1. www.ucd.ie/foodandhealth Personalised Nutrition Eileen R Gibney UCD Institute of Food and Health

2. 2002: Institute of the Future Palo Alto

3. “The Direct Market Will be Sizeable… Our conservative forecast indicates that at least one third of consumers will be making some changes in their nutrient intake in response to personalised nutrition by 2010”.

4. General healthy eating guidelines Individualised dietary analysis Phenotype (Biochemical profile) Genetic profile Personalised Nutrition Using knowledge to optimise an individuals diet

5. Population dietary advice Gene based individual dietary advice Clustered dietary advice

6. Genetic variation Height Eye Colour Hair Colour Nutrient requirements

7. Nutrient-gene examples Association studies: –Linking a genetic variation to a physical trait FTO gene and body composition TAS2R38 taste receptor and food intake Intervention studies –Where we can demonstrate that response to specific interventions vary according to genotype Weight loss Salt sensitivity Lipid metabolism

8. Association studies Genetic variation in taste –Influence of genotype on food choice –TAS2R38 gene Genetic variation in body composition –Influence of genotype on adiposity levels –FTO gene

9. Genetic variation in TAS2R38? TAS2R38 Bitter receptor gene in FP in tongue 3 SNPs polymorphisms result in amino acid substitutions  At position 49-amino acid encoded is either proline or alanine  At position 262-amino acid encoded is either alanine or valine  At position 296-amino acid encoded is either valine or isoleucine PPAA VV II TastersNon-tasters PPPAPAAA AVAVVV VIVIII Super Tasters Medium- tasters Non- tasters Table 1- amino acids substitutions giving rise to variations Table 2- Taster sub-groups

10. TAS2R38 genetic variation Supertasters V Non / Medium tasters: –More sensitive to taste of sugar –Find fats creamier –Detect / Bitter substances at lower levels Suggested effect on –Fruit and veg intake (bitter) –Fat intake –Alcohol intake Examine effect of genetic variation on habitual food intake (F & V) in Irish children (FIRM 2006-2010) Feeney et al, Proc Nut Soc 2011

11. Anthropometry breakdown Children n 525Adults n 165 Males n 225 Females n 300 Males n 39 Females n 126 MeanS.DMeanS.D.MeanS.D.MeanS.D. Age / years10.481.5610.091.3145.566.2439.579.03 Weight / kg38.9410.7938.2010.5484.3611.7068.9914.62 Height / cm143.6610.96140.729.88176.997.92163.087.63 BMI / kg m -2 18.583.3419.013.3926.963.6825.915.16 Characteristics of participants Feeney et al (in prep)

12. Significant Differences* in Food Group Intake in Children *Denotes significances of p≤0.05 Total children Rice, pasta, grains & starches (NT > MT) Processed potato products (ST > MT & NT) Carrots (NT > ST) Yoghurts (NT > MT) Boys Rice, pasta, grains & starches (NT > MT & ST) Biscuits & cakes (ST > MT & NT) Girls Yoghurts (NT & ST > MT) Fish (MT > ST) High-calorie beverages (ST >MT & NT) Genotype PROP Taster Status O’Brien et al (in prep)

13. Dietary cluster analysis 2-Cluster Solution “High Fruit & Vegetable” and “Low Fruit & Vegetable” Genotype / Taster Status no influence on Cluster Membership “High F&V” cluster sig. higher mean daily intakes of many nutrients O’Brien et al (in prep)

14. 9 year old fat mass via DEXA scan 9 year old lean mass via DEXA scan

15. Intervention studies Examine whether responsiveness to a particular nutrient / diet is influenced by a particular genotype –Weight loss responsiveness –Salt restriction –n-3 PUFA intake

16. Joint Conference - 50th Cardiovascular Disease Epidemiology and Prevention - and - Nutrition, Physical Activity and Metabolism – 2010 Genetic Phenotype Predicts Weight Loss Success: The Right Diet Does Matter

17. 101 Caucasian women on one of 4 diets over one year Low CHO, high protein diet Very low carbohydrate diet Low fat diet Very low fat diet 3 genotypes were tested based on an array of genes Low CHO responsive genotype Low fat diet responsive genotype Balanced diet responsive genotype

19. Salt sensitivity Natural variation in response of BP to changes in salt intake – genetic variation in enzymes responsible for hypertension. Obarzanek et al; Hypertension. 2003 Oct;42(4):459-67.

20. Variation according to genotype of Angiotension gene RR of intervention versus usual care Hunt et al; Hypertension. 1998 Sep;32(3):393-401

21. FP6 Lipgene study “Lipids, genetics & the metabolic syndrome”

22. The Lipgene study 480 subjects WITH the metabolic syndrome 12 weeks dietary intervention Variation in NOS gene SF A MUF A n-6 PUF A n-3 PUF A Type 16126Usua l HSFA 8206Usua l HMUFA 8116Usua l LFHCC 8116+1.24LFHCC n-3

25. Many pathways Multiple enzymes in each pathway Multiple genetic variations in each enzyme Nothing works in isolation Interaction of enzymes and variations due to genetic variation

29. Personalised dietary analysis: A question of balance Ease of use Accuracy

31. UCD Institute of Food and Health –National Nutrition Phenotype Database –JINGO (Joint Irish Nutrigenomic Organisation www.ucd.ie/jingo/) Personalised Nutrition at UCD

32. “Personalised Nutrition: An integrated analysis of opportunities and challenges” €9m 2011-2014 Coordinated by University College Dublin 22 partners

33. A detailed consultative scenario analysis of potential business models An 8 country study of consumer attitudes to all aspects of the use of personalised nutrition A detailed review of available technologies and the development of new software and algorithms for personalised nutrition An internet based proof-of-principle study for all three levels of personalised nutrition in 8 centres (160/centre) A comprehensive communication programme to all stakeholders (web-site, podcasts, citizens juries, workshops) A detailed review of prevailing and upcoming ethical and legal issues

34. 2002: Institute of the Future Palo Alto “The Direct Market Will be Sizeable… Our conservative forecast indicates that at least one third of consumers will be making some changes in their nutrient intake in response to personalised nutrition by 2010”.