Design and Implementation of a Cross-sectional Nutritional Phenotyping Study in Healthy US Adults

Overview

Journal BMC Nutr

Publisher Biomed Central

Specialty Nutritional Sciences

Date 2020 Mar 11

PMID 32153856

Citations 21

Authors

Lacey M Baldiviez

Nancy L Keim

Kevin D Laugero

Daniel H Hwang

Liping Huang

Leslie R Woodhouse

Dustin J Burnett

Melissa S Zerofsky

Ellen L Bonnel

Lindsay H Allen

John W Newman

Charles B Stephensen

Affiliations

Soon will be listed here.

Abstract

Background: Metabolic imbalance is a key determinant of risk of chronic diseases. Metabolic health cannot be assessed solely by body mass calculations or by static, fasted state biochemical readouts. Although previous studies have described temporal responses to dietary challenges, these studies fail to assess the environmental factors associated with certain metabolic phenotypes and therefore, provide little scientific rationale for potentially effective intervention strategies.

Methods/design: In this phenotyping study of healthy US adults, we are evaluating lifestyle, biological and environmental factors in addition to metabolic parameters to determine the factors associated with variations in metabolic health. A series of practical fitness, dietary, and emotional challenges are introduced and temporal responses in various areas of specialization, including immunology, metabolomics, and endocrinology, are monitored. We expect that this study will identify key factors related to healthy or unhealthy metabolic phenotypes (metabotypes) that may be modifiable targets for the prevention of chronic diseases in an individual.

Discussion: This study will provide novel insights into metabolic variability among healthy adults in balanced strata defined by sex, age and body mass index. Usual dietary intake and physical activity will be evaluated across these strata to determine how diet is associated with health status defined using many indicators including immune function, metabolism, body composition, physiology, response to exercise andmeal challenges and neuroendocrine assessment. A principal study goal is to identify dietary and other personal factors that will differentiate different levels of "health" among study participants.

Trial Registration: ClinicalTrials.gov NCT02367287.

Citing Articles

Diet, Microbiome, and Inflammation Predictors of Fecal and Plasma Short-Chain Fatty Acids in Humans.

Oliver A, Alkan Z, Stephensen C, Newman J, Kable M, Lemay D J Nutr. 2024; 154(11):3298-3311.

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Atypical monocyte dynamics in healthy humans in response to fasting and refeeding are distinguished by fasting HDL and postprandial cortisol.

Snodgrass R, Stephensen C, Laugero K Am J Physiol Endocrinol Metab. 2024; 327(2):E229-E240.

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Specific circulating miRNAs are associated with plasma lipids in a healthy American cohort.

Evans L, Durbin-Johnson B, Sutton K, Yam P, Bouzid Y, Cervantes E Physiol Genomics. 2024; 56(7):492-505.

PMID: 38557280 PMC: 11368566. DOI: 10.1152/physiolgenomics.00087.2023.

Lower Diet Quality Associated with Subclinical Gastrointestinal Inflammation in Healthy United States Adults.

Bouzid Y, Wilson S, Alkan Z, Stephensen C, Lemay D J Nutr. 2024; 154(4):1449-1460.

PMID: 38432562 PMC: 11347802. DOI: 10.1016/j.tjnut.2024.02.030.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure.

Oliver A, Kay M, Lemay D Bioinform Adv. 2023; 3(1):vbad165.

PMID: 38046097 PMC: 10689668. DOI: 10.1093/bioadv/vbad165.

References

Kardinaal A, van Erk M, Dutman A, Stroeve J, van de Steeg E, Bijlsma S . Quantifying phenotypic flexibility as the response to a high-fat challenge test in different states of metabolic health. FASEB J. 2015; 29(11):4600-13. DOI: 10.1096/fj.14-269852. View

Perez-Martinez P, Alcala-Diaz J, Delgado-Lista J, Garcia-Rios A, Gomez-Delgado F, Marin-Hinojosa C . Metabolic phenotypes of obesity influence triglyceride and inflammation homoeostasis. Eur J Clin Invest. 2014; 44(11):1053-64. DOI: 10.1111/eci.12339. View

Kayano M, Imoto S, Yamaguchi R, Miyano S . Multi-omics approach for estimating metabolic networks using low-order partial correlations. J Comput Biol. 2013; 20(8):571-82. DOI: 10.1089/cmb.2013.0043. View

Wildman R, Muntner P, Reynolds K, McGinn A, Rajpathak S, Wylie-Rosett J . The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med. 2008; 168(15):1617-24. DOI: 10.1001/archinte.168.15.1617. View

Backhed F, Ding H, Wang T, Hooper L, Koh G, Nagy A . The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 2004; 101(44):15718-23. PMC: 524219. DOI: 10.1073/pnas.0407076101. View

Wopereis S, Wolvers D, van Erk M, Gribnau M, Kremer B, van Dorsten F . Assessment of inflammatory resilience in healthy subjects using dietary lipid and glucose challenges. BMC Med Genomics. 2013; 6:44. PMC: 4015956. DOI: 10.1186/1755-8794-6-44. View

Guenther P, Casavale K, Reedy J, Kirkpatrick S, Hiza H, Kuczynski K . Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet. 2013; 113(4):569-80. PMC: 3810369. DOI: 10.1016/j.jand.2012.12.016. View

Diep T, Madsen A, Holst B, Kristiansen M, Wellner N, Hansen S . Dietary fat decreases intestinal levels of the anorectic lipids through a fat sensor. FASEB J. 2010; 25(2):765-74. DOI: 10.1096/fj.10-166595. View

Balasoiu M, Balasoiu A, Stepan A, Dinescu S, Avramescu C, Dumitrescu D . Proatherogenic adipocytokines levels in metabolic syndrome. Rom J Morphol Embryol. 2014; 55(1):29-33. View

10.

Taylor-Piliae R, Fair J, Haskell W, Varady A, Iribarren C, Hlatky M . Validation of the Stanford Brief Activity Survey: examining psychological factors and physical activity levels in older adults. J Phys Act Health. 2010; 7(1):87-94. PMC: 4696759. DOI: 10.1123/jpah.7.1.87. View

11.

Bechara A, Damasio A, Damasio H, Anderson S . Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994; 50(1-3):7-15. DOI: 10.1016/0010-0277(94)90018-3. View

12.

Steptoe A, Pollard T, Wardle J . Development of a measure of the motives underlying the selection of food: the food choice questionnaire. Appetite. 1995; 25(3):267-84. DOI: 10.1006/appe.1995.0061. View

13.

Finlayson G, King N, Blundell J . Is it possible to dissociate 'liking' and 'wanting' for foods in humans? A novel experimental procedure. Physiol Behav. 2006; 90(1):36-42. DOI: 10.1016/j.physbeh.2006.08.020. View

14.

Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G . Richness of human gut microbiome correlates with metabolic markers. Nature. 2013; 500(7464):541-6. DOI: 10.1038/nature12506. View

15.

Gearhardt A, Corbin W, Brownell K . Preliminary validation of the Yale Food Addiction Scale. Appetite. 2009; 52(2):430-6. DOI: 10.1016/j.appet.2008.12.003. View

16.

Hall L, Kimlin M, Aronov P, Hammock B, Slusser J, Woodhouse L . Vitamin D intake needed to maintain target serum 25-hydroxyvitamin D concentrations in participants with low sun exposure and dark skin pigmentation is substantially higher than current recommendations. J Nutr. 2010; 140(3):542-50. PMC: 2821886. DOI: 10.3945/jn.109.115253. View

17.

Stunkard A, Messick S . The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res. 1985; 29(1):71-83. DOI: 10.1016/0022-3999(85)90010-8. View

18.

Harris P, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J . Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2008; 42(2):377-81. PMC: 2700030. DOI: 10.1016/j.jbi.2008.08.010. View

19.

van Oostrom A, Rabelink T, Verseyden C, Sijmonsma T, Plokker H, De Jaegere P . Activation of leukocytes by postprandial lipemia in healthy volunteers. Atherosclerosis. 2004; 177(1):175-82. DOI: 10.1016/j.atherosclerosis.2004.07.004. View

20.

Moerland M, Kales A, Schrier L, van Dongen M, Bradnock D, Burggraaf J . Evaluation of the EndoPAT as a Tool to Assess Endothelial Function. Int J Vasc Med. 2012; 2012:904141. PMC: 3303545. DOI: 10.1155/2012/904141. View