ArticleCureus2024
Evaluating Risk Factors and the Burden of Silent Myocardial Ischemia Among Diabetic Patients.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Frequency of Silent Myocardial Ischemia in Diabetic Patients Presenting With Non-cardiac Complaints.Cureus · 2025Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSilent myocardial ischemia (SMI) is a significant concern for diabetic patients, often remaining undetected until severe complications arise. Prolonged hyperglycemia, poor glycemic control, and lifestyle factors contribute to its risk, with older adults and those with long-standing diabetes particularly vulnerable.
objectiveTo assess the prevalence and predictors of SMI in adults with diabetes, emphasizing long-term management and monitoring. METHODOLOGY: A longitudinal observational study was conducted at Nishtar Medical University, Multan, from October 2020 to September 2022, involving 388 adults with diabetes for at least five years. Patients with significant cardiovascular diseases or recent medication-affecting biomarkers were excluded. Data collection included demographics, medical history, and clinical assessments such as HbA1c levels, 12-lead resting ECGs, and treadmill exercise stress tests, with myocardial perfusion imaging (MPI) for those unable to perform stress tests. Statistical analyses using IBM SPSS (version 22) incorporated univariate and multivariate logistic regression to identify predictors of SMI, adjusting for confounders such as age, sex, smoking, and blood pressure. Thresholds included microalbuminuria at 30-300 mg/24 hours and HbA1c ≥7%, with missing data addressed through multiple imputations.
resultsAmong the participants, the prevalence of SMI was 48%, increasing to 58% in those with microalbuminuria. Significant predictors included the duration of diabetes (OR 1.28, 95% CI: 1.08-1.52, p=0.001), HbA1c levels (OR 1.75, 95% CI: 1.42-2.16, p<0.001), age (OR 1.05, 95% CI: 1.01-1.09, p=0.035), and smoking status (OR 1.42, 95% CI: 1.05-1.92, p=0.025). Smoking status was based on self-report. Notably, microalbuminuria showed a strong association with SMI (OR 2.89, 95% CI: 2.10-3.98, p<0.001). The distribution of participants was balanced in terms of age and gender, with a mean age of 58 years (SD 9.4), and 52% were male. No unexpected findings were observed, and the results aligned with the anticipated relationships between the variables.
conclusionThis study highlights a concerning prevalence of SMI among diabetic patients, emphasizing the importance of monitoring diabetes duration and glycemic control, particularly in individuals with microalbuminuria. Regular follow-up care, including routine ECGs, stress tests, and biomarker assessments, is crucial for improving cardiovascular outcomes in this high-risk population. Limitations such as the observational design and potential self-report bias in smoking status may affect the generalizability of the findings. Future research should focus on large-scale, multicenter studies to validate these findings and explore interventions that could reduce the burden of SMI in diabetic patients.
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