ObesityNCT02367287
USDA Western Human Nutrition Research Center (WHNRC) Cross-Sectional Nutritional Phenotyping Study
An observational study of Obesity and Inflammation, run by USDA, Western Human Nutrition Research Center at 1 site in United States.
Trial summary
Although the diet of the US population meets or exceeds recommended intake levels of most essential nutrients, the quality of the diet consumed by many Americans is sub-optimal due to excessive intake of added sugars, solid fats, refined grains, and sodium. The foundations and outcomes of healthy vs. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Obesity, Inflammation
- How many people
- 393 participants
- Where
- 1 siteUnited States
- Study dates
- May 2015to 24 Jul 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Healthy people in the general population
Who may be able to join
- 18-65 y
- Male or female
- Body Mass Index 18.5-45.0 kg/m2 (Normal to obese)
Who cannot join
- Pregnant or lactating women
- 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
Show all 19Show fewer
- 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)
Trial locations and contacts
1 site in 1 country.
United States1 site
USDA, Western Human Nutrition Research Center
Davis, California, 95616
About this study
Official title: Assessing the Impact of Diet on Inflammation in Healthy and Obese Adults in a Cross-Sectional Phenotyping Study
Although the diet of the US population meets or exceeds recommended intake levels of most essential nutrients, the quality of the diet consumed by many Americans is sub-optimal due to excessive intake of added sugars, solid fats, refined grains, and sodium. The foundations and outcomes of healthy vs. unhealthy eating habits and activity levels are complex and involve interactions between the environment and innate physiologic/genetic background. For instance, emerging research implicates chronic and acute stress responses and perturbations in the Hypothalamic-Pituitary-Adrenal axis in triggering obesity-promoting metabolic changes and poor food choices. In addition, the development of many chronic diseases, including cardiovascular disease, diabetes, cancer, asthma and autoimmune disease, results from an overactive immune response to host tissue or environmental antigens (e.g. inhaled allergens). A greater understanding is needed of the distribution of key environment-physiology interactions that drive overconsumption, create positive energy balance, and put health at risk.
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.
Read the detailed description
Many inflammatory responses can be modulated by specific dietary components. For example, in cardiovascular disease, macrophages and T-cells react with oxidized LDL (an endogenous modified antigen) to produce arterial plaque and subsequent blockage of coronary arteries. High intake of saturated fats (or simple sugars that drive synthesis of saturated fatty acids) may promote this inflammation by affecting macrophages and T-cells. Conversely, increased intake of omega-3 fatty acids may decrease inflammation by suppression of macrophage and T-cell pro-inflammatory activity. Long-term sub-clinical inflammation caused by intestinal bacteria has been linked to the development of Irritable Bowel Disease and related disorders. Low intake of fruits, vegetables, or whole grains or high intake of saturated fats may promote sub-clinical gut inflammation by promoting dysbiosis of the gut microbiota. Allergic asthma develops in predisposed individuals as a result of an overactive allergic-type immune response to inhaled environmental allergens. Dietary factors such as vitamin D and omega-3 fatty acids may diminish pro-inflammatory responses to environmental allergens by promoting the development of T-regulatory cells and other anti-inflammatory factors.
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:
- pro-inflammatory T-helper cells (Th1, Th2, and Th17 cells) and related cytokines
- anti-inflammatory T-regulatory cells and related cytokines
- dysbiosis of the gut microbiota and markers of gut inflammation (e.g. neopterin and myeloperoxidase)
- and to evaluate the association between dysbiosis of the gut microbiota, gut inflammation, and systemic immune activation
- plasma metabolomic response to a mixed macronutrient challenge meal (includes diet quality and physical activity as independent variables)
- 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:
- postprandial monocyte activation
- 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
- plasma amino acid metabolomics
- glucose metabolism and metabolic flexibility (i.e. the ability to switch from glucose to lipid oxidation as energy sources)
- changes in endocrinology and self-report of hunger and satiety
- postprandial free cortisol
Specific Aim 3: To determine the mechanisms of:
- postprandial monocyte activation
- 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:
- endocrinology of hunger and satiety
- plasma metabolomic responses
- vulnerability and resistance to stress
- endothelial (dys)function and vascular reactivity
- 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
- Obesity, Inflammation
- Keywords
- Diet, Phenotype, Immunology, Microbiota, Metabolomics, Physical activity, Neuroendocrine system, Nutritional Physiological Phenomena
- Medical subject headings
- Obesity, Inflammation, Motor Activity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Baseline level and change in systemic immune activation following challenge meal
Number and activation level of pro-inflammatory T-helper (Th) cells (Th1, Th2 and Th17), T-regulatory (Treg) cells, and B cells will be measured in fasting blood. Monocytes and neutrophils will be measured in fasting and postprandial blood.
Measured: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in plasma metabolome
Plasma fatty acid profiles of non-esterified fatty acids, phospholipids, triacylglycerols, red blood cell fatty acids, endocannabinoids, bile acids, eicosanoids and related oxylipins, ceramides, sphingoid bases, acylcarnitines, amino acids and other metabolites measured in response to a challenge meal.
Measured: 0, 0.5, 3, and 6 hours postprandial
Secondary outcomes (39)
Baseline level and change in glucose metabolism
Glucose and insulin measured in response to a challenge meal.
Measured: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in appetitive hormones
Cholecystokinin, incretins, Peptide YY 3-36, ghrelin measured in response to a challenge meal.
Measured: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in mitogen activated protein (MAP) kinase activity
Mononuclear cells or B cells will be measured for MAP kinase activities in fasting and postprandial blood.
Measured: 0, 0.5, 3 and 6 hours postprandial
Baseline level and change in dietary-responsive, circulating microRNA
Plasma microRNA measured in response to a challenge meal
Measured: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in RNA transcriptome
Transcriptome RNA sequenced in whole blood
Measured: 0, 3, and 6 hours postprandial
Genome Wide Association Study (GWAS)
DNA sequence from whole blood will be analyzed
Measured: 0 hours (fasting)
General health
Clinical chemistry panel and complete blood count
Measured: 0 hours (Fasting)
Anthropometrics
Height (cm), weight (kg), waist and hip circumference (cm)
Measured: single time point
Vital signs
Blood pressure (mmHg), pulse rate (beats per minute) and temperature (degrees F).
Measured: single time point
Body composition
Body composition (percent body fat) and bone mineral density by Dual energy X-ray Absorptiometry scan.
Measured: single time point
Resting and change in metabolism
Resting and postprandial metabolic rates, including respiratory exchange ratios.
Measured: 0, 0.5, 3, and 6 hours postprandial
Gut microbiota
Gut microbiota composition and gene content will be assessed in stool using polymerase chain reaction (PCR) and sequencing
Measured: single time point
Gut microbiota fermentation capacity
The fermentation capacity of microbiota will be measured from a single stool sample
Measured: single time point
Gut microbiota pathogen resistance capacity
The pathogen resistance capability of microbiota will be measured from a single stool sample
Measured: single time point
Gut inflammation
Gut inflammation will be assessed by measuring molecules in stool and/or the response of intestinal epithelial cell cultures to fecal waters from a single stool sample.
Measured: single time point
Stool metabolites
Volatile and short chain fatty acids and bile acids will be measured in a single stool sample.
Measured: single time point
Stool RNA markers
RNA markers will provide a measure of genes expressed by cells of the colon naturally present in a single stool sample
Measured: single time point
Baseline and change in hunger and appetite
Perceived hunger and fullness will be measured using a visual analog scale. Responses will be a marked on an unsegmented line from 0 or "not at all" to 5 or "extremely."
Measured: 0, 1, 2, 3, 4, 5, and 6 hours postprandial
Baseline and change in gut fermentation profile
Breath hydrogen and methane measured in response to a challenge meal.
Measured: 0, 1, 2, 3, 4, 5, and 6 hours postprandial
Recent dietary intake
Random selection of 2 week days and 1 weekend day for 24-hour recall using an automated multi-pass method
Measured: Three 24-hour dietary recalls collected at home
Dietary intake
Food frequency questionnaire (FFQ)
Measured: single time point
Behavior assessment
Chronic stress questionnaire, food choice questionnaires, and a food preference activity.
Measured: single time point
Taste thresholds
Sampling tastes of sweet, salty, and bitter solutions in comparison to water to determine threshold of taste detection.
Measured: single time point
Skin reflectance
Spectrophotometric measure of skin pigmentation for assessment of vitamin D status.
Measured: single time point
Peripheral arterial tone
Use of the EndoPAT system to measure blood vessel tone.
Measured: single time point
Pulmonary function
Forced expiratory lung volume test
Measured: single time point
Pulmonary inflammation
Pulmonary inflammation measured as exhaled nitric oxide (NO)
Measured: single time point
Executive function
Executive function will be assessed using Cambridge Neuropsychological Test Automated Battery (CANTAB) and Iowa Gambling Task
Measured: single time point
Cognitive function
Measured by Wechsler Abbreviated Standard Intelligence test.
Measured: single time point
Aerobic fitness assessment
Pulse rate (bpm) and recovery after a 3 min YMCA Step Test
Measured: single time point
Submaximal oxygen consumption
The submaximal volume of oxygen consumed during a 4 minute treadmill walking protocol (VO2max) (ml/kg*min)
Measured: single time point
Physical activity
Use of an accelerometer worn on the hip for 7 days
Measured: daily, for 7 days
Usual physical activity
Activity recall using a questionnaire
Measured: single time point
Heart rate variability and autonomic nerve conductivity
Monitoring of autonomic balance, cardiac performance, and respiratory measurements and activity using MindWare Mobile Impedance Cardiograph.
Measured: single time point
Allostatic Load
An aggregate score derived from measures of urinary cortisol, norepinephrine, epinephrine, blood cholesterol, high sensitivity c-reactive protein, and hemoglobin A1C.
Measured: single time point
Baseline and change in salivary cortisol in response to test meal
Salivary cortisol measured by enzyme-linked immunosorbent assay (ELISA)
Measured: 0, immediately post-prandial, 30, 60, and 90 minutes post-prandial
Baseline and change in salivary cortisol in response to exercise
Salivary cortisol measured by enzyme-linked immunosorbent assay (ELISA)
Measured: 0, immediately post-exercise, 30, 60, and 90 minutes post-exercise
Baseline and change in salivary cortisol in response to emotional recall task
Salivary cortisol measured by enzyme-linked immunosorbent assay (ELISA)
Measured: 0, immediately post-task, 30, 60, and 90 minutes post-task
Baseline and change in breath aldehydes
The concentration of aldehydes present in human breath before and after a high-fat meal will be measured by mass spectrometry
Measured: 0, 1, 4 and 6 hours postprandial
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Feb 2015 (estimated)
- Study startMay 2015
- Primary completion24 Jul 2019
- Study completion24 Jul 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (31)Papers the sponsor has linked to this study.
- Other trials for ObesityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 393 participants (actual)
Sponsor and investigators
- Lead sponsor
- USDA, Western Human Nutrition Research Center
- Sponsor type
- US federal agency
- Responsible party
- Sponsor
- Investigators
- Charles B Stephensen, Ph.D., Principal investigator, USDA, Western Human Nutrition Research CenterBrian J Bennett, Ph.D., Principal investigator, USDA, Western Human Nutrition Research Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02367287
- Sponsor's protocol ID
- 691654
- Other IDs
- 2032-53000-001-00
- Status last verified
- Mar 2026
- First submitted
- 12 Feb 2015
- Last updated
- 25 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
Publications
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.
PubMed 24160467Pellis 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.
PubMed 22448156Krug 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.
PubMed 22426117Robles 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.
PubMed 23360877Baldiviez 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.
PubMed 32153856Oliver 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.
PubMed 39173973James KL, Gertz ER, Kirschke CP, Allayee H, Huang L, Kable ME, Newman JW, Stephensen CB, Bennett BJ. Trimethylamine N-Oxide Response to a Mixed Macronutrient Tolerance Test in a Cohort of Healthy United States Adults. Int J Mol Sci. 2023 Jan 20;24(3):2074. doi: 10.3390/ijms24032074.
PubMed 36768394Riazati N, Kable ME, Newman JW, Adkins Y, Freytag T, Jiang X, Stephensen CB. Associations of microbial and indoleamine-2,3-dioxygenase-derived tryptophan metabolites with immune activation in healthy adults. Front Immunol. 2022 Sep 29;13:917966. doi: 10.3389/fimmu.2022.917966. eCollection 2022.
PubMed 36248784Chin 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.
PubMed 31405126Bouzid 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.
PubMed 33763626Lemay 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.
PubMed 33704458Arrington CE, Tacad DKM, Allayee H, Sutton KJ, Dombroski C, Keim NL, Newman JW, Bennett BJ. Genetic determinants of BMI, diet, and fitness interact to partially explain anthropometric obesity traits but not the metabolic consequences of obesity in men and women. Int J Obes (Lond). 2026 Apr;50(4):938-946. doi: 10.1038/s41366-026-02027-0. Epub 2026 Feb 20.
PubMed 41720915Blecksmith 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 Nov;155(11):3757-3768. doi: 10.1016/j.tjnut.2025.08.027. Epub 2025 Sep 1.
PubMed 40902732Tang Y, Oliver A, Alkan Z, Korf I, Huang L, Kable ME, Lemay DG. Association of lactose intake and lactase persistence genotype with microbial taxa and function in healthy multi-ethnic U.S. adults. Food Funct. 2025 Sep 15;16(18):7393-7407. doi: 10.1039/d5fo01640a.
PubMed 40880079Tacad DKM, Borkowski K, Keim NL. Differential associations of eating behavior traits, food preference, motivations of food choice on diet intake and diet quality in adult females and males from the USDA nutritional phenotyping study. Appetite. 2025 Sep 1;213:108048. doi: 10.1016/j.appet.2025.108048. Epub 2025 May 7.
PubMed 40339649
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessing the Impact of Diet on Inflammation in Healthy and Obese Adults in a Cross-Sectional Phenotyping Study
- ClinicalTrials.gov ID
- NCT02367287
- Acronym / sponsor ID
- 691654
- Conditions
- Obesity, Inflammation
- Keywords
- Diet, Phenotype, Immunology, Microbiota, Metabolomics, Physical activity, Neuroendocrine system, Nutritional Physiological Phenomena
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 393 participants (actual)
- Lead sponsor
- USDA, Western Human Nutrition Research CenterUS federal agency
- Study start
- May 2015 (actual)
- Primary completion
- 24 Jul 2019 (actual)
- Study completion
- 24 Jul 2019 (actual)
- First posted
- 20 Feb 2015 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Although the diet of the US population meets or exceeds recommended intake levels of most essential nutrients, the quality of the diet consumed by many Americans is sub-optimal due to excessive intake of added sugars, solid fats, refined grains, and sodium. The foundations and outcomes of healthy vs. unhealthy eating habits and activity levels are complex and involve interactions between the environment and innate physiologic/genetic background. For instance, emerging research implicates chronic and acute stress responses and perturbations in the Hypothalamic-Pituitary-Adrenal axis in triggering obesity-promoting metabolic changes and poor food choices. In addition, the development of many chronic diseases, including cardiovascular disease, diabetes, cancer, asthma and autoimmune disease, results from an overactive immune response to host tissue or environmental antigens (e.g. inhaled allergens). A greater understanding is needed of the distribution of key environment-physiology interactions that drive overconsumption, create positive energy balance, and put health at risk.
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.
Detailed description
Many inflammatory responses can be modulated by specific dietary components. For example, in cardiovascular disease, macrophages and T-cells react with oxidized LDL (an endogenous modified antigen) to produce arterial plaque and subsequent blockage of coronary arteries. High intake of saturated fats (or simple sugars that drive synthesis of saturated fatty acids) may promote this inflammation by affecting macrophages and T-cells. Conversely, increased intake of omega-3 fatty acids may decrease inflammation by suppression of macrophage and T-cell pro-inflammatory activity. Long-term sub-clinical inflammation caused by intestinal bacteria has been linked to the development of Irritable Bowel Disease and related disorders. Low intake of fruits, vegetables, or whole grains or high intake of saturated fats may promote sub-clinical gut inflammation by promoting dysbiosis of the gut microbiota. Allergic asthma develops in predisposed individuals as a result of an overactive allergic-type immune response to inhaled environmental allergens. Dietary factors such as vitamin D and omega-3 fatty acids may diminish pro-inflammatory responses to environmental allergens by promoting the development of T-regulatory cells and other anti-inflammatory factors.
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:
- pro-inflammatory T-helper cells (Th1, Th2, and Th17 cells) and related cytokines
- anti-inflammatory T-regulatory cells and related cytokines
- dysbiosis of the gut microbiota and markers of gut inflammation (e.g. neopterin and myeloperoxidase)
- and to evaluate the association between dysbiosis of the gut microbiota, gut inflammation, and systemic immune activation
- plasma metabolomic response to a mixed macronutrient challenge meal (includes diet quality and physical activity as independent variables)
- 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:
- postprandial monocyte activation
- 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
- plasma amino acid metabolomics
- glucose metabolism and metabolic flexibility (i.e. the ability to switch from glucose to lipid oxidation as energy sources)
- changes in endocrinology and self-report of hunger and satiety
- postprandial free cortisol
Specific Aim 3: To determine the mechanisms of:
- postprandial monocyte activation
- 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:
- endocrinology of hunger and satiety
- plasma metabolomic responses
- vulnerability and resistance to stress
- endothelial (dys)function and vascular reactivity
- 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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Healthy people in the general population
Key inclusion criteria (3 listed)
- 18-65 y
- Male or female
- Body Mass Index 18.5-45.0 kg/m2 (Normal to obese)
Key exclusion criteria (19 listed)
- Pregnant or lactating women
- 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:
- Recent minor surgery (within 4 wk) or major surgery (within 16 wk)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Baseline level and change in systemic immune activation following challenge meal
Time frame: 0, 0.5, 3, and 6 hours postprandial
Number and activation level of pro-inflammatory T-helper (Th) cells (Th1, Th2 and Th17), T-regulatory (Treg) cells, and B cells will be measured in fasting blood. Monocytes and neutrophils will be measured in fasting and postprandial blood.
Baseline level and change in plasma metabolome
Time frame: 0, 0.5, 3, and 6 hours postprandial
Plasma fatty acid profiles of non-esterified fatty acids, phospholipids, triacylglycerols, red blood cell fatty acids, endocannabinoids, bile acids, eicosanoids and related oxylipins, ceramides, sphingoid bases, acylcarnitines, amino acids and other metabolites measured in response to a challenge meal.
39 secondary outcomes
Baseline level and change in glucose metabolism
Time frame: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in appetitive hormones
Time frame: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in mitogen activated protein (MAP) kinase activity
Time frame: 0, 0.5, 3 and 6 hours postprandial
Baseline level and change in dietary-responsive, circulating microRNA
Time frame: 0, 0.5, 3, and 6 hours postprandial
Baseline level and change in RNA transcriptome
Time frame: 0, 3, and 6 hours postprandial
Genome Wide Association Study (GWAS)
Time frame: 0 hours (fasting)
General health
Time frame: 0 hours (Fasting)
Anthropometrics
Time frame: single time point
Vital signs
Time frame: single time point
Body composition
Time frame: single time point
Resting and change in metabolism
Time frame: 0, 0.5, 3, and 6 hours postprandial
Gut microbiota
Time frame: single time point
Gut microbiota fermentation capacity
Time frame: single time point
Gut microbiota pathogen resistance capacity
Time frame: single time point
Gut inflammation
Time frame: single time point
Stool metabolites
Time frame: single time point
Stool RNA markers
Time frame: single time point
Baseline and change in hunger and appetite
Time frame: 0, 1, 2, 3, 4, 5, and 6 hours postprandial
Baseline and change in gut fermentation profile
Time frame: 0, 1, 2, 3, 4, 5, and 6 hours postprandial
Recent dietary intake
Time frame: Three 24-hour dietary recalls collected at home
Dietary intake
Time frame: single time point
Behavior assessment
Time frame: single time point
Taste thresholds
Time frame: single time point
Skin reflectance
Time frame: single time point
Peripheral arterial tone
Time frame: single time point
Pulmonary function
Time frame: single time point
Pulmonary inflammation
Time frame: single time point
Executive function
Time frame: single time point
Cognitive function
Time frame: single time point
Aerobic fitness assessment
Time frame: single time point
Submaximal oxygen consumption
Time frame: single time point
Physical activity
Time frame: daily, for 7 days
Usual physical activity
Time frame: single time point
Heart rate variability and autonomic nerve conductivity
Time frame: single time point
Allostatic Load
Time frame: single time point
Baseline and change in salivary cortisol in response to test meal
Time frame: 0, immediately post-prandial, 30, 60, and 90 minutes post-prandial
Baseline and change in salivary cortisol in response to exercise
Time frame: 0, immediately post-exercise, 30, 60, and 90 minutes post-exercise
Baseline and change in salivary cortisol in response to emotional recall task
Time frame: 0, immediately post-task, 30, 60, and 90 minutes post-task
Baseline and change in breath aldehydes
Time frame: 0, 1, 4 and 6 hours postprandial
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2026.
- Background: PubMed 24160467Wopereis 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.
- Background: PubMed 22448156Pellis 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.
- Background: PubMed 22426117Krug 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.
- Background: PubMed 23360877Robles 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.
- Background: PubMed 32153856Baldiviez 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.
- Results publication: PubMed 39173973Oliver 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.
- Results publication: PubMed 36768394James KL, Gertz ER, Kirschke CP, Allayee H, Huang L, Kable ME, Newman JW, Stephensen CB, Bennett BJ. Trimethylamine N-Oxide Response to a Mixed Macronutrient Tolerance Test in a Cohort of Healthy United States Adults. Int J Mol Sci. 2023 Jan 20;24(3):2074. doi: 10.3390/ijms24032074.
- Results publication: PubMed 36248784Riazati N, Kable ME, Newman JW, Adkins Y, Freytag T, Jiang X, Stephensen CB. Associations of microbial and indoleamine-2,3-dioxygenase-derived tryptophan metabolites with immune activation in healthy adults. Front Immunol. 2022 Sep 29;13:917966. doi: 10.3389/fimmu.2022.917966. eCollection 2022.
- Results publication: PubMed 31405126Chin 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.
- Results publication: PubMed 33763626Bouzid 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.
- Results publication: PubMed 33704458Lemay 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.
- Results publication: PubMed 41720915Arrington CE, Tacad DKM, Allayee H, Sutton KJ, Dombroski C, Keim NL, Newman JW, Bennett BJ. Genetic determinants of BMI, diet, and fitness interact to partially explain anthropometric obesity traits but not the metabolic consequences of obesity in men and women. Int J Obes (Lond). 2026 Apr;50(4):938-946. doi: 10.1038/s41366-026-02027-0. Epub 2026 Feb 20.
- WHNRC websiteLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Charles B Stephensen, Ph.D. principal investigator
USDA, Western Human Nutrition Research Center
Brian J Bennett, Ph.D. principal investigator
USDA, Western Human Nutrition Research Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obesity and Inflammation, run by USDA, Western Human Nutrition Research Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 19 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Baseline level and change in systemic immune activation following challenge meal (time frame: 0, 0.5, 3, and 6 hours postprandial). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02367287, which also lists 31 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2026.