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Evaluation of patients diagnosed with essential arterial hypertension through network analysis
L Suciu1, C Cristescu2, A Topîrceanu3
1Pharmacology-Clinical Pharmacy Department, "Victor Babes" University of Medicine and Pharmacy, 2, Eftimie Murgu Square, 300041, Timisoara, Romania. lianads@yahoo.com.
Insights
Network medicine reveals distinct patient clusters in hypertension treatment. This approach aids in assessing the efficacy of different medications like nebivolol, perindopril, and candesartan cilexetil.
Area of Science:
- Cardiology
- Network Medicine
- Computational Biology
Background:
- Essential hypertension is a chronic condition often leading to severe complications.
- Late diagnosis and complex risk factors hinder effective disease management.
- Controlling hypertension is crucial to prevent long-term health issues.
Purpose of the Study:
- To identify specific treatment response patterns in arterial hypertension.
- To analyze the intricate connections between risk factors in hypertensive patients.
- To leverage network medicine for a deeper understanding of hypertension.
Main Methods:
- Utilized network science and network medicine methodologies.
- Constructed a complex network of hypertensive patients based on shared medical conditions.
- Applied community-based representation to uncover hypertension development patterns.
Main Results:
- Identified distinct patient clusters with shared characteristics in each treatment group (nebivolol, perindopril, candesartan cilexetil).
- Network-based clustering facilitated a more nuanced assessment of treatment efficacy.
- Revealed previously uncharted patterns in hypertension development.
Conclusions:
- Network medicine offers a novel approach to understanding and managing hypertension.
- Patient stratification based on network topology can enhance treatment evaluation.
- This methodology supports personalized medicine strategies for hypertension care.
Background:
Essential hypertension is a chronic pathology that causes long-term complications due to late diagnosis of patients, the inability to control the disease through medication, or due to the complexity of associated risk factors.
Aims:
Our study sets out to identify specific patterns of response to arterial hypertension treatment, by taking into consideration the multiple connections between risk factors in a relevant population of hypertensive patients.
Methods:
Network science is an emerging paradigm, branching over multiple aspects of physical, biological and social phenomena. One such branch, which has brought significant contributions to medical science, is the field of network medicine. To apply this methodology, we create a complex network of hypertensive patients based on their common medical conditions. Consequently, we obtain a community-based representation which pinpoints specific-and previously uncharted-patterns of hypertension development. This approach creates incentives for evaluating patient's treatment efficacy, by considering its network topological position.
Results:
Distinct clusters of patients with common properties have emerged for each study group (group A-treated with nebivolol, group B-treated with perindopril and group C-treated with candesartan cilexetil). Therefore, our network-based clustering allows for a better treatment assessment.
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