Professor Samuli Ripatti, FIMM, University of Helsinki, Finland, at Genomics to Healthcare EU side event, 13 Sept 2019, THL, Helsinki

1. Polygenic Risk and the GeneRISK Study
Samuli Ripatti, PhD, Professor
Institute for Molecular Medicine Finland (FIMM)
Public Health, University of Helsinki
Broad Institute of MIT and Harvard, US
EU2019.fi / THL Sep 13 2019

2. Four universal “truths” about the utility of
genomic data in disease prevention:
1. Genetic data does not matter, particularly when you know the
family history of the disease
2. We cannot return genetic risk information to individuals because
they do not understand risks and only worry
3. One cannot change genes, so there is no benefit of communicating
genetic risk
4. Everyone should eat healthy food, exercise more and people are
not doing any life style changes anyway

3. 1. Genetic data does not matter,
particularly when you know the
family history of the disease

4. www.fimm.fi
Nature Oct 10th 2018: “The approach to predictive medicine
that is taking genomics research by storm”

5. Type 2 diabetes
HR 0.22
HR 3.45
Nina Mars et al, BioRXiv 2019
Polygenic risk score
FinnGen Study n=170,399
T2D cases = 22514

6. HR 12.80
HR 2.93
HR 0.23
N cases = 5,812
Effect only slightly attenuated by taking into account
the family history

7. 16.8% 14.4%
24.2% 26.5% 25.8%
9.1%
6.9%
0.8%
15.8%
51.9%
1.7%
58.2%
26.9%
73.3% 73.5% 74.2%
CHD T2D AF Breast cancer Prostate cancer
Age <55 Age <45 Age <60 Age <40 Age <55
Early-onset disease
Nina Mars et al.
bioRxiv August 06, 2019

9. 2. We cannot return genetic risk
information to individuals because
they do not understand risks and
only worry

10. Elisabeth Widén14.03.2019
10
• Primary AIM: To assess whether genome-based disease prediction can improve
the prevention of cardiovascular disease
Recruit a population cohort for
prospective and biobank studies
Promoting practical
implementations of genome-
based medicine
A prospective population study initiated in 2015 with 7,350 randomly-sampled middle-
aged Finns
GeneRISK – Study Aim

11. Communicating Disease Risk
Overall
personal risk
for CVD
CHD-risk
displayed as a
function of age
Disease risk in
the population

12. Communicating Disease Risk
Overall
personal risk
for CVD
CHD-risk
displayed as a
function of age
Disease risk in
the population

13. Attitudes at 1.5 Years of Follow-up (n=5,196)
Elisabeth Widén14.03.2019
13
• My personal risk information was easy to understand 89%
• My results were useful 90%
• My results were unexpected 22%
• My results were concerning 29%
• Genetic factors importantly influence my disease risk 97%
• I can impact on my disease risk through my lifestyle 99%
• My personal risk information motivates me to take better care of my health 89%
• Doctors know how to interpret and utilize genome information 75%

14. 3. One cannot change genes, so
there is no benefit of communicating
genetic risk
4. Everyone should eat healthy food,
exercise more and people are not
doing any life style changes anyway

15. Genetic Risk and Health Behavior
›Personal disease risk information based on a few variants
with weak effects does not motivate change in health
behavior (Hollands et al 2016 BMJ)
›Genetic information based on single rare DNA variants
with a moderately strong effect on disease risk often
prompts health behaviors such as screening, medication
and surgery
Based on previous studies

16. Evidence for strong statin response in high
genetic risk group
Mega et al, Lancet 2015
High genetic risk
Risk
reduction

17. GeneRISK: Personal Disease Risk Information
› All participants receive personal information on their 10-year CVD-
risk
› The risk estimate is based on both traditional risk factors and a
polygenic risk score (49,000 SNPs) (Abrahams et al. 2016)
› The risk information is interpreted and communicated utilizing an in-
house interactive web-tool, KardioKompassi®
Elisabeth Widén 17

18. CVD-risk > 10%
n = 685
CVD-risk < 10%
n = 3,996
Sustained weight loss (% of study
participants)
15.9 * 12.3
Quit smoking
(% of smokers)
14.3 15.3
Seen a physician (%) 20.7 *** 8.3
Any of the above (%) 36.2 *** 20.8
Elisabeth Widén
*p=0.01; ***p<0.001
Actions Taken at Follow-up

19. Characteristic
Intervention
Yes (n = 232) No (n = 413) p
% males 68.5 % 75.8 % p=0.03
Age (yrs) 61.0 + 4.0 60.6+4.4
CVD-risk (%) 16.3+6.6 15.2+5.4
p=0.00
6
BMI (kg/m2) 28.2+4.2 28.1+4.4
Cholesterol (mmol/l) 5.7+0.9 5.7+1.0
CVD polygenic risk score (SD) 0.33+1.0 0.18+0.96 p=0.009
Smoker at baseline 34.0 % 34.6 %
Elisabeth Widén
Individuals with high CVD-risk
Motivator to Take Action

20. Who visited doctor during the followup?
↓ ↑

21. BreastcancerProstatecancer
PRS distribution

22. 40 50 69
2% breast cancer
prevalence
2% prostate cancer
prevalence
BreastcancerProstatecancer

23. Genomic Risk and Health Behavior
Elisabeth Widén
23
INTENTION
Participants report that
personal risk
information inspires to
improve lifestyle
ACTION
Individuals at high risk
take action to lower
their risk more often
than others
MOTIVATION
Action to lower the risk
associates with an
increased polygenic
load
Elevated genomic risk + interactive tool for communication
= motivation for change
The GeneRISK –study

24. Genome - Based Disease Prediction for All Patients
Elisabeth Widén
›Utilize genomic data together
with clinical information
›Integrate with preventive
healthcare IT-systems and
national guidelines
›Ongoing clinical pilot studies
in Finland
in Estonia

25. Acknowledgements
14.03.2019
GeneRISK Collaborators
 Carea - Kymenlaakso Social and Health
Services: Pasi Pöllänen
 Duodecim Medical Publications Ltd: Pekka
Mustonen
 Mehiläinen: Kristina Hotakainen
 Finnish Red Cross Blood Service: Jukka
Partanen
 Yhtyneet Medix/SYNLAB: Sakari Jokiranta
 Elisabeth Widén
 Nina Mars
 Johanna Aro
 Sanni Ruotsalainen
 Ida Surakka
 Pietari Ripatti
 Kati Donner
 Javier Nuñez-Fortanau
 Kari Pitkänen
 Timo Miettinen
 Robert Mills