ClinicalTrials.gg
CompletedNCT00342732Updated Nov 9, 2026

The Food Intake Phenotype: Assessing Eating Behavior and Food Preferences as Risk Factors for Obesity

An observational study in Obesity, Overweight and Overnutrition, sponsored by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Completed at 1 site in United States. Open to participants aged 18 Years to 65 Years. Per ClinicalTrials.gov, last updated 2026-11-09.

Sponsored by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) · Observational

Study type
Observational
Model
Ecologic or community
Time perspective
Cross-sectional
Enrollment
539
Ages
18 Years to 65 Years
Sex
All
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Study summary

The prevalence of obesity in the United States has reached alarming proportions with 33% of adults over the age of 20 being overweight. Obesity is more than twice as prevalent, however, in the Pima Indians of Arizona. Although there have been a number of advances in our understanding of the genetics of obesity, the environmental influences on the genetic expression of obesity requires further investigation.

In an effort to understand some of the influences on the high prevalence of obesity in the Pima Indians, the present study was designed to investigate eating behaviors and food preferences, most especially the preference for high fat foods, in sib-pairs of Pima Indians who have been previously genotyped in our genomic scan for loci linked to diabetes/obesity. Most specifically, we will utilize several questionnaires and methods of assessing eating behavior and the preference for high fat foods to create a food intake phenotype. In addition, we will study Caucasians so that comparisons can be made between these two groups. We will make these evaluations by assessing eating behavior, food preferences including usual fat intake and preferences for high fat foods, body image perceptions, and energy expenditure. It is hoped that the data gathered from this study will elucidate some of the risk factors for the development of obesity among the Pima Indians.

Read the detailed description

The prevalence of obesity in the United States has reached alarming proportions with 33% of adults over the age of 20 being overweight. In some populations, obesity is even more prevalent. Although there have been a number of advances in our understanding of the genetics of obesity, the environmental influences on the genetic expression of obesity requires further investigation.

In an effort to understand some of the influences on the high prevalence of obesity, the present study was designed to investigate what drives how much people eat. More specifically, we will try to understand what drives food intake utilizing 1) questionnaires that assess eating behavior, 2) measurements in blood, urine or fat tissue, and 3) genotypic associations to investigate the various factors that control what and how much people eat. We will make these evaluations by assessing eating behavior, food preferences including usual fat intake and preferences for high fat foods, body image perceptions, and energy expenditure. It is hoped that the data gathered from this study will elucidate some of the risk factors for the development of obesity.

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Conditions studied

  • Obesity
  • Overweight
  • Overnutrition

Keywords

  • Genetics
  • Fat Intake
  • Weight Control
  • Appetite Control
  • Natural History
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In context

Obesity

11,522 studies on the registry are indexed under Obesity; 2,033 are open to participants now.

This study's enrollment of 539 is above the median of 114 across 2,154 observational studies indexed under Obesity.

Browse Obesity studies →

Lead sponsor

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) is the lead sponsor of 538 studies on the registry; 55 are open to participants now.

Of its 80 completed or terminated interventional studies of FDA-regulated products, 51 (64%) have results posted.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years to 65 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Residents of Phoenix metropolitan area

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  1. Healthy, as determined by medical history, physical examination, and laboratory tests.

    1. 18-65 years old
    2. Non-diabetic status

Exclusion criteria

Exclusion Criteria:

  1. Hypo- or hyper-thyroid
  2. Blood pressure greater than 160/95
  3. Cardiovascular disease
  4. Gallbladder disease
  5. Alcohol and/or current use of drugs (more than 2 drinks per day and regular use of drugs such as amphetamines, cocaine, or heroin)
  6. Psychiatric conditions or behavior that would be incompatible with safe and successful participation in this study, including claustrophobia and eating disorders such as anorexia or bulimia nervosa

8. Use of medications affecting metabolism and appetite

9. Pregnancy

10. Current use of nicotine products, including tobacco, electronic cigarettes, and nicotine replacement therapies that exceed Very Low Dependence on the Fagerstr(SqrRoot)(Delta)m Test for Nicotine Dependence Tool (score greater than 2).

Subjects may be excluded or withdrawn from the study if they have any conditions not specifically mentioned above that may interfere with the collection of the food intake. This includes such issues as not following study and unit policies and procedures, diagnosis of following admission, and development of illness/infection unrelated to the study. For example, volunteers who do not comply with the vending machine protocol (i.e., share food, do not record what they eat, eat outside of room, etc) may be withdrawn from the study.

Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Contraindication

A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.

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Study design

Observational model
Ecologic or community
Time perspective
Cross-sectional
Enrollment
539 participants (actual)

Groups and cohorts

  • non-diabetic volunteers

    non-diabetic volunteers aged 18-65 who are healthy as determined by medical history, physical examination, and laboratory tests

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What researchers measure

Primary outcomes

  1. Ad libitum food intake

    Ad libitum food intake measured in kcal/day averaged over 3 days

    Time frame: Baseline, up to day 3

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Study locations

1 site
  • NIDDK, Phoenix
    Phoenix, Arizona 85014, United States
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References and documents

Publications

  • Aydin BN, Stinson EJ, Cabeza De Baca T, Ando T, Travis KT, Piaggi P, Krakoff J, Chang DC. Investigation of seasonality of human spontaneous physical activity and energy expenditure in respiratory chamber in Phoenix, Arizona. Eur J Clin Nutr. 2024 Jan;78(1):27-33. doi: 10.1038/s41430-023-01347-y. Epub 2023 Oct 13. PubMed 37833567 ↗
  • Booker JM, Chang DC, Stinson EJ, Mitchell CM, Votruba SB, Krakoff J, Gluck ME, Cabeza de Baca T. Food insecurity is associated with higher respiratory quotient and lower glucagon-like peptide 1. Obesity (Silver Spring). 2022 Jun;30(6):1248-1256. doi: 10.1002/oby.23437. PubMed 35674698 ↗
  • Cabeza de Baca T, Piaggi P, Gluck ME, Krakoff J, Votruba SB. Meal-to-meal and day-to-day macronutrient variation in an ad libitum vending food paradigm. Appetite. 2022 Apr 1;171:105944. doi: 10.1016/j.appet.2022.105944. Epub 2022 Jan 21. PubMed 35074459 ↗
  • Basolo A, Hollstein T, Shah MH, Walter M, Krakoff J, Votruba SB, Piaggi P. Higher fasting plasma FGF21 concentration is associated with lower ad libitum soda consumption in humans. Am J Clin Nutr. 2021 Oct 4;114(4):1518-1522. doi: 10.1093/ajcn/nqab204. PubMed 34159373 ↗
  • Basolo A, Ando T, Chang DC, Hollstein T, Krakoff J, Piaggi P, Votruba S. Reduced Albumin Concentration Predicts Weight Gain and Higher Ad Libitum Energy Intake in Humans. Front Endocrinol (Lausanne). 2021 Mar 11;12:642568. doi: 10.3389/fendo.2021.642568. eCollection 2021. PubMed 33776937 ↗
  • Hollstein T, Basolo A, Ando T, Votruba SB, Krakoff J, Piaggi P. Urinary Norepinephrine Is a Metabolic Determinant of 24-Hour Energy Expenditure and Sleeping Metabolic Rate in Adult Humans. J Clin Endocrinol Metab. 2020 Apr 1;105(4):1145-56. doi: 10.1210/clinem/dgaa047. PubMed 32002540 ↗
  • Stinson EJ, Graham AL, Thearle MS, Gluck ME, Krakoff J, Piaggi P. Cognitive dietary restraint, disinhibition, and hunger are associated with 24-h energy expenditure. Int J Obes (Lond). 2019 Jul;43(7):1456-1465. doi: 10.1038/s41366-018-0305-9. Epub 2019 Jan 16. PubMed 30651576 ↗
  • Stinson EJ, Votruba SB, Venti C, Perez M, Krakoff J, Gluck ME. Food Insecurity is Associated with Maladaptive Eating Behaviors and Objectively Measured Overeating. Obesity (Silver Spring). 2018 Dec;26(12):1841-1848. doi: 10.1002/oby.22305. Epub 2018 Nov 14. PubMed 30426695 ↗
  • Basolo A, Heinitz S, Stinson EJ, Begaye B, Hohenadel M, Piaggi P, Krakoff J, Votruba SB. Fasting glucagon-like peptide 1 concentration is associated with lower carbohydrate intake and increases with overeating. J Endocrinol Invest. 2019 May;42(5):557-566. doi: 10.1007/s40618-018-0954-5. Epub 2018 Oct 3. PubMed 30284224 ↗
  • Stinson EJ, Piaggi P, Ibrahim M, Venti C, Krakoff J, Votruba SB. High Fat and Sugar Consumption During Ad Libitum Intake Predicts Weight Gain. Obesity (Silver Spring). 2018 Apr;26(4):689-695. doi: 10.1002/oby.22124. Epub 2018 Mar 4. PubMed 29504262 ↗
  • Stinson EJ, Krakoff J, Gluck ME. Depressive symptoms and poorer performance on the Stroop Task are associated with weight gain. Physiol Behav. 2018 Mar 15;186:25-30. doi: 10.1016/j.physbeh.2018.01.005. Epub 2018 Jan 9. PubMed 29326031 ↗
  • Basolo A, Votruba SB, Heinitz S, Krakoff J, Piaggi P. Deviations in energy sensing predict long-term weight change in overweight Native Americans. Metabolism. 2018 May;82:65-71. doi: 10.1016/j.metabol.2017.12.013. Epub 2018 Jan 3. PubMed 29305947 ↗
  • Ibrahim M, Thearle MS, Krakoff J, Gluck ME. Perceived stress and anhedonia predict short-and long-term weight change, respectively, in healthy adults. Eat Behav. 2016 Apr;21:214-9. doi: 10.1016/j.eatbeh.2016.03.009. Epub 2016 Mar 3. PubMed 27002703 ↗
  • Piaggi P, Thearle MS, Krakoff J, Votruba SB. Higher Daily Energy Expenditure and Respiratory Quotient, Rather Than Fat-Free Mass, Independently Determine Greater ad Libitum Overeating. J Clin Endocrinol Metab. 2015 Aug;100(8):3011-20. doi: 10.1210/jc.2015-2164. Epub 2015 Jun 18. PubMed 26086330 ↗
  • Bundrick SC, Thearle MS, Venti CA, Krakoff J, Votruba SB. Soda consumption during ad libitum food intake predicts weight change. J Acad Nutr Diet. 2014 Mar;114(3):444-449. doi: 10.1016/j.jand.2013.09.016. Epub 2013 Dec 8. PubMed 24321742 ↗
  • He J, Votruba S, Pomeroy J, Bonfiglio S, Krakoff J. Measurement of ad libitum food intake, physical activity, and sedentary time in response to overfeeding. PLoS One. 2012;7(5):e36225. doi: 10.1371/journal.pone.0036225. Epub 2012 May 22. PubMed 22629311 ↗
  • Venti CA, Votruba SB, Franks PW, Krakoff J, Salbe AD. Reproducibility of ad libitum energy intake with the use of a computerized vending machine system. Am J Clin Nutr. 2010 Feb;91(2):343-8. doi: 10.3945/ajcn.2009.28315. Epub 2009 Nov 18. PubMed 19923376 ↗
  • Votruba SB, Kirchner H, Tschop M, Salbe AD, Krakoff J. Morning ghrelin concentrations are not affected by short-term overfeeding and do not predict ad libitum food intake in humans. Am J Clin Nutr. 2009 Mar;89(3):801-6. doi: 10.3945/ajcn.2008.27011. Epub 2009 Jan 21. PubMed 19158212 ↗

Individual participant data

Plan to share: Undecided

09

Updates

2 registry updates since Sep 25, 2026
Minor edits
Nothing that changes what the study is or who can join. Edited: description
2 updates, last Nov 9, 2026
Show all 2 updates
  1. Nov 9, 2026
    Minor edits only
    + 1 other change: description
  2. Nov 5, 2026
    Minor edits only
    + 1 other change: description

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

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Registry details

Key details

Study ID
NCT00342732
Responsible party
Sponsor
First posted
Jun 21, 2006
Start date
Nov 24, 1999
Last update
Nov 9, 2026

Study contacts

Susanne M Votruba, Ph.D.
principal investigator · National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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This study is completed, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.

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