Emerging Predictors by Non-HDL-C/HDL-C Ratio and Novel Biomarkers for Coronary Slow Flow Phenomenon

Abstract

The coronary slow-flow (CSF) phenomenon is a condition characterized by delayed coronary opacification during diagnostic angiography without the presence of epicardial coronary artery disease. This mini-review explores various emerging predictors and biomarkers associated with CSF, aiming to address the potential diagnostic tools. A comprehensive analysis of recent studies has investigated different biomarkers, including growth differentiation factor 15, galectin 3, microRNA (miRNA)-22, miRNA-155, interleukin 34, soluble vascular cell adhesion molecule-1, long non-coding RNA, plasma choline, adropin, and lipid markers non-high-density lipoprotein cholesterol (HDL-C)/HDL-C ratio to enhance understanding and predict CSF. Additionally, we have summarizes the major findings and significant limitations observed in various studies on CSF biomarkers. The implications of these findings suggest significant advancements in personalized treatment strategies and improved prognostic outcomes for patients exhibiting CSF.

First author

Corresponding author

Abbreviations

ALP, Alkaline phosphatase

CSF, Coronary slow flow

CSFP, Coronary slow flow phenomenon

Gal-3, Galectin 3

GDF-15, Growth differentiation factor 15

HDL-C, High-density lipoprotein cholesterol

IL-34, Interleukin 34

LDL, Low-density lipoprotein

lncRNA, Long non-coding RNAs

miRNA, MicroRNA

NHHR, Non-HDL-C/HDL-C ratio

non-HDL-C, Non-HDL cholesterol

SCF, Slow coronary flow

sVCAM-1, Soluble vascular cell adhesion molecule-1

TFC, Thrombolysis in myocardial infarction frame count

TIMI, Thrombolysis in myocardial infarction

VLDL, Very-low-density lipoprotein

INTRODUCTION

In 1972, Tambe et al. were the first to document cases of patients presenting with chest pain and significantly reduced blood flow rates due to the absence of detectable lesions on coronary angiography.¹ Coronary slow flow (CSF) is a special form of coronary microangiopathy, characterized by normal coronary angiography and a delayed passage of contrast agents into the distal vessels.^2,3 Research indicates that a notable majority of patients diagnosed with CSF (80% to 90%) suffer from recurrent chest pain. Among these patients, 33% require hospital readmission, significantly impacting their quality of life. Additionally, approximately 2.5% of individuals with CSF experience a poor prognosis characterized by an elevated risk of cardiovascular mortality, potentially leading to critical outcomes such as sudden death.^4-7 The pathophysiology of CSF remains unclear. However, extensive research suggests that CSF may be linked to endothelial dysfunction, inflammatory responses, microvascular abnormalities, subclinical atherosclerosis, and genetic factors.⁸ In clinical practice, a thrombolysis in myocardial infarction (TIMI) flow grade of 1-2 is considered indicative of CSF.⁹ Slow blood flow is often linked to adverse cardiovascular events such as sudden cardiac death indicating a poor prognosis.¹⁰ Consequently, early diagnosis and prevention of CSF are crucial.

In this mini-review, we aim to provide an overview of recent advancements in the research on the non-high-density lipoprotein cholesterol (HDL-C)/HDL-C ratio and novel biomarkers relevant to CSF. We focus on their potential for predicting and diagnosing CSF, highlighting their significant clinical implications. A schematic diagram illustrating emerging predictors of the non-HDL-C/HDL-C ratio and various novel biomarkers for CSF is presented in Figure 1.

Figure 1.

A schematic diagram of emerging predictors and various biomarkers for coronary slow flow phenomenon. GAL-3, galectin 3; GDF-15, growth differentiation factor 15; IL-34, interleukin 34; lncRNA, long non-coding RNA; miRNA, microRNA; sVCAM-1, soluble vascular cell adhesion molecule-1.

THE POTENTIAL BIOMARKERS FOR CSF

The World Health Organization states that biomarkers serve as indicators of pathogenic processes and play a crucial role in diagnosing and managing various diseases.¹¹ In recent years, several studies have investigated different biomarkers to enhance understanding and predict CSF. Yasin Yuksel et al. determined specific cutoff values for growth differentiation factor 15 (GDF-15) and galectin 3 (GAL-3) (182.18 pg/mL and 8.58 ng/mL) that predicted coronary slow flow phenomenon (CSFP) with high sensitivities (76% and 87.5%) and specificities (84% and 75%), respectively.¹² These findings suggest that GDF-15 and GAL-3 may serve as useful biomarkers for the diagnosis and severity assessment of CSF. Qiang Su et al. explored the relationship between plasma microRNA (miRNA)-155 levels and CSF, a condition associated with cardiovascular events.¹³ Also, miRNA-22 has been utilized as a valuable biomarker for the early diagnosis of CSF patients with sensitivity and specificity of 75.0% and 88.1%, respectively.¹⁴ They suggest that miRNA-22 may serve as a potential biomarker for identifying and stratifying patients with CSF, thereby offering implications for risk stratification and targeted therapy. Mehdi Karasu et al. investigated the elevated levels of the interleukin-34 (IL-34) biomarker about inflammation in the pathogenesis of slow coronary flow (SCF). The results showed a significant positive correlation between the IL-34 biomarker and average thrombolysis in myocardial infarction frame count (TFC) [for all participants: r = 0.514, p < 0.001; for SCF patients: r = 0.526, p < 0.001; for normal controls: r = -0.288, p > 0.05], indicating that IL-34 is a more effective indicator than high-sensitivity C-reactive protein (hs-CRP) in SCF patients.¹⁵

Qing Zhu et al. identified soluble vascular cell adhesion molecule-1 (sVCAM-1) as a potential biomarker for predicting CSF, demonstrating a sensitivity of 87% and a specificity of 73%.¹⁶ Jiang et al. studied the correlation between long non-coding RNA (lncRNA) AF131217.1 and CSF.¹⁷ Their results showed that lncRNA AF131217.1 expression was activated in the CSF model and positively correlated with the mean thrombolysis in TFC and high-sensitivity C-reactive protein levels. Plasma choline can be utilized as a novel diagnostic biomarker for SCF, as evidenced by a significant positive correlation between plasma choline levels and TIMI frame counts (r = 0.364, p = 0.002).¹⁸ Zhao et al. reported serum adropin was identified as an independent negative predictor of CSFP.¹⁹ The cell-free DNA is also utilized as a potential marker for CSFP patients.²⁰ Salusin-β was identified as a potential biomarker for CSF and may play a key role in its pathophysiology.²¹ Oxidative stress biomarkers including total oxidative status, oxidative stress index and lipid hydroxy peroxide may contribute to the development of CSF.²² Another important, the newly developed inflammatory marker of pan-immune-inflammation value shows promise as a diagnostic marker for CSFP (p < 0.05).²³

Wang et al. investigated the relationship between serum alkaline phosphatase (ALP) levels and the CSFP.²⁴ Their results indicated that a serum ALP level greater than 67.5 U/L is a predictor of CSFP, with a sensitivity of 83.3% and a specificity of 84.1%. Bei Liu et al. investigated the association between CD45RO+CD8+T cells and CSF in patients without significant coronary stenosis.²⁵ The results indicated that CSF patients had higher levels of hemoglobin and uric acid compared to non-CSF patients. Additionally, CD45RO+CD8+T cell levels were positively associated with microvascular resistance, suggesting a role for these cells in coronary microvascular dysfunction and the development of CSF. Aytun Çanga et al. studied the relationship between leukocyte counts, hs-CRP, and SCF in patients with angiographically normal coronary arteries.²⁶ The results showed that hs-CRP and monocyte count were significantly positively correlated with SCF as determined by the TIMI frame count method. Tahir Durmaz et al. study found significant differences between the CSF and normal coronary flow groups in terms of depression and anxiety scores.²⁷ SCF is associated with higher microvolt T-wave alternans, particularly in the right coronary artery which may increase the risk of ventricular tachyarrhythmia and sudden cardiac death.²⁸ Zekeriya Dogan et al., found that patients with CSF had increased arterial stiffness, as indicated by higher pulse wave velocity, and decreased choroidal thickness compared to the control group.²⁹ Li et al. reported the triglyceride glucose-body mass index was an independent predictor for SCFP with sensitivity and specificity were 88.6% and 68.5%.³⁰ Their findings suggest that CSF may be a manifestation of a systemic microvascular disorder affecting not only the coronary arteries but also other vascular structures.

THE LIPID MARKERS AS PREDICTORS OF CSF

Recent research has focused on the potential roles of both traditional and non-traditional lipid markers in CSF.³¹ Non-HDL cholesterol (non-HDL-C) includes low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), and intermediate-density lipoprotein.³² On the other hand, HDL-C is known for its protective role against atherosclerosis.³³ The non-HDL-C/HDL-C ratio is an important lipid marker that reflects the balance between atherogenic and anti-atherogenic lipoproteins.³⁴ A higher non-HDL-C/HDL-C ratio is indicative of a lipid profile to atherogenesis and has been associated with an increased risk of cardiovascular events.³⁵ This ratio is emerging as a more reliable predictor of cardiovascular risk compared to traditional markers like total cholesterol or LDL-C alone.³⁶ However, more studies are focused on the role of the non-HDL-C/HDL-C ratio in predicting CSF. Recently, Yu et al. reported that the non-HDL-C/HDL-C ratio (NHHR) serves as a predictor of all-cause and cardiovascular mortality.³⁷ Their study, which included a total of 12,578 participants, revealed a U-shaped correlation between NHHR and all-cause mortality, as well as an L-shaped correlation between NHHR and cardiovascular mortality.

One recent research article, Kenan Toprak et al. published in "Acta Cardiologica Sinica," investigated the relationship between NHHR and CSF, comparing its significance to other traditional and non-traditional lipid markers.³⁸ The results indicated that an NHHR cut-off value of 3.3 provided the best prediction of CSF with a sensitivity of 74% and a specificity of 72%. In addition, the non-HDL-C/HDL-C ratio showed a strong correlation (r = 0.3593, p < 0.0001) with the TIMI frame count, compared to other non-traditional lipid profiles. Diagnostic accuracies of NHHR were superior to other lipid profiles in predicting CSF (area under the curve: 0.785; confidence interval: 0.730-0.840; p < 0.001) were also compared.

LIMITATIONS IN VARIOUS STUDIES ON CSF BIOMARKERS

Table 1 summarizes the major findings and significant limitations observed in various studies on CSF biomarkers.

Table 1. Summary of major findings and significant limitations in CSF biomarker studies.

S. No	Biomarkers	Major findings	Limitations	Ref.
1	Adropin	Independent negative predictor of CSFP (n = 82).	Relate to its cross-sectional design and relatively small study population.	Zhao et al.19
2	Cell-free DNA	The plasma cfDNA level significantly increased to 5.04 ± 2.37 ng/μL in the CSFP patients (n = 46).	Lack of follow-up and exclusion of isolated coronary artery slow flow.	Yolcu et al.20
3	GAL-3 and GDF-15	GAL-3 and GDF-15 levels were posi-tively correlated with the TFC (n = 40).	Single-center design with a relatively small sample size and lacking long-term follow-up.	Yuksel et al.12
4	miRNA-155	CSF showed a statistically significant positive correlation between mean TFC and plasma hs-CRP levels (n = 132).	Single-center study and lack of mechanistic understanding.	Su et al.13
5	miRNA-22	The expression of serum miRNA-22 in CSF is upregulated (n = 44).	The small sample size of 86 patients.	Chen et al.16
6	Non-HDL-C/HDL-C ratio	NHHR showed a stronger correlation with TIMI frame count than other non-traditional lipid profiles (r = 0.3593, p < 0.0001) (n = 9112).	Retrospective and observational study design and lack of comprehensive imaging and biomarker data.	Kenan et al.38
7	sVCAM-1	Plasma sVCAM-1 level is an inde-pendent predictor of CSF. Positively correlated with mean cTFC (n = 46).	Small sample size and strict exclusion criteria and limitations of plasma sVCAM-1 measurement.	Zhu et al.16

cfDNA, cell-free DNA; CSF, coronary slow flow; CSFP, coronary slow flow phenomenon; Gal-3, galectin 3; GDF-15, growth differentiation factor 15; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; miRNA, microRNA; NHHR, non-HDL-C/HDL-C ratio; sVCAM-1, soluble vascular cell adhesion molecule-1; TFC, thrombolysis in myocardial infarction frame count; TIMI, thrombolysis in myocardial infarction.

CONCLUSION

In conclusion, the identification of reliable biomarkers and predictors is crucial for the early diagnosis and management of CSF to prevent adverse cardiovascular events. Recent studies have highlighted several promising biomarkers, including GDF-15, GAL-3, miRNA-22, miRNA-155, IL-34, sVCAM-1, lncRNA, plasma choline, and adropin, each demonstrating significant correlations with CSF. Furthermore, the non-HDL-C/HDL-C ratio has emerged as a superior predictor of CSF compared to traditional lipid markers. These advancements in biomarker research provide various approaches to understanding, diagnosing, and potentially treating CSF.

DECLARATION OF CONFLICT OF INTEREST

All the authors declare no conflict of interest.

FUNDING

This work was supported by a research grant from the National Health Research Institutes (NHRI-112A1-CACO-02232311) and the Ministry of Science and Technology (111-2314-B-006-017-MY3), Taiwan. ROC.