Precision medicine for complex diseases uses individual-level characteristics to improve prediction of risk, therapeutic response and prognosis.
Precision medicine for complex diseases uses individual-level characteristics to improve prediction of risk, therapeutic response and prognosis. Many precision medicine studies leverage existing data types and analytic methods to reveal new insights; however, beyond oncology, there has been limited success in translating precision medicine research for complex diseases into clinical practice.
Thus, there is a need to identify areas for improvement, particularly in translation-oriented analytical methods and study designs. In this perspective article, we outline five fundamental tenets to enhance the efficient clinical translation of precision medicine research. These tenets focus on addressing heterogeneity in risk, response and prognosis; signal robustness; structured statistical benchmarking against key performance indicators; precision trial designs; and risks and benefits to individuals and society. Our intention is to promote clinically meaningful, reproducible, scalable and equitable health outcomes through precision medicine, beyond those possible through contemporary approaches.Fig. 1: Precision and contemporary approaches in medicine and healthcare.Tannock, I. F. et al. 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Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis.Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases.Google ScholarBenkirane, H. et al. Multimodal CustOmics: a unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.Van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S. & Vermunt, J. K. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies.Haq, I., Anwar, S., Shah, K., Khan, M. T. & Shah, S. A. Fuzzy logic based edge detection in smooth and noisy clinical images.Yamamoto, K. et al. 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Effect of intensive versus standard blood pressure treatment according to baseline prediabetes status: a post hoc analysis of a randomized trial.Dennis, J. M. et al. Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study.Shields, B. M. et al. Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study.Dennis, J. M. et al. A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study.Lipkovich, I., Svensson, D., Ratitch, B. & Dmitrienko, A. Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data.Assmann, S. F., Pocock, S. J., Enos, L. E. & Kasten, L. E. Subgroup analysis and other uses of baseline data in clinical trials.Selker, H. P., Dulko, D., Greenblatt, D. J., Palm, M. & Trinquart, L. The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations.Redman, M. W. et al. Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer : a biomarker-driven master protocol.Antoniou, M., Jorgensen, A. L. & Kolamunnage-Dona, R. Biomarker-guided adaptive trial designs in phase II and phase III: a methodological review.Dwibedi, C. et al. Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology.Shields, B. M. et al. Patient preference for second- and third-line therapies in type 2 diabetes: a prespecified secondary endpoint of the TriMaster study.Lancaster, T. et al. Proof-of-concept recall-by-genotype study of extremely low and high Alzheimer’s polygenic risk reveals autobiographical deficits and cingulate cortex correlates.Paulus, J. K. & Kent, D. M. Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.Quintero, A. et al. Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.A.J.D. is supported by the National Institutes of Health /National Institute of Diabetes and Digestive and Kidney Diseases . M.S.U. is supported by NIH/NIDDK and the Doris Duke Foundation . D.E.C., L.M.C., G.N.G., E.R.P. and P.W.F. were supported by grants from the European Commission , Swedish Research Council, Novo Nordisk Foundation, Vinnova and Swedish Foundation for Strategic Research and Exodiab . D.E.C. was supported by an EFSD/Lilly research grant.Genetic & Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Helsingborg, SwedenJennifer L. SargentDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USAPrograms in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USAEwan R. Pearson Julius Center for Health Sciences and Primary Care, Department of Data Science and Biostatistics, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands D.E.C. and P.W.F. conceived the core ideas described in this study. D.E.C., J.L.S. and P.W.F. wrote the initial draft of the manuscript. All authors contributed to the ideas described herein and reviewed and edited the manuscript before submission.M.S.U. has consulting activity and research supported in collaboration with Novo Nordisk. J.L.S. has received consulting fees from the World Health Organization, University of Bergen, University of Heidelberg, Lund University, Karolinska University, Springer Nature, ABC Labs and European Diabetes Forum; travel support from University of Tubingen, University of Silensia, and European Association for Study of Diabetes; she is also founder of BabelFiskAB, which provides consulting services in global public health. P.W.F. has received consulting fees from Novo Nordisk and Zoe Ltd, and travel support and speaker fees from Menarini Group. P.W.F. has received research grants from Boehringer Ingelheim, Novo Nordisk, Medtronic, Pfizer and Lilly as part of the Innovative Health Initiative of the European Union. The remaining authors declare no competing interests.thanks the anonymous reviewer for their contribution to the peer review of this work. Primary Handling Editor: Karen O’Leary, in collaboration with theSpringer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author or other rightsholder; author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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