www.ucd.ie/foodandhealth

Personalised Nutrition

Eileen R GibneyUCD Institute of Food and Health

2002: Institute of the Future Palo Alto

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”.

Personalised Nutrition

Using knowledge to optimise an individuals diet

Populationdietary advice

Gene based individualdietary advice

Clustereddietary advice

Genetic variation

Height

Eye Colour

Hair Colour

Nutrient requirements

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

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

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

PP AA

AA VV

VV II

Tasters Non-tasters

PP PA AA

AA AV VV

VV VI II

Super Tasters

Medium-tasters

Non-tasters

Table 1- amino acids substitutions giving rise to variations

Table 2- Taster sub-groups

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

Anthropometry breakdown

Children n 525 Adults n 165

Males n 225

Females n 300

Malesn 39

Females n 126

Mean S.D Mean S.D. Mean S.D. Mean S.D.

Age / years 10.48 1.56 10.09 1.31 45.56 6.24 39.57 9.03

Weight / kg 38.94 10.79 38.20 10.54 84.36 11.70 68.99 14.62

Height / cm 143.66 10.96 140.72 9.88 176.99 7.92 163.08 7.63

BMI / kg m-2 18.58 3.34 19.01 3.39 26.96 3.68 25.91 5.16

Characteristics of participants

Feeney et al (in prep)

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)

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)

9 year old fat mass via

DEXA scan

9 year old lean mass via

DEXA scan

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

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

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

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.

Variation according to genotype of Angiotension geneRR of intervention versus usual care

Hunt et al; Hypertension. 1998 Sep;32(3):393-401

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

The Lipgene study

• 480 subjects WITH the metabolic syndrome• 12 weeks dietary intervention• Variation in NOS gene

SFA

MUFA

n-6PUF

A

n-3PUFA

Type

16 12 6 Usual HSFA

8 20 6 Usual HMUFA

8 11 6 Usual LFHCC

8 11 6 +1.24 LFHCC n-3

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

Personalised dietary analysis: A question of balance

Ease of use

Accuracy

• UCD Institute of Food and Health– National Nutrition Phenotype Database

– JINGO • (Joint Irish Nutrigenomic Organisation www.ucd.ie/jingo/)

Personalised Nutrition at UCD

Joint Irish Nutrigenomics Organisation

University College Cork

University College Dublin

Trinity College Dublin

University of Ulster

“Personalised Nutrition: An integrated analysis of opportunities and challenges”

€9m 2011-2014Coordinated by University College Dublin

22 partners

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”.