Horm Metab Res 2021; 53(01): 41-48
DOI: 10.1055/a-1199-2378
Endocrine Care

Cardiovascular Biomarkers and Calculated Cardiovascular Risk in Orally Treated Type 2 Diabetes Patients: Is There a Link?

Aleksandra Markova
1   Department of Internal Medicine, Clinic of Endocrinology and Metabolism, University Hospital “Alexandrovska”, Medical University Sofia, Sofia, Bulgaria
,
Mihail Boyanov
1   Department of Internal Medicine, Clinic of Endocrinology and Metabolism, University Hospital “Alexandrovska”, Medical University Sofia, Sofia, Bulgaria
,
Deniz Bakalov
1   Department of Internal Medicine, Clinic of Endocrinology and Metabolism, University Hospital “Alexandrovska”, Medical University Sofia, Sofia, Bulgaria
,
Atanas Kundurdjiev
2   Department of Internal Medicine, Clinic of Nephrology, University Hospital “St. Ivan Rilski”, Medical University Sofia, Sofia, Bulgaria
,
Adelina Tsakova
3   Department of Clinical Laboratory and Clinical Immunology, Central Clinical Laboratory, University Hospital “Alexandrovska”, Medical University Sofia, Sofia, Bulgaria
› Author Affiliations
Funding Information This work was partly supported by the Council for Medical Science at the Medical University (Grant 14D – 2015). The funding body did not in any way affect the collection, analysis and interpretation of the data, the writing of the report and the decision to submit it for publication.

Abstract

The aim of the study was to test the correlation of serum levels of asymmetric dimethylarginine (ADMA), endothelin 1 (ET-1), N-terminal brain natriuretic pro-peptide (NT-proBNP), and placental growth factor (PIGF-1) with estimated cardiovascular (CV) risk. The study group was composed of 102 women and 67 men with type 2 diabetes, having their glycemic and metabolic parameters assessed. All were on oral antidiabetic drugs. Serum levels of NT-proBNP and PIGF-1 were measured by electro-hemi-luminescence on an Elecsys 2010 analyzer. Enzymatic immunoassays were used for ADMA and ET-1. The Framingham Risk Score (FRS), the UKPDS 2.0 and the ADVANCE risk engines were used to calculate cardiovascular risks while statistical analysis was performed on SPSS. Levels of PIGF-1 showed no correlation with the calculated CV risks. The same was true for ADMA, except for a weak correlation with the UKPDS-based 10-year risk for stroke (Pearsons’s R=0.167, p=0.039). Plasma levels of ET-1 were correlated with the UKPDS-based 10-year risk for stroke (R=0.184, p=0.032) and fatal stroke (R=0.215, p=0.012) only. NT-proBNP was significantly correlated with all CV risk calculations: ADVANCE-based 4-yr risk (Spearman’s Rho=0.521, p<0.001); UKPDS-based 10-year risk for: CHD (Rho=0.209, p=0.01), fatal CHD (Rho=0.282, p<0.001), stroke (Rho=0.482, p<0.001), fatal stroke (Rho=0.505, p<0.001); and 10-year FRS risk (Rho=0.246, p=0.002). In conclusion, ADMA and PIGF-1 did not seem useful in stratifying CV risk while ET-1 is linked to the risk of stroke, and NT-proBNP to all CV risk estimations.



Publication History

Received: 14 January 2020

Accepted after revision: 08 June 2020

Article published online:
06 July 2020

© 2020. Thieme. All rights reserved.

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  • References

  • 1 Einarson TR, Acs A, Ludwig C. et al. Prevalence of cardiovascular disease in type 2 diabetes: A systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol 2018; 17: 83
  • 2 Fox CS, Golden SH, Anderson C. et al. Update on Prevention of Cardiovascular Disease in Adults With Type 2 Diabetes Mellitus in Light of Recent Evidence: A Scientific Statement From the American Heart Association and the American Diabetes Association. Diabetes Care 2015; 38: 1777-1803
  • 3 Rydén L, Grant PJ, Anker SD. et al. Diabetes, pre-diabetes, and cardiovascular diseases. Eur Heart J 2013; 34: 3035-3087
  • 4 Wells BJ, Roth R, Nowacki AS. et al. Prediction of morbidity and mortality in patients with type 2 diabetes. Peer J 2013; 1: e87
  • 5 Jellinger PS, Handelsman Y, Rosenblit PD. et al. The development of these guidelines is mandated by the American Association of Clinical Endocrinologists (AACE). Endocr Pract 2017; 23: 1-87
  • 6 National Institute for Health Care and Excellence (NICE). Cardiovascular disease: Risk assessment and reduction, including lipid modification. Clinical Guideline NICE CG181, July 2014, Last updated: Sep 2016; https://www.nice.org.uk/guidance/cg181
  • 7 Allan GM, Nouri F, Korownyk C. et al. Agreement among cardiovascular disease risk calculators. Circulation. 2013; 127: 1948-1956
  • 8 Kengne AP. The ADVANCE cardiovascular risk model and current strategies for cardiovascular disease risk evaluation in people with diabetes. Cardiovasc J Afr 2013; 24: 376-381
  • 9 Song SH, Brown PM. Coronary heart disease risk assessment in diabetes mellitus: Comparison of UKPDS risk engine with Framingham risk assessment function and its clinical implications. Diabet Med 2004; 21: 238-245
  • 10 Fujihara K, Suzuki H, Sato A. et al. Comparison of the Framingham risk score, UK prospective diabetes Study (UKPDS) risk engine, Japanese atherosclerosis longitudinal study-Existing cohorts combine (JALS-ECC) and maximum carotid intima-media thickness for predicting coronary artery stenosis in patients with asymptomatic type 2 diabetes. J Atheroscler Thromb 2014; 21: 799-815
  • 11 Pokharel DR, Khadka D, Sigdel M. et al. Estimation of 10-year risk of coronary heart disease in Nepalese patients with type 2 diabetes: Framingham versus United Kingdom prospective diabetes study. N Am J Med Sci 2015; 7: 347-355
  • 12 Echouffo-Tcheugui JB, Kengne AP. Comparative performance of diabetes-specific and general population-based cardiovascular risk assessment models in people with diabetes mellitus. Diabetes Metab 2013; 39: 389-396
  • 13 Bansal D, Nayakallu RSR, Gudala K. et al. Agreement between framingham risk score and united kingdom prospective diabetes study risk engine in identifying high coronary heart disease risk in North Indian population. Diabetes Metab J 2015; 39: 321-327
  • 14 Simmons RK, Coleman RL, Price HC. et al. Performance of the UK prospective diabetes study risk engine and the framingham risk equations in estimating cardiovascular disease in the EPIC- norfolk cohort. Diabetes Care 2009; 32: 708-713
  • 15 van der Leeuw J, Beulens JWJ, van Dieren S. et al. Novel biomarkers to improve the prediction of cardiovascular event risk in type 2 diabetes mellitus. J Am Heart Assoc 2016; 5 DOI: pii: e003048; doi: 10.1161/JAHA.115.003048.
  • 16 Sciacqua A, Grillo N, Quero M. et al. Asymmetric dimethylarginine plasma levels and endothelial function in newly diagnosed type 2 diabetic patients. Int J Mol Sci 2012; 13: 13804-13815
  • 17 Anderssohn M, Schwedhelm E, Lüneburg N. et al. Asymmetric dimethylarginine as a mediator of vascular dysfunction and a marker of cardiovascular disease and mortality: An intriguing interaction with diabetes mellitus. Diab Vasc Dis Res 2010; 7: 105-118
  • 18 Ganz T, Wainstein J, Gilad S. et al. Serum asymmetric dimethylarginine and arginine levels predict microvascular and macrovascular complications in type 2 diabetes mellitus. Diabetes Metab Res Rev 2017; DOI: 10.1002/dmrr.2836.
  • 19 Hsu CP, Hsu PF, Chung MY. et al. Asymmetric dimethylarginine and long-term adverse cardiovascular events in patients with type 2 diabetes: Relation with the glycemic control. Cardiovasc Diabetol 2014; 13: 156
  • 20 Jawalekar SL, Karnik A, Bhutey A. Risk of cardiovascular diseases in diabetes mellitus and serum concentration of asymmetrical dimethylarginine. Bioch Res Int 2013; 189430
  • 21 Chen S, Li N, Deb-Chatterji M, Dong Q. et al. Asymmetric Dimethyarginine as marker and mediator in Ischemic stroke. Int J Mol Sci 2012; 13: 15983-16004
  • 22 Kalogeropoulou K, Mortzos G, Migdalis I. et al. Carotid atherosclerosis in type 2 diabetes mellitus: Potential role of endothelin-1, lipoperoxides, and prostacyclin. Angiology 2002; 53: 279-285
  • 23 Zhang C-L, Xie S, Qiao X. et al. Plasma endothelin-1-related peptides as the prognostic biomarkers for heart failure: A PRISMA-compliant meta-analysis. Medicine 2017; 96: e9342
  • 24 El-Mesallamy H, Suwailem S, Hamdy N. Evaluation of C-reactive protein, endothelin-1, adhesion molecule(s), and lipids as inflammatory markers in type 2 diabetes mellitus patients. Mediators Inflamm 2007; 73635
  • 25 Ju C, Ye M, Li F. Plasma brain natriuretic peptide, endothelin-1, and matrix metalloproteinase 9 expression and significance in type 2 diabetes mellitus patients with ischemic heart disease. Med Sci Monit 2015; 21: 2094-2099
  • 26 Kartamihardja AS. Correlation between transient ischemic dilation index and endothelin-1 level in patients with Type 2 diabetes mellitus. World J Nucl Med 2016; 15: 109
  • 27 Sayama H, Nakamura Y, Saito etal. Does the plasma endothelin-1 concentration reflect atherosclerosis in the elderly?. Gerontology 1999; 45: 312-316
  • 28 Li W, Kelly-Cobbs AI, Mezzetti EM. et al. Endothelin-1-mediated cerebrovascular remodeling is not associated with increased ischemic brain injury in diabetes. Can J Physiol Pharmacol 2010; 88: 788-795
  • 29 James-Todd T, Cohen A, Wenger J. et al. Time-specific placental growth factor (PlGF) across pregnancy and infant birth weight in women with preexisting diabetes. Hypertens Pregnancy 2016; 35: 436-446
  • 30 Mishra RK, Beatty AL, Jaganath R. et al. B-type natriuretic peptides for the prediction of cardiovascular events in patients with stable coronary heart disease: the Heart and Soul Study. J Am Heart Assoc 2014; 3: e000907 DOI: doi: 10.1161/JAHA.114.000907.
  • 31 Wolsk E, Claggett B, Pfeffer MA. et al. Role of B-type natriuretic peptide and N-terminal prohormone BNP as predictors of cardiovascular morbidity and mortality in patients with a recent coronary event and type 2 diabetes mellitus. J Am Heart Assoc 2017; 6: e004743. DOI: doi: 10.1161/JAHA.116.004743.
  • 32 Resl M, Clodi M, Vila G. et al. Targeted multiple biomarker approach in predicting cardiovascular events in patients with diabetes. Heart 2016; 102: 1963-1968
  • 33 Kavaric N, Klisic A, Ninic A. Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes. Open Med 2018; 13: 610-617
  • 34 Vassalle C, Sabatino L, Cecco Pdi. et al. Relationship between bone health biomarkers and cardiovascular risk in a general adult population. Diseases 2017; 5: 24
  • 35 Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia RepoRt of a WHO/IDf ConsultatIon. 2006; https://apps.who.int/iris/handle/10665/43588
  • 36 WHO. Obesity: Preventing and managing the global epidemic. World Health Organization: Technical Report Series. WHO Technical Report Series, no. 894. 2000; /www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/
  • 37 Davies MJ, D’Alessio DA, Fradkin J. et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2018; 41: 2669-2701
  • 38 Catapanoc AL, Graham I, De Backer G. et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J 2016; 37: 2999-3058
  • 39 Levey AS, Bosch JP, Lewis JB. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999; 130: 461-470
  • 40 Triches CB, Mayer S, Quinto BMR. et al. Association of endothelial dysfunction with cardiovascular risk factors and new-onset diabetes mellitus in patients with hypertension. J Clin Hypertens 2018; 20: 935-941
  • 41 Borgeraas H, Hertel JK, Svingen GFT. et al. Association between body mass index, asymmetric dimethylarginine and risk of cardiovascular events and mortality in norwegian patients with suspected stable angina pectoris. PLoS One 2016; 11: e0152029 DOI: 10.1371/journal.pone.0152029.
  • 42 Seligman BGS, Biolo A, Polanczyk CA. et al. Increased plasma levels of endothelin 1 and von Willebrand factor in patients with type 2 diabetes and dyslipidemia. Diabetes Care 2000; 23: 1395-1400
  • 43 Kostov K, Blazhev A, Atanasova M. et al. Serum concentrations of endothelin-1 and matrix metalloproteinases-2, -9 in pre-hypertensive and hypertensive patients with type 2 diabetes. Int J Mol Sci 2016; 17: 1182
  • 44 Kuo YR, Chien CM, Kuo MJ. et al. Endothelin-1 expression associated with lipid peroxidation and nuclear factor-KB activation in type 2 diabetes mellitus patients with angiopathy and limb amputation. Plast Reconstr Surg 2016; 137: 187e-195e
  • 45 Harris AK, Hutchinson JR, Sachidanandam K. et al. Type 2 diabetes causes remodeling of cerebrovasculature via differential regulation of matrix metalloproteinases and collagen synthesis: Role of endothelin-1. Diabetes 2005; 54: 2638-2644
  • 46 Miyauchi T, Sakai S. Endothelin and the heart in health and diseases. Peptides 2019; 111: 77-88
  • 47 Sachidanandam K, Hutchinson JR, Elgebaly MM. et al. Glycemic control prevents microvascular remodeling and increased tone in type 2 diabetes: Link to endothelin-1. Am J Physiol Regul Integr Comp Physiol 2009; 296: R952-R959
  • 48 Sánchez SS, Aybar MJ, Velarde MS. et al. Relationship between plasma Endothelin-1 and glycemic control in type 2 diabetes mellitus. Horm Metab Res 2001; 33: 748-751
  • 49 Gao R, Wang J, Zhang S. et al. The value of combining plasma d-dimer and endothelin-1 levels to predict no-reflow after percutaneous coronary intervention of st-segment elevation in acute myocardial infarction patients with a type 2 diabetes mellitus history. Med Sci Monit 2018; 24: 3549-3556
  • 50 Pernow J, Shemyakin A, Böhm F. New perspectives on endothelin-1 in atherosclerosis and diabetes mellitus. In: Life Sci 2012; 91: 507-516
  • 51 Harris AK, Elgebaly MM, Li W. et al. Effect of chronic endothelin receptor antagonism on cerebrovascular function in type 2 diabetes. Am J Physiol Regul Integr Comp Physiol 2008; 294: R1213-R1219
  • 52 Ahn HR, Shin MH, Yun WJ. et al. Comparison of the framingham risk score, UKPDS risk engine, and SCORE for predicting carotid atherosclerosis and peripheral arterial disease in Korean type 2 diabetic patients. Korean J Fam Med 2011; 32: 189-196
  • 53 Jiao FF, Lam CLK, Fung C. et al. Comparison of four cardiovascular risk prediction functions among Chinese patients with diabetes mellitus in the primary care setting. J Diabetes Investig 2014; 5: 606-614
  • 54 Goliasch G, Silbernagel G, Kleber ME. et al. Refining Long-Term Prediction of Cardiovascular Risk in Diabetes-The VILDIA Score. Sci Rep 2017; 7: 4700
  • 55 Wind AE, Gorter KJ, van den Donk M. et al. Impact of UKPDS risk estimation added to a first subjective risk estimation on management of coronary disease risk in type 2 diabetes - An observational study. Prim Care Diabetes 2016; 10: 27-35