Skip to main content
SpringerLink
Log in

Evaluation of patients diagnosed with essential arterial hypertension through network analysis

  • Original Article
  • Published:
Irish Journal of Medical Science (1971 -) Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Cortez-Diaz N, Martins SR, Belo A, VALSIM Study investigators et al (2013) Association of metabolic risk factors with uncontrolled hypertension: comparison of the several definitions of metabolic syndrome. J Hypertens 31(10):1991–1997. doi:10.1097/HJH.0b013e32836342f7

    Article  Google Scholar 

  2. Elperin DT, Pelter MA, Deamer RL et al (2014) A large cohort study evaluating risk factors associated with uncontrolled hypertension. J Clin Hypertens. 16(2):149–154. doi:10.1111/jch.12259

    Article  Google Scholar 

  3. Marotta T, Liccardo M, Schettini F et al (2015) Association of hyperuricemia with conventional cardiovascular risk factors in elderly patients. J Clin Hypertens (Greenwhich). 17(1):27–32. doi:10.1111/jch.12434

    Article  CAS  Google Scholar 

  4. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256. doi:10.1137/S003614450342480

    Article  Google Scholar 

  5. Wang XF, Chen G (2003) Complex networks: small-world, scale-free and beyond. Circuits Syst Mag IEEE 3(1):6–20. doi:10.1109/MCAS.2003.1228503

    Article  Google Scholar 

  6. Papo D, Buldú JM, Boccaletti S et al (2014) Complex network theory and the brain. The Royal Society publishing, Philosophical transactions B. doi:10.1098/rstb.2013.0520

    Google Scholar 

  7. Barabási AL (2013) Network science. Phil Trans Royal Soc A Math Phys Eng Sci 371(1987):20120375. doi:10.1098/rsta.2012.0375

    Article  Google Scholar 

  8. Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68. doi:10.1038/nrg2918

    Article  PubMed  PubMed Central  Google Scholar 

  9. Loscalzo J, Barabasi AL (2011) Systems biology and the future of medicine. Wiley Interdiscip Rev Syst Biol Med 3(6):619–627. doi:10.1002/wsbm.144

    Article  PubMed  PubMed Central  Google Scholar 

  10. Goh KI, Choi IG (2012) Exploring the human diseasome: the human disease network. Brief Funct Genomics 11(6):533–542. doi:10.1093/bfgp/els032

    Article  CAS  PubMed  Google Scholar 

  11. Silverman EK, Loscalzo J (2012) Network medicine approaches to the genetics of complex diseases. Discov Med 14(75):143–152

    PubMed  PubMed Central  Google Scholar 

  12. Cho D-Y, Kim Y-A, Przytycka TM (2012) Network Biology Approach to Complex Diseases. PLoS Comput Biol 8(12):e1002820. doi:10.1371/journal.pcbi.1002820

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Udrescu M, Topirceanu A, Avram R, Mihaicuta S (2014) AER score: a social-network-inspired predictor for sleep apnea syndrome. Chest 145(3):609. doi:10.1378/chest.1824353

    Article  Google Scholar 

  14. Wang K, Sun J, Zhou S et al (2013) Correction: prediction of drug-target interactions for drug repositioning only based on genomic expression similarity. PLoS Comput Biol 9:11. doi:10.1371/annotation/958d4c23-4f1e-4579-b6ef-8ae1f828b1dd.10.1371/annotation/958d4c23-4f1e-4579-b6ef-8ae1f828b1dd

    CAS  Google Scholar 

  15. Mancia G et al (2007) The task force for the management of arterial hypertension of the European Society of Hypertension, the task force for the management of arterial hypertension of the european society of cardiology. Guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the european society of hypertension (ESH) and of the european society of cardiology (ESC). Eur Heart J 28(12):1462–1536

    PubMed  Google Scholar 

  16. Mancia G et al (2013) Guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J 34(28):2159–2219

    Article  PubMed  Google Scholar 

  17. American Diabetes Association (2010) Standards of Medical Care in Diabetes—2010. Diabetes Care 33(Suppl1):S11–S61

    Article  PubMed Central  Google Scholar 

  18. Jacomy M, Venturini T, Heymann S, Bastian M (2014) ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS One 9(6):e98679

    Article  PubMed  PubMed Central  Google Scholar 

  19. Barabási AL (2007) Network medicine—from obesity to the “diseasome”. N Engl J Med 357(4):404–407. doi:10.1056/NEJMe078114

    Article  PubMed  Google Scholar 

  20. Heart Score Program available at http://www.heartscore.org/ro/Pages/Welcome.aspx, Accessed: 12-01-2015

  21. Cooney MT, Dudina A, D’Agostino R, Graham IM (2010) Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation 122(3):300–310. doi:10.1161/CIRCULATIONAHA.109.852756

    Article  PubMed  Google Scholar 

  22. Ulusoy S (2013) Assessment of cardiovascular risk in hypertensive patients: a comparison of commonly used risk scoring programs. Kidney Int Suppl 3(4):340–342. doi:10.1038/kisup.2013.72

    Article  Google Scholar 

  23. Poldervaart JM, Reitsma JB, Koffijberg H et al (2013) The impact of the HEART risk score in the early assessment of patients with acute chest pain: design of a stepped wedge, cluster randomised trial. BMC Cardiovasc Disord 13:77. doi:10.1186/1471-2261-13-77

    Article  PubMed  PubMed Central  Google Scholar 

  24. Noack A (2009) Modularity clustering is force-directed layout. Phys Rev E 79(2):026102. doi:10.1103/PhysRevE.79.026102

    Article  Google Scholar 

  25. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113. doi:10.1103/PhysRevE.69.026113

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pharmacology-Clinical Pharmacy Department, “Victor Babes” University of Medicine and Pharmacy, 2, Eftimie Murgu Square, 300041, Timisoara, Romania

    L. Suciu, C. Cristescu & V. Buda

  2. Computer and Software Engineering Department, University Politehnica Timisoara, 2, Vasile Parvan Boulevard, 300223, Timisoara, Romania

    A. Topîrceanu & M. Udrescu

  3. Drug Analysis Department, “Victor Babes” University of Medicine and Pharmacy, 2, Eftimie Murgu Square, 300041, Timisoara, Romania

    L. Udrescu

  4. Internal Medicine Department, “Victor Babes” University of Medicine and Pharmacy, 2, Eftimie Murgu Square, 300041, Timisoara, Romania

    M. C. Tomescu

Authors
  1. L. Suciu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Cristescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Topîrceanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Udrescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Udrescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. V. Buda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. C. Tomescu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Suciu.

Ethics declarations

Ethical committee approval

The present study has been approved by the Ethical Committee at “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania (no.10/2013). Furthermore, all the patients included in the study have signed the informed consent, thus agreeing to participate in this research.

Conflict of interest

None.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suciu, L., Cristescu, C., Topîrceanu, A. et al. Evaluation of patients diagnosed with essential arterial hypertension through network analysis. Ir J Med Sci 185, 443–451 (2016). https://doi.org/10.1007/s11845-015-1342-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11845-015-1342-1

Keywords

Search

Navigation