Recipient organisationUniversity of DundeeSource-published name: University of Dundee
Funding£2.3M
PeriodMar 2023 — Feb 2029
In plain English
AI plain-English summary
More than half of people with high blood pressure do not have it under control, and doctors often prescribe drugs by trial and error. This project will test whether a set of biological markers—drawn from a person’s genes, metabolism, and other molecular data—can tell doctors which patients have a curable form of the condition and which drug will work best for the rest. High blood pressure is the world’s leading risk factor for death and illness. Treatment fails partly because secondary forms such as endocrine hypertension go undetected, and partly because individual responses to drugs vary unpredictably. The current trial-and-error approach frustrates patients and clinicians alike. The researchers will run three clinical trials to validate two biomarker panels. One panel will identify patients with endocrine hypertension, who can be cured with targeted therapy. The other will predict which drug will lower blood pressure most effectively in patients with primary hypertension. Machine learning will integrate genetic, genomic, and metabolomic data to generate treatment recommendations. An economic evaluation will assess whether this approach saves money compared with standard care. If successful, the project could replace guesswork with a personalised, cost-effective treatment strategy and prevent the heart and metabolic complications that uncontrolled hypertension causes.
View original technical description
In HT-ADVANCE we aim to revolutionise personalised management of arterial hypertension (HT) by using multi-omics (MOMICS) stratification biomarkers as companion diagnostics for the prescription of existing drugs. HT is the most important global risk factor for death and morbidity but is uncontrolled in more than 50% of patients. Causes of treatment failure include lack of identification of secondary forms such as endocrine hypertension (EHT) which can be curable by specific therapy. In addition, there is variable response to individual drugs and trial-and-error treatment leads to poor HT control and frustration for both patients and clinicians. The objective of HT-ADVANCE is to validate two multicomponent stratification biomarkers in patients with HT in order to i) identify patients with EHT, and ii) predict response to treatment in patients with primary hypertension. The hypothesis is that MOMICS biomarkers reflect specific forms of hypertension and susceptibility to specific drugs. To this end, we will run three clinical trials (HT-ENDO, HT-TREAT and HT-PREDICT) and apply machine learning techniques to integrate the genetic, genomic and metabolomic features that constitute the MOMICS biomarkers in order to generate accurate diagnostic and therapeutic response predictions for clinicians. We will also perform economic evaluation of the use of MOMICS for the treatment of HT, produce ethical and legal recommendations for clinical decision making, and develop a plan for their implementation as companion diagnostics. The study will be conducted by several HT Centres of Excellence and will build on the success of the ENSAT-HT project that has established methods and pipelines for integrating datasets derived from multiple platforms. We expect that HT-ADVANCE will provide a step change in the management of HT by enabling a personalised, more efficient and cost-effective treatment strategy, and importantly, will prevent the ensuing cardio-metabolic complications.
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