deltatrials
Completed OBSERVATIONAL NCT00342732

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

Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Updated 75 times since 2017 Last updated: Apr 30, 2026 Started: Nov 24, 1999
This information is for research purposes only and is not medical advice. Consult a healthcare provider before making any medical decision.

This observational or N/A phase trial investigates Obesity and Overnutrition and is currently completed. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) leads this study, which shows 75 recorded versions since 1999 — indicating substantial longitudinal coverage. The change history captured here reflects the iterative nature of clinical trial conduct.

Study Description(click to expand)

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.

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.

Status Flow

~Jan 2017 – ~May 2017 · 4 months · monthly snapshot~May 2017 – ~Jun 2017 · 31 days · monthly snapshot~Jun 2017 – ~Apr 2018 · 10 months · monthly snapshot~Apr 2018 – ~Jun 2018 · 2 months · monthly snapshot~Jun 2018 – ~Jul 2018 · 30 days · monthly snapshot~Jul 2018 – ~Aug 2018 · 31 days · monthly snapshot~Aug 2018 – ~Sep 2018 · 31 days · monthly snapshot~Sep 2018 – ~Oct 2018 · 30 days · monthly snapshot~Oct 2018 – ~Nov 2018 · 31 days · monthly snapshot~Nov 2018 – ~Jan 2019 · 2 months · monthly snapshot~Jan 2019 – ~Feb 2019 · 31 days · monthly snapshot~Feb 2019 – ~Jul 2019 · 5 months · monthly snapshot~Jul 2019 – ~Aug 2019 · 31 days · monthly snapshot~Aug 2019 – ~Sep 2019 · 31 days · monthly snapshot~Sep 2019 – ~Oct 2019 · 30 days · monthly snapshot~Oct 2019 – ~Dec 2019 · 2 months · monthly snapshot~Dec 2019 – ~Jan 2020 · 31 days · monthly snapshot~Jan 2020 – ~Apr 2020 · 3 months · monthly snapshot~Apr 2020 – ~May 2020 · 30 days · monthly snapshot~May 2020 – ~Jun 2020 · 31 days · monthly snapshot~Jun 2020 – ~Jul 2020 · 30 days · monthly snapshot~Jul 2020 – ~Aug 2020 · 31 days · monthly snapshot~Aug 2020 – ~Sep 2020 · 31 days · monthly snapshot~Sep 2020 – ~Nov 2020 · 2 months · monthly snapshot~Nov 2020 – ~Dec 2020 · 30 days · monthly snapshot~Dec 2020 – ~Jan 2021 · 31 days · monthly snapshot~Jan 2021 – ~Feb 2021 · 31 days · monthly snapshot~Feb 2021 – ~Mar 2021 · 28 days · monthly snapshot~Mar 2021 – ~Apr 2021 · 31 days · monthly snapshot~Apr 2021 – ~May 2021 · 30 days · monthly snapshot~May 2021 – ~Jun 2021 · 31 days · monthly snapshot~Jun 2021 – ~Sep 2021 · 3 months · monthly snapshot~Sep 2021 – ~Nov 2021 · 2 months · monthly snapshot~Nov 2021 – ~Dec 2021 · 30 days · monthly snapshot~Dec 2021 – ~Jan 2022 · 31 days · monthly snapshot~Jan 2022 – ~Apr 2022 · 3 months · monthly snapshot~Apr 2022 – ~May 2022 · 30 days · monthly snapshot~May 2022 – ~Jun 2022 · 31 days · monthly snapshot~Jun 2022 – ~Jul 2022 · 30 days · monthly snapshot~Jul 2022 – ~Sep 2022 · 2 months · monthly snapshot~Sep 2022 – ~Dec 2022 · 3 months · monthly snapshot~Dec 2022 – ~Jan 2023 · 31 days · monthly snapshot~Jan 2023 – ~Feb 2023 · 31 days · monthly snapshot~Feb 2023 – ~Mar 2023 · 28 days · monthly snapshot~Mar 2023 – ~Apr 2023 · 31 days · monthly snapshot~Apr 2023 – ~Jul 2023 · 3 months · monthly snapshot~Jul 2023 – ~Aug 2023 · 31 days · monthly snapshot~Aug 2023 – ~Sep 2023 · 31 days · monthly snapshot~Sep 2023 – ~Nov 2023 · 2 months · monthly snapshot~Nov 2023 – ~Dec 2023 · 30 days · monthly snapshot~Dec 2023 – ~Jan 2024 · 31 days · monthly snapshot~Jan 2024 – ~Feb 2024 · 31 days · monthly snapshot~Feb 2024 – ~Mar 2024 · 29 days · monthly snapshot~Mar 2024 – ~Apr 2024 · 31 days · monthly snapshot~Apr 2024 – ~May 2024 · 30 days · monthly snapshot~May 2024 – ~Jul 2024 · 2 months · monthly snapshot~Jul 2024 – ~Aug 2024 · 31 days · monthly snapshot~Aug 2024 – ~Sep 2024 · 31 days · monthly snapshot~Sep 2024 – ~Oct 2024 · 30 days · monthly snapshot~Oct 2024 – ~Dec 2024 · 2 months · monthly snapshot~Dec 2024 – ~Jan 2025 · 31 days · monthly snapshot~Jan 2025 – ~Feb 2025 · 31 days · monthly snapshot~Feb 2025 – ~Mar 2025 · 28 days · monthly snapshot~Mar 2025 – ~Apr 2025 · 31 days · monthly snapshot~Apr 2025 – ~May 2025 · 30 days · monthly snapshot~May 2025 – ~Jun 2025 · 31 days · monthly snapshot~Jun 2025 – ~Jul 2025 · 30 days · monthly snapshot~Jul 2025 – ~Sep 2025 · 2 months · monthly snapshot~Sep 2025 – ~Oct 2025 · 30 days · monthly snapshot~Oct 2025 – ~Nov 2025 · 31 days · monthly snapshot~Nov 2025 – ~Dec 2025 · 30 days · monthly snapshot~Dec 2025 – ~Jan 2026 · 31 days · monthly snapshot~Jan 2026 – ~Feb 2026 · 31 days · monthly snapshot~Feb 2026 – ~May 2026 · 3 months · monthly snapshotMay 4, 2026 – present · 2 months · daily API

Change History

75 versions recorded
  1. May 4, 2026 — Present [daily]

    Completed

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    Phase: NANone

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    First recorded

Nov 1999

Trial started

Per CT.gov start date — pre-dates our first snapshot

Eligibility 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....

Contact Information

Sponsor contact:
  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Data source: ClinicalTrials.gov

For direct contact, visit the study record on ClinicalTrials.gov .

Study Locations