Left Main and Right Coronary Artery Diameter and Left Ventricular Mass associated with coronary artery collaterals in Ischaemic heart disease: A Cardiovascular Imaging Study

Left Main and Right Coronary Artery Diameter and Left Ventricular Mass associated with coronary artery collaterals in Ischaemic heart disease: A Cardiovascular Imaging 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 Left Main and Right Coronary Artery Diameter and Left Ventricular Mass associated with coronary artery collaterals in Ischaemic heart disease: A Cardiovascular Imaging Study Ajeevan Gautam, Rexson Tse, Birat Krishna Timalsina, Rajib Chaulagain, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6825922/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 Purpose: Coronary artery collateral circulation is a novel factor in assessing ischemic heart disease (IHD). However, it requires specialised assessment and cannot be evaluated retrospectively. Theoretically, a ‘normalised’ coronary artery diameter would indicate the coronary artery collateral status. This study explores associations between coronary artery collaterals with left main and right coronary artery diameter, left ventricular mass, and a ratio of coronary artery diameters to left ventricular mass (CAA-LVM). We hypothesise an association between the status of coronary artery collaterals and the CAA-LVM ratio. Methods: A prospective study of 378 patients with suspected IHD to explore the association between the coronary artery collaterals and CAA-LVM ratio from angiograms and echocardiograms. Univariate and subsequent multivariable binary logistic regression were conducted to assess the associations. Results: The study's findings showed coronary artery collaterals was significantly associated with left main coronary artery (adjusted odds ratio, AOR: 16.321, 95% CI: 4.316, 61.713, p -value: <0.001), right coronary artery (AOR:6.056, 95% CI: 1.509, 24.305, p -value: 0.01), and CAA-LVM ratio (AOR: 3.256, 95% CI: 1.305, 8.125, p- value : 0.01). Conclusion: This study demonstrated that a larger CAA–LVM ratio was observed in patients with increased coronary collateral formation. This technique may serve as an adjunct to the currently available techniques in identifying collaterals and may also help assess collateral circulation amid limited resources. coronary angiography coronary artery collateral coronary artery disease echocardiography ischaemic heart disease Figures Figure 1 Figure 2 Figure 3 Introduction The coronary collateral circulation is a pre-existing network of anastomotic connections between primitive vessels that links one epicardial artery to another, functioning as a "natural bypass" mechanism and representing an adaptive vascular response to ischaemic heart disease (IHD) [ 1 – 4 ]. Well-developed collateral circulation is commonly believed to be associated with reduced infarct size, improved left ventricular function, and decreased mortality rate and reduces the incidence of myocardial necrosis and angina pectoris, thereby functioning as a crucial element in risk stratification and enhancing survival in individuals with IHD [ 5 ]. Various methods like warmup angina, angina during exertion, electrocardiogram (ECG) during occlusion, angiography, coronary and doppler pressure, and collateral perfusion index exist for assessing coronary artery collaterals; however, they possess considerable limitations, such as the inadequate representation of collateral prevalence, substantial interindividual variability, inapplicability to stenotic arteries, semiquantitative nature, high cost, operator dependency, and challenges in complex anatomical scenarios [ 6 ]. A limited expert workforce in developing countries further complicates this. In IHD, the left and right coronary arteries (CAA) increase calibre to compensate for the reduced perfusion from atherosclerotic narrowing or increased heart demand [ 7 – 9 ]. Since coronary collaterals are the interconnections in the coronary artery, well-developed collaterals suggest less heart remodelling [ 10 ] (measured by left ventricular mass (LVM)). Previous studies focused on associations between normal coronary artery diameters with sex [ 11 ], left ventricular mass [ 12 ], age, sex, anatomic variation, and left ventricular mass [ 13 ]. However, there is limited information on the association between collaterals and the coronary diameters and left ventricular mass. We hypothesise that there should be an association between coronary artery collaterals and the sum of the fourth power of coronary artery diameter and left ventricular mass (denoted as CAA-LVM ratio). This study explored the association between the coronary artery diameter and the CAA-LVM ratio. Methods Patient Population This clinical prospective observational study (December 2023–December 2024) was performed at a tertiary cardiac care centre hospital in Nepal. The study included patients who presented at the emergency department with chest pain and were admitted for further procedures and treatment. From patient’s history recorded sheets, the study collected demographic data (age, sex, weight, height), personal habits (smoking, alcohol, exercise), diet, occupation, history of chronic disease (diabetes, hypothyroidism, hyperuricaemia, hyperlipidaemia) and medication, heart, and coronary measurement (both measured and estimated), haematological and biochemistry findings (documented), and final diagnosis. Adults aged > 18 years were chosen because IHD is rare among those aged < 18 years. Ethics Approval This study was approved by the Nepal Health Research Council (Ref: 600) and Griffith University human research ethics (GU: Ref. NO: 2023/891). Exclusion Criteria The patient with a recent episode of myocardial infarction, because of insufficient time for collaterals to develop and for the heart to remodel. Patient with valvular disease due to the chance of increased coronary insufficiency. No documented or radiological evidence of previous cardiac disease. Cases with left ventricular ejection fraction (LVEF) < 40% due to compromised heart function. Cases with heart malignancy or secondaries due to possible direct invasion or compression of the coronary artery. Left Ventricular Mass Estimation on Echocardiogram All echocardiograms were performed using a Philips echocardiography system (Philips, Amsterdam, the Netherlands). During the diastolic phase, the left ventricular internal diameter, interventricular septal thickness, and posterior wall thickness were measured (Fig. 1 ). The Devereaux formula was used to calculate LVM (g): 0.8{1.04[([LVEDd + IVSd + PWd] 3 − LVEDd 3 )]} + 0.6 [ 14 ]; where LVEDd is left ventricular end diastolic diameter, IVSd is interventricular septal thickness at diastole, and PWd is posterior wall thickness at diastole. The model proposed uses only the left ventricle volume and not the whole heart volume. This was considered suitable for this study because the highest blood demand would be from the left ventricle. It is quick, simple and can be used easily in clinical cardiology practice (Fig. 1 ). Acquiring Coronary Angiography images All patients were examined, and a standard protocol for safety was applied before and during the procedure. All angiographic scans were performed using the Siemens Artis one Cath-Angio system (Siemens Healthineers, Erlangen, Germany), which features a 29 cm × 26 cm flat detector with a 1560 × 1420 pixels resolution. The angiographic scans were reconstructed using the syngo qca Frontier software package (Siemens Healthineers, Erlangen, Germany). They were then viewed and measured using the standard in-built tools (Fig. 2 ). All measurements were later performed on the sagittal and transverse sections. Patients were transferred out in stable condition (Fig. 2 ). Coronary Artery Diameter and Coronary Artery to Estimated Heart Mass Ratio After vascular access was obtained through the radial or femoral artery, diagnostic catheters were guided to the respective coronary ostia and cannulated. The radio-opaque contrast dye was injected through the catheter. A fluoroscope was used for real-time imaging, and multiple real-time images from different angles were taken to view the left main coronary artery (LMCA) and right coronary artery (RCA) comprehensively. The internal diameters were measured 10 mm away from the ostia after the infundibulum tapers and before branching. The proximal left anterior descending and left circumflex arteries were not measured because of the risk of overlapping vessel segments, anatomical variations, and difficulty achieving optimal view during angiography. Poiseuille’s equation for closed pipe flow (incompressible viscous fluid) states that flow is proportional to the diameter/radius of the pipe to the power of four [ 15 , 16 ]. As such, the coronary artery to estimated left ventricular mass ratio (CAA–LVM ratio) was calculated (diameter left main 4 + diameter right coronary artery 4 )/estimated LVM, in x10 − 3 mm 4 /g. Assessment of Collaterals Coronary angiography was analysed to determine the maturity of collaterals, which were graded according to Rentrop classification: Grade 0 = there are no collaterals; Grade 1 = the recipient artery's side branches are filled, but not the main epicardial artery; Grade 2 = the main epicardial recipient artery is filled; Grade 3 = the main epicardial recipient artery is filled [ 17 ]. For the analysis, patients were divided into two groups: those with compromised collateral recruitment (Rentrop Grade 0, 1, 2) and excellent collateral recruitment (Rentrop Grade 3), as has been done in previous study [ 18 ]. The predominant donor vessel was the epicardial coronary artery, which supplied the most collaterals (Fig. 3 ). Collection of Blood for Investigations After fasting for at least 8 hours, 5 ml of venous blood from the median cubital vein was withdrawn. For a complete blood count, 2 ml was transferred to a purple-coloured lavender-top (K2 EDTA) tube, and 1 ml was transferred to a grey-coloured tube containing citrate, which was used to examine blood sugar. For all other tests, 2 ml of blood was transferred to a yellow-top tube. A blood sample was stored in the refrigerator for at least 1 hour and was analysed. For hematology, three-part Coulter counters and five-part laser cell counters and biochemistry, Dade Behring, United States (RXL Max), and Randox, United Kingdom (RX Daytona), were used for the analysis. Statistical Analysis Statistical analysis was performed using IBM Statistical Package for Social Science 29 (IBM SPSS Statistics 29, IBM, Armonk, NY, United States), and a p -value of < 0.05 was considered significant. Continuous variables were described using means and standard deviations (SD), and categorical variables were presented as frequencies and percentages. The Shapiro-Wilk test was used to check for normality. Sample characteristics (including the coronary measures) were presented and compared between males and females using independent-sample t-tests and chi-square tests. Univariate and multivariable binary logistic regression was conducted to assess the associations of coronary measures (LVM, RCA, LMCA, CAA-LVM ratio) with ischaemic heart disease and coronary artery collaterals separately. Results Of the 384 subjects identified, 378 satisfied the inclusion criteria for patients with LVEF > 40%. The mean age was 62.5 years (SD: 7.7), and male predominance was M: F = 206 (55%):172 (45%). The mean weight, height, and body mass index (BMI) were 64.9 kg (SD: 8.1), 1.6 m (SD: 0.1), and 24.75 kg/m 2 (SD: 3.8), respectively (Table 1 ). Table 1 Baseline characteristics of Sex differences in the study cohort (n = 378) Variables Overall (n = 378) Males (n = 206) Females (n = 172) P value Age (Years) 62.5 (7.7) 63.4 (8.5) 61.4 (6.4) 0.015* Weight (kg) 64.9 (8.1) 67.7 (6.6) 61.7 (8.5) < 0.001* Height (m) 1.6 (0.1) 1.6 (0.1) 1.6 (0.1) <0.001* BMI (kg/m 2 ) 24.8 (3.8) 25.2 (3.0) 24.2 (4.5) 0.007* Smoking Yes: 205 (54) No: 172 (46) Yes:48 (67) No: 24 (33) Yes: 157 (52) No: 148 (48) 0.020* Alcohol Yes: 204 (54) No: 172 (46) Yes 45 (70) No: 19 (30) Yes: 159 (51) No: 153 (49) 0.005* Exercise Yes:204 (54) No:172 (46) Yes: 46 (71) No: 19 (29) Yes: 158 (51) No: 153 (49) 0.003* Diet Veg: 206 (55) Non-veg: 172 (45) Veg: 155 (61) Non-veg: 98 (39) Veg: 51 (41) Non-veg: 74 (59) < 0.001* Occupation Non-Sed: 172 (46) Sed: 206 (54) Non-Sed :139 (47) Sed :154 (53) Non-Sed: 33 (39) Sed: 52 (61) 0.160 LVM (gm) 228.0 (43.7) 232.7 (44.6) 222.3 (42.0) 0.022* LVEF (%) 58.3 (5.6) 58.0 (5.4) 58.7 (5.7) 0.171 LMCA diameter (mm) 3.2 (0.3) 3.2 (0.3) 3.2 (0.3) 0.526 RCA diameter (mm) 3.1 (0.2) 3.1 (0.2) 3.1 (0.2) 0.702 CAA-LVM ratio (mm 4 /gm) 1.0 (0.3) 1.0 (0.3) 1.0 (0.3) 0.512 Collateral Grades Excellent Compromised 378 (100) 224 (59) 154 (41) 206 (54) 116 (56) 90 (44) 172 (46) 108 (63) 64 (37) 0.202 Haemoglobin (gm%) 13.7 (1.2) 14.0 (1.1) 13.3 (1.3) <0.001* Platelets (10 3 cu/mm) 201.5 (39.6) 203.9 (37.4) 198.5 (42.0) 0.183 TLC (10 3 cells/mm 3 ) 8.7 (2.3) 8.7 (2.3) 8.7 (2.3) 0.831 Neutrophil (%) 68.9 (6.5) 69.3 (6.4) 68.5 (6.7) 0.239 Eosinophil (%) 6.7 (3.1) 6.5 (3.0) 6.9 (3.2) 0.280 Lymphocyte (%) 19.9 (5.8) 19.9 (6.0) 20.0 (5.5) 0.902 Monocyte (%) 4.2 (2.5) 4.2 (2.5) 4.3 (2.5) 0.776 Basophil (%) 0.2 (0.4) 0.2 (0.3) 0.3 (0.5) 0.071 FBS (mmol/L) 6.1 (3.1) 6.1 (3.1) 6.1 (3.0) 0.988 PPBS (mmol/L) 8.6 (4.8) 8.6 (4.8) 8.7 (4.8) 0.939 Uric Acid (mmol/L) 313.7 (46.6) 314.5 (49.7) 312.9 (42.6) 0.746 TSH (µIU/ml) 4.6 (1.8) 4.8 (2.0) 4.5 (1.5) 0.091 HDL (mmol/L) Missing-2 2.5 (0.6) 2.5 (0.6) 2.5 (0.6) 2 0.830 LDL (mmol/L) Missing-3 3.4 (2.3) 3.5 (2.3) 1 3.3 (2.2) 2 0.392 Triglyceride (mmol/L) Missing-7 4.7 (2.4) 4.7 (2.4) 5 4.8 (2.5) 2 0.524 Cholesterol (mmol/L) Missing-6 7.3 (2.9) 7.4 (2.9) 4 7.1 (2.7) 2 0.315 Diagnosis IHD: 254 (67) Non-IHD: 124 (33) IHD: 145 (70) Non-IHD: 61 (30) IHD: 109 (63) Non-IHD: 63 (37) 0.148 For continuous variable it is Mean (SD), for categorical variable it is n (%), LVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein Comparison of Parameters Between Males and Females Male patients were significantly older. In comparing personal habits, males exhibited higher rates of smoking (67% vs 52%), alcohol consumption (70% vs 51%), and regular exercise (71% vs 51%), while a greater proportion of females identified as non-vegetarian (59% vs 39%) with statistical significance (Table 1 ). The estimated LVM was higher in the male group. Left ventricular mass and haemoglobin were also higher in males than in females. A detailed comparison between various parameters is presented in Table 1 . Risk factors associated with cardiovascular disease. Univariate binary logistic regression analysis of cardiovascular disease showed that age, weight, non-vegetarian diet, haemoglobin, fasting blood sugar, postprandial blood sugar, and uric acid had a statistically significant association with cardiovascular disease (CVD) (Table 2 ). Multivariable binary logistic regression models separately for LVM, LMCA, RCA, and CAA-LVM (Table 3 ) showed that only RCA was associated with CVD (AOR: 4.51, 95% CI: 1.42, 14.31, p-value : 0.01), after adjusting for the above significant risk factors. The CAA–LVM ratio was not significantly associated with CVD. Table 2 Univariate binary logistic regression analyses of IHD with various independent factors (n = 378). Variable Odds Ratio (OR) 95% Confidence Interval P value Age (Years) 1.04 (1.01, 1.07) 0.02* Weight (kg) 1.03 (1.00, 1.06) 0.04* Height (m) 0.65 (0.05, 8.11) 0.75 Body Mass Index (kg/m 2 ) 0.65 (0.05, 8.11) 0.74 Smoker 1.35 (0.76, 2.37) 0.31 Alcoholic 1.59 (0.86, 2.93) 0.14 Exercise 1.63 (0.89, 3.00) 0.12 Non-Vegetarians 0.44 (0.27, 0.73) 0.001* Sedentary Occupation 0.61 (0.35, 1.05) 0.07 LVM (gm) 1.01 (1.00, 1.01) 0.03* LVEF (%) 1.07 (1.03, 1.12) < 0.001* LMCA diameter (mm) 2.15 (0.90, 5.13) 0.08 RCA diameter (mm) 4.60 (1.57, 13.48) 0.01* CAA-LVM ratio (mm 4 /gm) 1.26 (0.65, 2.46) 0.49 Haemoglobin (%) 0.83 (0.69, 0.99) 0.04* Platelets (cu/mm) 1.00 (1.00, 1.00) 0.34 TLC (cells/mm 3 ) 1.00 (1.00, 1.00) 0.08 Neutrophil (%) 0.99 (0.96, 1.03) 0.66 Eosinophil (%) 0.98 (0.91, 1.05) 0.48 Lymphocyte (%) 1.03 (0.99, 1.08) 0.09 Monocyte (%) 0.93 (0.85, 1.01) 0.09 Basophil (%) 0.75 (0.46, 1.21) 0.23 FBS (mmol/L) 0.90 (0.84, 0.96) 0.002* PPBS (mmol/L) 0.93 (0.89, 0.97) 0.002* Uric Acid (mmol/L) 0.99 (0.99, 0.10) < 0.001* TSH (µIU/ml) 0.95 (0.84, 1.07) 0.37 HDL (mmol/L) 1.12 (0.79, 1.59) 0.54 LDL (mmol/L) 0.95 (0.87, 1.05) 0.33 Triglyceride (mmol/L) 0.98 (0.90, 1.07) 0.65 Cholesterol (mmol/L) 0.10 (0.92, 1.07) 0.90 LVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein Table 3 Multivariate binary logistic regression analysis of IHD, after adjusting for age, weight, non-vegetarian diet, uric acid, and fasting blood sugar (n = 378). Variables Odds ratio (OR) 95% Confidence Interval P value LVM (gm) 1.01 (1.00, 1.01) 0.07 LMCA diameter (mm) 1.99 (0.78, 5.08) 0.15 RCA diameter (mm) 4.51 (1.42, 14.31) 0.01* CAA–LVM ratio (mm 4 /gm) 1.28 (0.65, 2.62) 0.50 LVM: Left Ventricular Mass, LMCA: Left main coronary artery, RCA: Right coronary artery, CAA-LVM: Sum of the fourth power of the diameter of the left main and right coronary artery to left ventricular mass. Risk factors associated with coronary artery collaterals. Univariate binary logistic regression analysis of coronary artery collaterals showed that age, non-vegetarian diet, sedentary occupation, fasting blood sugar, postprandial blood sugar, uric acid, thyroid-stimulating hormone, high-density lipoprotein, triglyceride and cholesterol had a statistically significant association with coronary artery collaterals (Table 4 ). Multivariable binary logistic regression models separately for LVM, LMCA, RCA, and CAA-LVM (Table 5 ) showed that LMCA (AOR: 16.32, 95% CI: 4.32, 61.71, p-value : <0.001), RCA (AOR: 6.06, 95% CI: 1.51, 24.31, p-value : 0.01), and CAA-LVM ratio (AOR: 3.26, 95% CI: 1.31, 8.13, p-value : 0.01) were associated with coronary artery collaterals, after adjusting for the above significant risk factors. Table 4 Univariate binary logistic regression analyses of coronary artery collaterals grades with various independent factors (n = 378). Variable Odds Ratio (OR) 95% Confidence Interval P value Age (Years) 1.03 (1.01, 1.06) 0.02* Weight (kg) 1.01 (0.99, 1.04) 0.39 Height (m) 1.42 (0.13, 15.62) 0.78 Body Mass Index (kg/m 2 ) 1.01 (0.96, 1.07) 0.71 Smoker 1.60 (0.93, 2.77) 0.09 Alcoholic 1.40 (0.80, 2.46) 0.24 Exercise 1.44 (0.82, 2.53) 0.20 Non-Vegetarians 0.17 (0.10, 0.29) 0.001* Sedentary Occupation 0.46 (0.27, 0.78) 0.004* LVM (gm) 1.00 (0.10, 1.01) 0.20 LVEF (%) 1.00 (0.97, 1.04) 0.79 LMCA diameter (mm) 11.92 (4.46, 31.84) < 0.001* RCA diameter (mm) 6.14 (2.16, 17.41) < 0.001* CAA-LVM ratio (mm 4 /gm) 2.98 (1.48, 5.10) 0.002* Haemoglobin (%) 0.96 (0.81, 1.14) 0.65 Platelets (cu/mm) 1.00 (1.00, 1.00) 0.08 TLC (cells/mm 3 ) 1.00 (1.00, 1.00) 0.89 Neutrophil (%) 1.00 (0.97, 1.04) 0.84 Eosinophil (%) 1.03 (0.96, 1.10) 0.41 Lymphocyte (%) 0.99 (0.96, 1.03) 0.59 Monocyte (%) 0.97 (0.89, 1.05) 0.45 Basophil (%) 0.87 (0.54, 1.39) 0.56 FBS (mmol/L) 0.91 (0.85, 0.97) 0.007* PPBS (mmol/L) 0.96 (0.92, 1.00) 0.05* Uric Acid (mmol/L) 0.98 (0.98, 0.99) < 0.001* TSH (µIU/ml) 0.59 (0.51, 0.68) < 0.001* HDL (mmol/L) 0.40 (0.27, 0.60) < 0.001* LDL (mmol/L) 0.95 (0.87, 1.04) 0.27 Triglyceride (mmol/L) 0.68 (0.61, 0.77) < 0.001* Cholesterol (mmol/L) 1.00 (0.93, 1.08) 0.95 LVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post-prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein. Table 5 Multivariate binary logistic regression analysis of coronary artery collaterals, after adjusting for non-vegetarian diet, sedentary occupation, fasting blood sugar, uric acid, thyroid-stimulating hormone, high-density lipoprotein, and triglyceride. Variable Odds Ratio (OR) 95% Confidence Interval P value LVM (gm) 1.00 (0.10, 1.01) 0.54 LMCA diameter (mm) 16.32 (4.32, 61.71) < 0.001* RCA diameter (mm) 6.06 (1.51, 24.31) 0.01* CAA-LVM ratio (mm 4 /gm) 3.26 (1.31, 8.13) 0.01* LVM: Left ventricular mass, LMCA: Left main coronary artery, RCA: Right coronary artery, CAA-LVM: Sum of the fourth power of the diameter of the left main and right coronary artery to left ventricular mass. Discussion The study explored whether the coronary artery collaterals were associated with CAA–LVM ratio among suspected patients of IHD. The CAA–LVM ratio was a robust parameter associated significantly with collaterals after adjusting for anthropometric, demographic, haematological and biochemical factors. An elevated CAA–LVM ratio increases the likelihood of well-developed coronary artery collaterals. Coronary artery atherosclerosis causes over 90% of IHD [ 19 ], which is the most common cause of cardiovascular disease encountered in the clinical cardiology setting in Nepal [ 20 , 21 ]. It commonly presents as chronic IHD, resulting in sudden cardiac death [ 22 , 23 ]. Angiographic examination of the coronary arteries commonly shows coronary artery narrowing from atherosclerosis [ 24 , 25 ]. In modern cardiology, coronary angiography is used to identify coronary artery collaterals [ 26 ]. To reduce the need for collateral grading, looking at the epicardial artery in angiography, which is tedious, technically challenging, requires expert training and even observer differences [ 17 ]. This study used the CAA–LVM ratio to detect coronary artery collaterals relying on pathophysiological changes, which is less reliant on technical and expert availability and can also be done retrospectively. Further, there is no difference in the CAA-LVM ratio between sexes. Physiologically, the coronary artery diameter determines the blood flow to the heart, while the left ventricular mass determines the blood demand [ 27 , 28 ]. During ischaemia, with insufficient blood supply, the myocardium undergoes a series of compensatory mechanisms, such as remodelling the myocardium and increasing coronary artery diameter to meet the heart's needs [ 15 , 29 ]. Coronary arteries start compensating by increasing the diameter of the heart to compensate for the increased demand from the heart [ 7 ]. Further, to meet the increased demand, coronary arteries try to compensate by activating collateral arteries and compensating blood from other arteries in the physiological and pathological state [ 7 , 30 ]. In IHD, the coronary artery may compensate disproportionately by increasing the diameter, activating the collateral to maintain adequate perfusion to the diseased heart [ 31 – 33 ]. As such, assuming a standard ratio between the coronary artery diameter and heart mass is present. In IHD, the ratio and the number of collateral arteries should change significantly. In our study, after adjustment, comparing CAA–LVM and coronary artery collaterals showed that the CAA–LVM ratio and coronary artery collaterals have a statistically significant association/correlation, suggesting that a higher or increased CAA–LVM ratio is associated with a higher number of coronary artery collaterals. The increase in the CAA–LVM ratio and a higher number of collaterals were because of the compensatory mechanism of the coronary arteries, which perfuses the heart because of the blockage of the coronary artery [ 15 , 34 ]. This study also studied the association between the CAA-LVM ratio and IHD. Normal heart anatomy, particularly heart mass and coronary artery diameter, is associated with demographics and anthropometry[ 35 , 36 ]. The association between the CAA–LVM ratio is explained by the physiological changes in the aging heart, such as myocardial hypertrophy, arterial stiffness, and endothelial dysfunction occur physiologically, requiring more blood perfusion [ 36 , 37 ]. Nonetheless, the CAA–LVM ratio was not associated with IHD after adjusting for anthropometric and demographic factors [ 38 , 39 ]. The explanation for this difference may include the pathophysiological changes in IHD being more noticeable on the CAA-LVM ratio than demography, anthropometry, adaptation, or remodelling, which could not be readily predicted. Likewise, the angiographic appearance of a lesion does not accurately reflect the distribution of plaque [ 40 ]. Although sophisticated scanning modalities can definitively identify collaterals, CAA–LVM is easy to obtain from echocardiography for routine clinical cardiology practice. We can use the CAA-LVM ratio to overview collateral health. It can be useful, especially in developing countries, where access to advanced diagnostic tools may be costly and limited. Besides this, the study's results show that the CAA–LVM ratio may serve as a surrogate method for detecting collaterals in those areas/regions where angiographic assessment of collaterals is challenging. However, while this finding is encouraging, it is important to consider several limitations when interpreting the findings. The information about smoking, alcohol, exercise, diet, and occupation was self-reported. The study was conducted in Nepal, which has a limited workforce; consequently, a single doctor obtained data. Other limitations might be single-centred, focused only on the anatomical aspect, and not on physiological function. The CAA-LVM ratio is the indirect method in assessing collaterals in IHD, which does not replace currently available methods. This can be used in places with limited resources. When applied to routine practice, further validation is needed with multi-centred analysis integrating physiological aspects for repeatability. Conclusion The study demonstrated that the CAA–LVM ratio is associated with coronary artery collateral. In addition to current parameters and gradings, the CAA–LVM ratio may serve as an adjunct to recognise collateral in the heart, which would help the clinician assess the outcome of the patient's IHD. Further validation and subsequent studies comparing and incorporating other echocardiography and angiographic parameters for IHD are recommended. Declarations Acknowledgement The author would like to acknowledge clinicians, Nursing staff and all other staff at Sahid Gangalal National Heart Centre, Kathmandu, Nepal, for their support during data collection. Conflict of Interest None Consent All patients who participated in the study provided written consent before enrolment; patients who did not were excluded from participation. Funding Ajeevan Gautam receives an International Postgraduate Research Scholarship from Griffith University. The funding body had no role in study design, data collection, analysis, interpretation of data, report writing, or submitting the article for publication. Data Availability statement The corresponding author has complete original collected study data, and anonymised data will be made available upon request for a reasonable purpose. References Seiler C, Stoller M, Pitt B et al (2013) The human coronary collateral circulation: development and clinical importance. Eur Heart J 34(34):2674–2682 Koerselman J, van der Graaf Y, de Jaegere PPT et al (2003) ; Coronary Collaterals: An Important and Underexposed Aspect of Coronary Artery Disease. Circulation: Journal of the American Heart Association. 107(19):2507-11 Allahwala UK, Ekmejian A, Mughal N et al (2021) Impact of coronary artery bypass grafting (CABG) on coronary collaterals in patients with a chronic total occlusion (CTO). The International Journal of Cardiovascular Imaging: X-Ray Imaging, Intravascular Imaging, Echocardiography, Nuclear Cardiology. Computed Tomography Magn Reson Imaging 37(12):3373–3380 Faber JE, Chilian WM, Deindl E et al (2014) A brief etymology of the collateral circulation. Arterioscler Thromb Vasc Biol 34(9):1854–1859 Li Z, Wang Y, Wu S et al (2023) Good coronary collateral circulation is not associated with better prognosis in patients with chronic total occlusion, regardless of treatment strategy. Hellenic J Cardiol 69:9–15 Traupe T, Gloekler S, de Marchi SF et al (2010) Assessment of the Human Coronary Collateral Circulation. Circulation 122(12):1210–1220 Duncker DJ, Koller A, Merkus D et al (2015) Regulation of coronary blood flow in health and ischemic heart disease. Prog Cardiovasc Dis 57(5):409–422 Goodwill AG, Dick GM, Kiel AM et al (2017) Regulation of Coronary Blood Flow. Compr Physiol 7(2):321–382 Tse R, Ajeevan G, Benjamin O et al An exploratory study using left main and right coronary artery diameter and heart mass assessed on post-mortem computed tomography to identify ischaemic heart disease. Australian Journal of Forensic Sciences.1–12. https://doi.org/10.1080/00450618.2025.2513084 Jamaiyar A, Juguilon C, Dong F et al (2019) Cardioprotection during ischemia by coronary collateral growth. Am J Physiol Heart Circ Physiol 316(1):H1–h9 Hiteshi AK, Li D, Gao Y et al (2014) Gender differences in coronary artery diameter are not related to body habitus or left ventricular mass. Clin Cardiol 37(10):605–609 Lewis BS, Gotsman MS (1973) Relation between coronary artery size and left ventricular wall mass. Br Heart J 35(11):1150–1153 Dodge JT Jr., Brown BG, Bolson EL et al (1992) Lumen diameter of normal human coronary arteries. Influence of age, sex, anatomic variation, and left ventricular hypertrophy or dilation. Circulation 86(1):232–246 Jafary FH (2007) Devereux formula for left ventricular mass–be careful to use the right units of measurement. J Am Soc Echocardiogr 20(6):783 Hall JE, Hall ME (2021) Guyton and Hall textbook of medical physiology. Philadelphia, PA: Elsevier; Available from: https://www.clinicalkey.com.au/dura/browse/bookChapter/3-s2.0-C20170004883 Barman SM, Barrett KE, Brooks HL et al (2024) Ganong's medical physiology examination & board review. [New York, New York]: Mcgraw Hill; Available from: http://accessmedicine.mhmedical.com/book.aspx?bookid=3413 Rentrop KP, Cohen M, Blanke H et al (1985) Changes in collateral channel filling immediately after controlled coronary artery occlusion by an angioplasty balloon in human subjects. J Am Coll Cardiol 5(3):587–592 Hu XY, Yang WX, Guan CD et al (2024) The prognostic value of collateral circulation in coronary chronic total occlusion underwent percutaneous coronary intervention. J Geriatr Cardiol 21(2):232–241 Kasper DL, Fauci AS, Hauser SL et al (2015) Harrison's principles of internal medicine. New York: McGraw Hill Education; Available from: http://accessmedicine.mhmedical.com/book.aspx?bookid=1130 Pandey AR, Dhimal M, Shrestha N et al (2023) ; Burden of Cardiovascular Diseases in Nepal from 1990 to 2019: The Global Burden of Disease Study, 2019. Global health, epidemiology and genomics. 2023:3700094 Bhattarai S, Aryal A, Pyakurel M et al (2020) Cardiovascular disease trends in Nepal - An analysis of global burden of disease data 2017. Int J Cardiol Heart Vasc 30:100602 Concistrè G (2023) Ischemic heart disease: from diagnosis to treatment. Cham, Switzerland: Springer; Available from: https://doi.org/10.1007/978-3-031-25879-4 Boccanelli A, Scardovi AB (2023) Sudden death in ischemic heart disease: looking for new predictors: polygenic risk. Eur Heart J Supplements 25(SupplementB):B31–B3 Eckert J, Schmidt M, Magedanz A et al (2015) Coronary CT angiography in managing atherosclerosis. Int J Mol Sci 16(2):3740–3756 Voros S, Rinehart S, Qian Z et al (2011) Coronary Atherosclerosis Imaging by Coronary CT Angiography: Current Status, Correlation With Intravascular Interrogation and Meta-Analysis. JACC: Cardiovasc Imaging 4(5):537–548 Hasanović A, Sisić F, Dilberović F et al (2005) Collateral circulation in human heart. Bosn J Basic Med Sci 5(2):87–91 Schelbert HR (2010) Anatomy and physiology of coronary blood flow. J Nucl Cardiol 17(4):545–554 Ramanathan T, Skinner H (2005) Coronary blood flow. Continuing Educ Anaesth Crit Care Pain 5(2):61–64 Heusch G (2024) Myocardial ischemia/reperfusion: Translational pathophysiology of ischemic heart disease. Med 5(1):10–31 Schaper W (2009) Collateral circulation. Basic Res Cardiol 104(1):5–21 Libby P, Theroux P (2005) Pathophysiology of coronary artery disease. Circulation 111(25):3481–3488 Ward MR, Pasterkamp G, Yeung AC et al (2000) Arterial remodeling. Mechanisms and clinical implications. Circulation 102(10):1186–1191 Meier P, Schirmer SH, Lansky AJ et al (2013) The collateral circulation of the heart. BMC Med 11:143 Cruickshank JM (1992) ; The role of coronary perfusion pressure. Eur Heart J 13 Suppl D :39–43 Bonarjee VVS (2018) Arterial Stiffness: A Prognostic Marker in Coronary Heart Disease. Available Methods and Clinical Application. Front Cardiovasc Med 5:64 Raut BK, Patil VN, Cherian G (2017) Coronary artery dimensions in normal Indians. Indian Heart J 69(4):512–514 Mohan A, Gopalakrishnan A, Chandran R et al (2023) Examining the Influence of Gender, Age, and Dominance on the Caliber of Normal Coronary Arteries in the South Indian Population. Cureus 15(12):e51146 Donato AJ, Machin DR, Lesniewski LA (2018) Mechanisms of Dysfunction in the Aging Vasculature and Role in Age-Related Disease. Circ Res 123(7):825–848 Zieman SJ, Melenovsky V, Kass DA (2005) Mechanisms, pathophysiology, and therapy of arterial stiffness. Arterioscler Thromb Vasc Biol 25(5):932–943 Mintz GS, Popma JJ, Pichard AD et al (1996) Limitations of Angiography in the Assessment of Plaque Distribution in Coronary Artery Disease: A Systematic Study of Target Lesion Eccentricity in 1446 Lesions. Circulation 93(5):924–931 Additional Declarations No competing interests reported. Supplementary Files GA.jpg Graphical Abstract 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-6825922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476727460,"identity":"24de811f-ffa9-4a95-ace2-faa6b2409a92","order_by":0,"name":"Ajeevan Gautam","email":"data:image/png;base64,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","orcid":"","institution":"Griffith University","correspondingAuthor":true,"prefix":"","firstName":"Ajeevan","middleName":"","lastName":"Gautam","suffix":""},{"id":476727461,"identity":"cff58a43-c269-45a5-8725-be86d79f0ed9","order_by":1,"name":"Rexson Tse","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Rexson","middleName":"","lastName":"Tse","suffix":""},{"id":476727462,"identity":"953d1e58-ede4-437a-96a3-8fb1ae3870a7","order_by":2,"name":"Birat Krishna Timalsina","email":"","orcid":"","institution":"Shahid Gangalal National Heart Centre","correspondingAuthor":false,"prefix":"","firstName":"Birat","middleName":"Krishna","lastName":"Timalsina","suffix":""},{"id":476727463,"identity":"1588b5b4-addb-4722-9db1-1b6bb60e130c","order_by":3,"name":"Rajib Chaulagain","email":"","orcid":"","institution":"Madhesh Institute of Health Science","correspondingAuthor":false,"prefix":"","firstName":"Rajib","middleName":"","lastName":"Chaulagain","suffix":""},{"id":476727464,"identity":"9b13383f-b715-40fc-b864-27ddfe4994ac","order_by":4,"name":"Shu-Kay Ng","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Shu-Kay","middleName":"","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2025-06-05 06:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6825922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6825922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85755997,"identity":"7a48c4dc-9d30-4b1f-a1a0-447c8433855e","added_by":"auto","created_at":"2025-07-01 10:46:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83521,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative echocardiographic image and measurement of Interventricular Septal thickness in diastole (IVSd), Left Ventricular Internal Diameter in diastole (LVIDd), and Posterior Wall thickness in diastole (PWd) to calculate the estimated left ventricular mass (LVM) to calculate left ventricular mass.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6825922/v1/627c49249b29b2e421f289dc.jpg"},{"id":85757386,"identity":"9f61c2ce-9e76-4f4f-a307-5239cc68ff44","added_by":"auto","created_at":"2025-07-01 10:54:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69259,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative coronary angiographic image for the (a) measurement of the diameter of the right coronary artery (RCA), (b) measurement of the left main coronary artery (LMCA) just before branching into the left anterior descending (LAD) and left circumflex artery (LCx).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6825922/v1/47249a9e0047b620aa523c1b.jpg"},{"id":85755996,"identity":"604a188c-8449-4ba4-a83a-bf3ec8e8b77f","added_by":"auto","created_at":"2025-07-01 10:46:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55123,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Coronary artery collaterals between the branch of the right coronary artery and the branch of the left anterior descending artery of the left main coronary artery; (b) Coronary artery collaterals between the branch of the left anterior descending artery and the left circumflex artery, both branches of the left main coronary artery.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6825922/v1/97f69d7890a843820d7be862.jpg"},{"id":93045912,"identity":"e89ac46d-b335-49d3-95f7-dc8840a8dbb3","added_by":"auto","created_at":"2025-10-08 13:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1331968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6825922/v1/58f92c41-4b8d-47ff-8868-b8071b63c3b8.pdf"},{"id":85755993,"identity":"88f408b9-c86f-4b30-93dd-82775e7d4524","added_by":"auto","created_at":"2025-07-01 10:46:56","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":63932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6825922/v1/5875afb863aaaa8ec8f5d8d7.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Left Main and Right Coronary Artery Diameter and Left Ventricular Mass associated with coronary artery collaterals in Ischaemic heart disease: A Cardiovascular Imaging Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronary collateral circulation is a pre-existing network of anastomotic connections between primitive vessels that links one epicardial artery to another, functioning as a \"natural bypass\" mechanism and representing an adaptive vascular response to ischaemic heart disease (IHD) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Well-developed collateral circulation is commonly believed to be associated with reduced infarct size, improved left ventricular function, and decreased mortality rate and reduces the incidence of myocardial necrosis and angina pectoris, thereby functioning as a crucial element in risk stratification and enhancing survival in individuals with IHD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Various methods like warmup angina, angina during exertion, electrocardiogram (ECG) during occlusion, angiography, coronary and doppler pressure, and collateral perfusion index exist for assessing coronary artery collaterals; however, they possess considerable limitations, such as the inadequate representation of collateral prevalence, substantial interindividual variability, inapplicability to stenotic arteries, semiquantitative nature, high cost, operator dependency, and challenges in complex anatomical scenarios [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A limited expert workforce in developing countries further complicates this.\u003c/p\u003e \u003cp\u003eIn IHD, the left and right coronary arteries (CAA) increase calibre to compensate for the reduced perfusion from atherosclerotic narrowing or increased heart demand [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Since coronary collaterals are the interconnections in the coronary artery, well-developed collaterals suggest less heart remodelling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] (measured by left ventricular mass (LVM)). Previous studies focused on associations between normal coronary artery diameters with sex [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], left ventricular mass [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], age, sex, anatomic variation, and left ventricular mass [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, there is limited information on the association between collaterals and the coronary diameters and left ventricular mass. We hypothesise that there should be an association between coronary artery collaterals and the sum of the fourth power of coronary artery diameter and left ventricular mass (denoted as CAA-LVM ratio). This study explored the association between the coronary artery diameter and the CAA-LVM ratio.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Population\u003c/h2\u003e \u003cp\u003eThis clinical prospective observational study (December 2023\u0026ndash;December 2024) was performed at a tertiary cardiac care centre hospital in Nepal. The study included patients who presented at the emergency department with chest pain and were admitted for further procedures and treatment. From patient\u0026rsquo;s history recorded sheets, the study collected demographic data (age, sex, weight, height), personal habits (smoking, alcohol, exercise), diet, occupation, history of chronic disease (diabetes, hypothyroidism, hyperuricaemia, hyperlipidaemia) and medication, heart, and coronary measurement (both measured and estimated), haematological and biochemistry findings (documented), and final diagnosis. Adults aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years were chosen because IHD is rare among those aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics Approval\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Nepal Health Research Council (Ref: 600) and Griffith University human research ethics (GU: Ref. NO: 2023/891).\u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe patient with a recent episode of myocardial infarction, because of insufficient time for collaterals to develop and for the heart to remodel.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatient with valvular disease due to the chance of increased coronary insufficiency.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNo documented or radiological evidence of previous cardiac disease.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCases with left ventricular ejection fraction (LVEF)\u0026thinsp;\u0026lt;\u0026thinsp;40% due to compromised heart function.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCases with heart malignancy or secondaries due to possible direct invasion or compression of the coronary artery.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eLeft Ventricular Mass Estimation on Echocardiogram\u003c/h3\u003e\n\u003cp\u003eAll echocardiograms were performed using a Philips echocardiography system (Philips, Amsterdam, the Netherlands). During the diastolic phase, the left ventricular internal diameter, interventricular septal thickness, and posterior wall thickness were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Devereaux formula was used to calculate LVM (g): 0.8{1.04[([LVEDd\u0026thinsp;+\u0026thinsp;IVSd\u0026thinsp;+\u0026thinsp;PWd]\u003csup\u003e3\u003c/sup\u003e \u0026minus; LVEDd\u003csup\u003e3\u003c/sup\u003e)]} + 0.6 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; where LVEDd is left ventricular end diastolic diameter, IVSd is interventricular septal thickness at diastole, and PWd is posterior wall thickness at diastole. The model proposed uses only the left ventricle volume and not the whole heart volume. This was considered suitable for this study because the highest blood demand would be from the left ventricle. It is quick, simple and can be used easily in clinical cardiology practice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAcquiring Coronary Angiography images\u003c/h3\u003e\n\u003cp\u003eAll patients were examined, and a standard protocol for safety was applied before and during the procedure. All angiographic scans were performed using the Siemens Artis one Cath-Angio system (Siemens Healthineers, Erlangen, Germany), which features a 29 cm \u0026times; 26 cm flat detector with a 1560 \u0026times; 1420 pixels resolution. The angiographic scans were reconstructed using the syngo qca Frontier software package (Siemens Healthineers, Erlangen, Germany). They were then viewed and measured using the standard in-built tools (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All measurements were later performed on the sagittal and transverse sections. Patients were transferred out in stable condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCoronary Artery Diameter and Coronary Artery to Estimated Heart Mass Ratio\u003c/h2\u003e \u003cp\u003eAfter vascular access was obtained through the radial or femoral artery, diagnostic catheters were guided to the respective coronary ostia and cannulated. The radio-opaque contrast dye was injected through the catheter. A fluoroscope was used for real-time imaging, and multiple real-time images from different angles were taken to view the left main coronary artery (LMCA) and right coronary artery (RCA) comprehensively. The internal diameters were measured 10 mm away from the ostia after the infundibulum tapers and before branching. The proximal left anterior descending and left circumflex arteries were not measured because of the risk of overlapping vessel segments, anatomical variations, and difficulty achieving optimal view during angiography. Poiseuille\u0026rsquo;s equation for closed pipe flow (incompressible viscous fluid) states that flow is proportional to the diameter/radius of the pipe to the power of four [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As such, the coronary artery to estimated left ventricular mass ratio (CAA\u0026ndash;LVM ratio) was calculated (diameter left main\u003csup\u003e4\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;diameter right coronary artery\u003csup\u003e4\u003c/sup\u003e)/estimated LVM, in x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e4\u003c/sup\u003e/g.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of Collaterals\u003c/h3\u003e\n\u003cp\u003eCoronary angiography was analysed to determine the maturity of collaterals, which were graded according to Rentrop classification: Grade 0\u0026thinsp;=\u0026thinsp;there are no collaterals; Grade 1\u0026thinsp;=\u0026thinsp;the recipient artery's side branches are filled, but not the main epicardial artery; Grade 2\u0026thinsp;=\u0026thinsp;the main epicardial recipient artery is filled; Grade 3\u0026thinsp;=\u0026thinsp;the main epicardial recipient artery is filled [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For the analysis, patients were divided into two groups: those with compromised collateral recruitment (Rentrop Grade 0, 1, 2) and excellent collateral recruitment (Rentrop Grade 3), as has been done in previous study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The predominant donor vessel was the epicardial coronary artery, which supplied the most collaterals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCollection of Blood for Investigations\u003c/h3\u003e\n\u003cp\u003eAfter fasting for at least 8 hours, 5 ml of venous blood from the median cubital vein was withdrawn. For a complete blood count, 2 ml was transferred to a purple-coloured lavender-top (K2 EDTA) tube, and 1 ml was transferred to a grey-coloured tube containing citrate, which was used to examine blood sugar. For all other tests, 2 ml of blood was transferred to a yellow-top tube. A blood sample was stored in the refrigerator for at least 1 hour and was analysed. For hematology, three-part Coulter counters and five-part laser cell counters and biochemistry, Dade Behring, United States (RXL Max), and Randox, United Kingdom (RX Daytona), were used for the analysis.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using IBM Statistical Package for Social Science 29 (IBM SPSS Statistics 29, IBM, Armonk, NY, United States), and a \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered significant. Continuous variables were described using means and standard deviations (SD), and categorical variables were presented as frequencies and percentages. The Shapiro-Wilk test was used to check for normality. Sample characteristics (including the coronary measures) were presented and compared between males and females using independent-sample t-tests and chi-square tests. Univariate and multivariable binary logistic regression was conducted to assess the associations of coronary measures (LVM, RCA, LMCA, CAA-LVM ratio) with ischaemic heart disease and coronary artery collaterals separately.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 384 subjects identified, 378 satisfied the inclusion criteria for patients with LVEF\u0026thinsp;\u0026gt;\u0026thinsp;40%. The mean age was 62.5 years (SD: 7.7), and male predominance was M: F\u0026thinsp;=\u0026thinsp;206 (55%):172 (45%). The mean weight, height, and body mass index (BMI) were 64.9 kg (SD: 8.1), 1.6 m (SD: 0.1), and 24.75 kg/m\u003csup\u003e2\u003c/sup\u003e (SD: 3.8), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of Sex differences in the study cohort (n\u0026thinsp;=\u0026thinsp;378)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;378)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMales (n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemales (n\u0026thinsp;=\u0026thinsp;172)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.4 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.4 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.9 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.7 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.7 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.8 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes: 205 (54)\u003c/p\u003e \u003cp\u003eNo: 172 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes:48 (67)\u003c/p\u003e \u003cp\u003eNo: 24 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes: 157 (52)\u003c/p\u003e \u003cp\u003eNo: 148 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes: 204 (54)\u003c/p\u003e \u003cp\u003eNo: 172 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes 45 (70)\u003c/p\u003e \u003cp\u003eNo: 19 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes: 159 (51)\u003c/p\u003e \u003cp\u003eNo: 153 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes:204 (54)\u003c/p\u003e \u003cp\u003eNo:172 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes: 46 (71)\u003c/p\u003e \u003cp\u003eNo: 19 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes: 158 (51)\u003c/p\u003e \u003cp\u003eNo: 153 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVeg: 206 (55)\u003c/p\u003e \u003cp\u003eNon-veg: 172 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVeg: 155 (61)\u003c/p\u003e \u003cp\u003eNon-veg: 98 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVeg: 51 (41)\u003c/p\u003e \u003cp\u003eNon-veg: 74 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Sed: 172 (46)\u003c/p\u003e \u003cp\u003eSed: 206 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Sed :139 (47)\u003c/p\u003e \u003cp\u003eSed :154 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Sed: 33 (39)\u003c/p\u003e \u003cp\u003eSed: 52 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVM (gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.0 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232.7 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222.3 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022*\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\u003e58.3 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.7 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAA-LVM ratio (mm\u003csup\u003e4\u003c/sup\u003e/gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollateral Grades\u003c/p\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003cp\u003eCompromised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (100)\u003c/p\u003e \u003cp\u003e224 (59)\u003c/p\u003e \u003cp\u003e154 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (54)\u003c/p\u003e \u003cp\u003e116 (56)\u003c/p\u003e \u003cp\u003e90 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (46)\u003c/p\u003e \u003cp\u003e108 (63)\u003c/p\u003e \u003cp\u003e64 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin (gm%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e cu/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201.5 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.9 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198.5 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLC (10\u003csup\u003e3\u003c/sup\u003ecells/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.3 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.5 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.0 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313.7 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314.5 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312.9 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003cp\u003eMissing-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 (0.6)\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003cp\u003eMissing-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.3)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.2)\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003cp\u003eMissing-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7 (2.4)\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8 (2.5)\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003cp\u003eMissing-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4 (2.9)\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1 (2.7)\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIHD: 254 (67)\u003c/p\u003e \u003cp\u003eNon-IHD: 124 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIHD: 145 (70)\u003c/p\u003e \u003cp\u003eNon-IHD: 61 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIHD: 109 (63)\u003c/p\u003e \u003cp\u003eNon-IHD: 63 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFor continuous variable it is Mean (SD), for categorical variable it is n (%), LVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Parameters Between Males and Females\u003c/h2\u003e \u003cp\u003eMale patients were significantly older. In comparing personal habits, males exhibited higher rates of smoking (67% vs 52%), alcohol consumption (70% vs 51%), and regular exercise (71% vs 51%), while a greater proportion of females identified as non-vegetarian (59% vs 39%) with statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe estimated LVM was higher in the male group. Left ventricular mass and haemoglobin were also higher in males than in females. A detailed comparison between various parameters is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk factors associated with cardiovascular disease.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnivariate binary logistic regression analysis of cardiovascular disease showed that age, weight, non-vegetarian diet, haemoglobin, fasting blood sugar, postprandial blood sugar, and uric acid had a statistically significant association with cardiovascular disease (CVD) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariable binary logistic regression models separately for LVM, LMCA, RCA, and CAA-LVM (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that only RCA was associated with CVD (AOR: 4.51, 95% CI: 1.42, 14.31, \u003cem\u003ep-value\u003c/em\u003e: 0.01), after adjusting for the above significant risk factors. The CAA\u0026ndash;LVM ratio was not significantly associated with CVD.\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\u003eUnivariate binary logistic regression analyses of IHD with various independent factors (n\u0026thinsp;=\u0026thinsp;378).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.01, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.05, 8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.05, 8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.76, 2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.86, 2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.89, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Vegetarians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.27, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.35, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVM (gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03*\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.03, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.90, 5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.57, 13.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAA-LVM ratio (mm\u003csup\u003e4\u003c/sup\u003e/gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.65, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.69, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (cu/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLC (cells/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.91, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.99, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.85, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.46, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.84, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.89, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.99, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.84, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.79, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.87, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.90, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.92, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate binary logistic regression analysis of IHD, after adjusting for age, weight, non-vegetarian diet, uric acid, and fasting blood sugar (n\u0026thinsp;=\u0026thinsp;378).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eOdds ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVM (gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.78, 5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.42, 14.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAA\u0026ndash;LVM ratio (mm\u003csup\u003e4\u003c/sup\u003e/gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.65, 2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLVM: Left Ventricular Mass, LMCA: Left main coronary artery, RCA: Right coronary artery, CAA-LVM: Sum of the fourth power of the diameter of the left main and right coronary artery to left ventricular mass.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk factors associated with coronary artery collaterals.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnivariate binary logistic regression analysis of coronary artery collaterals showed that age, non-vegetarian diet, sedentary occupation, fasting blood sugar, postprandial blood sugar, uric acid, thyroid-stimulating hormone, high-density lipoprotein, triglyceride and cholesterol had a statistically significant association with coronary artery collaterals (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Multivariable binary logistic regression models separately for LVM, LMCA, RCA, and CAA-LVM (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that LMCA (AOR: 16.32, 95% CI: 4.32, 61.71, \u003cem\u003ep-value\u003c/em\u003e: \u0026lt;0.001), RCA (AOR: 6.06, 95% CI: 1.51, 24.31, \u003cem\u003ep-value\u003c/em\u003e: 0.01), and CAA-LVM ratio (AOR: 3.26, 95% CI: 1.31, 8.13, \u003cem\u003ep-value\u003c/em\u003e: 0.01) were associated with coronary artery collaterals, after adjusting for the above significant risk factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate binary logistic regression analyses of coronary artery collaterals grades with various independent factors (n\u0026thinsp;=\u0026thinsp;378).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.01, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.99, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.13, 15.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.93, 2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.80, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.82, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Vegetarians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.10, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.27, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVM (gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.10, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.97, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.46, 31.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.16, 17.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAA-LVM ratio (mm\u003csup\u003e4\u003c/sup\u003e/gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.48, 5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.81, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (cu/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLC (cells/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.97, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.89, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.54, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.85, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPBS (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.92, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.98, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.51, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.27, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.87, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.61, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.93, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLVM: Left ventricular mass, LVEF: Left ventricular ejection fraction, LMCA: Left main coronary artery, RCA: Right coronary artery, TLC: Total leucocyte count, FBS: Fasting blood sugar, PPBS: Post-prandial blood sugar, TSH: Thyroid stimulating hormone, HDL: High density lipoprotein, LDL: Low density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate binary logistic regression analysis of coronary artery collaterals, after adjusting for non-vegetarian diet, sedentary occupation, fasting blood sugar, uric acid, thyroid-stimulating hormone, high-density lipoprotein, and triglyceride.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVM (gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.10, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.32, 61.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.51, 24.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAA-LVM ratio (mm\u003csup\u003e4\u003c/sup\u003e/gm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.31, 8.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLVM: Left ventricular mass, LMCA: Left main coronary artery, RCA: Right coronary artery, CAA-LVM: Sum of the fourth power of the diameter of the left main and right coronary artery to left ventricular mass.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study explored whether the coronary artery collaterals were associated with CAA\u0026ndash;LVM ratio among suspected patients of IHD. The CAA\u0026ndash;LVM ratio was a robust parameter associated significantly with collaterals after adjusting for anthropometric, demographic, haematological and biochemical factors. An elevated CAA\u0026ndash;LVM ratio increases the likelihood of well-developed coronary artery collaterals.\u003c/p\u003e \u003cp\u003eCoronary artery atherosclerosis causes over 90% of IHD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which is the most common cause of cardiovascular disease encountered in the clinical cardiology setting in Nepal [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It commonly presents as chronic IHD, resulting in sudden cardiac death [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Angiographic examination of the coronary arteries commonly shows coronary artery narrowing from atherosclerosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In modern cardiology, coronary angiography is used to identify coronary artery collaterals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To reduce the need for collateral grading, looking at the epicardial artery in angiography, which is tedious, technically challenging, requires expert training and even observer differences [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study used the CAA\u0026ndash;LVM ratio to detect coronary artery collaterals relying on pathophysiological changes, which is less reliant on technical and expert availability and can also be done retrospectively. Further, there is no difference in the CAA-LVM ratio between sexes.\u003c/p\u003e \u003cp\u003ePhysiologically, the coronary artery diameter determines the blood flow to the heart, while the left ventricular mass determines the blood demand [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. During ischaemia, with insufficient blood supply, the myocardium undergoes a series of compensatory mechanisms, such as remodelling the myocardium and increasing coronary artery diameter to meet the heart's needs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Coronary arteries start compensating by increasing the diameter of the heart to compensate for the increased demand from the heart [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Further, to meet the increased demand, coronary arteries try to compensate by activating collateral arteries and compensating blood from other arteries in the physiological and pathological state [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In IHD, the coronary artery may compensate disproportionately by increasing the diameter, activating the collateral to maintain adequate perfusion to the diseased heart [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As such, assuming a standard ratio between the coronary artery diameter and heart mass is present. In IHD, the ratio and the number of collateral arteries should change significantly.\u003c/p\u003e \u003cp\u003eIn our study, after adjustment, comparing CAA\u0026ndash;LVM and coronary artery collaterals showed that the CAA\u0026ndash;LVM ratio and coronary artery collaterals have a statistically significant association/correlation, suggesting that a higher or increased CAA\u0026ndash;LVM ratio is associated with a higher number of coronary artery collaterals. The increase in the CAA\u0026ndash;LVM ratio and a higher number of collaterals were because of the compensatory mechanism of the coronary arteries, which perfuses the heart because of the blockage of the coronary artery [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study also studied the association between the CAA-LVM ratio and IHD. Normal heart anatomy, particularly heart mass and coronary artery diameter, is associated with demographics and anthropometry[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The association between the CAA\u0026ndash;LVM ratio is explained by the physiological changes in the aging heart, such as myocardial hypertrophy, arterial stiffness, and endothelial dysfunction occur physiologically, requiring more blood perfusion [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Nonetheless, the CAA\u0026ndash;LVM ratio was not associated with IHD after adjusting for anthropometric and demographic factors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The explanation for this difference may include the pathophysiological changes in IHD being more noticeable on the CAA-LVM ratio than demography, anthropometry, adaptation, or remodelling, which could not be readily predicted. Likewise, the angiographic appearance of a lesion does not accurately reflect the distribution of plaque [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough sophisticated scanning modalities can definitively identify collaterals, CAA\u0026ndash;LVM is easy to obtain from echocardiography for routine clinical cardiology practice. We can use the CAA-LVM ratio to overview collateral health. It can be useful, especially in developing countries, where access to advanced diagnostic tools may be costly and limited. Besides this, the study's results show that the CAA\u0026ndash;LVM ratio may serve as a surrogate method for detecting collaterals in those areas/regions where angiographic assessment of collaterals is challenging.\u003c/p\u003e \u003cp\u003eHowever, while this finding is encouraging, it is important to consider several limitations when interpreting the findings. The information about smoking, alcohol, exercise, diet, and occupation was self-reported. The study was conducted in Nepal, which has a limited workforce; consequently, a single doctor obtained data. Other limitations might be single-centred, focused only on the anatomical aspect, and not on physiological function. The CAA-LVM ratio is the indirect method in assessing collaterals in IHD, which does not replace currently available methods. This can be used in places with limited resources. When applied to routine practice, further validation is needed with multi-centred analysis integrating physiological aspects for repeatability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrated that the CAA\u0026ndash;LVM ratio is associated with coronary artery collateral. In addition to current parameters and gradings, the CAA\u0026ndash;LVM ratio may serve as an adjunct to recognise collateral in the heart, which would help the clinician assess the outcome of the patient's IHD. Further validation and subsequent studies comparing and incorporating other echocardiography and angiographic parameters for IHD are recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to acknowledge clinicians, Nursing staff and all other staff at Sahid Gangalal National Heart Centre, Kathmandu, Nepal, for their support during data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients who participated in the study provided written consent before enrolment; patients who did not were excluded from participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAjeevan Gautam receives an International Postgraduate Research Scholarship from Griffith University. The funding body had no role in study design, data collection, analysis, interpretation of data, report writing, or submitting the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author has complete original collected study data, and anonymised data will be made available upon request for a reasonable purpose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeiler C, Stoller M, Pitt B et al (2013) The human coronary collateral circulation: development and clinical importance. Eur Heart J 34(34):2674\u0026ndash;2682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoerselman J, van der Graaf Y, de Jaegere PPT et al (2003) ; Coronary Collaterals: An Important and Underexposed Aspect of Coronary Artery Disease. Circulation: Journal of the American Heart Association. 107(19):2507-11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllahwala UK, Ekmejian A, Mughal N et al (2021) Impact of coronary artery bypass grafting (CABG) on coronary collaterals in patients with a chronic total occlusion (CTO). The International Journal of Cardiovascular Imaging: X-Ray Imaging, Intravascular Imaging, Echocardiography, Nuclear Cardiology. Computed Tomography Magn Reson Imaging 37(12):3373\u0026ndash;3380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaber JE, Chilian WM, Deindl E et al (2014) A brief etymology of the collateral circulation. Arterioscler Thromb Vasc Biol 34(9):1854\u0026ndash;1859\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Wang Y, Wu S et al (2023) Good coronary collateral circulation is not associated with better prognosis in patients with chronic total occlusion, regardless of treatment strategy. Hellenic J Cardiol 69:9\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraupe T, Gloekler S, de Marchi SF et al (2010) Assessment of the Human Coronary Collateral Circulation. Circulation 122(12):1210\u0026ndash;1220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncker DJ, Koller A, Merkus D et al (2015) Regulation of coronary blood flow in health and ischemic heart disease. Prog Cardiovasc Dis 57(5):409\u0026ndash;422\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodwill AG, Dick GM, Kiel AM et al (2017) Regulation of Coronary Blood Flow. Compr Physiol 7(2):321\u0026ndash;382\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTse R, Ajeevan G, Benjamin O et al An exploratory study using left main and right coronary artery diameter and heart mass assessed on post-mortem computed tomography to identify ischaemic heart disease. Australian Journal of Forensic Sciences.1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00450618.2025.2513084\u003c/span\u003e\u003cspan address=\"10.1080/00450618.2025.2513084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamaiyar A, Juguilon C, Dong F et al (2019) Cardioprotection during ischemia by coronary collateral growth. Am J Physiol Heart Circ Physiol 316(1):H1\u0026ndash;h9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiteshi AK, Li D, Gao Y et al (2014) Gender differences in coronary artery diameter are not related to body habitus or left ventricular mass. Clin Cardiol 37(10):605\u0026ndash;609\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis BS, Gotsman MS (1973) Relation between coronary artery size and left ventricular wall mass. Br Heart J 35(11):1150\u0026ndash;1153\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodge JT Jr., Brown BG, Bolson EL et al (1992) Lumen diameter of normal human coronary arteries. Influence of age, sex, anatomic variation, and left ventricular hypertrophy or dilation. Circulation 86(1):232\u0026ndash;246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJafary FH (2007) Devereux formula for left ventricular mass\u0026ndash;be careful to use the right units of measurement. J Am Soc Echocardiogr 20(6):783\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall JE, Hall ME (2021) Guyton and Hall textbook of medical physiology. Philadelphia, PA: Elsevier; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.clinicalkey.com.au/dura/browse/bookChapter/3-s2.0-C20170004883\u003c/span\u003e\u003cspan address=\"https://www.clinicalkey.com.au/dura/browse/bookChapter/3-s2.0-C20170004883\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman SM, Barrett KE, Brooks HL et al (2024) Ganong's medical physiology examination \u0026amp; board review. [New York, New York]: Mcgraw Hill; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://accessmedicine.mhmedical.com/book.aspx?bookid=3413\u003c/span\u003e\u003cspan address=\"http://accessmedicine.mhmedical.com/book.aspx?bookid=3413\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRentrop KP, Cohen M, Blanke H et al (1985) Changes in collateral channel filling immediately after controlled coronary artery occlusion by an angioplasty balloon in human subjects. J Am Coll Cardiol 5(3):587\u0026ndash;592\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu XY, Yang WX, Guan CD et al (2024) The prognostic value of collateral circulation in coronary chronic total occlusion underwent percutaneous coronary intervention. J Geriatr Cardiol 21(2):232\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasper DL, Fauci AS, Hauser SL et al (2015) Harrison's principles of internal medicine. New York: McGraw Hill Education; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://accessmedicine.mhmedical.com/book.aspx?bookid=1130\u003c/span\u003e\u003cspan address=\"http://accessmedicine.mhmedical.com/book.aspx?bookid=1130\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey AR, Dhimal M, Shrestha N et al (2023) ; Burden of Cardiovascular Diseases in Nepal from 1990 to 2019: The Global Burden of Disease Study, 2019. Global health, epidemiology and genomics. 2023:3700094\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattarai S, Aryal A, Pyakurel M et al (2020) Cardiovascular disease trends in Nepal - An analysis of global burden of disease data 2017. Int J Cardiol Heart Vasc 30:100602\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcistr\u0026egrave; G (2023) Ischemic heart disease: from diagnosis to treatment. Cham, Switzerland: Springer; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-25879-4\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-25879-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoccanelli A, Scardovi AB (2023) Sudden death in ischemic heart disease: looking for new predictors: polygenic risk. Eur Heart J Supplements 25(SupplementB):B31\u0026ndash;B3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckert J, Schmidt M, Magedanz A et al (2015) Coronary CT angiography in managing atherosclerosis. Int J Mol Sci 16(2):3740\u0026ndash;3756\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoros S, Rinehart S, Qian Z et al (2011) Coronary Atherosclerosis Imaging by Coronary CT Angiography: Current Status, Correlation With Intravascular Interrogation and Meta-Analysis. JACC: Cardiovasc Imaging 4(5):537\u0026ndash;548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasanović A, Sisić F, Dilberović F et al (2005) Collateral circulation in human heart. Bosn J Basic Med Sci 5(2):87\u0026ndash;91\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchelbert HR (2010) Anatomy and physiology of coronary blood flow. J Nucl Cardiol 17(4):545\u0026ndash;554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamanathan T, Skinner H (2005) Coronary blood flow. Continuing Educ Anaesth Crit Care Pain 5(2):61\u0026ndash;64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeusch G (2024) Myocardial ischemia/reperfusion: Translational pathophysiology of ischemic heart disease. Med 5(1):10\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaper W (2009) Collateral circulation. Basic Res Cardiol 104(1):5\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLibby P, Theroux P (2005) Pathophysiology of coronary artery disease. Circulation 111(25):3481\u0026ndash;3488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard MR, Pasterkamp G, Yeung AC et al (2000) Arterial remodeling. Mechanisms and clinical implications. Circulation 102(10):1186\u0026ndash;1191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeier P, Schirmer SH, Lansky AJ et al (2013) The collateral circulation of the heart. BMC Med 11:143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruickshank JM (1992) ; The role of coronary perfusion pressure. Eur Heart J 13 Suppl D :39\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonarjee VVS (2018) Arterial Stiffness: A Prognostic Marker in Coronary Heart Disease. Available Methods and Clinical Application. Front Cardiovasc Med 5:64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaut BK, Patil VN, Cherian G (2017) Coronary artery dimensions in normal Indians. Indian Heart J 69(4):512\u0026ndash;514\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohan A, Gopalakrishnan A, Chandran R et al (2023) Examining the Influence of Gender, Age, and Dominance on the Caliber of Normal Coronary Arteries in the South Indian Population. Cureus 15(12):e51146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonato AJ, Machin DR, Lesniewski LA (2018) Mechanisms of Dysfunction in the Aging Vasculature and Role in Age-Related Disease. Circ Res 123(7):825\u0026ndash;848\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZieman SJ, Melenovsky V, Kass DA (2005) Mechanisms, pathophysiology, and therapy of arterial stiffness. Arterioscler Thromb Vasc Biol 25(5):932\u0026ndash;943\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMintz GS, Popma JJ, Pichard AD et al (1996) Limitations of Angiography in the Assessment of Plaque Distribution in Coronary Artery Disease: A Systematic Study of Target Lesion Eccentricity in 1446 Lesions. Circulation 93(5):924\u0026ndash;931\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 angiography, coronary artery collateral, coronary artery disease, echocardiography, ischaemic heart disease","lastPublishedDoi":"10.21203/rs.3.rs-6825922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6825922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eCoronary artery collateral circulation is a novel factor in assessing ischemic heart disease (IHD). However, it requires specialised assessment and cannot be evaluated retrospectively. Theoretically, a ‘normalised’ coronary artery diameter would indicate the coronary artery collateral status. This study explores associations between coronary artery collaterals with left main and right coronary artery diameter, left ventricular mass, and a ratio of coronary artery diameters to left ventricular mass (CAA-LVM). We hypothesise an association between the status of coronary artery collaterals and the CAA-LVM ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A prospective study of 378 patients with suspected IHD to explore the association between the coronary artery collaterals and CAA-LVM ratio from angiograms and echocardiograms. Univariate and subsequent multivariable binary logistic regression were conducted to assess the associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study's findings showed coronary artery collaterals was significantly associated with left main coronary artery (adjusted odds ratio, AOR: 16.321, 95% CI: 4.316, 61.713, \u003cem\u003ep\u003c/em\u003e-value: \u0026lt;0.001), right coronary artery (AOR:6.056, 95% CI: 1.509, 24.305, \u003cem\u003ep\u003c/em\u003e-value: 0.01), and CAA-LVM ratio (AOR: 3.256, 95% CI: 1.305, 8.125, \u003cem\u003ep- value\u003c/em\u003e: 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study demonstrated that a larger CAA–LVM ratio was observed in patients with increased coronary collateral formation. This technique may serve as an adjunct to the currently available techniques in identifying collaterals and may also help assess collateral circulation amid limited resources.\u003c/p\u003e","manuscriptTitle":"Left Main and Right Coronary Artery Diameter and Left Ventricular Mass associated with coronary artery collaterals in Ischaemic heart disease: A Cardiovascular Imaging Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:46:51","doi":"10.21203/rs.3.rs-6825922/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":"c6dc68dc-6d44-4d11-8576-7c5786d999f7","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T13:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 10:46:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6825922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6825922","identity":"rs-6825922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}