Abstract
Introduction
Hypertension is a leading cause of death worldwide and a primary risk factor for coronary artery disease, stroke and chronic kidney disease (1). These complications could be largely avoided with an effective anti-hypertensive drug (AHD) treatment. The Renin-Angiotensin-Aldosterone System (RAAS) is a critical pathway responsible for the long-term control of blood pressure (2). The main hormones of RAAS are angiotensin II that is generated from angiotensin I via c-terminal cleavage by angiotensin-converting enzyme (ACE) and aldosterone, a mineralocorticoid hormone whose production is stimulated by the binding of angiotensin II to angiotensin II type 1 receptor. The RAAS pathway is widely therapeutically targeted by either ACE inhibitors (ACEi) or angiotensin II receptor blockers (ARB). Combinations of an ACEi or an ARB with a calcium channel blocker or a diuretic is the recommended first-line treatment for hypertension (3). Despite the availability of a number of AHDs, hypertension remains poorly controlled. The complexity of the blood pressure regulation, characterized by multiple levels of controls and based on different molecular mechanisms (4) can result in poor AHD efficacy. This highlights the importance and need to investigate the molecular pathways in their complexity rather than one hormone at a time. Additionally, due to technical difficulties in the simultaneous and accurate measurement of angiotensin I, angiotensin II and aldosterone, there are currently insufficient studies about the distribution of these RAAS hormones in the general population.
Aim
Aim of this study was the joint characterization of the three functionally related RAAS hormones using cluster analysis in participants under different types of AHD treatment, and the assessment of cluster-specific clinical features.
Methods
This study was based on the Cooperative Health Research in South Tyrol (CHRIS) study, which consists of 13393 participants from the general population recruited between 2011 and 2018 (5). The current study focused on 800 CHRIS participants (aged between 43 and 90 years; 54% females); of them 300 were not taking any AHD and 500 had a documented history of AHD. AHDs groups were ACEI or ARB plain or in combination with a diuretic (N=100 each), and beta blocker (N=100). We used the novel RAAS Triple-A assay to obtain high quality RAAS data from standard clinical serum samples: Angiotensin I, Angiotensin II and Aldosterone were simultaneously quantified by liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) analysis.
The K-means unsupervised clustering technique was adopted to explore subgroups of participants based on the three RAAS hormones. The clustering algorithm assigns each data point to one of K groups based on a similarity feature computed from the covariance matrix of the three hormones. The hormones were first turned into three independent principal components, which were then used to stratify the 800 CHRIS participants into the K clusters. The quality of the separation of the clusters was assessed using the proportion of the between cluster variability and total explained variability. Clinical and laboratory variables were evaluated according to the cluster analysis. ANOVA and Chi-square tests were used to assess the differences of continuous and categorical variables across clusters at a significance level of 0.05.
Results
The principal component analysis indicated that the first component explained 62% of the RAAS total variability, while the second and the third components explained 28% and 10% of the variation, respectively. All the individuals were eventually stratified into K=3 clusters. The widest cluster comprised 55% of all participants, whereas the remaining clusters included 30% and 15% participants, respectively. The first cluster comprised participants with no active AHDs treatment or taking plain beta blocker only. The second cluster comprised participants with a single AHD treatment (either ACEi or ARB). Participants with combined treatments, which included diuretics, characterized the third cluster.
Among the clinical characteristics, individuals included in cluster 2 had higher average BMI than those in cluster 1 and cluster 3 (P-value<0.0001). The three clusters were also heterogeneous by systolic blood pressure, although the differences across the clusters were not statistically significant (P-value=0.0614). Cluster 1 and cluster 3 individuals had mean glycated hemoglobin (HbA1c) level > 6%, substantially higher than for individuals in cluster 2 (mean=5.7%, P-value=0.0028).
Conclusions
This study indicated that cluster analysis based on RAAS system is a feasible approach for investigating the heterogeneity of participants with hypertension in a population-based study. In particular, the cluster analysis reliably identified individuals under different AHD treatment. Finally, cluster analysis indicated differences in important clinical characteristics across groups.
References
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* Williams, B., Mancia, G., Spiering, W. et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Cardiology (ESC) and the European Society of Hypertension (ESH). Eur Heart J. 2018 Sep; 39(33), 3021-3104.
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* Pattaro, C., Gögele, M., Mascalzoni, D. et al. The Cooperative Health Research in South Tyrol (CHRIS) study: rationale, objectives, and preliminary results. J Transl Med., 2015 Nov; 13(1), 1-16