USDA Western Human Nutrition Research Center (WHNRC) Cross-Sectional Nutritional Phenotyping Study

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.

Study Overview

• Status: Completed
• Conditions: Inflammation; Obesity

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:

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.

• Study Type: Observational
• Enrollment (Actual): 393

Contacts and Locations

Study Locations

• United States
  • California
    • Davis, California, United States, 95616
      • USDA, Western Human Nutrition Research Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 65 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Healthy people in the general population

Description

Inclusion Criteria:

• 18-65 y
• Male or female
• Body Mass Index 18.5-45.0 kg/m2 (Normal to obese)

Exclusion Criteria:

• 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
  • 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)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	0, 0.5, 3, and 6 hours postprandial

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Baseline level and change in glucose metabolism	Glucose and insulin measured in response to a challenge meal.	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.	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.	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	0, 0.5, 3, and 6 hours postprandial
Baseline level and change in RNA transcriptome	Transcriptome RNA sequenced in whole blood	0, 3, and 6 hours postprandial
Genome Wide Association Study (GWAS)	DNA sequence from whole blood will be analyzed	0 hours (fasting)
General health	Clinical chemistry panel and complete blood count	0 hours (Fasting)
Anthropometrics	Height (cm), weight (kg), waist and hip circumference (cm)	single time point
Vital signs	Blood pressure (mmHg), pulse rate (beats per minute) and temperature (degrees F).	single time point
Body composition	Body composition (percent body fat) and bone mineral density by Dual energy X-ray Absorptiometry scan.	single time point
Resting and change in metabolism	Resting and postprandial metabolic rates, including respiratory exchange ratios.	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	single time point
Gut microbiota fermentation capacity	The fermentation capacity of microbiota will be measured from a single stool sample	single time point
Gut microbiota pathogen resistance capacity	The pathogen resistance capability of microbiota will be measured from a single stool sample	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.	single time point
Stool metabolites	Volatile and short chain fatty acids and bile acids will be measured in a single stool sample.	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	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."	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.	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	Three 24-hour dietary recalls collected at home
Dietary intake	Food frequency questionnaire (FFQ)	single time point
Behavior assessment	Chronic stress questionnaire, food choice questionnaires, and a food preference activity.	single time point
Taste thresholds	Sampling tastes of sweet, salty, and bitter solutions in comparison to water to determine threshold of taste detection.	single time point
Skin reflectance	Spectrophotometric measure of skin pigmentation for assessment of vitamin D status.	single time point
Peripheral arterial tone	Use of the EndoPAT system to measure blood vessel tone.	single time point
Pulmonary function	Forced expiratory lung volume test	single time point
Pulmonary inflammation	Pulmonary inflammation measured as exhaled nitric oxide (NO)	single time point
Executive function	Executive function will be assessed using Cambridge Neuropsychological Test Automated Battery (CANTAB) and Iowa Gambling Task	single time point
Cognitive function	Measured by Wechsler Abbreviated Standard Intelligence test.	single time point
Aerobic fitness assessment	Pulse rate (bpm) and recovery after a 3 min YMCA Step Test	single time point
Submaximal oxygen consumption	The submaximal volume of oxygen consumed during a 4 minute treadmill walking protocol (VO2max) (ml/kg*min)	single time point
Physical activity	Use of an accelerometer worn on the hip for 7 days	daily, for 7 days
Usual physical activity	Activity recall using a questionnaire	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.	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.	single time point
Baseline and change in salivary cortisol in response to test meal	Salivary cortisol measured by enzyme-linked immunosorbent assay (ELISA)	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)	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)	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	0, 1, 4 and 6 hours postprandial

Collaborators and Investigators

• Sponsor: USDA, Western Human Nutrition Research Center
• Investigators: Principal Investigator: Charles B Stephensen, Ph.D., USDA, Western Human Nutrition Research Center; Principal Investigator: Brian J Bennett, Ph.D., USDA, Western Human Nutrition Research Center

Publications and helpful links

General Publications

• 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.
• 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.
• 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.

Helpful Links

• WHNRC recruiting website

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2015
• Primary Completion (Actual): July 24, 2019
• Study Completion (Actual): July 24, 2019

Study Registration Dates

• First Submitted: February 12, 2015
• First Submitted That Met QC Criteria: February 19, 2015
• First Posted (Estimate): February 20, 2015

Study Record Updates

• Last Update Posted (Actual): March 30, 2021
• Last Update Submitted That Met QC Criteria: March 25, 2021
• Last Verified: March 1, 2021

More Information

Terms related to this study

• Keywords: Immunology; Microbiota; Metabolomics; Physical activity; Diet; Phenotype; Nutritional Physiological Phenomena; Neuroendocrine system
• Additional Relevant MeSH Terms: Pathologic Processes; Inflammation
• Other Study ID Numbers: 691654; 2032-53000-001-00 (Other Grant/Funding Number: United States Department of Agriculture)