Elevated plasma lipoprotein-associated phospholipase A2 levels are associated with poor prognosis in patients with coronary slow flow: A retrospective study

Elevated plasma lipoprotein-associated phospholipase A2 levels are associated with poor prognosis in patients with coronary slow flow: A retrospective 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 Elevated plasma lipoprotein-associated phospholipase A2 levels are associated with poor prognosis in patients with coronary slow flow: A retrospective study Xianjin Wang, Xianguan Zhu, Liling Zhang, Jing Chen, Biyun Qian, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4651584/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 slow flow (CSF) is characterized by reduced coronary circulation and delayed contrast media opacity during angiography. Inflammatory responses have been observed in CSF. The correlation between lipoprotein-associated phospholipase A2 (LpPLA2) as a vascular inflammatory factor and the prognosis of CSF patients remains unclear. This study aims to investigate the potential correlation between plasma LpPLA2 levels and the occurrence of major adverse cardiovascular events (MACE) in CSF patients. Methods: A retrospective study was conducted on 285 patients with CSF who were admitted to Anqing Municipal Hospital from January 2019 to March 2023. LpPLA2 plasma levels and baseline data were collected from hospital records, mean thrombolysis in myocardial infarction frame count (mTFC) was calculated for each patient. Follow-up was conducted by telephone call or cardiology clinic data, and the occurrence and timing of MACE were recorded. Results: The median follow-up duration was 25 (95%CI: 23-27) months. During follow-up, MACE occurred in 54 patients (18.9%), including 4 individuals had non-fatal myocardial infarction, 6 had arrhythmias (4 of atrial fibrillation, 2 of frequent ventricular premature beats), and 44 had rehospitalizations due to unstable angina pectoris. No cardiac death or cerebrovascular events occurred during follow-up. We categorized LpPLA2 into quartiles, and multivariable adjusted Cox models showed an association between LpPLA2 levels and MACE incidence among CSF patients. Compared to the lowest quartiles, the hazard ratios (HR) for the highest quartiles of LpPLA2 levels was 3.34(95%CI: 1.09-10.22) (p-trend < 0.01). Though the restricted cubic spline (RCS) curve indicates a linear relationship between LpPLA2 and MACE incidence in patients with CSF (p for non-linearity = 0.212), the risk of MACE was relatively flat until 247.7 ng/ml of predicted LpPLA2 levels and increased afterwards, with a HR of 1.78 (95%CI: 1.17 to 2.71) per standard deviation. Kaplan-Meier survival assessments revealed that patients in the highest LpPLA2 quartile exhibited a worse prognosis for MACE compared to those in the lower quartiles of LpPLA2 levels (log-rank p = 0.002). Additionally, multivariable Cox regression analyses identified mTFC, LDL-C, and diabetes as other factors associated with occurrence of MACE in patients with CSF. Conclusion: Elevated plasma LpPLA2 levels may serve as an independent predictor of MACE in patients with CSF. In addition to LpPLA2, dyslipidemia, diabetes, and mTFC may also be correlated with the prognosis of CSF. Coronary slow flow Lipoprotein-associated phospholipase A2 Coronary artery disease Major adverse cardiovascular events Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Coronary slow flow (CSF) is characterized by a slow progression of coronary flow velocity following the injection of contrast agent with normal or near-normal coronary vessels during coronary angiography [ 1 , 2 ]. This phenomenon was initially documented by Tambe et al. [ 3 ]. The incidence rate of CSF in coronary angiograms ranges from 1–7% and is frequently observed in male individuals, smokers, and patients at high cardiovascular risk [ 2 ]. Historically, CSF patients were considered to have favorable prognoses due to the absence of significant coronary artery stenosis. However, some observational studies have indicated an association between CSF and major adverse cardiovascular events (MACE), such as myocardial infarction, sudden death, arrhythmias, cerebrovascular events, rehospitalization due to unstable angina, etc., suggesting a poor prognosis [ 4 – 6 ]. Despite being well-established clinical and angiographic findings, the precise pathogenic mechanisms underlying CSF remain unclear. Numerous studies have proposed that subclinical atherosclerosis, endothelial dysfunction, and microvascular abnormalities may be involved in its pathogenesis [ 1 ]. Furthermore, inflammatory responses have been observed in CSF and are implicated in the pathological processes of CSF [ 1 , 7 ]. Lipoprotein-associated phospholipase A2 (LpPLA2), a novel vascular-specific inflammation cytokine and enzyme involved in stress reaction has also been reported to be associated with the presence and severity of CSF [ 8 , 9 ]. LpPLA2 is secreted by macrophage cells often detected in unstable coronary plaques [ 10 ], and has been shown to mediate the inflammatory process within the vascular wall [ 11 ]. Its proinflammatory properties make LpPLA2 an independent predictive biomarker for future cardiovascular events among patients with atherosclerotic cardiovascular disease [ 12 – 14 ]. However, it remains unknown whether plasma LpPLA2 can predict outcomes for CSF. In this study, the aim was to explore MACE rates among CSF patients with varying levels of LpPLA2. Methods Study Population This retrospective cohort study, conducted at Anqing Municipal Hospital in Anhui Province, China from January 1, 2019 through March 31, 2023, enrolled a total of 8780 consecutive patients. Among them, 315 patients were identified as having coronary slow flow (CSF) confirmed by angiography using the thrombolysis in myocardial infarction frame count (TFC) and exhibiting normal or near-normal coronary vessels (angiographic stenosis < 40%) [ 15 ]. Exclusion criteria included incomplete angiography recordings, angiographic stenosis ≥ 40%, history of coronary revascularization, left ventricular ejection fraction < 40%, pulmonary heart disease, organic heart disease (e.g., congenital heart disease, severe valvular heart disease, cardiomyopathy), coronary ectasia, coronary myocardial bridge, coronary artery spasm, atrial fibrillation; severe liver or kidney dysfunction; cancer; missing LpPLA2 data; and loss to follow-up at the beginning (Fig. 1 ). Demographic data including age, sex, body mass index (BMI), smoking status and diagnosed medical conditions (hypertension and diabetes) were collected at the time of angiography. Discharge cardiovascular medications (statins, antiplatelet agents, and nitrates) were documented. Echocardiogram data pertaining to left ventricular ejection fraction (LVEF), interventricular septal thickness (IVST), left ventricular end-diastolic dimension (LVEDD), left atrial anteroposterior diameter (LAAP), and the value of e/a ratio of mitral annulus (E/A ratio) were recorded. Laboratory data obtained at the time of angiography from electronic medical records included triglyceride, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and estimated glomerular filtration rate (eGFR). Uric acid, urea, creatinine, triglyceride, total cholesterol, LDL-C, and HDL-C levels were measured using a chemiluminescence method with a Roche Diagnostics Cobas analyzer Cobas8000, c702 module. The eGFR was calculated by Chinese modified MDRD equations [ 16 ]. Plasma LpPLA2 activity was measured using chemiluminescence immunoassay (Hotgen Biology Technology Co., Ltd. Beijin, China). The study was approved by the ethics committee of Anqing Municipal Hospital [Medical Ethics Review(2022) No.74], and informed consent was obtained from all patients or their family members. Coronary angiography All patients underwent coronary angiography using the standard Judkins technique with either radial or femoral artery approach. Coronary blood flow was evaluated using thrombolysis in myocardial infarction frame count (TFC), which is considered an objective and quantitative measure. TFC was originally defined by Gibson and refers to the number of film frames required for the contrast agent to reach distal landmarks. The first frame was determined when the contrast agent completely filled the entrance of the coronary artery. Distal landmarks included the distal left anterior descending (LAD) artery bifurcation (known as the "tail of the whale"), distal circumflex (CX) artery bifurcation (the longest lateral left ventricular wall artery branch), and distal right coronary artery (RCA) bifurcation (the first posterolateral artery branch) (Figs. 2 and 3 extracted from the work of Aksoy S et al.[ 17 ]). CSF was defined as TFC > 27 frames based on absence of obstructive epicardial coronary artery disease. The TFC value of LAD was divided by 1.7 to obtain a corrected TFC (CTFC) due to its longer length compared to other coronary arteries. Since criteria for TFC values were counted at a speed of 30 frames per second during angiography, we multiplied our study's acquisition rate of 15 frames/second by two when calculating TFC values. The mean TFC (mTFC) calculated from averaging LAD, CX, and RCA TFCs was recorded. Finally, two interventional cardiologists blinded to our study made decisions regarding TFC values. Follow up After completing the collection of demographic and coronary angiographic data from medical records, patients initially diagnosed with CSF were followed up through phone call invitations or available outpatient cardiology clinic data. The follow-up evaluation focused on the occurrence of major adverse cardiovascular events (MACE), and recorded the date when MACE occurred. In our study, MACE was defined as nonfatal myocardial infarction, cardiac death, severe arrhythmias (e.g., sick sinus syndrome, second degree or higher atrioventricular block, frequent ventricular premature beats, ventricular tachycardia or ventricular fibrillation, and paroxysmal or persistent atrial fibrillation), rehospitalization due to unstable angina, and cerebrovascular events. Statistical analysis All analyses were performed using R statistical software version 3.6.1 (R Foundation). Data were described as means and SDs for normally distributed continuous variables and as medians and interquartile ranges for non-normally distributed continuous variables. The Shapiro-Wilk test was conducted to determine the distribution of quantitative variables. Categorical variables were described using frequency with percentage. To clinically examine the association between plasma LpPLA2 levels and incident MACE in CSF patients, LpPLA2 levels were divided into quartiles. Baseline characteristics are summarized according to LpPLA2 quartiles and compared using analysis of variance or Kruskal-Wallis test or chi-square test depending on data types and distributions. We calculated the person-time of follow-up for each participant included in the study from the date of CSF diagnosis (January 1, 2019 to March 31, 2023) survey until the occurrence of MACE, loss to follow-up, or the end of follow-up (March 31, 2024), whichever came first. Incidence rates of MACE per 100 person-years were calculated based on LpPLA2 quartiles. Cox regression analysis was conducted to calculate hazard ratios (HRs) with 95% CIs, using LpPLA2 as the exposure variable and considering the lowest quartiles as reference. We estimated three models: model 1 adjusted for age and sex; model 2 adjusted for age, sex, LVEF, LA, IVSD, LVEDD, E/A ratio, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, uric acid, BMI, number of CSF vessels, mTFC, triglyceride, total cholesterol, HDL-C, LDL-C, and eGFR; and model 3 adjusted for variables in model 2 plus history of hypertension, diabetes, smoking, and use of discharge cardiovascular medications (statins, antiplatelet agents, and nitrates). The trend test utilized median values from each LpPLA2 quartile as a continuous variable within the mode. Kaplan-Meier curves depicted MACE rates across LpPLA2 quartiles assessed via log-rank testing. Additionally, potential non-linear relationships were explored through 4-knotted restricted cubic spline regression (the knots being determined by Akaike information criterion between 3 and 7). Reference value (HR = 1) was set at tenth percentiles while knots corresponded to 5th, 35th, 65th, and 95th percentiles. Meanwhile, univariate and multivariate Cox proportional hazards regression were performed to determine predictive variables for occurrence of MACE among CSF patients. In multivariable analyses only those variables with p ≤ 0.1 based on univariate testing were taken into account, and LpPLA2 was handled as a unprocessed continuous variable in these models. Of the 285 patients who completed follow-up, 28 had missing covariate information pertaining to echocardiographic data, blood lipids, BMI, eGFR, and uric acid. These missing data were assumed to be missing at random. Sensitivity analyses utilized multiple imputation with chained equations based on pooled results from five replications. Two-sided P < 0.05 was deemed statistically significant. Results 315 patients were identified as having CSF from 8780 patients evaluated, resulting in an incidence rate of 3.59%. Among them, 21 patients missing data of LpPLA2 and nine patients lacked end-point status because of missing contact information, we include the remaining 285 patients for analyze in our study. Furthermore, in this cohort, a subset of 28 cases exhibited missing data across various covariates including Echocardiograms, blood lipids, BMI, eGFR, and uric acid. The subsequent analysis focused on the remaining 257 cases which possessed a complete data set. Table 1 presents the comparison of baseline data across LpPLA2 quartiles (Quartile 1: 275.0 ng/ml). Among the 285 patients, 194 (68.1%) were male, with a mean age of 54.1 years and a mean mTFC of 31.2 frames. Statistically significant differences were observed in LVEDD, mTFC, LDL-C, and triglyceride levels among different LpPLA2 quartiles. Following a median follow-up period of 25 (95%CI: 23–27) months calculated using the reverse Kaplan-Meier method, MACE occurred in 54 patients (18.9%). This included nonfatal myocardial infarction in four individuals (three with complete data), arrhythmias in six individuals (four with atrial fibrillation and two with frequent ventricular premature beats), and rehospitalization due to unstable angina pectoris in 44 patients (42 with complete data). No cardiac deaths or cerebrovascular events occurred during the CSF patient follow-up period in our study. The associations between plasma LpPLA2 levels and incident MACE in CSF patients with complete data are presented in Table 2 . In models 1 to 3, statistically significant associations were observed when comparing the highest versus the lowest quartiles of LpPLA2 levels, with multivariate-adjusted HRs (95% CIs) of 4.43 (95%CI: 1.66–11.78), 3.22 (95%CI: 1.09–9.49), and 3.34 (95%CI: 1.09–10.22) respectively. The p-values for trend tests were all significant (p < 0.01). The incidence rates of MACE ranged from 3.96 to 15.67 per 100 person-years across the quartiles of LpPLA2 levels. Similar results were obtained when analyzing imputed data based on pooled results (eTable 1 in the Supplement). Kaplan-Meier survival assessment revealed a poorer outcome for CSF patients in the highest quartile compared to those in the lower quartiles of LpPLA2 levels (log-rank test p = 0.002) (Fig. 4 ). A restricted cubic spline analysis, based on a multivariable corrected model identical to model 3, was employed to visually represent the relationship between plasma LpPLA2 levels and incident MACE in CSF patients. The risk of MACE incidence remained relatively stable until around a predicted LpPLA2 level of 247.7 ng/ml, after which it began to increase though with a linear trend overall (p for non-linearity = 0.212); above this threshold, the hazard ratio per standard deviation higher predicted LpPLA level was found to be 1.78 (95%CI: 1.17 to 2.71) (Fig. 5 ). Similar results were obtained when the analysis was performed on data following multiple imputation with 5 replications(eFigure 1 in the Supplement) The occurrence of MACE among patients with CSF was investigated using univariate and multiple Cox regression analyses to identify independent predictors. The multiple Cox regression analyses, which included variables with p ≤ 0.1 based on the univariate analysis, revealed that the incidence of MACE was associated with levels of LpPLA2 (as an unprocessed continuous variable analyzed in the mode) (HR: 1.01, 95%CI: 1.00-1.02, p = 0.005), mTFC (HR: 1.13, 95%CI: 1.03–1.23, p = 0.011), LDL-C (HR: 1.54, 95% CI: 1.07–2.23, p = 0.021), and diabetes (HR: 3.38, 95%CI: l .60-7.12, p = 0.001). Furthermore, a statistically significant association was also found between HDL-C level and MACE incidence after conducting multiple Cox regression analyses on data following multiple imputation based on pooled results (Table 3 ). Table 1 Baseline characteristics of 285 participants according to LpPLA2 quartiles Variables Quartile 1 ( 275.0 ng/ml) (N = 71) p LVEF (%) a 64.5 (60.0 to 68.0) 63.0 (58.0 to 68.0) 62.0 (57.0 to 69.0) 62.0 (58.0 to 66.0) 0.226 LAAP (mm) a 33.0 (30.0 to 35.0) 32.0 (30.0 to 35.0) 32.0 (30.0 to 36.0) 34.0 (30.0 to 40.0) 0.246 IVSD (mm) a 10.0 (8.0 to 10.0) 9.0 (9.0 to 10.0) 9.0 (9.0 to 10.0) 9.5 (8.0 to 10.0) 0.916 LVEDD (mm) a 46.0 (42.0 to 50.0) 46.0 (42.0 to 50.0) 46.0 (43.0 to 48.0) 48.0 (45.0 to 51.0) 0.010 E/A ratio a 0.8 (0.7 to 1.3) 0.8 (0.7 to 1.5) 0.8 (0.7 to 1.4) 0.9 (0.7 to 1.4) 0.780 Platelet to lymphocyte ratio 116.0 (87.0 to 151.4) 105.2 (90.0 to 131.7) 106.4 (87.7 to 151.2) 105.7 (76.8 to 141.7) 0.675 Neutrophil to lymphocyte ratio 1.8 (1.5 to 2.8) 2.2 (1.5 to 2.8) 2.2 (1.6 to 3.1) 2.0 (1.4 to 3.9) 0.548 Uric acid (µmol/l) a 327.5 (282.5 to 396.0) 322.0 (281.0 to 401.0) 337.0 (288.0 to 388.5) 348.0 (292.0 to 418.5) 0.413 BMI (kg/m2 ) a 24.5 (21.9 to 25.9) 24.2 (21.9 to 25.9) 24.5 (22.5 to 26.8) 24.7 (22.7 to 26.5) 0.640 Age (years) 60.0 (52.0 to 69.0) 56.5 (53.0 to 64.0) 60.0 (52.5 to 66.5) 57.0 (50.5 to 68.5) 0.516 CSF vessels number 2.0 (1.0 to 3.0) 2.0 (2.0 to 3.0) 2.0 (2.0 to 3.0) 2.0 (1.0 to 3.0) 0.063 mTFC (frames) 30.7 (26.7 to 33.0) 31.7 (27.0 to 34.7) 32.0 (29.9 to 35.1) 32.0 (28.9 to 35.0) 0.034 Triglyceride (mmol/l) a 1.2 (0.9 to 1.7) 1.6 (1.3 to 1.8) 1.2 (0.9 to 1.7) 1.5 (1.2 to 1.9) 0.031 Total cholesterol (mmol/l) a 3.9 ± 0.9 4.2 ± 0.8 4.2 ± 0.9 4.2 ± 0.9 0.161 HDL-C (mmol/l) a 1.2 (1.0 to 1.4) 1.2 (1.0 to 1.3) 1.1 (1.0 to 1.4) 1.1 (0.9 to 1.3) 0.066 LDL-C (mmol/l) a 2.0 (1.4 to 2.3) 2.2 (1.7 to 2.7) 2.4 (1.8 to 2.8) 2.3 (1.8 to 2.8) 0.003 eGFR (ml/min) a 93.2 ± 19.4 98.8 ± 21.1 94.9 ± 22.4 90.4 ± 22.6 0.127 Male 46 (63%) 47 (67.1%) 48 (67.6%) 53 (74.6%) 0.511 Hypertension 23 (31.5%) 28 (40%) 28 (39.4%) 30 (42.3%) 0.568 Diabetes 7 (9.6%) 10 (14.3%) 9 (12.7%) 5 (7%) 0.517 Smoking history 25 (34.2%) 19 (27.1%) 21 (29.6%) 25 (35.2%) 0.695 Discharge cardiovascular medications Nitrates 26 (35.6%) 31 (44.3%) 40 (56.3%) 36 (50.7%) 0.076 Antiplatelet agents 41 (56.2%) 37 (52.9%) 33 (46.5%) 31 (43.7%) 0.419 Statins 59 (80.8%) 56 (80%) 53 (74.6%) 49 (69%) 0.317 a Missing data exists (refer to the flowchart for details) Table 2 LpPLA2 quartiles and the hazard ratio of MACE in 257 participants with complete data Lp-PLA2 (ng/ml, median [range]) Cases Person years Model 1 a Model 2 b Model 3 c HR (95%CI) P for trend HR (95%CI) P for trend HR (95%CI) P for trend Q1 (208.3 [< 121.7]) 5 126.33 Reference Reference Reference Q2 (234.3 [121.7–147.7]) 6 132.83 1.20 (0.37–3.97) 0.70 (0.19–2.54) 0.60 (0.15–2.41) Q3 (260.3 [147.7–175.0]) 17 179.50 2.01 (0.73–5.52) 1.40 (0.47–4.17) 1.28 (0.40–4.04) Q4 (289.2 [> 175.0]) 23 146.75 4.43 (1.66–11.78) < 0.001 3.22 (1.09–9.49) 0.003 3.34(1.09–10.22) 0.003 a Model 1 was adjusted for age and sex. b Model 2 was adjusted for age, sex, LVEF, LA, IVSD, LVEDD, E/A ratio, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, uric acid, BMI, number of CSF vessels, mTFC, triglyceride, total cholesterol, HDL-C, LDL-C, and eGFR c Model 3 was adjusted as model 2 plus history of hypertension, diabetes, smoking, and use of discharge cardiovascular medications (statins, antiplatelet agents, and nitrates) Table 3 Univariate and multivariate predictors of incident MACE in CSF patients Univariable Multivariable Multivariable after multiple imputation HR (95%CI) P HR (95%CI) P HR (95%CI) P Age 1.02 (1.00-1.05) 0.103 Male 1.32 (0.67–2.58) 0.420 LpPLA2 1.01 (1.01–1.02) < 0.001 1.01 (1.00-1.02) 0.005 1.01 (1.00-1.02) 0.012 LVEF 1.00 (0.97–1.03) 0.961 LAAP 1.01 (0.97–1.05) 0.629 IVSD 1.01 (0.82–1.25) 0.925 LVEDD 1.01 (0.96–1.07) 0.600 E/A ratio 0.95 (0.54–1.67) 0.849 Platelet to lymphocyte ratio 1.01 (1.00-1.01) 0.019 1.00 (0.99–1.01) 0.134 1.01 (0.99–1.01) 0.117 Neutrophil to lymphocyte ratio 1.19 (1.04–1.35) 0.009 1.04 (0.86–1.26) 0.676 1.05 (0.87–1.27) 0.596 Uric acid 1.00 (1.00–1.00) 0.922 BMI 0.96 (0.88–1.05) 0.379 CSF vessels number 1.42 (1.00-2.03) 0.052 0.93 (0.53–1.66) 0.817 0.85 (0.48–1.53) 0.588 mTFC 1.12 (1.05–1.19) < 0.001 1.13 (1.03–1.23) 0.011 1.13 (1.03–1.23) 0.012 Triglyceride 1.22 (0.91–1.63) 0..186 Total cholesterol 1.21 (0.87–1.67) 0..258 HDL-C 0.42 (0.16–1.06) 0.067 0.35 (0.12–1.01) 0.053 0.33 (0.11–0.99) 0.049 LDL-C 1.54 (1.08–2.21) 0.017 1.54 (1.07–2.23) 0.021 1.51 (1.04–2.18) 0.043 eGFR 0.99 (0.98-1.00) 0.072 0.99 (0.98-1.00) 0.155 0.99 (0.98-1.00) 0.103 Hypertension 1.17 (0.68–2.04) 0.571 Diabetes 2.06 (1.03–4.13) 0.041 3.38 (1.60–7.12) 0.001 2.83 (1.38–5.84) 0.006 Smoking history 1.71 (0.98–2.97) 0.057 1.73 (0.95–3.13) 0.073 1.64 (0.91–2.96) 0.101 Nitrates 1.35 (0.77–2.35) 0.294 Antiplatelet agents 0.82 (0.47–1.41) 0.470 Statins 1.36 (0.69–2.64) 0.373 Discussion In our study, 315 patients were identified with CSF out of 8780 patients who underwent angiography, resulting in an incidence rate of 3.59%, consistent with the previously reported range of 1–7% [ 1 ]. Following a median follow-up period of 25 months, CSF patients meeting the inclusion criteria exhibited a high incidence rate (18.9%) of MACE, with rehospitalization due to unstable angina accounting for the majority (15.4%). Our analysis suggests that LpPLA2 may independently predict the occurrence of MACE during follow-up in CSF patients. The risk of MACE incidence remained relatively stable until approximately 247.7 ng/ml of predicted LpPLA2 level and then began to increase thereafter, displaying an overall linear trend in the restricted cubic spline analysis. In addition to LpPLA2, multiple Cox regression analyses indicated that mTFC, LDL-C, and diabetes were also associated with the occurrence of MACE in CSF patients. Common characteristics of CSF were usually identified by comparing clinical data between patients with CSF and those with normal coronary artery flow though several observational studies. Most studies have reported a higher prevalence of CSF in men, smokers, and patients with high CV risks [ 2 ]. A cross-sectional study involving 124 patients revealed that CSF patients were predominantly male, had a history of smoking and hypertension, as well as elevated laboratory indicators such as lipids and mean platelet volume. Furthermore, these subjects displayed subtle declines in diastolic function alongside an overall reduction in longitudinal strain during cardiac ultrasound assessments [ 18 ]. Notably highlighted by Yilmaz et al. [ 19 ], within the context of suspected atherosclerotic heart disease necessitating coronary angiography procedures, those affected by CSF exhibited an increased likelihood of presenting metabolic syndrome hallmarked by hyperglycemia, hyperlipidemia, and overweight when compared to counterparts demonstrating normal coronary blood flow velocities. Cohort studies have shown that around two-thirds of patients with CSF present with acute coronary syndrome [ 2 ], typically characterized by recurrent chest pain, mainly at rest, and accompanied by electrocardiogram changes. While most CSF patients have a favorable prognosis in terms of major cardiovascular events, the persistent episodes of chest pain can significantly impact their quality of life. Additionally, several cases have reported that clinical manifestations of CSF also include malignant arrhythmias and sudden death [ 5 , 20 ]. Further studies have identified CSF as a risk predictor for myocardial ischemia, myocardial infarction, arrhythmia, and sudden cardiac death [ 21 – 23 ]. Although CSF has been found to be associated with vascular endothelial dysfunction, microvascular disease, insulin resistance, oxidative stress, and adipocytokines, the exact mechanisms remain unclear [ 1 ]. Increasing evidence suggests that endothelial cells play a crucial role in the regulation of vascular tension, platelet activity, white blood cell adhesion, vascular smooth muscle hyperplasia, and are closely associated with the development of atherosclerosis. A reduction in endothelium-dependent flow-mediated dilation of the brachial artery has been observed in patients with CSF, suggesting a link between endothelial dysfunction and the etiology of CSF [ 24 ]. Kanar et al. [ 25 ] utilized optical coherence tomography to identify thinner nerve fiber layers in the subfoveal choroid and perioptic disc in patients with CSF, indicating extensive endothelial dysfunction and increased microvascular resistance. The coronary artery system contains not only epicardial large vessels but also a significant number of microvessels smaller than 400 µm which play a role in regulating myocardial blood flow. Microvascular dysfunction has been identified as a key factor in the pathogenesis of CSF. Mangieri et al. [ 26 ] provided direct evidence of microvascular disease in endocardial muscle biopsy samples from CSF patients including thickening of the microvascular walls, reduction in lumen size, mitochondrial abnormalities, and decreased glycogen content. Pekdemir et al. [ 27 ] utilized intravascular ultrasound (IVUS) technology and flow rate measurements to demonstrate diffuse intimal thickening, extensive calcification of coronary artery walls, and nonobstructive atherosclerosis changes in patients with CSF, suggesting an early involvement of atherosclerosis in the development of CSF. Inflammation plays a crucial role in the human immune response, serving dual functions in defense mechanisms. Initially, it protects physiological homeostasis against infection and tissue damage but should be promptly resolved once infectious agents are eliminated or initial tissue injuries are repaired. Failure to resolve inflammation can lead to tissue dysfunction and other adverse consequences [ 28 ]. Studies have confirmed that inflammation is a risk factor for various cardiovascular diseases [ 29 ], and inflammatory responses have also been observed in CSF [ 1 , 7 ]. Plasma soluble adhesion molecules and inflammatory markers were found to be significantly elevated in CSF patients, including C-reactive protein [ 30 ], interleukin-6 [ 30 ], platelet to lymphocyte ratio [ 31 ], neutrophil to lymphocyte ratio [ 32 ], matrix metallopro-teinase-9 and soluble CD40 ligand [ 33 ], etc. LpPLA2 has been proposed as a novel independent inflammatory marker and has been associated with various vascular diseases in epidemiological studies [ 12 , 34 ]. Numerous studies have established LpPLA2 activity as an independent predictor of CHD outcomes in the general population [ 14 , 35 ]. Recent research has shown that elevated plasma LpPLA2 levels are also linked to the onset and severity of CSF [ 8 , 9 ]. Our study suggests that LpPLA2 can function as an independent prognostic indicator for poor outcomes in CSF patients. LpPLA2, originally known as platelet-activating factor acetylhydrolase due to its hydrolytic activity on platelet-activating factor [ 36 ], is primarily secreted by macrophages and circulates in the bloodstream bound to LDL and HDL [ 37 ]. LpPLA2 has the ability to hydrolyze and oxidize LDL into two biologically active products, oxidized nonesterified fatty acids and lysophosphatidylcholine, which act as proinflammatory substances derived from LpPLA2, inducing immune responses and oxidative stress [ 38 ]. These inflammatory effects may contribute to the development of atherosclerotic plaques [ 14 ]. In our study, when LpPLA2 was divided into quartiles, the results of multi-model Cox regression indicated that the highest level of LpPLA2 was associated with a higher incidence of MACE compared to the lowest level in CSF patients. When considered as a continuous variable, although the RCS curve suggests a overall linear relationship between LpPLA2 and MACE rate, the linear relationship becomes more pronounced when plasma levels of LpPLA2 exceed 247.7 ng/ml. The mechanisms underlying the association between LpPLA2 and poor prognosis in CSF are not fully understood. The most plausible hypothesis is that unresolved inflammatory responses mediated by LpPLA2 may lead to vascular endothelial dysfunction, contributing to the onset and progression of CSF. In addition to LpPLA2, multiple Cox regression analyses have shown that mTFC, LDL-C, and diabetes are associated with the occurrence of MACE in CSF patients. HDL-C also demonstrated a statistically significant association when analyzed using data after multiple imputation. Previous studies have found that dyslipidemia [ 6 , 17 ](2,3) and mTFC [ 17 ] are associated with the occurrence of MACE in CSF patients, which is consistent with our findings. Isik T et al. [ 39 ] identified diabetes as a potential predictor of CSF. Jiang Yu et al. [ 4 ] demonstrated that diabetes was an independent prognostic predictor of MACE in patients with normal coronary artery during a mean 3.5 years of follow-up. Our study suggests that diabetes is linked to poor prognosis in CSF patients. Previous studies have indicated that hypertension independently predicts MACE in patients with CSF[ 6 , 17 ], leading to a certain number of cardiovascular deaths over a relatively long follow-up period. However, our study suggests that there is no significant correlation between hypertension and the occurrence of MACE in patients with CSF, this may be due to insufficient follow-up time and a limited number of cases of cardiovascular death. Limitations of this study: Despite suggesting that LpPLA2 may have predictive value for clinical outcomes in patients with CSF, there are several limitations that should be acknowledged. Firstly, this was a single-center retrospective study primarily relying on telephone follow-up, which could result in incomplete information, recall bias, and the omission of crucial details regarding clinical outcomes and events. Secondly, the study had a small sample size and a short follow-up period due to our hospital only commencing clinical testing of LpPLA2 in 2019, leading to a low incidence of clinical events such as cardiovascular death that could impact the statistical power of the results and may restrict the generalizability and extensibility of our findings. Lastly, as coronary artery stenosis is often not apparent in patients with CSF, only a few patients underwent repeated coronary angiography tests resulting in an inadequate assessment of coronary artery stenosis and mTFC during CSF follow-up In conclusion, elevated levels of LpPLA may function as an independent predictor of risk for the occurrence of MACE in patients with CSF, potentially offering a novel therapeutic target for CSF treatment. In addition to LpPLA2, this study suggests that dyslipidemia, diabetes, and mTFC may also be associated with the prognosis of CSF. However, further investigation is needed to elucidate the exact pathophysiological mechanism of LpPLA2 in CSF, and prospective studies involving multiple centers and large sample sizes are required to validate our findings. Abbreviations CSF Coronary slow flow LpPLA2 Lipoprotein-associated phospholipase A2 MACE Major adverse cardiovascular events TFC Thrombolysis in myocardial infarction frame count mTFC Mean thrombolysis in myocardial infarction frame count HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol LAD Left anterior descending CX Circumflex RCA Right coronary artery BMI Body mass index LVEF Left ventricular ejection fraction IVST Interventricular septal thickness, LVEDD:Left ventricular end-diastolic dimension, LAAP:Left atrial anteroposterior diameter E/A ratio The value of e/a ratio of mitral annulus eGFR Estimated glomerular filtration rate. Declarations Authors’ contributions Xianjin Wang, Xuejun Xiang, Rui Qiao, Yuanxi Zheng, and Liangchuan Chen conducted and designed the study. Statistical analyses were performed by Xianjin Wang, Xianguan Zhu, and Liangchuan Chen. The article was written by Xianjin Wang. Liling Zhang, Jing Chen, Biyun Qian, and Jingfang Cheng conducted follow-ups through phone call invitations or available outpatient cardiology clinic data. Decisions regarding TFC values were made by Xuejun Xiang and Rui Qiao. All authors have reviewed and approved the final manuscript. Acknowledgements The authors thank all the staff for their help. Data Availability Data are available upon request to the corresponding author. Funding Foundation of Anqing Science and Technology Bureau (2023Z1025). Conflict of interest All authors declare that there are no conflicts of interest. Ethics approval and consent to participate The study was approved by the ethics committee of Anqing Municipal Hospital [Medical Ethics Review(2022) No.74], and informed consent was obtained from all patients or their family members. References Chalikias G, Tziakas D. Slow Coronary Flow: Pathophysiology, Clinical Implications, and Therapeutic Management. Angiology. 2021;72(9):808–18. Aparicio A, Cuevas J, Morís C, Martín M. Slow Coronary Blood Flow: Pathogenesis and Clinical Implications. Eur Cardiol. 2022;17:e08. Tambe AA, Demany MA, Zimmerman HA, Mascarenhas E. Angina pectoris and slow flow velocity of dye in coronary arteries–a new angiographic finding. Am Heart J. 1972;84(1):66–71. Yu J, Yi D, Yang C, Zhou X, Wang S, Zhang Z et al. Major Adverse Cardiovascular Events and Prognosis in Patients With Coronary Slow Flow. Curr Probl Cardiol. 2024;49(1 Pt B):102074. Wozakowska-Kapłon B, Niedziela J, Krzyzak P, Stec S. Clinical manifestations of slow coronary flow from acute coronary syndrome to serious arrhythmias. Cardiol J. 2009;16(5):462–8. Zhu X, Shen H, Gao F, Wu S, Ma Q, Jia S, et al. Clinical Profile and Outcome in Patients with Coronary Slow Flow Phenomenon. Cardiol Res Pract. 2019;2019:9168153. Wang X, Nie SP. The coronary slow flow phenomenon: characteristics, mechanisms and implications. Cardiovasc diagnosis therapy. 2011;1(1):37–43. Liang Q, Lei X, Huang X, Fan L, Yu H. Elevated Lipoprotein-Associated Phospholipase A2 is Valuable in Prediction of Coronary Slow Flow in Non-ST-Segment Elevation Myocardial Infarction Patients. Curr Probl Cardiol. 2021;46(3):100596. Ding YD, Pei YQ, Wang R, Yang JX, Zhao YX, Liu XL, et al. Increased plasma lipoprotein-associated phospholipase A2 levels are associated with coronary slow flow. BMC Cardiovasc Disord. 2020;20(1):248. Li J, Wang H, Tian J, Chen B, Du F. 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Clinical and coronary angiographic characteristics of patients with coronary slow flow. Acta Cardiol. 2008;63(5):579–84. Saya S, Hennebry TA, Lozano P, Lazzara R, Schechter E. Coronary slow flow phenomenon and risk for sudden cardiac death due to ventricular arrhythmias: a case report and review of literature. Clin Cardiol. 2008;31(8):352–5. Zhu Q, Wang S, Huang X, Zhao C, Wang Y, Li X, et al. Understanding the pathogenesis of coronary slow flow: Recent advances. Trends Cardiovasc Med. 2024;34(3):137–44. Sen T. Coronary Slow Flow Phenomenon Leads to ST Elevation Myocardial Infarction. Korean circulation J. 2013;43(3):196–8. Amasyali B, Turhan H, Kose S, Celik T, Iyisoy A, Kursaklioglu H, et al. Aborted sudden cardiac death in a 20-year-old man with slow coronary flow. Int J Cardiol. 2006;109(3):427–9. Çelik O, Demirci E, Aydın M, Karabag T, Kalçık M. Evaluation of ghrelin levels and endothelial functions in patients with coronary slow flow phenomenon. Interventional Med Appl Sci. 2017;9(3):154–9. Kanar HS, Arsan A, Kup A, Kanar BG, Tanyıldız B, Akaslan D, et al. Comparison of subfoveal choroidal thickness and retinal nerve fiber layer thickness in patients with coronary slow flow phenomenon and microvascular angina: Optical coherence tomography based study. Photodiagn Photodyn Ther. 2021;33:102189. Mangieri E, Macchiarelli G, Ciavolella M, Barillà F, Avella A, Martinotti A, et al. Slow coronary flow: clinical and histopathological features in patients with otherwise normal epicardial coronary arteries. Cathet Cardiovasc Diagn. 1996;37(4):375–81. Pekdemir H, Cin VG, Ciçek D, Camsari A, Akkus N, Döven O, et al. Slow coronary flow may be a sign of diffuse atherosclerosis. Contribution of FFR and IVUS. Acta Cardiol. 2004;59(2):127–33. Buckley CD, Gilroy DW, Serhan CN. Proresolving lipid mediators and mechanisms in the resolution of acute inflammation. Immunity. 2014;40(3):315–27. Sprague AH, Khalil RA. Inflammatory cytokines in vascular dysfunction and vascular disease. Biochem Pharmacol. 2009;78(6):539–52. Li JJ, Qin XW, Li ZC, Zeng HS, Gao Z, Xu B, et al. Increased plasma C-reactive protein and interleukin-6 concentrations in patients with slow coronary flow. Clin Chim Acta. 2007;385(1–2):43–7. Qiu Z, Jiang Y, Jiang X, Yang R, Wu Y, Xu Y, et al. Relationship Between Platelet to Lymphocyte Ratio and Stable Coronary Artery Disease: Meta-Analysis of Observational Studies. Angiology. 2020;71(10):909–15. Yılmaz M, Korkmaz H, Bilen MN, Uku Ö, Kurtoğlu E. Could neutrophil/lymphocyte ratio be an indicator of coronary artery disease, coronary artery ectasia and coronary slow flow? J Int Med Res. 2016;44(6):1443–53. Zhang X, Ding J, Xia S. A preliminary study of MMP-9 and sCD40L in patients with coronary slow flow. Annals Palliat Med. 2021;10(1):657–63. Siddiqui MK, Kennedy G, Carr F, Doney ASF, Pearson ER, Morris AD, et al. Lp-PLA(2) activity is associated with increased risk of diabetic retinopathy: a longitudinal disease progression study. Diabetologia. 2018;61(6):1344–53. Corsetti JP, Rainwater DL, Moss AJ, Zareba W, Sparks CE. High lipoprotein-associated phospholipase A2 is a risk factor for recurrent coronary events in postinfarction patients. Clin Chem. 2006;52(7):1331–8. Dennis EA, Cao J, Hsu YH, Magrioti V, Kokotos G. Phospholipase A2 enzymes: physical structure, biological function, disease implication, chemical inhibition, and therapeutic intervention. Chem Rev. 2011;111(10):6130–85. Tellis CC, Tselepis AD. Pathophysiological role and clinical significance of lipoprotein-associated phospholipase A₂ (Lp-PLA₂) bound to LDL and HDL. Curr Pharm Design. 2014;20(40):6256–69. Zalewski A, Macphee C. Role of lipoprotein-associated phospholipase A2 in atherosclerosis: biology, epidemiology, and possible therapeutic target. Arteriosclerosis, thrombosis, and vascular biology. 2005;25(5):923–31. Isik T, Ayhan E, Uyarel H, Ergelen M, Tanboga IH, Kurt M, et al. Increased mean platelet volume associated with extent of slow coronary flow. Cardiol J. 2012;19(4):355–62. Additional Declarations No competing interests reported. Supplementary Files eFigure1.docx eTable1.docx 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-4651584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327150944,"identity":"efb35afb-b86d-477f-bde1-3f0be38a5242","order_by":0,"name":"Xianjin Wang","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianjin","middleName":"","lastName":"Wang","suffix":""},{"id":327150945,"identity":"ced299f2-68a9-4169-86f7-a95f08f30055","order_by":1,"name":"Xianguan Zhu","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianguan","middleName":"","lastName":"Zhu","suffix":""},{"id":327150946,"identity":"72df7431-0ceb-4673-a059-7b3099507d3b","order_by":2,"name":"Liling Zhang","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liling","middleName":"","lastName":"Zhang","suffix":""},{"id":327150948,"identity":"d7afcd8c-b8ac-48a3-b83b-17388a03bd54","order_by":3,"name":"Jing Chen","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":327150950,"identity":"df752014-5b67-4794-a3ab-6b44161b6aa6","order_by":4,"name":"Biyun Qian","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Biyun","middleName":"","lastName":"Qian","suffix":""},{"id":327150955,"identity":"cca454ce-fa52-45b0-9a48-02f0d57af979","order_by":5,"name":"Jingfang Cheng","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingfang","middleName":"","lastName":"Cheng","suffix":""},{"id":327150958,"identity":"31b3b9b0-a67e-457a-ad0b-19ff07f53e6e","order_by":6,"name":"Xuejun Xiang","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"Xiang","suffix":""},{"id":327150963,"identity":"3c9f56f4-12a5-4cf9-86fa-a7b4359ddaa0","order_by":7,"name":"Rui Qiao","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Qiao","suffix":""},{"id":327150964,"identity":"c17077ae-40f5-4696-a1a2-50946d7bfefd","order_by":8,"name":"Yuanxi Zheng","email":"","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanxi","middleName":"","lastName":"Zheng","suffix":""},{"id":327150966,"identity":"5c1e70e6-14f5-480c-a157-adbc752fb0b8","order_by":9,"name":"Liangchuan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYDACdgb2D4l/bHj4+RuI1cLMwMbwsSFNRnLGARK0MM5sOGxj0JBApA7+ZuZnj3l3nOcxYDjA+OFjDhFaJA6zmRvznrnNY87cwCw5cxsRWgyYeRikedhu81g2HGBj5iVByzkegwMJxGthk5zZdoAELUC/GBt8OJPMIznjYDNxfuFvb374IKHCzp6fv/ngh4/EaEECjA2kqR8Fo2AUjIJRgBsAAJ8kMSEr8LCDAAAAAElFTkSuQmCC","orcid":"","institution":"Anqing Municipal Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liangchuan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-06-28 02:39:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4651584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4651584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60851054,"identity":"aa0a2cb3-89a9-4d8f-af44-449214c24ef4","added_by":"auto","created_at":"2024-07-22 20:57:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187275,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart depicts the procedure for patient enrollment and follow-up\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/afebb555567cb67495df500c.jpeg"},{"id":60851938,"identity":"250d0aea-86f7-414e-a7bd-d6829412606c","added_by":"auto","created_at":"2024-07-22 21:05:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271524,"visible":true,"origin":"","legend":"\u003cp\u003e(extracted from the work of Aksoy S et al. [17] ). The illustration depicts the distal landmarks for LAD bifurcation, also known as the \"tail of the whale,\" and CX bifurcation, which represents the longest branch of the lateral left ventricular wall artery.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/cf6da2e509be57634cc41dad.png"},{"id":60851061,"identity":"7ba7af35-2416-4ba6-b60b-41a45bb5cc07","added_by":"auto","created_at":"2024-07-22 20:57:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290264,"visible":true,"origin":"","legend":"\u003cp\u003e(extracted from the work of Aksoy S et al. [17] ). The illustration depicts the distal landmark of the RCA bifurcation, defined as the first posterolateral arterial branch.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/40d4a2d06a93dd717df9fa3c.jpeg"},{"id":60852924,"identity":"60d2c21e-46a3-4191-893f-e64cd64fed37","added_by":"auto","created_at":"2024-07-22 21:13:43","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373703,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of MACE rates in 285 CSF patients across LpPLA2 quartiles\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/c9c34f0bb1e9d0ac2752b4d4.jpeg"},{"id":60851941,"identity":"d3ed0f54-935d-4e9f-aaa7-f693fd533ecb","added_by":"auto","created_at":"2024-07-22 21:05:43","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":290411,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of predicted LpPLA2 levels with incident MACE in 257 CSF patients through 4-knotted RCS curve adjusted for covariates in model 3. Hazard ratio is indicated by solid line and 95% CI by shaded area. Reference point was set at tenth percentiles while knots corresponded to 5th, 35th, 65th, and 95th percentiles. Standard deviation for LpPLA2 levels was 37.88 ng/ml.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/921c6e289d1b11a731fdcd69.jpeg"},{"id":80744987,"identity":"6142b582-07d0-453c-bfdc-fd6555031de6","added_by":"auto","created_at":"2025-04-16 15:08:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2417022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/a0b25c85-639b-4e49-837e-0053d9d62014.pdf"},{"id":60851058,"identity":"9b342ed1-e12e-401e-b263-5a1682ed7dd7","added_by":"auto","created_at":"2024-07-22 20:57:43","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":463437,"visible":true,"origin":"","legend":"","description":"","filename":"eFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/ac1cb49cf255993565a0a760.docx"},{"id":60852923,"identity":"950ef071-b572-4403-924c-122af5723688","added_by":"auto","created_at":"2024-07-22 21:13:43","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":15663,"visible":true,"origin":"","legend":"","description":"","filename":"eTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4651584/v1/233a8fb04ef08db496c3c1c7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated plasma lipoprotein-associated phospholipase A2 levels are associated with poor prognosis in patients with coronary slow flow: A retrospective study","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronary slow flow (CSF) is characterized by a slow progression of coronary flow velocity following the injection of contrast agent with normal or near-normal coronary vessels during coronary angiography [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This phenomenon was initially documented by Tambe et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The incidence rate of CSF in coronary angiograms ranges from 1\u0026ndash;7% and is frequently observed in male individuals, smokers, and patients at high cardiovascular risk [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Historically, CSF patients were considered to have favorable prognoses due to the absence of significant coronary artery stenosis. However, some observational studies have indicated an association between CSF and major adverse cardiovascular events (MACE), such as myocardial infarction, sudden death, arrhythmias, cerebrovascular events, rehospitalization due to unstable angina, etc., suggesting a poor prognosis [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite being well-established clinical and angiographic findings, the precise pathogenic mechanisms underlying CSF remain unclear. Numerous studies have proposed that subclinical atherosclerosis, endothelial dysfunction, and microvascular abnormalities may be involved in its pathogenesis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, inflammatory responses have been observed in CSF and are implicated in the pathological processes of CSF [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLipoprotein-associated phospholipase A2 (LpPLA2), a novel vascular-specific inflammation cytokine and enzyme involved in stress reaction has also been reported to be associated with the presence and severity of CSF [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. LpPLA2 is secreted by macrophage cells often detected in unstable coronary plaques [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and has been shown to mediate the inflammatory process within the vascular wall [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its proinflammatory properties make LpPLA2 an independent predictive biomarker for future cardiovascular events among patients with atherosclerotic cardiovascular disease [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, it remains unknown whether plasma LpPLA2 can predict outcomes for CSF. In this study, the aim was to explore MACE rates among CSF patients with varying levels of LpPLA2.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study, conducted at Anqing Municipal Hospital in Anhui Province, China from January 1, 2019 through March 31, 2023, enrolled a total of 8780 consecutive patients. Among them, 315 patients were identified as having coronary slow flow (CSF) confirmed by angiography using the thrombolysis in myocardial infarction frame count (TFC) and exhibiting normal or near-normal coronary vessels (angiographic stenosis\u0026thinsp;\u0026lt;\u0026thinsp;40%) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Exclusion criteria included incomplete angiography recordings, angiographic stenosis\u0026thinsp;\u0026ge;\u0026thinsp;40%, history of coronary revascularization, left ventricular ejection fraction\u0026thinsp;\u0026lt;\u0026thinsp;40%, pulmonary heart disease, organic heart disease (e.g., congenital heart disease, severe valvular heart disease, cardiomyopathy), coronary ectasia, coronary myocardial bridge, coronary artery spasm, atrial fibrillation; severe liver or kidney dysfunction; cancer; missing LpPLA2 data; and loss to follow-up at the beginning (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDemographic data including age, sex, body mass index (BMI), smoking status and diagnosed medical conditions (hypertension and diabetes) were collected at the time of angiography. Discharge cardiovascular medications (statins, antiplatelet agents, and nitrates) were documented. Echocardiogram data pertaining to left ventricular ejection fraction (LVEF), interventricular septal thickness (IVST), left ventricular end-diastolic dimension (LVEDD), left atrial anteroposterior diameter (LAAP), and the value of e/a ratio of mitral annulus (E/A ratio) were recorded. Laboratory data obtained at the time of angiography from electronic medical records included triglyceride, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and estimated glomerular filtration rate (eGFR). Uric acid, urea, creatinine, triglyceride, total cholesterol, LDL-C, and HDL-C levels were measured using a chemiluminescence method with a Roche Diagnostics Cobas analyzer Cobas8000, c702 module. The eGFR was calculated by Chinese modified MDRD equations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Plasma LpPLA2 activity was measured using chemiluminescence immunoassay (Hotgen Biology Technology Co., Ltd. Beijin, China).\u003c/p\u003e \u003cp\u003e The study was approved by the ethics committee of Anqing Municipal Hospital [Medical Ethics Review(2022) No.74], and informed consent was obtained from all patients or their family members.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCoronary angiography\u003c/h2\u003e \u003cp\u003eAll patients underwent coronary angiography using the standard Judkins technique with either radial or femoral artery approach. Coronary blood flow was evaluated using thrombolysis in myocardial infarction frame count (TFC), which is considered an objective and quantitative measure. TFC was originally defined by Gibson and refers to the number of film frames required for the contrast agent to reach distal landmarks. The first frame was determined when the contrast agent completely filled the entrance of the coronary artery. Distal landmarks included the distal left anterior descending (LAD) artery bifurcation (known as the \"tail of the whale\"), distal circumflex (CX) artery bifurcation (the longest lateral left ventricular wall artery branch), and distal right coronary artery (RCA) bifurcation (the first posterolateral artery branch) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e extracted from the work of Aksoy S et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]). CSF was defined as TFC\u0026thinsp;\u0026gt;\u0026thinsp;27 frames based on absence of obstructive epicardial coronary artery disease. The TFC value of LAD was divided by 1.7 to obtain a corrected TFC (CTFC) due to its longer length compared to other coronary arteries. Since criteria for TFC values were counted at a speed of 30 frames per second during angiography, we multiplied our study's acquisition rate of 15 frames/second by two when calculating TFC values. The mean TFC (mTFC) calculated from averaging LAD, CX, and RCA TFCs was recorded. Finally, two interventional cardiologists blinded to our study made decisions regarding TFC values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFollow up\u003c/h3\u003e\n\u003cp\u003eAfter completing the collection of demographic and coronary angiographic data from medical records, patients initially diagnosed with CSF were followed up through phone call invitations or available outpatient cardiology clinic data. The follow-up evaluation focused on the occurrence of major adverse cardiovascular events (MACE), and recorded the date when MACE occurred. In our study, MACE was defined as nonfatal myocardial infarction, cardiac death, severe arrhythmias (e.g., sick sinus syndrome, second degree or higher atrioventricular block, frequent ventricular premature beats, ventricular tachycardia or ventricular fibrillation, and paroxysmal or persistent atrial fibrillation), rehospitalization due to unstable angina, and cerebrovascular events.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R statistical software version 3.6.1 (R Foundation). Data were described as means and SDs for normally distributed continuous variables and as medians and interquartile ranges for non-normally distributed continuous variables. The Shapiro-Wilk test was conducted to determine the distribution of quantitative variables. Categorical variables were described using frequency with percentage. To clinically examine the association between plasma LpPLA2 levels and incident MACE in CSF patients, LpPLA2 levels were divided into quartiles. Baseline characteristics are summarized according to LpPLA2 quartiles and compared using analysis of variance or Kruskal-Wallis test or chi-square test depending on data types and distributions.\u003c/p\u003e \u003cp\u003eWe calculated the person-time of follow-up for each participant included in the study from the date of CSF diagnosis (January 1, 2019 to March 31, 2023) survey until the occurrence of MACE, loss to follow-up, or the end of follow-up (March 31, 2024), whichever came first. Incidence rates of MACE per 100 person-years were calculated based on LpPLA2 quartiles. Cox regression analysis was conducted to calculate hazard ratios (HRs) with 95% CIs, using LpPLA2 as the exposure variable and considering the lowest quartiles as reference. We estimated three models: model 1 adjusted for age and sex; model 2 adjusted for age, sex, LVEF, LA, IVSD, LVEDD, E/A ratio, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, uric acid, BMI, number of CSF vessels, mTFC, triglyceride, total cholesterol, HDL-C, LDL-C, and eGFR; and model 3 adjusted for variables in model 2 plus history of hypertension, diabetes, smoking, and use of discharge cardiovascular medications (statins, antiplatelet agents, and nitrates). The trend test utilized median values from each LpPLA2 quartile as a continuous variable within the mode. Kaplan-Meier curves depicted MACE rates across LpPLA2 quartiles assessed via log-rank testing. Additionally, potential non-linear relationships were explored through 4-knotted restricted cubic spline regression (the knots being determined by Akaike information criterion between 3 and 7). Reference value (HR\u0026thinsp;=\u0026thinsp;1) was set at tenth percentiles while knots corresponded to 5th, 35th, 65th, and 95th percentiles. Meanwhile, univariate and multivariate Cox proportional hazards regression were performed to determine predictive variables for occurrence of MACE among CSF patients. In multivariable analyses only those variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.1 based on univariate testing were taken into account, and LpPLA2 was handled as a unprocessed continuous variable in these models. Of the 285 patients who completed follow-up, 28 had missing covariate information pertaining to echocardiographic data, blood lipids, BMI, eGFR, and uric acid. These missing data were assumed to be missing at random. Sensitivity analyses utilized multiple imputation with chained equations based on pooled results from five replications. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e315 patients were identified as having CSF from 8780 patients evaluated, resulting in an incidence rate of 3.59%. Among them, 21 patients missing data of LpPLA2 and nine patients lacked end-point status because of missing contact information, we include the remaining 285 patients for analyze in our study. Furthermore, in this cohort, a subset of 28 cases exhibited missing data across various covariates including Echocardiograms, blood lipids, BMI, eGFR, and uric acid. The subsequent analysis focused on the remaining 257 cases which possessed a complete data set.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the comparison of baseline data across LpPLA2 quartiles (Quartile 1: \u0026lt; 221.7 ng/ml, Quartile 2: 221.7\u0026ndash;247.7 ng/ml, Quartile 3: 247.7\u0026ndash;275.0 ng/ml, Quartile 4: \u0026gt; 275.0 ng/ml). Among the 285 patients, 194 (68.1%) were male, with a mean age of 54.1 years and a mean mTFC of 31.2 frames. Statistically significant differences were observed in LVEDD, mTFC, LDL-C, and triglyceride levels among different LpPLA2 quartiles.\u003c/p\u003e \u003cp\u003eFollowing a median follow-up period of 25 (95%CI: 23\u0026ndash;27) months calculated using the reverse Kaplan-Meier method, MACE occurred in 54 patients (18.9%). This included nonfatal myocardial infarction in four individuals (three with complete data), arrhythmias in six individuals (four with atrial fibrillation and two with frequent ventricular premature beats), and rehospitalization due to unstable angina pectoris in 44 patients (42 with complete data). No cardiac deaths or cerebrovascular events occurred during the CSF patient follow-up period in our study.\u003c/p\u003e \u003cp\u003eThe associations between plasma LpPLA2 levels and incident MACE in CSF patients with complete data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In models 1 to 3, statistically significant associations were observed when comparing the highest versus the lowest quartiles of LpPLA2 levels, with multivariate-adjusted HRs (95% CIs) of 4.43 (95%CI: 1.66\u0026ndash;11.78), 3.22 (95%CI: 1.09\u0026ndash;9.49), and 3.34 (95%CI: 1.09\u0026ndash;10.22) respectively. The p-values for trend tests were all significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The incidence rates of MACE ranged from 3.96 to 15.67 per 100 person-years across the quartiles of LpPLA2 levels. Similar results were obtained when analyzing imputed data based on pooled results (eTable 1 in the Supplement). Kaplan-Meier survival assessment revealed a poorer outcome for CSF patients in the highest quartile compared to those in the lower quartiles of LpPLA2 levels (log-rank test p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA restricted cubic spline analysis, based on a multivariable corrected model identical to model 3, was employed to visually represent the relationship between plasma LpPLA2 levels and incident MACE in CSF patients. The risk of MACE incidence remained relatively stable until around a predicted LpPLA2 level of 247.7 ng/ml, after which it began to increase though with a linear trend overall (p for non-linearity\u0026thinsp;=\u0026thinsp;0.212); above this threshold, the hazard ratio per standard deviation higher predicted LpPLA level was found to be 1.78 (95%CI: 1.17 to 2.71) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similar results were obtained when the analysis was performed on data following multiple imputation with 5 replications(eFigure 1 in the Supplement)\u003c/p\u003e \u003cp\u003eThe occurrence of MACE among patients with CSF was investigated using univariate and multiple Cox regression analyses to identify independent predictors. The multiple Cox regression analyses, which included variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.1 based on the univariate analysis, revealed that the incidence of MACE was associated with levels of LpPLA2 (as an unprocessed continuous variable analyzed in the mode) (HR: 1.01, 95%CI: 1.00-1.02, p\u0026thinsp;=\u0026thinsp;0.005), mTFC (HR: 1.13, 95%CI: 1.03\u0026ndash;1.23, p\u0026thinsp;=\u0026thinsp;0.011), LDL-C (HR: 1.54, 95% CI: 1.07\u0026ndash;2.23, p\u0026thinsp;=\u0026thinsp;0.021), and diabetes (HR: 3.38, 95%CI: l .60-7.12, p\u0026thinsp;=\u0026thinsp;0.001). Furthermore, a statistically significant association was also found between HDL-C level and MACE incidence after conducting multiple Cox regression analyses on data following multiple imputation based on pooled results (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 characteristics of 285 participants according to LpPLA2 quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;221.7 ng/ml)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003cp\u003e(221.7\u0026ndash;247.7 ng/ml)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003cp\u003e(247.7\u0026ndash;275.0 ng/ml)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;275.0 ng/ml)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eLVEF (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.5 (60.0 to 68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (58.0 to 68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.0 (57.0 to 69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.0 (58.0 to 66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAAP (mm) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0 (30.0 to 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0 (30.0 to 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.0 (30.0 to 36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.0 (30.0 to 40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVSD (mm) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (8.0 to 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0 (9.0 to 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.0 (9.0 to 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5 (8.0 to 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD (mm) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.0 (42.0 to 50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.0 (42.0 to 50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.0 (43.0 to 48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.0 (45.0 to 51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A ratio \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.7 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.7 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.7 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.7 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet to lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.0 (87.0 to 151.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.2 (90.0 to 131.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.4 (87.7 to 151.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.7 (76.8 to 141.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil to lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.5 to 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.5 to 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (1.6 to 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0 (1.4 to 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/l)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327.5 (282.5 to 396.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322.0 (281.0 to 401.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337.0 (288.0 to 388.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348.0 (292.0 to 418.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2 ) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5 (21.9 to 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2 (21.9 to 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5 (22.5 to 26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.7 (22.7 to 26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.0 (52.0 to 69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5 (53.0 to 64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.0 (52.5 to 66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.0 (50.5 to 68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF vessels number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.0 to 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (2.0 to 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (2.0 to 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0 (1.0 to 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emTFC (frames)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.7 (26.7 to 33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7 (27.0 to 34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.0 (29.9 to 35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.0 (28.9 to 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/l) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.9 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.3 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.9 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 (1.2 to 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/l) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/l) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (1.0 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (1.0 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (1.0 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.9 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/l) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.4 to 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.7 to 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (1.8 to 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3 (1.8 to 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.511\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarge cardiovascular medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.317\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\u003ea Missing data exists (refer to the flowchart for details)\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\u003eLpPLA2 quartiles and the hazard ratio of MACE in 257 participants with complete data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLp-PLA2 (ng/ml, median [range])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerson years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (208.3 [\u0026lt;\u0026thinsp;121.7])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (234.3 [121.7\u0026ndash;147.7])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.37\u0026ndash;3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.19\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.60 (0.15\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (260.3 [147.7\u0026ndash;175.0])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01 (0.73\u0026ndash;5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.40 (0.47\u0026ndash;4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.28 (0.40\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (289.2 [\u0026gt;\u0026thinsp;175.0])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43 (1.66\u0026ndash;11.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.22 (1.09\u0026ndash;9.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.34(1.09\u0026ndash;10.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\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\u003ea Model 1 was adjusted for age and sex.\u003c/p\u003e \u003cp\u003eb Model 2 was adjusted for age, sex, LVEF, LA, IVSD, LVEDD, E/A ratio, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, uric acid, BMI, number of CSF vessels, mTFC, triglyceride, total cholesterol, HDL-C, LDL-C, and eGFR\u003c/p\u003e \u003cp\u003ec Model 3 was adjusted as model 2 plus history of hypertension, diabetes, smoking, and use of discharge cardiovascular medications (statins, antiplatelet agents, and nitrates)\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\u003eUnivariate and multivariate predictors of incident MACE in CSF patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable after multiple imputation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00-1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.67\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLpPLA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.97\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.82\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.54\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet to lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil to lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.04\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.86\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.87\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.88\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF vessels number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (1.00-2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.53\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.48\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.05\u0026ndash;1.19)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.03\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (1.03\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.91\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0..186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (0.87\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0..258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.16\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35 (0.12\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33 (0.11\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.08\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.07\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51 (1.04\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.68\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06 (1.03\u0026ndash;4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.38 (1.60\u0026ndash;7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.83 (1.38\u0026ndash;5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (0.98\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (0.95\u0026ndash;3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64 (0.91\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.77\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.47\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (0.69\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, 315 patients were identified with CSF out of 8780 patients who underwent angiography, resulting in an incidence rate of 3.59%, consistent with the previously reported range of 1\u0026ndash;7% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Following a median follow-up period of 25 months, CSF patients meeting the inclusion criteria exhibited a high incidence rate (18.9%) of MACE, with rehospitalization due to unstable angina accounting for the majority (15.4%). Our analysis suggests that LpPLA2 may independently predict the occurrence of MACE during follow-up in CSF patients. The risk of MACE incidence remained relatively stable until approximately 247.7 ng/ml of predicted LpPLA2 level and then began to increase thereafter, displaying an overall linear trend in the restricted cubic spline analysis. In addition to LpPLA2, multiple Cox regression analyses indicated that mTFC, LDL-C, and diabetes were also associated with the occurrence of MACE in CSF patients.\u003c/p\u003e \u003cp\u003eCommon characteristics of CSF were usually identified by comparing clinical data between patients with CSF and those with normal coronary artery flow though several observational studies. Most studies have reported a higher prevalence of CSF in men, smokers, and patients with high CV risks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A cross-sectional study involving 124 patients revealed that CSF patients were predominantly male, had a history of smoking and hypertension, as well as elevated laboratory indicators such as lipids and mean platelet volume. Furthermore, these subjects displayed subtle declines in diastolic function alongside an overall reduction in longitudinal strain during cardiac ultrasound assessments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably highlighted by Yilmaz et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], within the context of suspected atherosclerotic heart disease necessitating coronary angiography procedures, those affected by CSF exhibited an increased likelihood of presenting metabolic syndrome hallmarked by hyperglycemia, hyperlipidemia, and overweight when compared to counterparts demonstrating normal coronary blood flow velocities.\u003c/p\u003e \u003cp\u003eCohort studies have shown that around two-thirds of patients with CSF present with acute coronary syndrome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], typically characterized by recurrent chest pain, mainly at rest, and accompanied by electrocardiogram changes. While most CSF patients have a favorable prognosis in terms of major cardiovascular events, the persistent episodes of chest pain can significantly impact their quality of life. Additionally, several cases have reported that clinical manifestations of CSF also include malignant arrhythmias and sudden death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Further studies have identified CSF as a risk predictor for myocardial ischemia, myocardial infarction, arrhythmia, and sudden cardiac death [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough CSF has been found to be associated with vascular endothelial dysfunction, microvascular disease, insulin resistance, oxidative stress, and adipocytokines, the exact mechanisms remain unclear [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Increasing evidence suggests that endothelial cells play a crucial role in the regulation of vascular tension, platelet activity, white blood cell adhesion, vascular smooth muscle hyperplasia, and are closely associated with the development of atherosclerosis. A reduction in endothelium-dependent flow-mediated dilation of the brachial artery has been observed in patients with CSF, suggesting a link between endothelial dysfunction and the etiology of CSF [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Kanar et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] utilized optical coherence tomography to identify thinner nerve fiber layers in the subfoveal choroid and perioptic disc in patients with CSF, indicating extensive endothelial dysfunction and increased microvascular resistance. The coronary artery system contains not only epicardial large vessels but also a significant number of microvessels smaller than 400 \u0026micro;m which play a role in regulating myocardial blood flow. Microvascular dysfunction has been identified as a key factor in the pathogenesis of CSF. Mangieri et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] provided direct evidence of microvascular disease in endocardial muscle biopsy samples from CSF patients including thickening of the microvascular walls, reduction in lumen size, mitochondrial abnormalities, and decreased glycogen content. Pekdemir et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] utilized intravascular ultrasound (IVUS) technology and flow rate measurements to demonstrate diffuse intimal thickening, extensive calcification of coronary artery walls, and nonobstructive atherosclerosis changes in patients with CSF, suggesting an early involvement of atherosclerosis in the development of CSF.\u003c/p\u003e \u003cp\u003eInflammation plays a crucial role in the human immune response, serving dual functions in defense mechanisms. Initially, it protects physiological homeostasis against infection and tissue damage but should be promptly resolved once infectious agents are eliminated or initial tissue injuries are repaired. Failure to resolve inflammation can lead to tissue dysfunction and other adverse consequences [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies have confirmed that inflammation is a risk factor for various cardiovascular diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and inflammatory responses have also been observed in CSF [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Plasma soluble adhesion molecules and inflammatory markers were found to be significantly elevated in CSF patients, including C-reactive protein [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], interleukin-6 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], platelet to lymphocyte ratio [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], neutrophil to lymphocyte ratio [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], matrix metallopro-teinase-9 and soluble CD40 ligand [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], etc.\u003c/p\u003e \u003cp\u003eLpPLA2 has been proposed as a novel independent inflammatory marker and has been associated with various vascular diseases in epidemiological studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Numerous studies have established LpPLA2 activity as an independent predictor of CHD outcomes in the general population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Recent research has shown that elevated plasma LpPLA2 levels are also linked to the onset and severity of CSF [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our study suggests that LpPLA2 can function as an independent prognostic indicator for poor outcomes in CSF patients. LpPLA2, originally known as platelet-activating factor acetylhydrolase due to its hydrolytic activity on platelet-activating factor [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], is primarily secreted by macrophages and circulates in the bloodstream bound to LDL and HDL [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. LpPLA2 has the ability to hydrolyze and oxidize LDL into two biologically active products, oxidized nonesterified fatty acids and lysophosphatidylcholine, which act as proinflammatory substances derived from LpPLA2, inducing immune responses and oxidative stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These inflammatory effects may contribute to the development of atherosclerotic plaques [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study, when LpPLA2 was divided into quartiles, the results of multi-model Cox regression indicated that the highest level of LpPLA2 was associated with a higher incidence of MACE compared to the lowest level in CSF patients. When considered as a continuous variable, although the RCS curve suggests a overall linear relationship between LpPLA2 and MACE rate, the linear relationship becomes more pronounced when plasma levels of LpPLA2 exceed 247.7 ng/ml. The mechanisms underlying the association between LpPLA2 and poor prognosis in CSF are not fully understood. The most plausible hypothesis is that unresolved inflammatory responses mediated by LpPLA2 may lead to vascular endothelial dysfunction, contributing to the onset and progression of CSF.\u003c/p\u003e \u003cp\u003eIn addition to LpPLA2, multiple Cox regression analyses have shown that mTFC, LDL-C, and diabetes are associated with the occurrence of MACE in CSF patients. HDL-C also demonstrated a statistically significant association when analyzed using data after multiple imputation. Previous studies have found that dyslipidemia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e](2,3) and mTFC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] are associated with the occurrence of MACE in CSF patients, which is consistent with our findings. Isik T et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] identified diabetes as a potential predictor of CSF. Jiang Yu et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] demonstrated that diabetes was an independent prognostic predictor of MACE in patients with normal coronary artery during a mean 3.5 years of follow-up. Our study suggests that diabetes is linked to poor prognosis in CSF patients. Previous studies have indicated that hypertension independently predicts MACE in patients with CSF[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], leading to a certain number of cardiovascular deaths over a relatively long follow-up period. However, our study suggests that there is no significant correlation between hypertension and the occurrence of MACE in patients with CSF, this may be due to insufficient follow-up time and a limited number of cases of cardiovascular death.\u003c/p\u003e \u003cp\u003eLimitations of this study: Despite suggesting that LpPLA2 may have predictive value for clinical outcomes in patients with CSF, there are several limitations that should be acknowledged. Firstly, this was a single-center retrospective study primarily relying on telephone follow-up, which could result in incomplete information, recall bias, and the omission of crucial details regarding clinical outcomes and events. Secondly, the study had a small sample size and a short follow-up period due to our hospital only commencing clinical testing of LpPLA2 in 2019, leading to a low incidence of clinical events such as cardiovascular death that could impact the statistical power of the results and may restrict the generalizability and extensibility of our findings. Lastly, as coronary artery stenosis is often not apparent in patients with CSF, only a few patients underwent repeated coronary angiography tests resulting in an inadequate assessment of coronary artery stenosis and mTFC during CSF follow-up\u003c/p\u003e \u003cp\u003eIn conclusion, elevated levels of LpPLA may function as an independent predictor of risk for the occurrence of MACE in patients with CSF, potentially offering a novel therapeutic target for CSF treatment. In addition to LpPLA2, this study suggests that dyslipidemia, diabetes, and mTFC may also be associated with the prognosis of CSF. However, further investigation is needed to elucidate the exact pathophysiological mechanism of LpPLA2 in CSF, and prospective studies involving multiple centers and large sample sizes are required to validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary slow flow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLpPLA2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLipoprotein-associated phospholipase A2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThrombolysis in myocardial infarction frame count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emTFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean thrombolysis in myocardial infarction frame count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft anterior descending\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCircumflex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight coronary artery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterventricular septal thickness, LVEDD:Left ventricular end-diastolic dimension, LAAP:Left atrial anteroposterior diameter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE/A ratio\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe value of e/a ratio of mitral annulus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated glomerular filtration rate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianjin Wang, Xuejun Xiang, Rui Qiao, Yuanxi Zheng, and Liangchuan Chen conducted and designed the study. Statistical analyses were performed by Xianjin Wang, Xianguan Zhu, and Liangchuan Chen. The article was written by Xianjin Wang. Liling Zhang, Jing Chen, Biyun Qian, and Jingfang Cheng conducted follow-ups through phone call invitations or available outpatient cardiology clinic data. Decisions regarding TFC values were made by Xuejun Xiang and Rui Qiao. All authors have reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the staff for their help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFoundation of Anqing Science and Technology Bureau (2023Z1025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Anqing Municipal Hospital [Medical Ethics Review(2022) No.74], and informed consent was obtained from all patients or their family members.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChalikias G, Tziakas D. 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Cardiol J. 2012;19(4):355\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\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":"Coronary slow flow, Lipoprotein-associated phospholipase A2, Coronary artery disease, Major adverse cardiovascular events","lastPublishedDoi":"10.21203/rs.3.rs-4651584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4651584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCoronary slow flow (CSF) is characterized by reduced coronary circulation and delayed contrast media opacity during angiography. Inflammatory responses have been observed in CSF. The correlation between lipoprotein-associated phospholipase A2 (LpPLA2) as a vascular inflammatory factor and the prognosis of CSF patients remains unclear. This study aims to investigate the potential correlation between plasma LpPLA2 levels and the occurrence of major adverse cardiovascular events (MACE) in CSF patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective study was conducted on 285 patients with CSF who were admitted to Anqing Municipal Hospital from January 2019 to March 2023. LpPLA2 plasma levels and baseline data were collected from hospital records, mean thrombolysis in myocardial infarction frame count (mTFC) was calculated for each patient. Follow-up was conducted by telephone call or cardiology clinic data, and the occurrence and timing of MACE were recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The median follow-up duration was 25 (95%CI: 23-27) months. During follow-up, MACE occurred in 54 patients (18.9%), including 4 individuals had non-fatal myocardial infarction, 6 had arrhythmias (4 of atrial fibrillation, 2 of frequent ventricular premature beats), and 44 had rehospitalizations due to unstable angina pectoris. No cardiac death or cerebrovascular events occurred during follow-up. We categorized LpPLA2 into quartiles, and multivariable adjusted Cox models showed an association between LpPLA2 levels and MACE incidence among CSF patients. Compared to the lowest quartiles, the hazard ratios (HR) for the highest quartiles of LpPLA2 levels was 3.34(95%CI: 1.09-10.22) (p-trend \u0026lt; 0.01). Though the restricted cubic spline (RCS) curve indicates a linear relationship between LpPLA2 and MACE incidence in patients with CSF (p for non-linearity = 0.212), the risk of MACE was relatively flat until 247.7 ng/ml of predicted LpPLA2 levels and increased afterwards, with a HR of 1.78 (95%CI: 1.17 to 2.71) per standard deviation. Kaplan-Meier survival assessments revealed that patients in the highest LpPLA2 quartile exhibited a worse prognosis for MACE compared to those in the lower quartiles of LpPLA2 levels (log-rank p = 0.002). Additionally, multivariable Cox regression analyses identified mTFC, LDL-C, and diabetes as other factors associated with occurrence of MACE in patients with CSF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Elevated plasma LpPLA2 levels may serve as an independent predictor of MACE in patients with CSF. In addition to LpPLA2, dyslipidemia, diabetes, and mTFC may also be correlated with the prognosis of CSF.\u003c/p\u003e","manuscriptTitle":"Elevated plasma lipoprotein-associated phospholipase A2 levels are associated with poor prognosis in patients with coronary slow flow: A retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 20:57:38","doi":"10.21203/rs.3.rs-4651584/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":"282e9e4e-5e8c-483b-be39-4d58202889bf","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-16T15:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 20:57:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4651584","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4651584","identity":"rs-4651584","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}