon MC, Moore SC, Derkach A, Reedy J, Subar AF, Sampson JN, Albanes D, Gu F, Kontto J, Lassale C, 
 
Liao LM, Mannisto S, Mondul AM, Weinstein SJ, Irwin ML, Mayne ST, Stolzenberg-Solomon  
R. Identifying 
 
biomarkers of dietary patterns by using metabolomics. Am J Clin Nutr. 2017;105(2):450- 65. Epub 2016/12/30. 
 
doi: 10.3945/ajcn.116.144501. PubMed PMID: 28031192; PMCID: PMC5267308. 
 
68. 
Gibbons H, Carr E, McNulty BA, Nugent AP, Walton J, Flynn A, Gibney MJ, Brennan L. Metabolomic-based 
 
identification of clusters that reflect dietary patterns. Mol Nutr Food Res. 2017;61(10). Epub 2017/06/07. doi: 
 
10.1002/mnfr.201601050. PubMed PMID: 28586169. 
 
69. 
Bondia-Pons I, Cañellas N, Abete I, Rodríguez M, Perez-Cornago A, Navas-Carretero S, Zulet M, Correig X, 
 
Martínez JA. Nutri-metabolomics: subtle serum metabolic differences in healthy subjects by NMR-based 
 
metabolomics after a short-term nutritional intervention with two tomato sauces. OMICS. 2013;17(12):611-8. Epub 
 
2013/10/22. doi: 10.1089/omi.2013.0027. PubMed PMID: 24138070. 
 
70. 
Keski-Rahkonen P, Kolehmainen M, Lappi J, Micard V, Jokkala J, Rosa-Sibakov N, Pihlajamäki J, 
 
Kirjavainen PV, Mykkänen H, Poutanen K, Gunter MJ, Scalbert A, Hanhineva K. Decreased plasma 
 
serotonin and other metabolite changes in healthy adults after consumption of wholegrain rye: an  untargeted 
 
metabolomics study. Am J Clin Nutr. 2019;109(6):1630-9. Epub 2019/05/29. doi:  10.1093/ajcn/nqy394. PubMed 
 
PMID: 31136658. 
 
71. 
Hanhineva K, Lankinen MA, Pedret A, Schwab U, Kolehmainen M, Paananen J, de Mello V, Sola R, 
 
Lehtonen M, Poutanen K, Uusitupa M, Mykkänen H. Nontargeted metabolite profiling discriminates diet-specific 
 
biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr. 
 
2015;145(1):7-17. Epub 2014/12/21. doi: 10.3945/jn.114.196840. PubMed PMID: 25527657. 
 
72. 
Derkach A, Sampson J, Joseph J, Playdon MC, Stolzenberg-Solomon RZ. Effects of dietary sodium on 
 
metabolites: the Dietary Approaches to Stop Hypertension (DASH)-Sodium Feeding Study. Am J Clin Nutr. 
 
2017;106(4):1131-41. Epub 2017/09/01. doi: 10.3945/ajcn.116.150136. PubMed PMID: 28855223; PMCID: 
 
PMC5611778. 
 
73. 
Acar E, Gurdeniz G, Khakimov B, Savorani F, Korndal SK, Larsen TM, Engelsen SB, Astrup A, Dragsted LO. 
 
Biomarkers of individual foods, and separation of diets using untargeted LC-MS-based plasma metabolomics in a 
 
randomized controlled trial. Mol Nutr Food Res. 2019;63(1):e1800215. Epub 2018/08/11. doi: 
 
10.1002/mnfr.201800215. PubMed PMID: 30094970. 
 
74. 
Ulven SM, Christensen JJ, Nygård O, Svardal A, Leder L, Ottestad I, Lysne V, Laupsa-Borge J, Ueland  PM, 
 
Midttun Ø, Meyer K, McCann A, Andersen LF, Holven KB. Using metabolic profiling and gene expression 
 
analyses to explore molecular effects of replacing saturated fat with polyunsaturated fat-a randomized controlled 
 
dietary intervention study. Am J Clin Nutr. 2019;109(5):1239-50. Epub 2019/05/06. doi: 10.1093/ajcn/nqy356. 
 
PubMed PMID: 31051508; PMCID: PMC6499508. 
 
75. 
Cheung W, Keski-Rahkonen P, Assi N, Ferrari P, Freisling H, Rinaldi S, Slimani N, Zamora-Ros R, Rundle M, 
 
Frost G, Gibbons H, Carr E, Brennan L, Cross AJ, Pala V, Panico S, Sacerdote C, Palli D, Tumino R, Kühn T, 
 
Kaaks R, Boeing H, Floegel A, Mancini F, Boutron-Ruault MC, Baglietto L,  
Trichopoulou A, Naska A, 
 
Orfanos P, Scalbert A. A metabolomic study of biomarkers of meat and fish intake. Am J Clin Nutr. 
 
2017;105(3):600-8. Epub 2017/01/27. doi: 10.3945/ajcn.116.146639. PubMed PMID: 28122782. 
 
76. 
Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, Rappaport SM, van der Hooft JJ, 
 
Wishart DS. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99(6):1286-308. Epub 
 
2014/04/25. doi: 10.3945/ajcn.113.076133. PubMed PMID: 24760973. 

 
EAT-WELL Study Protocol_V3  
20 
 
 
77. 
Ijssennagger N, Belzer C, Hooiveld GJ, Dekker J, van Mil SW, Müller M, Kleerebezem M, van der Meer  R. Gut 
 
microbiota facilitates dietary heme-induced epithelial hyperproliferation by opening the mucus barrier in colon.  
 
Proc Natl Acad Sci U S A. 2015;112(32):10038-43. Epub 2015/07/29. doi: 10.1073/pnas.1507645112. PubMed 
 
PMID: 26216954; PMCID: PMC4538683. 
 
78. 
Gregory LG, Bond PL, Richardson DJ, Spiro S. Characterization of a nitrate-respiring bacterial community using 
 
the nitrate reductase gene (narG) as a functional marker. Microbiology. 2003;149(Pt 1):229-37. Epub 2003/02/11. 
 
doi: 10.1099/mic.0.25849-0. PubMed PMID: 12576596. 
 
79. 
Zhang Y, Xu Z, Li J, Liu D, Yuan Y, Chen Z, Wang G. Cooperation between two strains of Enterobacter  and 
 
Klebsiella in the simultaneous nitrogen removal and phosphate accumulation processes. Bioresour Technol. 
 
2019;291:121854. Epub 2019/07/30. doi: 10.1016/j.biortech.2019.121854. PubMed PMID: 31357041. 
 
80. 
Wedekind R, Kiss A, Keski-Rahkonen P, Viallon V, Rothwell JA, Cross AJ, Rostgaard-Hansen AL, 
 
Sandanger TM, Jakszyn P, Schmidt JA, Pala V, Vermeulen R, Schulze MB, Kühn T, Johnson T,  Trichopoulou A, 
 
Peppa E, La Vechia C, Masala G, Tumino R, Sacerdote C, Wittenbecher C, de  Magistris MS, Dahm CC, Severi 
 
G, Mancini FR, Weiderpass E, Gunter MJ, Huybrechts I, Scalbert A. A metabolomic study of red and processed 
 
meat intake and acylcarnitine concentrations in human urine and blood. Am J Clin Nutr. 2020. Epub 
 
2020/06/04. doi: 10.1093/ajcn/nqaa140. PubMed PMID:  32492168. 
 
81. 
Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ, Bright IJ, Lucero MY, 
 
Hiddessen AL, Legler TC, Kitano TK, Hodel MR, Petersen JF, Wyatt PW, Steenblock ER, Shah PH, Bousse LJ, 
 
Troup CB, Mellen JC, Wittmann DK, Erndt NG, Cauley TH, Koehler RT, So AP, Dube S,  Rose KA, Montesclaros 
 
L, Wang S, Stumbo DP, Hodges SP, Romine S, Milanovich FP, White HE, Regan JF, Karlin-Neumann GA, 
 
Hindson CM, Saxonov S, Colston BW. High-throughput droplet digital PCR system for absolute quantitation of 
 
DNA copy number. Anal Chem. 2011;83(22):8604-10. Epub 2011/11/01. doi: 10.1021/ac202028g. PubMed PMID: 
 
22035192; PMCID: PMC3216358. 
 
82. 
Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T, 
 
Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT, Bustin SA. The digital MIQE guidelines: Minimum 
 
Information for Publication of Quantitative Digital PCR Experiments. Clin Chem.  2013;59(6):892-902. Epub 
 
2013/04/11. doi: 10.1373/clinchem.2013.206375. PubMed PMID: 23570709. 
 
83. 
Cao Y, Raith MR, Griffith JF. Droplet digital PCR for simultaneous quantification of general and human-
 
associated fecal indicators for water quality assessment. Water Res. 2015;70:337-49. Epub 2014/12/30. doi: 
 
10.1016/j.watres.2014.12.008. PubMed PMID: 25543243. 
 
84. 
Nadkarni MA, Martin FE, Jacques NA, Hunter N. Determination of bacterial load by real-time PCR using a broad-
 
range (universal) probe and primers set. Microbiology. 2002;148(Pt 1):257-66. Epub 2002/01/10. doi: 
 
10.1099/00221287-148-1-257. PubMed PMID: 11782518. 
 
85. 
Neuhouser ML, Schwarz Y, Wang C, Breymeyer K, Coronado G, Wang CY, Noar K, Song X, Lampe JW. A low-
 
glycemic load diet reduces serum C-reactive protein and modestly increases adiponectin in overweight and obese 
 
adults. J Nutr. 2012;142(2):369-74. Epub 2011/12/23. doi: 10.3945/jn.111.149807. PubMed PMID: 22190020; 
 
PMCID: PMC3260063. 
 
86. 
Prentice RL, Mossavar-Rahmani Y, Huang Y, Van Horn L, Beresford SA, Caan B, Tinker L, Schoeller D, Bingham 
 
S, Eaton CB, Thomson C, Johnson KC, Ockene J, Sarto G, Heiss G, Neuhouser ML. Evaluation and comparison 
 
of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers. Am J 
 
Epidemiol. 2011;174(5):591-603. Epub 2011/07/19. doi: 10.1093/aje/kwr140. PubMed PMID: 21765003; PMCID: 
 
PMC3202154. 
 
87. 
Lampe JW, Huang Y, Neuhouser ML, Tinker LF, Song X, Schoeller DA, Kim S, Raftery D, Di C, Zheng  
C, 
 
Schwarz Y, Van Horn L, Thomson CA, Mossavar-Rahmani Y, Beresford SA, Prentice RL. Dietary biomarker 
 
evaluation in a controlled feeding study in women from the Women's Health Initiative cohort. Am J Clin Nutr. 
 
2017;105(2):466-75. doi: 10.3945/ajcn.116.144840. PubMed PMID: 28031191; PMCID: PMC5267309. 
 
88. 
Runchey SS, Valsta LM, Schwarz Y, Wang C, Song X, Lampe JW, Neuhouser ML. Effect of low- and high-
 
glycemic load on circulating incretins in a randomized clinical trial. Metabolism. 2013;62(2):188-95. Epub 
 
2012/09/11. doi: 10.1016/j.metabol.2012.07.006. PubMed PMID: 22959497; PMCID: PMC3519963. 
 

 
EAT-WELL Study Protocol_V3  
21 
 
89. 
Santiago-Torres M, Kratz M, Lampe JW, Tapsoba J de D, Breymeyer KL, Levy L, Villasenor A, Wang CY, Song 
 
X, Neuhouser ML. Metabolic responses to a traditional Mexican diet compared with a commonly consumed US 
 
diet in women of Mexican descent: a randomized crossover feeding trial. Am J Clin Nutr. 2016;103(2):366-74. 
 
doi: 10.3945/ajcn.115.119016. PubMed PMID: 26718418; PMCID: PMC4733259. 
 
90. 
Neuhouser ML, Clowry C, Beatty SJ, Wang CY, Drewnowski A, Perrigue MM. Rationale and design of the 
 
frequency of eating and Satiety Hormones (FRESH) study: A randomized cross-over clinical trial.  Contemp Clin 
 
Trials Commun. 2019;14:100334. Epub 2019/03/06. doi: 10.1016/j.conctc.2019.100334. PubMed PMID: 
 
30834353; PMCID: PMC6384307. 
 
91. 
Staten LK, Taren DL, Howell WH, Tobar M, Poehlman ET, Hill A, Reid PM, Ritenbaugh C. Validation of the 
 
Arizona Activity Frequency Questionnaire using doubly labeled water. Med Sci Sports Exerc. 2001;33(11):1959-
 
67. Epub 2001/11/02. PubMed PMID: 11689750. 
 
92. 
Mifflin MD, St. Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh Y. A new predictive equation for resting energy 
 
expenditure in healthy individuals. Am J Clin Nutr. 1990;51(2):241-7. 
 
93. 
Nagana Gowda GA, Raftery D. Overview of NMR Spectroscopy-Based Metabolomics: Opportunities and 
 
Challenges. Methods Mol Biol. 2019;2037:3-14. Epub 2019/08/30. doi: 10.1007/978-1-4939-9690-2_1. PubMed 
 
PMID: 31463836. 
 
94. 
Nagana Gowda GA, Raftery D. Analysis of Plasma, Serum, and Whole Blood Metabolites Using (1)H NMR 
 
Spectroscopy. Methods Mol Biol. 2019;2037:17-34. Epub 2019/08/30. doi: 10.1007/978-1-4939- 9690-2_2. 
 
PubMed PMID: 31463837. 
 
95. 
Emwas AH, Roy R, McKay RT, Ryan D, Brennan L, Tenori L, Luchinat C, Gao X, Zeri AC, Gowda GA, Raftery D, 
 
Steinbeck C, Salek RM, Wishart DS. Recommendations and standardization of biomarker quantification using 
 
NMR-based metabolomics with particular focus on urinary analysis. J Proteome Res. 2016;15(2):360-73. Epub 
 
2016/01/09. doi: 10.1021/acs.jproteome.5b00885. PubMed PMID: 26745651; PMCID: PMC4865177. 
 
96. 
Emwas AH, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah- Daouk R, Zeri 
 
AC, Nagana Gowda GA, Raftery D, Wang Y, Brennan L, Wishart DS. Standardizing the  experimental conditions 
 
for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. 
 
Metabolomics. 2015;11(4):872-94. Epub 2015/06/26. doi: 10.1007/s11306-014-0746-7. PubMed PMID: 
 
26109927; PMCID: PMC4475544. 
 
97. 
Fei Q, Wang D, Jasbi P, Zhang P, Nagana Gowda GA, Raftery D, Gu H. Combining NMR and MS with 
 
Chemical Derivatization for Absolute Quantification with Reduced Matrix Effects. Anal Chem. 
 
2019;91(6):4055-62. Epub 2019/02/26. doi: 10.1021/acs.analchem.8b05611. PubMed PMID: 30801179. 
 
98. 
Marshall DD, Lei S, Worley B, Huang Y, Garcia-Garcia A, Franco R, Dodds ED, Powers R. Combining DI-ESI-MS 
 
and NMR datasets for metabolic profiling. Metabolomics. 2015;11(2):391-402. Epub  
2015/03/17. doi: 
 
10.1007/s11306-014-0704-4. PubMed PMID: 25774104; PMCID: PMC4354777. 
 
99. 
Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple 
 
testing. J Roy Stat Soc. 1995;57(1):289-300. 
 
100. 
Li F, Hullar MA, Lampe JW. Optimization of terminal restriction fragment polymorphism (TRFLP)  analysis of 
 
human gut microbiota. J Microbiol Methods. 2007;68(2):303-11. Epub 2006/10/31. doi: 
 
10.1016/j.mimet.2006.09.006. PubMed PMID: 17069911; PMCID: PMC1853299. 
 
101. 
Fu BC, Randolph TW, Lim U, Monroe KR, Cheng I, Wilkens LR, Le Marchand L, Hullar MA, Lampe JW. 
 
Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience. Ann 
 
Epidemiol. 2016;26(5):373-9. Epub 2016/04/04. doi: 10.1016/j.annepidem.2016.02.009. PubMed PMID: 
 
27039047; PMCID: PMC4892953. 
 
102. 
Fu BC, Randolph TW, Lim U, Monroe KR, Cheng I, Wilkens LR, Le Marchand L, Lampe JW, Hullar MAJ. 
 
Temporal Variability and Stability of the Fecal Microbiome: The Multiethnic Cohort Study. Cancer  Epidemiol 
 
Biomarkers Prev. 2019;28(1):154-62. Epub 2018/09/13. doi: 10.1158/1055-9965.epi-18- 0348. PubMed PMID: 
 
30206059; PMCID: PMC6625800. 
 

 
EAT-WELL Study Protocol_V3  
22 
 
103. 
Vogelstein B, Kinzler KW. Digital PCR. Proc Natl Acad Sci U S A. 1999;96(16):9236-41. Epub 1999/08/04. doi: 
 
10.1073/pnas.96.16.9236. PubMed PMID: 10430926; PMCID: PMC17763. 
 
104. 
Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of  
high-
 
throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth 
 
experiments by compositional data analysis. Microbiome. 2014;2:15. Epub 2014/06/10. doi: 10.1186/2049-2618-
 
2-15. PubMed PMID: 24910773; PMCID: PMC4030730. 
 
105. 
Koh H, Li Y, Zhan X, Chen J, Zhao N. A Distance-Based Kernel Association Test Based on the Generalized 
 
Linear Mixed Model for Correlated Microbiome Studies. Front Genet. 2019;10:458. Epub  2019/06/04. doi: 
 
10.3389/fgene.2019.00458. PubMed PMID: 31156711; PMCID: PMC6532659. 
 
106. 
McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z, Gonzalez A, Knight R.  Striped 
 
UniFrac: enabling microbiome analysis at unprecedented scale. Nat Methods. 2018;15(11):847- 8. Epub 
 
2018/11/01. doi: 10.1038/s41592-018-0187-8. PubMed PMID: 30377368; PMCID: PMC7250580. 
 
107. 
Lozupone CA, Knight R. The UniFrac significance test is sensitive to tree topology. BMC  Bioinformatics. 
 
2015;16(1):211. Epub 2015/07/08. doi: 10.1186/s12859-015-0640-y. PubMed PMID: 26150095; PMCID: P
 
MC4492014. 
 
108. 
Randolph TW, Zhao S, Copeland W, Hullar M, Shojaie A. KERNEL-penalized Regression for analysis  
of 
 
micorbiome data. Ann Appl Stat. 2018;12(1):540-66. Epub 2018/09/19. doi: 10.1214/17-aoas1102. PubMed 
 
PMID: 30224943; PMCID: PMC6138053. 
 
109. 
Bae S, Ulrich CM, Neuhouser ML, Malysheva O, Bailey LB, Xiao L, Brown EC, Cushing-Haugen KL,  Zheng Y, 
Cheng TY, Miller JW, Green R, Lane DS, Beresford SA, Caudill MA. Plasma choline  metabolites and colorectal cancer 
risk in the Women's Health Initiative Observational Study. Cancer Res. 2014;74(24):7442-52. Epub 2014/10/23. doi: 
10.1158/0008-5472.can-14-1835. PubMed PMID: 25336191; PMCID: PMC4268282. 
 
110. 
Nyamundanda G, Gomley IC, Fan Y, Gallagher M, Brennan L. MetSizeR: selecting the optimal sample size 
for metabolomic studies using an analysis based approach. BMC Bioinformatics; 2013; 14:338.