Food4Me Project update: how findings may influence the delivery of personalised nutrition John Mathers

Transcription

1 Food4Me Project update: how findings may influence the delivery of personalised nutrition John Mathers This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration. (Contract n )

2 Introduction Diet has major influence on global burden of ill-health * Nutrition-related problems GBD 2013 Risk Factors Collaborators (2015) Lancet 386,

3 Percentage individuals meeting 5 a day Current strategies have limited success 5 a day in the UK: 32% 30% 28% 30% 29% 26% 24% 22% 23% 24% 24% 25% 28% 27% 26% 26% 27% 20% Source: UK Health and Social Care Information Centre (2012): Health Survey for England , Trend tables 18% Year of survey

4 Introduction Can Personalised Nutrition do better? One size fits all Personalised nutrition

5 Why and how for personalised nutrition? Designed according to key characteristics of individual participants e.g. Socioeconomic characteristics Health status Food preferences Need to collect relevant information about individual participants More personalised means more complexity Digital approaches Celis-Morales C et al. (2015) Proc. Nutr. Soc. 74,

6 The new era of personalised medicine

7 Genotype: lifestyle interactions and heart disease.1 Aim & Methods Research question: To what extent can genetic risk of coronary artery disease be offset by healthy lifestyle? Study populations: 7,814 participants in Atherosclerosis Risk in Communities 21,222 participants in Women s Genome Health Study 22,389 participants in Malmö Diet and Cancer Study Outcome: 10-year incidence of coronary events Khera AV et al. (2016) NEJM November 13, 2016 DOI: /NEJMoa

8 Genotype: lifestyle interactions and heart disease.2 Methods Lifestyle: No smoking BMI<30 PA at least 1/week Healthy dietary pattern fruit, nuts, vegetables, dairy, fish and wholegrains refined grains, red and processed meats, SSB, [trans fats, sodium] Polygenic risk score: Up to 50 SNPs that achieved genome-wide significance in previous studies Individual participant score: No. of risk alleles x sum of literature-based effect size Khera AV et al. (2016) NEJM November 13, 2016 DOI: /NEJMoa

9 Genotype: lifestyle interactions and heart disease. 3 Results Khera AV et al. (2016) NEJM November 13, 2016 DOI: /NEJMoa

10 Genotype: lifestyle interactions and heart disease. 4 Conclusion...Among participants at high genetic risk, a favorable lifestyle was associated with a nearly 50% lower relative risk of coronary artery disease than was an unfavorable lifestyle. Khera AV et al. (2016) NEJM November 13, 2016 DOI: /NEJMoa

11 Introduction Diet-gene interactions influence health Genetic information may: 1. Identify the right diet for you and me (personalisation) 2. Motivate us to adopt healthier eating patterns

12 A Proof of Principle study of Personalised Nutrition across Europe: The Food4Me intervention study This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration. (Contract n )

13 Research questions Is personalised nutrition advice more effective than general healthy eating guidelines? Is phenotypic or genotypic information more effective than diet-based advice alone? Is the internet a successful delivery method? Does more feedback mean more compliance?

14 Recruitment countries Led by John Mathers, Newcastle University 7 recruitment sites 1. University College Dublin (Ireland) 2. Maastricht University (The Netherlands) 3. University of Navarra (Spain) 4. University of Reading (UK) 5. National Food and Nutrition Institute Warsaw (Poland) 6. Harokopio University Athens (Athens) 7. Technische Universitaet Muenchen (Germany)

15 Randomised to 4 treatments Level 0: Generic dietary advice (Control) Level 1: Personalisation based on DIETARY analysis Level 2: Personalisation based on DIETARY + PHENOTYPIC analysis Level 3: Personalisation based on DIETARY + PHENOTYPIC + GENOMIC analysis

16 Study Design summary Level 0 = Control (non-personalised) intervention versus Level 1 = Personalised Nutrition (PN) based on diet only Level 2 = PN based on diet and phenotype Level 3 = PN based on diet, phenotype and genotype

17 Genotype-based dietary advice Gene MTHFR Nutritional issues associated with gene variants Benefit by increasing intake of the vitamin folate. FTO TCF7L2 ApoE4 FADS1 Greater need to maintain a healthy body weight and engage in physical activity. Improved weight loss when following a low fat diet compared to other weight loss diets. Greater need to maintain healthy cholesterol levels, by decreasing saturated fat intake. Benefit by increasing intake of the healthy omega-3 fat found in oily fish.

18 Generating personalised nutrition advice

19 Personalised nutrition improved dietary behaviour 1. All participants Celis-Morales C et al. (2016) Int. J. Epidemiol. doi: /ije/dyw186

20 PN improved dietary behaviour 2. participants receiving targeted advice Celis-Morales C et al. (2016) Int. J. Epidemiol. doi: /ije/dyw186

21 Mediterranean dietary pattern Livingstone KM et al. (2016) Am. J. Clin. Nutr. 104,

22 Med Diet score Mediterranean diet score after 6 months 5.7 P= P= L0 (L1+L2+L3 L1 L2 L3 Livingstone KM et al. (2016) Am. J. Clin. Nutr. 104,

23 Mediterranean diet score after 6 months what changed? P=0.039 P=0.001 Livingstone KM et al. (2016) Am. J. Clin. Nutr. 104,

24 Take home messages Personalised nutrition works No added advantage of phenotypic or genetic information Internet-based delivery is effective

25 Who benefits from Personalised Nutrition?

26 Methodology.1 Personalised nutrition intervention Baseline 6 months Livingstone KM et al. (unpublished)

27 Methodology.2 Most benefit Least benefit Livingstone KM et al. (unpublished)

28 Age (years) Female (%) Who benefits from Personalised Nutrition? Age and sex P< P= Most Least Most Least Livingstone KM et al. (unpublished)

29 Who benefits from Personalised Nutrition? Nationality Bigger benefit: Spain Smaller benefit: Germany The Netherlands Livingstone KM et al. (unpublished)

30 Who benefits from Personalised Nutrition? Aspirations.1 % P<0.002 I want to know what foods are best for me Most Least Livingstone KM et al. (unpublished)

31 Who benefits from Personalised Nutrition? Aspirations.2 % Most P<0.05 Least I want to improve my health Livingstone KM et al. (unpublished)

32 Who benefits from Personalised Nutrition? Aspirations.3 % P<0.012 I want to improve my wellbeing 0 Most Least Livingstone KM et al. (unpublished)

33 Factors that did not matter.1 Genotype FTO APOE MTHFR TCF7L2 FADS1 Livingstone KM et al. (unpublished)

34 Factors that did not matter.2 Lifestyle, occupation, health Livingstone KM et al. (unpublished)

35 How can we deliver more effective PN?.1 Food4Me Current diet Genotype Phenotype

36 How can we deliver more effective PN?.2 Barriers and facilitators Afshin A et al. (2014) in Handbook of Global Health Policy, Wiley-Blackwell, San Francisco

37 Barriers to health eating. 1 Methodology What are the most commonly reported barriers and motivators to healthy eating among adults? Lara J, Yong V & Mathers JC (unpublished)

38 Barriers to health eating. 2 Identified studies Lara J, Yong V & Mathers JC (unpublished)

39 Top 5 perceived barriers to health eating Barrier Time 1 3 Cost 2 1= Knowledge 3 4 Taste 4 1= Cooking skills 5 5 Lara J, Yong V & Mathers JC (unpublished)

40 Potential implementation for more effective PN Participant characteristics Barriers & facilitators Personalised nutrition Selfmonitoring Health & Wellbeing Aspirations etc.

41 Public health implications

42 Should we eat for our genes?

43 Companies already on the market

44 More research on personalised nutrition encourages an increased focus on research that can lead to more individualized advice for promoting health and preventing disease

45 Socioeconomic inequalities in life expectancy Chetty R et al. (2016) JAMA 315,

46 life expectancy for the wealthy in USA: Chetty R et al. (2016) JAMA 315,

47 Health behaviours correlate with wealth Chetty R et al. (2016) JAMA 315,

48 Public health implications? Amid the excitement over personalized medicine, the fact remains that a patient s zip code may be more useful for targeting therapy than his or her genotype Woolf SH & Purnell JQ (2016) JAMA 315,

49 What will Mr Trump do for income (and health) inequalities?

50 Acknowledgments Acknowledgments All participants in the Food4Me study This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration. (Contract n ) Food4Me intervention study team: Newcastle University (UK) Carlos Celis-Morales; Katherine Livingstone; John Matthews; John Mathers University College Dublin (Ireland) Hannah Forster; Clare O Donovan; Clara Woolhead; Eileen Gibney; Lorraine Brennan; Marianne Walsh; Mike Gibney University of Reading (UK) Rosalind Fallaize; Anna Macready; Julie Lovegrove Maastricht University (NL) Cyril Marsaux; Wim Saris University of Navarra (Spain) Santiago Navas-Carretero; Rodrigo San-Cristobal; Alfredo Martinez Harokopio University (Greece) Lydia Tsirigoti; Christina Lambrinou; George Moschonis; Yannis Manios Technische Universität München (Germany) Silvia Kolossa; Jacqueline Hallmann; Hannelore Daniel National Food & Nutrition Institute IZZ (Poland) Magdalena Godlewska; Agnieszka Surwiłło ; Iwona Traczyk University of Oslo (Norway) Christian Drevon