Prevalence and Associating Factors of Ischemic Heart Disease Among Type 2 Diabetes Mellitus: A Cross-sectional Study in a Rural Community

Abstract

Background

Ischemic Heart Disease (IHD) is the leading cause of mortality among cardiovascular disorders. There is inadequate knowledge about the prevalence of IHD in Bangladesh, particularly in diabetes mellitus patients. This research aimed to ascertain the frequency of IHD in diabetic individuals and to identify risk factors for IHD in rural areas.

Methods

A cross-sectional survey was in a rural community in a diabetic cohort. Simple random sampling was used to select 150 diagnosed diabetic patients of both genders, aged 30 to 65, from registration numbers within the diabetic cohort. Diagnosis of IHD was based on the history of established IHD and ECG and echocardiography findings.

Results

The results showed that the prevalence of IHD among diabetic patients is 4%. The univariate binary logistic regression analysis identified significant factors associated with IHD, including older age (OR 1.15; 95% CI 1.01-1.31), higher body mass index (BMI) (OR 1.30; 95% CI 1.07-1.52), increased duration of diabetes (OR 1.13; 95% CI 1.00-1.28), and smokeless tobacco use (OR 5.26; 95% CI 1.00-27.7). However, after adjustment in the logistic regression model, a significantly higher risk of IHD was found to be associated with patients having a higher BMI (AOR 1.43, 95% CI 1.14-1.80) and a longer duration of diabetes (AOR 1.26, 95% CI 1.07-1.48).

Conclusion

The present data indicate that the prevalence of IHD in Bangladesh is 4%, which is a significant issue for diabetic individuals. Regular screening and careful monitoring are necessary among diabetic patients to reduce the risk of cardiovascular diseases, including IHD.

Keywords

Ischemic heart disease diabetes mellitus prevalence associated factors Bangladesh

Background

Ischemic heart disease (IHD) refers to cardiovascular issues resulting from the narrowing coronary arteries responsible for supplying blood to the heart muscle. Although various factors can contribute to the narrowing, such as blood clot formation or blood vessel constriction, the predominant cause is plaque buildup, referred to as atherosclerosis.¹ On the other hand, Type 2 diabetes mellitus (T2DM) is a chronic and progressive metabolic disorder marked by insulin resistance and the functional decline of pancreatic beta cells.² T2DM has a delayed, probably ten-year natural history. It can initially appear as macroangiopathy, especially in cases with IHD.² Compared to the general rural population, those with T2DM have a noticeably higher chance of developing IHD.¹ Over a third (32.2%) of individuals diagnosed with T2DM experience some form of cardiovascular disease (CVD), with IHD being the most prevalent at 21.2%.³ Diabetes mellitus raises the independent risk of IHD by around 1.5 times in men, whereas in women, it increases by about 1.7 times.⁴ In a comparable study conducted in 2007 among diabetic patients, the age-standardized incidence rate (per 1000 person-years) of initial coronary heart disease (CHD) events was 23.3 for females and 28.8 for males.⁵ CVD is the leading cause of mortality in individuals with diabetes, with IHD accounting for the majority of fatalities.¹

Several countries have examined epidemiological data on IHD in Asian populations. In China, the prevalence of CHD was 0.63% in 2007 and 2008.⁶ The overall CHD prevalence in India was 8.2% in 2002.⁷ According to a study, the projected number of deaths in Southeast Asia due to IHD increased from 5.73 million to 8.14 million between 1990 and 2013.⁸ According to estimates from the International Diabetes Federation (IDF), 463 million individuals worldwide were affected by diabetes in 2019, and 79% of those individuals reside in countries with low and middle incomes (LMIC).⁹ The prevalence of diabetes among adults has shown a marked increase in low- and middle-income countries such as Bangladesh, surging from around 5% in 2001 to approximately 14% in 2017.⁹ However, there is limited knowledge regarding the prevalence of IHD in Bangladesh. In a rural community, 3.4% of individuals were found to have IHD, which was defined as the presence of a pathological Q wave on an ECG or current medication usage. Patients with significant risk factors, such as diabetes mellitus, were more likely to have IHD.¹⁰

Several notable risk factors linked to CVD in diabetic subjects include advanced age, smoking, insufficient physical activity, obesity, hypertension, dyslipidemia (characterized by imbalances in high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride levels), as well as suboptimal glycemic control.³ Nonetheless, there is a significant lack of pertinent data from LMIC, such as Bangladesh. Low- and middle-income countries experience a significantly higher burden of CVD compared to developed nations due to inadequate access to adequate and equitable healthcare services, which includes early identification and treatment of CVD risk factors. Therefore, it is crucial to investigate the prevalence of IHD in these places, particularly in various high-risk groups, including T2DM. Limited studies have been conducted to reveal the prevalence of IHD among diabetic patients in Bangladesh. Therefore, the present study aims to determine the prevalence of IHD among diabetic patients through a cross-sectional survey. The secondary objective of this study is to identify the variables associated with IHD.

Method

Study Design and Sampling

This cross-sectional study was conducted in a rural community of Sirajdikhan Upazila, a subdistrict of Munshiganj District of Bangladesh, focusing on diagnosed diabetic patients. The study was carried out at a diabetes center affiliated with the Bangladesh Diabetic Association (BADAS), the leading provider of diabetic care in the country. Since 2015, this center has continuously managed a cohort of diabetic patients in the Roushania Union of Sirajdikhan Upazila, maintaining a comprehensive registry of all these patients. Participants for the study were selected through simple random sampling, using the registration numbers as the sampling frame. As there were no prior studies on the prevalence of IHD at the community level among diabetic patients, this study assumed a 50% prevalence rate for IHD in this group. With a desired precision level of 8%, this assumption necessitated a sample size of 150 participants.

This study included diagnosed diabetic patients of any sex aged 30 to 65 years who were permanent residents of the study area. A participant was considered to have diabetes mellitus (DM) if diagnosed with a fasting blood glucose level of ≥ 7.0 mmol/L and/or a 2-hour post-load glucose concentration of ≥ 11.1 mmol/L, or if they were currently receiving treatment for DM.¹¹ The study excluded severely ill patients who required assistance, as well as pregnant or lactating women. The current study took patients who were diagnosed with IHD by a cardiologist or through ECG and echocardiographic evidence of IHD.

Data Collection

A semi-structured questionnaire was used to facilitate the collection of data. Before collection of data, two data collectors, one phlebotomist and one supervisor, were recruited. These individuals underwent a comprehensive two-day training session aimed at enhancing their proficiency in self-introduction, elucidating the purpose of the study, obtaining informed consent, and effectively utilizing the data collection tool. Throughout the data collection phase, the principal researcher conducted weekly debriefings with the team to monitor their progress and address any challenges. After obtaining written informed consent from the participants, the phlebotomist obtained 8.0 ml of venous blood from each individual for biochemical markers, including fasting and postprandial blood glucose levels (measured two hours after breakfast) and the serum lipid profile. Additionally, three consulting cardiologists, including the study’s lead author, visited the center throughout the data collection period to perform echocardiograms and ECGs on all participants. The data collection process for this study was conducted in June 2022.

Statistical Analysis

The data were coded and entered into SPSS, Version 22. Categorical data were presented as frequencies and percentages, while continuous data were presented as means and standard deviations (SDs). The prevalence of the condition was analyzed using descriptive statistics and reported as a percentage with a 95% confidence interval. Data normality was evaluated by the Shapiro-Wilk test. The chi-square test was employed to compare categorical data, while the t-test was used to compare continuous data. In the case of skewed distribution, the Mann-Whitney U test was done for continuous variables.

Odds ratios (OR) were calculated to analyze the factors associated with IHD. The magnitude of these associations was presented as crude ORs with a 95% confidence interval. Additionally, binary logistic regression analysis was performed to determine the effects of associated factors on diabetic patients with IHD. The Hosmer-Lemeshow goodness-of-fit for the logistic regression models was evaluated, yielding a P value of .09. The omnibus test indicated statistical significance with χ²⁴ = 17.64, P < .001, and the model explained 33.4% (Nagelkerke R²) of the variance and correctly classified 97.3% of cases. The P value was considered significant at a 5% level.

Result

Demographic Data

The mean (SD) age of the participants is 49.62 (9.0) years, and more than two-thirds are female. Around 64% of the patients have no formal education, 13.3% are current smokers, 17.3% use smokeless tobacco, and 54.7% report adding extra salt to their food (Table 1).

Table 1.

Prevalence and Associated Factors of IHD Among Diabetic Patient.

Variables	Total Mean ± SD or n (%)	IHD Mean ± SD or n (%)	No IHD Mean ± SD or n (%)	Crude Odd Ratio OR (95% CI)
Age	49.62 ± 9	58.66 ± 4.1	49.25 ± 9.0	1.15 (1.01–1.31)
Sex
Male	46 (30.7)	2 (4.3)	44 (95.7)	Ref
Female	104 (69.3)	4 (3.8)	100 (96.2)	1.13 (0.201–6.43)
Education
No formal education	96 (64)	5 (5.2)	91 (94.8)	Ref
Secondary and above	54 (36)	1 (1.9)	53 (98.1)	3.43 (0.039–3.01)
Occupation
Paid job	97 (64.7)	4 (4.1)	93 (95.9)	Ref
Non-paid job	53 (35.3)	2 (3.8)	51 (96.2)	0.912 (0.161–5.15)
BMI	25.92 ± 3.8	30.44 ± 5.1	25.73 ± 3.6	1.30 (1.07–1.52)
Duration of diabetes	7.2 ± 0.3	11.16 ± 5.8	7.05 ± 4.3	1.13 (1.00–1.28)
Smoking
Nonsmoker	111 (74)	4 (3.6)	107 (96.4)	Ref
Ever smoker	19 (12.7)	2 (10.5)	17 (89.5)	3.14 (0.54–18.52)
Current smoker	20 (13.3)	−	20 (100)	−
Alcohol consumption
No	143 (95.3)	5 (3.5)	138 (96.5)	Ref
Yes	7 (4.7)	1 (14.3)	6 (85.7)	4.6 (0.46–45.76)
Smokeless tobacco
No	124 (82.7)	3 (2.4)	121 (97.6)	Ref
Yes	26 (17.3)	3 (11.5)	23 (88.5)	5.26 (1.00–27.70)
Added salt in food
No	68 (45.3)	−	68 (100)	Ref
Yes	82 (54.7)	6 (7.3)	76 (92.7)	1.07 (1.05–1.14)
Fruits consumption
No	46 (30.7)	4 (8.7)	42 (91.3)	Ref
1 serving per day	104 (69.3)	2 (1.9)	102 (98.1)	0.206 (0.04–1.16)
Vegetable consumption
Up to 1 serve	111 (74)	5 (4.5)	106 (95.5)	Ref
2 or more serve	39 (26)	1 (2.6)	38 (97.4)	0.558 (0.06–4.92)
Hypertension
No	35 (23.3)	−	35 (100)
Yes	115 (76.7)	6 (5.2)	109 (94.8)
Dyslipidemia
No	32 (21.3)	−	32 (100)
Yes	118 (78.7)	6 (5.1)	112 (94.9)
FBS	8.42 ± 3.07	9.83 ± 2.9	8.36 ± 3.0	1.13 (0.91–1.31)
Blood sugar 2 hours after breakfast	12.04 ± 4.83	14.26 ± 2.7	11.94 ± 4.8	1.08 (0.94–1.24)
HbA1c level	9.53 ± 2.46	9.77 ± 1.6	9.52 ± 2.4	1.04 (0.65–1.66)
Cholesterol level	174.1 ± 63.9	146 ± 27.1	175 ± 64.7	0.98 (0.96–1.01)
Triglyceride level	177.7 ± 90.8	159 ± 35.1	178.5 ± 92.4	0.99 (0.98–1.0)
LDL level	91.4 ± 39.64	74.44 ± 20.6	92.15 ± 40.1	0.98 (0.96–1.01)
HDL level	42.69 ± 11.82	37.4 ± 4.4	42.92 ± 11.9	0.94 (0.85–1.05)

Note: IHD, ischemic heart disease; OR odds ratio; CI, confidence interval; SD, standard deviation; BMI, body mass index; FBS, fasting blood sugar; LDL, low-density lipoprotein; HDL, high-density lipoprotein.

Prevalence and Associated Factors with IHD Among Diabetic Patients

In this study, the prevalence of IHD among diabetic patients is 4%. The mean (SD) duration of diabetes among patients is 7.2 (0.3) years, and HbA1c level stands at 9.53 (2.46%). Approximately three-fourths of the patients have hypertension. Results from the univariate binary logistic regression analysis identify significant associated factors, including older age, higher body mass index (BMI), increased duration of diabetes, added salt in food, and smokeless tobacco. However, after adjusting for potential modifiers, the binary logistic regression analysis finds that a significantly higher risk of IHD is associated with patients having a higher BMI (AOR 1.43, 95% CI: 1.14-1.80) and a longer duration of DM (AOR 1.26, 95% CI: 1.07-1.48) (Table 2).

Table 2.

Binary Logistic Regression for Factors Associated with IHD Among Diabetic Patients.

Variables	Presence of IHD
Variables	Adjusted OR*	95% CI	P Value
BMI	1.43	1.14–1.80	.002
Duration of diabetes	1.26	1.07–1.48	.005

Note: Independent variables: age (year), smokeless tobacco, BMI, duration of diabetes.

Forward LR method.

Nagelkerke R2 = 33.4%.

Discussion

This study found that the prevalence of IHD among diabetic patients residing in rural areas is slightly higher at 4% compared to 2% for the general rural population of Bangladesh.¹² This outcome was expected and can be justified, considering that our diabetic cohort is likely to have a higher burden of the disease. While the prevalence observed in this study aligns with the findings among diabetic patients in Thailand (3.54%),¹³ it is significantly lower than the global prevalence of IHD within the diabetic population, which stands at 32.2%.³ Furthermore, recent meta-analysis data on the prevalence of CVD among the diabetic population in India indicated a much higher rate of 21.1%.¹⁴ Similarly, Iran, in the Middle East region, reported a substantially higher prevalence of 37.4% among diabetic patients, according to a systematic review and meta-analysis.¹⁵ The relatively lower prevalence of IHD in this study could be attributed to the relatively younger age profile of the participants, who mainly consisted of middle-aged adults, and potentially to geographical variations compared to the regions mentioned in the studies. Urban lifestyle and stressful life are contributors to IHD.¹⁶ This study population consists of rural dwellers, which may impact the prevalence, as there is a higher prevalence in urban than rural dwellers, with persistent disparity.¹⁷

The present study identifies several factors significantly associated with IHD among diabetic patients in the crude model. These factors include older age, higher BMI, longer duration of diabetes, added salt in food, and smokeless tobacco. However, after adjusting for confounding factors using multiple regression analysis, only a higher BMI and prolonged duration of DM remain significantly associated with IHD in this demographic. It is important to note that the incidence of IHD increases with age, consistent with findings from previous research conducted in Bangladesh and Thailand.¹³ In older individuals, changes in endothelial function, such as decreased arterial elasticity and compliance, contribute to accelerated vascular aging and degenerative processes, ultimately leading to atherosclerotic disease development.¹⁸ Furthermore, a lower frequency of physical activity in older individuals may also contribute to an elevated risk of IHD.¹³ Smokeless tobacco is linked to multiple harmful health consequences. Like smoked tobacco, smokeless tobacco includes nicotine, a substance known for its addictive qualities. Previous research has been conducted in Western populations to investigate the link between consuming smokeless tobacco and experiencing adverse cardiovascular events such as IHD, strokes, and myocardial infarction.¹⁹ In addition, a recent study found that higher frequency of adding salt to food increases the risk of CVD, particularly IHD.²⁰

When interpreting the findings reported here, several study limitations need to be noted: first, this study was based on data collected from only a rural community, not the urban people, making it less representative of the entire population. However, this study provides a unique perspective since it has analyzed IHD among diabetic patients while considering some well-established cardiovascular risk factors. Due to the concurrent assessment of both outcome and exposure variables in a cross-sectional study, delineating causal relationships presents a considerable challenge. Despite these limitations, the main contribution of this investigation stems from evidence suggesting that screening for IHD among diabetic patients should be routinely performed in primary healthcare settings.

Reducing the burden of IHD in diabetic patients necessitates a comprehensive approach that encompasses prevention and management. The following recommendations are proposed: First, it is crucial to provide diabetic patients with extensive education regarding the correlation between IHD and BMI and emphasize the significance of effectively managing diabetes. This education should encompass the benefits of regular screenings, lifestyle adjustments, and adherence to medication and monitoring routines. Second, healthcare providers should be offered training programs on IHD prevention and management in diabetic patients. This initiative can enhance their knowledge and skills in identifying and addressing cardiovascular risk factors and promoting patient-centered care. Third, it is imperative to establish support systems for diabetic patients, such as support groups or counseling services, to aid them in coping with the challenges of managing their condition and reducing the risk of IHD. These support systems can provide emotional support, share resources and strategies, and encourage adherence to lifestyle modifications. Last, advocating for policy changes prioritizing IHD prevention in diabetic patients is of utmost importance. This can entail initiatives to improve access to healthcare services, promote healthy environments, and implement regulations on tobacco use and unhealthy food marketing.

Conclusions

The study found the prevalence of IHD in diabetic patients in rural Bangladesh at 4%. Risk factors such as higher BMI and prolonged duration of diabetes increase the incidence of IHD among diabetic patients. The current study findings indicate the prevalence of IHD as a concern in individuals with diabetes. The cross-sectional nature and small sample size of this study prevent the establishment of causality and generalization to the entire population. Nevertheless, the primary advantage of this study lies in its ability to identify modifiable risk factors among diabetic patients through regular screening tests. The outcomes of this study will contribute to the existing literature in the pertinent field, primarily by establishing a baseline prevalence of IHD among individuals diagnosed with diabetes. Thus, regular assessment of IHD and optimizing risk factor control are essential for diabetic patients. Furthermore, national health policymakers and medical professionals must implement additional preventive measures to mitigate the impact of CVD.

Footnotes

Acknowledgments

The authors would like to express their gratitude to all the study subjects for their active participation. We also extend our thanks to Debabrata Ghosh, Project Director at the Santi Sir Memorial Diabetic Center of Sirajdikhan, Munshiganj, for his invaluable assistance in data collection.

Authors’ Contribution

AIJ, FIKNB, NB and MAH conceived the project. AIJ, FIKNB and NB collected the data. TA, TC and MAH performed statistical analysis. RI and NB drafted the manuscript. AIJ and MAH supervised the study. All authors reviewed and approved the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

Ethical clearance was obtained from the Institutional Review Board of BSMMU (Memo No. BSMMU/2022/228).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by Bangabandhu Sheikh Mujib Medical University, Bangladesh.

Patient consent

Data collection was done after taking appropriate consent from the patient.

ORCID iDs

Fakhrul Islam Khaled

Nilima Barman

Taslima Chowdhury

Rakia Ishra

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