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| ID | Type | Description | Link |
|---|---|---|---|
| 2032-53000-001-00 | Other Grant/Funding Number | United States Department of Agriculture |
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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.
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)
a. 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:
Specific Aim 3: To determine the mechanisms of:
Specific Aim 4: To evaluate the associations between eating behavior, physical activity, and/or anthropometry and the outcomes:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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 |
| 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 |
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Inclusion Criteria:
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:
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)
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Healthy people in the general population
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| Name | Affiliation | Role |
|---|---|---|
| Charles B Stephensen, Ph.D. | USDA, Western Human Nutrition Research Center | Principal Investigator |
| Brian J Bennett, Ph.D. | USDA, Western Human Nutrition Research Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| USDA, Western Human Nutrition Research Center | Davis | California | 95616 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 24160467 | Background | 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. | |
| 22448156 |
| Label | URL |
|---|---|
| WHNRC website | View source |
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| ID | Term |
|---|---|
| D009765 | Obesity |
| D007249 | Inflammation |
| D009043 | Motor Activity |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
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Whole blood, stool
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 |
| 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. |
| 22426117 | Background | 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. |
| 23360877 | Background | 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. |
| 32153856 | Background | 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. |
| 39173973 | Result | 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. |
| 36768394 | Result | James 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. |
| 36248784 | Result | Riazati 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. |
| 31405126 | Result | 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. |
| 33763626 | Result | 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. |
| 33704458 | Result | 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. |
| 41720915 | Result | Arrington 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. |
| 40902732 | Result | 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 Nov;155(11):3757-3768. doi: 10.1016/j.tjnut.2025.08.027. Epub 2025 Sep 1. |
| 40880079 | Result | Tang 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. |
| 40339649 | Result | Tacad 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. |
| 39788323 | Result | 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. |
| 41970507 | Derived | Keim NL, Shilts MK, Diaz Rios LK, Zeng X, Sun J, Drake CM, Townsend MS. Validation of a Brief Vegetable Variety Questionnaire to Assess Diet Quality. Curr Dev Nutr. 2026 Feb 28;10(4):107667. doi: 10.1016/j.cdnut.2026.107667. eCollection 2026 Apr. |
| 39716659 | Derived | 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. |
| 39163972 | Derived | 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. |
| 38432562 | Derived | 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. |
| 37354976 | Derived | 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. |
| 37353855 | Derived | 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. |
| 36913444 | Derived | 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. |
| 36104734 | Derived | 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. |
| 36050800 | Derived | 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. |
| 35634390 | Derived | 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. |
| 35536006 | Derived | 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. |
| 34958387 | Derived | 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. |
| 34313764 | Derived | 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. |
| 34154665 | Derived | 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. |
| 32040591 | Derived | 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. |
| D001835 |
| Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D010335 | Pathologic Processes |
| D001519 | Behavior |