Precision medicine for cardiometabolic disease : a framework for clinical translation
(2023) In The Lancet Diabetes and Endocrinology 11(11). p.822-835- Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a... (More)
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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- author
- Franks, Paul W. LU ; Cefalu, William T. ; Dennis, John ; Florez, Jose C. ; Mathieu, Chantal ; Morton, Robert W. ; Ridderstråle, Martin LU ; Sillesen, Henrik H. and Stehouwer, Coen D.A.
- organization
- publishing date
- 2023-11
- type
- Contribution to journal
- publication status
- published
- subject
- in
- The Lancet Diabetes and Endocrinology
- volume
- 11
- issue
- 11
- pages
- 14 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:37804856
- scopus:85173493760
- ISSN
- 2213-8587
- DOI
- 10.1016/S2213-8587(23)00165-1
- language
- English
- LU publication?
- yes
- id
- 32dbab90-3c1a-495b-befe-fedb69eb89d8
- date added to LUP
- 2024-01-15 13:11:01
- date last changed
- 2024-04-16 00:55:24
@article{32dbab90-3c1a-495b-befe-fedb69eb89d8,
abstract = {{<p>Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.</p>}},
author = {{Franks, Paul W. and Cefalu, William T. and Dennis, John and Florez, Jose C. and Mathieu, Chantal and Morton, Robert W. and Ridderstråle, Martin and Sillesen, Henrik H. and Stehouwer, Coen D.A.}},
issn = {{2213-8587}},
language = {{eng}},
number = {{11}},
pages = {{822--835}},
publisher = {{Elsevier}},
series = {{The Lancet Diabetes and Endocrinology}},
title = {{Precision medicine for cardiometabolic disease : a framework for clinical translation}},
url = {{http://dx.doi.org/10.1016/S2213-8587(23)00165-1}},
doi = {{10.1016/S2213-8587(23)00165-1}},
volume = {{11}},
year = {{2023}},
}