Biomarkers of nutrition status: recent

advances with metabolomics

Claudine MANACH

Human Nutrition Unit,

INRA Clermont-Ferrand, France

Organised by

The ILSI Europe Nutrition and Mental Performance Task Force

THE PURPOSE OF NUTRITIONAL ASSESSMENT

• Identify population groups who do not have adequate nutrition for

preventing diseases or for optimal performance

• Develop nutrition programs for at risk populations and monitor their

impact

2

Study health

effects of dietary

patterns, foods,

nutrients

?

? Characterize

dietary patterns

of individuals

Assess exposure

to nutrients and

non-nutrients

THE CHALLENGE OF NUTRITIONAL ASSESSMENT

• Still a huge challenge in the post-genomic era:

- Heterogeneity and variability of food choices

- Limited knowledge of the composition of foods beyond the

~60 essential nutrients & best-known food bioactives

• Methods must be accurate, sensitive and applicable to

many populations

• Existing methods:

3

1- Anthropometric methods

2- Clinical examination

3- Questionnaires

4- Biomarkers

• BMI, Hip/waist ratio…

4

1-ANTHROPOMETRIC METHODS

Indicator of adequacy of energy intakes Correlation with diet quality?

• Physical signs associated to severe vitamin or mineral deficiencies

5

2-CLINICAL EXAMINATION

Not for mild deficiencies

Vit D deficiency (Rickets) Vit C deficiency (Scurvy) Iron deficiency (Anemia)

Vit A deficiency (Xeropthalmia)

• Today dietary assessment mainly relies on questionnaire instruments

- Food Frequency Questionnaires

(10-300 foods)

- 24hr recalls

- Food records

• New tools (web-based; digital camera)

6

3- QUESTIONNAIRES

Which food consumed How much How often

Tucker et al., Annu. Rev. Nutr. 2013. 33:349–71

• Recall errors, Misreporting, Difficulty to assess portion size

(literacy problems, desire to reduce the inconvenience of reporting, desire

to be perceived as compliant to a socially acceptable behaviour…)

• Inappropriate for some populations (children, obese people, elderly with cognitive impairment…)

Attenuation of associations with health outcomes

7

3- QUESTIONNAIRES: LIMITATIONS

• Targeted analysis in biofluids (blood, serum, urine, saliva) or tissues (hair, nail, skin, erythrocytes, adipose tissue)

8

4- BIOMARKERS

• Some biomarkers are nutrients or bioactives

and reflect their status or exposure

• Some are used as surrogate biomarkers of

food intake

List of commonly used biomarkers

Doubly labeled water

Urinary nitrogen

Urinary sodium

Urinary sucrose and fructose

Fatty acids (erythrocytes, adip tissue)

Plasma Vitamin C

Plasma carotenoids

Plasma alkylresorcinols

Urine Methylhistidine

TMAO

Urine polyphenols

Total energy expenditure

Protein intake

Sodium intake

Sugar intake

Dietary fats, fatty acids

F&V

F&V

Whole grain wheat and rye

Meat

Fish

Red wine, citrus, tea, soy, olive oil…

Jenab et al., Hum Gen 2009; Perez-Jimenez et al., AJCN 2010; Hedrick et al., Nutr J 2012

• Previous knowledge of a specific compound of the food

• Bioavailability and method of analysis of the specific compound

•

Specificity?

High dependence on factors affecting the biomarker concentration

(food matrix, interindividual variation in absorption and metabolism,

smoking, condition of sampling and storage, analytical method…)

• New biomarkers needed to cover a larger spectrum of foods

9

HYPOTHESIS-DRIVEN APPROACH FOR BIOMARKER

DISCOVERY

One food or food group One compound

• Metabolic profiling of

plasma/urine samples of

consumers and non-consumers

of a given diet/food/nutrient (cohort or intervention studies)

• Comparison with

multivariate statistics

10

METABOLOMICS: A DATA-DRIVEN APPROACH FOR BIOMARKER

DISCOVERY

Food metabolome = All metabolites

that directly derive from digestion and

metabolism of food chemicals

11

WORKFLOW FOR FOOD METABOLOME ANALYSES

3 Identification of discriminating ions

Spectral data (MS/MS,…) Databases, Librairies of spectra Analysis of standards

High Resolution Mass spectrometry or NMR analysis

1 Multivariate statistics (PCA, PLS-DA…)

2

Visualization

Discriminating ions

X samples N groups

Non-targeted method: Detection of as many

compounds as possible

Group 2 Group 1

Plasma, urine, saliva, etc…

HMDB In-house librairies

Pre-processing

• Controlled intervention study with orange juice

12

DISCOVERY OF BIOMARKERS OF FOOD INTAKE

12 volunteers – 4 weeks

500 ml/d Orange juice / Control drink

Usual diet, Cross-over design

24h urine D30; LC-ESI+-Qtof

105 significant ions

Score plot PLSDA

Nootkatone-diol

Limonene-diol Proline betaine

Pujos-Guillot et al., J Proteome Res, 2013

Hesperetin gluc Naringenin gluc

HCA heatmap

m/z 312.21

m/z 144.06 CO

group

OJ

group

m/z 232.09

CO OJ

02

00

04

00

06

00

0

Ion 144

CO OJ

02

00

04

00

06

00

08

00

01

00

00

12

00

0

Ion 312

CO OJ

05

00

10

00

15

00

20

00

25

00

30

00

Ion 303

CO OJ

02

00

40

06

00

80

01

00

01

20

0

Ion 273

CO OJ

01

00

20

03

00

40

05

00

Ion 144.06

CO OJ

05

00

10

00

15

00

Ion 347

CO OJ

05

00

10

00

15

00

Ion 235

CO OJ

05

01

00

15

02

00

25

03

00

Ion 232

CO OR CO OR CO OR

CO OR CO OR CO OR

CO OR CO OR

13

BIOMARKER VALIDATION: PROLINE BETAINE AS AN EXAMPLE

Heinzmann et al., 2010, Lloyd et al., 2011&2013, Pujos-Guillot et al., 2013, May et al., 2013, Andersen et al., 2014

Heinzmann et al., 2010; de Zwart et al., 2003; Slow et al., 2005

Found almost exclusively in citrus fruits, with dominance in orange

Associated with citrus intake in 3 acute studies,

3 medium-term interventions , 3 cohort studies

Detected with NMR, LC-QTof, FIE-MS

In morning spot urines, 24hr urine & post-prandial

urine kinetics

250 ml orange juice challenge

Heinzmann et al., AJCN 2010

Pharmacokinetics data

Training set n=220

Validation set n=279

« Excellent biomarker »

ROC curve

Heinzmann et al., AJCN 2010

Validation in INTERMAP-UK cohort

• Acute or medium-term

intervention studies (4 days-12 weeks; 4-61 subjects)

• >90% used urine samples (Spots, 24hr urines, or kinetics)

• NMR (8 studies), LC-MS (18

studies) or GC-MS (4 studies),

multiplatform analyses (5

studies)

14

DISCOVERY OF BIOMARKERS OF FOOD INTAKE

Citrus, Apple,

Raspberry, Aronia,

Strawberry,

Tomato, Soy,

Cruciferous

vegetables,

Beetroot,

Almonds, Nuts

Cocoa drink

Coffee

Red wine

Grape juice

Black tea, Green

tea

Whole rye grain

Milk, Cheese

Salmon, Cod

21 foods studied

145 candidate biomarkers (75%= phytochemical metabolites)

Scalbert et al., AJCN 2014, 99(6):1286-1308

To be validated

15

Six 24h recalls (1994-2002)

+FFQ 2007-2009

Matched on: Sex Age class (10 ans) Sampling season BMI in 2 classes

Selection of low and high consumers for 20 plant foods or food groups

Correlations between consumptions

Distribution of food consumption

SU.VI.MAX2 sub-cohort (210 M & F; 55-70 y) Coll. S. Hercberg, P. Galan, M. Touvier UREN, Inserm/INRA/CNAM/Paris 13

Comparison of urine metabolomes of low and high consumers

UPLC-ESI-Qtof-MS (pos&neg)

Biobank (One morning spot urine)

PhenoMeNEp project

METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE

BIOMARKERS

16

METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE

BIOMARKERS

Good discrimination for most foods,

especially those consumed frequently

& rich in phytochemicals

High consumers

Low consumers

• Databases are not

complete

• Many food chemical

metabolites are still

unknown

• Standards are lacking

17

IDENTIFICATION OF METABOLITES: THE BOTTLENECK

List of discriminating ions Spectral data

m/z100 200 300 400 500 600 700 800 900 1000

%

0

100

CHZ OR 0-6 473 (14.063) 1: TOF MS ES+ 2.33e3369.0930

347.1316

311.1183153.1078

293.0958

715.2088370.1058

693.2535371.1097

716.2155

Names and chemical structures ?

Databases and librairies of spectra

Analysis of authentic standards

75% of the detected ions are not easily identified

and require additional work (Analytical chemistry + bioinformatics)

BIOMARKERS OF COFFEE INTAKE

• Coffee contains various bioactives implicated with

human health and well-being

• Uncertainties on the effect of coffee on diabetes,

cardiovascular diseases, neurodegenerative diseases,

cancer…

…maybe due to inaccuracy of dietary assessment

• Different methods of preparation: Poor comparability

across studies

• No specific biomarker available

19

BIOMARKERS OF COFFEE INTAKE

QTOf RT ESI m/z Hypothesis P_value VIP Identification

0.7 p 138.052 8.68E-06 2.57

3.6 n

n

181.04

138.033

1-Methyluric acid [M-H]-

1-Methyluric acid [M-H]- loss HCNO

6.48E-06

6.75E-05

2.60

2.43 1-Methyluric acid*

4.4 n 165.045 1-MethylXanthine [M-H]- 8.51E-07 2.71 1-MethylXanthine*

5.3 p 357.104 Theobromine+AnhGlu [M+H]+ 6.75E-05 2.43 Theobromine-Glu

5.6 p 200.108 6.75E-05 2.42

5.8 p

n

n

p

197.068

195.056

180.034

198.071

1,7-Dimethyluric acid [M+H]+

1,7-Dimethyluric acid [M-H]-

1,7-Dimethyluric acid [M+H]+ loss CH3

1,7-Dimethyluric acid [M+H]+ isotope M+1

8.51E-07

6.75E-05

6.75E-05

6.75E-05

2.72

2.42

2.42

2.42

1,7-Dimethyluric

acid*

5.8 p 292.158 6.75E-05 2.41

7.4 p 243.135 1.02E-04 2.38

8.4 p 211.146 1.61E-07 2.81

11.3 n

p

p

p

p

p

p

p

p

495.226

520.225

519.221

321.194

322.202

285.187

303.195

257.191

479.199

7.49E-09

8.53E-09

2.95E-08

1.61E-07

6.50E-07

5.90E-07

8.32E-06

1.58E-04

1.77E-04

2.98

2.96

2.90

2.82

2.78

2.75

2.58

2.34

2.33

11.4 p

p

329.186

527.193

2.29E-06

7.60E-05

2.66

2.40

Not

specific

enough

* Validated with standard

1,3,7-trimethylxanthine mass 194.080376

1, 7-dimethylxanthine 180.064726

3, 7-dimethylxanthine 180.064726

mass 166.049075

166.049075

166.049075

166.049075

196.05964

182.04399

182.04399

196.05964 210.07529

1, 3-dimethylxanthine 180.064726

Many caffeine metabolites

25 very strong discriminating ions

PhenoMeNEp project

BIOMARKERS OF COFFEE INTAKE

QTOf RT ESI m/z Hypothesis P_value VIP Identification

0.7 p 138.052 Trigonelline [M+H]+ 8.68E-06 2.57 Trigonelline*

3.6 n

n

181.04

138.033

1-Methyluric acid [M-H]-

1-Methyluric acid [M-H]- loss HCNO

6.48E-06

6.75E-05

2.60

2.43 1-Methyluric acid*

4.4 n 165.045 1-MethylXanthine [M-H]- 8.51E-07 2.71 1-MethylXanthine*

5.3 p 357.104 Theobromine+AnhGlu [M+H]+ 6.75E-05 2.43 Theobromine-Glu

5.6 p 200.108 Unknown 1 6.75E-05 2.42

5.8 p

n

n

p

197.068

195.056

180.034

198.071

1,7-Dimethyluric acid [M+H]+

1,7-Dimethyluric acid [M-H]-

1,7-Dimethyluric acid [M+H]+ loss CH3

1,7-Dimethyluric acid [M+H]+ isotope M+1

8.51E-07

6.75E-05

6.75E-05

6.75E-05

2.72

2.42

2.42

2.42

1,7-Dimethyluric

acid*

5.8 p 292.158 Unknown 2 6.75E-05 2.41

7.4 p 243.135 Unknown 3 1.02E-04 2.38

8.4 p 211.146 Cyclo(leucyl-prolyl) [M+H]+ 1.61E-07 2.81 Cyclo(isoleucyl-

prolyl)*

11.3 n

p

p

p

p

p

p

p

p

495.226

520.225

519.221

321.194

322.202

285.187

303.195

257.191

479.199

Actractyligenin-AnhGlu [M-H]-

Actractyligenin-AnhGlu [M+Na]+ isotope

M+1

Actractyligenin-AnhGlu [M+Na]+

Actractyligenin [M+H]+

Actractyligenin [M+H]+ isotope M+1

Actractyligenin [M+H]+ loss 2H2O)

Actractyligenin [M+H]+ loss H2O)

Actractyligenin [M+H]+ loss 2H2O and CO

Actractyligenin-AnhGlu [M+H]+ loss H2O

7.49E-09

8.53E-09

2.95E-08

1.61E-07

6.50E-07

5.90E-07

8.32E-06

1.58E-04

1.77E-04

2.98

2.96

2.90

2.82

2.78

2.75

2.58

2.34

2.33

Actractyligenin-Glu

11.4 p

p

329.186

527.193

Kawheol oxide [M+H]+

Kawheol oxide-AnhGlu [M+Na]+

2.29E-06

7.60E-05

2.66

2.40 Kawheol oxide-Glu

Diterpene metabolite

alkaloid

diketopiperazine alkaloid

* Validated with standard

Rothwell et al., Plos One 2014, 9:e93474

21

BIOMARKERS PERFORMANCE (ROC CURVES)

AUC : 0.9-1 : excellent

0.8-0.9 : good

0.7-0.8 : fair

0.6-0.7 : poor

0.5-0.6 : fail

Atractyligenine glucuronide

Cyclo- (Isoleucyl-prolyl) Trigonelline

AUC: 0.979 AUC: 0.968 AUC: 0.937

Caffeine

AUC: 0.808 AUC: 0.796

Hippuric acid

Rothwell et al., Plos One 2014, 9:e93474

22

BIOMARKERS PERFORMANCE (ROC CURVES)

Cyclo(isoleucyl-prolyl)

1-Methylxanthine

Trigonelline

RO

C C

UR

VE

A

UC

Atractyligenin glucuronide

• Combination of markers

increases performance

• Metabolomics provides

multiple biomarkers

Rothwell et al., Plos One 2014, 9:e93474

• Validation required

(quantitative analysis in

other studies)

23

DATA SHARING FOR BIOMARKER VALIDATION

PhenoMeNEp project

IARC, UCD Dublin Univ Barcelona, Univ Copenhagen, Univ Glasgow, …..

Candidate biomarkers

identified in Study A

Correlation with coffee

intake in all available

studies? Raw data Metadata

• Common data repository with metabolic profiles and food intake data

• Consensus on most needed biomarkers

• Develop databases and bioinformatic tools for identification of

unknowns

• Sharing data and resources (SOPs, chemical standards, samples, raw

data…)

• Databasing biomarkers with a new validation scoring system

24

1ST INTERNATIONAL WORSHOP ON THE FOOD METABOLOME

AND BIOMARKERS FOR DIETARY EXPOSURE (GLASGOW 2013)

Define common objectives and priorities

AJCN 2014, 99(6):1286-1308

• An online database for dietary phytochemicals and their human

metabolites

25

PHYTOHUB

(www.phytohub.eu)

Dietary sources

Known metabolites

Predicted metabolites

Spectral data

Physico-chemical data

Links to other databases

1,000 dietary phytochemicals

26

TOWARDS THE USE OF METABOLIC PROFILES FOR NUTRITIONAL

ASSESSMENT

Cohort studies

Socio-economic data,

Anthropometric data

Dietary habits (FFQ), Lifestyle,

Genotype, Gut microbiota,

Biological and Medical data,

Other phenotypic data…

Food metabolome

profiles

Biobank

samples

Standardized Food Metabolome profiling

(Biomarkers + known bioactives+ others) Diet-Health associations

New food bioactives

Adequacy of dietary behaviour?

Detailed information Actual internal exposures of individuals

27

CONCLUSION

• Nutritional exposures are more complex than what has been

covered so far: many non-nutrients to consider

• The Food metabolome contains a wealth of information that

we are just starting to explore with MS-based metabolomics

• Profiling of urine or plasma metabolomes in interventions or

cohorts studies is efficient to discover new candidate

biomarkers of food intake (data-driven approach)

• International collaboration is essential to move forward

(validation)

• Beyond biomarker discovery, food metabolome profiling in

biofluids may become a new method for nutritional

assessment

ACKNOWLEDGMENTS

Edmonton, Canada

Yoann FILLATRE Joe ROTHWELL Mercedes QUINTANA Bruno CHABANAS Christine MORAND André MAZUR

JRU1019- Human Nutrition Unit Serge HERCBERG Pilar GALAN Mathilde TOUVIER Leopold FEZEU

UREN, Inserm/INRA/CNAM/Paris 13

Estelle PUJOS-GUILLOT Charlotte JOLY Bernard LYAN Jean-François MARTIN Frank GIACOMONI

Craig KNOX Roman EISNER

Citrus department of Florida

French National Agency for

Research

Post-doctoral grant

Fundings

THANK YOU FOR YOUR ATTENTION