Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies

BackgroundHypertension is a leading cause of death worldwide. Population-based studies offer an opportunity to assess the effectiveness of anti-hypertensive drugs (AHD) in real-world scenarios. However, lack of quality AHD documentation, especially when electronic health record linkage is unavailable, leads to reporting and classification bias. Here we assessed to which extent Renin-Angiotensin-Aldosterone System (RAAS) biomarkers can identify AHD treatments in the general population. MethodAngiotensin I, angiotensin II and aldosterone levels were simultaneously determined through mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented AHD treatment. We conducted unsupervised cluster analysis, assessing agreement, sensitivity and specificity of the resulting clusters against known AHD treatment. Through lasso penalized regression we identified clinical characteristics associated with RAAS biomarkers, accounting for the effects of cluster and treatment classifications. ResultsWe identified three well-separated clusters: cluster 1 (n=444) preferentially including individuals not receiving RAAS-targeting AHD; cluster 2 (n=235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa {kappa}w=74%; sensitivity=73%; specificity=83%); and cluster 3 (n=121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users ({kappa}w=81%; sensitivity=55%; specificity=90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. ConclusionsUnsupervised clustering of angiotensin I, angiotensin II and aldosterone is a viable technique to identify individuals on ACEi and ARB AHD treatment outside of a controlled clinical setting.