Prevalence and Predictors of Silent Myocardial Ischemia in Diabetic Patients
Hafiz Abdul Manan1,2, Hammad Rauf Chishti3, Franklin Dzigbodi Hewlett4
1Cardiology, London Northwest University Healthcare NHS, London, GBR.
Background:
Silent myocardial ischemia (SMI), a frequently underdiagnosed condition in diabetic patients, contributes significantly to increased cardiovascular morbidity and mortality. The absence of typical symptoms, such as angina (particularly in diabetic individuals with altered pain perception due to autonomic neuropathy), allows ischemic episodes to go unnoticed, while chronic hyperglycemia, endothelial dysfunction, oxidative stress, and low-grade inflammation further predispose them to SMI. Yet despite its serious consequences, it often remains undetected due to both symptomlessness and limitations in standard screening, highlighting the urgent need for proactive risk stratification and targeted diagnostic approaches.
Objective:
To determine the prevalence and key risk factors associated with SMI in diabetic adults, aiming to enhance early detection through targeted risk stratification and inform tailored screening strategies to reduce cardiovascular events in this high-risk group.
Methodology:
This cross-sectional study was conducted at Jinnah Hospital, Lahore, from May 2020 to May 2021. A total of 162 diabetic patients aged ≥40 years with a minimum five-year duration of diabetes and no prior history of overt coronary artery disease, ascertained through clinical history review and verification from available medical records, were enrolled using convenient sampling. This approach was selected due to time and resource constraints within the clinical setting. All participants underwent comprehensive evaluation, including detailed medical history, physical examination, laboratory tests (HbA1c and lipid profile), and cardiac assessments using myocardial perfusion imaging, exercise stress testing, and resting ECG. Multivariate logistic regression analysis was applied to identify independent predictors of SMI, with results reported as odds ratios (OR) and 95% confidence intervals (CIs). A p-value of <0.05 was considered statistically significant.
Results:
SMI was detected in 61 (37.65%) diabetic patients. Multivariate logistic regression analysis identified several independent predictors of SMI: smoking (OR: 2.67; 95% CI: 1.32-5.41; p = 0.006), hypertension (OR: 1.89; 95% CI: 1.02-3.51; p = 0.041), dyslipidemia (OR: 2.15; 95% CI: 1.18-3.93; p = 0.012), diabetes duration >10 years (OR: 2.34; 95% CI: 1.22-4.48; p = 0.010), and poor glycemic control (HbA1c >7%) (OR: 3.21; 95% CI: 1.59-6.49; p = 0.001). Advancing age was also significantly associated with increased SMI risk (OR per year: 1.05; 95% CI: 1.02-1.09; p = 0.003), with a marked rise in prevalence observed beyond the age of 55. These findings highlight a high burden of silent myocardial ischemia among diabetic individuals and underscore the importance of identifying key predictors to support early detection, targeted screening, and risk-based management strategies to mitigate adverse cardiovascular outcomes.
Conclusion:
SMI affects over one-third of diabetic individuals, often going undetected due to the absence of symptoms and limitations in routine screening. Key risk factors include smoking, poor glycemic control, long-standing diabetes, dyslipidemia, hypertension, and increasing age-particularly beyond 55 years. Incorporating non-invasive cardiac assessments such as stress ECG and myocardial perfusion imaging into screening protocols for high-risk diabetics can enable earlier diagnosis, timely intervention, and ultimately reduce the burden of future cardiovascular events.
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