Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Tuomo Kiiskinen; Pyry Helkkula; Kristi Krebs; Juha Karjalainen; Elmo Saarentaus; Nina Mars; Arto Lehisto; Wei Zhou; Mattia Cordioli; Sakari Jukarainen; Joel T Rämö; Juha Mehtonen; Kumar Veerapen; Markus Räsänen; Sanni Ruotsalainen; Mutaamba Maasha; FinnGen; Teemu Niiranen; Tiinamaija Tuomi; Veikko Salomaa; Mitja Kurki; Matti Pirinen; Aarno Palotie; Mark Daly; Andrea Ganna; Aki S Havulinna; Lili Milani; Samuli Ripatti

doi:10.1038/s41591-022-02122-5

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Nat Med. 2023 Jan;29(1):209-218. doi: 10.1038/s41591-022-02122-5. Epub 2023 Jan 18.

Authors

Tuomo Kiiskinen^#^{1

2

3}, Pyry Helkkula^#¹, Kristi Krebs^#⁴, Juha Karjalainen^{1

3

5}, Elmo Saarentaus^{1

6}, Nina Mars¹, Arto Lehisto¹, Wei Zhou³, Mattia Cordioli¹, Sakari Jukarainen¹, Joel T Rämö¹, Juha Mehtonen¹, Kumar Veerapen³, Markus Räsänen⁷, Sanni Ruotsalainen¹, Mutaamba Maasha³; FinnGen; Teemu Niiranen^{2

8

9}, Tiinamaija Tuomi^{1

10

11

12

13}, Veikko Salomaa², Mitja Kurki^{1

3

5}, Matti Pirinen^{1

14

15}, Aarno Palotie^{1

5

16}, Mark Daly^{1

5

16}, Andrea Ganna^{1

3}, Aki S Havulinna^{1

2}, Lili Milani⁴, Samuli Ripatti^{17

18

19}

Affiliations

¹ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
² Finnish Institute for Health and Welfare, Helsinki, Finland.
³ Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
⁴ Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
⁵ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
⁶ Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
⁷ Wihuri Research Institute, University of Helsinki, Helsinki, Finland.
⁸ Department of Internal Medicine, University of Turku, Turku, Finland.
⁹ Division of Medicine, Turku University Hospital, Turku, Finland.
¹⁰ Research Programs Unit, Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.
¹¹ Lund University Diabetes Center, Malmo, Sweden.
¹² Folkhälsan Research Centre, Helsinki, Finland.
¹³ Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland.
¹⁴ Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
¹⁵ Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
¹⁶ Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹⁷ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. samuli.ripatti@helsinki.fi.
¹⁸ Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. samuli.ripatti@helsinki.fi.
¹⁹ Lund University Diabetes Center, Malmo, Sweden. samuli.ripatti@helsinki.fi.

^# Contributed equally.

Abstract

Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10^-9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cardiovascular Diseases* / drug therapy
Cardiovascular Diseases* / epidemiology
Cardiovascular Diseases* / genetics
Coronary Artery Disease* / drug therapy
Coronary Artery Disease* / epidemiology
Coronary Artery Disease* / genetics
Diabetes Mellitus, Type 2* / drug therapy
Diabetes Mellitus, Type 2* / epidemiology
Diabetes Mellitus, Type 2* / genetics
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Male
Risk Factors

Grants and funding

MC_PC_17228/MRC_/Medical Research Council/United Kingdom