Volume 28, Issue 3 (Summer 2022)                   Intern Med Today 2022, 28(3): 354-365 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abbasi Ranjbar Z, Salari A, Hasandokht T, Fakhr Mousavi A, Jaefarpour S. Risk of Developing Diabetes in Patients Undergoing Coronary Artery Angiography Based on American Diabetes Association Risk Score: A Cross-Sectional Study. Intern Med Today 2022; 28 (3) :354-365
URL: http://imtj.gmu.ac.ir/article-1-3791-en.html
1- Department of Internal Medicine, Faculty of Medicine, Gilan University of Medical Sciences, Rasht, Iran.
2- Department of Cardiology, Cardiovascular Research Center, Heshmat Hospital, Faculty of Medicine, Gilan University of Medical Sciences, Rasht, Iran.
3- Department of Cardiology, Cardiovascular Research Center, Heshmat Hospital, Faculty of Medicine, Gilan University of Medical Sciences, Rasht, Iran. , tolou.hasandokht@gmail.com
Full-Text [PDF 3154 kb]   (217 Downloads)     |   Abstract (HTML)  (437 Views)
Full-Text:   (377 Views)
Cardiovascular diseases are known as one of the crucial causes of death and disability in the world, especially in people with diabetes and hypertension [1]. On the other hand, diabetes is also known as a critical health problem with a high prevalence in recent decades all over the world with a death rate of about 82.4 per hundred thousand people [2]. The prevalence of diabetes in the world is increasing due to the increase in the aging population, socio-economic status, urbanization, increased consumption of unhealthy foods, and inactivity. Since the risk of reoccurrence of coronary artery events after the first heart attack in people with diabetes is reported to be higher than in non-diabetic people [3], the treatment costs of these people are also estimated to be higher [4].
Recent studies have reported that the risk of heart disease in the pre-diabetes stage is higher even with a small increase in blood sugar [5, 6, 7]. Optimal care of heart patients, including lifestyle modification and proper management of cardiovascular risk factors, including diabetes along with correct use of medications are effective in improving the health status and survival of patients. The results of recent studies indicate that for the increase of an uncontrolled risk factor, the survival rate of patients decreases and the probability of adverse cardiac events gradually increases [8, 9]. Evidence from clinical trial studies with a randomized control group has shown that although only drug treatment of risk factors in heart patients with comorbidity is very effective in improving health status, a significant number of these people do not reach the desired treatment goal [10]. In recent studies, the mortality rate in heart patients with diabetes with uncontrolled risk factors has been reported to be at least twice as high as in heart patients with controlled heart risk factors [11]. On the other hand, due to the asymptomatic nature of type 2 diabetes in the early stages of the disease, the prevalence of undiagnosed cases has been reported high [12]. Also, complications of diabetes, such as retinopathy, neuropathy, and coronary artery occlusion are very common in newly diagnosed diabetics and it shows that these problems can appear even in the early and undiagnosed phases [13].
In a study conducted on 3266 candidates for coronary angiography, about 18% of non-diabetic people had undiagnosed diabetes, and the risk of significant coronary artery stenosis was reported higher in these people than in the non-diabetic group [14]. In this regard, early detection and timely treatment of diabetes can have beneficial effects on the vascular complications of diabetes, and the implementation of early diabetes detection programs, especially in cardiovascular patients, seems necessary [15]. Today, clinical guidelines suggest primary screening with the aim of early diagnosis of diabetes and undiagnosed cases using non-invasive methods, such as predictive models [1617], including Finnish diabetes risk score (FINDRISC), Australian type 2 (AUSDRISK), diabetes risk assessment tool, and American diabetes association risk score (ADA). 
Materials and Methods 
The current cross-sectional-descriptive study was conducted to investigate the frequency of people at risk of diabetes based on the ADA tool in non-diabetic patients who were candidates for angiography in 2018 in an academic center. Based on the study of Lotfaliany et al. [18], the prevalence of people at high risk of diabetes based on the ADA tool in the studied population in the sugar and lipid cohort was 12.4%. Considering d=0.3p and α=1.96, the sample size was calculated at 300 patients. All non-diabetic people over 30 years of age who were candidates for angiography and consented to the study were included in the study. Diabetes was defined as self-reported diabetes or the use of oral and injectable blood sugar control drugs. The suitable subjects for the study were selected based on the inclusion criteria from the inpatients and outpatients who were candidates for angiography by the convenience sampling method. The design assistant student collected the information according to the checklist prepared for this study. This information includes background variables, such as age, gender, marital status, place of residence, occupation, literacy level, and smoking. Anthropometric measurements, including weight (kg), and height (cm) without shoes, with light clothes, and with a standard scale and height gauge were performed by a trained student. The people studied in this research were classified into three groups with body mass index (BMI) <18.5 (thin), 24.9-18.5 (normal weight), and >25 (overweight and obesity). The ADA risk assessment model in the non-diabetic (self-reported) American population over 20 years old was designed to identify undiagnosed cases of type 2 diabetes, including 7 questions based on age, sex, family history of diabetes, blood pressure, weight, height, and physical activity [19]. Family history of diabetes based on questions from the patient, history of diabetes in one of the first-degree relatives, such as parents or siblings, history of high blood pressure based on current medication use due to previous diagnosis of high blood pressure or, according to the attending physician, history of diabetes during pregnancy or the birth of a baby more than 4.5 kg, the level of physical activity were considered. Then, based on the variables of the questionnaire, the final score between 0 and 11 is calculated and the risk status of diabetes is determined. In the ADA tool, a score of 4 is equivalent to moderate risk or pre-diabetes, a score of 5 or more is equivalent to high risk or diabetes, and a score below 4 is considered to be equivalent to low risk [20].
In the current research, the consent form and the research checklist have been approved as a medical student’s thesis by the Vice President of Research and Technology of Guilan University of Medical Sciences with the code of ethics IR.GUMS.REC.1398.330. After qualitative evaluation, the variables were entered into SPSS software, version 16. After checking the normality of the data, quantitative variables were reported as mean and standard deviation, and qualitative variables were reported as frequency and percentage. A significance level of less than 0.5 was considered.
In the present study, the data of 300 non-diabetic people who underwent angiography was investigated among whom 59.3% were men and 40.7% were women. The average age of the subjects was 15.7±59.44 years, the lowest age was 31 years and the highest age was 88 years. Almost one-third of the subjects were smokers and 44.3% had high blood pressure and nearly 60% of subjects had a BMI above 25. 22% with a family history of diabetes, and 9% of women (11 out of 122 women) reported a history of gestational diabetes. Almost 90% of people had coronary artery involvement, and the most involvement (47.3%) was in the form of three-vessel involvement. According to the classification of the ADA risk assessment model, only 24.3% of the subjects were at low risk of diabetes, the rest were at moderate risk (32%), and at high risk of diabetes (43.7%). Table 1 presents the basic characteristics of patients with undiagnosed diabetes in subjects undergoing angiography according to the risk of developing diabetes.

As shown in Table 1, the age of the subjects in the group with a high risk of developing diabetes was significantly higher than the other two groups. Also, the frequency of people with high blood pressure and a family history of diabetes in the high-risk group of diabetes was higher than in the low-risk and medium-risk groups, and this difference was statistically significant. Also, the percentage of people with a high risk of diabetes with a BMI below 18.5 and above 25 is more than the normal BMI (P < 0.000). As shown, the frequency of people with involvement of at least two coronary vessels and above in the group with a high risk of diabetes (43.1%) is higher than the two groups of medium risk (34.4%) and low risk (5.5%). 
In this study, using the ADA diabetes risk prediction model, we investigated the status of diabetes risk in the group of heart patients in whom the control of risk factors is vital. The results of our study showed that a significant number of heart patients (75%) with coronary artery involvement are at moderate to high risk of diabetes and only a very small percentage of them are at low risk. This issue shows the attention to the careful follow-up of heart patients after discharge from the hospital in terms of cardiometabolic risk factors. The result of the national study of the risk factors of non-communicable diseases in Iran shows that the prevalence of diabetes is about 7.7% in the adult population, about half of these cases are undiagnosed, and about 16.8% of this population are in the pre-diabetes stage [21]. In addition, based on the Persian cohort study, the prevalence of diabetes (self-reported, drug use, or fasting blood sugar above 126) was reported at about 24% in the population of Guilan [22]. The percentage of people with a high risk of diabetes in the current study is more than the prevalence of diabetes in other studies, although the current study was in the population of heart patients as a high-risk group in terms of cardiometabolic risk factors, which is different from the general population. Similarly, in the study of Gunnar Taubert et al., the prevalence of diabetes in coronary patients was reported to be 31.9%, which was about 10% more than the general population [14]. According to the results of other studies, 175 million people in the world live with undiagnosed diabetes, of which 83.8% are in low- and middle-income countries [23].
In the present study, the share of women with a high risk of diabetes was reported higher than men, which is similar to the results of the national study of non-communicable disease risk factors in 2008 [21]. Previous studies have shown that the risk of heart disease in diabetic men is 2.4 times higher than the non-diabetic men, while this risk increases 3.5 times in diabetic women [24]. A critical point in previous studies is the younger age of heart infarction in women with diabetes compared to men with diabetes, and in addition, the risk of fatal cardiac events is higher in women with diabetes than men with diabetes [2, 25]. According to previous studies, a direct relationship was observed between the incidence of type 2 diabetes and increasing age, the frequency of people with a high risk of diabetes was higher in older age groups. The incidence of diabetes up to the age of 65 years increases with increasing age and the possibility of diabetes is low after this age [26]. 
The results of the present study indicate a higher frequency of people with a high risk of diabetes with a BMI above 25. In previous studies, obesity and overweight have been identified as risk factors for type 2 diabetes and metabolic syndrome [27]. The increase in the prevalence of obesity is reported consistent with the increase in the number of people with diabetes and metabolic syndrome [28]. In most people, an increase in BMI is considered a strong risk factor for the occurrence of diabetes [29]. In addition, obesity and an increase in BMI increase the risk of heart disease and increase the cost of treatment through an indirect effect of increasing the possibility of high blood pressure, blood fat, insulin resistance, and coronary endothelial dysfunction [30]. Although in our study, the frequency of people with a high risk of diabetes with a BMI below 18.5 was higher than the group with a normal BMI, which can be attributed to the lack of some micronutrients, including vitamin D, chromium, biotin, thiamin, and vitamins A, E and, C [31, 32]. The present study was conducted specifically on angiography candidate patients, which limits the generalizability of this study to other people with other underlying diseases or even the general population. Considering the high burden of cardiovascular diseases, it is necessary to study this group of patients with the aim of early identification of one of the important comorbidities. The next issue is the limitation of the sample size in this study, which limited the comparison of diabetes risk status in groups with and without coronary artery involvement. Also, due to the cross-sectional nature of the study, it was not possible to compare the predicted risk with the observed risk. So, it is recommended to conduct longitudinal studies and follow up with the study subjects in terms of the occurrence of the desired event. According to the International Diabetes Federation (IDF) estimate, 10.9% of the world’s population will be affected by diabetes by 2045 [33] and the results of our study show that it is necessary to design interventions to increase people’s awareness of diabetes to modify lifestyle and unhealthy behaviors to prevent diabetes.
Nearly half of the subjects undergoing angiography were at high risk of diabetes (>5). These results show the importance of careful follow-up of cardiac patients in terms of cardiometabolic risk factors after discharge from the hospital.

Ethical Considerations
Compliance with ethical guidelines

The present study, the consent form, and the research checklist have been approved as a medical student’s thesis by the Vice President of Research and Technology of the Guilan University of Medical Sciences with the code of ethics IR.GUMS.REC.1398.330.

This article is taken from the thesis of Ms. Sofia Jafarpour, a medical student from Gilan University of Medical Sciences.

Authors' contributions
Presenting the plan: Zahra Abbasi Ranjbar, Tolue Hasandokht, Arsalan Salari; Collecting information: Zahra Abbasi Ranjbar, Arsalan Salari, AFM, and SJP.Data analysis: Tolou Hasandokht; Writing the article: Tolue Hasandokht, Arsalan Salari, Abuzar Fakhr Mousavi, and Sofia Jaefarpour; Interpretation of results: Arsalan Salari, Abuzar Fakhr Mousavi, and Sofia Jaefarpour; Read and approved the final draft of the article: All authors.

Conflicts of interest
The authors declared no conflict of interest.

The authors of this study are grateful to the Research and Technology Vice-Chancellor of the Guilan University of Medical Sciences for their financial support. 

  1. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2020 update: A report from the American Heart Association. Circulation. 2020; 141(9):e139-596. [doi:/10.1161/cir.0000000000000757]
  2. Glovaci D, Fan W, Wong ND. Epidemiology of diabetes mellitus and cardiovascular disease. Current Cardiology Reports. 2019; 21(4):21.   [DOI:10.1007/s11886-019-1107-y] [PMID]
  3. Komaru Y, Takeuchi T, Suzuki L, Asano T, Urayama KY. Recurrent cardiovascular events in patients with newly diagnosed acute coronary syndrome: Influence of diabetes and its management with medication. Journal of Diabetes and its Complications. 2020; 34(3):107511. [DOI:10.1016/j.jdiacomp.2019.107511] [PMID]
  4. Weng W, Liang Y, Brett J, Hobbs T, Baeres F. Longitudinal analysis of atherosclerotic cardiovascular disease risk and healthcare costs in newly diagnosed type 2 diabetes in a real-world setting. Journal of Diabetes and its Complications. 2020; 34(5):107500. [DOI:10.1016/j.jdiacomp.2019.107500] [PMID]
  5. Di Pino A, Scicali R, Calanna S, Urbano F, Mantegna C, Rabuazzo AM, et al. Cardiovascular risk profile in subjects with prediabetes and new-onset type 2 diabetes identified by HbA1c according to American diabetes association criteria. Diabetes Care. 2014; 37(5):1447-53. [DOI:10.2337/dc13-2357] [PMID]
  6. Brannick B, Dagogo-Jack S. Prediabetes and cardiovascular disease: pathophysiology and interventions for prevention and risk reduction. Endocrinology and Metabolism Clinics of North America. 2018; 47(1):33-50. [DOI:10.1016/j.ecl.2017.10.001] [PMID] [PMCID]
  7. Welsh C, Welsh P, Celis-Morales CA, Mark PB, Mackay D, Ghouri N, et al. Glycated hemoglobin, prediabetes, and the links to cardiovascular disease: Data from UK Biobank. Diabetes Care. 2020; 43(2):440-5. [DOI:10.2337/dc19-1683] [PMID]
  8. Rawshani A, Rawshani A, Franzén S, Sattar N, Eliasson B, Svensson A-M, et al. Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes. New England Journal of Medicine. 2018; 379(7):633-44. [DOI:10.1056/NEJMoa1800256] [PMID]
  9. Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson A-M, Miftaraj M, et al. Range of risk factor levels: Control, mortality, and cardiovascular outcomes in type 1 diabetes mellitus. Circulation. 2017; 135(16):1522-31. [DOI:10.1161/CIRCULATIONAHA.116.025961] [PMID] [PMCID]
  10. Farkouh ME, Boden WE, Bittner V, Muratov V, Hartigan P, Ogdie M, et al. Risk factor control for coronary artery disease secondary prevention in large randomized trials. Journal of the American College of Cardiology. 2013; 61(15):1607-15. [DOI:10.1016/j.jacc.2013.01.044] [PMID]
  11. Bittner V, Bertolet M, Felix RB, Farkouh ME, Goldberg S, Ramanathan KB, et al. Comprehensive cardiovascular risk factor control improves survival: the BARI 2D trial. Journal of the American College of Cardiology. 2015; 66(7):765-73. [DOI:10.1016/j.jacc.2015.06.019] [PMID] [PMCID]
  12. Huang Y-T, Steptoe A, Zaninotto P. Prevalence of undiagnosed diabetes in 2004 and 2012: Evidence from the English longitudinal study of aging. The Journals of Gerontology: Series A. 2021; 76(5):922-8.  [DOI:10.1093/gerona/glaa179] [PMID] [PMCID]
  13. Harris MI, Eastman RC. Early detection of undiagnosed diabetes mellitus: a US perspective. Diabetes/Metabolism Research and Reviews. 2000; 16(4):230-6. [DOI:10.1002/1520-7560(2000)9999:9999<::aid-dmrr122>3.0.co;2-w] [PMID]
  14. Taubert G, Winkelmann BR, Schleiffer T, März W, Winkler R, Gök R, et al. Prevalence, predictors, and consequences of unrecognized diabetes mellitus in 3266 patients scheduled for coronary angiography. American Heart Journal. 2003; 145(2):285-91. [DOI:10.1067/mhj.2003.134] [PMID]
  15. Ekoe J-M, Goldenberg R, Katz P. Screening for diabetes in adults. Canadian Journal of Diabetes. 2018; 42(S 1):S16-9. [DOI:10.1016/j.jcjd.2017.10.004] [PMID]
  16. Alberti KGMM, Zimmet P, Shaw J. International Diabetes Federation: A consensus on Type 2 diabetes prevention. Diabetic Medicine. 2007; 24(5):451-63. [DOI:10.1111/j.1464-5491.2007.02157.x] [PMID]
  17. Paulweber B, Valensi P, Lindström J, Lalic N, Greaves C, McKee M, et al. A European evidence-based guideline for the prevention of type 2 diabetes. Hormone and Metabolic Research. 2010; 42(S 1):S3-36. [DOI:10.1055/s-0029-1240928] [PMID]
  18. Lotfaliany M, Hadaegh F, Asgari S, Mansournia MA, Azizi F, Oldenburg B, et al. Non-invasive risk prediction models in identifying undiagnosed type 2 diabetes or predicting future incident cases in the Iranian population. Archives of Iranian Medicine. 2019; 22(3):116-24. [PMID]
  19. Bang H, Edwards AM, Bomback AS, Ballantyne CM, Brillon D, Callahan MA, et al. Development and validation of a patient self-assessment score for diabetes risk. Annals of Internal Medicine. 2009; 151(11):775-83. [DOI:10.7326/0003-4819-151-11-200912010-00005] [PMID] [PMCID]
  20. Poltavskiy E, Kim DJ, Bang H. Comparison of screening scores for diabetes and prediabetes. Diabetes Research and Clinical Practice. 2016; 118:146-53. [DOI:10.1016/j.diabres.2016.06.022] [PMID] [PMCID]
  21. Esteghamati A, Gouya MM, Abbasi M, Delavari A, Alikhani S, Alaedini F, et al. Prevalence of diabetes and impaired fasting glucose in the adult population of Iran: National Survey of Risk Factors for Non-Communicable Diseases of Iran. Diabetes Care. 2008; 31(1):96-8. [DOI:10.2337/dc07-0959] [PMID]
  22. Mansour-Ghanaei F, Joukar F, Naghipour MR, Sepanlou SG, Poustchi H, Mojtahedi K, et al. The PERSIAN Guilan Cohort Study (PGCS). Archives of Iranian Medicine. 2019; 22(1):39-45. [PMID]
  23. Pinchevsky Y, Butkow N, Raal FJ, Chirwa T, Rothberg A. Demographic and clinical factors associated with development of type 2 diabetes: A review of the literature. International Journal of General Medicine. 2020; 13:121-9. [DOI:10.2147/IJGM.S226010] [PMID] [PMCID]
  24. Barrett-Connor E, Wingard DL. Sex differential in ischemic heart disease mortality in diabetics: A prospective population-based study. American Journal of Epidemiology. 1983; 118(4):489-96. [DOI:10.1093/oxfordjournals.aje.a113654] [PMID]
  25. Huxley R, Barzi F, Woodward M. Excess risk of fatal coronary heart disease associated with diabetes in men and women: Meta-analysis of 37 prospective cohort studies. Bmj. 2006; 332(7533):73-8. [DOI:10.1136/bmj.38678.389583.7C] [PMID] [PMCID]
  26. Cooper-Patrick L, Gallo JJ, Gonzales JJ, Vu HT, Powe NR, Nelson C, et al. Race, gender, and partnership in the patient-physician relationship. Jama. 1999; 282(6):583-9. [DOI:10.1001/jama.282.6.583] [PMID]
  27. Rios S. Relationship between obesity and the increased risk of major complications in non-insulin-dependent diabetes mellitus. European Journal of Clinical Investigation. 1998; 28(S 2):14-7. [DOI:10.1046/j.1365-2362.1998.0280s2014.x] [PMID]
  28. Ginsberg HN, MacCallum PR. The obesity, metabolic syndrome, and type 2 diabetes mellitus pandemic: Part I. Increased cardiovascular disease risk and the importance of atherogenic dyslipidemia in persons with the metabolic syndrome and type 2 diabetes mellitus. Journal of the Cardiometabolic Syndrome. 2009; 4(2):113-9. [DOI:10.1111/j.1559-4572.2008.00044.x] [PMID] [PMCID]
  29. Schnurr TM, Jakupović H, Carrasquilla GD, Ängquist L, Grarup N, Sørensen TI, et al. Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: A case-cohort study. Diabetologia. 2020; 63(7):1324-32. [DOI:10.1007/s00125-020-05140-5] [PMID]
  30. le Roux CW, Hartvig NV, Haase CL, Nordsborg RB, Olsen AH, Satylganova A. Obesity, cardiovascular risk and healthcare resource utilization in the UK. European Journal of Preventive Cardiology. 2021; 28(11):1235-41. [DOI:10.1177/2047487320925639] [PMID]
  31. Via M. The malnutrition of obesity: Micronutrient deficiencies that promote diabetes. ISRN Endocrinology. 2012; 2012:103472.  [DOI:10.5402/2012/103472] [PMID] [PMCID]
  32. Dubey P, Thakur V, Chattopadhyay M. Role of minerals and trace elements in diabetes and Insulin resistance. Nutrients. 2020; 12(6):1864. [DOI:10.3390/nu12061864] [PMID] [PMCID]
  33. Cho N, Shaw J, Karuranga S, Huang Y, da Rocha Fernandes J, Ohlrogge A, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Research and Clinical Practice. 2018; 138:271-81. [DOI:10.1016/j.diabres.2018.02.023] [PMID]
Type of Study: Original | Subject: Internal Medicine
Received: 2021/09/9 | Accepted: 2022/06/22 | Published: 2022/07/1

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2023 CC BY-NC 4.0 | Internal Medicine Today

Designed & Developed by : Yektaweb