Background: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. Method: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. Results: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 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. Conclusions: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting.
|Journal||BMC Medical Research Methodology|
|State||Published - Dec 2023|
Bibliographical noteFunding Information:
The CHRIS study was funded by the Department of Innovation, Research and University of the Autonomous Province of Bolzano-South Tyrol and supported by the European Regional Development Fund (FESR1157). The present research was conducted within the project “Molecular profiling of uncontrolled and treatment resistant hypertension in the general population: the HyperProfile study” funded by Department of Innovation, Research and University of the Autonomous Province of Bolzano, grant “Legge Provinciale 14/2006”, unique project code D52F19000130003.
Authors thank CHRIS study participants from the middle and upper Vinschgau/Val Venosta, the general practitioners, the personnel of the Hospital of Schlanders/Silandro, the field study team. The CHRIS biobank was assigned the “Bioresource Research Impact Factor” (BRIF) code BRIF6107. The authors thank the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano for covering the Open Access publication costs.
© 2023, The Author(s).
- Antihypertensive drugs
- CHRIS study
- Cluster analysis
- Lasso regression
ASJC Scopus subject areas
- Health Informatics