USDA Western Human Nutrition Research Center (WHNRC) Cross-Sectional Nutritional Phenotyping Study
NCT ID: NCT02367287
Last Updated: 2021-03-30
Study Results
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Basic Information
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COMPLETED
393 participants
OBSERVATIONAL
2015-05-31
2019-07-24
Brief Summary
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Researchers from the United States Department of Agriculture (USDA) Western Human Nutrition Research Center are conducting a cross-sectional "metabolic phenotyping" study of healthy people in the general population. Observational measurements include the interactions of habitual diet with the metabolic response to food intake, production of key hormones, the conversion of food into energy: the metabolism of fats, proteins, and carbohydrates, characteristics of the immune system, stress response, gut microbiota (bacteria in the intestinal tract), and cardiovascular health. Most outcomes will be measured in response to a mixed macronutrient/high fat challenge meal.
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Detailed Description
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Individual variability in chronic disease risk is well recognized. For example, why does excess adiposity lead to disease in some individuals and not others? The nature of the fat tissue rather than the abundance, may impact cross-talk with other metabolically-relevant tissues and affect disease risk. It is important to characterize healthy vs. unhealthy phenotypes across various tissues and to understand how micro- and macro-nutrients interact with molecular and metabolic pathways to support a healthy body weight. This study brings together scientists with expertise in nutritional sciences, immunology, analytical chemistry, physiology, neuroendocrinology, and behavior to understand how diet impacts metabolism and disease risk through the interplay and coordination of signals and metabolites arising from multiple organ systems.
The overall objective is to characterize the phenotypic profile of participants according to their immunologic, physiologic, neuroendocrine, and metabolic responses to a dietary challenge and a physical fitness challenge by addressing the specific aims listed below. The cross-sectional study is organized into two study visits (Visit 1 and Visit 2) separated by approximately two weeks of at-home specimen and data collection.
Specific Aim 1: To determine if diet quality is independently associated with systemic immune activation, inflammation, or oxidative stress differentiated by:
1. pro-inflammatory T-helper cells (Th1, Th2, and Th17 cells) and related cytokines
2. anti-inflammatory T-regulatory cells and related cytokines
3. dysbiosis of the gut microbiota and markers of gut inflammation (e.g. neopterin and myeloperoxidase)
a. and to evaluate the association between dysbiosis of the gut microbiota, gut inflammation, and systemic immune activation
4. plasma metabolomic response to a mixed macronutrient challenge meal (includes diet quality and physical activity as independent variables)
5. endothelial (dys)function and vascular reactivity
Specific Aim 2: To determine if a high fat/sugar challenge meal induces differential effects over time (0-6h postprandial) according to habitual diet characteristics, physical activity levels, stress levels, age, sex, or BMI on:
1. postprandial monocyte activation
2. plasma lipid metabolomic responses including non-esterified fatty acids, phospholipids, triacylglycerols, red blood cell fatty acids, endocannabinoids, bile acids, eicosanoids and related oxylipins, ceramides, sphingoid bases, and acylcarnitines
3. plasma amino acid metabolomics
4. glucose metabolism and metabolic flexibility (i.e. the ability to switch from glucose to lipid oxidation as energy sources)
5. changes in endocrinology and self-report of hunger and satiety
6. postprandial free cortisol
Specific Aim 3: To determine the mechanisms of:
1. postprandial monocyte activation
2. suppression of challenge-meal induced monocyte activation by docosahexaenoic acid (DHA) (in an ex vivo experiment using a subset of samples)
Specific Aim 4: To evaluate the associations between eating behavior, physical activity, and/or anthropometry and the outcomes:
1. endocrinology of hunger and satiety
2. plasma metabolomic responses
3. vulnerability and resistance to stress
4. endothelial (dys)function and vascular reactivity
5. prediction of insulin sensitivity
Specific Aim 5: To determine how genetic variants affect nutrient metabolism, cardiovascular physiology, and immune function and improve understanding of how dietary factors affect these metabolic, cardiovascular and immune phenotypes.
Conditions
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Study Design
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OTHER
CROSS_SECTIONAL
Study Groups
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Sampling strata
Stratified analyses of primary and secondary outcomes based on variables of interest (e.g. sex, age, or BMI) may occur prior to achieving the target for total study enrollment.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Male or female
* Body Mass Index 18.5-45.0 kg/m2 (Normal to obese)
Exclusion Criteria
* Known allergy to egg-white protein
* Systolic blood pressure greater than 140 mm Hg or diastolic blood pressure greater than 90 mm Hg measured on three separate occasions
* Diagnosed active chronic diseases for which the individual is currently taking daily medication, including but not limited to:
* Diabetes mellitus
* Cardiovascular disease
* Cancer
* Gastrointestinal disorders
* Kidney disease
* Liver disease
* Bleeding disorders
* Asthma
* Autoimmune disorders
* Hypertension
* Osteoporosis
* Recent minor surgery (within 4 wk) or major surgery (within 16 wk)
* Recent antibiotic therapy (within 4 wk)
* Recent hospitalization (within 4 wk)
* Use of prescription medications at the time of the study that directly affect endpoints of interest (e.g. hyperlipidemia, glycemic control, steroids, statins, anti-inflammatory agents, and over-the-counter weight loss aids)
18 Years
65 Years
ALL
Yes
Sponsors
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USDA, Western Human Nutrition Research Center
FED
Responsible Party
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Principal Investigators
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Charles B Stephensen, Ph.D.
Role: PRINCIPAL_INVESTIGATOR
USDA, Western Human Nutrition Research Center
Brian J Bennett, Ph.D.
Role: PRINCIPAL_INVESTIGATOR
USDA, Western Human Nutrition Research Center
Locations
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USDA, Western Human Nutrition Research Center
Davis, California, United States
Countries
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References
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Wopereis S, Wolvers D, van Erk M, Gribnau M, Kremer B, van Dorsten FA, Boelsma E, Garczarek U, Cnubben N, Frenken L, van der Logt P, Hendriks HF, Albers R, van Duynhoven J, van Ommen B, Jacobs DM. Assessment of inflammatory resilience in healthy subjects using dietary lipid and glucose challenges. BMC Med Genomics. 2013 Oct 27;6:44. doi: 10.1186/1755-8794-6-44.
Pellis L, van Erk MJ, van Ommen B, Bakker GC, Hendriks HF, Cnubben NH, Kleemann R, van Someren EP, Bobeldijk I, Rubingh CM, Wopereis S. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics. 2012 Apr;8(2):347-359. doi: 10.1007/s11306-011-0320-5. Epub 2011 May 28.
Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, Raffler J, Romisch-Margl W, Adamski J, Prehn C, Frank T, Engel KH, Hofmann T, Luy B, Zimmermann R, Moritz F, Schmitt-Kopplin P, Krumsiek J, Kremer W, Huber F, Oeh U, Theis FJ, Szymczak W, Hauner H, Suhre K, Daniel H. The dynamic range of the human metabolome revealed by challenges. FASEB J. 2012 Jun;26(6):2607-19. doi: 10.1096/fj.11-198093. Epub 2012 Mar 16.
Robles Alonso V, Guarner F. Linking the gut microbiota to human health. Br J Nutr. 2013 Jan;109 Suppl 2:S21-6. doi: 10.1017/S0007114512005235.
Baldiviez LM, Keim NL, Laugero KD, Hwang DH, Huang L, Woodhouse LR, Burnett DJ, Zerofsky MS, Bonnel EL, Allen LH, Newman JW, Stephensen CB. Design and implementation of a cross-sectional nutritional phenotyping study in healthy US adults. BMC Nutr. 2017 Oct 19;3:79. doi: 10.1186/s40795-017-0197-4. eCollection 2017.
Chin EL, Huang L, Bouzid YY, Kirschke CP, Durbin-Johnson B, Baldiviez LM, Bonnel EL, Keim NL, Korf I, Stephensen CB, Lemay DG. Association of Lactase Persistence Genotypes (rs4988235) and Ethnicity with Dairy Intake in a Healthy U.S. Population. Nutrients. 2019 Aug 10;11(8):1860. doi: 10.3390/nu11081860.
Bouzid YY, Arsenault JE, Bonnel EL, Cervantes E, Kan A, Keim NL, Lemay DG, Stephensen CB. Effect of Manual Data Cleaning on Nutrient Intakes Using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24). Curr Dev Nutr. 2021 Feb 2;5(3):nzab005. doi: 10.1093/cdn/nzab005. eCollection 2021 Mar.
Lemay DG, Baldiviez LM, Chin EL, Spearman SS, Cervantes E, Woodhouse LR, Keim NL, Stephensen CB, Laugero KD. Technician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load. J Nutr. 2021 Jun 1;151(6):1443-1452. doi: 10.1093/jn/nxab019.
Blecksmith SE, Oliver A, Alkan Z, Lemay DG. Gut Microbiome Genes Involved in Plant and Mucin Breakdown Correlate with Diet and Gastrointestinal Inflammation in Healthy United States Adults. J Nutr. 2025 Sep 1:S0022-3166(25)00533-4. doi: 10.1016/j.tjnut.2025.08.027. Online ahead of print.
Stephensen CB, Jiang X, Gale B, Peerson JM. Association of Healthy Eating Index-2015 Total and Component Scores with Measures of Inflammation and Immune Activation in Healthy Adults. J Nutr. 2025 Mar;155(3):994-1004. doi: 10.1016/j.tjnut.2025.01.005. Epub 2025 Jan 7.
Riazati N, Engle-Stone R, Stephensen CB. Association of Vitamin D Status with Immune Markers in a Cohort of Healthy Adults. J Nutr. 2025 Feb;155(2):621-633. doi: 10.1016/j.tjnut.2024.12.010. Epub 2024 Dec 21.
Oliver A, Alkan Z, Stephensen CB, Newman JW, Kable ME, Lemay DG. Diet, Microbiome, and Inflammation Predictors of Fecal and Plasma Short-Chain Fatty Acids in Humans. J Nutr. 2024 Nov;154(11):3298-3311. doi: 10.1016/j.tjnut.2024.08.012. Epub 2024 Aug 20.
Wilson SM, Oliver A, Larke JA, Naveja JJ, Alkan Z, Awika JM, Stephensen CB, Lemay DG. Fine-Scale Dietary Polyphenol Intake Is Associated with Systemic and Gastrointestinal Inflammation in Healthy Adults. J Nutr. 2024 Nov;154(11):3286-3297. doi: 10.1016/j.tjnut.2024.08.010. Epub 2024 Aug 18.
Bouzid YY, Wilson SM, Alkan Z, Stephensen CB, Lemay DG. Lower Diet Quality Associated with Subclinical Gastrointestinal Inflammation in Healthy United States Adults. J Nutr. 2024 Apr;154(4):1449-1460. doi: 10.1016/j.tjnut.2024.02.030. Epub 2024 Mar 1.
Kable ME, Chin EL, Huang L, Stephensen CB, Lemay DG. Association of Estimated Daily Lactose Consumption, Lactase Persistence Genotype (rs4988235), and Gut Microbiota in Healthy Adults in the United States. J Nutr. 2023 Aug;153(8):2163-2173. doi: 10.1016/j.tjnut.2023.06.025. Epub 2023 Jun 23.
Snodgrass RG, Jiang X, Stephensen CB, Laugero KD. Cumulative physiological stress is associated with age-related changes to peripheral T lymphocyte subsets in healthy humans. Immun Ageing. 2023 Jun 23;20(1):29. doi: 10.1186/s12979-023-00357-5.
Larke JA, Bacalzo N, Castillo JJ, Couture G, Chen Y, Xue Z, Alkan Z, Kable ME, Lebrilla CB, Stephensen CB, Lemay DG. Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults. J Nutr. 2023 Jan;153(1):106-119. doi: 10.1016/j.tjnut.2022.12.008. Epub 2022 Dec 26.
Snodgrass RG, Jiang X, Stephensen CB. Monocyte subsets display age-dependent alterations at fasting and undergo non-age-dependent changes following consumption of a meal. Immun Ageing. 2022 Sep 14;19(1):41. doi: 10.1186/s12979-022-00297-6.
Wang YE, Kirschke CP, Woodhouse LR, Bonnel EL, Stephensen CB, Bennett BJ, Newman JW, Keim NL, Huang L. SNPs in apolipoproteins contribute to sex-dependent differences in blood lipids before and after a high-fat dietary challenge in healthy U.S. adults. BMC Nutr. 2022 Sep 1;8(1):95. doi: 10.1186/s40795-022-00592-x.
Newman JW, Krishnan S, Borkowski K, Adams SH, Stephensen CB, Keim NL. Assessing Insulin Sensitivity and Postprandial Triglyceridemic Response Phenotypes With a Mixed Macronutrient Tolerance Test. Front Nutr. 2022 May 11;9:877696. doi: 10.3389/fnut.2022.877696. eCollection 2022.
Oliver A, Xue Z, Villanueva YT, Durbin-Johnson B, Alkan Z, Taft DH, Liu J, Korf I, Laugero KD, Stephensen CB, Mills DA, Kable ME, Lemay DG. Association of Diet and Antimicrobial Resistance in Healthy U.S. Adults. mBio. 2022 Jun 28;13(3):e0010122. doi: 10.1128/mbio.00101-22. Epub 2022 May 10.
Kable ME, Chin EL, Storms D, Lemay DG, Stephensen CB. Tree-Based Analysis of Dietary Diversity Captures Associations Between Fiber Intake and Gut Microbiota Composition in a Healthy US Adult Cohort. J Nutr. 2022 Mar 3;152(3):779-788. doi: 10.1093/jn/nxab430.
Chin EL, Van Loan M, Spearman SS, Bonnel EL, Laugero KD, Stephensen CB, Lemay DG. Machine Learning Identifies Stool pH as a Predictor of Bone Mineral Density in Healthy Multiethnic US Adults. J Nutr. 2021 Nov 2;151(11):3379-3390. doi: 10.1093/jn/nxab266.
Artegoitia VM, Krishnan S, Bonnel EL, Stephensen CB, Keim NL, Newman JW. Healthy eating index patterns in adults by sex and age predict cardiometabolic risk factors in a cross-sectional study. BMC Nutr. 2021 Jun 22;7(1):30. doi: 10.1186/s40795-021-00432-4.
Mo Z, Huang S, Burnett DJ, Rutledge JC, Hwang DH. Endotoxin May Not Be the Major Cause of Postprandial Inflammation in Adults Who Consume a Single High-Fat or Moderately High-Fat Meal. J Nutr. 2020 May 1;150(5):1303-1312. doi: 10.1093/jn/nxaa003.
Related Links
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WHNRC recruiting website
Other Identifiers
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2032-53000-001-00
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
691654
Identifier Type: -
Identifier Source: org_study_id
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