Hypertension is the leading cause of death worldwide and the most prevalent disease in South Tyrol. While angiotensin converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARBs) are effective anti-hypertensive drugs (AHD), success in blood pressure control is limited, resulting in a high proportion of uncontrolled and treatment resistant hypertensive individuals. Data from 13,393 adults from the Cooperative Health Research In South Tyrol (CHRIS) study show that ~50% of individuals under AHD have blood pressure levels above the recommended thresholds. In this context, we propose the HyperProfile study to evaluate the causes of AHD inefficiency and to identify molecular markers of drug resistance. In particular, we will assess circulating AHD metabolite levels, the joint distribution of the renin-angiotensin-aldosterone system (RAAS) markers, and the behavior of corticosteroids in hypertensive individuals from the general population, and their relationship to environmental and genetic components.
To this end, we identified 1815 CHRIS study participants who documented the use of AHD and for whom deep cardiovascular, metabolic and genetic characterization is available, including over >19 million high quality genetic variants.
Using an innovative mass-spectrometry approach called RAAS Triple-A analysis, we will simultaneously measure the levels of Ang I, Ang II and aldosterone on these participants, and derive plasma renin activity, Ang I–to-Ang II ratio, and the aldosterone-to-Ang II ratio. At the same time, we will determine the presence of the pharmacological active compounds in plasma, which will be used as a proxy for AHD adherence. Finally, we will measure 13 corticosteroid hormones in order to study the comprehensive action of AHD on the RAAS and the entire mineralocorticoid chain, and assess whether any AHD combination may induce compensatory mechanisms on adrenal glucocorticoid production.
Statistical analyses will take into account dietary behavior, sleep, and innate genetic predisposition, through the derivation of genetic risk scores of predisposition to hypertension, to the effectiveness to, or intolerance of AHD, and the integration of polymorphisms known to inhibit the effectiveness of specific AHD, such as those on the UMOD gene. Unsupervised machine learning techniques will be employed to identify data aggregation in relation to hypertension resistance, with available external data for replication.
Results obtained through this project will allow to gain insight into the mechanisms involved in resistance to AHD treatment and to identify biomarkers for this condition, as well as to determine the overall compliance to treatment in the general population. They will open new perspectives for the implementation of efficient strategies for reducing treatment resistant hypertension in the general population. The project team includes members from the Eurac Research Institute for Biomedicine (I), Inserm (F), and University of Glasgow (UK).