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Table of Contents
Year : 2022  |  Volume : 4  |  Issue : 2  |  Page : 59-65

Socioeconomic and clinical determinants of coronary artery disease in symptom-free type 2 diabetes mellitus patients

1 Department of Health Affairs, Al Isawiya General Hospital, Al Qurayyat Governorate, Qurayyat, Kingdom of Saudi Arabia
2 Department of Community Medicine, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra, Nigeria
3 Department of Internal Medicine, Garki Hospital, Garki, Abuja, Nigeria
4 Department of Radiology, Faculty of Medicine, College of Health Sciences, Nnamdi Azikiwe University, Nnewi, Anambra, Nigeria
5 Department of Internal Medicine, Medical College of Wisconsin, Madison, Wisconsin, USA
6 Department of Internal Medicine, Faculty of Medicine, College of Health Sciences, Nnamdi Azikiwe University, Nnewi, Anambra, Nigeria
7 Department of Internal Medicine, College of Medicine and Health Sciences, Bingham University, Jos Campus, Jos, Nigeria

Date of Submission22-Dec-2022
Date of Decision24-Jan-2023
Date of Acceptance25-Jan-2023
Date of Web Publication02-Mar-2023

Correspondence Address:
Dr. Chikezie Hart Onwukwe
Department of Health Affairs, Al Isawiya General Hospital, Al Qurayyat Governorate, Qurayyat
Kingdom of Saudi Arabia
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ACCJ.ACCJ_25_22

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Background: There are global reports of rising cardiovascular burden in persons living with type 2 diabetes mellitus (T2DM) patients. The presence of coronary artery disease (CAD) increases mortality risk in T2DM patients. There are currently no data on the determinants of CAD in Nigerian T2DM patients. Objective: The objective was to determine the determinants of CAD in persons with T2DM. Methods: This was a cross-sectional study involving T2DM patients with and without CAD attending the diabetes clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria. The diagnosis of CAD was made based on personal information obtained using the Rose angina questionnaire and resting electrocardiogram findings. Medical history and other clinical evaluations were done to identify the socioeconomic and clinical variables in the study participants. Data obtained were analyzed using appropriate statistical software. Results: The study involved 400 asymptomatic T2DM patients with a median age of 60 years and a female-to-male ratio of 1.3:1. Sixty-four (16%) participants had CAD. The male: female ratio in CAD and non-CAD groups was 1.8:1 and 0.7:1 (χ2 = 1.7, P = 0.22). Formal education (χ2 = 4.1, P = 0.02), upper socioeconomic class (χ2 = 5.1, P = 0.02), hypertension (χ2 = 2.2, P = 0.03), dyslipidemia (χ2 = 4.7, P = 0.02), cerebrovascular disease (χ2 = 5.2, P = 0.01), smoking (χ2 = 9.1, P = 0.01), waist circumference (Mann–Whitney U = 358, P = 0.02), carotid intima-media thickness (Mann–Whitney U = 300, P = 0.01), and ankle brachial pressure index (Mann–Whitney U = 315, P = 0.01) were significantly associated with CAD in the study participants. Multivariate logistic regression analysis showed that formal education had the least odds of predicting CAD (odd ratio [OR] =2.1, 95% confidence interval [CI] =1.6–6.2; P = 0.02), while low-density lipoprotein cholesterol had the highest odds of predicting CAD (OR = 5.2, 95% CI = 2.1–9.5, P = 0.01) among the study participants. Conclusions: Early screening for comorbidities and lipid abnormalities in T2DM patients is required, especially in those with formal education and within the high socioeconomic class.

Keywords: Clinical, determinants, socioeconomic, type 2 diabetes

How to cite this article:
Onwukwe CH, Chikezie NI, Okorie KK, Umeh EO, Odenigbo CC, Osuji CU, Ohwovoriole AE. Socioeconomic and clinical determinants of coronary artery disease in symptom-free type 2 diabetes mellitus patients. Ann Clin Cardiol 2022;4:59-65

How to cite this URL:
Onwukwe CH, Chikezie NI, Okorie KK, Umeh EO, Odenigbo CC, Osuji CU, Ohwovoriole AE. Socioeconomic and clinical determinants of coronary artery disease in symptom-free type 2 diabetes mellitus patients. Ann Clin Cardiol [serial online] 2022 [cited 2023 Jun 4];4:59-65. Available from:

  Introduction Top

About 415 million persons are living with diabetes mellitus (DM), and this number is likely to get to 642 million by 2040.[1] Data from Nigeria show that about 6 million (5.8%) adult Nigerians are living with DM and two-thirds of cases are undiagnosed.[2],[3]

Type 2 DM (T2DM) is the most common type of DM which accounts for up to 90% of DM cases.[1] Naik et al. reported the occurrence of T2DM in 34.6% of health workers with an average atherosclerotic cardiovascular disease score of 57.8%.[4] Worsening glycemic status in affected individuals increases the risk of coexisting cardiovascular disease. A systematic review of 4,549,481 persons with T2DM showed a 32.2% overall prevalence of macrovascular complications, and coronary artery disease (CAD) was the most reported (21.2%). Acute myocardial infarction was the second-most common cause of death after sudden cardiac death in these individuals. T2DM is regarded as a coronary heart disease “equivalent” as T2DM without preceding occurrence of coronary heart disease indicates the same or even higher chance of CAD than prior CAD.

A few Nigerian reports have shown high cardiovascular risk burden in persons living with T2DM, but none have described the predictors of CAD in Nigerians with T2DM.[5],[6],[7] This study aimed to determine the socioeconomic and clinical determinants of asymptomatic Nigerian patients with T2DM.

  Methods Top

This research work was a cross-sectional study done in the medical ward side laboratory of the Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi, Nigeria. The study was approved by the NAUTH, Nnewi, Research and Ethics Committee. Nnewi town is situated in Anambra, South-East Nigeria. The study participants were asymptomatic T2DM patients recruited consecutively from the diabetes clinic of NAUTH using convenience sampling. Consent was sought after full details of the research work were explained to the participants. A researcher-administered study protocol was used to obtain personal information, social history, medical history, smoking history, dietary history, diabetes duration, physical examination findings, and test results. This research study was carried out from August 2016 to January 2017 (a 6-month period).

Classification of the study participants into upper, middle, and lower socioeconomic class was done using the revised socioeconomic stratification instrument developed by Ibadin and Akpede.[8] The Rose angina questionnaire (RAQ) was included in this protocol with information about history of previous MI, unstable angina, coronary artery bypass graft (CABG) surgery, noninvasive tests for CAD, coronary angiography, use of medications for CAD, and hospital admissions for CAD.[9],[10] The WHO STEPS anthropometry tool was used to determine the weight, height, and waist circumference (WC).[11] Weight measurement was done using a weighing scale (Seca 770 Floor Digital Scale, Hamburg) with the individual wearing light clothes and standing in an upright position with bare feet on each side of the scale. Height was simultaneously measured by reading off the exact value from the back using a stadiometer (Seca 240 wall-mounted, Hamburg) with the feet together and the patient looking straight. The body mass index (BMI) was calculated as weight in kilograms divided by square of height in meters. WC was measured midway between the lower border of the last rib and the iliac bone using a measuring tape.

A mercury sphygmomanometer (Accoson, England) was used to obtain the right arm blood pressure (BP) with the individual seated on a chair with the arm resting on a table at the heart level. This measurement was done after the individual must have rested for at least 15 min. BP measurement was done with the sphygmomanometer cuff snuggly applied on the lower arm and a stethoscope placed on the brachial artery. Cuff was inflated to determine the systolic (Korotkoff I) and diastolic (Korotkoff 5) BP. Three consecutive readings were taken 3 min apart with the average of the last two readings calculated as the BP. Hypertension was defined as systolic BP (SBP) of 140 mmHg and above, or diastolic BP 90 mmHg and above, or history of antihypertensive use.[12]

Resting 12-lead electrocardiography (ECG) was performed using a three-channel digital electrocardiograph device and tracings coded using the University of Minnesota coding system for resting electrocardiograms. Electrocardiograms were interpreted independently by two experienced cardiologists in a blinded manner.[13] CAD was defined as any of the following: evidence of previous acute coronary syndrome (ACS) or treatment for CAD, history of previous coronary angioplasty or CABG surgery, coronary angiography evidence of more than 50% epicardial coronary stenosis, ECG evidence of pathological Q waves (any of Minnesota Code 1-1-1 to 1-1-7 or 1-2-1 to 1-2-5 or 1-2-7), echocardiographic evidence of a region of loss of viable myocardium that is thinned with motion abnormality in the absence of a nonischemic ECG finding, or ECG with Minnesota Codes 4-1-1, 4-1-2, 4-2 or 5-1, 5-2 without RAQ angina.[14] Information about previous coronary angiography and echocardiography was obtained from the hospital case files of the study participants.

A Logiq P5 general electric ultrasound machine with a 7.5–10 MHz frequency linear transducer was used to perform B-mode carotid artery Doppler ultrasound.[15] With the individual in supine position, ultrasound of the common carotid arteries was done using the transducer. Carotid intima-media thickness (CIMT), which is the distance between the lumen-intima border and the media-adventitia border, was measured. Ultrasound of the distal centimeter of both common carotid arteries, carotid bifurcation, and the proximal centimeter of the internal carotid arteries in anterior, lateral, and posterior positions were performed. The higher intima-media distance of each of the three carotid territories on both sides was averaged to determine the CIMT for each study participant.

Ultrasonography of the peripheral arteries was done with an Imex PD II Doppler device and a 5 MHz probe.[16] The sphygmomanometer BP cuff was applied on each arm to determine the brachial systolic BP (SBP) and just above each ankle to determine the dorsalis pedis and posterior tibial SBP. Ankle–brachial pressure index (ABPI) in each lower extremity was calculated as the ratio of the higher of the dorsalis pedis and posterior tibial SBP to the higher of the brachial SBP. The higher of both ABPI readings was analyzed.

Cannulation of the intercubital vein was done, and 10 mL of venous blood collected between eight and ten o'clock in the morning, following a 12–14 h overnight fast. Two milliliters of blood was introduced in a fluoride oxalate tube for plasma glucose measurement using the Trinder glucose oxidase method, while one mL of blood was put in an ethylenediaminetetraacetic acid bottle for total hemoglobin estimation (hemiglobincyanide method) and glycosylated hemoglobin (HbA1c) measurement (boronate affinity chromatography method).[17],[18],[19] The remaining 7 mL was introduced in a plain tube for plasma high-density lipoprotein cholesterol (HDL-C) and HDL3-C subfraction estimation using precipitation method, plasma total cholesterol (TC) level estimation using the modified Liebermann–Burchard method, plasma triglyceride (TG) level estimation using lipase-based lysis of TGs, plasma low-density lipoprotein-cholesterol (LDL-C) estimation (precipitation technique), serum creatinine estimation (isotope dilution mass spectrometry), and serum alanine transaminase measurement (dinitrophenylhydrazine technique) in the NAUTH clinical chemistry laboratory.[20],[21],[22],[23],[24],[25] Plasma HDL2-C subfraction was obtained by subtraction of HDL3-C from the total HDL-C. Clinical samples were preserved in a freezer (Haier Thermocool, UK) at a temperature of -20°C before spectrophotometry was done (Spectronic ZOD, Milton Roy Company, UK). The unknown concentrations of biochemical analytes were determined by interpolation of absorbance on the linear regression curves. Inter- and intra-assay coefficient of variation were <10%, which is the acceptable limit of variation.[26]

Glycosylated hemoglobin >6.5% defined a poor glycemic control.[27] Dyslipidemia was defined as any of the following: TC >200 mg/dL (5.17 mmol/L), LDL-C >130 mg/dL (3.36 mmol/L), TGs >150 mg/dL (1.7 mmol/L), HDL-C <40 mg/dL (1.03 mmol/L) for males and <50 mg/dL (1.3 mmol/L) for females, or lipid lowering drug use.[28]

Use of hormonal contraceptive drugs, steroids, pregnancy, estimated glomerular filtration rate (eGFR) of <60 mL/1.73 m2/min, serum alanine transaminase >45 IU/L which was the upper reference limit for alanine transaminase in the NAUTH chemical pathology laboratory), anemia (defined as hemoglobin <12 g/dL), and sickle cell disease were the exclusion criteria for the study participants.[29],[30] eGFR was calculated based on the modification of diet in renal disease equation.[31]

Statistical analysis

Data were transferred from the study protocol sheets to Microsoft Office Excel® 2010 for data management, followed by statistical analysis of verified data using the Statistical Package for the Social Sciences (SPSS) version 26 (the International Business Machines Corporation, Armonk, New York, United States of America). The Kolmogorov–Smirnov test was used to determine the presence or absence of skewed data set. Quantitative variables were presented as median (interquartile range), while qualitative variables were presented as proportions, n (%). Difference in continuous variables across two groups was analyzed using the Mann–Whitney U-test. Data obtained from medical history, socioeconomic profile, and clinical evaluations were compared among CAD and non-CAD groups of patients.

Logistic regression was used to determine the relationship between independent variables and CAD. P < 0.05 and odd ratio (OR) ≥1.22 defined statistical and clinical significance, respectively.[32] Description of results was done using tables and text.

Ethical statement

The NAUTH Research Ethics Committee, Nnewi, Nigeria, approved this research study on September 5, 2014, with reference ID NAUTH/CS/66/VOL 5/06 before commencement of the research work. Costs were borne by the principal researcher, and study participants were at liberty to withdraw at any stage of the study. All processes carried out during the study index were in accordance with ethical requirements of the committee on human experimentation and the 1975 Helsinki Declaration as revised in 2000.

  Results Top

Four hundred asymptomatic type 2 diabetic patients consisting of 174 (43.5%) males and 226 (56.5%) females participated in this research work. [Table 1] summarizes the participant descriptive profile. The median age of participants was 60 years, and 64 (16%) participants had CAD with a male-to-female ratio of 1.8:1. Age and sex were not significantly different in CAD and non-CAD groups. Sixty (93.8%) CAD patients had formal education compared to 239 (71.1%) non-CAD patients who had formal education (χ2 = 4.1, P = 0.02). Fifty-eight (90.6%) CAD patients were within the upper socioeconomic class compared to 168 (50%) non-CAD patients in the same socioeconomic class (χ2 = 5.1, P = 0.02). There was no significant difference in employment status between both groups of patients (χ2 = 1.1, P = 0.32).
Table 1: Descriptive profile of the study participants

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[Table 2] and [Table 3] summarized the association of baseline medical histories with CAD and the relationship between clinical indices and CAD, respectively. History of hypertension, dyslipidemia, cerebrovascular disease (CVD), and smoking were significantly associated with CAD. There were also significant differences in BMI, WC, CIMT, ABPI, TC, LDL-C, HDL-C, and HDL3-C between both patient groups.
Table 2: Medical history of the study participants

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Table 3: Clinical characteristics of the participants

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[Table 4] shows the odds at which significant independent variables predicted CAD after controlling for confounding variables. LDL-C had the highest odds of predicting CAD (OR = 5.2, 95% confidence interval [CI] =2.1–9.5, P = 0.01), while formal education was the least clinically significant predictor of CAD with OR = 2.1 (95% CI for OR = 1.6–6.2; P = 0.02).
Table 4: Predictors of coronary artery disease in the study participants (n=400)

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  Discussion Top

The index research work aims to describe the socioeconomic and clinical determinants of CAD in asymptomatic T2DM. The current research work found that formal education, upper socioeconomic class, hypertension, dyslipidemia, CVD, smoking, anthropometric indices, CIMT, ABPI, TC, LDL-C, HDL-C, and the HDL3-C phenotype were significant predicting variables of CAD among the study participants with T2DM in the current work.

The United Kingdom Prospective Diabetes Study and the Fremantle Diabetes Study did not find a statistically significant gender relationship with CAD in diabetic patients, while the ACCORD study did not find a statistically or clinically significant relationship between age and CAD in diabetic patients.[33],[34],[35] These findings from previous studies were similar to findings in the current study.

The Basel Asymptomatic high-Risk Diabetics' Outcome Trial (BARDOT) reported male gender, duration of diabetes, peripheral arterial disease, smoking history, and SBP as the predictors of asymptomatic CAD in type 2 diabetic patients with a mean age of 65 years.[36] These findings from the BARDOT were obtained from 400 T2DM patients with CAD occurring in 87 of them. The current study did not find an association of CAD with sex, diabetes duration, or SBP among the study participants probably due to difference in methodologies for defining CAD as the BARDOT used abnormal myocardial perfusion single-photon emission computed tomography (SPECT) image findings to define CAD.

Tanaka et al. determined the predictors of CAD in asymptomatic T2DM patients at baseline.[37] The study found that BMI and LDL-C were significantly associated with coronary artery plaque progression. This report was similar to findings from the index research work in which BMI and LDL-C were significant predictors of CAD with LDL-C having an OR of 5.2 in predicting CAD, despite the study by Tanaka et al. using CT coronary angiography to define CAD. The sample size of this study was 70 with a mean age of 65 years.

The Progression of AtheRosclerotic PlAque Determined by Computed TomoGraphic Angiography IMaging (PARADIGM) research study found that male sex, current smoking, and HbA1c were significant independent predictors of CAD in 325 T2DM patients.[38] This previous study reported no significant association between CAD and any of age, BMI, hypertension, and LDL-C, despite the use of coronary artery angiography to define CAD. These findings were different from what was found in the current study and findings by Tanaka et al. The retrospective design of this aspect of the PARADIGM study may explain the disparity in the study findings.

Cordola Hsu et al. studied the risk factors that predicted CAD in 1613 adult DM patients. This previous study showed that age, male gender, hypertension, current smoking, C-reactive protein, and TG levels were the strong predictors of CAD in DM patients. Of these identified significant predictors of CAD in the research carried out by Cordola Hsu et al., only hypertension and smoking were found to predict CAD in the study participants involved in the current study. A possible explanation for this may be the varied methods of defining CAD in the study by Cordola Hsu et al. as their study design was a retrospective approach to identify risk factors for CAD in diabetic patients.[39]

Lu et al. demonstrated that BMI was the strongest predictor of CAD among 796 T2DM patients studied. This finding was similar to findings from the current study despite the fact that thallium-201 myocardial perfusion imaging was used to identify participants with CAD.[40]

Heinsen et al. studied the association of baseline risk factors and CAD in 230 T2DM patients with no symptoms. This previous study demonstrated that male sex, tobacco exposure, and HbA1c were significant independent predictors of CAD among the study participants. The difference in study findings between the research study by Heinsen et al. and the current study may possibly be explained by the use of computed tomography (CT) coronary angiography in diagnosing CAD among the study participants.[41]

Study limitations

The main limitation of this study was the inability to perform noninvasive CT coronary angiography, which is the gold-standard investigation for diagnosing CAD. This was due to its high cost and unavailability in the study location. Non-CT coronary angiography is no longer encouraged due to its invasive nature. Another limitation was the cross-sectional design of the study. The cross-sectional study design of the current study was another study limitation as a prospective approach with a larger sample size may add more information to the study findings.

The use of clinical variables contained in the RAQ to identify patients with CAD was the main strength of this research work considering the unavailability of CT coronary angiography in most Nigerian medical facilities.

  Conclusions Top

Coronary heart disease is prevalent in Nigerian T2DM patients who are symptomless. Formal education, upper socioeconomic class, comorbidities, and lipid abnormalities significantly predicted CAD in Nigerians with T2DM. LDL-C had the highest odds of predicting CAD in these patients.

Early screening for comorbidities and lipid abnormalities should be carried out in Nigerians with T2DM, especially those who received formal education and those within the upper socioeconomic class. Appropriate health policy making is important in the quality improvement process at all levels of health care with good referral systems in place.

Future research involving the use of coronary angiography in defining CAD is recommended with patients followed up over time. This will add more data to the findings already obtained from the current study.


We hereby acknowledge Professor Ogonna Oguejiofor of the Endocrinology, Diabetes, and Metabolism Unit of NAUTH, Nnewi, Nigeria, Professor Geoffery Onyemelukwe of the Clinical Immunology Unit, Ahmadu Bello University Teaching Hospital, Zaria, Nigeria, and the Radiology Department, NAUTH, Nnewi, Nigeria, for their support throughout the duration of the study.

Financial support and sponsorship


Conflicts of interest

C. Onwukwe and N. Chikezie accessed all data obtained from participants. All authors made huge contributions to the conception, design, data acquisition, and interpretation of these data. Each author revised the manuscript and made relevant input to the actualization of the final version, which was approved by them. There was no conflict of interest by any of the authors regarding this manuscript.

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  [Table 1], [Table 2], [Table 3], [Table 4]


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