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Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases.
Kiiskinen, Tuomo; Helkkula, Pyry; Krebs, Kristi; Karjalainen, Juha; Saarentaus, Elmo; Mars, Nina; Lehisto, Arto; Zhou, Wei; Cordioli, Mattia; Jukarainen, Sakari; Rämö, Joel T; Mehtonen, Juha; Veerapen, Kumar; Räsänen, Markus; Ruotsalainen, Sanni; Maasha, Mutaamba; Niiranen, Teemu; Tuomi, Tiinamaija; Salomaa, Veikko; Kurki, Mitja; Pirinen, Matti; Palotie, Aarno; Daly, Mark; Ganna, Andrea; Havulinna, Aki S; Milani, Lili; Ripatti, Samuli.
Affiliation
  • Kiiskinen T; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Helkkula P; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Krebs K; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Karjalainen J; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Saarentaus E; Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
  • Mars N; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Lehisto A; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Zhou W; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Cordioli M; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Jukarainen S; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Rämö JT; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Mehtonen J; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Veerapen K; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Räsänen M; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Ruotsalainen S; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Maasha M; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Niiranen T; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Tuomi T; Wihuri Research Institute, University of Helsinki, Helsinki, Finland.
  • Salomaa V; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Kurki M; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Palotie A; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Daly M; Department of Internal Medicine, University of Turku, Turku, Finland.
  • Ganna A; Division of Medicine, Turku University Hospital, Turku, Finland.
  • Havulinna AS; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Milani L; Research Programs Unit, Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.
  • Ripatti S; Lund University Diabetes Center, Malmo, Sweden.
Nat Med ; 29(1): 209-218, 2023 01.
Article in En | MEDLINE | ID: mdl-36653479
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Coronary Artery Disease / Cardiovascular Diseases / Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male Language: En Journal: Nat Med Journal subject: BIOLOGIA MOLECULAR / MEDICINA Year: 2023 Document type: Article Affiliation country: Finland
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Coronary Artery Disease / Cardiovascular Diseases / Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male Language: En Journal: Nat Med Journal subject: BIOLOGIA MOLECULAR / MEDICINA Year: 2023 Document type: Article Affiliation country: Finland