Correlation of Stromelysin-1 and Tissue Inhibitor of Metalloproteinase-1 with Lipid Profile and Atherogenic Indices in End-Stage Renal Disease Patients: A Neural Network Study
-
Published:2023-06-13
Issue:4
Volume:31
Page:
-
ISSN:2231-8526
-
Container-title:Pertanika Journal of Science and Technology
-
language:en
-
Short-container-title:JST
Author:
Abdalsada Habiba Khdair,Hadi Hadi Hassan,F. Almulla Abbas,Najm Asawer Hassan,Al-Isa Ameer,Al-Hakeim Hussein Kadhem
Abstract
End-stage renal disease (ESRD) patients are prone to cardiovascular disease (CVD). The search for a biomarker that determines patients at great risk of CVD is still a hot topic of study. In the present study, stromelysin-1 and its inhibitor (TIMP1), in addition to atherogenic indices, were studied in ESRD patients. We assessed stromelysin-1, TIMP1, and lipid profile parameters in the serum of 60 ESRD patients and 30 healthy controls. A neural network study was conducted to determine the best factors for predicting ESRD patients more susceptible to developing CVD using the cut-off value of the atherogenic index of plasma (AIP) >0.24. ESRD patients have dyslipidemia, high atherogenic indices, and elevated levels of stromelysin-1 and TIMP1. There is a correlation between the rise in stromelysin-1 and its inhibitor and several atherogenic indices and lipids in those patients. The neural network results indicated that the area under the curve predicting CVD, using the measured eight parameters, was 0.833, with 80 % sensitivity and 100% specificity. The relative importance of the top four most effective input variables that represent the most important determinants for the prediction of high risk of CVD stromelysin-1 (100%), followed by eGFR (77.9%), TIMP1 (66.0%), and TIMP1/stromelysin-1 (30.7%). ESRD patients have dyslipidemia and are prone to CVD, and stromelysin-1 is the best parameter for predicting CVD in ESRD patients.
Publisher
Universiti Putra Malaysia
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference91 articles.
1. Alge-Priglinger, C. S., Kreutzer, T., Obholzer, K., Wolf, A., Mempel, M., Kernt, M., Kampik, A., & Priglinger, S. G. (2009). Oxidative Stress-Mediated Induction of MMP-1 and MMP-3 in Human RPE Cells. Investigative Ophthalmology & Visual Science, 50(11), 5495-5503. https://doi.org/10.1167/iovs.08-3193 2. Altemtam, N., El Nahas, M., & Johnson, T. (2012). Urinary matrix metalloproteinase activity in diabetic kidney disease: A potential marker of disease progression. Nephron Extra, 2(1), 219-232. https://doi.org/10.1159/000339645 3. Andreucci, M., Provenzano, M., Faga, T., Michael, A., Patella, G., Mastroroberto, P., Serraino, G. F., Bracale, U. M., lelapi, N., & Serra, R. (2021). Aortic Aneurysms, Chronic Kidney Disease and Metalloproteinases. Biomolecules, 11(2), Article 194. https://doi.org/10.3390%2Fbiom11020194 4. Arpino, V., Brock, M., & Gill, S. E. (2015). The role of TIMPs in regulation of extracellular matrix proteolysis. Matrix Biology, 44-46, 247-254. https://doi.org/10.1016/j.matbio.2015.03.005 5. Benjamin, O., & Lappin, S. L. (2021). End-Stage Renal Disease. StatPearls Publishing.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|