Coronary slow flow: role of systemic inflammation and biomarkers in its pathophysiology

Coronary slow flow: role of systemic inflammation and biomarkers in its pathophysiology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coronary slow flow: role of systemic inflammation and biomarkers in its pathophysiology Myrna Vianney Muñoz Flores, Arguiñe Ivonne Urraza Robledo, Faviel Francisco González Galarza, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7653116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 17 You are reading this latest preprint version Abstract Background: Coronary slow flow (CSF) is characterized by reduced coronary blood flow velocity in the absence of significant obstruction, and it has been associated with systemic inflammation and endothelial dysfunction. This study aimed to evaluate the association between CSF and inflammatory parameters to better understand its pathophysiology and potential utility as clinical markers. Methods: A cross-sectional study was conducted in 77 patients undergoing coronary angiography, including those with CSF (n = 43) and controls with angiographically normal coronary arteries (n = 34). Clinical, biochemical, and hematological variables, TNF-α levels, and inflammatory indices (TyG, NLR, LMR, NPR, PIV, and SIRI) were assessed. Statistical tests and logistic regression were applied to identify risk factors associated with CSF. Results: The prevalence of CSF was 2.6%, with hypertension and dyslipidemia being the most frequent comorbidities. Patients with CSF exhibited significantly higher levels of total cholesterol (p = 0.024), LDL cholesterol (p = 0.016), monocytes (p = 0.017), and neutrophils (p = 0.015). Inflammatory indices TyG, NLR, NPR, PIV, and SIRI were significantly elevated in the CSF group (p < 0.05), whereas LMR was lower. No significant differences were found in TNF-α levels, although elevated values were observed in both groups. Logistic regression identified cholesterol and the SIRI index as significant risk factors for CSF. Conclusions: CSF is associated with systemic inflammation and dyslipidemia, with the SIRI index emerging as an accessible and practical tool to assess inflammation and cardiovascular risk in CSF patients. Its incorporation into clinical practice may facilitate earlier diagnosis and more effective treatment strategies. Clinical Trial number: Not applicable Coronary slow flow systemic inflammation SIRI index dyslipidemia coronary angiography biomarkers. Background Cardiovascular diseases (CVDs) represent a major global public health problem. According to the World Health Organization (WHO), they are the leading cause of death worldwide, accounting for 17.9 million annual deaths [ 1 ]. Among CVDs, coronary slow flow (CSF) is characterized by reduced coronary blood flow velocity in the absence of significant obstructive coronary artery disease [ 2 ]. Although the underlying pathophysiological mechanisms of CSF remain unclear [ 3 ], it has been observed in patients with anginal symptoms [ 4 ], underscoring its clinical relevance and impact on patient quality of life. Inflammatory processes are among the factors implicated in the pathophysiology of CSF, with cytokines playing a central role, as they have been linked to both myocardial infarction type (with or without ST-segment elevation) and coronary blood flow in the affected artery. Inflammatory responses, endothelial function, and thrombus formation are closely interconnected [ 5 ]. Cytokines, produced by various cell types, include interleukins (ILs), chemokines, interferons (IFNs), and tumor necrosis factor (TNF) [ 6 ]. These proteins are pivotal in critical stages of the atherosclerotic cascade, such as endothelial activation and dysfunction, transcytosis of low-density lipoproteins (LDL), and monocyte adhesion and transmigration. They also contribute to extracellular matrix remodeling and vascular smooth muscle cell proliferation, promoting the development of lipid-rich cores surrounded by thin fibrous caps [ 6 ]. Several studies have investigated the potential relationship between CSF and inflammatory parameters, suggesting that systemic inflammation may play a key role in its development [ 7 ]. Tumor necrosis factor-alpha (TNF-α), primarily secreted by immune cells such as monocytes, macrophages, neutrophils, natural killer cells (NK), and CD4 + T lymphocytes [ 8 ], is widely recognized as an inflammatory marker [ 9 ]. Moreover, hematological parameters, whether considered individually or as inflammatory indices, have become relevant tools for assessing and predicting a wide range of cardiovascular and systemic inflammatory diseases. Examples include the neutrophil-to-lymphocyte ratio (NLR) [ 10 ], lymphocyte-to-monocyte ratio (LMR) [ 11 ], neutrophil-to-platelet ratio (NPR) [ 12 ], pan-immune-inflammation value (PIV) [ 13 ], and systemic inflammatory response index (SIRI) [ 14 ]. These indices are closely associated with inflammation and cardiovascular risk. Elevated NLR levels have been reported in patients with coronary artery disease and CSF, linking them to atherosclerosis, endothelial dysfunction, and inflammation [ 10 ]. Similarly, SIRI, together with NLR, has been associated with cardiovascular mortality in postmenopausal women with osteoporosis and osteopenia [ 14 ]. On the other hand, LMR has been correlated with the severity of coronary atherosclerosis [ 11 ], while NPR has been shown to increase in patients with ischemic stroke [ 12 ]. Additionally, the triglyceride–glucose index (TyG index) is used for the early identification of individuals at high risk of cardiovascular events [ 15 ]. The elevation of inflammatory markers in patients with CSF may reflect endothelial activation and inflammation, processes that are integral to the pathological pathways involved in this condition [ 16 ]. The aim of the present study was to evaluate the association between CSF and inflammatory parameters, with the purpose of contributing to a better understanding of its pathophysiology and its potential utility as clinical markers. Identifying risk factors associated with CSF may provide valuable insights to improve diagnostic and therapeutic approaches in these patients. Methods Study Population This study was conducted at the Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), in the High Specialty Medical Unit Hospital de Especialidades No. 71 (UMAE HE No. 71), located in Torreón, Coahuila, from June 2023 to June 2024. The study included patients who underwent coronary angiography in the hemodynamics unit and were diagnosed with CSF (case group), as well as those with angiographically normal coronary arteries (control group). Eligible participants were adults over 18 years of age, of either sex, undergoing coronary angiography for the first time. Patients were excluded if they had a history of diagnosed cardiovascular disease, known coronary lesions, prior acute myocardial infarction, autoimmune or chronic inflammatory disorders, cancer, trauma, or major surgery within the past 6 months, to minimize potential bias in inflammatory measurements. The study was approved by the Research Ethics Committee and the Local Health Research Committee No. 501 at UMAE HE No. 71 (Approval No.: R-2023-501-046). All participants received detailed information about the study, and written informed consent was obtained. The research was conducted in accordance with the principles of the Declaration of Helsinki and the Belmont Report. Coronary Angiography Coronary angiography was performed via femoral or radial arterial access using the Seldinger technique, with superficial and deep local anesthesia (2% lidocaine) prior to arterial puncture. Iopamidol, a low-osmolarity contrast medium, was used during the procedure. The diagnosis of CSF was based on the definition proposed by Beltrame (2012), which requires the absence of significant angiographic lesions (≥ 40%) and delayed distal vessel contrast opacification corresponding to TIMI grade 2 flow (i.e., requiring three cardiac cycles to fully opacify the vessel). In contrast, patients with TIMI grade 3 flow and no obstruction or alterations in major epicardial arteries were classified as having “angiographically normal arteries.” All angiographic results were interpreted and validated by two interventional cardiologists. Biochemical and Hematological Parameters Biochemical and hematological parameters were obtained from patient medical records. Prior to coronary angiography, patients underwent laboratory testing, including blood chemistry, lipid profile, and complete blood count. Biochemical analyses were performed using the Vitros® 4600 automated dry chemistry analyzer, and hematological parameters were measured with the XN-1000 automated analyzer, following the manufacturers’ specifications. Determination of TNF-α Quantification of TNF-α was performed using a quantitative sandwich enzyme-linked immunosorbent assay (ELISA) for human TNF-α (lot: 20114909, code: SK00109-01, Aviscera Bioscience). Reagents, including wash solution, TNF-α standard solution, positive control, detection antibody concentrate, and streptavidin-HRP conjugate, were prepared according to the manufacturer’s instructions. Microplate readings were conducted with the Stat Fax 4700 reader at 450 nm. Statistical Analysis Descriptive statistical analysis was performed. Qualitative variables were expressed as absolute frequencies and percentages. Normality of quantitative variables was assessed using the Shapiro–Wilk test. Quantitative variables were reported as mean ± standard deviation or as median and interquartile range, depending on distribution. Comparisons between the two study groups were performed using the chi-square test for qualitative variables, and either Student’s t-test or the Mann–Whitney U test for quantitative variables, according to distribution. Regression analysis was conducted to identify variables associated with CSF, with odds ratios (OR) and 95% confidence intervals (CIs) calculated. Statistical analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). A p-value < 0.05 was considered statistically significant. Results The present study was conducted between July 2023 and July 2024. During this period, a total of 1,650 coronary angiographies were performed, of which 2.6% (n = 43) of patients were diagnosed with CSF. After applying inclusion and exclusion criteria, the final sample consisted of 77 participants (43 with CSF and 34 without CSF); 53% were male (Table 1 ). The mean age of participants was 62 ± 9.57 years. The mean body mass index (BMI) was 29.13 ± 4.80 kg/m², while the mean heart rate was 70.83 ± 12.51 beats per minute. Hypertension was the most prevalent comorbidity (82%), followed by dyslipidemia (41%) and diabetes mellitus (37%). Additionally, 45% of patients were smokers. No statistically significant differences were observed between the CSF and control groups in sociodemographic, clinical, or anthropometric characteristics (Table 1 ). Table 1 Sociodemographic, clinical, and anthropometric characteristics Parameters N = 77 CSF Group (n = 43) Control Group (n = 34) p-value Sex 0.428 Male n(%) 41(53) 22(51) 19(56) Female n(%) 36(47) 21(49) 15(44) Age (years) 62 ± 9.57 63 ± 9 62 ± 9 0.612 BMI(kg/m 2 ) 29.13 ± 4.80 28.80 ± 4.64 29.55 ± 5.05 0.286 Heart rate (lpm) 70.83 ± 12.51 70.32 ± 12.19 71.47 ± 13.05 0.821 Hypertension n(%) 64(82) 38(88) 26(76) 0.141 Dyslipidemia n(%) 32(41) 20(46) 11(32) 0.224 Diabetes n(%) 29(37) 18(42) 11(32) 0.269 Smoking n(%) 35(45) 19(44) 16(47) 0.491 The results are exressed as mean ± standard deviation. BMI: body mass index; bpm: beats per minute; CSF: slow flow coronary. Biochemical and hematological analyses revealed significant differences between groups. Total cholesterol levels were significantly higher in the CSF group (165 [153.00–186.00] vs. 139 [118.00–178.50]; p = 0.024), as were LDL cholesterol levels (96.16 ± 29.18 vs. 80.29 ± 26.82; p = 0.016). Monocyte counts were also higher in the CSF group (0.64 ± 0.12 vs. 0.57 ± 0.15; p = 0.017), as were neutrophil counts (4.40 [4.00–5.30] vs. 4.00 [3.53–4.70]; p = 0.015) (Table 2 ). Table 2 Biochemical and hematological parameters Parameter CSF Group (n = 43) Control Group (n = 34) p-value Glucose (mg/dL) 100.00 (91–121) 90.50 (81.50–114.00) 0.073 BUN (mg/dL) 16.86 (12.60-20.35) 14.00 (12.00-18.19) 0.353 Creatinine (mg/dL) 0.90 (0.70-1.00) 0.80 (0.60-1.00) 0.283 Uric acid (mg/dL) 5.17 ± 1.50 5.29 ± 1.82 0.748 Triglycerides (mg/dL) 147.00 (110.00-200.00) 115.50 (93.00-167.50) 0.054 Cholesterol (mg/dL) 165.00(153.00-186.00) 139.00 (118.00-178.50) 0.024* HDL (mg/dL) 39.99 ± 10.78 39.69 ± 10.50 0.901 LDL (mg/dL) 96.16 ± 29.18 80.29 ± 26.82 0.016* VLDL (mg/dL) 29.40 (22.00–40.00) 23.10 (18.60–33.50) 0.054 Leucocytes (10 3 / µl) 7.20 (6.70–8.02) 7.19 (6.19–8.60) 0.898 Lymphocytes (10 3 / µl) 1.78 (1.58–2.20) 1.99 (1.60–2.81) 0.133 Monocytes (10 3 / µl) 0.64 ± 0.12 0.57 ± 0.15 0.017* Eosinophils (10 3 / µl) 0.11 (0.10–0.20) 0.12 (0.10–0.20) 0.688 Basophils (10 3 / µl) 0.04 (0.03–0.06) 0.04 (0.02–0.05) 0.331 Neutrophils (10 3 / µl) 4.40 (4.00-5.30) 4.0 (3.57–4.70) 0.015* Erythrocytes (10 6 / µl) 4.64 ± 0.64 4.85 ± 0.55 0.129 Hemoglobin (g/dL) 13.86 ± 1.96 14.34 ± 1.77 0.277 Hematocrit (%) 41.68 ± 5.79 43.40 ± 5.45 0.188 MCV (fL) 89.87 ± 5.62 89.43 ± 6.46 0.752 MCH (pg) 30.11 ± 2.28 29.59 ± 2.66 0.361 MCHC (g/dL) 33.10 ± 1.58 33.05 ± 1.28 0.881 Platelets (10 3 / µl) 231.72 ± 66.58 248.44 ± 64.60 0.271 The results are expressed as mean ± standard deviation or median (interquartile range) according to their distribution. BUN: urea nitrogen; HDL: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very-low-density lipoprotein; MCV: mean corpuscular volumen; MCH: mean corpuscular hemoglobin; MCHC: mean corpuscular hemoglobin concentration. * p-value < 0.05 indicates a significant difference between groups. No statistically significant differences were observed in TNF-α levels; however, values were elevated above the normal range in both groups (Table 3). Analysis of indices associated with inflammation and insulin resistance, including TyG, NLR, LMR, NPR, PIV, and SIRI, revealed significant differences, with higher values in the CSF group, suggesting a heightened inflammatory response and greater insulin resistance (Table 3). Table 3. Inflammation parameters Parameters CSF Group (n = 43) Control Group (n = 34) p-value TNF-α (pg/ml) 46.21 (16.91–60.02) 54.13 (14.15–62.51) 0.704 TyG Index 9.03 ± 0.52 8.76 ± 0.61 0.042* NLR 2.77 ± 1.15 2.06 ± 0.70 0.002* LMR 2.99 ± 0.98 4.13 ± 1.85 0.001* NPR 19.15(15.85–27.83) 16.60(11.86–20.94) 0.009* PIV 353.16(273.69–516.60) 265.76(174.17-377.24) 0.006* SIRI 1.49(1.20–2.28) 1.17(0.73–1.39) < 0.001* The results are expressed as mean ± standard deviation or median (interquartile range) according to their distribution. TNF-α: tumor necrosis factor-alpha; TyG Index: triglyceride/glucose index; NLR: neutrophil-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; NPR: neutrophil-to-platelet ratio; PIV: pan-immune-inflammation value; SIRI: systemic inflammatory response index. * p-value < 0.05 indicates a significant difference between groups. Logistic regression analysis identified cholesterol and the SIRI index as the most significant risk factors for CSF (Table 4 ). Table 4 Logistic regression analysis between biochemical, hematological variables, and systemic inflammatory response index with slow coronary flow OR 95%IC p-value SIRI 4.985 1.761–14.112 0.002 Cholesterol 1.039 1.002–1.077 0.040 LDL 0.989 0.941–1.018 0.979 LDL: low-density lipoprotein; SIRI: systemic inflammatory response index. Discussion Coronary slow flow (CSF) is a cardiac phenomenon that has been linked to biochemical and hematological alterations. However, its pathophysiology remains unclear [ 17 ]. The prevalence of CSF observed in this study was 2.6%, which falls within the range reported in the literature (1–7% of patients undergoing coronary angiography) [ 18 ]. Nonetheless, some studies have reported higher rates, such as 23.7% in an Indian population [ 19 ], underscoring the variability of this condition across different populations and geographic contexts. CSF has been associated with inflammatory processes that impair endothelial function[ 16 ]. In this regard, obesity plays a pivotal role as a trigger of chronic low-grade systemic inflammation, known as “metabolic inflammation,” which is implicated in the pathogenesis of endothelial dysfunction, atherosclerosis, and other microvascular alterations [ 20 ]. Body mass index (BMI) is a widely used measure for classifying overweight and obesity. Elevated BMI is associated with adipose tissue expansion and inflammation, including epicardial adipose tissue, which secretes proinflammatory cytokines and disrupts vascular function [ 21 ]. This adipose expansion is frequently accompanied by cellular hypoxia, tissue fibrosis, and macrophage infiltration, generating both local and systemic inflammation [ 22 ]. In this study, patients with CSF (BMI: 28.80 ± 4.64) and those without CSF (BMI: 29.55 ± 5.05) both exhibited values consistent with overweight according to WHO criteria. This suggests that both groups share a chronic inflammatory profile, although the presence of CSF may be modulated by additional factors such as microvascular dysfunction, oxidative stress, or more specific inflammatory markers. Lipid abnormalities, commonly referred to as dyslipidemias, are key contributors to increased cardiovascular risk because they adversely affect cardiac and vascular function, partly through impairing the production and availability of nitric oxide, a critical molecule for vascular health [ 23 ]. Hypercholesterolemia is one of the primary risk factors for atherosclerosis, manifesting as progressive arterial obstruction [ 24 ]. Similarly, elevated LDL cholesterol plays a central role in this process, as it can undergo oxidative modification in the vascular endothelium, giving rise to oxidized LDL particles. These oxidized particles are highly atherogenic and trigger an inflammatory response that promotes endothelial dysfunction and damage to the coronary microcirculation [ 23 , 25 ]. This sequence fosters a proinflammatory and proatherogenic state, resulting in cellular injury and increased production of reactive oxygen species (ROS) [ 23 ]. In the present study, total cholesterol and LDL cholesterol levels were significantly higher in patients with CSF compared with the control group. This finding is consistent with previous studies reporting similar trends [ 26 ]. Identifying cholesterol as a key risk factor for CSF is crucial, as it supports the implementation of preventive and therapeutic strategies targeting cholesterol levels to reduce the incidence of cardiovascular events. Monocytes are a key subset of innate immune leukocytes that respond to inflammatory stimuli and play a central role in host defense. They actively participate in various immune responses and pathological processes, including atherosclerosis, where they differentiate into macrophages [ 27 ]. Both monocytes [ 28 ] and neutrophils [ 29 ] contribute significantly to atherogenesis, leading to foam cell formation and the development of early atherosclerotic lesions. Neutrophils can induce endothelial cell apoptosis or release proteases that disrupt endothelial adhesion to the vessel wall [ 30 ]. In this study, monocyte and neutrophil counts were significantly higher in patients with CSF than in controls. Similarly, Yu et al. reported neutrophilia in patients with CSF in a Chinese population [ 31 ]. Since both neutrophils and monocytes are immune cells with proinflammatory activity, their elevated levels may not only reflect underlying inflammation but also directly contribute to endothelial dysfunction and impaired coronary flow, strengthening their potential value as prognostic biomarkers in CSF patients. Various inflammatory biomarkers have been associated with cardiovascular diseases, among them tumor necrosis factor-alpha (TNF-α). This cytokine is not only a biological driver of metabolic syndrome but also a major risk factor for type 2 diabetes and atherosclerosis [ 32 ]. Moreover, TNF-α plays a critical role during the early inflammatory phase, promoting multiple cardiovascular pathologies, as demonstrated by animal models and clinical studies [ 33 ]. TNF-α has been implicated in endothelial dysfunction, coronary artery disease, acute coronary syndromes, and restenosis [ 34 ]. Elevated TNF-α concentrations have also been observed in patients with CSF following percutaneous coronary intervention for non–ST-segment elevation acute coronary syndrome [ 35 ]. In the present study, no significant differences were found in TNF-α levels between groups. However, concentrations were elevated in both CSF and non-CSF groups, indicating the presence of systemic inflammation in both. In the context of chronic systemic inflammation, TNF-α may induce microvascular endothelial dysfunction [ 36 ], which is not always detectable by conventional angiography. Patients with CSF typically receive a diagnosis and treatment to mitigate their symptoms, whereas patients without CSF but with angiographically normal arteries often remain undiagnosed despite being symptomatic. This may suggest that non-CSF patients also experience chronic inflammation leading to microvascular alterations that remain undetected, potentially resulting in a lack of targeted treatment. This discrepancy highlights the need for further research to examine the impact of inflammation on microcirculation in both groups, ensuring more comprehensive patient care. The TyG index, NLR, LMR, NPR, PIV, and SIRI are important markers associated with inflammation and cardiovascular risk. Each has been studied in different contexts of inflammatory diseases, and their role in the pathogenesis and prognosis of cardiovascular disorders is well established. NLR has been shown to be elevated in patients with CSF, coronary artery disease, and coronary artery ectasia compared with angiographically normal individuals [ 17 ]. Conversely, LMR has been inversely correlated with the prevalence and severity of cardiovascular diseases such as aortic dilatation [ 37 ]. NPR has been identified as a predictor of short-term mortality in patients with ST-segment elevation myocardial infarction [ 38 ]. Elevated PIV levels have been reported in CSF patients, suggesting its potential utility as a diagnostic predictor of CSF [ 39 ]. Similarly, SIRI has demonstrated predictive value in CSF patients, proving more effective than isolated levels of neutrophils, monocytes, or lymphocytes [ 40 ]. In this study, significant differences were observed between groups in TyG (CSF: 9.03 ± 0.52 vs. control: 8.76 ± 0.61; p = 0.042), NLR (CSF: 2.77 ± 1.15 vs. control: 2.06 ± 0.70; p = 0.002), NPR (CSF: 19.15 [15.85–27.83] vs. control: 16.60 [11.86–20.94]; p = 0.009), PIV (CSF: 353.16 [273.69–516.60] vs. control: 265.76 [174.17–377.24]; p = 0.006), and SIRI (CSF: 1.49 [1.20–2.28] vs. control: 1.17 [0.73–1.39]; p < 0.001), with higher values in the CSF group. In contrast, LMR levels were higher in the control group compared with CSF patients (CSF: 2.99 ± 0.98 vs. control: 4.13 ± 1.85; p < 0.001), demonstrating an inverse relationship between groups. This may be explained by the role of neutrophils as first responders in inflammation, infiltrating endothelial tissue and releasing pro-oxidant and proinflammatory mediators [ 41 ]. Patients with CSF exhibited increased neutrophil counts and decreased lymphocyte counts, suggesting an inverse relationship between these cells: as neutrophil levels rise, lymphocyte levels decline. These indices may provide a comprehensive picture of the inflammatory state and could serve as useful tools for cardiovascular risk assessment. For the analysis of significant variables, several statistical models were tested, and the one showing the highest significance was selected. This model identified SIRI and cholesterol as the most important risk factors for the development of CSF. The relevance of this model lies in its practicality, as it integrates biochemical and hematological parameters that are routinely evaluated in patients undergoing angiography. These readily available results facilitate early detection of CSF without requiring additional procedures. The simplicity, accessibility, and low risk of this approach may contribute substantially to improving patient quality of life through timely and accurate diagnosis. Limitations This study provides evidence regarding the relationship between systemic inflammation and CSF; however, larger sample sizes and prospective multicenter studies are required to confirm the clinical utility of SIRI and other inflammatory indices in the diagnosis and prognosis of CSF. Microcirculatory dysfunction has been proposed as a mechanism in the development of CSF. Nevertheless, this study did not include methods to evaluate such dysfunction, as UMAE HE No. 71 lacks the resources to assess microvascular damage. Therefore, it is important to conduct a thorough evaluation of control groups presenting with inflammation but without a definitive diagnosis. Conclusion This study observed a chronic inflammatory state in both patients with CSF and those without CSF, as evidenced by elevated TNF-α levels, suggesting that metabolic alterations may play an important role in the development of this condition. The differences identified in inflammatory indices, including TyG, NLR, NPR, PIV, and SIRI, may be linked to a metabolically dysfunctional profile, reflecting a potentially significant systemic inflammatory state. Notably, the SIRI index showed an association with CSF and may serve as a more useful tool than individual immune cell counts, providing an integrated view of the inflammatory state. This index is particularly valuable as it is easy to obtain, cost-effective, and accessible in clinical practice, enabling clinicians to implement timely interventions, thereby improving patient care and quality of life in those with CSF. Nonetheless, further research is essential to better understand this phenomenon and to enhance diagnostic and therapeutic strategies. Abbreviations BMI body mass index BUN urea nitrogen CSF coronary slow flow CVDs cardiovascular diseases ELISA enzyme-linked immunosorbent assay HDL high-density lipoprotein IFNs interferons ILs interleukins LDL low-density lipoprotein LMR lymphocyte-to-monocyte ratio MCH mean corpuscular hemoglobin MCHC mean corpuscular hemoglobin concentration MCV mean corpuscular volume NK natural killer cells NLR neutrophil-to-lymphocyte ratio NPR neutrophil-to-platelet ratio OR odds ratios PIV pan-immune-inflammation value ROS reactive oxygen species SIRI systemic inflammatory response index TNF tumor necrosis factor TNF-α tumor necrosis factor-alpha TyG triglyceride–glucose index VLDL very-low-density lipoprotein Declarations Ethics approval and consent to participate The study was approved by the Research Ethics Committee and the Local Health Research Committee No. 501 at UMAE HE No. 71 (Approval No.: R-2023-501-046). All participants received detailed information about the study, and written informed consent was obtained. The research was conducted in accordance with the principles of the Declaration of Helsinki and the Belmont Report. Consent for publication “Not applicable” Competing interests The authors have no conflicts of interest to declare. Authors´ contributions MVMF: Sample collection, TNF-α quantification, analysis and interpretation of results, manuscript writing. AIUR: Analysis and interpretation of results, manuscript writing. FFGG: Statistical analysis and manuscript writing. FKSL: Statistical analysis and manuscript writing. HADA: Methodological advisor, TNF-α quantification AEBR: Interventional specialist physician, responsible for diagnosing the study population. JNPM: Interventional specialist physician, responsible for diagnosing the study population. ARS: Interventional specialist physician, responsible for diagnosing the study population. FCLM: Generator of clinical, biochemical, and hematological database. Funding This work was funded by the National Council of Humanities, Sciences and Technologies (665980). All the support provided was used for the development of the research project (materials, reagents, supplies). Acknowledgements “Not applicable” Data Availability The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request. References World Health Organization. Enfermedades cardiovasculares. 2024. Disponible en: https://www.who.int/es/health-topics/cardiovasculardiseases#tab=tab_1 . Accesed 20 marzo 2024. Wang X, Nie SP. The coronary slow flow phenomenon: characteristics, mechanisms and implications. Cardiovasc Diagn Ther. 2011;1(1):37–43. 10.3978/j.issn.2223-3652.2011.10.01 . Chalikias G, Tziakas D. Slow Coronary Flow: Pathophysiology, Clinical Implications, and Therapeutic Management. Angiology. 2021;72(9):808–18. 10.1177/00033197211004390 . 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Cureus. 2022;14(2):e22711. 10.7759/cureus.22711 . Pappan N, Awosika AO, y Rehman A. Dyslipidemia. En National Center for Biotechnology Information. 2024. https://www.ncbi.nlm.nih.gov/sites/books/NBK560891/ . Accesed 20 marzo 2024. American Heart Association. Consecuencias del colesterol alto. 2022. Disponible en: https://www.heart.org/-/media/Files/Health-Topics/Cholesterol/Consequences-of-High-Cholesterol-Spanish.pdf . Accesed 20 septiembre 2024. Ross R. The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature. 1993;362(6423):801–9. 10.1038/362801a0 . Toprak K, Karataş M, Kaplangoray M, Dursun A, Taşcanov MB, Altıparmak İH, Biçer A, y, Demirbağ R. Comparison of the Effect of Non- HDL-C/HDL-C Ratio on Coronary Slow Flow with Other Non-Traditional Lipid Markers. Acta Cardiol Sin . 2024;40(4):388–401. 10.6515/ACS.202407_40(4).20240419A Karlmark KR, Tacke F, Dunay IR. Monocytes in health and disease - Minireview. Eur J Microbiol Immunol (Bp). 2012;2(2):97–102. 10.1556/EuJMI.2.2012.2.1 . Chistiakov DA, Grechko AV, Myasoedova VA, Melnichenko AA, Orekhov AN. The role of monocytosis and neutrophilia in atherosclerosis. J Cell Mol Med. 2018;22(3):1366–82. 10.1111/jcmm.13462 . Silvestre-Roig C, Braster Q, Ortega-Gomez A, Soehnlein O. Neutrophils as regulators of cardiovascular inflammation. Nat Rev Cardiol. 2020;17(6):327–40. 10.1038/s41569-019-0326-7 . Sreejit G, Johnson J, Jaggers RM, Dahdah A, Murphy AJ, Hanssen NMJ, y, Nagareddy PR. Neutrophils in cardiovascular disease: warmongers, peacemakers, or both? Cardiovasc Res . 2022;118(12):2596–2609. 10.1093/cvr/cvab302 Yu J, Ran Y, Yi D, Yang C, Zhou X, Wang S, Li H, Yu W, Sun Z, Zhang Z, y, Yan M. Establishment and verification of a nomogram that predicts the risk for coronary slow flow. Front Endocrinol (Lausanne) . 2024;15:1337284. 10.3389/fendo.2024.1337284 Bruunsgaard H. Physical activity and modulation of systemic low-level inflammation. J Leukoc Biol. 2005;78(4):819–35. 10.1189/jlb.0505247 . Rolski F, Błyszczuk P. Complexity of TNF-α Signaling in Heart Disease. J Clin Med. 2020;9(10):3267. 10.3390/jcm9103267 . Mehaffey E, Majid DSA. Tumor necrosis factor-α, kidney function, and hypertension. Am J Physiol Ren Physiol. 2017;313(4):F1005–8. 10.1152/ajprenal.00535.2016 . Wang C, Wu Y, Su Y, Mao B, Luo Y, Yan Y, Hu K, Lu Y, Che W, y, Wan M. Elevated levels of sIL-2R, TNF-α and hs-CRP are independent risk factors for post percutaneous coronary intervention coronary slow flow in patients with non-ST segment elevation acute coronary syndrome. Int J Cardiovasc Imaging . 2022;38(6):1191–1202. 10.1007/s10554-022-02529-8 Caraba A, Stancu O, Crișan V, Georgescu D. Anti TNF-Alpha Treatment Improves Microvascular Endothelial Dysfunction in Rheumatoid Arthritis Patients. Int J Mol Sci. 2024;25(18):9925. 10.3390/ijms25189925 . Chen S, Wu Z, Yun Y, et al. Lymphocyte-to-monocyte ratio associated with severe post-stenotic aortic dilation in a case-control study. BMC Cardiovasc Disord. 2022;22(1):195. 10.1186/s12872-022-02636-3 . Somaschini A, Cornara S, Demarchi A, et al. Neutrophil to platelet ratio: A novel prognostic biomarker in ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention. Eur J Prev Cardiol. 2020;27(19):2338–40. 10.1177/2047487319894103 . Kaplangoray M, Toprak K, Deveci E, Caglayan C, Şahin E. Could Pan- Immune-Inflammation Value be a Marker for the Diagnosis of Coronary Slow Flow Phenomenon? Cardiovasc Toxicol. 2024;24(5):519–26. 10.1007/s12012-024-09855-4 . Chen YD, Wen ZG, Long JJ, Wang Y. Association Between Systemic Inflammation Response Index and Slow Coronary Flow Phenomenon in Patients with Ischemia and No Obstructive Coronary Arteries. Int J Gen Med. 2024;17:4045–53. 10.1186/s12889-025-21423-1 . Dai XT, Kong TZ, Zhang XJ, Luan B, Wang Y, Hou AJ. Relationship between increased systemic immune-inflammation index and coronary slow flow phenomenon. BMC Cardiovasc Disord. 2022;22(1):362. 10.1186/s12872-022-02798-0 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 29 Sep, 2025 Editor assigned by journal 26 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 18 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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09:58:21","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132313,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7653116/v1/9e54985223799dfaaa4d04fa.html"},{"id":107927820,"identity":"88755849-651e-40e8-9bd9-54af335d00a5","added_by":"auto","created_at":"2026-04-27 16:04:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":377697,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7653116/v1/6a2a9708-f6da-40a1-9467-9814f3281021.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coronary slow flow: role of systemic inflammation and biomarkers in its pathophysiology","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular diseases (CVDs) represent a major global public health problem. According to the World Health Organization (WHO), they are the leading cause of death worldwide, accounting for 17.9\u0026nbsp;million annual deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among CVDs, coronary slow flow (CSF) is characterized by reduced coronary blood flow velocity in the absence of significant obstructive coronary artery disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the underlying pathophysiological mechanisms of CSF remain unclear [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], it has been observed in patients with anginal symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], underscoring its clinical relevance and impact on patient quality of life.\u003c/p\u003e\u003cp\u003eInflammatory processes are among the factors implicated in the pathophysiology of CSF, with cytokines playing a central role, as they have been linked to both myocardial infarction type (with or without ST-segment elevation) and coronary blood flow in the affected artery. Inflammatory responses, endothelial function, and thrombus formation are closely interconnected [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCytokines, produced by various cell types, include interleukins (ILs), chemokines, interferons (IFNs), and tumor necrosis factor (TNF) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These proteins are pivotal in critical stages of the atherosclerotic cascade, such as endothelial activation and dysfunction, transcytosis of low-density lipoproteins (LDL), and monocyte adhesion and transmigration. They also contribute to extracellular matrix remodeling and vascular smooth muscle cell proliferation, promoting the development of lipid-rich cores surrounded by thin fibrous caps [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have investigated the potential relationship between CSF and inflammatory parameters, suggesting that systemic inflammation may play a key role in its development [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Tumor necrosis factor-alpha (TNF-α), primarily secreted by immune cells such as monocytes, macrophages, neutrophils, natural killer cells (NK), and CD4\u0026thinsp;+\u0026thinsp;T lymphocytes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], is widely recognized as an inflammatory marker [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, hematological parameters, whether considered individually or as inflammatory indices, have become relevant tools for assessing and predicting a wide range of cardiovascular and systemic inflammatory diseases. Examples include the neutrophil-to-lymphocyte ratio (NLR) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], lymphocyte-to-monocyte ratio (LMR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], neutrophil-to-platelet ratio (NPR) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], pan-immune-inflammation value (PIV) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and systemic inflammatory response index (SIRI) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These indices are closely associated with inflammation and cardiovascular risk. Elevated NLR levels have been reported in patients with coronary artery disease and CSF, linking them to atherosclerosis, endothelial dysfunction, and inflammation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, SIRI, together with NLR, has been associated with cardiovascular mortality in postmenopausal women with osteoporosis and osteopenia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. On the other hand, LMR has been correlated with the severity of coronary atherosclerosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while NPR has been shown to increase in patients with ischemic stroke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the triglyceride\u0026ndash;glucose index (TyG index) is used for the early identification of individuals at high risk of cardiovascular events [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe elevation of inflammatory markers in patients with CSF may reflect endothelial activation and inflammation, processes that are integral to the pathological pathways involved in this condition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The aim of the present study was to evaluate the association between CSF and inflammatory parameters, with the purpose of contributing to a better understanding of its pathophysiology and its potential utility as clinical markers. Identifying risk factors associated with CSF may provide valuable insights to improve diagnostic and therapeutic approaches in these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis study was conducted at the Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), in the High Specialty Medical Unit Hospital de Especialidades No. 71 (UMAE HE No. 71), located in Torre\u0026oacute;n, Coahuila, from June 2023 to June 2024. The study included patients who underwent coronary angiography in the hemodynamics unit and were diagnosed with CSF (case group), as well as those with angiographically normal coronary arteries (control group). Eligible participants were adults over 18 years of age, of either sex, undergoing coronary angiography for the first time. Patients were excluded if they had a history of diagnosed cardiovascular disease, known coronary lesions, prior acute myocardial infarction, autoimmune or chronic inflammatory disorders, cancer, trauma, or major surgery within the past 6 months, to minimize potential bias in inflammatory measurements.\u003c/p\u003e\u003cp\u003eThe study was approved by the Research Ethics Committee and the Local Health Research Committee No. 501 at UMAE HE No. 71 (Approval No.: R-2023-501-046). All participants received detailed information about the study, and written informed consent was obtained. The research was conducted in accordance with the principles of the Declaration of Helsinki and the Belmont Report.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCoronary Angiography\u003c/h3\u003e\n\u003cp\u003eCoronary angiography was performed via femoral or radial arterial access using the Seldinger technique, with superficial and deep local anesthesia (2% lidocaine) prior to arterial puncture. Iopamidol, a low-osmolarity contrast medium, was used during the procedure. The diagnosis of CSF was based on the definition proposed by Beltrame (2012), which requires the absence of significant angiographic lesions (\u0026ge;\u0026thinsp;40%) and delayed distal vessel contrast opacification corresponding to TIMI grade 2 flow (i.e., requiring three cardiac cycles to fully opacify the vessel). In contrast, patients with TIMI grade 3 flow and no obstruction or alterations in major epicardial arteries were classified as having \u0026ldquo;angiographically normal arteries.\u0026rdquo; All angiographic results were interpreted and validated by two interventional cardiologists.\u003c/p\u003e\n\u003ch3\u003eBiochemical and Hematological Parameters\u003c/h3\u003e\n\u003cp\u003eBiochemical and hematological parameters were obtained from patient medical records. Prior to coronary angiography, patients underwent laboratory testing, including blood chemistry, lipid profile, and complete blood count. Biochemical analyses were performed using the Vitros\u0026reg; 4600 automated dry chemistry analyzer, and hematological parameters were measured with the XN-1000 automated analyzer, following the manufacturers\u0026rsquo; specifications.\u003c/p\u003e\n\u003ch3\u003eDetermination of TNF-α\u003c/h3\u003e\n\u003cp\u003eQuantification of TNF-α was performed using a quantitative sandwich enzyme-linked immunosorbent assay (ELISA) for human TNF-α (lot: 20114909, code: SK00109-01, Aviscera Bioscience). Reagents, including wash solution, TNF-α standard solution, positive control, detection antibody concentrate, and streptavidin-HRP conjugate, were prepared according to the manufacturer\u0026rsquo;s instructions. Microplate readings were conducted with the Stat Fax 4700 reader at 450 nm.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistical analysis was performed. Qualitative variables were expressed as absolute frequencies and percentages. Normality of quantitative variables was assessed using the Shapiro\u0026ndash;Wilk test. Quantitative variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or as median and interquartile range, depending on distribution. Comparisons between the two study groups were performed using the chi-square test for qualitative variables, and either Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test for quantitative variables, according to distribution. Regression analysis was conducted to identify variables associated with CSF, with odds ratios (OR) and 95% confidence intervals (CIs) calculated. Statistical analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe present study was conducted between July 2023 and July 2024. During this period, a total of 1,650 coronary angiographies were performed, of which 2.6% (n\u0026thinsp;=\u0026thinsp;43) of patients were diagnosed with CSF. After applying inclusion and exclusion criteria, the final sample consisted of 77 participants (43 with CSF and 34 without CSF); 53% were male (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of participants was 62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57 years. The mean body mass index (BMI) was 29.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80 kg/m\u0026sup2;, while the mean heart rate was 70.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51 beats per minute. Hypertension was the most prevalent comorbidity (82%), followed by dyslipidemia (41%) and diabetes mellitus (37%). Additionally, 45% of patients were smokers. No statistically significant differences were observed between the CSF and control groups in sociodemographic, clinical, or anthropometric characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic, clinical, and anthropometric characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;77\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCSF Group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41(53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19(56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15(44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate (lpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.32\u0026thinsp;\u0026plusmn;\u0026thinsp;12.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.47\u0026thinsp;\u0026plusmn;\u0026thinsp;13.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38(88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26(76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11(32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11(32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results are exressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. BMI: body mass index; bpm: beats per minute; CSF: slow flow coronary.\u003c/p\u003e\u003cp\u003eBiochemical and hematological analyses revealed significant differences between groups. Total cholesterol levels were significantly higher in the CSF group (165 [153.00\u0026ndash;186.00] vs. 139 [118.00\u0026ndash;178.50]; p\u0026thinsp;=\u0026thinsp;0.024), as were LDL cholesterol levels (96.16\u0026thinsp;\u0026plusmn;\u0026thinsp;29.18 vs. 80.29\u0026thinsp;\u0026plusmn;\u0026thinsp;26.82; p\u0026thinsp;=\u0026thinsp;0.016). Monocyte counts were also higher in the CSF group (0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 vs. 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; p\u0026thinsp;=\u0026thinsp;0.017), as were neutrophil counts (4.40 [4.00\u0026ndash;5.30] vs. 4.00 [3.53\u0026ndash;4.70]; p\u0026thinsp;=\u0026thinsp;0.015) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBiochemical and hematological parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCSF Group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00 (91\u0026ndash;121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.50 (81.50\u0026ndash;114.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.86 (12.60-20.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.00 (12.00-18.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90 (0.70-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 (0.60-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147.00 (110.00-200.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.50 (93.00-167.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e165.00(153.00-186.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e139.00 (118.00-178.50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.024*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e96.16\u0026thinsp;\u0026plusmn;\u0026thinsp;29.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e80.29\u0026thinsp;\u0026plusmn;\u0026thinsp;26.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.016*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVLDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.40 (22.00\u0026ndash;40.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.10 (18.60\u0026ndash;33.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeucocytes\u0026nbsp; (10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.20 (6.70\u0026ndash;8.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.19 (6.19\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78 (1.58\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99 (1.60\u0026ndash;2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonocytes (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u003c/b\u003e \u003cb\u003e\u0026micro;l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophils (10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11 (0.10\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12 (0.10\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasophils (10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.03\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04 (0.02\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophils (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u003c/b\u003e \u003cb\u003e\u0026micro;l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.40 (4.00-5.30)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.0 (3.57\u0026ndash;4.70)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.015*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythrocytes (10\u003csup\u003e6\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.68\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCH (pg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCHC (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/ \u0026micro;l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231.72\u0026thinsp;\u0026plusmn;\u0026thinsp;66.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248.44\u0026thinsp;\u0026plusmn;\u0026thinsp;64.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eThe results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) according to their distribution. BUN: urea nitrogen; HDL: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very-low-density lipoprotein; MCV: mean corpuscular volumen; MCH: mean corpuscular hemoglobin; MCHC: mean corpuscular hemoglobin concentration.\u003c/p\u003e\u003cp\u003e* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference between groups.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNo statistically significant differences were observed in TNF-α levels; however, values were elevated above the normal range in both groups (Table\u0026nbsp;3). Analysis of indices associated with inflammation and insulin resistance, including TyG, NLR, LMR, NPR, PIV, and SIRI, revealed significant differences, with higher values in the CSF group, suggesting a heightened inflammatory response and greater insulin resistance (Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;3. Inflammation parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCSF Group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-α (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e46.21 (16.91\u0026ndash;60.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.13 (14.15\u0026ndash;62.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTyG Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.042*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNPR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e19.15(15.85\u0026ndash;27.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.60(11.86\u0026ndash;20.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e353.16(273.69\u0026ndash;516.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265.76(174.17-377.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSIRI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.49(1.20\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17(0.73\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eThe results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) according to their distribution. TNF-α: tumor necrosis factor-alpha; TyG Index: triglyceride/glucose index; NLR: neutrophil-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; NPR: neutrophil-to-platelet ratio; PIV: pan-immune-inflammation value; SIRI: systemic inflammatory response index.\u003c/p\u003e\u003cp\u003e* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference between groups.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLogistic regression analysis identified cholesterol and the SIRI index as the most significant risk factors for CSF (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis between biochemical, hematological variables, and systemic inflammatory response index with slow coronary flow\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%IC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.985\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.761\u0026ndash;14.112\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholesterol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.039\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.002\u0026ndash;1.077\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.941\u0026ndash;1.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eLDL: low-density lipoprotein; SIRI: systemic inflammatory response index.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCoronary slow flow (CSF) is a cardiac phenomenon that has been linked to biochemical and hematological alterations. However, its pathophysiology remains unclear [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The prevalence of CSF observed in this study was 2.6%, which falls within the range reported in the literature (1\u0026ndash;7% of patients undergoing coronary angiography) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nonetheless, some studies have reported higher rates, such as 23.7% in an Indian population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], underscoring the variability of this condition across different populations and geographic contexts.\u003c/p\u003e\u003cp\u003eCSF has been associated with inflammatory processes that impair endothelial function[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this regard, obesity plays a pivotal role as a trigger of chronic low-grade systemic inflammation, known as \u0026ldquo;metabolic inflammation,\u0026rdquo; which is implicated in the pathogenesis of endothelial dysfunction, atherosclerosis, and other microvascular alterations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Body mass index (BMI) is a widely used measure for classifying overweight and obesity. Elevated BMI is associated with adipose tissue expansion and inflammation, including epicardial adipose tissue, which secretes proinflammatory cytokines and disrupts vascular function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This adipose expansion is frequently accompanied by cellular hypoxia, tissue fibrosis, and macrophage infiltration, generating both local and systemic inflammation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, patients with CSF (BMI: 28.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64) and those without CSF (BMI: 29.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05) both exhibited values consistent with overweight according to WHO criteria. This suggests that both groups share a chronic inflammatory profile, although the presence of CSF may be modulated by additional factors such as microvascular dysfunction, oxidative stress, or more specific inflammatory markers.\u003c/p\u003e\u003cp\u003eLipid abnormalities, commonly referred to as dyslipidemias, are key contributors to increased cardiovascular risk because they adversely affect cardiac and vascular function, partly through impairing the production and availability of nitric oxide, a critical molecule for vascular health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hypercholesterolemia is one of the primary risk factors for atherosclerosis, manifesting as progressive arterial obstruction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, elevated LDL cholesterol plays a central role in this process, as it can undergo oxidative modification in the vascular endothelium, giving rise to oxidized LDL particles. These oxidized particles are highly atherogenic and trigger an inflammatory response that promotes endothelial dysfunction and damage to the coronary microcirculation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This sequence fosters a proinflammatory and proatherogenic state, resulting in cellular injury and increased production of reactive oxygen species (ROS) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the present study, total cholesterol and LDL cholesterol levels were significantly higher in patients with CSF compared with the control group. This finding is consistent with previous studies reporting similar trends [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Identifying cholesterol as a key risk factor for CSF is crucial, as it supports the implementation of preventive and therapeutic strategies targeting cholesterol levels to reduce the incidence of cardiovascular events.\u003c/p\u003e\u003cp\u003eMonocytes are a key subset of innate immune leukocytes that respond to inflammatory stimuli and play a central role in host defense. They actively participate in various immune responses and pathological processes, including atherosclerosis, where they differentiate into macrophages [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Both monocytes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and neutrophils [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] contribute significantly to atherogenesis, leading to foam cell formation and the development of early atherosclerotic lesions. Neutrophils can induce endothelial cell apoptosis or release proteases that disrupt endothelial adhesion to the vessel wall [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, monocyte and neutrophil counts were significantly higher in patients with CSF than in controls. Similarly, Yu et al. reported neutrophilia in patients with CSF in a Chinese population [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Since both neutrophils and monocytes are immune cells with proinflammatory activity, their elevated levels may not only reflect underlying inflammation but also directly contribute to endothelial dysfunction and impaired coronary flow, strengthening their potential value as prognostic biomarkers in CSF patients.\u003c/p\u003e\u003cp\u003eVarious inflammatory biomarkers have been associated with cardiovascular diseases, among them tumor necrosis factor-alpha (TNF-α). This cytokine is not only a biological driver of metabolic syndrome but also a major risk factor for type 2 diabetes and atherosclerosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, TNF-α plays a critical role during the early inflammatory phase, promoting multiple cardiovascular pathologies, as demonstrated by animal models and clinical studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. TNF-α has been implicated in endothelial dysfunction, coronary artery disease, acute coronary syndromes, and restenosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Elevated TNF-α concentrations have also been observed in patients with CSF following percutaneous coronary intervention for non\u0026ndash;ST-segment elevation acute coronary syndrome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the present study, no significant differences were found in TNF-α levels between groups. However, concentrations were elevated in both CSF and non-CSF groups, indicating the presence of systemic inflammation in both. In the context of chronic systemic inflammation, TNF-α may induce microvascular endothelial dysfunction [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which is not always detectable by conventional angiography. Patients with CSF typically receive a diagnosis and treatment to mitigate their symptoms, whereas patients without CSF but with angiographically normal arteries often remain undiagnosed despite being symptomatic. This may suggest that non-CSF patients also experience chronic inflammation leading to microvascular alterations that remain undetected, potentially resulting in a lack of targeted treatment. This discrepancy highlights the need for further research to examine the impact of inflammation on microcirculation in both groups, ensuring more comprehensive patient care.\u003c/p\u003e\u003cp\u003eThe TyG index, NLR, LMR, NPR, PIV, and SIRI are important markers associated with inflammation and cardiovascular risk. Each has been studied in different contexts of inflammatory diseases, and their role in the pathogenesis and prognosis of cardiovascular disorders is well established. NLR has been shown to be elevated in patients with CSF, coronary artery disease, and coronary artery ectasia compared with angiographically normal individuals [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, LMR has been inversely correlated with the prevalence and severity of cardiovascular diseases such as aortic dilatation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. NPR has been identified as a predictor of short-term mortality in patients with ST-segment elevation myocardial infarction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Elevated PIV levels have been reported in CSF patients, suggesting its potential utility as a diagnostic predictor of CSF [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, SIRI has demonstrated predictive value in CSF patients, proving more effective than isolated levels of neutrophils, monocytes, or lymphocytes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In this study, significant differences were observed between groups in TyG (CSF: 9.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 vs. control: 8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61; p\u0026thinsp;=\u0026thinsp;0.042), NLR (CSF: 2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15 vs. control: 2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70; p\u0026thinsp;=\u0026thinsp;0.002), NPR (CSF: 19.15 [15.85\u0026ndash;27.83] vs. control: 16.60 [11.86\u0026ndash;20.94]; p\u0026thinsp;=\u0026thinsp;0.009), PIV (CSF: 353.16 [273.69\u0026ndash;516.60] vs. control: 265.76 [174.17\u0026ndash;377.24]; p\u0026thinsp;=\u0026thinsp;0.006), and SIRI (CSF: 1.49 [1.20\u0026ndash;2.28] vs. control: 1.17 [0.73\u0026ndash;1.39]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher values in the CSF group. In contrast, LMR levels were higher in the control group compared with CSF patients (CSF: 2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98 vs. control: 4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating an inverse relationship between groups. This may be explained by the role of neutrophils as first responders in inflammation, infiltrating endothelial tissue and releasing pro-oxidant and proinflammatory mediators [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Patients with CSF exhibited increased neutrophil counts and decreased lymphocyte counts, suggesting an inverse relationship between these cells: as neutrophil levels rise, lymphocyte levels decline. These indices may provide a comprehensive picture of the inflammatory state and could serve as useful tools for cardiovascular risk assessment.\u003c/p\u003e\u003cp\u003eFor the analysis of significant variables, several statistical models were tested, and the one showing the highest significance was selected. This model identified SIRI and cholesterol as the most important risk factors for the development of CSF. The relevance of this model lies in its practicality, as it integrates biochemical and hematological parameters that are routinely evaluated in patients undergoing angiography. These readily available results facilitate early detection of CSF without requiring additional procedures. The simplicity, accessibility, and low risk of this approach may contribute substantially to improving patient quality of life through timely and accurate diagnosis.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study provides evidence regarding the relationship between systemic inflammation and CSF; however, larger sample sizes and prospective multicenter studies are required to confirm the clinical utility of SIRI and other inflammatory indices in the diagnosis and prognosis of CSF.\u003c/p\u003e\u003cp\u003eMicrocirculatory dysfunction has been proposed as a mechanism in the development of CSF. Nevertheless, this study did not include methods to evaluate such dysfunction, as UMAE HE No. 71 lacks the resources to assess microvascular damage. Therefore, it is important to conduct a thorough evaluation of control groups presenting with inflammation but without a definitive diagnosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study observed a chronic inflammatory state in both patients with CSF and those without CSF, as evidenced by elevated TNF-α levels, suggesting that metabolic alterations may play an important role in the development of this condition. The differences identified in inflammatory indices, including TyG, NLR, NPR, PIV, and SIRI, may be linked to a metabolically dysfunctional profile, reflecting a potentially significant systemic inflammatory state. Notably, the SIRI index showed an association with CSF and may serve as a more useful tool than individual immune cell counts, providing an integrated view of the inflammatory state. This index is particularly valuable as it is easy to obtain, cost-effective, and accessible in clinical practice, enabling clinicians to implement timely interventions, thereby improving patient care and quality of life in those with CSF. Nonetheless, further research is essential to better understand this phenomenon and to enhance diagnostic and therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eurea nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCSF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecoronary slow flow\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCVDs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiovascular diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eELISA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eenzyme-linked immunosorbent assay\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIFNs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterferons\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eILs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterleukins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elymphocyte-to-monocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCH\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean corpuscular hemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCHC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean corpuscular hemoglobin concentration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean corpuscular volume\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNK\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural killer cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNPR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-platelet ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eodds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epan-immune-inflammation value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSIRI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic inflammatory response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor necrosis factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor necrosis factor-alpha\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglyceride\u0026ndash;glucose index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eVLDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003every-low-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe study was approved by the Research Ethics Committee and the Local Health Research Committee No. 501 at UMAE HE No. 71 (Approval No.: R-2023-501-046). All participants received detailed information about the study, and written informed consent was obtained. The research was conducted in accordance with the principles of the Declaration of Helsinki and the Belmont Report.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e\u0026ldquo;Not applicable\u0026rdquo;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors\u0026acute; contributions\u003c/h2\u003e\u003cp\u003eMVMF: Sample collection, TNF-α quantification, analysis and interpretation of results, manuscript writing.\u003c/p\u003e\u003cp\u003eAIUR: Analysis and interpretation of results, manuscript writing.\u003c/p\u003e\u003cp\u003eFFGG: Statistical analysis and manuscript writing.\u003c/p\u003e\u003cp\u003eFKSL: Statistical analysis and manuscript writing.\u003c/p\u003e\u003cp\u003eHADA: Methodological advisor, TNF-α quantification\u003c/p\u003e\u003cp\u003eAEBR: Interventional specialist physician, responsible for diagnosing the study population.\u003c/p\u003e\u003cp\u003eJNPM: Interventional specialist physician, responsible for diagnosing the study population.\u003c/p\u003e\u003cp\u003eARS: Interventional specialist physician, responsible for diagnosing the study population.\u003c/p\u003e\u003cp\u003eFCLM: Generator of clinical, biochemical, and hematological database.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was funded by the National Council of Humanities, Sciences and Technologies (665980). All the support provided was used for the development of the research project (materials, reagents, supplies).\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003e\u0026ldquo;Not applicable\u0026rdquo;\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Enfermedades cardiovasculares. 2024. Disponible en: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/es/health-topics/cardiovasculardiseases#tab=tab_1\u003c/span\u003e\u003cspan address=\"https://www.who.int/es/health-topics/cardiovasculardiseases#tab=tab_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Relationship between increased systemic immune-inflammation index and coronary slow flow phenomenon. BMC Cardiovasc Disord. 2022;22(1):362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12872-022-02798-0\u003c/span\u003e\u003cspan address=\"10.1186/s12872-022-02798-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary slow flow, systemic inflammation, SIRI index, dyslipidemia, coronary angiography, biomarkers.","lastPublishedDoi":"10.21203/rs.3.rs-7653116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7653116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoronary slow flow (CSF) is characterized by reduced coronary blood flow velocity in the absence of significant obstruction, and it has been associated with systemic inflammation and endothelial dysfunction. This study aimed to evaluate the association between CSF and inflammatory parameters to better understand its pathophysiology and potential utility as clinical markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted in 77 patients undergoing coronary angiography, including those with CSF (n = 43) and controls with angiographically normal coronary arteries (n = 34). Clinical, biochemical, and hematological variables, TNF-α levels, and inflammatory indices (TyG, NLR, LMR, NPR, PIV, and SIRI) were assessed. Statistical tests and logistic regression were applied to identify risk factors associated with CSF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of CSF was 2.6%, with hypertension and dyslipidemia being the most frequent comorbidities. Patients with CSF exhibited significantly higher levels of total cholesterol (p = 0.024), LDL cholesterol (p = 0.016), monocytes (p = 0.017), and neutrophils (p = 0.015). Inflammatory indices TyG, NLR, NPR, PIV, and SIRI were significantly elevated in the CSF group (p \u0026lt; 0.05), whereas LMR was lower. No significant differences were found in TNF-α levels, although elevated values were observed in both groups. Logistic regression identified cholesterol and the SIRI index as significant risk factors for CSF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCSF is associated with systemic inflammation and dyslipidemia, with the SIRI index emerging as an accessible and practical tool to assess inflammation and cardiovascular risk in CSF patients. Its incorporation into clinical practice may facilitate earlier diagnosis and more effective treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial number: \u003c/strong\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"Coronary slow flow: role of systemic inflammation and biomarkers in its pathophysiology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 09:58:17","doi":"10.21203/rs.3.rs-7653116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T11:35:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T18:40:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T10:21:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98959662988819365467505395011279876696","date":"2025-10-18T13:35:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213980776461194135754848471564260181030","date":"2025-10-17T18:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193854170948570607358940913682966636488","date":"2025-10-17T02:33:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T21:40:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T15:44:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T12:47:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33076256250636632440725419250001419643","date":"2025-10-16T12:19:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17608516748527221219716968797723334047","date":"2025-10-16T05:18:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170805629052513982814444527541633557177","date":"2025-10-16T04:09:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-16T03:35:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-29T12:34:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T02:17:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T02:16:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-18T22:56:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b816dede-a209-453e-839d-7a35a6221f5b","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:02:33+00:00","versionOfRecord":{"articleIdentity":"rs-7653116","link":"https://doi.org/10.1186/s12872-026-05861-2","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2026-04-21 15:57:19","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2025-10-30 09:58:17","video":"","vorDoi":"10.1186/s12872-026-05861-2","vorDoiUrl":"https://doi.org/10.1186/s12872-026-05861-2","workflowStages":[]},"version":"v1","identity":"rs-7653116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7653116","identity":"rs-7653116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}