Risk stratification using genomes and lifestyle

Samuli Ripatti, Prof, PhD

Public Health, Faculty of Medicine, University of HelsinkiInstitute for Molecular Medicine Finland (FIMM)

Wellcome Trust Sanger Institute, UK

Causes of death in Finland

Suomen virallinen tilasto: Kuolemansyyt 2010

Cardiovascular

diseases

Tumors

Dementia,

Alzheimer’s disease

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CHD prevention based on traditional risk

factors

2008 guidelines: statin

treatment for those >20% risk

Problem: 83% of cases have

risk < 20%

74% of those with risk >20%

do not get the disease (in 10

years)

4

Risk distribution

Fre

qu

ency

0.0 0.2 0.4 0.6 0.8 1.0

01

000

2000

30

00

40

00

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Risk distribution

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

0100

02

000

30

00

400

0

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40% of cases have risk

< 7.5%

83% of those with risk

>7.5% do not get the

disease (in 10 years)

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Risk distribution

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

0100

02

000

30

00

400

0

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Genome-wide studies have provided

unprecedented information about genetic

background of complex diseases and traits

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How to use the genome in prevention?

1. Database: Large-scale prospective data with genomic screen

and follow-up recordings of health

2. Algorithms: to estimate the personalized risks

3. Apps: to communicate the risk to individuals

› Possibility to lower the risks through intervention

1

0

Aarno Palotie

Sequencing Initiative Suomi

Type 2 Diabetes

> 10 000 individuals

High Blood pressure

Severe mental health:

- schizophrenia, depression

- > 5000 individuals

Migraine15 000 individuals

Old age dementia, ~ 5000 individuals

Cancer

> 10 000 cases

Life style and socio-economic data

- education, economic state, smoking

Cardiovascular events

stroke, CHD25 000 individuals

Life course events

Prescription medication data18 000 statin users

20 000 estrogen substitution th

Cause of death data

Cardiovascular risk factor data100 000 individuals

National Biobank

201 858 individuals

National Biobanks Finland130 000 individuals from population cohorts

70 000 individuals from disease collections

Social security number

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40 year health event follow-up through health registries

SISU-projectSequencing Initiative Suomi

National

Biobank

Disease genes

discovery

Extensive health,

phenotype,

metabolomic data

Population

cohorts

Disease specific

collections

Genome wide genotype

data

70,000

Genome/exome sequences

~ 16,000

more than double in a year

200 000 individuals

4% of the population

Variation

reference/Imputation

The 200K

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Apply to an individual patient

SISu provides a reference

database of genomic variation

www.sisuproject.fi

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LPA loss-of-function variant protecting

from CHD

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Rare high-impact vs. common low-impact variants

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Single rare variant

dominating the risk

(>5×risk, >0.5sd)

Multiple genetic and other risk

factors contributing (<2×risk,

<0.2sd)

Growing evidence

for full range of

variants

contributing to the

risks of common

diseases

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Low frequency and functional

candidates contribute to lipid variances

Ida SurakkaHDL-C LDL-C TC TG

Trait variance explained in FRCoreExome9702 cohort

Varian

ce e

xpla

ine

d (

r2)

0.0

00

.05

0.1

00

.15

0.2

0

Adding low frequency lead SNPs

Common SNPs

FL SNPs and new

functional candidate SNPs0.128

0.1950.188

0.093

0.119

0.163 0.162

0.082

0.021

0.067

0.056

0.027

Surakka 2015 Nat Genet

The evidence:

• Familial hypercholesterolemia (FH) although frequent is underdiagnosed

Some of the most centralized health care

systems worldwide NL, NO ,IS, UK

Finland:10,000 – 20,000 undiagnosedFH cases

Nordenstgaard et al Eur Heart J 2013

Risk factor distributions overlapping

Framingham risk score at baseline:

age, sex, total cholesterol, HDL, BMI, systolic blood pressure,

blood pressure treatment,

current smoking status, diabetes mellitus, family history of CHD

Incident CHD cases

Ripatti Lancet 2010

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CHD risk variants

22CARDIoGRAMplusC4D Consortium, Nat Genet 2013

Predicting heart disease risk with genetic risk scores

Incident cases

Non-cases

Refining the risk

estimates using

genetic data Ripatti Lancet 2010Tikkanen ATVB 2013

Stage 2: Screeningbased on genetic risk score

Stage 1: Screening for Framingham risk factors

100,000 screened

>20% Risk

N=16169

- 3745 cases

- 12423 non-cases

Assume treated

10-20% Risk

N=18966

- 2481 cases

- 16485 non-cases

Assume no treatment,

test GRS

>20% Risk

N=2196

- 694 cases

- 1502 non-cases

10-20% Risk

N=13545

- 1630 cases

- 11915 non-cases

<10% Risk

N=3225

- 157 cases

- 3069 non-cases

<10% Risk

N=64865

- 2458 cases

- 62408 non-cases

Assume no treatment norfurther testing

Assuming:- 20% risk reduction with statin treatment- Treatment compliance and efficacy similar in Stage 1 and Stage 2 high risk groups

694*0.2 = 139 cases prevented in 14 years / 100,000 individuals (119 in 10 years) Compared to 17 for lp(a) (Di Angelantonio, JAMA 2012) and 30 for CRP (Kaptoge, NEJM

2012)

Statin treatment

added

Tikkanen et al ATVB 2013

12% reclassified,28% of cases

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Examples

› Using GRS on top of

traditional risk factors

25

58-year-old female

AGE

58 Baseline examinationTotal cholesterol 5.0HDL cholesterol 1.4Systolic blood pressure 169, treatedNon-smokerNo diabetesNo family history of CVD

CHD risk 13.7%High genetic risk score

CHD risk 21%

70 S422 Fracture of upper end of humerus

73 N179 Acute kidney failure, unspecifiedI2141 Non-ST elevation (NSTEMI) myocardial infarctionI509 Heart failure, unspecified

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Using genome-wide marker data for

prediction

Abraham, under review

Training with Cardiogram+C4D

Testing with Finrisk

Replicating in independent

cohorts

C index

41,000 SNPs

28 SNPs

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KardioKompassi: communicating risk to citizens

27

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- Consent

- Assessment of

cardiovascular

disease risk

- Ordering DNA-test

Blood samplingKardiokompassi

web-service

DNA-test

Participant X

Pilot:

Communicating

the risk

Traditional

risk factors

200 healthy volunteers

FIMM, SITRA,

Finnish Red Cross

Elisabeth Widén

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OPINIONS ABOUT THE KARDIOKOMPASSI APPLICATION

Disagree (%)

No opinion (%)

Agree (%)

I learned useful information regarding my health males 16 4 80

females 12.7 12.7 74.5

My personal disease risk information was reassuring males 12 28 60

females 26.5 25.5 48.1

My personal disease risk information motivated me to take better care of my health males 12 24 64

females 11.8 25.5 62.6

The information I received was worrying males 64 32 4

females 65.7 16.7 17.7

The information I received was interesting males 0 4 96

females 3.9 5.9 90.2

My personal genetic risk information was confusing males 52 32 16

females 60.8 24.5 14.7

I was indifferent to the information provided on my personal genetic risk males 84 12 4

females 83.3 9.8 6.9

The information on my genetic risk, in particular, motivated me to take better care of my health males 12 24 64

females 16.7 18.6 64.7

Evidence for strong statin response in high genetic risk group

Mega et al, Lancet 2015

High genetic risk

Risk reduction

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Acknowledgements› Magnetic consortium:

Analysis team: Johannes Kettunen,

Tom Haller, Rene Pool, Ayse

Demirkan, Rajesh Rawal, Tune Pers,

Tonu Esko, Mari Niemi, Taru

Tukiainen, Harm-Jan Westra

Cohorts: KORA, Finrisk, NFBC, YFS,

HBCS, FTC, Estonian biobank, ERF,

Dutch Twin Registry

› ENGAGE lipids scan:

Analysis team: Ida Surakka, Momoko

Horikoshi, Reedik Magi, Antti-Pekka

Sarin, Anubha Mahajan, Vasiliki

Lagou, Letizia Marullo, Teresa

Ferreira, Benjamin Miraglio, Sanna

Timonen, Johannes Kettunen, Matti

Pirinen, Juha Karjalainen, Gudmar

Thorleifsson, Sara Hagg, Jouke-Jan

Hottenga, Aaron Isaacs, Claes

Ladenvall, Marian Beekman, Tonu

Esko, Janina S Ried, Christopher P

Nelson, Christina Willenborg, Harm-

Jan Westra

22 cohortsdd.mm.yyyy 33

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Prediction: 13 SNP study

Emmi Tikkanen

Kaisa Silander

Leena Peltonen

Marju Orho-Melander

Amitabh Sharma

Olle Melander

Aki S. Havulinna

Markus Perola

Antti Jula

Veikko Salomaa

Candace Guiducci

Elena Gonzalez

Sekar Kathiresan

HUCHJuha Sinisalo

Markku S Nieminen

Haartman InstituteMarja-Liisa Lokki

Ripatti Lancet 2010

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Prediction: 28 SNP study

Emmi Tikkanen

Aarno Palotie

Aki Havulinna

Veikko Salomaa

Tikkanen ATVB 2013

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Kardiokompassi & GRS trial teams

› FIMM:

Elisabeth Widén

Kimmo Pitkänen

Emmi Tikkanen

Timo Miettinen

Olli Kallioniemi

› Carea:

Pasi Pöllänen

› Duodecim:

Pekka Mustonen

› Mehiläinen:

Kristiina Hotakainen

› Finnish Red Cross

Blood Service:

Jukka Partanen

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Ida Surakka: Lipid GWAS

Emmi Tikkanen: Risk Prediction