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Phenotypic flexibility in nutrition research to quantify human variability: building the bridge to personalised nutrition
Published online by Cambridge University Press: 12 December 2022
Abstract
Phenotypic flexibility is a methodology that accurately assesses health in terms of mechanistic understanding of the interrelationship of multiple metabolic and physiological processes. This starts from the perspective that a healthy person is better able to cope with changes in environmental stressors that affect homeostasis compared to people with a compromised health state. The term ‘phenotypic flexibility’ expresses the cumulative ability of overarching physiological processes to return to homeostatic levels after short-term perturbations. The concept of phenotypic flexibility to define biomarkers for nutrition-related health was introduced in 2009 in the area of health optimisation and prevention and delay of non-communicable disease. The core approach consists of the combination of imposing a challenge test to the body followed by time-resolved analysis of multiple biomarkers. This new approach may better facilitate nutritional health research in intervention studies since it may show effects on early derailed physiological markers and the biomarker response can be extended by perturbing the system, thereby making them more sensitive in detecting health effects from food and nutrition. At the same time, interindividual variation can also be extended and compressed by challenge tests, facilitating the bridge to personalised nutrition. This review will overview where the science is in this research arena and what the phenotypic flexibility potential is for the nutrition field.
Keywords
- Type
- Conference on ‘Food and nutrition: Pathways to a sustainable future’
- Information
- Copyright
- Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society
References
Hill, MA, Sowers, JR & Mantzoros, CS (2021) Commentary: COVID-19 and obesity pandemics converge into a syndemic requiring urgent and multidisciplinary action. Metabolism 114, 154408.CrossRefGoogle ScholarPubMed
Arena, R, Pronk, NP, Laddu, D et al. (2022) Mapping one million COVID-19 deaths and unhealthy lifestyle behaviors in the United States: recognizing the syndemic pattern and taking action. Am J Med S, 0002–9343, 00495–00498.Google Scholar
World Health Organisation (2021) Draft recommendations for the prevention and management of obesity over the life course, including potential targets. WHO Discussion Paper. https://doi.org/10.1017/S2040174421000027.CrossRefGoogle Scholar
Willett, WC (2002) Balancing life-style and genomics research for disease prevention. Science 296, 695–698.CrossRefGoogle ScholarPubMed
van Ommen, B, van der Greef, J, Ordovas, JM et al. (2014) Phenotypic flexibility as key factor in the human nutrition and health relationship. Genes Nutr 9, 423.CrossRefGoogle ScholarPubMed
van Ommen, B, Keijer, J, Heil, SG et al. (2009) Challenging homeostasis to define biomarkers for nutrition related health. Mol Nutr Food Res 53, 795–804.CrossRefGoogle ScholarPubMed
Huber, M, Knottnerus, JA, Green, L et al. (2011) How should we define health? Br Med J 343, d4163.CrossRefGoogle ScholarPubMed
Kardinaal, AFM, van Erk, MJ, Dutman, AE et al. (2015) Quantifying phenotypic flexibility as the response to a high-fat challenge test in different states of metabolic health. FASEB J 29, 4600–4613.CrossRefGoogle ScholarPubMed
Heaney, RP (2008) Nutrients, endpoints, and the problem of proof. J Nutr 138, 1591–1595.CrossRefGoogle ScholarPubMed
Witkamp, RF (2021) Nutrition to optimise human health-how to obtain physiological substantiation? Nutrients 13, 2155.CrossRefGoogle ScholarPubMed
Ayres, JS (2020) The biology of physiological health. Cell 181, 250–269.CrossRefGoogle ScholarPubMed
Stenvers, D, Scheer, FAJL, Schrauwen, P et al. (2019) Circadian clocks and insulin resistance. Nat Rev Endocrinol 15, 75–89.CrossRefGoogle ScholarPubMed
Lépine, G, Tremblay-Franco, M, Bouder, S et al. (2022) Investigating the postprandial metabolome after challenge tests to assess metabolic flexibility and dysregulations associated with cardiometabolic diseases. Nutrients 14, 472.CrossRefGoogle ScholarPubMed
Rubio-Aliaga, I, de Roos, B, Duthie, SJ et al. (2011) Metabolomics of prolonged fasting in humans reveals new catabolic markers. Metabolomics 7, 375–387.CrossRefGoogle Scholar
Jarrar, AH, Beasley, JM, Ohuma, EO et al. (2019) Effect of high fiber cereal intake on satiety and gastrointestinal symptoms during Ramadan. Nutrients 11, 939.CrossRefGoogle ScholarPubMed
Newman, JW, Krishnan, S, Borkowski, K et al. (2022) Assessing insulin sensitivity and postprandial triglyceridemic response phenotypes with a mixed macronutrient tolerance test. Front Nutr 9, 877696.CrossRefGoogle ScholarPubMed
Pellis, L, van Erk, MJ, van Ommen, B et al. (2012) Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics 8, 347–359.CrossRefGoogle ScholarPubMed
van Bussel, IPG, Fazelzadeh, P, Frost, GS et al. (2019) Measuring phenotypic flexibility by transcriptome time-course analyses during challenge tests before and after energy restriction. FASEB J 33, 10280–10290.CrossRefGoogle ScholarPubMed
Stroeve, JHM, van Wietmarschen, H, Kremer, BHA et al. (2015) Phenotypic flexibility as a measure of health: the optimal nutritional stress response test. Genes Nutr 10, 459.CrossRefGoogle ScholarPubMed
Wopereis, S, Stroeve, JHM, Stafleu, A et al. (2017) Multi-parameter comparison of a standardized mixed meal tolerance test in healthy and type 2 diabetic subjects: the PhenFlex challenge. Genes Nutr 12, 21.CrossRefGoogle ScholarPubMed
van den Broek, TJ, Bakker, GCM, Rubingh, CM et al. (2017) Ranges of phenotypic flexibility in healthy subjects. Genes Nutr 12, 32.CrossRefGoogle ScholarPubMed
van Ommen, B & Wopereis, S (2016) Next-generation biomarkers of health. Nestlé Nutr Inst Work Ser 84, 25–33.CrossRefGoogle ScholarPubMed
Esser, D, Mars, M, Oosterink, E et al. (2014) Dark chocolate consumption improves leukocyte adhesion factors and vascular function in overweight men. FASEB J 28, 1464–1473.CrossRefGoogle ScholarPubMed
Furst, T, Massaro, A, Miller, C et al. (2018) β-Alanine supplementation increased physical performance and improved executive function following endurance exercise in middle aged individuals. J Int Soc Sports Nutr 15, 32.CrossRefGoogle ScholarPubMed
Miranda-Castro, S, Aidar, FJ, de Moura Samara, S et al. (2022) The curcumin supplementation with piperine can influence the acute elevation of exercise-induced cytokines: double-blind crossover study. Biology 11, 573.CrossRefGoogle ScholarPubMed
Carol, A, Witkamp, RF, Wichers, HJ et al. (2011) Bovine colostrum supplementation's lack of effect on immune variables during short-term intense exercise in well-trained athletes. Int J Sport Nutr Exerc Metab 21, 135–145.CrossRefGoogle ScholarPubMed
Figueroa, A, Alvarez-Alvarado, S, Jaime, SJ et al. (2016) l-Citrulline supplementation attenuates blood pressure, wave reflection and arterial stiffness responses to metaboreflex and cold stress in overweight men. Br J Nutr 116, 279–285.CrossRefGoogle ScholarPubMed
Afkhami, R & Johnson, S (2021) Wave reflection: more than a round trip. Med Eng Phys 92, 40–44.CrossRefGoogle ScholarPubMed
Albers, R, Antoine, J, Bourdet-Sicard, R et al. (2005) Markers to measure immunomodulation in human nutrition intervention studies. Br J Nutr 94, 452–481.CrossRefGoogle ScholarPubMed
Wolvers, DAW, Van Herpen-Broekmans, WMR, Logman, MHGM et al. (2006) Effect of a mixture of micronutrients, but not of bovine colostrum concentrate, on immune function parameters in healthy volunteers: a randomized placebo-controlled study. Nutr J 5, 28.CrossRefGoogle Scholar
Albers, R, van der Wielen, RPJ, Brink, EJ et al. (2003) Effects of cis-9, trans-11 and trans-10, cis-12 conjugated linoleic acid (CLA) isomers on immune function in healthy men. Eur J Clin Nutr 57, 595–603.CrossRefGoogle ScholarPubMed
Albers, R, Bourdet-Sicard, R, Braun, D et al. (2013) Monitoring immune modulation by nutrition in the general population: identifying and substantiating effects on human health. Br J Nutr 110(Suppl), S1–30.CrossRefGoogle ScholarPubMed
Turner, RB, Woodfolk, JA, Borish, L et al. (2017) Effect of probiotic on innate inflammatory response and viral shedding in experimental rhinovirus infection – a randomised controlled trial. Benef Microbes 8, 207–215.CrossRefGoogle ScholarPubMed
Bovee-Oudenhoven, IMJ, Lettink-Wissink, MLG, Van Doesburg, W et al. (2003) Diarrhea caused by enterotoxigenic Escherichia coli infection of humans is inhibited by dietary calcium. Gastroenterology 125, 469–476.CrossRefGoogle ScholarPubMed
Ten Bruggencate, SJM, Girard, SA, Floris-Vollenbroek, EGM et al. (2015) The effect of a multi-strain probiotic on the resistance toward Escherichia coli challenge in a randomized, placebo-controlled, double-blind intervention study. Eur J Clin Nutr 69, 385–391.CrossRefGoogle Scholar
Ten Bruggencate, SJ, Frederiksen, PD, Pedersen, SM et al. (2016) Dietary milk-fat-globule membrane affects resistance to diarrheagenic Escherichia coli in healthy adults in a randomized, placebo-controlled, double-blind study 1,2. J Nutr Nutr Dis 146, 249–255.Google Scholar
Ulfman, LH, Schloesser, JEL, Kortman, GAM et al. (2022) A double-blind, randomized intervention study on the effect of a whey protein concentrate on E. coli-induced diarrhea in a human infection model. Nutrients 14, 1204.CrossRefGoogle Scholar
Ouwehand, AC, Ten Bruggencate, SJM, Schonewille, AJ et al. (2014) Lactobacillus acidophilus supplementation in human subjects and their resistance to enterotoxigenic Escherichia coli infection. Br J Nutr 111, 465–473.CrossRefGoogle ScholarPubMed
van Hoffen, E, Mercenier, A, Vidal, K et al. (2021) Characterization of the pathophysiological determinants of diarrheagenic Escherichia coli infection using a challenge model in healthy adults. Sci Rep 11, 6060.CrossRefGoogle ScholarPubMed
Hoevenaars, FPM, Esser, D, Schutte, S et al. (2019) Whole grain wheat consumption affects postprandial inflammatory response in a randomized controlled trial in overweight and obese adults with mild hypercholesterolemia in the Graandioos study. J Nutr 149, 2133–2144.CrossRefGoogle Scholar
Schutte, S, Esser, D, Siebelink, E et al. (2022) Diverging metabolic effects of 2 energy-restricted diets differing in nutrient quality: a 12-week randomized controlled trial in subjects with abdominal obesity. Am J Clin Nutr 116, 132–150.CrossRefGoogle ScholarPubMed
Fiamoncini, J, Rundle, M, Gibbons, H et al. (2018) Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements. FASEB J 32, 5447–5458.CrossRefGoogle ScholarPubMed
Fazelzadeh, P, Hangelbroek, RWJ, Joris, PJ et al. (2018) Weight loss moderately affects the mixed meal challenge response of the plasma metabolome and transcriptome of peripheral blood mononuclear cells in abdominally obese subjects. Metabolomics 14, 46.CrossRefGoogle ScholarPubMed
Vis, DJ, Westerhuis, JA, Jacobs, DM et al. (2015) Analyzing metabolomics-based challenge tests. Metabolomics 11, 50–63.CrossRefGoogle Scholar
O'Donovan, SD, Erdős, B, Jacobs, DM et al. (2022) Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model. ISCIENCE. https://doi.org/https://doi.org/10.1016/j.isci.2022.105206.CrossRefGoogle Scholar
Bakker, GCM, van Erk, MJ, Pellis, L et al. (2010) An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr 91, 1044–1059.CrossRefGoogle Scholar
Bouwman, J, Vogels, JTWE, Wopereis, S et al. (2012) Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes. BMC Med Genomics 5, 1.CrossRefGoogle ScholarPubMed
van den Brink, W, van Bilsen, J, Salic, K et al. (2019) Current and future nutritional strategies to modulate inflammatory dynamics in metabolic disorders. Front Nutr 6, 129.CrossRefGoogle ScholarPubMed
Hoevenaars, F, van der Kamp, JW, van den Brink, WD et al. (2020) Next generation health claims based on resilience: the example of whole-grain wheat. Nutrients 12, 1–13.CrossRefGoogle ScholarPubMed
Hardy, A, Benford, D, Halldorsson, T et al. (2017) Guidance on the assessment of the biological relevance of data in scientific assessments. EFSA Journal Eur Food Saf Auth 15, e04970.Google ScholarPubMed
de Boer, A (2021) Fifteen years of regulating nutrition and health claims in Europe: the past, the present and the future. Nutrients 13, 1725.CrossRefGoogle ScholarPubMed
Morris, C, O'Grada, C, Ryan, M et al. (2013) Identification of differential responses to an oral glucose tolerance test in healthy adults. PLoS ONE 8, e72890.CrossRefGoogle Scholar
Fiamoncini, J, Donado-Pestana, CM, Duarte, GBS et al. (2022) Plasma metabolic signatures of healthy overweight subjects challenged with an oral glucose tolerance test. Front Nutr 9, 898782.CrossRefGoogle ScholarPubMed
Krug, S, Kastenmüller, G, Stückler, F et al. (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26, 2607–2619.CrossRefGoogle ScholarPubMed
Berry, SE, Valdes, AM, Drew, DA et al. (2020) Human postprandial responses to food and potential for precision nutrition. Nat Med 26, 964–973.CrossRefGoogle ScholarPubMed
de Hoogh, IM, Winters, BL, Nieman, KM et al. (2021) A novel personalized systems nutrition program improves dietary patterns, lifestyle behaviors and health-related outcomes: results from the habit study. Nutrients 13, 1763.CrossRefGoogle ScholarPubMed
Griffiths, JC, De Vries, J, McBurney, MI et al. (2020) Measuring health promotion: translating science into policy. Eur J Nutr 59, 11–23.CrossRefGoogle ScholarPubMed
van den Brink, W, Bloem, R, Ananth, A et al. (2021) Digital resilience biomarkers for personalized health maintenance and disease prevention. Front Digit Health 2, 614670.CrossRefGoogle ScholarPubMed
Oosterman, JE, Wopereis, S & Kalsbeek, A (2020) The circadian clock, shift work, and tissue-specific insulin resistance. Endocrinology 161, bqaa180.CrossRefGoogle ScholarPubMed
Esser, D, van Dijk, SJ, Oosterink, E et al. (2015) High fat challenges with different fatty acids affect distinct atherogenic gene expression pathways in immune cells from lean and obese subjects. Mol Nutr Food Res 59, 1563–1572.CrossRefGoogle ScholarPubMed
Fatima, A, Connaughton, RM, Weiser, A et al. (2018) Weighted gene co-expression network analysis identifies gender specific modules and hub genes related to metabolism and inflammation in response to an acute lipid challenge. Mol Nutr Food Res 62, 1700388.CrossRefGoogle Scholar
Norris, PC, Skulas-Ray, AC, Riley, I et al. (2018) Identification of specialized pro-resolving mediator clusters from healthy adults after intravenous low-dose endotoxin and omega-3 supplementation: a methodological validation. Sci Rep 8, 19816.CrossRefGoogle ScholarPubMed
Dillingh, MR, Van Poelgeest, EP, Malone, KE et al. (2014) Characterization of inflammation and immune cell modulation induced by low-dose LPS administration to healthy volunteers. J Inflamm 11, 1–9.CrossRefGoogle Scholar
Salehi, M, Mashhadi, NS, Esfahani, PS et al. (2021) The effects of curcumin supplementation on muscle damage, oxidative stress, and inflammatory markers in healthy females with moderate physical activity: a randomized, double-blind, placebo-controlled clinical trial. Int J Prev Med 12, 94.Google ScholarPubMed
Janssen Duijghuijsen, LM, Van Norren, K, Grefte, S et al. (2017) Endurance exercise increases intestinal uptake of the peanut allergen Ara h 6 after peanut consumption in humans. Nutr 9, 84.Google ScholarPubMed
Schutte, S, Esser, D, Hoevenaars, FPM et al. (2018) A 12-wk whole-grain wheat intervention protects against hepatic fat: the Graandioos study, a randomized trial in overweight subjects. Am J Clin Nutr 108, 1264–1274.CrossRefGoogle ScholarPubMed