Diabetes & Metabolism Journal

Original Articles

Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population: Dipika Bansal, Ramya S. R. Nayakallu, Kapil Gudala, Rajavikram Vyamasuni, Anil Bhansali; Diabetes Metab J. 2015;39(4):321-327. Published online July 8, 2015; DOI: https://doi.org/10.4093/dmj.2015.39.4.321

3,841 View
33 Download
11 Web of Science
12 Crossref

Background

The aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them.

Methods

A cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined.

Results

The FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (κ=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin.

Conclusion

The FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.

Citations

Citations to this article as recorded by

Endocan is Related to Increased Cardiovascular Risk in Type 2 Diabetes Mellitus Patients
Aleksandra Klisic, Jelena Kotur-Stevuljevic, Ana Ninic
Metabolic Syndrome and Related Disorders.2023; 21(7): 362. CrossRef
Estimated risk of cardiovascular events and long-term complications: The projected future of diabetes patients in Delhi from the DEDICOM-II survey
Swapnil Rawat, Ramasheesh Yadav, Siddhi Goyal, Jitender Nagpal
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2023; 17(11): 102880. CrossRef
Cardiovascular Biomarkers and Calculated Cardiovascular Risk in Orally Treated Type 2 Diabetes Patients: Is There a Link?
Aleksandra Markova, Mihail Boyanov, Deniz Bakalov, Atanas Kundurdjiev, Adelina Tsakova
Hormone and Metabolic Research.2021; 53(01): 41. CrossRef
Risk of coronary heart disease and stroke based on United Kingdom prospective diabetes study in type 2 DM patients in Medan
R Amelia, J Harahap, H Wijaya, I I Fujiati
IOP Conference Series: Earth and Environmental Science.2021; 912(1): 012081. CrossRef
Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors
Ankush Jamthikar, Deep Gupta, Narendra N. Khanna, Luca Saba, John R. Laird, Jasjit S. Suri
Indian Heart Journal.2020; 72(4): 258. CrossRef
Current Data Regarding the Relationship between Type 2 Diabetes Mellitus and Cardiovascular Risk Factors
Cosmin Mihai Vesa, Loredana Popa, Amorin Remus Popa, Marius Rus, Andreea Atena Zaha, Simona Bungau, Delia Mirela Tit, Raluca Anca Corb Aron, Dana Carmen Zaha
Diagnostics.2020; 10(5): 314. CrossRef
Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound
Ankush D. Jamthikar, Deep Gupta, Luca Saba, Narendra N. Khanna, Klaudija Viskovic, Sophie Mavrogeni, John R. Laird, Naveed Sattar, Amer M. Johri, Gyan Pareek, Martin Miner, Petros P. Sfikakis, Athanasios Protogerou, Vijay Viswanathan, Aditya Sharma, Georg
Computers in Biology and Medicine.2020; 126: 104043. CrossRef
Additive and Synergistic Cardiovascular Disease Risk Factors and HIV Disease Markers' Effects on White Matter Microstructure in Virally Suppressed HIV
Maëliss Calon, Kritika Menon, Andrew Carr, Roland G. Henry, Caroline D. Rae, Bruce J. Brew, Lucette A. Cysique
JAIDS Journal of Acquired Immune Deficiency Syndromes.2020; 84(5): 543. CrossRef
Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study
Narendra N. Khanna, Ankush D. Jamthikar, Deep Gupta, Andrew Nicolaides, Tadashi Araki, Luca Saba, Elisa Cuadrado-Godia, Aditya Sharma, Tomaz Omerzu, Harman S. Suri, Ajay Gupta, Sophie Mavrogeni, Monika Turk, John R. Laird, Athanasios Protogerou, Petros P.
Computers in Biology and Medicine.2019; 105: 125. CrossRef
Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes
Nebojsa Kavaric, Aleksandra Klisic, Ana Ninic
Open Medicine.2018; 13(1): 610. CrossRef
Differential Association of Metabolic Risk Factors with Open Angle Glaucoma according to Obesity in a Korean Population
Hyun-Ah Kim, Kyungdo Han, Yun-Ah Lee, Jin A Choi, Yong-Moon Park
Scientific Reports.2016;[Epub] CrossRef
The Association between Diabetic Retinopathy and Framingham Risk Score in Koreans with Type II Diabetes
Da Yeong Kim, Su Jeong Song, Jeong Hun Bae, Cheol-Young Park, Eun-Jung Rhee
Journal of the Korean Ophthalmological Society.2016; 57(5): 779. CrossRef

Smaller Mean LDL Particle Size and Higher Proportion of Small Dense LDL in Korean Type 2 Diabetic Patients: Sunghwan Suh, Hyung-Doo Park, Se Won Kim, Ji Cheol Bae, Alice Hyun-Kyung Tan, Hye Soo Chung, Kyu Yeon Hur, Jae Hyeon Kim, Kwang-Won Kim, Moon-Kyu Lee; Diabetes Metab J. 2011;35(5):536-542. Published online October 31, 2011; DOI: https://doi.org/10.4093/dmj.2011.35.5.536

5,941 View
52 Download
15 Crossref

Abstract PDF PubReader

Background

Small dense low density lipoprotein (sdLDL) has recently emerged as an important risk factor of coronary heart disease.

Methods

The mean LDL particle size was measured in 203 patients with type 2 diabetes mellitus (T2DM) and 212 matched subjects without diabetes using polyacrylamide tube gel electrophoresis. Major vascular complications were defined as stroke, angiographically-documented coronary artery disease or a myocardial infarction. Peripheral vascular stenosis, carotid artery stenosis (≥50% in diameter) or carotid artery plaque were considered minor vascular complications. Overall vascular complications included both major and minor vascular complications.

Results

Diabetic patients had significantly smaller mean-LDL particle size (26.32 nm vs. 26.49 nm) and a higher percentage of sdLDL to total LDL compared to those of subjects without diabetes (21.39% vs. 6.34%). The independent predictors of sdLDL in this study were serum triglyceride level and body mass index (odds ratio [OR], 1.020 with P<0.001 and OR 1.152 with P<0.027, respectively). However, no significant correlations were found between sdLDL and major vascular complications (P=0.342), minor vascular complications (P=0.573) or overall vascular complications (P=0.262) in diabetic subjects.

Conclusion

Diabetic patients had a smaller mean-LDL particle size and higher proportion of sdLDL compared to those of subjects without diabetes. Obese diabetic patients with hypertriglyceridemia have an increased risk for atherogenic small dense LDL. However, we could not verify an association between LDL particle size and vascular complications in this study.

Citations

Citations to this article as recorded by

Evaluation of measured and calculated small dense low-density lipoprotein in capillary blood and association with the metabolic syndrome
Sara Deza, Inmaculada Colina, Oscar Beloqui, José Ignacio Monreal, Estéfani Martínez-Chávez, Julia Maroto-García, Carmen Mugueta, Alvaro González, Nerea Varo
Clinica Chimica Acta.2024; 557: 117897. CrossRef
Nrf2 Signaling Pathway as a Key to Treatment for Diabetic Dyslipidemia and Atherosclerosis
Michelle Yi, Arvin John Toribio, Yusuf Muhammad Salem, Michael Alexander, Antoney Ferrey, Lourdes Swentek, Ekamol Tantisattamo, Hirohito Ichii
International Journal of Molecular Sciences.2024; 25(11): 5831. CrossRef
Association between measured or calculated small dense low‐density lipoprotein cholesterol and oxidized low‐density lipoprotein in subjects with or without type 2 diabetes mellitus
Hyun‐Ki Kim, Jinyoung Hong, Sunyoung Ahn, Woochang Lee, Sail Chun, Won‐Ki Min
Journal of Clinical Laboratory Analysis.2023;[Epub] CrossRef
The association of apolipoprotein in the risk of ST-elevation myocardial infarction in patients with documented coronary artery disease
Astuti Giantini, Nur Gifarani Pratiwi, Renan Sukmawan, Joedo Prihartono, Suzanna Immanuel, Merci Monica Pasaribu, Sri Suryo Adiyanti, Yusuf Bahasoan
International Journal of Cardiology Cardiovascular Risk and Prevention.2023; 18: 200194. CrossRef
Atherogenic Index of Plasma and Its Association with Risk Factors of Coronary Artery Disease and Nutrient Intake in Korean Adult Men: The 2013–2014 KNHANES
Hye Ran Shin, SuJin Song, Jin Ah Cho, Sun Yung Ly
Nutrients.2022; 14(5): 1071. CrossRef
The Atherogenic Index of Plasma: A Powerful and Reliable Predictor for Coronary Artery Disease in Patients With Type 2 Diabetes
Kuo Zhou, Zheng Qin, Jinfan Tian, Kongyong Cui, Yunfeng Yan, Shuzheng Lyu
Angiology.2021; 72(10): 934. CrossRef
Direct bilirubin is associated with low-density lipoprotein subfractions and particle size in overweight and centrally obese women
Y.-J. Kwon, H.-S. Lee, J.-W. Lee
Nutrition, Metabolism and Cardiovascular Diseases.2018; 28(10): 1021. CrossRef
Correlation between Cholesterol, Triglycerides, Calculated, and Measured Lipoproteins: Whether Calculated Small Density Lipoprotein Fraction Predicts Cardiovascular Risks
Sikandar Hayat Khan, Nadeem Fazal, Athar Abbas Gilani Shah, Syed Mohsin Manzoor, Naveed Asif, Aamir Ijaz, Najmusaqib Khan Niazi, Muhammad Yasir
Journal of Lipids.2017; 2017: 1. CrossRef
Effects of Small Dense LDL in Diabetic Nephropathy in Females with Type 2 Diabetes Mellitus
Seongyul Ryu, Youngwoo Kim, Mee Kyoung Kim, Hyuk-Sang Kwon, Ki-Hyun Baek, Ki-Ho Song, Kyung-Jin Yun
Journal of Lipid and Atherosclerosis.2016; 5(1): 11. CrossRef
Prepregnancy Adverse Lipid Profile and Subsequent Risk of Gestational Diabetes
Emily S. Han, Ronald M. Krauss, Fei Xu, Sneha B. Sridhar, Assiamira Ferrara, Charles P. Quesenberry, Monique M. Hedderson
The Journal of Clinical Endocrinology & Metabolism.2016; 101(7): 2721. CrossRef
Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus
Xiao-Wei Zhu, Fei-Yan Deng, Shu-Feng Lei
Primary Care Diabetes.2015; 9(1): 60. CrossRef
Higher levels of small dense low‐density lipoprotein (LDL) are associated with cardiac autonomic neuropathy in patients with Type 2 diabetes
E.‐H. Jang, Y.‐M. Park, J. Hur, M.‐K. Kim, S.‐H. Ko, K.‐H. Baek, K.‐H. Song, K.‐W. Lee, H.‐S. Kwon
Diabetic Medicine.2013; 30(6): 694. CrossRef
Sleep Status and Low-Density Lipoprotein Particle Size in a General Japanese Female Population: The Mima Study
Kazuhiko Kotani, Kokoro Tsuzaki, Shinji Fujiwara, Naoki Sakane
Medical Principles and Practice.2013; 22(5): 510. CrossRef
Serum small-dense LDL abnormalities in chronic renal disease patients
M. Chu, A. Y. M. Wang, I. H. S. Chan, S. H. Chui, C. W. K. Lam
British Journal of Biomedical Science.2012; 69(3): 99. CrossRef
Small Dense Low-density Lipoprotein and Cardiovascular Disease
Sunghwan Suh, Moon-Kyu Lee
Journal of Lipid and Atherosclerosis.2012; 1(1): 1. CrossRef

Comparison of the Predictability of Cardiovascular Disease Risk According to Different Metabolic Syndrome Criteria of American Heart Association/National Heart, Lung, and Blood Institute and International Diabetes Federation in Korean Men.: Do Young Lee, Eun Jung Rhee, Eun Suk Choi, Ji Hoon Kim, Jong Chul Won, Cheol Young Park, Won Young Lee, Ki Won Oh, Sung Woo Park, Sun Woo Kim; Korean Diabetes J. 2008;32(4):317-327. Published online August 1, 2008; DOI: https://doi.org/10.4093/kdj.2008.32.4.317

3,083 View
22 Download
7 Crossref

Abstract PDF

BACKGROUND
We compared the prevalences of two criteria of metabolic syndrome, that is, American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) and International Diabetes Federation (IDF), in Korean male adults and compared the predictability of insulin resistance and future cardiovascular diseases using Framingham Risk Score. METHODS: In total 23,467 male adults (mean age 43.3 years) who participated in medical check-up in 2005, the prevalences of metabolic syndrome according to AHA/NHLBI and IDF criteria and the presence of insulin resistance, defined by the highest quartile of Homeostasis Model Assessment of insulin resistance index (HOMA-IR), were compared. The relative risk (calculated risk/average risk) for 10-year risk for coronary artery disease (CHD) assessed by Framingham Risk Score were compared. RESULTS: 5.8% of the subjects had diabetes mellitus. 20.7% and 13.2%of the subjects had metabolic syndrome defined by AHA/NHLBI and IDF criteria, and the two criteria showed high agreement with kappa value of 0.737 (P < 0.01). More subjects in IDF-defined group had insulin resistance compared with AHA/NHLBI definition (59.8 vs. 54%, P < 0.01). The odds ratio for increased relative risk (> 1.0) for 10-year CHD were higher in AHA/NHLBI-defined subjects compared with IDF-defined subject (3.295 vs. 3.082). The Kappa values for the analysis of agreement between each criteria and prediction of insulin resistance or cardiovascular disease risk, were too low for comparison. CONCLUSION: In Korean males, the prevalence for metabolic syndrome defined by AHA/NHLBI criteria was higher than those defined by IDF criteria. IDF criteria detected more subjects with insulin resistance, but didn't have better predictability for CHD compared with AHA/NHLBI criteria.

Citations

Citations to this article as recorded by

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model
Muhammad Zulqarnain, Habib Shah, Rozaida Ghazali, Omar Alqahtani, Rubab Sheikh, Muhammad Asadullah
Brain Sciences.2023; 13(7): 994. CrossRef
Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction
Jaekwon Kim, Ungu Kang, Youngho Lee
Healthcare Informatics Research.2017; 23(3): 169. CrossRef
Relationship between Abdominal Fat Area Measured by Screening Abdominal Fat CT and Metabolic Syndrome in Asymptomatic Korean Individuals
Dae Woong Park, Noh Hyuck Park, Ji Yeon Park, Seon-Jeong Kim
Journal of the Korean Society of Radiology.2017; 77(1): 1. CrossRef
Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree
Jaekwon Kim, Jongsik Lee, Youngho Lee
Healthcare Informatics Research.2015; 21(3): 167. CrossRef
Implication of high‐body‐fat percentage on cardiometabolic risk in middle‐aged, healthy, normal‐weight adults
Ji Young Kim, Sang‐Hwan Han, Bong‐Min Yang
Obesity.2013; 21(8): 1571. CrossRef
Cardio-Metabolic Features of Type 2 Diabetes Subjects Discordant in the Diagnosis of Metabolic Syndrome
Sa Rah Lee, Ying Han, Ja Won Kim, Ja Young Park, Ji Min Kim, Sunghwan Suh, Mi-Kyoung Park, Hye-Jeong Lee, Duk Kyu Kim
Diabetes & Metabolism Journal.2012; 36(5): 357. CrossRef
Comparison of Cardiovascular Health Status and Health Behaviors in Korean Women based on Household Income
Young-Joo Park, Nah-Mee Shin, Ji-Won Yoon, Jiwon Choi, Sook-Ja Lee
Journal of Korean Academy of Nursing.2010; 40(6): 831. CrossRef

Value of Coronary Calcium Score in Type 2 Diabetics.: Ji Eun Lee, Mi Jung Eun, Kyung Ah Chun, Jae Hong Kim, Ji Sung Yoon, Ihn Ho Cho, Kyu Chang Won, Hyoung Woo Lee; Korean Diabetes J. 2006;30(4):303-311. Published online July 1, 2006; DOI: https://doi.org/10.4093/jkda.2006.30.4.303

2,722 View
22 Download

Abstract PDF: BACKGROUND
Cardiovascular disease including coronary heart disease (CHD) is the most common cause of morbidity and mortality in patients with diabetes. But traditional risk factor assessment is limited to predict CHD in asymptomatic high-risk individuals. In this study, relationship between coronary calcium score (CCS) and CHD was evaluated to determine value of coronary artery calcification detected by multi-slice spiral computed tomography to predict CHD in high risk asymptomatic patients with type 2 diabetes. METHODS: 127 patients were enrolled who admitted in Yeungnam University Hospital between December 2004 and May 2005. Standard cardiovascular risk factors and the CCS measured by multi-slice spiral computed tomography were assessed. RESULTS: Enrolled subjects were consisted of 56 subjects with diabetes and 71 subjects without diabetes. The mean CCS was significantly greater in patients with diabetes than without diabetics (P < 0.01). In both groups, patients with higher CCS had higher prevalence of CHD (P < 0.05). In all subjects, LDL cholesterol levels and CCS were significantly associated in multi-variate analysis (P < 0.05). In patients without diabetes, age was only associated with presence of CHD (P < 0.05). CCS was only associated with CHD in patients with diabetes, even after adjusting for the effects of age, LDL cholesterol and CRP (P < 0.05). CONCLUSION: Therefore, multi-slice spiral computed tomography can non-invasively and accurately detect coronary calcification. By detection of coronary artery calcification, it may be possible to predict coronary heart disease early in high-risk asymptomatic patients with type 2 diabetes.

The Association of Pro12Ala Polymorphism in PPAR-gamma Gene with Coronary Artery Disease in Korean Subjects.: Chang Hee Kwon, Eun Jung Rhee, Se Yeon Kim, Eun Ran Kim, Chang Uk Chon, Chan Hee Jung, Ji Ho Yun, Byung Jin Kim, Ki Chul Sung, Bum Su Kim, Won Young Lee, Ki Won Oh, Jin Ho Kang, Sun Woo Kim, Man Ho Lee, Jung Roe Park; Korean Diabetes J. 2006;30(2):122-129. Published online March 1, 2006; DOI: https://doi.org/10.4093/jkda.2006.30.2.122

2,540 View
18 Download

Abstract PDF: BACKGROUND
PPAR-gamma, a member of nuclear family, which is involved in the differentiation of adipose tissue, is reported to be associated in the pathogenesis of type 2 diabetes mellitus, insulin resistance and atherosclerosis. We conducted a research to see whether the prevalence of coronary artery disease is associated with Pro12Ala polymorphism in exon B of PPAR-gamma in Korean adults. METHODS: The study was conducted in 161 subjects (97 males, 64 females, mean age 57 year old) who underwent coronary angiogram due to chest pain. We assessed cardiovascular risk factors in all subjects, such as blood pressure, body mass index (BMI), fasting blood sugar and serum lipid profiles. Subjects were divided into four groups as normal, 1-vessel, 2-vessel and 3-vessel disease according to the number of stenosed coronary arteries. Genotypings of Pro12Ala polymorphism were done with Real-time polymerase chain reaction. RESULTS: Allelic frequency for proline was 0.957 and 0.043 for alanine, and they were in compliance with Hardy-Weinberg equilibrium (P = 0.85). 79 subjects (43.5%) had normal coronary artery, 52 subjects (31%), 1-vessel disease, 24 subjects (14.9%), 2-vessel disease and 15 subjects (9.3%), 3-vessel disease. When the cardiovascular risk factors were compared among these four groups, there were no meaningful differences except the age and high-density lipoprotein cholesterol levels, which were lost after adjustment for age and BMI. There were no significant differences in the prevalence or severity of coronary artery diseases according to the different genotypes of Pro12Ala polymorphism. CONCLUSIONS: There was no significantassociation between Pro12Ala polymorphism in exon B of PPAR-gamma and prevalence or severity of coronary artery disease in Korean adults. It is considered that further studies on the correlation between Pro12Ala polymorphism and coronary artery disease should be carried out in larger Korean population in the future

First
Prev
Page of 1
Next
Last

Search