Can we track what we ate from our blood? From our pee? The Nutri-Markers team from UCD are exploring this as part of the UCD-Nutrimarkers study, gaining a deeper understanding of the link between nutrition and health. The potential for this is huge - no more food diaries for a start. The science of nutrigenomics, metabolomics, is still very much in it's infancy, with advanced testing with DNA and blood analysis. You can find out more on the UCD project on their website - http://www.ucdnutrimarkers.com/

1. www.ucd.ie/foodandhealth
Nutrition Biomarkers and Health
Prof Lorraine Brennan
School of Agriculture & Food Science
UCD Conway Institute
UCD, Belfield, Dublin 4, Ireland

2. Outline
Personalised Nutrition

3. Challenge
• What did you eat yesterday?
• What was the portion size?
• Can you describe your habitual diet?

4. The need for Dietary Biomarkers
Limitations:
• Measurements over short periods of time may be
unrepresentative
• Recall of eating behaviour can be difficult
• Errors in reporting intakes
• Recording process alters dietary habits
• Difficult to validate
Food diaries, FFQ, 24 hr recalls

5.  Metabolomics is the study of metabolites present in biological
samples
 aims to study alterations in metabolism and metabolic
pathways under different conditions
 aims to profile all of the metabolites present in the samples:
the metabolome.
 metabolites represent biological end points and are now
implicated in the development of a number of human
diseases
What is metabolomics?

6. Introduction to metabolomics
Metabolomics: large amount of metabolites, multivariate
data analysis
NMR
LC-MS
GC-MS
Traditional metabolic studies: targeted specific pathways
univariate statistics

7. 1. Biomarkers of Dietary Intake
Metabolomics in nutrition

8. Strategies for biomarker discovery
Acute intervention with
specific foods
metabolomics
Biomarker Identification
Biomarker assessment in
cohort
Prior knowledge:
database

9. Examples of biomarkers
Heinzman et al AJCN 2010

10. Examples of biomarkers

11. Our recent discoveries
Biomarkers of
- Citrus fruit
- Red meat
- White meat
- Dairy intake
- SSB
Examples of on-going work:
Biomarkers of
- Legumes
- Carrots
- Fruit (apples)
- Cake
- Dietary Fat
- …….

12. Markers
Health/
Disease Risk
Factors
Biomarkers reflective of dietary
intake
Compliance
Dietary Intake

13. Link with Quantified Self?

14. Precision Medicine
Precision Nutrition
Metabolomics based biomarkers
image courtesy of Metabolon

15. Is PN advice more effective than conventional healthy eating
guidelines?
Develop a method for delivery of PN remotely

16. 22
0
22
0
22
0
22
022
0
22
0
22
0
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)
Coordinated by Newcastle
University
Courtesy John Mathers, UNew
Proof of Principle Study

17. Level 0 Level 1 PN Level 2 PN Level 3 PN
• Generic
healthy eating
guidelines
• Dietary intake
• Dietary intake
• Phenotype
• Dietary intake
• Phenotype
• Genotype
Generic Personalised
Baseline Month 3 Month 6Month 2Month 1
Dietary data   
Physical activity   
Blood sample   
DNA sample 

18. Collection of dietary data

19. Collection of Phenotypic Data
Vitas

20. Metabolite measurements on DBS
Examples
Fatty acids (%)
Myristic acid C14:0
Pentadecyclic acid C15:0
Palmitic acid C16:0
Stearic acid C18:0
cisVaccenic acid C18:1 cis
Oleic acid C18:1
Total saturated fat*
Omega 3 index**
Linoleic acid C18:2 n6
Arachidonic acid C20:4 n6
Carotenoids (µM)
aCaroten
bCaroten
bCryptoxanthin
Lutein
Lycopene
Zeaxanthin

21. Collection of genetic data

22. • Health and lifestyle
questionnaires
• Physical activity
Collection of Data

23. Algorithms for Decision Trees
(Females <88 cm; Males <102cm)
< 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8 < 5 5 to 8 >8
Note:
* Glucose and Cholesterol are mesured from blood and expressed in mmol units
Cholesterol: Low <5 ; Normal 5 to 8; High >8 mmol
Glucose: Low <6.1 ; Normal 6.1 to 7.0 ; High >7.0
Sedentary Lightly Active Active
Check Glucose levels? Check Glucose levels? Check Glucose levels?
FTO (rs9939609)
Body weight
Check Physical Activity level?
Carriers of the Risk Variant (AA or TA)
Check BMI
Normal Waist Circumference
Underweight (BMI <18.5 kg.m2)
>7.0
Check cholesterol levels? Check cholesterol levels? Check cholesterol levels? Check cholesterol levels? Check cholesterol levels? Check cholesterol levels? Check cholesterol levels?
<6.1 6.1 to 7.0 >7.0 <6.1 6.1 to 7.0 >7.0 <6.1 6.1 to 7.0
MessageNo
L3.1.009
MessageNo
L3.1.010
MessageNo
L3.1.011
MessageNo
L3.1.012
MessageNo
L3.1.013
MessageNo
3.1.014
Check cholesterol levels? Check cholesterol levels?
MessageNo
L3.1.001
MessageNo
L3.1.002
MessageNo
L3.1.003
MessageNo
L3.1.004
MessageNo
L3.1.005
MessageNo
L3.1.006
MessageNo
L3.1.007
MessageNo
L3.1.008
MessageNo
L3.1.027
MessageNo
L3.1.021
MessageNo
L3.1.022
MessageNo
L3.1.023
MessageNo
L3.1.024
MessageNo
L3.1.025
MessageNo
L3.1.026
MessageNo
L3.1.015
MessageNo
L3.1.016
MessageNo
L3.1.017
MessageNo
L3.1.018
MessageNo
L3.1.019
MessageNo
L3.1.020
Gene (SNP) (2)
Body weight (3)
Waist (2)
Physical activity (3)
Glucose level (3)
Cholesterol levels (3)
Normal weight

24. Primary Outcomes
Participants ate significantly healthier diets after
receiving PN compared with the control group

25. Future Opportunities
More detailed profiling
diet markers
disease risk markers
Personalise Dietary
Advice

26. Beger et al Metabolomics 2016

27. Overall Conclusions
Personalised Nutrition Advice

28. Metabolomics team
A McNamara, Dr A O Gorman
H Gibbons, Dr K Horner, C
Erraught’ X Yin, V O Sullivan
Dr Harsha Pepadti, Dr L Kirwan
K Li, R AbudulWahab, A Curran
J Davison, Dr O Grant, T Alegare,
Dr M Clarke,
Collaborators
Nutritech partners: G Frost, Imperial College
JINGO partners
Food4me partners (M Gibney, E Gibney, H Roche)