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  • 1.
    Johansson, Åsa
    et al.
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Andreassen, Ole A.
    NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway.
    Brunak, Søren
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Rigshospitalet, Blegdamsvej 9, Copenhagen University Hospital, Copenhagen, Denmark.
    Franks, Paul W.
    Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Lund, Sweden; Novo Nordisk Foundation, Hellerup, Denmark.
    Hedman, Harald
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Loos, Ruth J. F.
    Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, NY, New York, United States.
    Meder, Benjamin
    Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University of Heidelberg, Heidelberg, Germany.
    Melén, Erik
    Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden; Sachs’ Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden.
    Wheelock, Craig E.
    Unit of Integrative Metabolomics, Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden; Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden.
    Jacobsson, Bo
    Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden; Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway.
    Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification2023In: Journal of Internal Medicine, ISSN 0954-6820, E-ISSN 1365-2796, Vol. 294, no 4, p. 378-396Article, review/survey (Refereed)
    Abstract [en]

    Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

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