Periodic Reporting for period 1 - Hypermarker (Personalised pharmacometabolomic optimisation of treatment for hypertension)
Période du rapport: 2023-01-01 au 2024-06-30
Hypertension, a prevalent condition affecting millions worldwide, remains a significant public health challenge due to its complex and multifactorial nature. The HYPERMARKER project aims to address this challenge by leveraging pharmacometabolomics to personalize antihypertensive treatment. By identifying metabolic biomarkers associated with treatment response, the project seeks to optimize therapeutic strategies, thereby reducing the burden of uncontrolled hypertension.
The project operates within a broader context of increasing emphasis on precision medicine, where the integration of social sciences and humanities plays a crucial role in addressing the ethical, legal, and societal implications of using advanced AI-driven technologies in healthcare.
HYPERMARKER's objectives include the development of predictive models and clinical decision support tools, validated through randomized controlled trials, that will ultimately inform international hypertension guidelines. The project's pathway to impact is clear: by improving hypertension management, it aims to reduce morbidity and mortality associated with cardiovascular diseases, significantly impacting public health at both European and global levels.
The project operates within a broader context of increasing emphasis on precision medicine, where the integration of social sciences and humanities plays a crucial role in addressing the ethical, legal, and societal implications of using advanced AI-driven technologies in healthcare.
HYPERMARKER's objectives include the development of predictive models and clinical decision support tools, validated through randomized controlled trials, that will ultimately inform international hypertension guidelines. The project's pathway to impact is clear: by improving hypertension management, it aims to reduce morbidity and mortality associated with cardiovascular diseases, significantly impacting public health at both European and global levels.
During the reporting period, HYPERMARKER has made significant progress in several key areas. The project successfully established a comprehensive governance and communication structure, facilitating collaboration across its multidisciplinary consortium. Technical achievements include the selection and shipment of samples from three large cohorts of patients and the initiation of data analysis to identify biomarkers predictive of antihypertensive treatment response.
The project also started developing an optimized metabolomics and exposomics platform, which will be critical for the ongoing and future analyses. Furthermore, efforts to integrate existing cohort data and harmonize these datasets have progressed, despite delays in accessing some data sources. The machine learning models for predicting treatment outcomes are in early development, with preliminary models already being tested using available data.
The project also started developing an optimized metabolomics and exposomics platform, which will be critical for the ongoing and future analyses. Furthermore, efforts to integrate existing cohort data and harmonize these datasets have progressed, despite delays in accessing some data sources. The machine learning models for predicting treatment outcomes are in early development, with preliminary models already being tested using available data.
After just 1.5 years this is too early to report. However, the integration of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles will ensure that the project's findings are easily accessible and applicable in real-world settings.