Nutrigenetics: What Does the Future Hold?Peter J Jones, PhD

Richardson Centre for Functional Foods and Nutraceuticals

Departments of Human Nutritional Sciences and Food Sciences

University of Manitoba, Winnipeg, Manitoba, Canada

ABIC Meeting

Winnipeg, Manitoba

September 26th, 2017

Sept 26th, 2017

Objectives

• Explore whether genetic factors play a role in explaining the variability of responsiveness of disease risk related biomarkers

• Case studies: plant sterols/ dairy consumption

• Consider whether sufficient evidence exists for development of “personalized nutrition” platforms

Case Study: Plant sterols as natural inhibitors of

cholesterol absorption

Average daily plant sterol intake of adults

150 - 400 mg/day

Major food sources:

• fruits and vegetables

• fat and oils

• bread and cereals

• nuts

* International Atherosclerosis Society, 2003; NCEP III Expert Panel, JAMA 2001, EAS 2014

Recommended intake of

plant sterol-enriched foods

for a significant

cholesterol-lowering effect

2 g/day*

Plant sterol/ stanol meta-analyses results show

high variability between trials

Demonty et al. J Nutr 2009 Musa-Veloso et al. PLEFA 2011

Maximal average LDL-C lowering,

PSa vs PSe:17.3 vs 8.4%*

Pooled LDL-C reduction,2.15 g/d

-8.8% CV =(9.4, 8.3)

84 trials, 141 trial arms

114 trials, 182 trial arms

Large variability exists in LDL-C response to

plant sterols within trials

Rideout et al Lipids Health Dis 2009

Healthy individuals were given 2 g/d plant sterols for 4 wksconsumed under supervision

Large variability exists in LDL-C response to

plant sterols within trials

Rideout et al Lipids Health Dis 2009

Healthy individuals were given 2 g/d plant sterols for 4 wksconsumed under supervision

Is response to plant sterols repeatable?

Percentage Reduction Low-Density Lipoprotein

Cholesterol Compared Control

R2 = 0.7168

-30

-20

-10

0

10

20

30

40

50

-30 -20 -10 0 10 20 30 40 50

Sterol Intake Phase 1

(% Reduction)

Ste

ro

l In

ta

ke

Ph

as

e 2

(%

Re

du

ctio

n)

Percentage Reduction in Total Cholesterol

Compared to Control

R2 = 0.6298

-20

-10

0

10

20

30

40

-20 -10 0 10 20 30 40

Sterol Intake Phase 1

(% Reduction)

Ste

ro

l In

ta

ke

Ph

as

e 2

(%

Re

du

ctio

n)

Rudkowska I, Abumweis SS, Nicolle C, Jones PJ. Association between non-responsiveness to plant sterol intervention and polymorphisms in cholesterol metabolism genes: A case-control study. Appl Physiol Nutr Metab 2008;33:728-34.

Plant sterol response is a phenotype

Objective: Are genetics associated with

cholesterol metabolism predictive of

cholesterol lowering in response to

plant sterol consumption ?

Collaborator: David Baer, USDA, Beltsville

Dual center (Winnipeg, Beltsville), randomized, crossover, single blinded, placebo controlled design

Plant sterol study design

Mackay et al AJCN 2015a

N = 130 65

Lathosterol/cholesterol ratio and LDL-C

response to plant sterol consumption

Low L/C ratio-0.29 ± 0.05 mmol/L, p<0.0001

High L/C ratio-0.04 ± 0.07 mmol/L, p=0.4904

-1.5

-1.0

-0.5

0.0

0.5

1.0

L

DL

-c (

mm

ol/

L)

High L/C ratio=Low L/C ratio= Mackay et al AJCN 2015a

Gene

SNP

Type of SNP Variation Minor allelic frequency Function of gene

ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 Cholesterol efflux pump in the cellular lipid removal pathway.

rs2230808 NS - missense C to T T=0.410

rs2066714 NS - missense T to C C=0.366

ABCG5 ATP-binding cassette, sub-family G (WHITE), member 5 Half-transporter (with ABCG8) that promotes intestinal and biliary

excretion of sterols.rs6720173 NS - missense G to C C=0.211

rs6756629 NS - missense G to A A=0.074

rs2278356 3’-UTR A to C C=0.403

rs11887534 Near gene-5’ G to C C=0.065

ABCG8 ATP-binding cassette, sub-family G (WHITE), member 8 Half-transporter (with ABCG5) that promotes intestinal and biliary

excretion of sterols.rs4148211 NS - missense A to G G=0.441

rs4148217 NS - missense C to A A=0.218

rs6544718 NS - missense C to T T=0.106

CETP Cholesteryl ester transfer protein Facilitates the transport of cholesteryl esters and triglycerides between the

lipoproteinsrs5882 NS - missense A to G G=0.448

CYP7A1 Cholesterol 7 alpha-hydroxylase The rate-limiting enzyme in the synthesis of bile acid in the classic

pathway.rs3808607 Near gene-5’ T to G G=0.450

DHCR7 7-dehydrocholesterol reductase Enzyme which catalyzes the conversion of 7-dehydrocholesterol to

cholesterol.rs760241 NS - missense G to A A=0.179

LDLR low density lipoprotein receptor A cell surface protein involved in receptor-mediated endocytosis.

rs688 Synonymous A to G G=0.280

LSS Lanosterol synthase The protein encoded by this gene catalyzes the conversion of (S)-2,3

oxidosqualene to lanosterol.rs2839158 NS - missense C to T T=0.137

rs34115287 NS - missense T to C C=0.136

PCSK9 Proprotein convertase subtilisin/kexin type 9 A convertase belonging to the proteinase K subfamily which induces

LDLR degradationrs562556 NS - missense A to G G=0.148

SCAP SREBF chaperone a protein with a sterol sensing domain which is involved in SREBFs

regulation rs12487736 NS - missense C to T T=0.476

SREBF2 Sterol regulatory element binding transcription factor 2 Transcription factor that controls cholesterol homeostasis by stimulating

transcription of sterol-regulated genesrs2228314 NS - missense G to C C=0.366

rs2228313 NS - missense G to C C=0.066

ApoE variant Ε2 Ε3 Ε4 Apolipoprotein E is a glycoprotein present in human plasma; ApoE is

associated with triglyceride-rich lipoproteins (chylomicrons and VLDLs)

and HDL.Typical

frequency*

7.9% 78.6% 13.5%

APOE isoform and rs38038607 in CYP7A1

on LDL-C response to plant sterols

-1.5

-1.0

-0.5

0.0

0.5

1.0

L

DL

-c (

mm

ol/

L)

T/T=G/T=G/G=

ε2 ε3 ε4

MacKay et al AJCN 2015b

Canada’s Food Guiderecommends 2-3 servings/d of low-fat milk and alternatives

Unique fatty acid profile

Variability exists across studies in response of cholesterol metabolism to dairy consumption

Results for butter are consistent (Tholstrup et al. 2004, Nestel et al. 2005)

Milk, fermented products, and cheese data are less conclusive(St-Onge et al. 2000, Nestel 2008, Ohlsson 2010)

Dairy Fat Efficacy Assessment StudyCollaborator Benoit Lamarche, Laval University

Contribution of genomic architecture to such observations has yet to be examined

Study visitDiet. recommendations

Diet. counselingAnthropometry

Blood sample3D FR+PA, and FFQ

24hr recallEndoPAT

DXACholesterol absorption

and biosynthesis

2 3 4Wk 0 61 5-1-2 7 8 9 10 11 12Sc

ree

nin

g

Run-in

CONTROL

DAIRY

DAIRY

CONTROL

Washout

Dairy randomized cross-over study design

Clinical assessmentn = 337

Eligiblen = 212

Excluded (n = 125)Did not meet inclusion criteria

Randomizedn = 137

Completed studyn = 124

Declined (n = 75)Chose not to participateor could not becontacted

Dropped out (n = 13)Never started, n = 7Protocol too demanding, n = 6

RCFFN, Winnipeg (n = 68)INAF, Quebec City (n = 69)

RCFFN, Winnipeg (n = 64)INAF, Quebec City (n = 60)

Genotypedn = 99

RCFFN, Winnipeg (n = 41)INAF, Quebec City (n = 58)

Could not be contacted to provideconsent for genotyping (n = 23)

Telephone screeningn = 623

Eligiblen = 346

Declined (n = 9)Time commitment

Excluded (n = 277)Did not meet inclusion criteria

Recruitment

chart

Abdullah et al. J Nutr. 2016

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

TC LDL-C

mm

ol/

L

Range: -1.10-1.15 mmol/L Range: -0.77-1.03 mmol/L

Changes from control of total (TC) and LDL

cholesterol (LDL-C) in response to dairy (n=99)

Abdullah et al. J Nutr. 2016

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

TC LDL-C

Δ, m

mo

l/L

GG + G + AGG + G + GGGG + TT + AGG + TT + GGC + G + AC + G + GGC + TT + AC + TT + GG

a

a

a

aa

a

a

aa

b

b

ab

ab

ab abab

Responses of serum total cholesterol and LDL-cholesterol concentrations to the 4-wk dairy diet compared with control in healthy adults stratified by ABCG5 rs6720173 + CYP7A1 rs3808607 + DHCR7 rs760241 genotypes (n = 7 rs6720173-GG + rs3808607-G + rs760241-A, n = 42 rs6720173-GG + rs3808607-G + rs760241-GG, n = 5 rs6720173-GG + rs3808607-TT + rs760241-A, n = 17 rs6720173-GG + rs3808607-TT + rs760241-GG, n = 6 rs6720173-C + rs3808607-G + rs760241-A, n = 11 rs6720173-C + rs3808607-G + rs760241-GG, n = 2 rs6720173-C + rs3808607-TT + rs760241-A, n = 9 rs6720173-C + rs3808607-TT + rs760241-GG). Values are least-square means ± SEMs. Effects of treatment by combined genotypes were assessed by using SAS ANCOVA, with gender as a fixed effect, and Tukey-Kramer adjustments. Labeled means without a common superscript letter are significantly different, P ≤ 0.05. ABCG5, ATP-binding cassette sub-family G member 5; CYP7A1, cholesterol 7 a-hydroxylase; DHCR7, 7-dehydrocholesterol reductase; LDL-C, LDL-cholesterol; TC, total cholesterol; Δ, change in cholesterol [post-diet (wk 4) dairy minus post-diet (wk 4) control values].

Response of Total and LDL-C to Dairy Intake as a Function of Three SNPs Working in Combination

(ABCG5 rs6720173, CYP7A1 rs3808607, DHCR7 rs760241)

• Responses of serum cholesterol to dairy fat product intake associate with genetic variants related to cholesterol metabolism

• In particular, common variants of ABCG5, CYP7A1, and DHCR7 genes may, individually and in combination, be involved in determining responses of serum cholesterol to the recommended level of dairy fat intake

Dairy Trial Conclusions

What other evidence exists in support

of genetic mechanisms explaining

inter-individual differences in

responsiveness to dietary bioactives?

n = 173 articles of ‘cholesterol-related gene-diet

interaction’ identified between 2003 and 2013

n = 69 observational and dietary intervention

articles included in the review

n = 49 articles (including 29 genes and 54

SNPs) showed significant gene-diet interactions

n = 104 articles

excluded; did not

investigate fasting

cholesterol response, did

not provide clear

associations, provided

limited research details,

review articles, animal

studies, and non-English

language

n = 20 articles showed no

significant cholesterol-

related gene-diet

interactions

2015

Reference Gene/SNP Design Diet Cholesterol effect

Viturro et al. 2006ABCG5

rs6720173

Cross-

sectional

14.3-34.1 g/d SFA

intake

↑ TC & LDL-C in

C>G vs. C/C male

carriers

De Castro-Oros

et al. 2010

CYP7A1

rs3808607Parallel

2 and 3.2 g/d PS

esters

↓ TC in G allele vs.

T/T carriers

Tucker

et al. 2010

APOE

E2, E3, E4Crossover

2/d whole grain

wheat or white

bread

↑ LDL-C in E3/E3

carriers with

whole grain vs.

white bread intake

Tipping point has been reached in understanding

that genetics play a role in determining the degree of

responsiveness of biomarkers to diet intervention…

Adapted from Abdullah et al. 2015

Impac

t on P

hen

oty

pe

Common

with smaller impact

Allele Frequency

Common

with large impact

Low

with moderate impact

Rare

with smaller impact

Rare

with large impact

<0.1% >5.0%

ABCG8 rs4148217-A

+5

+4

+3

+2+1

■

■

■

♦ABCG8 p.Pro231Thr♦

ABCG8 p.Arg263Gln

ABCG8 p.Gly574Arg♦

ABCA1 rs9282541-CABCA1 rs9282541-C

ABCG1 rs448102-A

ABCG1 rs448102-A

ABCG8 rs4148217-A■

ADIPOQ rs1501299-GADIPOQ rs1501299-T

APOA1 rs670-G

APOA1 rs1799837-G

APOA1 rs670-G APOA5 rs3135506-G

APOC3 rs5128-G

APOC3 rs202197102-T

APOC3 rs5128-C

APOE E2

APOE E2

APOE E2■

APOE E2 APOE E4■

■

CYP7A1 rs3808607-C ■

NPC1L1 rs2072183-G/rs10264715-A ■

■

■

■■

■■

■■

■

■

■

ABCG8 rs6544718-C

■

■

■

Abdullah et al. 2015

Allele frequency – phenotypic impact relationship

Impac

t on P

hen

oty

pe

Common

with smaller impact

Allele Frequency

Common

with large impact

Low

with moderate impact

Rare

with smaller impact

Rare

with large impact

<0.1% >5.0%

ABCG8 rs4148217-A

+5

+4

+3

+2+1

■

■

■

♦ABCG8 p.Pro231Thr♦

ABCG8 p.Arg263Gln

ABCG8 p.Gly574Arg♦

ABCA1 rs9282541-CABCA1 rs9282541-C

ABCG1 rs448102-A

ABCG1 rs448102-A

ABCG8 rs4148217-A■

ADIPOQ rs1501299-GADIPOQ rs1501299-T

APOA1 rs670-G

APOA1 rs1799837-G

APOA1 rs670-G APOA5 rs3135506-G

APOC3 rs5128-G

APOC3 rs202197102-T

APOC3 rs5128-C

APOE E2

APOE E2

APOE E2■

APOE E2 APOE E4■

■

CYP7A1 rs3808607-C ■

NPC1L1 rs2072183-G/rs10264715-A ■

■

■

■■

■■

■■

■

■

■

ABCG8 rs6544718-C

■

■

■

Abdullah et al. 2015

“The majority of current gene-diet interactions are

associated with common variants. Through epistatic

interactions of multiple common variants, effect sizes

could dramatically increase while the frequency for

each combination decreases”

Predicting biomarker response based on multiple SNPs

Majority of PopulationPossess Random SNP

Allocation

Mild Responsiveness

Minority of PopulationPossess Positive SNP Allocation

High Responsiveness

Minority of PopulationPossess Negative SNP Allocation

Non / Adverse Responsiveness

Gene-nutrient interactions can be seen in smaller nutritional intervention trials provided:

-Nutrient/ compliance is well controlled

-Response is accurately phenotyped

-Biological impact of SNP is large enough

A place exists given current knowledge for development of single and multiple SNP functional tests to distinguish between responders vs non-responders for dietary bioactives

Summary

AcknowledgementsMohammad AbdullahNancy AmesDavid BaerIsabelle DemontyDylan MacKayDavid PuTodd RideoutIwona RudkowskaSylvia SantosaPeter SujithElke TrautweinKrista VaradyYanan WangHailin Zhao

Canadian Institute for Health ResearchNatural Sciences and Engineering Research CouncilForbes Medi-Tech IncUnilever IncDanone IncWhitewave Inc

•Richardson Centre for Functional Foods and Nutraceuticals

•SmartPark, University of Manitoba, Winnipeg

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