Meteorin-like Protein (Metrnl) and Coronary Collateral Circulation in Patients with Chronic Total Occlusion: A Prospective Observational Study

Meteorin-like Protein (Metrnl) and Coronary Collateral Circulation in Patients with Chronic Total Occlusion: A Prospective Observational Study | 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 Meteorin-like Protein (Metrnl) and Coronary Collateral Circulation in Patients with Chronic Total Occlusion: A Prospective Observational Study Hasan Akkaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9059019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Coronary collateral circulation (CCC) mitigates ischemia, preserves myocardial viability, and improves outcomes in patients with advanced coronary artery disease. However, molecular determinants of inter-individual variability in CCC remain poorly defined. Meteorin-like protein (Metrnl) is a novel adipomyokine with cardioprotective, metabolic, and immunoregulatory properties, but its relationship with CCC has not been clarified. Objectives To evaluate the association between serum Metrnl levels and CCC in patients with chronic coronary syndrome and chronic total occlusion, and to investigate the relationship between Metrnl and angiographic severity scores. Methods This prospective, single-center study included 98 patients with chronic coronary syndrome and at least one chronic total occlusion in a major epicardial coronary artery. CCC was assessed angiographically using the Rentrop classification. Patients were stratified into poor CCC (Rentrop 0–1, n = 33) and good CCC (Rentrop 2–3, n = 65) groups. Baseline clinical characteristics, medications, biochemical and hematological parameters, inflammatory indices, and angiographic findings were recorded. Serum Metrnl levels were measured using a standardized ELISA. Coronary disease severity was quantified with SYNTAX II and Gensini scores. Results Clinical features, prior medication use, lipid profile, and most inflammatory markers did not differ significantly between poor and good CCC groups. Serum Metrnl levels were comparable in poor versus good CCC (1.73 ± 0.15 vs. 1.67 ± 0.16 ng/mL, p = 0.093), and Metrnl did not correlate with body mass index, lipid parameters, or high-sensitivity C-reactive protein. In contrast, Metrnl levels were inversely associated with angiographic severity: Gensini score (r = − 0.360, p < 0.001) and SYNTAX score (r = − 0.372, p < 0.001). ROC analysis showed no discriminatory value of Metrnl for distinguishing poor from good CCC (AUC 0.520, p = 0.789). In univariate logistic regression, higher SYNTAX score was significantly associated with good CCC (odds ratio 1.069; 95% confidence interval 1.004–1.138; p = 0.038). Conclusions In patients with chronic coronary syndrome and chronic total occlusion, serum Metrnl levels are not linked to the extent of CCC but are inversely correlated with coronary atherosclerotic burden as reflected by SYNTAX and Gensini scores. Metrnl may represent a biomarker of disease severity rather than collateralization. Meteorin-like protein Metrnl Chronic coronary syndrome Chronic total occlusion Coronary collateral circulation SYNTAX score Biomarker Figures Figure 1 Background Coronary artery disease (CAD) remains one of the leading causes of morbidity and mortality worldwide [1]. Coronary collateral circulation (CCC), which develops in response to chronic myocardial ischemia, constitutes an alternative vascular network that supplies blood to ischemic myocardial regions distal to severely stenotic or occluded epicardial coronary arteries. By providing supplementary perfusion in the presence of coronary obstruction, collateral vessels play an important protective role in reducing ischemic burden. The development of coronary collaterals is regulated by a complex interplay of factors, including inflammation, oxidative stress, metabolic alterations, and vascular growth mediators [2]. However, the molecular mechanisms underlying the substantial inter-individual variability in CCC formation remain incompletely understood. Meteorin-like protein (Metrnl) is a recently identified adipomyokine composed of a 311-amino-acid precursor protein with a molecular weight of approximately 25–30 kDa. It is predominantly expressed in white adipose tissue and skeletal muscle, with lower expression levels in the myocardium. Also referred to as Meteorin-β (Metrnβ) or interleukin-41 (IL-41), Metrnl has gained increasing attention due to its potential roles in cardiovascular homeostasis [3]. Experimental evidence indicates that Metrnl enhances endothelial function and metabolic homeostasis by reducing vascular inflammation, thereby suggesting a possible indirect contribution to angiogenic processes required for collateral vessel development [4]. Clinical studies have demonstrated significantly reduced serum Metrnl levels in patients with CAD compared with healthy individuals, and an inverse association between Metrnl concentrations and disease severity has been reported [5]. Moreover, decreased Metrnl levels in patients with acute coronary syndrome (ACS) have been shown to correlate with higher troponin concentrations and adverse clinical outcomes [6]. Metrnl levels are reduced in metabolic disorders such as metabolic syndrome and obesity, and lower circulating concentrations have been associated with an atherogenic lipid profile—characterized by decreased high-density lipoprotein and elevated low-density lipoprotein and triglycerides [7]. These findings suggest that Metrnl may play a role in atherogenic dyslipidemia and, consequently, in the pathogenesis of chronic coronary syndrome (CCS). Additionally, in elderly patients with chronic heart failure, reduced Metrnl levels have been linked to unintentional weight loss, severe cardiac dysfunction, and increased cardiovascular mortality [8]. Considering these cardioprotective properties, Metrnl may, with further investigation, emerge as a novel diagnostic biomarker, a therapeutic target, or even a potential therapeutic agent for CAD. Given its pro-angiogenic and endothelial-protective characteristics, it is plausible that Metrnl may also influence the development of coronary collaterals. Although previous studies have explored the general cardiovascular risk profile and the association between Metrnl and CAD severity, its direct relationship with the degree of CCC has not yet been adequately investigated. Therefore, the aim of the present study was to evaluate the association between serum Metrnl levels and CCC development in patients with chronic total occlusion (CTO) of the coronary arteries. Methods Study Design and Population This single-center, prospective observational study was conducted between April 2024 and February 2026 at the [Blinded for Review].. A total of 98 patients of both sexes, aged over 18 years, who presented with ischemic symptoms and were diagnosed with CCS were enrolled. All included patients had at least one CTO in a major epicardial coronary artery confirmed by coronary angiography. Written informed consent was obtained from each patient. The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Local Clinical Research Ethics Committee of [Blinded for Review]. Inclusion and Exclusion Criteria Patients with stable CAD who met the angiographic indications according to current guidelines—characterized by typical chest pain, ischemic findings on electrocardiography, ischemia on myocardial perfusion scintigraphy, or critical lesions on coronary computed tomography angiography—and demonstrated total occlusion of at least one major epicardial coronary artery were included. Exclusion criteria were as follows: a diagnosis of ACS within the last 6 months; a history of coronary artery bypass grafting or valvular surgery; moderate-to-severe valvular heart disease; acute or chronic rheumatological or inflammatory diseases; acute or chronic renal failure (glomerular filtration rate <30 mL/min); known malignancy; heart failure symptoms (New York Heart Association [NYHA] class III–IV); hepatic failure; moderate or severe chronic obstructive pulmonary disease; and acute or chronic infectious diseases. Diabetes mellitus was defined as a fasting plasma glucose of ≥126 mg/dL on multiple measurements or the current use of antidiabetic medication. Hypertension was defined as repeated blood pressure measurements >140/90 mmHg or the use of antihypertensive therapy. Clinical and Laboratory Assessment Prior to angiography, all patients underwent a comprehensive clinical evaluation, including medical history, electrocardiography, and echocardiographic assessment (performed by a single cardiologist) under outpatient conditions. Clinical characteristics (age, sex, presence of diabetes mellitus, presence of hypertension, smoking status, body mass index [BMI; calculated as weight in kilograms divided by the square of height in meters], left ventricular ejection fraction [LVEF; using the Simpson method], and blood pressure values); current medications (angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, calcium channel blockers, beta-blockers, nitrates, ranolazine, trimetazidine, statins); baseline biochemical parameters (fasting blood glucose, hemoglobin A1c [HbA1c], urea, creatinine, aspartate aminotransferase [AST], alanine aminotransferase [ALT]); lipid parameters (total cholesterol, triglycerides, low-density lipoprotein [LDL], high-density lipoprotein [HDL]); complete blood count (hemoglobin, red blood cell count, mean corpuscular volume [MCV], white blood cell count, neutrophil, lymphocyte, platelet count, mean platelet volume [MPV], plateletcrit, platelet distribution width [PDW]); and inflammatory parameters (high-sensitivity C-reactive protein [hs-CRP], neutrophil/lymphocyte ratio [NLR], platelet/lymphocyte ratio [PLR], monocyte/lymphocyte ratio [MLR], Systemic Immuno-Inflammation Index [SII], Systemic Inflammation Response Index [SIRI]) were evaluated for all patients at the time of admission. Measurement of Serum Metrnl Levels Venous blood samples were collected following at least 10 hours of fasting. Samples were promptly centrifuged at 1000 g for 10 minutes at 4°C, and serum Metrnl levels were analyzed. The analysis range was 0.05–15 ng/mL. Following centrifugation, serum samples were stored at −80°C until analysis. Commercially available human Metrnl enzyme-linked immunosorbent assay (ELISA) kits (Catalog No.: HDP3836PU, Shanghai YL Biotech Co., Ltd., Shanghai, China) were used. Metrnl levels were measured by expert biochemists in accordance with the manufacturer's instructions using a total of two kits. The inter-assay coefficient of variation was less than 9%, and the intra-assay coefficient of variation was less than 10%. Inflammatory Indices Inflammatory parameters were derived from routine complete blood counts. NLR, PLR, and MLR values were obtained through mathematical division. The SII was calculated using the formula: (neutrophils × platelets) / lymphocytes. The SIRI was calculated using the formula: (neutrophils × monocytes) / lymphocytes. Angiographic Evaluation Angiographic images were evaluated via a Picture Archiving and Communication System (PACS) by an experienced cardiologist. In cases of ambiguity, a second physician was consulted. Total occlusions in at least one major (diameter ≥2.5 mm) coronary artery (excluding the left main coronary artery) were identified in patients with CCS. Rentrop, SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery), and Gensini scores were calculated for each patient. Rentrop Classification The Rentrop grading system was used to assess the degree of CCC [9]. According to this classification: Rentrop 0: no visible coronary collateral circulation; Rentrop 1: barely detectable collateral circulation (filling of side branches without any filling of the epicardial segment); Rentrop 2: partial collateral circulation (filling of the epicardial segment, but incomplete opacification); Rentrop 3: complete perfusion of the occluded epicardial artery via collaterals (full opacification). Consistent with previous literature, Rentrop grades 0–1 were categorized as poor CCC, while grades 2–3 were categorized as good CCC [9]. SYNTAX Score Calculation The SYNTAX score was determined using the online calculator (www.syntaxscore.com) by answering 12 interactive questions (dominance, number of lesions, segments per lesion, total occlusion, trifurcation, bifurcation, aorto-ostial lesion, severe tortuosity, lesion length, calcification, presence of thrombus, diffuse disease/small vessels). Upon completion of the algorithm, the software provides a report specifying the characteristics of each lesion, its score, and the total SYNTAX score. The SYNTAX II score was utilized in this study. Gensini Score Calculation The Gensini score was calculated manually. In this system, lesions are assigned points based on the degree of angiographic stenosis: 1 to 32 points (1 point for ≤25% stenosis, 2 points for 26%–50% stenosis, 4 points for 51%–75% stenosis, 8 points for 76%–90% stenosis, 16 points for 91%–99% stenosis, and 32 points for total occlusion). These points are then multiplied by a factor determined by the location of the lesion (×5 for the left main coronary artery; ×2.5 for the proximal left anterior descending artery; ×1.5 for the mid-left anterior descending and proximal circumflex artery; ×1 for the distal left anterior descending, right coronary artery, first diagonal branch, and obtuse marginal branches; and ×0.5 for the posterolateral and other side branches). The final score is the sum of these weighted values. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software, version 22.018 (MedCalc Software Ltd, Ostend, Belgium). The normality of continuous variables was assessed using the Shapiro–Wilk test. Normally distributed numerical data are presented as mean ± standard deviation (SD), while non-normally distributed variables are expressed as median and interquartile range (IQR). Categorical variables are reported as frequencies (n) and percentages (%). For intergroup comparisons, the Independent Samples t-test was employed for normally distributed data, and the Mann–Whitney U test was used for non-normally distributed data. Relationships between categorical variables were evaluated using the Chi-square test. Correlation analyses were performed using Pearson's correlation coefficient for normally distributed variables and Spearman's rank correlation coefficient for non-normally distributed variables. Univariate binary logistic regression analysis was conducted to identify predictors of good CCC. Model fitness was verified using the Hosmer–Lemeshow goodness-of-fit test. The diagnostic performance of numerical variables was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. Optimal cut-off values were determined, and sensitivity, specificity, and their corresponding 95% confidence intervals (CIs) are presented. A p-value <0.05 was considered statistically significant for all analyses. Results A total of 98 patients with CTO in at least one epicardial coronary artery were included in the study. The patients were divided into two groups based on their collateral status. The poor CCC group consisted of 33 patients (Rentrop 0: 11 [33.3%], Rentrop 1: 22 [66.7%]), while the good CCC group consisted of 65 patients (Rentrop 2: 42 [64.6%], Rentrop 3: 23 [35.4%]). No significant differences were observed between the groups regarding clinical characteristics, including age, sex, presence of diabetes mellitus, presence of hypertension, smoking status, BMI, LVEF, and blood pressure levels. Similarly, there were no significant differences between the groups in terms of prior medication use. Serum Metrnl levels were 1.73 ± 0.15 ng/mL in the poor CCC group and 1.67 ± 0.16 ng/mL in the good CCC group, with no statistically significant difference observed (p = 0.093). Furthermore, no significant differences were found between the two groups in other baseline biochemical parameters, lipid profiles, or complete blood count parameters. The NLR was 2.77 ± 1.60 in the poor CCC group and 4.13 ± 2.27 in the good CCC group, representing a borderline significant difference (p = 0.044). No significant differences were observed among other inflammatory parameters, such as hs-CRP, PLR, MLR, SII, and SIRI. Regarding coronary angiographic characteristics, no left main coronary artery occlusion was observed in any patient. There were no significant differences between the groups in the frequency of left anterior descending artery occlusion, circumflex artery occlusion, or right coronary artery occlusion. The SYNTAX score was 19.12 ± 6.15 in the poor CCC group and 22.54 ± 8.01 in the good CCC group (p = 0.022). The Gensini score was 39.88 ± 12.06 in the poor CCC group and 42.85 ± 14.91 in the good CCC group, showing no statistically significant difference (p = 0.377) (Table 1 ). When all patients in the study group were evaluated, no significant correlations were observed between Metrnl and age, HbA1c, hs-CRP, BMI, total cholesterol, HDL, triglycerides, LDL, NLR, PLR, MLR, SII, or SIRI. However, a low-level negative linear correlation was observed between Metrnl levels and the Gensini score (r = − 0.360; p < 0.001), as well as a low-level negative linear correlation with the SYNTAX score (r = − 0.372; p < 0.001) (Table 2 ). The discriminatory power of Metrnl for distinguishing between poor CCC and good CCC cases was evaluated using ROC curve analysis. The performance of this parameter in classification was not statistically significant (p = 0.789). The AUC was 0.520 (95% CI: 0.39–0.65), and the cut-off value was determined to be 1.64 (Fig. 1 ). Clinical and laboratory parameters associated with the development of good CCC were evaluated using binary logistic regression analysis. In the univariate analysis, the SYNTAX score showed a statistically significant positive association with the development of good CCC (odds ratio [OR] = 1.069; 95% CI: 1.004–1.138; p = 0.038). This finding indicates that as the complexity of coronary lesions increases, the probability of having well-developed collateral circulation also increases (Table 3 ). Table 1 Baseline clinical characteristics, medications, laboratory parameters, and angiographic findings of the study population. Clinical characteristics Poor CCC (n = 33) Good CCC (n = 65) P Age, years, mean (SD) 70.67 ± 11.10 68.40 ± 12.11 0.376 Female, n (%) 14 (42.4%) 23 (35.4%) 0.497 Diabetes mellitus, n (%) 15 (45.5%) 17 (26.2%) 0.056 Hypertension, n (%) 11 (33.3%) 17 (26.2%) 0.457 Current smoker, n (%) 8 (24.2%) 16 (24.6%) 0.968 Body mass index, mean (SD), kg/m² 24.44 ± 2.26 24.12 ± 1.61 0.422 Left ventricular ejection fraction, %, mean (SD) 49.76 ± 7.16 52.38 ± 5.75 0.052 Systolic blood pressure, mean (SD), mmHg 134.30 ± 14.57 130.17 ± 16.14 0.219 Diastolic blood pressure, mean (SD), mmHg 75.91 ± 8.20 73.42 ± 8.58 0.171 Medications ACE inhibitor or ARB usage, n (%) 17 (51.5%) 24 (36.9%) 0.166 Calcium channel blocker usage, n (%) 6 (18.2%) 19 (29.2%) 0.263 β-Blocker usage, n (%) 23 (69.7%) 48 (73.8%) 0.664 Nitrate usage, n (%) 4 (12.1%) 10 (15.4%) 0.663 Ranolazine usage, n (%) 6 (18.2%) 5 (7.7%) 0.174 Trimetazidine usage, n (%) 4 (12.1%) 8 (12.3%) 0.979 Statin usage, n (%) 7 (21.2%) 19 (29.2%) 0.395 Laboratory Parameters Metrnl, mean (SD), ng/mL 1.73 ± 0.15 1.67 ± 0.16 0.093 Fasting glucose, mean (SD), mg/dL 189.91 ± 25.47 165.03 ± 21.67 0.220 HbA1c, mean (SD), % 7.12 ± 1.11 6.83 ± 0.84 0.206 Urea, mean (SD), mg/dL 40.06 ± 16.98 39.17 ± 16.66 0.804 Creatinine, mean (SD), mg/dL 0.95 ± 0.27 1.02 ± 0.54 0.602 AST, mean (SD), U/L 21.76 ± 6.86 24.80 ± 15.85 0.288 ALT, mean (SD), U/L 15.82 ± 7.26 17.78 ± 8.62 0.264 Total cholesterol, mean (SD), mg/dL 193.18 ± 31.09 194.51 ± 28.22 0.845 Triglyceride, median (IQR), mg/dL 164.00 (60.00) 156.00 (72.00) 0.157 LDL, mean (SD), mg/dL 109.39 ± 26.80 112.78 ± 33.32 0.613 HDL, mean (SD), mg/dL 44.94 ± 5.31 44.29 ± 6.59 0.626 Hemoglobin, mean (SD), g/dL 13.93 ± 1.59 13.73 ± 2.25 0.640 Red blood cell, mean (SD), ×10⁶/µL 4.86 ± 0.50 4.82 ± 0.85 0.796 MCV, mean (SD), fL 86.61 ± 6.74 86.50 ± 6.61 0.934 White blood cell, mean (SD), ×10⁹/L 9.77 ± 2.91 9.72 ± 3.36 0.937 Neutrophil, mean (SD), ×10⁹/L 5.95 ± 1.91 6.51 ± 2.26 0.366 Lymphocyte, mean (SD), ×10⁹/L 2.89 ± 0.90 2.26 ± 0.77 0.083 Platelet, mean (SD), ×10⁹/L 364.82 ± 61.07 275.86 ± 54.46 0.580 MPV, mean (SD), fL 10.55 ± 0.88 10.21 ± 1.48 0.231 Plateletcrit, mean (SD), % 0.27 ± 0.05 0.28 ± 0.10 0.681 PDW, mean (SD), % 12.23 ± 3.02 11.86 ± 1.68 0.518 hs-CRP, median (IQR), mg/L 3.60 (5.50) 4.40 (6.45) 0.222 NLR, mean (SD) 2.77 ± 1.60 4.13 ± 2.27 0.044 PLR, mean (SD) 123.14 ± 41.24 149.40 ± 37.29 0.191 MLR, mean (SD) 0.29 ± 0.14 0.42 ± 0.17 0.125 SII, mean (SD) 745.25 ± 172.80 1164.86 ± 345.77 0.063 SIRI, mean (SD) 1.80 ± 0.18 3.65 ± 1.11 0.193 Angiographic findings LAD occlusion, n (%) 13 (39.4%) 25 (38.5%) 0.929 Cx occlusion, n (%) 8 (24.2%) 22 (33.8%) 0.330 RCA occlusion, n (%) 14 (42.4%) 26 (40.0%) 0.818 SYNTAX score, mean (SD) 19.12 ± 6.15 22.54 ± 8.01 0.022 Gensini score, mean (SD) 39.88 ± 12.06 42.85 ± 14.91 0.377 ACE: Angiotensin-converting enzyme; ARB: Angiotensin receptor blocker; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; CCC: Coronary collateral circulation; Cx: Circumflex artery; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; IQR: Interquartile range; LAD: Left anterior descending artery; LDL: Low-density lipoprotein; MCV: Mean corpuscular volume; MLR: Monocyte/lymphocyte ratio; MPV: Mean platelet volume; NLR: Neutrophil/lymphocyte ratio; PACS: Picture Archiving and Communication System; PDW: Platelet distribution width; PLR: Platelet/lymphocyte ratio; RCA: Right coronary artery; SD: Standard deviation; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index. Table 2 Correlation between Metrnl level and other variables of the study population. Variable r p Age −0.005 0.964 HbA1c 0.019 0.850 hs-CRP 0.019* 0.852 Body mass index −0.008 0.941 Total cholesterol 0.018 0.863 HDL −0.052 0.614 Triglyceride 0.165* 0.104 LDL −0.042 0.681 NLR 0.038 0.711 PLR 0.025 0.808 MLR 0.037 0.719 SII 0.057 0.576 SIRI 0.057 0.580 SYNTAX score −0.372 < 0.001 Gensini score −0.360 < 0.001 * Spearman's rank correlation coefficient. HDL: High-density lipoprotein; HbA1c: Hemoglobin A1c; hs-CRP: High-sensitivity C-reactive protein; LDL: Low-density lipoprotein; MLR: Monocyte/lymphocyte ratio; NLR: Neutrophil/lymphocyte ratio; PLR: Platelet/lymphocyte ratio; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index. Table 3 Binary logistic regression analysis showing independent predictors of good CCC. Variable P OR (95% CI) Metrnl levels 0.112 0.103 (0.006–1.504) Age 0.373 0.984 (0.949–1.020) HbA1c 0.167 0.731 (0.469–1.140) hs-CRP 0.203 1.050 (0.974–1.132) Body mass index 0.422 0.912 (0.728–1.142) Diabetes mellitus 0.057 0.425 (0.176–1.025) Hypertension 0.458 0.708 (0.285–1.761) Current smoker 0.968 1.020 (0.385–2.708) Total cholesterol 0.843 1.001 (0.988–1.015) HDL 0.623 0.983 (0.919–1.052) Triglyceride 0.181 0.995 (0.987–1.003) LDL 0.610 1.004 (0.990–1.018) NLR 0.130 1.160 (0.957–1.405) PLR 0.200 1.004 (0.998–1.009) MLR 0.080 14.171 (0.726–27.707) SII 0.612 1.000 (0.997–1.004) SIRI 0.435 1.004 (0.804–1.113) SYNTAX score 0.038 1.069 (1.004–1.138) Gensini score 0.419 1.006 (0.991–1.022) LAD occlusion 0.906 1.061 (0.399–2.819) Cx occlusion 0.948 0.965 (0.342–2.720) RCA occlusion 0.534 1.367 (0.510–3.661) CI: Confidence interval; CCC: Coronary collateral circulation; Cx: Circumflex artery; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; LAD: Left anterior descending artery; LDL: Low-density lipoprotein; MLR: Monocyte/lymphocyte ratio; NLR: Neutrophil/lymphocyte ratio; OR: Odds ratio; PLR: Platelet/lymphocyte ratio; RCA: Right coronary artery; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index. Discussion To the best of our knowledge, this is the first study in the literature to investigate the association between Metrnl and CCC in patients presenting with CCS and CTO. Furthermore, it provides the first clinical data evaluating the relationship between Metrnl and the SYNTAX score, a robust indicator of CAD complexity. Our primary finding indicates that serum Metrnl levels do not significantly correlate with the degree of CCC development. However, it is noteworthy that an increased SYNTAX score was associated with a higher probability of well-developed collateral circulation, and Metrnl levels exhibited a negative correlation with both SYNTAX and Gensini scores. These observations suggest that Metrnl may be more reflective of the overall atherosclerotic burden rather than the specific mechanisms of collateralization. CAD is a progressive clinical entity characterized by chronic inflammation and atherosclerotic plaque-induced myocardial oxygen supply–demand mismatch [10]. CCC represents a critical adaptive response to chronic ischemia resulting from high-grade coronary stenosis [11]. Well-developed collaterals, particularly in the setting of CTO, preserve myocardial viability, limit infarct size, and sustain ventricular function by maintaining perfusion to ischemic territories [12]. Collateral vessel formation primarily involves angiogenesis and arteriogenesis [13]. Arteriogenesis is the clinically paramount process, involving the transformation of pre-existing vessels into mature, functional arteries driven by mechanical stress, increased flow, and growth factors [13]. Conversely, angiogenesis refers to the formation of new capillaries in response to hypoxia. The equilibrium between these two mechanisms is a fundamental determinant of the inter-individual variability observed in collateral capacity [14]. In clinical practice, robust collateral circulation is instrumental in preserving myocardial integrity and cardiac function during CTO [12]. Metrnl is a pleiotropic protein secreted by skeletal muscle, adipose tissue, and barrier tissues that modulates endothelial and vascular smooth muscle functions via metabolic and immunological pathways [15]. Experimental models have demonstrated that Metrnl regulates endothelial function through the KIT receptor tyrosine kinase and enhances cardiac repair by promoting angiogenic responses post–myocardial infarction [16]. Similarly, in doxorubicin-induced cardiotoxicity models, Metrnl supplementation has been shown to mitigate oxidative stress and apoptosis, thereby preserving cardiac function [17]. These experimental findings underscore the potential of Metrnl as a cardioprotective molecule. Human studies have linked low serum Metrnl levels to impaired glucose tolerance, insulin resistance, elevated inflammatory markers, endothelial dysfunction, and subclinical atherosclerosis [18]. In patients with CAD, Metrnl levels are significantly lower than in healthy controls and are inversely proportional to the severity of atherosclerosis as measured by the Gensini score [5]. Furthermore, in ACS, Metrnl levels are reduced compared to healthy individuals, showing a negative correlation with necrosis markers (troponin-I and creatine kinase-MB [CK-MB]) and continuing to decline as the duration from symptom onset to hospital admission increases [6]. Metrnl has also been characterized as an immunoregulatory cytokine. Evidence suggests that Metrnl expression is upregulated during inflammation and modulates inflammatory responses through macrophages and other immune cells [19]. In a cohort of 60 CAD patients and 60 healthy controls, Metrnl levels were significantly lower in the CAD group and correlated positively with HDL, while showing a negative correlation with LDL and inflammatory markers (tumor necrosis factor-alpha [TNF-α], interleukin-1 beta [IL-1β], interleukin-6 [IL-6], hs-CRP) [4]. While our study also identified a negative correlation between Metrnl and the Gensini score, no significant correlation was found with HDL or LDL levels. In a study of 381 patients with ST-elevation myocardial infarction (STEMI), Metrnl levels measured 12 hours post-symptom onset were associated with CAD extent and acute complications such as heart failure and cardiogenic shock. Moreover, Metrnl independently predicted a 3-year composite endpoint of all-cause mortality and non-fatal myocardial infarction [20]. While some studies have reported negative correlations between Metrnl and total cholesterol/LDL and positive correlations with HDL [5,7], our study found no such associations. Furthermore, although Metrnl levels are typically lower in overweight and obese individuals [7], we found no significant correlation between BMI and Metrnl, consistent with findings in ACS cohorts [6]. Similarly, age did not correlate with Metrnl levels in our study, aligning with existing clinical data [6]. Despite reported negative correlations between Metrnl and hs-CRP in CAD [5], our cohort showed no such relationship, similar to findings in ACS [6]. Although literature regarding Metrnl and a comprehensive set of inflammatory indices is sparse, we observed that only the NLR was borderline significantly higher in the good CCC group (p = 0.044). Other indices showed no significant differences, suggesting that Metrnl's inflammatory associations may not directly translate into collateral development in CTO. Our study provides the first data regarding the relationship between the SYNTAX score and Metrnl. Consistent with prior research on CTO, the SYNTAX score was significantly higher in the good CCC group [21]. The negative correlation between Metrnl and both SYNTAX and Gensini scores, combined with the fact that the SYNTAX score was the sole predictor of good collateral development in our logistic regression, reinforces the link between lesion complexity and collateral response. Limitations The primary limitations of this study include the relatively small sample size and the lack of a control group with normal coronary anatomy. Collateral development is a dynamic, longitudinal process that may not be fully captured by cross-sectional measurements. Additionally, the Rentrop classification is a semi-quantitative angiographic method subject to technical and hemodynamic influences [9]. Invasive collateral flow index measurements, the gold standard, were not performed [22]. Conclusions In conclusion, serum Metrnl levels were not significantly associated with the degree of CCC in patients with CCS and CTO. However, Metrnl levels exhibited a negative correlation with SYNTAX and Gensini scores, reflecting the overall severity of CAD. Furthermore, the SYNTAX score emerged as an independent predictor of good collateral development, indicating that collateralization is closely linked to coronary lesion complexity. These findings suggest that Metrnl is more indicative of atherosclerotic burden than CCC, necessitating larger prospective studies to further elucidate these underlying mechanisms. Abbreviations ACS: Acute coronary syndrome; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; AUC: Area under the curve; BMI: Body mass index; CAD: Coronary artery disease; CCC: Coronary collateral circulation; CCS: Chronic coronary syndrome; CI: Confidence interval; CK-MB: Creatine kinase-MB; CTO: Chronic total occlusion; ELISA: Enzyme-linked immunosorbent assay; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; IL-1β: Interleukin-1 beta; IL-6: Interleukin-6; IL-41: Interleukin-41; IQR: Interquartile range; LDL: Low-density lipoprotein; LVEF: Left ventricular ejection fraction; MCV: Mean corpuscular volume; Metrnl: Meteorin-like protein; Metrnβ: Meteorin-β; MLR: Monocyte/lymphocyte ratio; MPV: Mean platelet volume; NLR: Neutrophil/lymphocyte ratio; NYHA: New York Heart Association; OR: Odds ratio; PACS: Picture Archiving and Communication System; PCI: Percutaneous coronary intervention; PDW: Platelet distribution width; PLR: Platelet/lymphocyte ratio; ROC: Receiver operating characteristic; SD: Standard deviation; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index; STEMI: ST-elevation myocardial infarction; SYNTAX: SYNergy between PCI with TAXUS and Cardiac Surgery; TNF-α: Tumor necrosis factor-alpha. Declarations Ethics Approval and Consent to Participate This study was approved by the Non-Interventional Clinical Research Ethics Committee of Niğde Ömer Halisdemir (Approval No: [2026/8], Date: [22.01.2026]). All procedures were conducted in accordance with the 1975 Declaration of Helsinki, updated in 2013. Written informed consent was obtained from all participants included in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding There was no external funding source for this study. Authors' Contributions HA conceived and designed the study, collected and acquired the data, performed statistical analysis, interpreted the results, drafted the manuscript, and critically revised it for important intellectual content. HA read and approved the final manuscript. Acknowledgements Not applicable. Clinical trial number Not applicable. References Shao C, Wang J, Tian J, Tang YD. Coronary Artery Disease: From Mechanism to Clinical Practice. In: Wang J, editor. Adv Exp Med Biol. 2020;1177:1–36. Meier P, Seiler C. The coronary collateral circulation--past, present and future. Curr Cardiol Rev. 2014;10(1):1. Rao RR, Long JZ, White JP, Svensson KJ, Lou J, Lokurkar I, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157(6):1279–1291. Liu J, Diao L, Xia W, Zeng X, Li W, Zou J, et al. Metrnl elevation post-exercise improved vascular inflammation among coronary artery disease patients by downregulating NLRP3 inflammasome activity. Aging (Albany NY). 2023;15(24):14720–14732. Liu ZX, Ji HH, Yao MP, Wang L, Wang Y, Zhou P, et al. Serum Metrnl is associated with the presence and severity of coronary artery disease. J Cell Mol Med. 2019;23(1):271–280. Giden R, Yasak IH. Metrnl decreases in acute coronary syndrome. Eur Rev Med Pharmacol Sci. 2023;27(1):208–214. Ding X, Chang X, Wang J, Bian N, An Y, Wang G, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Front Endocrinol (Lausanne). 2022;13:938341. Cai J, Wang QM, Li JW, Xu F, Bu YL, Wang M, et al. Serum Meteorin-like is associated with weight loss in the elderly patients with chronic heart failure. J Cachexia Sarcopenia Muscle. 2022;13(1):409–417. Rentrop KP, Cohen M, Blanke H, Phillips RA. Changes in collateral channel filling immediately after controlled coronary artery occlusion by an angioplasty balloon in human subjects. J Am Coll Cardiol. 1985;5(3):587–592. Libby P. Inflammation in atherosclerosis. Nature. 2002;420(6917):868–874. Seiler C. The human coronary collateral circulation. Eur J Clin Invest. 2010;40(5):465–476. Meier P, Hemingway H, Lansky AJ, Knapp G, Pitt B, Seiler C. The impact of the coronary collateral circulation on mortality: a meta-analysis. Eur Heart J. 2012;33(5):614–621. Heil M, Schaper W. Influence of mechanical, cellular, and molecular factors on collateral artery growth (arteriogenesis). Circ Res. 2004;95(5):449–458. Schaper W. Collateral circulation: past and present. Basic Res Cardiol. 2009;104(1):5–21. Li Z, Gao Z, Sun T, Zhang S, Yang S, Zheng M, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Front Immunol. 2023;14:1098570. Reboll MR, Klede S, Taft MH, Cai CL, Field LJ, Lavine KJ, et al. Meteorin-like promotes heart repair through endothelial KIT receptor tyrosine kinase. Science. 2022;376(6599):1343–1347. Hu C, Zhang X, Song P, Yuan YP, Kong CY, Wu HM, et al. Metrnl attenuates doxorubicin-induced cardiotoxicity via activating cAMP/PKA/SIRT1 pathway. Redox Biol. 2020;37:101747. El-Ashmawy HM, Selim FO, Hosny TAM, Almassry HN. Association of low serum Meteorin like (Metrnl) concentrations with worsening of glucose tolerance, impaired endothelial function and atherosclerosis. Diabetes Res Clin Pract. 2019;150:57–63. Ushach I, Arrevillaga-Boni G, Heller GN, Pone E, Hernandez-Ruiz M, Catalan-Dibene J, et al. Meteorin-like/Meteorin-β Is a Novel Immunoregulatory Cytokine Associated with Inflammation. J Immunol. 2018;201(12):3669–3676. Ferrer-Curriu G, Rueda F, Revuelta-López E, García-García C, Codina P, Gálvez-Montón C, et al. Metrnl is associated with a higher risk profile and predicts a worse outcome in patients with STEMI. Rev Esp Cardiol (Engl Ed). 2023;76(11):891–900. Hu XY, Yang WX, Guan CD, Xie LH, Dou KF, Wu YJ, et al. The prognostic value of collateral circulation in coronary chronic total occlusion underwent percutaneous coronary intervention. J Geriatr Cardiol. 2024;21(2):232–241. Seiler C, Fleisch M, Garachemani A, Meier B. Coronary collateral quantitation in patients with coronary artery disease using intravascular flow velocity or pressure measurements. J Am Coll Cardiol. 1998;32(5):1272–1279. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9059019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619871406,"identity":"602c15b2-863c-4aa2-abeb-233216394275","order_by":0,"name":"Hasan Akkaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDCCAzwMjA0HgAz2HjCfh494LTxngBwgxUa8FokcsBYGglr4bp89+HHGGbvE/plvDz7+mGMnw8bA/PDRDTxaJM/lJUtuuJGcOON2XrLBwW3JQIexGRvn4NFicIbHQPLBB+bEhts5ZhIHtzEDtfCwSRPQYvzzwYf6xPk3z4C01BOlxQzosMOJG27wgLQcJqxFEqjFcsaZ48Ybz+QYG5zddpyHjZmAX/iADrvZc6xadt7xM4YPKrdV2/OzNz98jE8LDDg2wJnMRCgHAXsi1Y2CUTAKRsFIBAAnflFRiEvlRQAAAABJRU5ErkJggg==","orcid":"","institution":"Niğde Ömer Halisdemir University","correspondingAuthor":true,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Akkaya","suffix":""}],"badges":[],"createdAt":"2026-03-07 14:09:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9059019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9059019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106543495,"identity":"dd7f61e9-2ecd-47ae-939b-653ad17f6f95","added_by":"auto","created_at":"2026-04-09 16:37:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver–operating characteristic (ROC) curve analysis for Metrnl level to predict good CCC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9059019/v1/ddb52fef4ecd7dcdf9ee80ab.png"},{"id":106862165,"identity":"607242e4-fef7-43f6-874c-f9d8ccac43ba","added_by":"auto","created_at":"2026-04-14 08:28:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9059019/v1/25d1f29d-ef1a-4c5d-b784-bdf603282b42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meteorin-like Protein (Metrnl) and Coronary Collateral Circulation in Patients with Chronic Total Occlusion: A Prospective Observational Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronary artery disease (CAD) remains one of the leading causes of morbidity and mortality worldwide [1]. Coronary collateral circulation (CCC), which develops in response to chronic myocardial ischemia, constitutes an alternative vascular network that supplies blood to ischemic myocardial regions distal to severely stenotic or occluded epicardial coronary arteries. By providing supplementary perfusion in the presence of coronary obstruction, collateral vessels play an important protective role in reducing ischemic burden. The development of coronary collaterals is regulated by a complex interplay of factors, including inflammation, oxidative stress, metabolic alterations, and vascular growth mediators [2]. However, the molecular mechanisms underlying the substantial inter-individual variability in CCC formation remain incompletely understood.\u003c/p\u003e \u003cp\u003eMeteorin-like protein (Metrnl) is a recently identified adipomyokine composed of a 311-amino-acid precursor protein with a molecular weight of approximately 25\u0026ndash;30 kDa. It is predominantly expressed in white adipose tissue and skeletal muscle, with lower expression levels in the myocardium. Also referred to as Meteorin-β (Metrnβ) or interleukin-41 (IL-41), Metrnl has gained increasing attention due to its potential roles in cardiovascular homeostasis [3]. Experimental evidence indicates that Metrnl enhances endothelial function and metabolic homeostasis by reducing vascular inflammation, thereby suggesting a possible indirect contribution to angiogenic processes required for collateral vessel development [4]. Clinical studies have demonstrated significantly reduced serum Metrnl levels in patients with CAD compared with healthy individuals, and an inverse association between Metrnl concentrations and disease severity has been reported [5]. Moreover, decreased Metrnl levels in patients with acute coronary syndrome (ACS) have been shown to correlate with higher troponin concentrations and adverse clinical outcomes [6].\u003c/p\u003e \u003cp\u003eMetrnl levels are reduced in metabolic disorders such as metabolic syndrome and obesity, and lower circulating concentrations have been associated with an atherogenic lipid profile\u0026mdash;characterized by decreased high-density lipoprotein and elevated low-density lipoprotein and triglycerides [7]. These findings suggest that Metrnl may play a role in atherogenic dyslipidemia and, consequently, in the pathogenesis of chronic coronary syndrome (CCS). Additionally, in elderly patients with chronic heart failure, reduced Metrnl levels have been linked to unintentional weight loss, severe cardiac dysfunction, and increased cardiovascular mortality [8]. Considering these cardioprotective properties, Metrnl may, with further investigation, emerge as a novel diagnostic biomarker, a therapeutic target, or even a potential therapeutic agent for CAD.\u003c/p\u003e \u003cp\u003eGiven its pro-angiogenic and endothelial-protective characteristics, it is plausible that Metrnl may also influence the development of coronary collaterals. Although previous studies have explored the general cardiovascular risk profile and the association between Metrnl and CAD severity, its direct relationship with the degree of CCC has not yet been adequately investigated. Therefore, the aim of the present study was to evaluate the association between serum Metrnl levels and CCC development in patients with chronic total occlusion (CTO) of the coronary arteries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, prospective observational study was conducted between April 2024 and February 2026 at the [Blinded for Review].. A total of 98 patients of both sexes, aged over 18 years, who presented with ischemic symptoms and were diagnosed with CCS were enrolled. All included patients had at least one CTO in a major epicardial coronary artery confirmed by coronary angiography. Written informed consent was obtained from each patient. The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Local Clinical Research Ethics Committee of [Blinded for Review].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with stable CAD who met the angiographic indications according to current guidelines\u0026mdash;characterized by typical chest pain, ischemic findings on electrocardiography, ischemia on myocardial perfusion scintigraphy, or critical lesions on coronary computed tomography angiography\u0026mdash;and demonstrated total occlusion of at least one major epicardial coronary artery were included. Exclusion criteria were as follows: a diagnosis of ACS within the last 6 months; a history of coronary artery bypass grafting or valvular surgery; moderate-to-severe valvular heart disease; acute or chronic rheumatological or inflammatory diseases; acute or chronic renal failure (glomerular filtration rate \u0026lt;30 mL/min); known malignancy; heart failure symptoms (New York Heart Association [NYHA] class III\u0026ndash;IV); hepatic failure; moderate or severe chronic obstructive pulmonary disease; and acute or chronic infectious diseases. Diabetes mellitus was defined as a fasting plasma glucose of \u0026ge;126 mg/dL on multiple measurements or the current use of antidiabetic medication. Hypertension was defined as repeated blood pressure measurements \u0026gt;140/90 mmHg or the use of antihypertensive therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and Laboratory Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to angiography, all patients underwent a comprehensive clinical evaluation, including medical history, electrocardiography, and echocardiographic assessment (performed by a single cardiologist) under outpatient conditions. Clinical characteristics (age, sex, presence of diabetes mellitus, presence of hypertension, smoking status, body mass index [BMI; calculated as weight in kilograms divided by the square of height in meters], left ventricular ejection fraction [LVEF; using the Simpson method], and blood pressure values); current medications (angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, calcium channel blockers, beta-blockers, nitrates, ranolazine, trimetazidine, statins); baseline biochemical parameters (fasting blood glucose, hemoglobin A1c [HbA1c], urea, creatinine, aspartate aminotransferase [AST], alanine aminotransferase [ALT]); lipid parameters (total cholesterol, triglycerides, low-density lipoprotein [LDL], high-density lipoprotein [HDL]); complete blood count (hemoglobin, red blood cell count, mean corpuscular volume [MCV], white blood cell count, neutrophil, lymphocyte, platelet count, mean platelet volume [MPV], plateletcrit, platelet distribution width [PDW]); and inflammatory parameters (high-sensitivity C-reactive protein [hs-CRP], neutrophil/lymphocyte ratio [NLR], platelet/lymphocyte ratio [PLR], monocyte/lymphocyte ratio [MLR], Systemic Immuno-Inflammation Index [SII], Systemic Inflammation Response Index [SIRI]) were evaluated for all patients at the time of admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Serum Metrnl Levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood samples were collected following at least 10 hours of fasting. Samples were promptly centrifuged at 1000 g for 10 minutes at 4\u0026deg;C, and serum Metrnl levels were analyzed. The analysis range was 0.05\u0026ndash;15 ng/mL. Following centrifugation, serum samples were stored at \u0026minus;80\u0026deg;C until analysis. Commercially available human Metrnl enzyme-linked immunosorbent assay (ELISA) kits (Catalog No.: HDP3836PU, Shanghai YL Biotech Co., Ltd., Shanghai, China) were used. Metrnl levels were measured by expert biochemists in accordance with the manufacturer\u0026apos;s instructions using a total of two kits. The inter-assay coefficient of variation was less than 9%, and the intra-assay coefficient of variation was less than 10%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInflammatory Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInflammatory parameters were derived from routine complete blood counts. NLR, PLR, and MLR values were obtained through mathematical division. The SII was calculated using the formula: (neutrophils \u0026times; platelets) / lymphocytes. The SIRI was calculated using the formula: (neutrophils \u0026times; monocytes) / lymphocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAngiographic Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAngiographic images were evaluated via a Picture Archiving and Communication System (PACS) by an experienced cardiologist. In cases of ambiguity, a second physician was consulted. Total occlusions in at least one major (diameter \u0026ge;2.5 mm) coronary artery (excluding the left main coronary artery) were identified in patients with CCS. Rentrop, SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery), and Gensini scores were calculated for each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRentrop Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Rentrop grading system was used to assess the degree of CCC [9]. According to this classification: Rentrop 0: no visible coronary collateral circulation; Rentrop 1: barely detectable collateral circulation (filling of side branches without any filling of the epicardial segment); Rentrop 2: partial collateral circulation (filling of the epicardial segment, but incomplete opacification); Rentrop 3: complete perfusion of the occluded epicardial artery via collaterals (full opacification). Consistent with previous literature, Rentrop grades 0\u0026ndash;1 were categorized as poor CCC, while grades 2\u0026ndash;3 were categorized as good CCC [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSYNTAX Score Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SYNTAX score was determined using the online calculator (www.syntaxscore.com) by answering 12 interactive questions (dominance, number of lesions, segments per lesion, total occlusion, trifurcation, bifurcation, aorto-ostial lesion, severe tortuosity, lesion length, calcification, presence of thrombus, diffuse disease/small vessels). Upon completion of the algorithm, the software provides a report specifying the characteristics of each lesion, its score, and the total SYNTAX score. The SYNTAX II score was utilized in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGensini Score Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gensini score was calculated manually. In this system, lesions are assigned points based on the degree of angiographic stenosis: 1 to 32 points (1 point for \u0026le;25% stenosis, 2 points for 26%\u0026ndash;50% stenosis, 4 points for 51%\u0026ndash;75% stenosis, 8 points for 76%\u0026ndash;90% stenosis, 16 points for 91%\u0026ndash;99% stenosis, and 32 points for total occlusion). These points are then multiplied by a factor determined by the location of the lesion (\u0026times;5 for the left main coronary artery; \u0026times;2.5 for the proximal left anterior descending artery; \u0026times;1.5 for the mid-left anterior descending and proximal circumflex artery; \u0026times;1 for the distal left anterior descending, right coronary artery, first diagonal branch, and obtuse marginal branches; and \u0026times;0.5 for the posterolateral and other side branches). The final score is the sum of these weighted values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software, version 22.018 (MedCalc Software Ltd, Ostend, Belgium). The normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Normally distributed numerical data are presented as mean \u0026plusmn; standard deviation (SD), while non-normally distributed variables are expressed as median and interquartile range (IQR). Categorical variables are reported as frequencies (n) and percentages (%). For intergroup comparisons, the Independent Samples t-test was employed for normally distributed data, and the Mann\u0026ndash;Whitney U test was used for non-normally distributed data. Relationships between categorical variables were evaluated using the Chi-square test. Correlation analyses were performed using Pearson\u0026apos;s correlation coefficient for normally distributed variables and Spearman\u0026apos;s rank correlation coefficient for non-normally distributed variables. Univariate binary logistic regression analysis was conducted to identify predictors of good CCC. Model fitness was verified using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test. The diagnostic performance of numerical variables was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. Optimal cut-off values were determined, and sensitivity, specificity, and their corresponding 95% confidence intervals (CIs) are presented. A p-value \u0026lt;0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 98 patients with CTO in at least one epicardial coronary artery were included in the study. The patients were divided into two groups based on their collateral status. The poor CCC group consisted of 33 patients (Rentrop 0: 11 [33.3%], Rentrop 1: 22 [66.7%]), while the good CCC group consisted of 65 patients (Rentrop 2: 42 [64.6%], Rentrop 3: 23 [35.4%]).\u003c/p\u003e \u003cp\u003eNo significant differences were observed between the groups regarding clinical characteristics, including age, sex, presence of diabetes mellitus, presence of hypertension, smoking status, BMI, LVEF, and blood pressure levels. Similarly, there were no significant differences between the groups in terms of prior medication use. Serum Metrnl levels were 1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 ng/mL in the poor CCC group and 1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 ng/mL in the good CCC group, with no statistically significant difference observed (p\u0026thinsp;=\u0026thinsp;0.093). Furthermore, no significant differences were found between the two groups in other baseline biochemical parameters, lipid profiles, or complete blood count parameters. The NLR was 2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 in the poor CCC group and 4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27 in the good CCC group, representing a borderline significant difference (p\u0026thinsp;=\u0026thinsp;0.044). No significant differences were observed among other inflammatory parameters, such as hs-CRP, PLR, MLR, SII, and SIRI.\u003c/p\u003e \u003cp\u003eRegarding coronary angiographic characteristics, no left main coronary artery occlusion was observed in any patient. There were no significant differences between the groups in the frequency of left anterior descending artery occlusion, circumflex artery occlusion, or right coronary artery occlusion. The SYNTAX score was 19.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15 in the poor CCC group and 22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01 in the good CCC group (p\u0026thinsp;=\u0026thinsp;0.022). The Gensini score was 39.88\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06 in the poor CCC group and 42.85\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91 in the good CCC group, showing no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.377) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen all patients in the study group were evaluated, no significant correlations were observed between Metrnl and age, HbA1c, hs-CRP, BMI, total cholesterol, HDL, triglycerides, LDL, NLR, PLR, MLR, SII, or SIRI. However, a low-level negative linear correlation was observed between Metrnl levels and the Gensini score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.360; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as a low-level negative linear correlation with the SYNTAX score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.372; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe discriminatory power of Metrnl for distinguishing between poor CCC and good CCC cases was evaluated using ROC curve analysis. The performance of this parameter in classification was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.789). The AUC was 0.520 (95% CI: 0.39\u0026ndash;0.65), and the cut-off value was determined to be 1.64 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical and laboratory parameters associated with the development of good CCC were evaluated using binary logistic regression analysis. In the univariate analysis, the SYNTAX score showed a statistically significant positive association with the development of good CCC (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.069; 95% CI: 1.004\u0026ndash;1.138; p\u0026thinsp;=\u0026thinsp;0.038). This finding indicates that as the complexity of coronary lesions increases, the probability of having well-developed collateral circulation also increases (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eBaseline clinical characteristics, medications, laboratory parameters, and angiographic findings of the study population.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor CCC (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood CCC (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.40\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \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\u003e14 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\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\u003e11 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, mean (SD), kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular ejection fraction, %, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.30\u0026thinsp;\u0026plusmn;\u0026thinsp;14.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.17\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.91\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE inhibitor or ARB usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium channel blocker usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Blocker usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanolazine usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrimetazidine usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin usage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory Parameters\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrnl, mean (SD), ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189.91\u0026thinsp;\u0026plusmn;\u0026thinsp;25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.03\u0026thinsp;\u0026plusmn;\u0026thinsp;21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, mean (SD), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.06\u0026thinsp;\u0026plusmn;\u0026thinsp;16.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.17\u0026thinsp;\u0026plusmn;\u0026thinsp;16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, mean (SD), U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.80\u0026thinsp;\u0026plusmn;\u0026thinsp;15.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, mean (SD), U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.18\u0026thinsp;\u0026plusmn;\u0026thinsp;31.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194.51\u0026thinsp;\u0026plusmn;\u0026thinsp;28.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, median (IQR), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164.00 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.00 (72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.39\u0026thinsp;\u0026plusmn;\u0026thinsp;26.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.78\u0026thinsp;\u0026plusmn;\u0026thinsp;33.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, mean (SD), g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell, mean (SD), \u0026times;10⁶/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV, mean (SD), fL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell, mean (SD), \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil, mean (SD), \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte, mean (SD), \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, mean (SD), \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364.82\u0026thinsp;\u0026plusmn;\u0026thinsp;61.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275.86\u0026thinsp;\u0026plusmn;\u0026thinsp;54.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV, mean (SD), fL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlateletcrit, mean (SD), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDW, mean (SD), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, median (IQR), mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.60 (5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.40 (6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.14\u0026thinsp;\u0026plusmn;\u0026thinsp;41.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.40\u0026thinsp;\u0026plusmn;\u0026thinsp;37.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e745.25\u0026thinsp;\u0026plusmn;\u0026thinsp;172.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1164.86\u0026thinsp;\u0026plusmn;\u0026thinsp;345.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAngiographic findings\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD occlusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCx occlusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA occlusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGensini score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.88\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.85\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.377\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\u003e \u003cem\u003eACE: Angiotensin-converting enzyme; ARB: Angiotensin receptor blocker; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; CCC: Coronary collateral circulation; Cx: Circumflex artery; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; IQR: Interquartile range; LAD: Left anterior descending artery; LDL: Low-density lipoprotein; MCV: Mean corpuscular volume; MLR: Monocyte/lymphocyte ratio; MPV: Mean platelet volume; NLR: Neutrophil/lymphocyte ratio; PACS: Picture Archiving and Communication System; PDW: Platelet distribution width; PLR: Platelet/lymphocyte ratio; RCA: Right coronary artery; SD: Standard deviation; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index.\u003c/em\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\u003eCorrelation between Metrnl level and other variables of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.165*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGensini score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e \u003cem\u003e* Spearman's rank correlation coefficient. HDL: High-density lipoprotein; HbA1c: Hemoglobin A1c; hs-CRP: High-sensitivity C-reactive protein; LDL: Low-density lipoprotein; MLR: Monocyte/lymphocyte ratio; NLR: Neutrophil/lymphocyte ratio; PLR: Platelet/lymphocyte ratio; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index.\u003c/em\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression analysis showing independent predictors of good CCC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrnl levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103 (0.006\u0026ndash;1.504)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984 (0.949\u0026ndash;1.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.731 (0.469\u0026ndash;1.140)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.050 (0.974\u0026ndash;1.132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.912 (0.728\u0026ndash;1.142)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.425 (0.176\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708 (0.285\u0026ndash;1.761)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.020 (0.385\u0026ndash;2.708)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001 (0.988\u0026ndash;1.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983 (0.919\u0026ndash;1.052)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995 (0.987\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004 (0.990\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.160 (0.957\u0026ndash;1.405)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004 (0.998\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.171 (0.726\u0026ndash;27.707)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000 (0.997\u0026ndash;1.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004 (0.804\u0026ndash;1.113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.069 (1.004\u0026ndash;1.138)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGensini score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.006 (0.991\u0026ndash;1.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.061 (0.399\u0026ndash;2.819)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCx occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965 (0.342\u0026ndash;2.720)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.367 (0.510\u0026ndash;3.661)\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\u003e \u003cem\u003eCI: Confidence interval; CCC: Coronary collateral circulation; Cx: Circumflex artery; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; LAD: Left anterior descending artery; LDL: Low-density lipoprotein; MLR: Monocyte/lymphocyte ratio; NLR: Neutrophil/lymphocyte ratio; OR: Odds ratio; PLR: Platelet/lymphocyte ratio; RCA: Right coronary artery; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study in the literature to investigate the association between Metrnl and CCC in patients presenting with CCS and CTO. Furthermore, it provides the first clinical data evaluating the relationship between Metrnl and the SYNTAX score, a robust indicator of CAD complexity. Our primary finding indicates that serum Metrnl levels do not significantly correlate with the degree of CCC development. However, it is noteworthy that an increased SYNTAX score was associated with a higher probability of well-developed collateral circulation, and Metrnl levels exhibited a negative correlation with both SYNTAX and Gensini scores. These observations suggest that Metrnl may be more reflective of the overall atherosclerotic burden rather than the specific mechanisms of collateralization.\u003c/p\u003e \u003cp\u003eCAD is a progressive clinical entity characterized by chronic inflammation and atherosclerotic plaque-induced myocardial oxygen supply\u0026ndash;demand mismatch [10]. CCC represents a critical adaptive response to chronic ischemia resulting from high-grade coronary stenosis [11]. Well-developed collaterals, particularly in the setting of CTO, preserve myocardial viability, limit infarct size, and sustain ventricular function by maintaining perfusion to ischemic territories [12].\u003c/p\u003e \u003cp\u003eCollateral vessel formation primarily involves angiogenesis and arteriogenesis [13]. Arteriogenesis is the clinically paramount process, involving the transformation of pre-existing vessels into mature, functional arteries driven by mechanical stress, increased flow, and growth factors [13]. Conversely, angiogenesis refers to the formation of new capillaries in response to hypoxia. The equilibrium between these two mechanisms is a fundamental determinant of the inter-individual variability observed in collateral capacity [14]. In clinical practice, robust collateral circulation is instrumental in preserving myocardial integrity and cardiac function during CTO [12].\u003c/p\u003e \u003cp\u003eMetrnl is a pleiotropic protein secreted by skeletal muscle, adipose tissue, and barrier tissues that modulates endothelial and vascular smooth muscle functions via metabolic and immunological pathways [15]. Experimental models have demonstrated that Metrnl regulates endothelial function through the KIT receptor tyrosine kinase and enhances cardiac repair by promoting angiogenic responses post\u0026ndash;myocardial infarction [16]. Similarly, in doxorubicin-induced cardiotoxicity models, Metrnl supplementation has been shown to mitigate oxidative stress and apoptosis, thereby preserving cardiac function [17]. These experimental findings underscore the potential of Metrnl as a cardioprotective molecule.\u003c/p\u003e \u003cp\u003eHuman studies have linked low serum Metrnl levels to impaired glucose tolerance, insulin resistance, elevated inflammatory markers, endothelial dysfunction, and subclinical atherosclerosis [18]. In patients with CAD, Metrnl levels are significantly lower than in healthy controls and are inversely proportional to the severity of atherosclerosis as measured by the Gensini score [5]. Furthermore, in ACS, Metrnl levels are reduced compared to healthy individuals, showing a negative correlation with necrosis markers (troponin-I and creatine kinase-MB [CK-MB]) and continuing to decline as the duration from symptom onset to hospital admission increases [6].\u003c/p\u003e \u003cp\u003eMetrnl has also been characterized as an immunoregulatory cytokine. Evidence suggests that Metrnl expression is upregulated during inflammation and modulates inflammatory responses through macrophages and other immune cells [19]. In a cohort of 60 CAD patients and 60 healthy controls, Metrnl levels were significantly lower in the CAD group and correlated positively with HDL, while showing a negative correlation with LDL and inflammatory markers (tumor necrosis factor-alpha [TNF-α], interleukin-1 beta [IL-1β], interleukin-6 [IL-6], hs-CRP) [4]. While our study also identified a negative correlation between Metrnl and the Gensini score, no significant correlation was found with HDL or LDL levels.\u003c/p\u003e \u003cp\u003eIn a study of 381 patients with ST-elevation myocardial infarction (STEMI), Metrnl levels measured 12 hours post-symptom onset were associated with CAD extent and acute complications such as heart failure and cardiogenic shock. Moreover, Metrnl independently predicted a 3-year composite endpoint of all-cause mortality and non-fatal myocardial infarction [20].\u003c/p\u003e \u003cp\u003eWhile some studies have reported negative correlations between Metrnl and total cholesterol/LDL and positive correlations with HDL [5,7], our study found no such associations. Furthermore, although Metrnl levels are typically lower in overweight and obese individuals [7], we found no significant correlation between BMI and Metrnl, consistent with findings in ACS cohorts [6]. Similarly, age did not correlate with Metrnl levels in our study, aligning with existing clinical data [6].\u003c/p\u003e \u003cp\u003eDespite reported negative correlations between Metrnl and hs-CRP in CAD [5], our cohort showed no such relationship, similar to findings in ACS [6]. Although literature regarding Metrnl and a comprehensive set of inflammatory indices is sparse, we observed that only the NLR was borderline significantly higher in the good CCC group (p\u0026thinsp;=\u0026thinsp;0.044). Other indices showed no significant differences, suggesting that Metrnl's inflammatory associations may not directly translate into collateral development in CTO.\u003c/p\u003e \u003cp\u003eOur study provides the first data regarding the relationship between the SYNTAX score and Metrnl. Consistent with prior research on CTO, the SYNTAX score was significantly higher in the good CCC group [21]. The negative correlation between Metrnl and both SYNTAX and Gensini scores, combined with the fact that the SYNTAX score was the sole predictor of good collateral development in our logistic regression, reinforces the link between lesion complexity and collateral response.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe primary limitations of this study include the relatively small sample size and the lack of a control group with normal coronary anatomy. Collateral development is a dynamic, longitudinal process that may not be fully captured by cross-sectional measurements. Additionally, the Rentrop classification is a semi-quantitative angiographic method subject to technical and hemodynamic influences [9]. Invasive collateral flow index measurements, the gold standard, were not performed [22].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, serum Metrnl levels were not significantly associated with the degree of CCC in patients with CCS and CTO. However, Metrnl levels exhibited a negative correlation with SYNTAX and Gensini scores, reflecting the overall severity of CAD. Furthermore, the SYNTAX score emerged as an independent predictor of good collateral development, indicating that collateralization is closely linked to coronary lesion complexity. These findings suggest that Metrnl is more indicative of atherosclerotic burden than CCC, necessitating larger prospective studies to further elucidate these underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACS: Acute coronary syndrome; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; AUC: Area under the curve; BMI: Body mass index; CAD: Coronary artery disease; CCC: Coronary collateral circulation; CCS: Chronic coronary syndrome; CI: Confidence interval; CK-MB: Creatine kinase-MB; CTO: Chronic total occlusion; ELISA: Enzyme-linked immunosorbent assay; HbA1c: Hemoglobin A1c; HDL: High-density lipoprotein; hs-CRP: High-sensitivity C-reactive protein; IL-1\u0026beta;: Interleukin-1 beta; IL-6: Interleukin-6; IL-41: Interleukin-41; IQR: Interquartile range; LDL: Low-density lipoprotein; LVEF: Left ventricular ejection fraction; MCV: Mean corpuscular volume; Metrnl: Meteorin-like protein; Metrn\u0026beta;: Meteorin-\u0026beta;; MLR: Monocyte/lymphocyte ratio; MPV: Mean platelet volume; NLR: Neutrophil/lymphocyte ratio; NYHA: New York Heart Association; OR: Odds ratio; PACS: Picture Archiving and Communication System; PCI: Percutaneous coronary intervention; PDW: Platelet distribution width; PLR: Platelet/lymphocyte ratio; ROC: Receiver operating characteristic; SD: Standard deviation; SII: Systemic Immuno-Inflammation Index; SIRI: Systemic Inflammation Response Index; STEMI: ST-elevation myocardial infarction; SYNTAX: SYNergy between PCI with TAXUS and Cardiac Surgery; TNF-\u0026alpha;: Tumor necrosis factor-alpha.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Non-Interventional Clinical Research\u0026nbsp;Ethics Committee of Niğde Ömer Halisdemir (Approval No: [2026/8], Date: [22.01.2026]).\u0026nbsp; All procedures were conducted in accordance with the 1975 Declaration of Helsinki, updated in 2013. Written informed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no external funding source for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHA conceived and designed the study, collected and acquired the data, performed statistical analysis, interpreted the results, drafted the manuscript, and critically revised it for important intellectual content. HA read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eShao C, Wang J, Tian J, Tang YD. Coronary Artery Disease: From Mechanism to Clinical Practice. In: Wang J, editor. Adv Exp Med Biol. 2020;1177:1\u0026ndash;36.\u003c/li\u003e\n \u003cli\u003eMeier P, Seiler C. The coronary collateral circulation--past, present and future. Curr Cardiol Rev. 2014;10(1):1.\u003c/li\u003e\n \u003cli\u003eRao RR, Long JZ, White JP, Svensson KJ, Lou J, Lokurkar I, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157(6):1279\u0026ndash;1291.\u003c/li\u003e\n \u003cli\u003eLiu J, Diao L, Xia W, Zeng X, Li W, Zou J, et al. Metrnl elevation post-exercise improved vascular inflammation among coronary artery disease patients by downregulating NLRP3 inflammasome activity. Aging (Albany NY). 2023;15(24):14720\u0026ndash;14732.\u003c/li\u003e\n \u003cli\u003eLiu ZX, Ji HH, Yao MP, Wang L, Wang Y, Zhou P, et al. Serum Metrnl is associated with the presence and severity of coronary artery disease. J Cell Mol Med. 2019;23(1):271\u0026ndash;280.\u003c/li\u003e\n \u003cli\u003eGiden R, Yasak IH. Metrnl decreases in acute coronary syndrome. Eur Rev Med Pharmacol Sci. 2023;27(1):208\u0026ndash;214.\u003c/li\u003e\n \u003cli\u003eDing X, Chang X, Wang J, Bian N, An Y, Wang G, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Front Endocrinol (Lausanne). 2022;13:938341.\u003c/li\u003e\n \u003cli\u003eCai J, Wang QM, Li JW, Xu F, Bu YL, Wang M, et al. Serum Meteorin-like is associated with weight loss in the elderly patients with chronic heart failure. J Cachexia Sarcopenia Muscle. 2022;13(1):409\u0026ndash;417.\u003c/li\u003e\n \u003cli\u003eRentrop KP, Cohen M, Blanke H, Phillips RA. Changes in collateral channel filling immediately after controlled coronary artery occlusion by an angioplasty balloon in human subjects. J Am Coll Cardiol. 1985;5(3):587\u0026ndash;592.\u003c/li\u003e\n \u003cli\u003eLibby P. Inflammation in atherosclerosis. Nature. 2002;420(6917):868\u0026ndash;874.\u003c/li\u003e\n \u003cli\u003eSeiler C. The human coronary collateral circulation. Eur J Clin Invest. 2010;40(5):465\u0026ndash;476.\u003c/li\u003e\n \u003cli\u003eMeier P, Hemingway H, Lansky AJ, Knapp G, Pitt B, Seiler C. The impact of the coronary collateral circulation on mortality: a meta-analysis. Eur Heart J. 2012;33(5):614\u0026ndash;621.\u003c/li\u003e\n \u003cli\u003eHeil M, Schaper W. Influence of mechanical, cellular, and molecular factors on collateral artery growth (arteriogenesis). Circ Res. 2004;95(5):449\u0026ndash;458.\u003c/li\u003e\n \u003cli\u003eSchaper W. Collateral circulation: past and present. Basic Res Cardiol. 2009;104(1):5\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eLi Z, Gao Z, Sun T, Zhang S, Yang S, Zheng M, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Front Immunol. 2023;14:1098570.\u003c/li\u003e\n \u003cli\u003eReboll MR, Klede S, Taft MH, Cai CL, Field LJ, Lavine KJ, et al. Meteorin-like promotes heart repair through endothelial KIT receptor tyrosine kinase. Science. 2022;376(6599):1343\u0026ndash;1347.\u003c/li\u003e\n \u003cli\u003eHu C, Zhang X, Song P, Yuan YP, Kong CY, Wu HM, et al. Metrnl attenuates doxorubicin-induced cardiotoxicity via activating cAMP/PKA/SIRT1 pathway. Redox Biol. 2020;37:101747.\u003c/li\u003e\n \u003cli\u003eEl-Ashmawy HM, Selim FO, Hosny TAM, Almassry HN. Association of low serum Meteorin like (Metrnl) concentrations with worsening of glucose tolerance, impaired endothelial function and atherosclerosis. Diabetes Res Clin Pract. 2019;150:57\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eUshach I, Arrevillaga-Boni G, Heller GN, Pone E, Hernandez-Ruiz M, Catalan-Dibene J, et al. Meteorin-like/Meteorin-\u0026beta; Is a Novel Immunoregulatory Cytokine Associated with Inflammation. J Immunol. 2018;201(12):3669\u0026ndash;3676.\u003c/li\u003e\n \u003cli\u003eFerrer-Curriu G, Rueda F, Revuelta-L\u0026oacute;pez E, Garc\u0026iacute;a-Garc\u0026iacute;a C, Codina P, G\u0026aacute;lvez-Mont\u0026oacute;n C, et al. Metrnl is associated with a higher risk profile and predicts a worse outcome in patients with STEMI. Rev Esp Cardiol (Engl Ed). 2023;76(11):891\u0026ndash;900.\u003c/li\u003e\n \u003cli\u003eHu XY, Yang WX, Guan CD, Xie LH, Dou KF, Wu YJ, et al. The prognostic value of collateral circulation in coronary chronic total occlusion underwent percutaneous coronary intervention. J Geriatr Cardiol. 2024;21(2):232\u0026ndash;241.\u003c/li\u003e\n \u003cli\u003eSeiler C, Fleisch M, Garachemani A, Meier B. Coronary collateral quantitation in patients with coronary artery disease using intravascular flow velocity or pressure measurements. J Am Coll Cardiol. 1998;32(5):1272\u0026ndash;1279.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Meteorin-like protein, Metrnl, Chronic coronary syndrome, Chronic total occlusion, Coronary collateral circulation, SYNTAX score, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-9059019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9059019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary collateral circulation (CCC) mitigates ischemia, preserves myocardial viability, and improves outcomes in patients with advanced coronary artery disease. However, molecular determinants of inter-individual variability in CCC remain poorly defined. Meteorin-like protein (Metrnl) is a novel adipomyokine with cardioprotective, metabolic, and immunoregulatory properties, but its relationship with CCC has not been clarified.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate the association between serum Metrnl levels and CCC in patients with chronic coronary syndrome and chronic total occlusion, and to investigate the relationship between Metrnl and angiographic severity scores.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective, single-center study included 98 patients with chronic coronary syndrome and at least one chronic total occlusion in a major epicardial coronary artery. CCC was assessed angiographically using the Rentrop classification. Patients were stratified into poor CCC (Rentrop 0\u0026ndash;1, n\u0026thinsp;=\u0026thinsp;33) and good CCC (Rentrop 2\u0026ndash;3, n\u0026thinsp;=\u0026thinsp;65) groups. Baseline clinical characteristics, medications, biochemical and hematological parameters, inflammatory indices, and angiographic findings were recorded. Serum Metrnl levels were measured using a standardized ELISA. Coronary disease severity was quantified with SYNTAX II and Gensini scores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eClinical features, prior medication use, lipid profile, and most inflammatory markers did not differ significantly between poor and good CCC groups. Serum Metrnl levels were comparable in poor versus good CCC (1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 vs. 1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 ng/mL, p\u0026thinsp;=\u0026thinsp;0.093), and Metrnl did not correlate with body mass index, lipid parameters, or high-sensitivity C-reactive protein. In contrast, Metrnl levels were inversely associated with angiographic severity: Gensini score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.360, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SYNTAX score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.372, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis showed no discriminatory value of Metrnl for distinguishing poor from good CCC (AUC 0.520, p\u0026thinsp;=\u0026thinsp;0.789). In univariate logistic regression, higher SYNTAX score was significantly associated with good CCC (odds ratio 1.069; 95% confidence interval 1.004\u0026ndash;1.138; p\u0026thinsp;=\u0026thinsp;0.038).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn patients with chronic coronary syndrome and chronic total occlusion, serum Metrnl levels are not linked to the extent of CCC but are inversely correlated with coronary atherosclerotic burden as reflected by SYNTAX and Gensini scores. Metrnl may represent a biomarker of disease severity rather than collateralization.\u003c/p\u003e","manuscriptTitle":"Meteorin-like Protein (Metrnl) and Coronary Collateral Circulation in Patients with Chronic Total Occlusion: A Prospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:37:33","doi":"10.21203/rs.3.rs-9059019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6808c8fb-a05a-4ce6-a3fa-0990d35bfcd9","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T08:27:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:37:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9059019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9059019","identity":"rs-9059019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}