Application of Nutrigenomics Toward

Personalized Dietary Recommendations

Johanna Lampe, PhD, RD

Division of Public Health Sciences

Fred Hutchinson Cancer Research Center, Seattle, WA

North American Menopause Society Annual Meeting

October12, 2013, Dallas, TX

How can genetic variation influence response to diet?

How can “omic” approaches be incorporated into the study of gene-diet interactions?

How do we move forward to personalized dietary recommendations, now and in the future?

Questions

Nutrigenomics: Study of the interactions between genes and diet.

Study of how genetic variation influences response to diet.

Health &

Disease Risk EPIGENOME

MICROBIOME

Gut bacterial

genome

GENOME

DIET

DNA mRNA Protein

Substrate Product

Down-stream

Effects

Protein Function

e.g., enzyme activity

Transcription Translation

Susceptibility

(e.g., genetic variation)

Nutrigenomic Approaches: Analysis of Gene-Diet Interactions

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5 10 15 20 25 30 35

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5 10 15 20 25 30 35

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a.

16

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c.

b.

5 10 15 20 25 30 35

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Transcriptomics

Proteomics

Metabolomics

Epi/genomics

The human diet is complex.

1000s of compounds

Variety of methods of food preparation Structure and particle size

Bioavailability to host

Food preference

Food tolerance

Absorption

Transport

Metabolism

Effect in target tissue

How can genetic variation

influence response to diet?

Lampe and Potter, in Gene-Environment Interactions (Costa and Eaton, eds), 2006

Metabolism: NAT2 Polymorphism Modifies Dietary-

Induced DNA Damage in Colorectal Mucosa

2-day vegetarian diet vs 2-day grilled meat diet

Measured DNA strand breaks in epithelial cells extracted from stool

samples

Kiss et al, Eur J Cancer Prev, 9:429, 2000

NAT2 Genotype

0

20

40

60

80

NAT2 rapid NAT2 slow

Ta

il m

om

en

t

Vegetarian

baseline

High-meat *

Increased Urinary Mutagenicity with Fried Red

Meat Intake in Individuals with UGT1A1*28

• 2-week controlled

feeding study in 60

subjects

• Fed red meat cooked at

250°C

• Mutagenicity of urine

tested using Salmonella

YG1024 (+S9)

Peters et al, Environ. Mol. Mutagenesis, 2004

P for interaction between UGT1A1 and meat intake = 0.04

Increase in urinary mutagenicity per 10 g meat

intake/day

UGT1A1

genotype

Point

estimate

95% CI P-value

6/6 747 -1166-2660 0.450

6/7 or 7/7 4062 1623-6511 0.003

Unhydrolyzed Urinary Mutagenicity and Cooked Meat:

Stratified by UGT1A1 Genotype

Metabolism of Chemopreventive Phytochemicals:

Isothiocyanates Conjugated by Glutathione

S-Transferases and Excreted in Urine

GST

Dithiocarbamates, excreted in urine

ITC

+

GSH

ITC-

glutathione

ITC-

cysteine-

glycine

ITC-

cysteine

ITC-

N-acetylcysteine

g-GT CG AT

GSTM1-null subjects had:

Greater rate of urinary excretion of sulforaphane in first 6 h

Higher % sulforaphane excretion over 24 h

Impact of GSTM1 Polymorphism

on Sulforaphane Pharmacokinetics

0

20

40

60

80

100

120

0 4 8 12 16 20 24

Time (h)

Uri

na

ry e

xc

reti

on

(%

)

GSTM1-null

GSTM1+

Gasper et al, Am J Clin Nutr, 2005

Integrating Genomics and Metabolomics:

A Cross-Sectional Study

284 men

GWA study data with serum

metabolomics-based quantitation of 363

metabolites.

Reported associations of frequent SNP

with differences in the metabolic

homoeostasis, explaining up to 12% of

observed variance.

Gieger C et al, PLoS Genet , 2008.

Metabolomics

Epi/genomics

Genotypes and Metabotypes:

Endogenous Metabolite-SNP Interactions

Gieger C et al, PLoS Genet , 2008.

• Associations of frequent SNP with

differences in the metabolic

homoeostasis explained up to 12%

of observed variance.

• Using ratios of certain metabolite

concentrations as proxy for

enzymatic activity, explained up to

28% of the variance (P-values 10-

16–10-21).

• Identified 4 variants in genes

coding for lipid metabolism

enzymes (FADS1, LIPC, SCAD

and MCAD), where corresponding

metabolic phenotype matched

pathways in which these enzymes

are active.

Genetically Determined Metabotypes:

A GWA study of metabolic traits in human urine

• Designed to investigate the

detoxification capacity of human body.

• Tested for associations between 59

metabolites in urine from 862 male

participants in the SHIP study and

replicated the results in independent

samples.

• Reported 5 loci with joint P values of

association from 3.2 × 10−19 to 2.1 ×

10−182. Variants at 3 loci previously

linked with important clinical outcomes:

SLC7A9 is a risk locus for chronic

kidney disease, NAT2 for coronary

artery disease and genotype-

dependent response to drug toxicity,

and SLC6A20 for iminoglycinuria.

Moreover, we identify rs37369 in

AGXT2 as the genetic basis of hyper-

-aminoisobutyric aciduria.

Suhre et al, Nat Genet 43: 565–69, 2011.

Gut microbial variation:

Metabolism of soy isoflavone daidzein

O

O

HO

OH

OHO

OH

O

O

HO

OH

OH

O

HO

OH

OHO

OHOH

Daidzein Dihydrodaidzein

EquolO-Desmethylangolensin

Cis/Trans-isoflavan-4-ol

1

100

10000

equol

30-50% of

individuals

produce

Urinary Equol Excretion

nmol/d

INTERVENTION: Lymphocyte Gene Expression Differentially

Induced in Equol-Producing and Nonproducing Women

30 postmenopausal women

~900 mg isoflavones for 84 d

Gene expression array of peripheral lymphocytes

27 genes changed with isoflavones

Stronger effect on estrogen-responsive genes in equol producers than nonproducers.

Niculescu et al, J Nutr Biochem 18:380, 2007.

Gut Microbiome Phenotypes: Enterotypes

Arumugam et al, Nature, 2011

View to the Future

Focusing efforts in areas where evidence is

suggestive, but inconclusive, most likely to

result in findings that can advance scientific

knowledge or change public health practice

Understand the mechanisms

Reconcile the heterogeneity

Application of genomic and systems approaches to

understand:

Biologic pathways and mechanisms

Complexity of effects of dietary patterns

Behavior

Ultimate Goal of Metabotyping

• Develop a novel approach to personalized health care

based on a combination of genotyping and metabolic

characterization.

• Identify genetically determined metabotypes that can

subscribe the risk for a certain medical phenotype, the

response to a given drug treatment, or the reaction to

a nutritional intervention or environmental challenge.

• Characterize microbial modification of effects of diet.

Gieger et al, PLoS Genet , 2008

Lampe et al, Proc Nutr Soc, 2013

What do we need to get there?

Sufficiently sensitive technologies to detect small, but physiologically relevant, differences or changes in response to diet.

Data analysis, visualization, and annotation methods.

Ability to integrate the various omics platforms in a systematic fashion.

Characterization of phenotypes.

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RP

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PB

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Pa

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ipa

nts

5 10 15 20 25 30 35

60

50

40

30

20

10

Pa

rtic

ipa

nts

5 10 15 20 25 30 35

60

50

40

30

20

10

a.

16

10

5 10 15 20 25 30 35

16

10

5 10 15 20 25 30 35

c.

b.

5 10 15 20 25 30 35

20

10

5 10 15 20 25 30 35

20

10

Transcriptomics

Proteomics

Metabolomics

SNP arrays

Is there potential for personalized, or more

precise, individual dietary recommendations?

Yes, but still have to: Establish the relevant parameters

Integrate the omics data

Don’t lose sight of the broader public health

messages that can have the greatest impact

on the largest number of people