Relationship between inflammatory markers and coronary slow flow in type 2 diabetic patients

The predictors of coronary slow flow in patients undergoing coronary angiography

BACKGROUND

A new challenge in coronary artery disease treatment has emerged, where specific populations exhibit ischemic symptoms without any obstruction in the epicardial coronary artery. Instead, they exhibit slow coronary contrast flow, referred to as coronary slow flow (CSF). This study aims to identify several predictors of CSF.

RESULTS

This case-control study was conducted at the Regional General Hospital of West Nusa Tenggara Province in Indonesia from December 2016 to February 2024. The study involved sixty subjects, with 30 in each group of CSF and normal epicardial coronary artery angiogram (NECA). CSF is enforced by the TIMI frame count (TFC) greater than 27 frames. Among all the predictors studied, coronary artery diameter (p < 0.001) and random blood sugar (p = 0.049) were found to affect the CSF significantly. In the multivariate analysis, coronary artery diameter remained a significant predictor (adjusted OR 10.08, 95% CI 2.64-38.50, p < 0.001), with an optimal cut-off point of more than 3.56 mm, a sensitivity of 76.7%, and a specificity of 70.7% (AUC = 0.787, p < 0.001).

CONCLUSION

The coronary artery diameter strongly predicts CSF in patients undergoing coronary angiography.

Single Nucleotide Polymorphisms in Coronary Microvascular Dysfunction

Coronary microvascular dysfunction is an underdiagnosed pathologic process that is associated with adverse clinical outcomes. There are data to suggest that coronary microvascular dysfunction, in some cases, may be genetically determined. We present an updated review of single nucleotide polymorphisms in coronary microvascular dysfunction.

Predictors of non-obstructive coronary slow flow in poorly controlled type 2 diabetes mellitus: a cross-sectional study

BACKGROUND

Coronary slow flow (CSF) can occur due to various factors, such as inflammation, small vessel disease, endothelial dysfunction, and inadequate glucose control. However, the exact pathological mechanisms behind CSF remain incompletely understood. The objective of this study was to identify the risk factors associated with slow coronary flow in individuals with Type 2 Diabetes Mellitus (T2DM) who have non-obstructive coronary artery disease (CAD) and experience CSF.

METHODS

We conducted a prospective cohort study involving 120 patients with T2DM who were referred for invasive coronary angiography due to typical chest pain or inconclusive results from non-invasive tests for myocardial ischemia. Using a 2 × 2 design, we categorized patients into groups based on their glycemic control (adequate or poor) and the presence of CSF (yes or no), defined by a TIMI frame count > 27. All patients had non-obstructive CAD, characterized by diameter stenosis of less than 40%. We identified many variables associated with CSF.

RESULTS

Our investigation revealed no significant differences in age, sex, family history of coronary artery disease, ECG ischemia abnormalities, or echocardiographic (ECHO) data between the groups. In patients with adequate glycemic control, hypertension increased the risk of CSF by 5.33 times, smoking by 3.2 times, while dyslipidemia decreased the risk by 0.142. Additionally, hematocrit increased the risk by 2.3, and the platelet-to-lymphocyte ratio (PLR) increased the risk by 1.053. Among patients with poor glycemic control, hematocrit increased the risk by 2.63, and the Neutrophil-to-Lymphocyte Ratio (NLR) by 24.6. Notably, NLR was positively correlated with glycemic control parameters in T2DM patients with CSF.

CONCLUSIONS

In T2DM patients with CSF, various factors strongly correlate with glycemic control parameters and can be employed to predict the likelihood of CSF. These factors encompass hypertension, smoking, increased body mass index (BMI), elevated platelet count, hematocrit, NLR, PLR, and C-reactive protein (CRP).

TRIAL REGISTRATION

Registry: ZU-IRB (ZU-IRB#9419-3-4-2022), Registered on: 3 April 2022, Email: IRB_123@medicine.zu.edu.eg.