FarFaR - Pharmacy Repository
University of Belgrade, Faculty of Pharmacy
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   FarFaR
  • Pharmacy
  • Radovi istraživača / Researchers’ publications
  • View Item
  •   FarFaR
  • Pharmacy
  • Radovi istraživača / Researchers’ publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes

Thumbnail
2018
3137.pdf (192.0Kb)
Authors
Kavarić, Nebojša
Klisić, Aleksandra
Ninić, Ana
Article (Published version)
Metadata
Show full item record
Abstract
Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Biochemical and anthropometric parameters, and blood pressure were obtained. More males than females were classified at high UKPDS risk category (p lt 0.001). Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. In multivariate regression analysis, triglycerides [Odds ratio (OR) =1.703, p=0.001] and creatinine concentration (OR=1.040, p lt 0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with ...CVD risk. HsCRP is not superior for CVD risk prediction by UKPDS risk engine algorithm over high triglyceride and creatinine levels in diabetic population, which suggests that the old traditional markers must not be underestimated when examining CVD risk in population with diabetes.

Keywords:
Cardiovascular risk / Type 2 diabetes / UKPDS risk engine
Source:
Open Medicine, 2018, 13, 1, 610-617
Publisher:
  • Sciendo, Warsaw
Funding / projects:
  • Interactive role of dyslipidemia, oxidative stress and inflammation in atherosclerosis and other diseases: genetic and biochemical markers (RS-175035)

DOI: 10.1515/med-2018-0086

ISSN: 2391-5463

PubMed: 30847393

WoS: 000474884700001

Scopus: 2-s2.0-85062549051
[ Google Scholar ]
5
1
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3139
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Kavarić, Nebojša
AU  - Klisić, Aleksandra
AU  - Ninić, Ana
PY  - 2018
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3139
AB  - Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Biochemical and anthropometric parameters, and blood pressure were obtained. More males than females were classified at high UKPDS risk category (p lt 0.001). Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. In multivariate regression analysis, triglycerides [Odds ratio (OR) =1.703, p=0.001] and creatinine concentration (OR=1.040, p lt 0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with CVD risk. HsCRP is not superior for CVD risk prediction by UKPDS risk engine algorithm over high triglyceride and creatinine levels in diabetic population, which suggests that the old traditional markers must not be underestimated when examining CVD risk in population with diabetes.
PB  - Sciendo, Warsaw
T2  - Open Medicine
T1  - Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes
VL  - 13
IS  - 1
SP  - 610
EP  - 617
DO  - 10.1515/med-2018-0086
ER  - 
@article{
author = "Kavarić, Nebojša and Klisić, Aleksandra and Ninić, Ana",
year = "2018",
abstract = "Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Biochemical and anthropometric parameters, and blood pressure were obtained. More males than females were classified at high UKPDS risk category (p lt 0.001). Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. In multivariate regression analysis, triglycerides [Odds ratio (OR) =1.703, p=0.001] and creatinine concentration (OR=1.040, p lt 0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with CVD risk. HsCRP is not superior for CVD risk prediction by UKPDS risk engine algorithm over high triglyceride and creatinine levels in diabetic population, which suggests that the old traditional markers must not be underestimated when examining CVD risk in population with diabetes.",
publisher = "Sciendo, Warsaw",
journal = "Open Medicine",
title = "Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes",
volume = "13",
number = "1",
pages = "610-617",
doi = "10.1515/med-2018-0086"
}
Kavarić, N., Klisić, A.,& Ninić, A.. (2018). Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes. in Open Medicine
Sciendo, Warsaw., 13(1), 610-617.
https://doi.org/10.1515/med-2018-0086
Kavarić N, Klisić A, Ninić A. Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes. in Open Medicine. 2018;13(1):610-617.
doi:10.1515/med-2018-0086 .
Kavarić, Nebojša, Klisić, Aleksandra, Ninić, Ana, "Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes" in Open Medicine, 13, no. 1 (2018):610-617,
https://doi.org/10.1515/med-2018-0086 . .

DSpace software copyright © 2002-2015  DuraSpace
About FarFaR - Pharmacy Repository | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceCommunitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About FarFaR - Pharmacy Repository | Send Feedback

OpenAIRERCUB