In this study, the prevalence of CSF was found to be 0.8%, which is lower than the prevalence reported by Nakanishi et al. (1–7%) [3]. However, Sanati et al. also published a similar result (< 1%) [5]. The mean age of the subjects with CSF was 50.63 ± 8.09 years, with males accounting for 53.3% of the cases. Several studies have reported that men are more likely to be affected by CSF, as shown by Huang et al. (61.4%) [6] and Yang et al. (63.4%) [11]. Interestingly, this study found that age did not significantly affect the incidence of CSF (p = 0.398), consistent with several other studies [5, 12] showing that age is not a significant predictor of CSF.

Additionally, most of the subjects in this study were non-smokers (81.7%), and smoking was not found to be a significant predictor of CSF (p = 0.506). This finding is not consistent with some other studies that have shown that smoking has a significant effect, such as those conducted by Altun et al. (p = 0.031) [12], Elsanan et al. (adjusted p = 0.006) [13], and Shui et al. (p < 0.001) [14]. However, these discrepancies could be attributed to the small number of smokers in this study, which may have biased the genuine relationship between smoking and CSF.

According to this study, BMI is not a significant predictor of CSF (p = 0.344), which differs from other publications' findings. Sanati et al. (adjusted p = 0.003) [5], Huang et al. (p = 0.010) [6], and Elsanan et al. (adjusted p < 0.001) [13] have published that BMI is a strong predictor of CSF. Increasing BMI has been shown to elevate the risk of cardiovascular mortality by increasing vasoconstriction mediated by the sympathetic nervous system and systemic inflammatory processes [15, 16]. Obese populations also experience coronary microvascular abnormalities associated with endothelial dysfunction and microvascular remodeling [17]. In this study, most subjects were classified as non-obese, with a median BMI of 26.97 (24.01–30.89), which could explain the difference in findings.

The coronary artery diameter, on the other hand, significantly predicts CSF (3.99 ± 0.53 vs. 3.41 ± 0.51, p < 0.001) in this study. Even after controlling for RBS and creatinine levels, the 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 with a sensitivity of 76.7% and specificity of 70.7% (AUC = 0.787, p < 0.001). This finding challenges the commonly held assumption that larger coronary diameters result in reduced probabilities of myocardial ischemia. It suggests that there exists a critical threshold beyond which coronary arteries, when exceeding a certain diameter, may detrimentally impact myocardial perfusion. Yang et al.'s publication shows that mean coronary artery diameter is also a significant predictor both in bivariate analysis (5.50 ± 0.85 mm vs. 5.18 ± 0.91 mm, p < 0.001) and in multivariate logistic regression analysis (adjusted OR 2.64, 95% CI 1.54–4.51, p < 0.001) [11].

The occurrence of CSF is seen in larger coronary artery diameters, according to the laws of physics, which state that the larger the radius of the blood vessels, the slower the speed of blood flow. This is calculated by the formula Q = πr2v, where Q is constant traffic, π is a constant of 3.14, r is the radius, and v is the flow velocity [11]. However, there are variations in the location of coronary arteries involved. In this study, most CSF cases were in the LCx (83.3%), while most publications report that LAD is the most commonly affected coronary artery [3, 5, 18]. LAD is a much longer vessel than LCx and RCA, which explains why CSF is more common in LAD [10]. This study found that LCx has greater tortuosity than LAD and RCA, affecting coronary blood flow. This explanation is in line with Mihic et al.'s publication, which states that tortuosity is a significant predictor (p < 0.001) in patients with non-obstructive ischemic symptoms, and LCx is the most tortuous vessel [19].

According to this study, RBS was found to be the second most significant predictor of CSF (105.5 (97.0–135.0) vs. 97.5 (95.0–115.0), p = 0.049). However, when multivariate logistic regression analysis was conducted, RBS was no longer significant (p = 0.066). Studies have shown that blood sugar levels, as determined by the HbA1C examination, can potentiate other predictors. Elsanan et al. published that in subjects with an HbA1C > 7, the NLR (r = 0.548, p < 0.001), Hb (r = 0.382, p = 0.018), and hematocrit (r = 0.542, p < 0.001) became significant predictors [13]. Hyperglycemia conditions have been shown to disrupt the physiology of blood flow. Kersten et al. published that hyperglycemia significantly disrupts coronary collateral blood flow through nitric oxide (NO)-mediated mechanisms [20]. The findings were reinforced by Angeli et al., who mentioned that hyperglycemia interferes with NO activation and increases the production of reactive oxygen species, worsening coronary blood flow in ACS cases [21].

Blood viscosity is an essential factor that affects blood flow, with hematocrit and plasma being the primary determinants. The characteristics of red blood cells (RBC) mainly determine microcirculation blood flow, so any deformities in RBC can increase blood viscosity. Therefore, parameters such as RDW are also crucial in determining the occurrence of CSF [22]. Platelet aggregation has been shown to increase significantly in people with CSF, so the platelet size presented by MPV becomes a critical marker describing platelet activity [12]. MPV is a biomarker of platelet activity that is very useful and easy to examine. MPV was also found to be a strong and independent predictor of impaired reperfusion and 6-month mortality in ST-segment elevation myocardial infarction patients undergoing PCI, as well as the incidence of restenosis and acute stent thrombosis [23].

Certain inflammatory predictors, like PLR and NLR, are known to be related to various inflammatory diseases, including cardiovascular disease because inflammation triggers endothelial dysfunction [6, 13]. An increased PLR level can even impact the prothrombotic status, slowing down the coronary blood flow [6]. High PLR levels are associated with a higher risk of recurrence of myocardial infarction, stroke, heart failure, and no-reflow syndrome after PCI [23]. Renal dysfunction also increases the risk of cardiovascular events and worsens prognosis. It is still associated with the mechanism of endothelial dysfunction and worsening of the atherosclerosis process caused by elevated creatinine levels [6]. Endothelial dysfunction affects the decrease in nitric oxide (NO) bioactivity, directly impacting the coronary microvascular [12].

The normal values for creatinine levels, RDW, PDW, and MPV vary depending on the laboratory's examination tools. This study's normal range for creatinine levels was 0.9–1.3 mg/dL, RDW 35.0–47.0 fL, PDW 9.0–13.0 fL, and MPV 7.2–11.1 fL. Several publications have indicated that these parameters significantly impact CSF. For creatinine levels, the results were 0.9 ± 0.2 [12] and 1.17 ± 0.23 [24], RDW 13.21 ± 1.76 [24], and MPV 13.10 ± 1.72 [24] in the CSF group. However, to date, there has been no publication on the effect of PDW on CSF. NLR and PLR are reliable indicators of systemic inflammation and have been extensively studied. However, there has been no consensus on the normal values of NLR and PLR as racial variations significantly influence them. For instance, a study on normal males and females in South China found the reference range for NLR to be 0.43–2.75 and 0.37–2.87, and for PLR to be 36.63–149.13 and 43.36–172.68, respectively [25]. Another publication reports that the normal NLR values in a healthy adult Belgian population are 0.78–3.53 [26]. Meanwhile, in the Iranian population with a mean sample age of 47.9 ± 9.29 years, the mean NLR and PLR were 1.70 ± 0.70 and 117.05 ± 47.73, respectively [27]. Several publications note the significant impact of NLR and PLR on CSF, with an NLR of 1.89 ± 0.58 [11] and a median PLR of 113.11 (91.13–140.11) [6]. Unfortunately, this study found that these parameters had no significant influence on CSF. The differences in results could be due to variations in the characteristics of the population studied in this research and the comparative study. The consistency and sample size, which originated from a single center, may have contributed to the disparity in results compared to the comparative study, which involved multiple centers and a larger number of subjects.

So far, the widely accepted pathophysiological approach for dealing with the CSF has been coronary microvascular dysfunction (CMD) and coronary endothelial dysfunction (CED). However, a surprising publication by Dutta et al. suggests that in patients with angina and non-obstructed coronary arteries, CSF and cTFC are not reliable indicators of CMD or CED. They propose that the guidelines supporting the use of cTFC in diagnosing CMD need to be reassessed. According to their findings, CSF had low diagnostic accuracy for both CMD (43.4%) and CED (31.7%), with poor sensitivity of 26.7% and 21.1%, respectively. Specificity was slightly higher at 65.2% for CMD and 56.0% for CED. Furthermore, cTFC could not predict CMD or CED, as indicated by ROC analyses with an AUC of 0.41 and 0.36, respectively [28]. Therefore, additional invasive or non-invasive tools are necessary to identify this clinical phenomenon when treating patients with CSF.

It is essential to note that although AF was an exclusion criterion in this study, there is a strong connection between AF and CSF. CAD and AF can exacerbate each other because they share similar risk factors and comorbidities [29, 30]. A study by Sharma et al. revealed that CSF was present in 42% of individuals with non-valvular AF. CSF can lead to myocardial ischemia even in the absence of obstructive CAD and may also increase hospitalization rates for AF patients due to fast ventricular response [29]. Furthermore, Gao et al. found that the incidence of CSF (adjusted OR 2.122, 95% CI 1.151–3.910, p = 0.016,) was significantly higher in the intraoperative AF episode group compared to the non-episode group. The proposed mechanism suggests that the duration of AF and the left atrial diameter can impact the TFC in AF patients. Additionally, acute AF leads to an increased demand for oxygen by the atria, potentially exceeding the oxygen supply. Moreover, a significant shortening of the diastolic phase can negatively affect diastolic-dominated coronary perfusion [30].

With the cause of CSF not fully understood, treatment options are limited. Administering anti-anginal medication only provides limited clinical benefits. Extensive studies testing pharmacological approaches to CSF are still lacking and existing evidence comes only from small studies with nonuniform inclusion criteria [7]. Empirical therapies based on several aspects include improving endothelial function by controlling cardiovascular risk factors, using nitrates to dilate coronary arteries, using beta-blockers to prolong coronary perfusion time, using antiplatelets to block platelet cross-linking and aggregation, and using calcium channel blockers to dilate coronary arteries and reduce myocardial contractility [6]. Physicians also widely use nicorandil, which has been proven to improve chest pain symptoms and the impaired function of the left ventricle. This improvement may be due to its potential to increase plasma NO and reduce endothelin-1 in CSF [31]. The effectiveness of nicorandil as a treatment is even better than that of nitroglycerin [32].

However, the study had several limitations. Even though data collection covered a span of seven years, the number of CSF subjects was relatively small. The study did not take into account biomolecular predictors that could have explained the mechanisms underlying CSF. Conducted in a single center with relatively homogeneous subjects, the results may not be easily generalized to the broader population. Furthermore, various echocardiography parameters, such as diastolic function, closely related to left ventricular end-diastolic pressure and CSF, could not be analyzed due to limited secondary data documentation. Lastly, many confounding variables, such as subject comorbidities and prior treatment, could not be controlled for.