Application of New Technologies and Tools to Nutrition Research (NutriTech)

The overall objective of the research is to develop new methods for studying the link between diet, health and disease.

The enquiry consists of two part. A metabolomic biomarker discovery study followed by a randomised controlled trial of moderate dietary induced weight loss. A cohort of moderately overweight men and women will undergo a metabolomic study follow by randomised controlled trial of a healthy diet with a 20% energy restriction for a 12 week period. This intervention will improve the volunteer's body composition and reduce their risk of developing diabetes. Extensive tests will be conducted before and after the dietary intervention using emerging technologies with current state-of-the-art methods. These tests will monitor the numerous physiological adaptations that have occurred on a gene, cell, organ and organismic level. The study will, therefore, aim for a scientific breakthrough by developing a series of novel biological "markers" that can accurately determine an individual's health status.

This inquiry took place prior to the randomized controlled trial and include 50 volunteers who will then be asked to volunteers of the weight loss study. Were randomly assigned to one of five different diets; red meat, fish, poultry, processed meat or a supplement and vegetarian option.

Metabolomic inquiry followed by a 12 week energy intake restriction. This study is part of a very large European study called EPIC. Only the results by Lee et al. 2016 have been presented here. The results of the study published by Cheung 2017 are from a different study. therefore the entries for the results are correct. For this particular study, participants were not randomly assigned to one of five different diets; red meat, fish, poultry, processed meat or a supplement and vegetarian option. In summary, both studies (lee and cheung) are considered as separate trials and results of the first phase where participants were assigned to one of five different diets, are reported separately. Unfortunately no different NCT number is available.

Glucose Infusion rate assessed by the euglycemic hyperinsulinemic clamp. Insulin Sensitivity assessment was only performed in the "Exercise" and "Exercise, Control" groups. No data were collected for this Outcome from the remaining groups.

Changes in blood plasma cholesterol in mg/dL- baseline to 12 weeks, after treatment. Statistical Analysis was performed only for the "Diet" Arm/Group.

Inclusion Criteria: Healthy overweight volunteers (body mass index (BMI) of 25-30 kg/m2) Age between 50-65 years Exclusion Criteria: Weight change of ≥ 3kg in the preceding 3 months Current smokers Substance abuse Excess alcohol intake Pregnancy Diabetes Cardiovascular disease Cancer Gastrointestinal disease e.g. inflammatory bowel disease or irritable bowel syndrome Kidney disease Liver disease Pancreatitis Have any metallic or magnetic implants such as pacemakers Use of medications likely to interfere with energy metabolism, appetite regulation and hormonal balance, including: anti inflammatory drugs or steroids, antibiotics, androgens, phenytoin, erythromycin or thyroid hormones.

Lee S, Norheim F, Langleite TM, Noreng HJ, Storas TH, Afman LA, Frost G, Bell JD, Thomas EL, Kolnes KJ, Tangen DS, Stadheim HK, Gilfillan GD, Gulseth HL, Birkeland KI, Jensen J, Drevon CA, Holen T; NutriTech Consortium. Effect of energy restriction and physical exercise intervention on phenotypic flexibility as examined by transcriptomics analyses of mRNA from adipose tissue and whole body magnetic resonance imaging. Physiol Rep. 2016 Nov;4(21):e13019. doi: 10.14814/phy2.13019.

Cheung W, Keski-Rahkonen P, Assi N, Ferrari P, Freisling H, Rinaldi S, Slimani N, Zamora-Ros R, Rundle M, Frost G, Gibbons H, Carr E, Brennan L, Cross AJ, Pala V, Panico S, Sacerdote C, Palli D, Tumino R, Kuhn T, Kaaks R, Boeing H, Floegel A, Mancini F, Boutron-Ruault MC, Baglietto L, Trichopoulou A, Naska A, Orfanos P, Scalbert A. A metabolomic study of biomarkers of meat and fish intake. Am J Clin Nutr. 2017 Mar;105(3):600-608. doi: 10.3945/ajcn.116.146639. Epub 2017 Jan 25.

Yin X, Gibbons H, Rundle M, Frost G, McNulty BA, Nugent AP, Walton J, Flynn A, Gibney MJ, Brennan L. Estimation of Chicken Intake by Adults Using Metabolomics-Derived Markers. J Nutr. 2017 Oct 1;147(10):1850-1857. doi: 10.3945/jn.117.252197.