Study protocol of a pragmatic randomized controlled trial incorporated into the Group Lifestyle Balance™ program: the nutrigenomics, overweight/obesity and weight management trial (the NOW trial)

Justine Horne, Jason Gilliland, Colleen O'Connor, Jamie Seabrook, Peter Hannaberg, Janet Madill, Justine Horne, Jason Gilliland, Colleen O'Connor, Jamie Seabrook, Peter Hannaberg, Janet Madill

Abstract

Background: The nutrigenomics, overweight/obesity and weight management trial (NOW Trial) is a pragmatic randomized controlled trial of community-dwelling adults recruited from the Group Lifestyle Balance™ (GLB™) Program. The GLB™ Program (formerly referred to as the Diabetes Prevention Program) is an evidence-based, intensive weight management program, which was offered to overweight/obese patients (BMI ≥ 25.0 kg/m2) in a rural Ontario community.

Methods: Patients enrolled in the GLB™ Program were invited to participate in this study. GLB™ groups were randomized 1:1 to receive either the standard GLB™ program + population-based lifestyle advice for weight management, or a modified GLB™ program + personalized, genetic-based lifestyle advice for weight management. The purpose of this study is to determine if the provision of genetic-based lifestyle guidelines is superior to the provision of population-based guidelines in a pragmatic clinical setting to promote changes in: body composition, weight, body mass index, dietary and physical activity habits, as well as attitudes, subjective norms, and behavioural control. The 12-month intervention protocol consists of 23 group-based sessions and 4 one-on-one sessions. Data collection time points include baseline in addition to 3, 6, and 12-month follow up. The comprehensive study design is described in the present manuscript, using both the extended CONSORT checklist for reporting pragmatic trials and the SPIRIT checklist as guidance during manuscript development.

Discussion: Overall, this study seeks to pragmatically determine if the provision of DNA-based lifestyle advice leads to improved health and lifestyle outcomes compared to the provision of standard, population-based lifestyle advice. The results of this trial can be used to inform clinical and community nutrition practice guidelines.

Trial registration: This study was registered with clinicaltrials.gov : NCT03015012 on January 9, 2017.

Keywords: Genetics; Lifestyle genomics; Nutrigenetics; Nutrigenomics; Nutrition; Obesity; Overweight; Physical activity.

Conflict of interest statement

Ethics approval and consent to participate

This study was approved by the Western University Research Ethics Board (#108511) and all participants completed an informed consenting process with a member of the research team. Personal information about participants collected during the consent/data collection processes are stored securely in a locked cabinet, in a locked office. Saliva samples are analyzed and securely stored at the Nutrigenomix, Inc. Laboratory. Each sample is coded with a 14-digit barcode; no personal identifying information are included on the samples. Any reported adverse events and unintended effects will be discussed amongst the research team and dealt with on a case-by-case basis. A data monitoring committee is not needed for the purposes of this study given that one author is not blinded, and that the overall risk for harm is low.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flow of Study Protocol
Fig. 2
Fig. 2
CONSORT 2010 Flow Diagram (Clinical Trial Registration Number: NCT03015012)
Fig. 3
Fig. 3
SPIRIT Flow Diagram of The NOW Trial Study Protocol at the EEFHT. SE: study entry; Mo: month

References

    1. Jensen MD, Ryan DH, Donato KA, Apovian CM, Ard JD, Comuzzie AG, et al. Guidelines (2013) for the Management of Overweight and Obesity in adults. Obesity. 2014;22:S1–410. doi: 10.1002/oby.20821.
    1. Devito NJ, French L, Goldacre B. Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age , 2007–2008 to 2015–2016. 2018;319(16):2016–2018. doi: 10.1001/jama.2018.3060.
    1. Canada S. Overweight and obese adults (self-reported), 2014 [internet] 2014.
    1. University of Pittsburgh. GLB Program Locations [Internet]. Available from: .
    1. Diabetes Prevention Program Research Group Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393. doi: 10.1056/NEJMoa012512.
    1. Ontario Ministry of Health and Long-Term Care. Schedule M program description: primary care diabetes prevention program 2017-2018. 2018.
    1. Diabetes Canada. Are you at risk? [Internet]. Available from: .
    1. Kramer MK, Kriska AM, Venditti EM, Semler LN, Miller RG, McDonald T, et al. A novel approach to diabetes prevention: evaluation of the group lifestyle balance program delivered via DVD. Diabetes Res Clin Pract [Internet] 2010;90(3):e60–e63. Available from: 10.1016/j.diabres.2010.08.013.
    1. Piatt GA, Seidel MC, yu CH, Powell RO, Zgibor JC. Two-year results of translating the diabetes prevention program into an urban, underserved community. Diabetes Educ. 2012;38(6):798–804. doi: 10.1177/0145721712458834.
    1. Field A, Camargo C, Ogino S. The merits of subtyping obesity one size does not fit all. JAMA. 2013;02115:4–5.
    1. Horne J, Madill J, O ‘Connor C, Shelley J, Gilliland J. A systematic review of genetic testing and lifestyle behaviour change: are we using high-quality genetic interventions and considering behaviour change theory? Lifestyle Genomics [Internet] 2018;11(1):49–63. doi: 10.1159/000488086.
    1. Hollands GJ, French DP, Griffin SJ, Prevost AT, Sutton S, King S, et al. The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ [Internet] 2016;352:i1102. doi: 10.1136/bmj.i1102.
    1. O’Donovan CB, Walsh MC, Gibney MJ, Brennan L, Gibney ER. Knowing your genes: does this impact behaviour change? Proc Nutr Soc [Internet] 2017;76(3):182–191. doi: 10.1017/S0029665116002949.
    1. French DP, Cameron E, Benton JS, Deaton C, Harvie M. Can communicating personalised disease risk promote healthy behaviour change? A systematic review of systematic reviews. Ann Behav Med. 2017;51(5):718–729. doi: 10.1007/s12160-017-9895-z.
    1. Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA. Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J [Internet]. 2007;6:29. doi: 10.1186/1475-2891-6-29.
    1. Frankwich KA, Egnatios J, Kenyon ML, Rutledge TR, Liao PS, Gupta S, et al. Differences in weight loss between persons on standard balanced vs Nutrigenetic diets in a randomized controlled trial. Clin Gastroenterol Hepatol [Internet] 2015;13(9):1625–1632. doi: 10.1016/j.cgh.2015.02.044.
    1. Celis-morales C, Marsaux CFM, Livingstone KM, Navas-carretero S, San-cristobal R, Fallaize R, et al. Can genetic-based advice help you lose weight ? Findings from the Food4Me European randomized controlled trial. 2017;105(5):1204–1213. doi: 10.3945/ajcn.116.145680.
    1. Gibney MJ, Walsh MC. The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc [Internet]. 2013;72(2):219–225. doi: 10.1017/S0029665112003436.
    1. Corella D, Peloso G, Arnett DK, Cupples LA, Tucker K, Lai C, et al. APOA2, dietary fat and body mass index: replication of a gene-diet Interacton in three independent populations. Arch Intern Med. 2009;169(20):1897–1906. doi: 10.1001/archinternmed.2009.343.
    1. Nagai N, Sakane N, Tsuzaki K, Moritani T. UCP1 genetic polymorphism (−3826 a/G) diminishes resting energy expenditure and thermoregulatory sympathetic nervous system activity in young females. Int J Obes. 2011;35(8):1050–1055. doi: 10.1038/ijo.2010.261.
    1. Zhang X, Qi Q, Zhang C, Smith SR, Hu FB, Sacks FM, et al. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: the POUNDS LOST trial. Diabetes [Internet] 2012;61(11):3005–11. doi: 10.2337/db11-1799.
    1. Grau K, Cauchi S, Holst C, Astrup A, Martinez JA, Saris WHM, et al. TCF7L2 rs7903146-macronutrient interaction in obese individuals’ responses to a 10-wk randomized hypoenergetic diet. Am J Clin Nutr. 2010;91(2):472–479. doi: 10.3945/ajcn.2009.27947.
    1. Mattei J, Qi Q, Hu F, Sacks F, Qi L. TCF7L2 genetic variants modulate the effect of dietary fat intake on changes in body composition during a weight-loss intervention1–3. Am J Clin Nutr 2012;961129–36 [Internet]. 2012;96:1129–1136. doi: 10.3945/ajcn.112.038125.
    1. Phillips CM, Kesse-Guyot E, McManus R, Hercberg S, Lairon D, Planells R, et al. High dietary saturated fat intake accentuates obesity risk associated with the fat mass and obesity-associated gene in adults. J Nutr [Internet] 2012;142(5):824–831. doi: 10.3945/jn.111.153460.
    1. Garaulet M, Smith CE, Hernández-González T, Lee YC, Ordovás JM. PPARγ Pro12Ala interacts with fat intake for obesity and weight loss in a behavioural treatment based on the Mediterranean diet. Mol Nutr Food Res. 2011;55(12):1771–1779. doi: 10.1002/mnfr.201100437.
    1. Stutzmann F, Cauchi S, Durand E, Calvacanti-Proença C, Pigeyre M, Hartikainen AL, et al. Common genetic variation near MC4R is associated with eating behaviour patterns in European populations. Int J Obes. 2009;33(3):373–378. doi: 10.1038/ijo.2008.279.
    1. Andreasen C, Stender-Peterson K, Mogensen M, Torekov S, Wegner L, Andersen G, et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes. 2008;57:95–101. doi: 10.2337/db07-0910..
    1. Ma F, Yang Y, Li X, Zhou F, Gao C, Li M, et al. The Association of Sport Performance with ACE and ACTN3 genetic polymorphisms: a systematic review and meta-analysis. PLoS One. 2013;8(1):1–9. doi: 10.1371/journal.pone.0054685..
    1. Ahmetov II, Kulemin NA, Popov DV, Naumov VA, Akimov EB, Bravy YR, et al. Genome-wide association study identifies three novel genetic markers associated with elite endurance performance. Biol Sport. 2015;32(1):3–9. doi: 10.5604/20831862.1124568.
    1. Zarebska A, Jastrzebski Z, Kaczmarczyk M, Ficek K, Maciejewska-Karlowska A, Sawczuk M, et al. The GSTP1 c.313A>G polymorphism modulates the cardiorespiratory response to aerobic training. Biol Sport. 2014;31(4):261–266. doi: 10.5604/20831862.1120932.
    1. He Z, Hu Y, Feng L, Lu Y, Liu G, Xi Y, et al. NRF2 genotype improves endurance capacity in response to training. Int J Sports Med. 2006;28(9):717–721. doi: 10.1055/s-2007-964913.
    1. Santiago C, Ruiz JR, Buxens A, Artieda M, Arteta D, González-Freire M, et al. Trp64Arg polymorphism in ADRB3 gene is associated with elite endurance performance. Br J Sports Med. 2011;45(2):147–149. doi: 10.1136/bjsm.2009.061366.
    1. Nutrigenomix Inc. Downloads [Internet]. Available from: .
    1. Moore S, Hall JN, Harper S, Lynch JW. Global and national socioeconomic disparities in obesity, overweight, and underweight status. J Obes. 2010:1–11. 10.1155/2010/514674.
    1. Eriksson J, Forsén T, Osmond C, Barker D. Obesity from cradle to grave. Int J Obes. 2003;27(6):722–727. doi: 10.1038/sj.ijo.0802278.
    1. Finkelstein EA, Ruhm CJ, Kosa KM. Economic causes and consequences of obesity. Annu Rev Public Health [Internet] 2005;26(1):239–257. doi: 10.1146/annurev.publhealth.26.021304.144628.
    1. Seabrook JA, Avison WR. Social Science & Medicine Genotype – environment interaction and sociology : contributions and complexities. Soc Sci Med [Internet] 2010;70(9):1277–1284. doi: 10.1016/j.socscimed.2010.01.016.
    1. Gilliland JA, Rangel CY, Healy MA, Tucker P, Loebach JE, Hess PM, et al. Linking Childhood Obesity to the Built Environment: A Multi-level Analysis of Home and School Neighbourhood Factors Associated With Body Mass Index. 2012;103(9):1–3. doi: 10.17269/cjph.103.3283.
    1. Sarma S, Zaric GS, Campbell MK, Gilliland J. Economics and human biology the effect of physical activity on adult obesity: evidence from the Canadian NPHS panel. Econ Hum Biol [Internet] 2014;14:1–21. doi: 10.1016/j.ehb.2014.03.002.
    1. Ajzen I. The theory of planned behavior. Orgnizational Behav Hum Decis Process. 1991;50:179–211. doi: 10.1016/0749-5978(91)90020-T.
    1. Horne J, Madill J, Gilliland J. Incorporating the ‘Theory of planned behavior’ into personalized healthcare behavior change research: a call to action. Per Med [Internet] 2017;14(6):521–529. doi: 10.2217/pme-2017-0038.
    1. Zwarentein M, Treweek S, Gagnier JJ, Altman DG, Tunis S, Haynes B, et al. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. J Chinese Integr Med. 2009;7(4):392–397. doi: 10.1136/bmj.a2390.
    1. Smilowitz JT, Wiest MM, Watkins SM, Teegarden D, Zemel MB, German JB, et al. Lipid metabolism predicts changes in body composition during energy restriction in overweight humans. J Nutr. 2009;139(2):222–229. doi: 10.3945/jn.108.095364..
    1. Williamson DA, Bray GA, Ryan DH. Is 5% weight loss a satisfactory criterion to define clinically significant weight loss? Obesity. 2015;23(12):2319–2320. doi: 10.1002/oby.21358.
    1. Imboden MT, Welch WA, Swartz AM, Montoye AHK, Finch HW, Harber MP, et al. Reference standards for body fat measures using GE dual energy x-ray absorptiometry in Caucasian adults. PLoS One. 2017;12(4):e0175110. doi: 10.1371/journal.pone.0175110..
    1. Dallal GE. 2017 [cited 2018 Aug 10]. Available from: .
    1. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr. 2003;77(5):1171–1178. doi: 10.1093/ajcn/77.5.1171.
    1. National Institutes of Health. 2018 [cited 2018]. Diet history questionnaire: web-based DHQ [internet]. 2018. Available from: .
    1. Ajzen I. Constructing a theory of planned behavior questionnaire [internet]. p. 7. Available from:
    1. Blackburn R, Osborn D, Walters K, Falcaro M, Nazareth I, Petersen I. Statin prescribing for people with severe mental illnesses: a staggered cohort study of ‘ real-world ’ impacts. BMJ Open. 2017;7:e013154. doi: 10.1136/bmjopen-2016-013154..
    1. Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015;350:h2147. doi: 10.1136/bmj.h2147..
    1. University of Pittsburgh. 2017 Diabetes prevention program group lifestyle balance™ materials [internet]. Available from: .
    1. Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375(5):454–463. doi: 10.1056/NEJMra1510059.

Source: PubMed

3
購読する