D-SPECT-derived myocardial perfusion imaging and coronary blood flow reserve in the clinical diagnosis of hypertensive patients with INOCA

D-SPECT-derived myocardial perfusion imaging and coronary blood flow reserve in the clinical diagnosis of hypertensive patients with INOCA | 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 Article D-SPECT-derived myocardial perfusion imaging and coronary blood flow reserve in the clinical diagnosis of hypertensive patients with INOCA Xuelian Bai, Lixia Yang, Xuemei Wang, Wei Wei, Guodong Ge, Yuyan Chai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7272519/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Long-term hypertension patients may experience structural changes in the myocardium, microvascular dysfunction, and myocardial ischemia, leading to a decrease in MBF and CFR, which exacerbates clinical symptoms and increases the incidence of MACE. This study aims to evaluate the characteristics of MPI and CFR, as well as their influencing factors, in hypertensive patients with LVH by utilizing a combination of D-SPECT with CAG or CTA. Materials and Methods This study is a retrospective analysis that selected patients with angina who visited Gansu Provincial People's Hospital from April 2023 to September 2024 and underwent D-SPECT, CAG, or coronary CTA. According to the 2024 ESC Guidelines for Hypertension Management, patients were categorized into non-hypertensive and hypertensive groups. The general data, laboratory indicators, echocardiographic parameters, and D-SPECT-related metrics of both groups were compared.Furthermore, based on the parameters obtained from echocardiography, a subgroup analysis was conducted for patients in the hypertension group to compare differences in MPI, MBF, and CFR between those with and without LVH. This study has received approval from the Ethics Committee of Gansu Provincial People's Hospital, with approval number: 2024 − 781. Result The SSS, SDS, TPD(s), and Extent(s) in the hypertension group were significantly higher than those in the control group ( P < 0.05). Additionally, the sMBF and CFR of the LAD, LCX, and RCA coronary arteries were significantly lower in the hypertension group compared to the control group ( P < 0.05). Subgroup analysis of hypertension revealed that patients with LVH exhibited a significantly lower CFR in the LAD, LCX, and RCA coronary arteries compared to those without LVH ( P < 0.05). Notably, both LAD and LCX had a CFR below 2.5, with the lowest CFR observed specifically in the distribution area of the LAD. However, no significant statistical differences were found among subgroups regarding sMBF ( P > 0.05).Through multivariate linear regression analysis, it was determined that SBP and IVS(D) are significant risk factors contributing to the reduction of CFR. Specifically, higher values of SBP and IVS(D) correlate with a lower CFR in coronary vessels. Furthermore, both sMBF and CFR demonstrate high sensitivity and specificity in disease diagnosis. The optimal cutoff values for diagnosing diseases were established through the maximum Youden index derived from the ROC curve; specifically, the optimal cutoff value for sMBF is 2.18, while that for CFR is 2.71. Conclusion The sMBF and CFR derived from D-SPECT demonstrate high sensitivity and specificity in disease diagnosis, making them reliable indicators for assessing coronary microcirculation in patients with hypertension. Furthermore, SBP and IVS(D) are identified as the primary risk factors contributing to the decline of CFR. Therefore, actively managing blood pressure and preventing the onset and progression of LVH is a crucial strategy for mitigating CMD. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Dynamic Single Photon Emission Computed Tomography Hypertension Ischaemia with Non-obstructive Coronary Arteries Myocardial Perfusion Imaging Coronary Flow Reserve Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Hypertension affects over one billion adults worldwide and is a major driving factor for the incidence and mortality of cardiovascular diseases. In China, the prevalence of hypertension is notably high; however, the treatment coverage and control rates remain relatively low. This situation significantly contributes to the epidemic of Hypertensive Heart Disease (HHD). The pathological features of HHD include Left Ventricular Hypertrophy (LVH), diastolic dysfunction, and diffuse interstitial fibrosis in the myocardium. When hypertension coexists with LVH, there is an increase in myocardial oxygen consumption that cannot be met by coronary artery dilation, leading to a decrease in Myocardial Blood Flow (MBF) and Coronary Flow Reserve (CFR). The reduction in CFR serves as an important underlying mechanism for left ventricular diastolic dysfunction and the occurrence of angina pectoris. These factors interact with each other, further exacerbating coronary microvascular disease and ultimately resulting in the development of Coronary Microvascular Dysfunction (CMD) [ 1 , 2 ] . Recent studies have indicated that coronary microvascular disease is a primary cause of Major Adverse Cardiovascular Events (MACE) in patients with non-obstructive epicardial coronary artery disease [ 3 ] . Ischaemia with Non-Obstructive Coronary Arteries (INOCA) is a distinct type of coronary artery disease characterized by the presence of coronary artery stenosis less than 50%. Despite this, patients often experience angina symptoms and/or evidence of myocardial ischaemia. Based on the pathophysiological characteristics of INOCA, this condition can be classified into two subtypes: Vasospastic Angina (VSA) and Microvascular Angina (MVA) [ 4 – 6 ] . The clinical presentation of MVA is characterized by the following key features: ① Clinical symptoms indicative of myocardial ischemia;② Results from coronary computed tomography angiography (CTA) or CAG revealing that the degree of stenosis in the epicardial coronary arteries is less than 50%; ③ Additional relevant examinations confirming the presence of myocardial ischemia; ④ A definitive diagnosis of CMD [ 7 , 8 ] .Due to the inability to assess coronary microcirculation during coronary CTA or CAG examinations, patients with CMD are often misdiagnosed as having non-cardiac causes when their test results return negative. This can lead to missed diagnoses, incorrect diagnoses, and repeated medical visits, resulting in emotional distress and anxiety for patients, as well as increased financial burdens. Therefore, it is particularly important to accurately evaluate the function of coronary microcirculation in clinical practice [ 9 ] . Currently, the assessment techniques for CMD in clinical practice include: ① Transthoracic Doppler Echocardiography (TTDE); ② Cardiovascular Magnetic Resonance (CMR); ③ Fractional Flow Reserve (FFR); ④ Index of Microcirculatory Resistance (IMR); ⑤ Single Photon Emission Computed Tomography (SPECT); ⑥ Positron Emission Tomography (PET) [ 7 , 10 , 11 ] .The aforementioned diagnostic techniques possess certain inherent drawbacks, such as high costs, complex surgical procedures, invasive operations, and suboptimal temporal and spatial resolution, along with various adverse reactions. These factors contribute to the reluctance of some patients to accept these methods; consequently, their clinical application remains limited at this stage. Nowadays, with the rapid advancement of nuclear medicine technology, the new generation of Dynamic Single Photon Emission Computed Tomography (D-SPECT) is gradually gaining recognition. This system not only offers ease of operation and lower costs but also demonstrates an 8–9 fold increase in sensitivity compared to traditional SPECT, along with a twofold improvement in spatial resolution. It has been widely adopted globally. Building upon conventional Myocardial Perfusion Imaging (MPI), D-SPECT utilizes myocardial blood flow quantification software to accurately calculate resting Myocardial Blood Flow (rMBF), stress Myocardial Blood Flow (sMBF), and CFR. This capability plays a crucial role in early disease diagnosis, risk stratification, clinical treatment guidance, and assessment of therapeutic outcomes [ 12 – 14 ] . Numerous studies have confirmed that D-SPECT exhibits high consistency with PET measurements of MBF and CFR, thereby providing clinicians with enhanced image quality and prognostic information [ 15 – 18 ] . Therefore, the objective of this study is to evaluate the characteristics and influencing factors related to myocardial perfusion and coronary blood flow reserve in patients with hypertension complicated by INOCA through D-SPECT analysis. 2 Materials and Methods 2.1 Refinement of Patient Selection The study received approval from Gansu Provincial People's Hospital, and all patients provided informed consent. Approval number: 2024 − 781. This study is a retrospective analysis that selected patients with angina who visited Gansu Provincial People's Hospital from April 2023 to September 2024 and underwent both D-SPECT and either CAG or coronary CTA. We retrospectively collected the patients' general information, laboratory indicators, echocardiographic parameters, and relevant D-SPECT metrics. Based on predetermined inclusion and exclusion criteria, the specific exclusions were as follows: (1) 105 patients did not undergo coronary CTA or CAG; (2) 174 patients had undergone coronary stent implantation; (3) 58 patients were confirmed to have epicardial coronary artery stenosis ≥ 50%; (4) D-SPECT data was incomplete for 40 patients, while cardiac ultrasound data was missing for 4 patients, and laboratory indicators were incomplete for 2 patients; (5) 4 patients were diagnosed with severe arrhythmias; (6) 5 patients were diagnosed with cardiomyopathy; (7) 14 patients had congenital heart disease or severe valvular heart disease. All of these cases were excluded from the study(Fig. 1 ). Typical angina pectoris is characterized by the following features: ① A sensation of pressure, heaviness, or tightness behind the sternum or in the precordial area, which may radiate to the left shoulder, inner side of the left arm, ring finger and little finger, as well as potentially extending to the neck, throat, or jaw; ② It is usually triggered by physical exertion or emotional stress and can be relieved within five minutes through rest or sublingual administration of nitrates [ 16 ] . 2.2 Research Subjects and Grouping 2.3 Research Methodology 2.3.1 Inclusion Criteria : (1) Age > 18 years; (2) Presence of symptoms related to angina pectoris or ischemic changes on electrocardiogram; (3) All patients have undergone comprehensive D-SPECT examination during hospitalization; (4) Patients with coronary artery stenosis < 50% as indicated by CTA or CAG. 2.3.2 Exclusion Criteria : (1) Patients who have not undergone CTA or CAG examinations; (2) Coronary artery stenosis of the epicardial coronary arteries ≥ 50%; (3) History of coronary stent implantation or coronary artery bypass grafting; (4) Incomplete clinical data; (5) Severe arrhythmias; (6) Myocarditis or myocardial disease; (7) Congenital heart disease or severe valvular heart disease. 2.3.3 D-SPECT Management (1) Preparation for D-SPECT examination: ① The patient must fast for at least 3 hours prior to the stress test; ② Discontinue the use of xanthine medications, β-blockers, and nitrate drugs within 24 hours before the examination; ③ Avoid coffee, cola, tea, and smoking for at least 12 hours prior to the test; ④ Prepare a high-fat meal (such as fried eggs or full-fat cake, along with whole milk) [ 19 ] . (2) During the D-SPECT examination, the following observation indicators should be noted: ① Assessment of myocardial perfusion: Record the number of ischemic segments in both resting and stress states, total myocardial perfusion score, MBF, CFR, as well as the Extent of perfusion abnormalities(Extent) and total perfusion defect (TPD); ② Adverse drug reactions: Observe and document any adverse reactions experienced by the subject, including symptoms such as facial flushing, cyanosis, sweating, generalized itching, dyspnea, asthma attacks, arrhythmias, hypotension, and nausea [ 20 ] . (3) D-SPECT Image Processing and Analysis: ① The processing of D-SPECT images is conducted by two chief physicians in the Department of Nuclear Medicine using a double-blind method for image analysis and evaluation. Images are collected from short-axis, vertical long-axis, and horizontal long-axis views, with the perfusion areas being delineated according to a 17-segment model.②The evaluation criteria for myocardial imaging agents, as established by the American Heart Association (AHA), are based on a five-point distribution scale: 0 = normal, 1 = mild sparsity, 2 = moderate sparsity, 3 = severe sparsity, and 4 = defect.③The Summed Stress Score (SSS) represents the cumulative scoring of myocardial perfusion across various segments:A score ranging from 0 to 3 indicates a low risk, while a score between 4 and 8 suggests moderate risk,Scores from 9 to 13 are indicative of high risk, and scores between 14 and 38 signify extremely high risk.④TPD = Summed Score (SS) × 100 / Sum of the Worst Possible Scores for All Myocardial Segments. The normal value for TPD is < 5% [ 21 – 23 ] . (4) D-SPECT Reference Ranges and Critical Values: ① The literature reports that the reference range for rMBF is between 0.5 and 2.0 mL·min⁻¹·g⁻¹, while the reference range for sMBF is between 1.5 and 5.0 mL·min⁻¹·g⁻¹;②Regarding the critical value of CFR, there is currently no unified standard in China. This study defines a CFR threshold of less than 2.5 as the diagnostic criterion for coronary microvascular dysfunction [ 23 , 24 ] . 2.3.4 Statistical Analysis The data was processed using the SPSS 26.0 statistical software.When the measurement data conforms to a normal distribution, it is expressed as mean ± standard deviation ( x ± s ),in cases where the data does not conform to a normal distribution, it is represented by the median (interquartile range, IQR).In intergroup comparisons, data that conform to a normal distribution should be analyzed using the t -test, while data that do not meet the criteria for normality should be subjected to non-parametric tests.The count data is expressed using rates or composition ratios, and inter-group comparisons are conducted using the χ 2 test.Correlation Analysis: For normally distributed data, Pearson correlation test is employed, while for non-normally distributed data, Spearman correlation test is utilized.The causal relationship between independent and dependent variables is analyzed through a multiple linear regression model.The Receiver Operating Characteristic Curve (ROC curve) is utilized to evaluate the sensitivity and specificity of diagnostic methods for diseases. By analyzing the ROC curve, we can calculate the Youden Index, which identifies the optimal threshold point corresponding to this index, thereby determining the best cutoff value for the diagnostic approach. P < 0.05 is considered to be statistically significant. 3 Result 3.1 General Data Comparison 3.1.1 Comparison of General Data Between the Hypertension Group and the Control Group According to the inclusion and exclusion criteria, this study ultimately included 259 patients with INOCA, who were divided into a non-hypertensive group (134 cases) and a hypertensive group (125 cases). The general clinical data of patients in each group are presented in Table 1 . The results indicated that there were 73 males (54.5%) in the non-hypertensive group, with an average age of 55.15 ± 10.26 years; whereas in the hypertensive group, there were 64 males (51.2%), with an average age of 60.18 ± 9.09 years. The hypertensive group exhibited higher values for age, SBP, and TT4 compared to the non-hypertensive group, while TG and TT3 levels were lower in the hypertensive group ( P < 0.05). However, no statistically significant differences were observed between the two groups regarding gender, smoking history, BMI, DBP, BUN, Cr, UA, HbA1c, TC, LDL-C, HDL-C as well as Hey and TSH levels ( P > 0.05). Table 1 Comparison of General Information Parameters Non-hypertensive group(n = 134) Hypertensive group(n = 125) Z/t/χ 2 -Value P- Value Age 55.15 ± 10.26 60.18 ± 9.09 −4.17 < 0.05 Males 73(54.50%) 64(51.20%) 0.28 0.60 Smoking 28(20.90%) 20(16%) 1.03 0.31 BMI,kg/m 2 24.77 ± 3.11 25.16 ± 3.13 −1 0.32 SBP,mmHg 120.18 ± 11.40 136.09 ± 16.24 −9.06 < 0.05 DBP,mmHg 77.18 ± 9.61 85.72 ± 11.48 −0.76 0.45 BUN,mmol/L 5.77 ± 1.55 5.87 ± 1.73 −0.50 0.62 Cr,umol/L 63.39 ± 17.44 64.11 ± 19.70 −0.31 0.76 UA,umol/L 337.71 ± 88.04 325.32 ± 89.34 1.12 0.26 HbA1c,% 5.70(5.50, 6) 5.80(5.50, 6.05) −0.83 0.40 TC,mmol/L 4.34 ± 0.98 4.11 ± 1.02 −1.88 0.06 TG,mmol/L 1.57(1.07, 2.41) 1.32(1.04, 1.84) −2.04 < 0.05 LDL-C,mmol/L 2.28 ± 0.67 2.17 ± 0.75 −1.16 0.25 HDL-C,mmol/L 1.00 ± 0.20 1.55 ± 6.26 −1.02 0.31 Hey,umol/L 14.87(11.37, 20.06) 15.01(12.55, 20.70) −0.62 0.53 TSH,mIU/L 1.78(1.19, 2.98) 1.76(1.12, 2.79) −0.36 0.72 TT3,nmol/L 1.58(1.40, 1.71) 1.49(1.33, 1.65) −2.53 < 0.05 TT4,nmol/L 96.18(86.39, 105.12) 100.47(87.30, 111.15) −2.02 < 0.05 Note : BMI:Body Mass Index;SBP༚Systolic Blood Pressure༛DBP༚Diastolic Blood Pressure༛BUN༚Blood Urea Nitrogen༛Cr༚Creatinine༛UA༚Uric Acid༛HbA1c༚Hemoglobin A1c༛TC༚Total Cholesterol༛TG༚Triglyceride༛LDL-C༚Low-Density Lipoprotein Cholesterol༛HDL-C༚High-Density Lipoprotein Cholesterol༛Hey༚Homocysteine༛TSH༚Thyroid Stimulating Hormone༛TT3༚Total Triiodothyronine༛TT4༚Total Thyroxine. 3.1.2 Comparison of General Data between the LVH Group and the Control Group This study categorized 125 hypertensive patients into two groups based on echocardiographic parameters: the non-LVH group (71 cases) and the LVH group (54 cases). A comparative analysis was conducted on the general clinical data of patients in each subgroup (see Table 2 ). The results indicated that the age, BMI, Cr, UA, and TG levels in the LVH group were significantly higher than those in the non-LVH group ( P 0.05). Table 2 Comparison of General Data Among Subgroups Parameters Non-LVH group(n = 71) LVH group(n = 54) Z/t/χ2 -Value P-Value Age 58.69 ± 8.78 62.15 ± 9.21 −2.14 < 0.05 Males 31(43.70%) 33(61.10%) 3.74 0.05 Smoking 14(19.70%) 6(11.10%) 1.68 0.19 BMI,kg/m 2 24.25 ± 3.17 26.36 ± 2.64 −3.96 < 0.05 SBP,mmHg 134.85 ± 14.29 137.72 ± 18.52 −0.98 0.33 DBP,mmHg 85.21 ± 11.40 86.39 ± 11.66 −0.57 0.57 BUN,mmol/L 5.74 ± 1.54 6.04 ± 1.94 −0.97 0.33 Cr,umol/L 59.50 ± 11.35 70.17 ± 25.93 −3.10 < 0.05 UA,umol/L 298.69 ± 77.56 360.34 ± 92.34 −4.05 < 0.05 HbA1c,% 5.70(5.50, 6.10) 5.80(5.50, 6.03) −1.28 0.20 TC,mmol/L 4.21 ± 1.03 3.97 ± 1.00 1.34 0.18 TG,mmol/L 1.23(1.03, 1.57) 1.48(1.12, 2.36) −2.38 < 0.05 LDL-C,mmol/L 2.23 ± 0.77 2.10 ± 0.71 0.95 0.35 HDL-C,mmol/L 1.03 ± 0.22 2.23 ± 9.53 −1.06 0.29 Hey,umol/L 15.00(12.51, 18.67) 15.30(12.93, 22.39) −0.96 0.34 TSH,mIU/L 1.75(1.20, 2.69) 1.78(1.08, 3.17) −0.08 0.94 TT3,nmol/L 1.49(1.30, 1.68) 1.49(1.36, 1.60) −0.22 0.82 TT4,nmol/L 97.46(85.04, 110.92) 104.21(95.11, 111.61) −1.39 1.64 3.2 Comparison of Echocardiographic Parameters 3.2.1 Comparison of Parameters Between the Hypertension Group and the Control Group The comparison of echocardiographic parameters between the two patient groups is presented in Table 3 . The results indicate that the median (IQR) values for IVS(D) in the non-hypertensive group and hypertensive group were 9 (8, 10) and 10 (9, 11), respectively. Similarly, the median (IQR) values for LVPW(D) were 9 (9, 10) in the non-hypertensive group and 10 (9, 11) in the hypertensive group. Notably, both IVS(D) and LVPW(D) measurements were significantly higher in the hypertensive group compared to the non-hypertensive group ( P < 0.05). Furthermore, analysis of E/A ratios revealed that patients in the hypertensive group exhibited a greater propensity for impaired cardiac diastolic function ( P 0.05). Table 3 Comparison of Echocardiographic Parameters Parameters Non-hypertensive group(n = 134) Hypertensive group(n = 125) Z/t- Value P-Value IVS(D),mm 9(8, 10) 10(9, 11) −5.59 < 0.05 LVPW(D),mm 9(9, 10) 10(9, 11) −6.32 < 0.05 EDV,ml 101.93 ± 21.09 104.61 ± 22.82 −0.98 0.33 ESV,ml 35(30, 41) 36(30, 43) −0.98 0.33 LVEF,% 64.65 ± 4.92 64.34 ± 5.40 −0.48 0.63 FS,% 35.30 ± 3.71 35.13 ± 4.05 −0.33 0.74 E/A Anomaly 29(21.6%) 41(32.8%) 4.08 < 0.05 Note : IVS(D):Interventricular septal thickness at end-diastole;LVPW(D):Left ventricular posterior wall thickness at end-diastole;EDV:End Diastolic Volume;ESV༚End Systolic Volume༛LVEF༚Left Ventricular Ejection Fraction༛FS༚Fractional Shortening. 3.2.2 Comparison of Parameters Between the LVH Group and the Control Group Table 4 presents a comparison of echocardiographic parameters between the hypertension subgroup. The results indicate that patients in the LVH group exhibited significantly higher values for IVS(D), LVPW(D), and EDV compared to those in the non-LVH group ( P 0.05). Table 4 Comparison of Echocardiographic Parameters Among Subgroups Parameters Non-LVH group(n = 71) LVH group(n = 54) Z/t- Value P- Value IVS(D),mm 9(9, 10) 11(11, 12) −9.77 < 0.05 LVPW(D),mm 10(9, 10) 11(10, 11) −7.17 < 0.05 EDV,ml 101.04 ± 20.59 109.31 ± 24.88 −2.73 < 0.05 ESV,ml 34(30, 39) 38.50(32.75, 47.50) −0.98 0.33 LVEF,% 65.43 ± 5.02 62.91 ± 5.60 2.65 < 0.05 FS,% 35.90 ± 3.83 34.08 ± 4.17 2.53 < 0.05 E/A Anomaly 19(26.8%) 22(40.7%) 2.72 0.10 3.3 D-SPECT MPI Parameter Comparison 3.3.1 Comparison of MPI Parameters Between the Hypertension Group and the Control Group Table 5 presents the specific data of D-SPECT MPI for two groups of patients. The results indicate that, compared to the non-hypertensive group, the hypertensive group exhibited significantly higher values in SSS, SDS, TPD(s), and Extent(s) ( P 0.05). Table 5 Comparative Analysis of D-SPECT MPI Parameters Parameters Non-hypertensive group(n = 134) Hypertensive group(n = 125) Z- Value P- Value SSS 1(0, 1) 1(0, 3) −3.61 < 0.05 SRS 0(0, 0) 0(0, 0) −0.75 0.45 SDS TPD(r) Extent(r) TPD(s) Extent(s) 0(0, 1) 0(0, 0) 0(0, 0) 1(1, 2) 1(0, 2) 1(0, 2) 0(0, 0) 0(0, 0) 2(1, 4) 1(0, 4) −3.57 −1.42 −0.35 −2.81 −3.15 < 0.05 0.16 0.73 < 0.05 < 0.05 Note : SSS：Summed Stress Score；SRS：Summed Rest Score；SDS：Summed Difference Score(SDS=SSS-SRS)；TPD:Total Perfusion Defect；Extent：Extent of perfusion abnormality. 3.3.2 Comparison of MPI Parameters Between the LVH Group and the Control Group According to the subgroup analysis based on echocardiographic parameters, patients in the hypertension group were evaluated. As shown in Table 6 , there was no significant statistical difference in the MPI between the non-LVH group and the LVH group ( P > 0.05). Table 6 Comparison of D-SPECT MPI Parameters Among Subgroups Parameters Non-LVH group(n = 71) LVH group(n = 54) Z- Value P- Value SSS 1(0, 3) 1(0, 3) −1.10 0.27 SRS 0(0, 0) 0(0, 1) −1.25 0.21 SDS TPD(r) Extent(r) TPD(s) Extent(s) 1(0, 2) 0(0, 0) 0(0, 0) 1(1, 3) 1(0, 3) 1(0, 3) 0(0, 0) 0(0, 0) 2(1, 4) 2(0, 4) −0.64 −0.24 −1.15 −0.88 −0.73 0.52 0.81 0.25 0.38 0.46 3.4 D-SPECT MBF and CFR Parameter Comparison 3.4.1Comparison of MBF and CFR Parameters Between the Hypertension Group and the Control Group In a study involving 259 patients, a total of 222 individuals underwent assessments of resting myocardial blood flow (rMBF), stress myocardial blood flow (sMBF), and coronary flow reserve (CFR) in the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). Among these participants, there were 111 patients in the hypertensive group and 111 patients in the non-hypertensive group. The conclusions drawn from Table 7 are as follows: (1) The sMBF and CFR of the LAD, LCX, and RCA coronary arteries in the hypertensive group were significantly lower than those in the non-hypertensive group ( P < 0.05). Among these vessels, the CFR of the LAD was found to be the lowest compared to that of the LCX and RCA. (2) There was no significant statistical difference in rMBF between the two groups for both LAD and LCX coronary arteries ( P > 0.05). Although a statistical difference was observed in rMBF for RCA between groups ( P < 0.05), both groups exhibited rMBF levels above normal values. A box plot has been created to visually illustrate the relationships among rMBF, sMBF, and CFR for LAD, LCX, and RCA coronary arteries between the two patient groups (Fig. 2 ). Table 7 Comparison of D-SPECT MBF and CFR Parameters Vascellum Parameters Non-hypertensive group(n = 111) Hypertensive group(n = 111) t- Value P- Value rMBF 0.83 ± 0.17 0.83 ± 0.24 −0.01 0.99 LAD sMBF 2.94 ± 0.65 1.96 ± 0.62 11.32 < 0.05 LCX CFR rMBF sMBF CFR 3.63 ± 0.82 0.73 ± 0.18 2.89 ± 0.75 4.12 ± 0.94 2.44 ± 0.50 0.69 ± 0.21 1.69 ± 0.50 2.59 ± 0.68 13.16 1.40 14.03 13.88 < 0.05 0.17 < 0.05 < 0.05 rMBF 0.68 ± 0.14 0.58 ± 0.21 4.30 < 0.05 RCA sMBF 2.86 ± 0.99 1.52 ± 0.52 12.45 < 0.05 CFR 4.17 ± 0.94 2.83 ± 0.86 11.06 < 0.05 Note：rMBF：rest Myocardial Blood Flow；sMBF：stress Myocardial Blood Flow；CFR：Coronary Flow Reserve. 3.4.2 Comparison of MBF and CFR Parameters Between the LVH Group and the Control Group The analysis of the above data indicates that the IVS(D) and LVPW(D) measurements in the hypertensive group are significantly higher than those in the non-hypertensive group, while sMBF and CFR values are markedly lower in the hypertensive group compared to their non-hypertensive counterparts. To clarify whether LVH is a contributing factor to the reduction in sMBF and CFR, we conducted a comparative analysis of rMBF, sMBF, and CFR across the LAD, LCX, and RCA coronary arteries among patients within the hypertensive subgroup. A total of 125 patients from the hypertensive group were categorized into two subgroups: non-LVH group and LVH group. In the non-LVH subgroup comprising 71 patients, 67 underwent assessments for rMBF, sMBF, and CFR across their LAD, LCX, and RCA coronary arteries. Meanwhile, among the LVH subgroup consisting of 54 patients, 49 also received evaluations for these parameters. Detailed analytical results can be found in Table 8 . The results indicate: (1) There are significant differences in the rMBF of the LAD, LCX, and RCA coronary arteries between the two patient groups ( P < 0.05).however, the rMBF values for both groups exceed 0.5 mL·min⁻¹·g⁻¹, which is above normal levels. (2) The CFR of the LAD, LCX, and RCA coronary arteries in the LVH group is significantly lower than that in the non-LVH group ( P < 0.05), with both LAD and LCX exhibiting a CFR less than 2.5;notably, the CFR in the distribution area of LAD is at its lowest. (3) Although there are significant differences in sMBF between the hypertensive group and control group, subgroup analysis reveals that these differences are not statistically significant ( P > 0.05), with all indicators falling within normal ranges. Box plots illustrating rMBF, sMBF, and CFR among LAD, LCX, and RCA coronary arteries for patients in the hypertensive subgroup can be found in Fig. 3 . Table 8 Comparison of D-SPECT MBF and CFR Parameters Among Subgroups Vascellum Parameters Non-LVH group(n = 67) LVH group(n = 49) t- Value P- Value rMBF 0.76 ± 0.22 0.91 ± 0.23 −3.57 < 0.05 LAD sMBF 1.92 ± 0.63 2.03 ± 0.59 −1.04 0.30 LCX CFR rMBF sMBF CFR 2.60 ± 0.53 0.62 ± 0.17 1.64 ± 0.43 2.78 ± 0.70 2.24 ± 0.41 0.77 ± 0.23 1.74 ± 0.55 2.36 ± 0.59 3.94 −3.96 −1.14 3.45 < 0.05 < 0.05 0.26 < 0.05 rMBF 0.52 ± 0.16 0.64 ± 0.25 −3.02 < 0.05 RCA sMBF 1.47 ± 0.43 1.58 ± 0.61 −1.09 0.31 CFR 2.99 ± 0.85 2.66 ± 0.86 2.03 < 0.05 3.5 Pearson Correlation Analysis In order to investigate the associations between age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, E/A values and SSS, SDS, TPD(S), Extent(S) as well as rMBF, sMBF and CFR of the LAD, LCX and RCA coronary arteries, a Pearson correlation analysis was conducted on the aforementioned data. 3.5.1 Correlation Analysis of MPI The Sperman correlation analysis of MPI is presented in Table 9 . The results indicate that while there are statistically significant associations ( P < 0.05) between TT4, LVEF, FS and SSS, TPD(s), Extent(s), as well as between EDV and SSS, SDS, TPD(s), Extent(s), and the E/A ratio with SSS and SDS, the correlation coefficients (r) are all less than 30%. This suggests that although these variables exhibit associative relationships, they do not demonstrate significant correlations. Table 9 Sperman Correlation Analysis of MPI Parameters SSS SDS TPD(s) Extent(s) r- Value P-Value r- Value P- Value r- Value P- Value r- Value P- Value Age −0.06 0.37 −0.04 0.51 −0.08 0.19 −0.10 0.11 BMI,kg/m 2 0.07 0.30 0.05 0.45 0.07 0.23 0.06 0.38 SBP,mmHg 0.09 0.15 0.07 0.26 0.11 0.09 0.10 0.12 Cr,umol/L 0.01 0.82 0.05 0.42 −0.01 0.91 −0.02 0.77 UA,umol/L 0.00 0.95 0.00 0.98 −0.03 0.69 −0.03 0.69 TG,mmol/L −0.08 0.20 −0.06 0.31 −0.08 0.21 −0.10 0.12 TT3,nmol/L −0.03 0.67 −0.04 0.56 −0.06 0.32 −0.08 0.20 TT4,nmol/L 0.15 < 0.05 0.04 0.50 0.14 < 0.05 0.13 < 0.05 IVS(D),mm 0.12 0.06 0.10 0.13 0.10 0.12 0.10 0.12 LVPW(D),mm 0.04 0.55 0.03 0.69 0.04 0.51 0.06 0.32 EDV,ml 0.21 < 0.05 0.13 < 0.05 0.21 < 0.05 0.21 < 0.05 LVEF,% −0.18 < 0.05 −0.06 0.33 −0.19 < 0.05 −0.18 < 0.05 FS,% −0.13 < 0.05 −0.04 0.52 −0.13 < 0.05 −0.13 < 0.05 E/A Ratio 0.14 < 0.05 0.22 < 0.05 0.11 0.08 0.12 0.06 3.5.2 Pearson Correlation Analysis of MBF and CFR Table 10 presents the Pearson correlation analysis of rMBF, sMBF, and CFR. The detailed results are analyzed as follows: (1) There exists a statistical association between age and the sMBF and CFR of coronary arteries ( P < 0.05). However, the correlation coefficient r is less than 30%, indicating that while an association is present, it does not reach a level of significant correlation. (2) There exists a statistically significant association between SBP and sMBF, as well as CFR in coronary arteries ( P < 0.05). The correlation coefficient for sMBF is -0.35 ( P < 0.05), while that for CFR is -0.36 ( P < 0.05). With 30% ≤ r < 50%, this indicates a notable weak correlation between SBP and both sMBF and CFR. It can be concluded that SBP has a significant negative impact on sMBF and CFR; specifically, higher SBP levels are associated with lower values of sMBF and CFR in the coronary arteries. (3) There exists a statistical association between IVS(D) and the coronary artery's sMBF and CFR ( P < 0.05). Notably, the correlation coefficient r between IVS(D) and CFR is -0.42, which exceeds 30%, indicating that IVS(D) significantly negatively impacts the CFR of coronary arteries; specifically, as IVS(D) increases, CFR decreases. However, the correlation coefficients r between IVS(D) and sMBF are all less than 30%. Although an association is present, it does not reach statistical significance. (4) There exists a statistical association between LVPW(D) and the sMBF and CFR of coronary arteries ( P < 0.05). However, given that the correlation coefficient r is less than 0.30, this indicates that while there is an associative relationship, it does not reach statistical significance. (5) There exists a statistically significant association between EDV and rMBF of the coronary arteries ( P < 0.05). However, the correlation coefficients (r) are all less than 0.30, indicating that while there is an associative relationship between them, it lacks substantial significance. Table 10 Pearson Correlation Analysis between MBF and CFR Parameters rMBF sMBF CFR r- Value P- Value r- Value P- Value r- Value P- Value Age −0.03 0.70 −0.22 < 0.05 −0.23 < 0.05 BMI,kg/m 2 0.03 0.71 −0.02 0.72 −0.03 0.62 SBP,mmHg −0.04 0.54 −0.35 < 0.05 −0.36 < 0.05 Cr,umol/L 0.04 0.57 −0.00 0.97 −0.07 0.31 UA,umol/L −0.01 0.92 0.03 0.62 0.02 0.74 TG,mmol/L −0.01 0.91 0.09 0.20 0.09 0.17 TT3,nmol/L 0.06 0.37 0.10 0.15 0.04 0.55 TT4,nmol/L 0.09 0.19 −0.04 0.52 −0.11 0.12 IVS(D),mm 0.13 0.05 −0.28 < 0.05 −0.42 < 0.05 LVPW(D),mm 0.02 0.75 −0.17 < 0.05 −0.20 < 0.05 EDV,ml 0.21 < 0.05 0.12 0.07 −0.04 0.53 LVEF,% 0.04 0.57 0.06 0.37 0.04 0.61 FS,% 0.04 0.55 0.06 0.36 0.03 0.68 E/A Ratio −0.01 0.84 −0.13 0.06 −0.13 0.06 3.6 Multiple Linear Regression Analysis To clarify the causal relationship between independent and dependent variables, a multiple linear regression analysis was conducted on two sets of data. The independent variables included age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, and E/A ratio. The dependent variables comprised SSS, SDS, TPD(S), Extent(S), as well as rMBF, sMBF, and CFR of coronary blood vessels. Statistical analysis revealed that only CFR demonstrated a good model fit with the respective independent variables. Table 11 :Before controlling for the variables, age, SBP,IVS(D) and LVPW(D) of the above independent variables can significantly affect the dependent variable, and after controlling for each of the test variables SBP,IVS(D) can still significantly affect the dependent variable. Table 11 Correlation analysis with CFR after adjusting for confounders Unadjusted Adjusted COR;95%CI P- Value AOR;95%CI P- Value Age −3.471 < 0.05 −1.673 0.096 SBP,mmHg −5.698 < 0.05 −3.726 < 0.05 IVS(D),mm −6.770 < 0.05 −4.313 < 0.05 LVPW(D),mm −2.947 < 0.05 0.654 0.514 Table 12 shows the data analysis of the causal relationship between the respective variables and CFR after controlling for the variables,The following is an interpretation of the results obtained from the table. The F -value is 9.41, with a P -value less than 0.05, indicating that the linear regression model is significant. This suggests that at least one of the fourteen independent variables has a statistically significant effect on the dependent variable. The specific impacts are as follows: (1) SBP: SBP has a significant effect on CFR ( P < 0.05), with a coefficient of -0.01, indicating that SBP exerts a notable negative influence on the dependent variable; specifically, for every increase of 1 mmHg in SBP, CFR decreases by 0.01. (2) IVS(D): IVS(D) also demonstrates a significant impact on CFR ( P < 0.05), with a coefficient of -0.23, signifying that IVS(D) significantly negatively affects the dependent variable; thus, for each increment of 1 mm in IVS(D), CFR declines by 0.23. Table 12 Multiple Linear Regression Analysis Unstandardized Coefficients Standardized Coefficient t-Value Significance VIF B Standard Error Beta (Constant) 6.95 1.65 4.22 < 0.05 Age −0.01 0.01 −0.10 −1.47 0.14 1.25 BMI,kg/m 2 0.01 0.02 0.03 0.46 0.65 1.19 SBP,mmHg −0.01 0.00 −0.23 −3.57 < 0.05 1.19 Cr,umol/L −0.00 0.00 −0.05 −0.66 0.51 1.49 UA,umol/L 0.00 0.00 0.04 0.52 0.61 1.65 TG,mmol/L 0.04 0.04 0.08 1.19 0.24 1.18 TT3,nmol/L 0.09 0.22 0.03 0.38 0.70 1.34 TT4,nmol/L −0.00 0.00 −0.08 −1.20 0.23 1.39 IVS(D),mm −0.23 0.05 −0.33 −4.29 < 0.05 1.70 LVPW(D),mm 0.04 0.05 0.05 0.68 0.50 1.48 EDV,ml 0.00 0.00 0.04 0.55 0.58 1.28 LVEF,% 0.02 0.06 0.14 0.41 0.69 33.43 FS,% −0.04 0.08 −0.15 −0.44 0.66 32.59 E/A值 −0.18 0.12 −0.09 −1.39 0.17 1.12 R Square 0.30 F 5.31 P <0.05 Dependent Variable: CFR 3.7 ROC Curve and Optimal Cut-off Values for D-SPECT Related Parameter 3.7.1 ROC Curves of MPI, MBF, and CFR The area under the ROC curve (AUC) for various diagnostic variables, including SSS, SDS, TPD(s), Extent(s), and the three vascular parameters rMBF, sMBF, and CFR, was plotted to evaluate their sensitivity and specificity in diagnosing diseases. Additionally, the optimal cutoff values for rMBF, sMBF, and CFR were determined from the ROC curves using the maximum Youden index. The following is an interpretation of the AUC corresponding to each variable's ROC curve.The analysis of Fig. 4 reveals that: The areas under the ROC curves (AUC) for SSS, SDS, TPD(s), and Extent(s) were 0.62, 0.62, 0.59, and 0.60 respectively. Although P < 0.05 indicates statistical significance, the relatively low sensitivity and specificity of these variables limit their diagnostic value for the disease. The AUC values for sMBF and CFR in the LAD vessels were 0.85 and 0.90, respectively ( P 0.05), suggesting that rMBF holds no diagnostic value for the condition. The AUC values for sMBF and CFR in the LCX vessels were 0.91 and 0.90, respectively ( P 0.05), suggesting that rMBF holds no diagnostic value for the condition. (4)The area under the curve (AUC) for sMBF and CFR in RCA vessels were 0.90 and 0.85, respectively (P < 0.05), indicating that both sMBF and CFR demonstrate high accuracy in disease diagnosis. In contrast, the AUC for rMBF was 0.68; although P < 0.05, the sensitivity and specificity of rMBF are relatively low, rendering its diagnostic value limited in this context. In summary, the sMBF and CFR of the three major coronary arteries demonstrate high sensitivity and specificity for diagnosing hypertension combined with INOCA. Therefore, both sMBF and CFR hold significant diagnostic value for this condition. In contrast, the SSS, SDS, TPD(s), Extent(s), and rMBF of the three vessels exhibit lower sensitivity and specificity, rendering them inadequate as standalone effective indicators for disease diagnosis. 3.7.2 The optimal cutoff values for MBF and CFR Table 13 : The optimal cutoff values for diagnosing the disease were determined from the ROC curve based on the maximum Youden index corresponding to rMBF, sMBF, and CFR of the LAD, LCX, and RCA coronary arteries. (1)The maximum Youden index for sMBF is 0.694, with an optimal cutoff value of 2.18. The sensitivity at this threshold is 0.81, and the specificity is 0.88. This indicates that using a cutoff point of 2.18 for disease diagnosis yields high sensitivity and specificity, thereby demonstrating significant value in the diagnostic process for the disease. (2)The maximum Youden index for CFR is 0.71, with an optimal cutoff value of 2.71. The sensitivity is 0.93 and the specificity is 0.78, indicating that using a cutoff point of 2.71 for disease diagnosis not only achieves high sensitivity and specificity but also demonstrates superior diagnostic value compared to sMBF. (3)The maximum Youden index for rMBF is 0.39, with an optimal cutoff value of 0.52. The sensitivity is 0.93, while the specificity stands at 0.46. Although the sensitivity is relatively high, the low specificity indicates that using a cutoff point of 0.52 for disease diagnosis may not be clinically significant. Table 13 Analysis of MBF and CFR in Disease Prediction Parameters Yoden Index Optimal Cut-off Value Sensitivity Specificity rMBF 0.39 0.52 0.93 0.46 sMBF 0.694 2.18 0.81 0.88 CFR 0.71 2.71 0.93 0.78 4 Conclusion The sMBF and CFR derived from D-SPECT represent a novel non-invasive diagnostic tool that holds significant value for the early diagnosis of patients with hypertension complicated by INOCA. Decreased CFR in hypertensive patients was significantly correlated with increased SBP and IVS(D), and the greater the SBP and IVS(D), the smaller the CFR in the coronary vasculature. sMBF and CFR demonstrated high sensitivity and specificity in the diagnosis with hypertension complicated by INOCA. The optimal cut-off values for rMBF, sMBF, and CFR were established from the ROC curves using the maximum Youden's index, with an optimal cut-off value of 2.18 for sMBF and 2.71 for CFR. 5 Discussion Previous studies have indicated that age is a significant risk factor for the development of hypertension. As individuals age, the prevalence of hypertension increases annually. This rise can primarily be attributed to the gradual onset of atherosclerosis in blood vessels, diminished endothelial function, and reduced vascular relaxation capacity, coupled with enhanced contraction ability, all contributing to elevated blood pressure in patients [ 25 ] . In our current study, we found that the average age of patients in the hypertensive group was significantly higher than that of those in the non-hypertensive group. Subgroup analyses yielded similar conclusions; specifically, patients with LVH were older on average. Furthermore, subgroup analysis revealed that the BMI of patients in the LVH group was significantly greater than that of non-LVH group. Hypertensive patients are prone to coronary microcirculatory dysfunction due to atherosclerosis of the coronary arteries and endothelial cell dysfunction. Additionally, as the left ventricular wall gradually thickens, the myocardial cells' demand for oxygen and nutrients increases. Concurrently, collagen accumulates in the vascular interstitial space, exerting pressure on the arteries, which leads to a reduction in CFR and subendocardial ischemia [ 26 ] .Zhang et al. [ 24 ] previously utilized Cadmium Zinc Telluride (CZT) SPECT to assess the prognostic value in patients with INOCA. The study revealed that the sMBF and CFR were significantly reduced in the MACE group, along with poorer left ventricular function parameters, including resting and stress LVEF, SSS, TPD, and Extent compared to other groups.Compared to previous studies, this research yielded similar results. By comparing the D-SPECT parameters of two patient groups, it was found that the SSS, SDS, TPD(s), and Extent(s) in the hypertensive group were significantly higher than those in the non-hypertensive group. Additionally, sMBF and CFR of the LAD, LCX, and RCA coronary arteries were also significantly reduced. Furthermore, subgroup analysis for hypertension revealed that patients with LVH exhibited a significant decrease in CFR for LAD, LCX, and RCA compared to the control group; notably, CFR values for both LAD and LCX fell below 2.5, with particularly low values observed in the LAD distribution area. The conclusions drawn from this study align with the myocardial territory supplied by coronary arteries. They indicate that hypertension and hypertension combined with LVH adversely affect coronary microcirculation leading to reductions in MBF and CFR—especially within regions supplied by LAD and LCX. Previous research has indicated that sMBF derived from D-SPECT is not influenced by rMBF,rather it directly reflects microvascular functional status—a distinction that sets it apart from CFR [ 27 ] . To clarify the causal relationships between age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, E/A values and MPI, rMBF, sMBF, CFR, we conducted correlation analysis and multiple linear regression analysis on the aforementioned data. The statistical analysis revealed that only CFR exhibited a good model fit with the independent variables. Notably, SBP and IVS(D) emerged as significant risk factors contributing to the decline in CFR. As both SBP and IVS(D) increase in value, there is a corresponding decrease in CFR of the coronary vessels. Therefore, actively managing blood pressure and preventing the onset and progression of LVH are crucial strategies for mitigating CMD. By determining the optimal cutoff values for MBF and CFR, we can effectively predict the clinical risk in patients with cardiovascular diseases. Traditionally, PET/CT has utilized a CFR threshold of less than 2 as the optimal cutoff value; however, there is currently no unified standard for CFR thresholds within domestic practices [ 28 ] . Zhang et al. [ 24 ] indicates that the threshold for sMBF is 3.16 mL·min⁻¹·g⁻¹ and that for CFR is 2.52, which allows effective risk stratification in patients with Ischemia with INOCA. Bom et al. [ 29 ] employed [ 15 O]H 2 O PET to assess risk prediction in 648 patients suspected of or diagnosed with Coronary Artery Disease (CAD). Their findings revealed that a hyperemic myocardial blood flow (hMBF) of less than 2.65 mL·min⁻¹·g⁻¹ and a CFR of less than 2.88 serve as optimal cutoff values for predicting MACE. Similarly, Farhad et al. [ 30 ] demonstrated that an sMBF range of 1.8–2.6 mL·min⁻¹·g⁻¹ and a CFR range of 1.8–2.4 are useful for risk stratification in cardiovascular disease patients. In this study, we extracted the optimal diagnostic cutoff values for rMBF, sMBF, and CFR from ROC curves using the maximum Youden index method; our results indicate that the best cutoff value for sMBF is 2.18 mL·min⁻¹·g⁻¹ while that for CFR is 2.71. In summary, both sMBF and CFR demonstrate high sensitivity and specificity in diagnosing diseases, serving as crucial reference indicators for assessing coronary artery function. They provide significant clinical value in early diagnosis, risk stratification, and prognosis evaluation—playing an essential role in diagnosing and assessing coronary heart disease as well as other cardiovascular conditions. Previous studies have shown that subtle variations in thyroid hormone levels within the normal range, particularly the ratio of free thyroxine (FT4) to free triiodothyronine (FT3), can modulate the cardiovascular system. In research conducted by Zhang et al. [ 31 ] it was found that an elevated FT4/FT3 ratio is associated with the occurrence of CMD in patients with normal thyroid function and ischemia with INOCA. This finding provides a novel biomarker for improving risk stratification. In this study, although significant differences were observed in TT3 and TT4 levels between the two patient groups, subsequent Pearson correlation analysis and multiple linear regression analysis did not reveal any causal relationship between these hormones and MPI, MBF, or CFR. The lack of association may be attributed to the small sample size inherent in retrospective studies; moreover, most clinical patients only underwent testing for three thyroid parameters: TSH, TT3, and TT4, without further assessment of FT3 or FT4. Currently, the relationship between UA levels in patients with CAD and cardiovascular prognosis has been extensively studied. Some scholars suggest that hyperuricemia is significantly associated with an increased risk of MACE in non-obstructive CAD; however, this conclusion remains a subject of debate. In the study conducted by Xu et al. [ 32 ] 695 CAD patients were evaluated for myocardial ischemia using D-SPECT MPI to determine the impact of uric acid on the severity of coronary artery stenosis and ischemia. The results indicated that hyperuricemia could elevate the MACE risk in non-obstructive CAD. Nevertheless, UA was not identified as a risk factor for abnormal MPI findings in this study. Future research may consider expanding the sample size to further investigate the influence of uric acid on the severity of coronary ischemia, thereby providing more valuable reference information for clinical practice. Furthermore, Xu et al. [ 33 ] investigated the prognostic value of D-SPECT detection in patients with normal ejection fraction but diastolic dysfunction associated with CAD. The study indicated that, despite preserved systolic function, diastolic dysfunction is significantly correlated with heart failure and all-cause mortality. Although this research compared the E/A ratios between two patient groups and found that those in the hypertensive group exhibited impaired ventricular diastolic function at an early stage, it did not delve deeply into the prognostic implications for these patients. Future clinical studies should further focus on the prognostic assessment of CAD patients who have a normal ejection fraction but present with diastolic dysfunction, aiming to provide a more comprehensive reference for clinical practice. 6 concluding remarks The incidence of CMD among patients with hypertension is significantly high within the spectrum of cardiovascular diseases. A considerable number of these patients are unable to receive accurate diagnosis and timely intervention in the early stages of their condition, leading not only to economic losses but also increasing the risk of MACE and delaying disease progression. With the continuous advancement in medical technology, D-SPECT-derived MBF and CFR can accurately assess microvascular function in CMD patients. This enables the formulation of personalized treatment plans, thereby enhancing the management of coronary heart disease and ultimately reducing MACE incidence while improving patients' quality of life. Abbreviations XLB—Research design, data collection, data analysis, and manuscript writing. LXY—Literature retrieval and data organization. XMW—Data Organization and Manuscript Revision. WW—Data Organization and Manuscript Revision. GDG—Data Organization and Analysis. YYC—literature search and data Organization. NW— Research guidance and thesis revision. Declarations Ethics Approval and Consent to Participate The patient gave informed written consent to publish her case (including the publication of images). Conflict of Interest The authors declare no conflict of interest.All authors declare that there are no conflicts of interest. Funding 2024 Gansu Province Science and Technology Program Projects(24JRRA604);2024 Lanzhou City Science and Technology Program Projects(2024-4-27). Author Contribution XLB—Research design, data collection, data analysis, and manuscript writing.LXY—Literature retrieval and data organization.XMW—Data Organization and Manuscript Revision.WW—Data Organization and Manuscript Revision.GDG—Data Organization and Analysis.YYC—literature search and data Organization.NW— Research guidance and thesis revision.All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Data Availability This retrospective study was approved by the Ethics Committee of Gansu Provincial People's Hospital and the methods were carried out in accordance with the approved guidelines.All the patients have been informed and signed informed consent before the experiments.The authors confirm that the data supporting the findings of this study are available within the article. References Ives CW, Sinkey R, Rajapreyar I, et al. 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Association between changes in predicted body composition and occurrence of heart failure: a nationwide population study [J]. Front Endocrinol (Lausanne), 2023, 14: 1210371. doi: 10.3389/fendo.2023.1210371. PMID: 37937051; PMCID: PMC10627176. Vorobeva DA, Ryabov VV, Lugacheva JG, et al. Relationships between indicators of prothrombotic activity and coronary microvascular dysfunction in patients with myocardial infarction with obstructive and non-obstructive coronary artery disease [J]. BMC Cardiovasc Disord, 2022, 22(1): 530.doi: 10.1186/s12872-022-02985-z. PMID: 36474151; PMCID: PMC9727929. Zhang H, Capbelli F, Che W, et al. The prognostic value of CZT SPECT myocardial blood flow (MBF) quantification in patients with ischemia and no obstructive coronary artery disease (INOCA): a pilot study [J]. Eur J Nucl Med Mol Imaging, 2023, 50(7): 1940-1953.doi: 10.1007/s00259-023-06125-3. Epub 2023 Feb 14. PMID: 36786817; PMCID: PMC10199834. Piskorz D. Hypertensive Mediated Organ Damage and Hypertension Management. How to Assess Beneficial Effects of Antihypertensive Treatments? [J]. High Blood Press Cardiovasc Prev, 2020, 27(1): 9-17.doi: 10.1007/s40292-020-00361-6. Epub 2020 Jan 23. PMID: 31975151. Đorđević DB, Koračević GP, Đorđević AD, Lović DB. Hypertension and left ventricular hypertrophy. J Hypertens. 2024 Sep 1;42(9):1505-1515. doi: 10.1097/HJH.0000000000003774. Epub 2024 May 15. PMID: 38747417. Loga R, Vontaobel J, Rovai D, et al. Multicentre multi-device hybrid imaging study of coronary artery disease: results from the EValuation of INtegrated Cardiac Imaging for the Detection and Characterization of Ischaemic Heart Disease (EVINCI) hybrid imaging population [J]. Eur Heart J Cardiovasc Imaging, 2016, 17(9): 951-960.doi: 10.1093/ehjci/jew038. Epub 2016 Mar 18. PMID: 26992419; PMCID: PMC5841878. Freitag MT, Bremerich J, Wild D, et al. Quantitative myocardial perfusion (82)Rb-PET assessed by hybrid PET/coronary-CT: Normal values and diagnostic performance [J]. J Nucl Cardiol, 2022, 29(2): 464-473.doi: 10.1007/s12350-020-02264-4. Epub 2020 Jul 16. PMID: 32676910. Bom MJ, Vansdiemen PA, Driessen RS, et al. Prognostic value of [15O]H2O positron emission tomography-derived global and regional myocardial perfusion [J]. Eur Heart J Cardiovasc Imaging, 2020, 21(7): 777-786.doi: 10.1093/ehjci/jez258. PMID: 31620792. Farhad H, Dunet V, Bachelard K, et al. Added prognostic value of myocardial blood flow quantitation in rubidium-82 positron emission tomography imaging [J]. Eur Heart J Cardiovasc Imaging, 2013, 14(12): 1203-1210.doi: 10.1093/ehjci/jet068. Epub 2013 May 9. PMID: 23660750. Zhang H, Che W, Shi K, et al. FT4/FT3 ratio: A novel biomarker predicts coronary microvascular dysfunction (CMD) in euthyroid INOCA patients [J]. Front Endocrinol (Lausanne), 2022, 13: 1021326.doi: 10.3389/fendo.2022.1021326. PMID: 36187090; PMCID: PMC9520241. Xu S, Liu L, Yin G, et al. Prognostic Significance of Uric Acid in Patients with Obstructive and Nonobstructive Coronary Artery Disease Undergoing D-SPECT [J]. Clin Interv Aging, 2021, 16: 1955-1965.doi: 10.2147/CIA.S339600. PMID: 34815667; PMCID: PMC8605808. Xu B, Liu L, Abdu FA, et al. Prognostic Value of Diastolic Dysfunction Derived From D-SPECT in Coronary Artery Disease Patients With Normal Ejection Fraction [J]. Front Cardiovasc Med, 2021, 8: 700027.doi: 10.3389/fcvm.2021.700027. PMID: 34336957; PMCID: PMC8319539. Additional Declarations No competing interests reported. Supplementary Files 3.titlepage.docx 4.Highhights.docx 6.Abstract.docx Availabilityofmaterialsanddata.docx Ethics.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers invited by journal 01 Sep, 2025 Editor assigned by journal 01 Sep, 2025 Editor invited by journal 01 Sep, 2025 Submission checks completed at journal 24 Aug, 2025 First submitted to journal 24 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/fbe486875afd89ffa403e2ca.jpg"},{"id":90898619,"identity":"fb3ee36b-3663-4a5a-96c8-e696639f9d72","added_by":"auto","created_at":"2025-09-09 11:54:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Analysis of rMBF, sMBF, and CFR in Two Patient Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/869577d0bbe6e1da310e37fc.jpg"},{"id":90899641,"identity":"c9696146-d34e-4983-83df-a2ee5b5683c2","added_by":"auto","created_at":"2025-09-09 12:02:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Analysis of rMBF, sMBF, and CFR in the Hypertension Subgroup\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/f8620b0d93326d48175d17a4.jpg"},{"id":90899643,"identity":"6cf8db67-77cf-406d-a1f3-0d5dde483eef","added_by":"auto","created_at":"2025-09-09 12:02:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis of MPI, MBFand CFR in two patient groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/dc22f7c479a944002c3e63e9.jpg"},{"id":90901804,"identity":"a63b8c1f-2bcc-49dc-8d7b-7246c31aea48","added_by":"auto","created_at":"2025-09-09 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12:02:14","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10802,"visible":true,"origin":"","legend":"","description":"","filename":"Availabilityofmaterialsanddata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/beac19b7d495ea541981c361.docx"},{"id":90898637,"identity":"ce348e77-a7f7-4393-bdcc-fc58e7c5d4bb","added_by":"auto","created_at":"2025-09-09 11:54:14","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1072680,"visible":true,"origin":"","legend":"","description":"","filename":"Ethics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7272519/v1/b7ef931339fede72dae4b2c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"D-SPECT-derived myocardial perfusion imaging and coronary blood flow reserve in the clinical diagnosis of hypertensive patients with INOCA","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHypertension affects over one billion adults worldwide and is a major driving factor for the incidence and mortality of cardiovascular diseases. In China, the prevalence of hypertension is notably high; however, the treatment coverage and control rates remain relatively low. This situation significantly contributes to the epidemic of Hypertensive Heart Disease (HHD). The pathological features of HHD include Left Ventricular Hypertrophy (LVH), diastolic dysfunction, and diffuse interstitial fibrosis in the myocardium. When hypertension coexists with LVH, there is an increase in myocardial oxygen consumption that cannot be met by coronary artery dilation, leading to a decrease in Myocardial Blood Flow (MBF) and Coronary Flow Reserve (CFR). The reduction in CFR serves as an important underlying mechanism for left ventricular diastolic dysfunction and the occurrence of angina pectoris. These factors interact with each other, further exacerbating coronary microvascular disease and ultimately resulting in the development of Coronary Microvascular Dysfunction (CMD)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Recent studies have indicated that coronary microvascular disease is a primary cause of Major Adverse Cardiovascular Events (MACE) in patients with non-obstructive epicardial coronary artery disease\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIschaemia with Non-Obstructive Coronary Arteries (INOCA) is a distinct type of coronary artery disease characterized by the presence of coronary artery stenosis less than 50%. Despite this, patients often experience angina symptoms and/or evidence of myocardial ischaemia. Based on the pathophysiological characteristics of INOCA, this condition can be classified into two subtypes: Vasospastic Angina (VSA) and Microvascular Angina (MVA)\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe clinical presentation of MVA is characterized by the following key features: ① Clinical symptoms indicative of myocardial ischemia;② Results from coronary computed tomography angiography (CTA) or CAG revealing that the degree of stenosis in the epicardial coronary arteries is less than 50%; ③ Additional relevant examinations confirming the presence of myocardial ischemia; ④ A definitive diagnosis of CMD\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Due to the inability to assess coronary microcirculation during coronary CTA or CAG examinations, patients with CMD are often misdiagnosed as having non-cardiac causes when their test results return negative. This can lead to missed diagnoses, incorrect diagnoses, and repeated medical visits, resulting in emotional distress and anxiety for patients, as well as increased financial burdens. Therefore, it is particularly important to accurately evaluate the function of coronary microcirculation in clinical practice\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, the assessment techniques for CMD in clinical practice include: ① Transthoracic Doppler Echocardiography (TTDE); ② Cardiovascular Magnetic Resonance (CMR); ③ Fractional Flow Reserve (FFR); ④ Index of Microcirculatory Resistance (IMR); ⑤ Single Photon Emission Computed Tomography (SPECT); ⑥ Positron Emission Tomography (PET)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.The aforementioned diagnostic techniques possess certain inherent drawbacks, such as high costs, complex surgical procedures, invasive operations, and suboptimal temporal and spatial resolution, along with various adverse reactions. These factors contribute to the reluctance of some patients to accept these methods; consequently, their clinical application remains limited at this stage.\u003c/p\u003e\u003cp\u003eNowadays, with the rapid advancement of nuclear medicine technology, the new generation of Dynamic Single Photon Emission Computed Tomography (D-SPECT) is gradually gaining recognition. This system not only offers ease of operation and lower costs but also demonstrates an 8\u0026ndash;9 fold increase in sensitivity compared to traditional SPECT, along with a twofold improvement in spatial resolution. It has been widely adopted globally. Building upon conventional Myocardial Perfusion Imaging (MPI), D-SPECT utilizes myocardial blood flow quantification software to accurately calculate resting Myocardial Blood Flow (rMBF), stress Myocardial Blood Flow (sMBF), and CFR. This capability plays a crucial role in early disease diagnosis, risk stratification, clinical treatment guidance, and assessment of therapeutic outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have confirmed that D-SPECT exhibits high consistency with PET measurements of MBF and CFR, thereby providing clinicians with enhanced image quality and prognostic information\u003csup\u003e[\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, the objective of this study is to evaluate the characteristics and influencing factors related to myocardial perfusion and coronary blood flow reserve in patients with hypertension complicated by INOCA through D-SPECT analysis.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Refinement of Patient Selection\u003c/h2\u003e\u003cp\u003eThe study received approval from Gansu Provincial People's Hospital, and all patients provided informed consent. Approval number: 2024\u0026thinsp;\u0026minus;\u0026thinsp;781.\u003c/p\u003e\u003cp\u003eThis study is a retrospective analysis that selected patients with angina who visited Gansu Provincial People's Hospital from April 2023 to September 2024 and underwent both D-SPECT and either CAG or coronary CTA. We retrospectively collected the patients' general information, laboratory indicators, echocardiographic parameters, and relevant D-SPECT metrics. Based on predetermined inclusion and exclusion criteria, the specific exclusions were as follows: (1) 105 patients did not undergo coronary CTA or CAG; (2) 174 patients had undergone coronary stent implantation; (3) 58 patients were confirmed to have epicardial coronary artery stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50%; (4) D-SPECT data was incomplete for 40 patients, while cardiac ultrasound data was missing for 4 patients, and laboratory indicators were incomplete for 2 patients; (5) 4 patients were diagnosed with severe arrhythmias; (6) 5 patients were diagnosed with cardiomyopathy; (7) 14 patients had congenital heart disease or severe valvular heart disease. All of these cases were excluded from the study(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTypical angina pectoris is characterized by the following features: ① A sensation of pressure, heaviness, or tightness behind the sternum or in the precordial area, which may radiate to the left shoulder, inner side of the left arm, ring finger and little finger, as well as potentially extending to the neck, throat, or jaw; ② It is usually triggered by physical exertion or emotional stress and can be relieved within five minutes through rest or sublingual administration of nitrates\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Subjects and Grouping\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Research Methodology\u003c/h2\u003e\u003cp\u003e\u003cb\u003e2.3.1 Inclusion Criteria\u003c/b\u003e: (1) Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; (2) Presence of symptoms related to angina pectoris or ischemic changes on electrocardiogram; (3) All patients have undergone comprehensive D-SPECT examination during hospitalization; (4) Patients with coronary artery stenosis\u0026thinsp;\u0026lt;\u0026thinsp;50% as indicated by CTA or CAG.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.2 Exclusion Criteria\u003c/b\u003e: (1) Patients who have not undergone CTA or CAG examinations; (2) Coronary artery stenosis of the epicardial coronary arteries\u0026thinsp;\u0026ge;\u0026thinsp;50%; (3) History of coronary stent implantation or coronary artery bypass grafting; (4) Incomplete clinical data; (5) Severe arrhythmias; (6) Myocarditis or myocardial disease; (7) Congenital heart disease or severe valvular heart disease.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 D-SPECT Management\u003c/h2\u003e\u003cp\u003e(1) Preparation for D-SPECT examination: ① The patient must fast for at least 3 hours prior to the stress test; ② Discontinue the use of xanthine medications, β-blockers, and nitrate drugs within 24 hours before the examination; ③ Avoid coffee, cola, tea, and smoking for at least 12 hours prior to the test; ④ Prepare a high-fat meal (such as fried eggs or full-fat cake, along with whole milk)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e(2) During the D-SPECT examination, the following observation indicators should be noted: ① Assessment of myocardial perfusion: Record the number of ischemic segments in both resting and stress states, total myocardial perfusion score, MBF, CFR, as well as the Extent of perfusion abnormalities(Extent) and total perfusion defect (TPD); ② Adverse drug reactions: Observe and document any adverse reactions experienced by the subject, including symptoms such as facial flushing, cyanosis, sweating, generalized itching, dyspnea, asthma attacks, arrhythmias, hypotension, and nausea\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e(3) D-SPECT Image Processing and Analysis: ① The processing of D-SPECT images is conducted by two chief physicians in the Department of Nuclear Medicine using a double-blind method for image analysis and evaluation. Images are collected from short-axis, vertical long-axis, and horizontal long-axis views, with the perfusion areas being delineated according to a 17-segment model.②The evaluation criteria for myocardial imaging agents, as established by the American Heart Association (AHA), are based on a five-point distribution scale: 0\u0026thinsp;=\u0026thinsp;normal, 1\u0026thinsp;=\u0026thinsp;mild sparsity, 2\u0026thinsp;=\u0026thinsp;moderate sparsity, 3\u0026thinsp;=\u0026thinsp;severe sparsity, and 4\u0026thinsp;=\u0026thinsp;defect.③The Summed Stress Score (SSS) represents the cumulative scoring of myocardial perfusion across various segments:A score ranging from 0 to 3 indicates a low risk, while a score between 4 and 8 suggests moderate risk,Scores from 9 to 13 are indicative of high risk, and scores between 14 and 38 signify extremely high risk.④TPD\u0026thinsp;=\u0026thinsp;Summed Score (SS) \u0026times; 100 / Sum of the Worst Possible Scores for All Myocardial Segments. The normal value for TPD is \u0026lt;\u0026thinsp;5%\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e(4) D-SPECT Reference Ranges and Critical Values: ① The literature reports that the reference range for rMBF is between 0.5 and 2.0 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1;, while the reference range for sMBF is between 1.5 and 5.0 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1;;②Regarding the critical value of CFR, there is currently no unified standard in China. This study defines a CFR threshold of less than 2.5 as the diagnostic criterion for coronary microvascular dysfunction\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe data was processed using the SPSS 26.0 statistical software.When the measurement data conforms to a normal distribution, it is expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cem\u003ex\u0026thinsp;\u0026plusmn;\u0026thinsp;s\u003c/em\u003e),in cases where the data does not conform to a normal distribution, it is represented by the median (interquartile range, IQR).In intergroup comparisons, data that conform to a normal distribution should be analyzed using the \u003cem\u003et\u003c/em\u003e-test, while data that do not meet the criteria for normality should be subjected to non-parametric tests.The count data is expressed using rates or composition ratios, and inter-group comparisons are conducted using the \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test.Correlation Analysis: For normally distributed data, Pearson correlation test is employed, while for non-normally distributed data, Spearman correlation test is utilized.The causal relationship between independent and dependent variables is analyzed through a multiple linear regression model.The Receiver Operating Characteristic Curve (ROC curve) is utilized to evaluate the sensitivity and specificity of diagnostic methods for diseases. By analyzing the ROC curve, we can calculate the Youden Index, which identifies the optimal threshold point corresponding to this index, thereby determining the best cutoff value for the diagnostic approach.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered to be statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 General Data Comparison\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Comparison of General Data Between the Hypertension Group and the Control Group\u003c/h2\u003e\u003cp\u003eAccording to the inclusion and exclusion criteria, this study ultimately included 259 patients with INOCA, who were divided into a non-hypertensive group (134 cases) and a hypertensive group (125 cases). The general clinical data of patients in each group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe results indicated that there were 73 males (54.5%) in the non-hypertensive group, with an average age of 55.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26 years; whereas in the hypertensive group, there were 64 males (51.2%), with an average age of 60.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09 years. The hypertensive group exhibited higher values for age, SBP, and TT4 compared to the non-hypertensive group, while TG and TT3 levels were lower in the hypertensive group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no statistically significant differences were observed between the two groups regarding gender, smoking history, BMI, DBP, BUN, Cr, UA, HbA1c, TC, LDL-C, HDL-C as well as Hey and TSH levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of General Information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hypertensive group(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive group(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ/t/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73(54.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(51.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.60\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\u003e28(20.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\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\u003e24.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.39\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.11\u0026thinsp;\u0026plusmn;\u0026thinsp;19.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337.71\u0026thinsp;\u0026plusmn;\u0026thinsp;88.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325.32\u0026thinsp;\u0026plusmn;\u0026thinsp;89.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70(5.50, 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.80(5.50, 6.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57(1.07, 2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32(1.04, 1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHey,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.87(11.37, 20.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.01(12.55, 20.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH,mIU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78(1.19, 2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.76(1.12, 2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT3,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58(1.40, 1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49(1.33, 1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT4,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.18(86.39, 105.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.47(87.30, 111.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: BMI:Body Mass Index;SBP༚Systolic Blood Pressure༛DBP༚Diastolic Blood Pressure༛BUN༚Blood Urea Nitrogen༛Cr༚Creatinine༛UA༚Uric Acid༛HbA1c༚Hemoglobin A1c༛TC༚Total Cholesterol༛TG༚Triglyceride༛LDL-C༚Low-Density Lipoprotein Cholesterol༛HDL-C༚High-Density Lipoprotein Cholesterol༛Hey༚Homocysteine༛TSH༚Thyroid Stimulating Hormone༛TT3༚Total Triiodothyronine༛TT4༚Total Thyroxine.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Comparison of General Data between the LVH Group and the Control Group\u003c/h2\u003e\u003cp\u003eThis study categorized 125 hypertensive patients into two groups based on echocardiographic parameters: the non-LVH group (71 cases) and the LVH group (54 cases). A comparative analysis was conducted on the general clinical data of patients in each subgroup (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results indicated that the age, BMI, Cr, UA, and TG levels in the LVH group were significantly higher than those in the non-LVH group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no statistically significant differences were observed between other indicators of both groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of General Data Among Subgroups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-LVH group(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLVH group(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ/t/χ2\u003c/em\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.69\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31(43.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(61.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\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\u003e14(19.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(11.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\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\u003e24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134.85\u0026thinsp;\u0026plusmn;\u0026thinsp;14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.21\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.17\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298.69\u0026thinsp;\u0026plusmn;\u0026thinsp;77.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e360.34\u0026thinsp;\u0026plusmn;\u0026thinsp;92.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70(5.50, 6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.80(5.50, 6.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23(1.03, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48(1.12, 2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHey,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00(12.51, 18.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.30(12.93, 22.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH,mIU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75(1.20, 2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78(1.08, 3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT3,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49(1.30, 1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49(1.36, 1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT4,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.46(85.04, 110.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.21(95.11, 111.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparison of Echocardiographic Parameters\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Comparison of Parameters Between the Hypertension Group and the Control Group\u003c/h2\u003e\u003cp\u003eThe comparison of echocardiographic parameters between the two patient groups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe results indicate that the median (IQR) values for IVS(D) in the non-hypertensive group and hypertensive group were 9 (8, 10) and 10 (9, 11), respectively. Similarly, the median (IQR) values for LVPW(D) were 9 (9, 10) in the non-hypertensive group and 10 (9, 11) in the hypertensive group. Notably, both IVS(D) and LVPW(D) measurements were significantly higher in the hypertensive group compared to the non-hypertensive group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, analysis of E/A ratios revealed that patients in the hypertensive group exhibited a greater propensity for impaired cardiac diastolic function (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences observed between the two groups regarding EDV, ESV, LVEF, and FS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of Echocardiographic Parameters\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hypertensive group(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive group(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ/t-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(8, 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(9, 11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(9, 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(9, 11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.93\u0026thinsp;\u0026plusmn;\u0026thinsp;21.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.61\u0026thinsp;\u0026plusmn;\u0026thinsp;22.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(30, 41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(30, 43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\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\u003e64.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A Anomaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: IVS(D):Interventricular septal thickness at end-diastole;LVPW(D):Left ventricular posterior wall thickness at end-diastole;EDV:End Diastolic Volume;ESV༚End Systolic Volume༛LVEF༚Left Ventricular Ejection Fraction༛FS༚Fractional Shortening.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Comparison of Parameters Between the LVH Group and the Control Group\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a comparison of echocardiographic parameters between the hypertension subgroup.\u003c/p\u003e\u003cp\u003eThe results indicate that patients in the LVH group exhibited significantly higher values for IVS(D), LVPW(D), and EDV compared to those in the non-LVH group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, LVEF and FS were notably lower in the LVH group than in the non-LVH group. There were no significant differences observed between the two groups regarding ESV and E/A ratios (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of Echocardiographic Parameters Among Subgroups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-LVH group(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLVH group(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ/t-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(9, 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(11, 12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;9.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(9, 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(10, 11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.04\u0026thinsp;\u0026plusmn;\u0026thinsp;20.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.31\u0026thinsp;\u0026plusmn;\u0026thinsp;24.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(30, 39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.50(32.75, 47.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\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\u003e65.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A Anomaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 D-SPECT MPI Parameter Comparison\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Comparison of MPI Parameters Between the Hypertension Group and the Control Group\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the specific data of D-SPECT MPI for two groups of patients. The results indicate that, compared to the non-hypertensive group, the hypertensive group exhibited significantly higher values in SSS, SDS, TPD(s), and Extent(s) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there were no significant statistical differences between the groups regarding SRS, TPD(r), and Extent(r) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\" width=\"100%\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of D-SPECT MPI Parameters\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hypertensive group(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive group(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDS\u003c/p\u003e\u003cp\u003eTPD(r)\u003c/p\u003e\u003cp\u003eExtent(r)\u003c/p\u003e\u003cp\u003eTPD(s)\u003c/p\u003e\u003cp\u003eExtent(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0, 1)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e1(1, 2)\u003c/p\u003e\u003cp\u003e1(0, 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0, 2)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e2(1, 4)\u003c/p\u003e\u003cp\u003e1(0, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.57\u003c/p\u003e\u003cp\u003e\u0026minus;1.42\u003c/p\u003e\u003cp\u003e\u0026minus;0.35\u003c/p\u003e\u003cp\u003e\u0026minus;2.81\u003c/p\u003e\u003cp\u003e\u0026minus;3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e0.73\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: SSS：Summed Stress Score；SRS：Summed Rest Score；SDS：Summed Difference Score(SDS=SSS-SRS)；TPD:Total Perfusion Defect；Extent：Extent of perfusion abnormality.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Comparison of MPI Parameters Between the LVH Group and the Control Group\u003c/h2\u003e\u003cp\u003eAccording to the subgroup analysis based on echocardiographic parameters, patients in the hypertension group were evaluated. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, there was no significant statistical difference in the MPI between the non-LVH group and the LVH group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of D-SPECT MPI Parameters Among Subgroups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-LVH group(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLVH group(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eZ-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDS\u003c/p\u003e\u003cp\u003eTPD(r)\u003c/p\u003e\u003cp\u003eExtent(r)\u003c/p\u003e\u003cp\u003eTPD(s)\u003c/p\u003e\u003cp\u003eExtent(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0, 2)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e1(1, 3)\u003c/p\u003e\u003cp\u003e1(0, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0, 3)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e0(0, 0)\u003c/p\u003e\u003cp\u003e2(1, 4)\u003c/p\u003e\u003cp\u003e2(0, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.64\u003c/p\u003e\u003cp\u003e\u0026minus;0.24\u003c/p\u003e\u003cp\u003e\u0026minus;1.15\u003c/p\u003e\u003cp\u003e\u0026minus;0.88\u003c/p\u003e\u003cp\u003e\u0026minus;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003cp\u003e0.81\u003c/p\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e0.38\u003c/p\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 D-SPECT MBF and CFR Parameter Comparison\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1Comparison of MBF and CFR Parameters Between the Hypertension Group and the Control Group\u003c/h2\u003e\u003cp\u003eIn a study involving 259 patients, a total of 222 individuals underwent assessments of resting myocardial blood flow (rMBF), stress myocardial blood flow (sMBF), and coronary flow reserve (CFR) in the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). Among these participants, there were 111 patients in the hypertensive group and 111 patients in the non-hypertensive group.\u003c/p\u003e\u003cp\u003eThe conclusions drawn from Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e are as follows: (1) The sMBF and CFR of the LAD, LCX, and RCA coronary arteries in the hypertensive group were significantly lower than those in the non-hypertensive group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these vessels, the CFR of the LAD was found to be the lowest compared to that of the LCX and RCA. (2) There was no significant statistical difference in rMBF between the two groups for both LAD and LCX coronary arteries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Although a statistical difference was observed in rMBF for RCA between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), both groups exhibited rMBF levels above normal values. A box plot has been created to visually illustrate the relationships among rMBF, sMBF, and CFR for LAD, LCX, and RCA coronary arteries between the two patient groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of D-SPECT MBF and CFR Parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascellum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-hypertensive group(n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHypertensive group(n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003cp\u003erMBF\u003c/p\u003e\u003cp\u003esMBF\u003c/p\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.16\u003c/p\u003e\u003cp\u003e1.40\u003c/p\u003e\u003cp\u003e14.03\u003c/p\u003e\u003cp\u003e13.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote：rMBF：rest Myocardial Blood Flow；sMBF：stress Myocardial Blood Flow；CFR：Coronary Flow Reserve.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2 Comparison of MBF and CFR Parameters Between the LVH Group and the Control Group\u003c/h2\u003e\u003cp\u003eThe analysis of the above data indicates that the IVS(D) and LVPW(D) measurements in the hypertensive group are significantly higher than those in the non-hypertensive group, while sMBF and CFR values are markedly lower in the hypertensive group compared to their non-hypertensive counterparts. To clarify whether LVH is a contributing factor to the reduction in sMBF and CFR, we conducted a comparative analysis of rMBF, sMBF, and CFR across the LAD, LCX, and RCA coronary arteries among patients within the hypertensive subgroup. A total of 125 patients from the hypertensive group were categorized into two subgroups: non-LVH group and LVH group. In the non-LVH subgroup comprising 71 patients, 67 underwent assessments for rMBF, sMBF, and CFR across their LAD, LCX, and RCA coronary arteries. Meanwhile, among the LVH subgroup consisting of 54 patients, 49 also received evaluations for these parameters. Detailed analytical results can be found in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe results indicate: (1) There are significant differences in the rMBF of the LAD, LCX, and RCA coronary arteries between the two patient groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).however, the rMBF values for both groups exceed 0.5 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1;, which is above normal levels. (2) The CFR of the LAD, LCX, and RCA coronary arteries in the LVH group is significantly lower than that in the non-LVH group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with both LAD and LCX exhibiting a CFR less than 2.5;notably, the CFR in the distribution area of LAD is at its lowest. (3) Although there are significant differences in sMBF between the hypertensive group and control group, subgroup analysis reveals that these differences are not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with all indicators falling within normal ranges. Box plots illustrating rMBF, sMBF, and CFR among LAD, LCX, and RCA coronary arteries for patients in the hypertensive subgroup can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of D-SPECT MBF and CFR Parameters Among Subgroups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascellum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-LVH group(n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLVH group(n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003cp\u003erMBF\u003c/p\u003e\u003cp\u003esMBF\u003c/p\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.94\u003c/p\u003e\u003cp\u003e\u0026minus;3.96\u003c/p\u003e\u003cp\u003e\u0026minus;1.14\u003c/p\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e0.26\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Pearson Correlation Analysis\u003c/h2\u003e\u003cp\u003eIn order to investigate the associations between age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, E/A values and SSS, SDS, TPD(S), Extent(S) as well as rMBF, sMBF and CFR of the LAD, LCX and RCA coronary arteries, a Pearson correlation analysis was conducted on the aforementioned data.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.5.1 Correlation Analysis of MPI\u003c/h2\u003e\u003cp\u003eThe Sperman correlation analysis of MPI is presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The results indicate that while there are statistically significant associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between TT4, LVEF, FS and SSS, TPD(s), Extent(s), as well as between EDV and SSS, SDS, TPD(s), Extent(s), and the E/A ratio with SSS and SDS, the correlation coefficients (r) are all less than 30%. This suggests that although these variables exhibit associative relationships, they do not demonstrate significant correlations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSperman Correlation Analysis of MPI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSDS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eTPD(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eExtent(s)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\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\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT3,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT4,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003e\u0026minus;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.5.2 Pearson Correlation Analysis of MBF and CFR\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the Pearson correlation analysis of rMBF, sMBF, and CFR. The detailed results are analyzed as follows:\u003c/p\u003e\u003cp\u003e(1) There exists a statistical association between age and the sMBF and CFR of coronary arteries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the correlation coefficient r is less than 30%, indicating that while an association is present, it does not reach a level of significant correlation.\u003c/p\u003e\u003cp\u003e(2) There exists a statistically significant association between SBP and sMBF, as well as CFR in coronary arteries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation coefficient for sMBF is -0.35 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while that for CFR is -0.36 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With 30% \u0026le; r\u0026thinsp;\u0026lt;\u0026thinsp;50%, this indicates a notable weak correlation between SBP and both sMBF and CFR. It can be concluded that SBP has a significant negative impact on sMBF and CFR; specifically, higher SBP levels are associated with lower values of sMBF and CFR in the coronary arteries.\u003c/p\u003e\u003cp\u003e(3) There exists a statistical association between IVS(D) and the coronary artery's sMBF and CFR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the correlation coefficient r between IVS(D) and CFR is -0.42, which exceeds 30%, indicating that IVS(D) significantly negatively impacts the CFR of coronary arteries; specifically, as IVS(D) increases, CFR decreases. However, the correlation coefficients r between IVS(D) and sMBF are all less than 30%. Although an association is present, it does not reach statistical significance.\u003c/p\u003e\u003cp\u003e(4) There exists a statistical association between LVPW(D) and the sMBF and CFR of coronary arteries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, given that the correlation coefficient r is less than 0.30, this indicates that while there is an associative relationship, it does not reach statistical significance.\u003c/p\u003e\u003cp\u003e(5) There exists a statistically significant association between EDV and rMBF of the coronary arteries (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the correlation coefficients (r) are all less than 0.30, indicating that while there is an associative relationship between them, it lacks substantial significance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson Correlation Analysis between MBF and CFR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003er-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT3,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT4,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\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\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Multiple Linear Regression Analysis\u003c/h2\u003e\u003cp\u003eTo clarify the causal relationship between independent and dependent variables, a multiple linear regression analysis was conducted on two sets of data. The independent variables included age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, and E/A ratio. The dependent variables comprised SSS, SDS, TPD(S), Extent(S), as well as rMBF, sMBF, and CFR of coronary blood vessels. Statistical analysis revealed that only CFR demonstrated a good model fit with the respective independent variables.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e :Before controlling for the variables, age, SBP,IVS(D) and LVPW(D) of the above independent variables can significantly affect the dependent variable, and after controlling for each of the test variables SBP,IVS(D) can still significantly affect the dependent variable.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis with CFR after adjusting for confounders\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAdjusted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOR;95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR;95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;5.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;3.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;6.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;4.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;2.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows the data analysis of the causal relationship between the respective variables and CFR after controlling for the variables,The following is an interpretation of the results obtained from the table. The \u003cem\u003eF\u003c/em\u003e-value is 9.41, with a \u003cem\u003eP\u003c/em\u003e-value less than 0.05, indicating that the linear regression model is significant. This suggests that at least one of the fourteen independent variables has a statistically significant effect on the dependent variable. The specific impacts are as follows: (1) SBP: SBP has a significant effect on CFR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a coefficient of -0.01, indicating that SBP exerts a notable negative influence on the dependent variable; specifically, for every increase of 1 mmHg in SBP, CFR decreases by 0.01. (2) IVS(D): IVS(D) also demonstrates a significant impact on CFR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a coefficient of -0.23, signifying that IVS(D) significantly negatively affects the dependent variable; thus, for each increment of 1 mm in IVS(D), CFR declines by 0.23.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Linear Regression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandardized Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003et-Value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.25\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\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP,mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA,umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG,mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT3,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT4,nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPW(D),mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDV,ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.28\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\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A值\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR Square\u003c/em\u003e 0.30\u003c/p\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e 5.31\u003c/p\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05\u003c/p\u003e\u003cp\u003eDependent Variable: CFR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.7 ROC Curve and Optimal Cut-off Values for D-SPECT Related Parameter\u003c/h2\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e3.7.1 ROC Curves of MPI, MBF, and CFR\u003c/h2\u003e\u003cp\u003eThe area under the ROC curve (AUC) for various diagnostic variables, including SSS, SDS, TPD(s), Extent(s), and the three vascular parameters rMBF, sMBF, and CFR, was plotted to evaluate their sensitivity and specificity in diagnosing diseases. Additionally, the optimal cutoff values for rMBF, sMBF, and CFR were determined from the ROC curves using the maximum Youden index. The following is an interpretation of the AUC corresponding to each variable's ROC curve.The analysis of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe areas under the ROC curves (AUC) for SSS, SDS, TPD(s), and Extent(s) were 0.62, 0.62, 0.59, and 0.60 respectively. Although \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance, the relatively low sensitivity and specificity of these variables limit their diagnostic value for the disease.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe AUC values for sMBF and CFR in the LAD vessels were 0.85 and 0.90, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that both sMBF and CFR possess high diagnostic accuracy for the disease. In contrast, the AUC for rMBF was found to be 0.52 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that rMBF holds no diagnostic value for the condition.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe AUC values for sMBF and CFR in the LCX vessels were 0.91 and 0.90, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that both sMBF and CFR possess high diagnostic accuracy for the disease. In contrast, the AUC for rMBF was found to be 0.55 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that rMBF holds no diagnostic value for the condition.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e(4)The area under the curve (AUC) for sMBF and CFR in RCA vessels were 0.90 and 0.85, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that both sMBF and CFR demonstrate high accuracy in disease diagnosis. In contrast, the AUC for rMBF was 0.68; although \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the sensitivity and specificity of rMBF are relatively low, rendering its diagnostic value limited in this context.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn summary, the sMBF and CFR of the three major coronary arteries demonstrate high sensitivity and specificity for diagnosing hypertension combined with INOCA. Therefore, both sMBF and CFR hold significant diagnostic value for this condition. In contrast, the SSS, SDS, TPD(s), Extent(s), and rMBF of the three vessels exhibit lower sensitivity and specificity, rendering them inadequate as standalone effective indicators for disease diagnosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e3.7.2 The optimal cutoff values for MBF and CFR\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e: The optimal cutoff values for diagnosing the disease were determined from the ROC curve based on the maximum Youden index corresponding to rMBF, sMBF, and CFR of the LAD, LCX, and RCA coronary arteries.\u003c/p\u003e\u003cp\u003e(1)The maximum Youden index for sMBF is 0.694, with an optimal cutoff value of 2.18. The sensitivity at this threshold is 0.81, and the specificity is 0.88. This indicates that using a cutoff point of 2.18 for disease diagnosis yields high sensitivity and specificity, thereby demonstrating significant value in the diagnostic process for the disease.\u003c/p\u003e\u003cp\u003e(2)The maximum Youden index for CFR is 0.71, with an optimal cutoff value of 2.71. The sensitivity is 0.93 and the specificity is 0.78, indicating that using a cutoff point of 2.71 for disease diagnosis not only achieves high sensitivity and specificity but also demonstrates superior diagnostic value compared to sMBF.\u003c/p\u003e\u003cp\u003e(3)The maximum Youden index for rMBF is 0.39, with an optimal cutoff value of 0.52. The sensitivity is 0.93, while the specificity stands at 0.46. Although the sensitivity is relatively high, the low specificity indicates that using a cutoff point of 0.52 for disease diagnosis may not be clinically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of MBF and CFR in Disease Prediction\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYoden Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimal Cut-off Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esMBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe sMBF and CFR derived from D-SPECT represent a novel non-invasive diagnostic tool that holds significant value for the early diagnosis of patients with hypertension complicated by INOCA.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDecreased CFR in hypertensive patients was significantly correlated with increased SBP and IVS(D), and the greater the SBP and IVS(D), the smaller the CFR in the coronary vasculature.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003esMBF and CFR demonstrated high sensitivity and specificity in the diagnosis with hypertension complicated by INOCA. The optimal cut-off values for rMBF, sMBF, and CFR were established from the ROC curves using the maximum Youden's index, with an optimal cut-off value of 2.18 for sMBF and 2.71 for CFR.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003ePrevious studies have indicated that age is a significant risk factor for the development of hypertension. As individuals age, the prevalence of hypertension increases annually. This rise can primarily be attributed to the gradual onset of atherosclerosis in blood vessels, diminished endothelial function, and reduced vascular relaxation capacity, coupled with enhanced contraction ability, all contributing to elevated blood pressure in patients\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In our current study, we found that the average age of patients in the hypertensive group was significantly higher than that of those in the non-hypertensive group. Subgroup analyses yielded similar conclusions; specifically, patients with LVH were older on average. Furthermore, subgroup analysis revealed that the BMI of patients in the LVH group was significantly greater than that of non-LVH group.\u003c/p\u003e\u003cp\u003eHypertensive patients are prone to coronary microcirculatory dysfunction due to atherosclerosis of the coronary arteries and endothelial cell dysfunction. Additionally, as the left ventricular wall gradually thickens, the myocardial cells' demand for oxygen and nutrients increases. Concurrently, collagen accumulates in the vascular interstitial space, exerting pressure on the arteries, which leads to a reduction in CFR and subendocardial ischemia\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003epreviously utilized Cadmium Zinc Telluride (CZT) SPECT to assess the prognostic value in patients with INOCA. The study revealed that the sMBF and CFR were significantly reduced in the MACE group, along with poorer left ventricular function parameters, including resting and stress LVEF, SSS, TPD, and Extent compared to other groups.Compared to previous studies, this research yielded similar results. By comparing the D-SPECT parameters of two patient groups, it was found that the SSS, SDS, TPD(s), and Extent(s) in the hypertensive group were significantly higher than those in the non-hypertensive group. Additionally, sMBF and CFR of the LAD, LCX, and RCA coronary arteries were also significantly reduced. Furthermore, subgroup analysis for hypertension revealed that patients with LVH exhibited a significant decrease in CFR for LAD, LCX, and RCA compared to the control group; notably, CFR values for both LAD and LCX fell below 2.5, with particularly low values observed in the LAD distribution area. The conclusions drawn from this study align with the myocardial territory supplied by coronary arteries. They indicate that hypertension and hypertension combined with LVH adversely affect coronary microcirculation leading to reductions in MBF and CFR\u0026mdash;especially within regions supplied by LAD and LCX. Previous research has indicated that sMBF derived from D-SPECT is not influenced by rMBF,rather it directly reflects microvascular functional status\u0026mdash;a distinction that sets it apart from CFR\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo clarify the causal relationships between age, BMI, SBP, Cr, UA, TG, TT3, TT4, IVS(D), LVPW(D), EDV, LVEF, FS, E/A values and MPI, rMBF, sMBF, CFR, we conducted correlation analysis and multiple linear regression analysis on the aforementioned data. The statistical analysis revealed that only CFR exhibited a good model fit with the independent variables. Notably, SBP and IVS(D) emerged as significant risk factors contributing to the decline in CFR. As both SBP and IVS(D) increase in value, there is a corresponding decrease in CFR of the coronary vessels. Therefore, actively managing blood pressure and preventing the onset and progression of LVH are crucial strategies for mitigating CMD.\u003c/p\u003e\u003cp\u003eBy determining the optimal cutoff values for MBF and CFR, we can effectively predict the clinical risk in patients with cardiovascular diseases. Traditionally, PET/CT has utilized a CFR threshold of less than 2 as the optimal cutoff value; however, there is currently no unified standard for CFR thresholds within domestic practices\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003eindicates that the threshold for sMBF is 3.16 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1; and that for CFR is 2.52, which allows effective risk stratification in patients with Ischemia with INOCA. Bom et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e employed [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET to assess risk prediction in 648 patients suspected of or diagnosed with Coronary Artery Disease (CAD). Their findings revealed that a hyperemic myocardial blood flow (hMBF) of less than 2.65 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1; and a CFR of less than 2.88 serve as optimal cutoff values for predicting MACE. Similarly, Farhad et al. \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003edemonstrated that an sMBF range of 1.8\u0026ndash;2.6 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1; and a CFR range of 1.8\u0026ndash;2.4 are useful for risk stratification in cardiovascular disease patients. In this study, we extracted the optimal diagnostic cutoff values for rMBF, sMBF, and CFR from ROC curves using the maximum Youden index method; our results indicate that the best cutoff value for sMBF is 2.18 mL\u0026middot;min⁻\u0026sup1;\u0026middot;g⁻\u0026sup1; while that for CFR is 2.71. In summary, both sMBF and CFR demonstrate high sensitivity and specificity in diagnosing diseases, serving as crucial reference indicators for assessing coronary artery function. They provide significant clinical value in early diagnosis, risk stratification, and prognosis evaluation\u0026mdash;playing an essential role in diagnosing and assessing coronary heart disease as well as other cardiovascular conditions.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that subtle variations in thyroid hormone levels within the normal range, particularly the ratio of free thyroxine (FT4) to free triiodothyronine (FT3), can modulate the cardiovascular system. In research conducted by Zhang et al. \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003eit was found that an elevated FT4/FT3 ratio is associated with the occurrence of CMD in patients with normal thyroid function and ischemia with INOCA. This finding provides a novel biomarker for improving risk stratification. In this study, although significant differences were observed in TT3 and TT4 levels between the two patient groups, subsequent Pearson correlation analysis and multiple linear regression analysis did not reveal any causal relationship between these hormones and MPI, MBF, or CFR. The lack of association may be attributed to the small sample size inherent in retrospective studies; moreover, most clinical patients only underwent testing for three thyroid parameters: TSH, TT3, and TT4, without further assessment of FT3 or FT4.\u003c/p\u003e\u003cp\u003eCurrently, the relationship between UA levels in patients with CAD and cardiovascular prognosis has been extensively studied. Some scholars suggest that hyperuricemia is significantly associated with an increased risk of MACE in non-obstructive CAD; however, this conclusion remains a subject of debate. In the study conducted by Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e695 CAD patients were evaluated for myocardial ischemia using D-SPECT MPI to determine the impact of uric acid on the severity of coronary artery stenosis and ischemia. The results indicated that hyperuricemia could elevate the MACE risk in non-obstructive CAD. Nevertheless, UA was not identified as a risk factor for abnormal MPI findings in this study. Future research may consider expanding the sample size to further investigate the influence of uric acid on the severity of coronary ischemia, thereby providing more valuable reference information for clinical practice.\u003c/p\u003e\u003cp\u003eFurthermore, Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003einvestigated the prognostic value of D-SPECT detection in patients with normal ejection fraction but diastolic dysfunction associated with CAD. The study indicated that, despite preserved systolic function, diastolic dysfunction is significantly correlated with heart failure and all-cause mortality. Although this research compared the E/A ratios between two patient groups and found that those in the hypertensive group exhibited impaired ventricular diastolic function at an early stage, it did not delve deeply into the prognostic implications for these patients. Future clinical studies should further focus on the prognostic assessment of CAD patients who have a normal ejection fraction but present with diastolic dysfunction, aiming to provide a more comprehensive reference for clinical practice.\u003c/p\u003e"},{"header":"6 concluding remarks","content":"\u003cp\u003eThe incidence of CMD among patients with hypertension is significantly high within the spectrum of cardiovascular diseases. A considerable number of these patients are unable to receive accurate diagnosis and timely intervention in the early stages of their condition, leading not only to economic losses but also increasing the risk of MACE and delaying disease progression. With the continuous advancement in medical technology, D-SPECT-derived MBF and CFR can accurately assess microvascular function in CMD patients. This enables the formulation of personalized treatment plans, thereby enhancing the management of coronary heart disease and ultimately reducing MACE incidence while improving patients' quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eXLB\u0026mdash;Research design, data collection, data analysis, and manuscript writing.\u003c/p\u003e\n\u003cp\u003eLXY\u0026mdash;Literature retrieval and data organization.\u003c/p\u003e\n\u003cp\u003eXMW\u0026mdash;Data Organization and Manuscript Revision.\u003c/p\u003e\n\u003cp\u003eWW\u0026mdash;Data Organization and Manuscript Revision.\u003c/p\u003e\n\u003cp\u003eGDG\u0026mdash;Data Organization and Analysis.\u003c/p\u003e\n\u003cp\u003eYYC\u0026mdash;literature search and data Organization.\u003c/p\u003e\n\u003cp\u003eNW\u0026mdash;\u0026nbsp;Research guidance and thesis revision.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\u003cp\u003e The patient gave informed written consent to publish her case (including the publication of images).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.All authors declare that there are no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e2024 Gansu Province Science and Technology Program Projects(24JRRA604);2024 Lanzhou City Science and Technology Program Projects(2024-4-27).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXLB\u0026mdash;Research design, data collection, data analysis, and manuscript writing.LXY\u0026mdash;Literature retrieval and data organization.XMW\u0026mdash;Data Organization and Manuscript Revision.WW\u0026mdash;Data Organization and Manuscript Revision.GDG\u0026mdash;Data Organization and Analysis.YYC\u0026mdash;literature search and data Organization.NW\u0026mdash; Research guidance and thesis revision.All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis retrospective study was approved by the Ethics Committee of Gansu Provincial People's Hospital and the methods were carried out in accordance with the approved guidelines.All the patients have been informed and signed informed consent before the experiments.The authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIves CW, Sinkey R, Rajapreyar I, et al. Preeclampsia-Pathophysiology and Clinical Presentations: JACC State-of-the-Art Review [J]. J Am Coll Cardiol, 2020, 76(14): 1690-1702.doi: 10.1016/j.jacc.2020.08.014. PMID: 33004135.\u003c/li\u003e\n\u003cli\u003eBoden WE, Decaterina R, Kaski JC, et al. 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Comparison between N(13)NH(3)-PET and (99m)Tc-Tetrofosmin-CZT SPECT in the evaluation of absolute myocardial blood flow and flow reserve [J]. J Nucl Cardiol, 2021, 28(5): 1906-1918.doi: 10.1007/s12350-019-01939-x. Epub 2019 Nov 14. PMID: 31728817.\u003c/li\u003e\n\u003cli\u003eYamamoto A, Nagao M, Ando K, et al. First Validation of Myocardial Flow Reserve Derived from Dynamic (99m)Tc-Sestamibi CZT-SPECT Camera Compared with (13)N-Ammonia PET [J]. Int Heart J, 2022, 63(2): 202-209.doi: 10.1536/ihj.21-487. PMID: 35354742.\u003c/li\u003e\n\u003cli\u003eBaolly M, Courtehoux M, Metrard G, et al. Dynamic CZT-SPECT: Characterizing the Lower Values of Myocardial Blood Flow and Reserve [J]. Clin Nucl Med, 2023, 48(11): 969-970. doi: 10.1097/RLU.0000000000004849. Epub 2023 Sep 18. PMID: 37756437; PMCID: PMC10581433.\u003c/li\u003e\n\u003cli\u003eKalantari F, Mohseninia N, Wetdsch A, et al. Head-to-Head Comparison of CZT-SPECT and SPECT/CT Myocardial Perfusion Imaging: Interobserver and Intraobserver Agreement and Diagnostic Performance [J]. Life (Basel), 2023, 13(9).doi: 10.3390/life13091879. PMID: 37763283; PMCID: PMC10532584.\u003c/li\u003e\n\u003cli\u003eMileva N, Paolisso P, Gallinoro E, et al. Diagnostic and Prognostic Role of Cardiac Magnetic Resonance in MINOCA: Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging. 2023 Mar;16(3):376-389. doi: 10.1016/j.jcmg.2022.12.029. PMID: 36889851.\u003c/li\u003e\n\u003cli\u003eWoo HG, Kim DH, Lee H, et al. Association between changes in predicted body composition and occurrence of heart failure: a nationwide population study [J]. Front Endocrinol (Lausanne), 2023, 14: 1210371. doi: 10.3389/fendo.2023.1210371. PMID: 37937051; PMCID: PMC10627176.\u003c/li\u003e\n\u003cli\u003eVorobeva DA, Ryabov VV, Lugacheva JG, et al. Relationships between indicators of prothrombotic activity and coronary microvascular dysfunction in patients with myocardial infarction with obstructive and non-obstructive coronary artery disease [J]. BMC Cardiovasc Disord, 2022, 22(1): 530.doi: 10.1186/s12872-022-02985-z. PMID: 36474151; PMCID: PMC9727929.\u003c/li\u003e\n\u003cli\u003eZhang H, Capbelli F, Che W, et al. The prognostic value of CZT SPECT myocardial blood flow (MBF) quantification in patients with ischemia and no obstructive coronary artery disease (INOCA): a pilot study [J]. Eur J Nucl Med Mol Imaging, 2023, 50(7): 1940-1953.doi: 10.1007/s00259-023-06125-3. Epub 2023 Feb 14. PMID: 36786817; PMCID: PMC10199834.\u003c/li\u003e\n\u003cli\u003ePiskorz D. Hypertensive Mediated Organ Damage and Hypertension Management. How to Assess Beneficial Effects of Antihypertensive Treatments? [J]. High Blood Press Cardiovasc Prev, 2020, 27(1): 9-17.doi: 10.1007/s40292-020-00361-6. Epub 2020 Jan 23. PMID: 31975151.\u003c/li\u003e\n\u003cli\u003eĐorđević DB, Koračević GP, Đorđević AD, Lović DB. Hypertension and left ventricular hypertrophy. J Hypertens. 2024 Sep 1;42(9):1505-1515. doi: 10.1097/HJH.0000000000003774. Epub 2024 May 15. PMID: 38747417.\u003c/li\u003e\n\u003cli\u003eLoga R, Vontaobel J, Rovai D, et al. Multicentre multi-device hybrid imaging study of coronary artery disease: results from the EValuation of INtegrated Cardiac Imaging for the Detection and Characterization of Ischaemic Heart Disease (EVINCI) hybrid imaging population [J]. Eur Heart J Cardiovasc Imaging, 2016, 17(9): 951-960.doi: 10.1093/ehjci/jew038. Epub 2016 Mar 18. PMID: 26992419; PMCID: PMC5841878.\u003c/li\u003e\n\u003cli\u003eFreitag MT, Bremerich J, Wild D, et al. Quantitative myocardial perfusion (82)Rb-PET assessed by hybrid PET/coronary-CT: Normal values and diagnostic performance [J]. J Nucl Cardiol, 2022, 29(2): 464-473.doi: 10.1007/s12350-020-02264-4. Epub 2020 Jul 16. PMID: 32676910.\u003c/li\u003e\n\u003cli\u003eBom MJ, Vansdiemen PA, Driessen RS, et al. Prognostic value of [15O]H2O positron emission tomography-derived global and regional myocardial perfusion [J]. Eur Heart J Cardiovasc Imaging, 2020, 21(7): 777-786.doi: 10.1093/ehjci/jez258. PMID: 31620792.\u003c/li\u003e\n\u003cli\u003eFarhad H, Dunet V, Bachelard K, et al. Added prognostic value of myocardial blood flow quantitation in rubidium-82 positron emission tomography imaging [J]. Eur Heart J Cardiovasc Imaging, 2013, 14(12): 1203-1210.doi: 10.1093/ehjci/jet068. Epub 2013 May 9. PMID: 23660750.\u003c/li\u003e\n\u003cli\u003eZhang H, Che W, Shi K, et al. FT4/FT3 ratio: A novel biomarker predicts coronary microvascular dysfunction (CMD) in euthyroid INOCA patients [J]. Front Endocrinol (Lausanne), 2022, 13: 1021326.doi: 10.3389/fendo.2022.1021326. PMID: 36187090; PMCID: PMC9520241.\u003c/li\u003e\n\u003cli\u003eXu S, Liu L, Yin G, et al. Prognostic Significance of Uric Acid in Patients with Obstructive and Nonobstructive Coronary Artery Disease Undergoing D-SPECT [J]. Clin Interv Aging, 2021, 16: 1955-1965.doi: 10.2147/CIA.S339600. PMID: 34815667; PMCID: PMC8605808.\u003c/li\u003e\n\u003cli\u003eXu B, Liu L, Abdu FA, et al. Prognostic Value of Diastolic Dysfunction Derived From D-SPECT in Coronary Artery Disease Patients With Normal Ejection Fraction [J]. Front Cardiovasc Med, 2021, 8: 700027.doi: 10.3389/fcvm.2021.700027. PMID: 34336957; PMCID: PMC8319539.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dynamic Single Photon Emission Computed Tomography, Hypertension, Ischaemia with Non-obstructive Coronary Arteries, Myocardial Perfusion Imaging, Coronary Flow Reserve","lastPublishedDoi":"10.21203/rs.3.rs-7272519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7272519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong-term hypertension patients may experience structural changes in the myocardium, microvascular dysfunction, and myocardial ischemia, leading to a decrease in MBF and CFR, which exacerbates clinical symptoms and increases the incidence of MACE. This study aims to evaluate the characteristics of MPI and CFR, as well as their influencing factors, in hypertensive patients with LVH by utilizing a combination of D-SPECT with CAG or CTA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods \u003c/strong\u003e\u0026nbsp;This study is a retrospective analysis that selected patients with angina who visited Gansu Provincial People's Hospital from April 2023 to September 2024 and underwent D-SPECT, CAG, or coronary CTA. According to the 2024 ESC Guidelines for Hypertension Management, patients were categorized into non-hypertensive and hypertensive groups. The general data, laboratory indicators, echocardiographic parameters, and D-SPECT-related metrics of both groups were compared.Furthermore, based on the parameters obtained from echocardiography, a subgroup analysis was conducted for patients in the hypertension group to compare differences in MPI, MBF, and CFR between those with and without LVH. This study has received approval from the Ethics Committee of Gansu Provincial People's Hospital, with approval number: 2024 − 781.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e The SSS, SDS, TPD(s), and Extent(s) in the hypertension group were significantly higher than those in the control group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Additionally, the sMBF and CFR of the LAD, LCX, and RCA coronary arteries were significantly lower in the hypertension group compared to the control group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Subgroup analysis of hypertension revealed that patients with LVH exhibited a significantly lower CFR in the LAD, LCX, and RCA coronary arteries compared to those without LVH (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Notably, both LAD and LCX had a CFR below 2.5, with the lowest CFR observed specifically in the distribution area of the LAD. However, no significant statistical differences were found among subgroups regarding sMBF (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).Through multivariate linear regression analysis, it was determined that SBP and IVS(D) are significant risk factors contributing to the reduction of CFR. Specifically, higher values of SBP and IVS(D) correlate with a lower CFR in coronary vessels. Furthermore, both sMBF and CFR demonstrate high sensitivity and specificity in disease diagnosis. The optimal cutoff values for diagnosing diseases were established through the maximum Youden index derived from the ROC curve; specifically, the optimal cutoff value for sMBF is 2.18, while that for CFR is 2.71.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The sMBF and CFR derived from D-SPECT demonstrate high sensitivity and specificity in disease diagnosis, making them reliable indicators for assessing coronary microcirculation in patients with hypertension. Furthermore, SBP and IVS(D) are identified as the primary risk factors contributing to the decline of CFR. Therefore, actively managing blood pressure and preventing the onset and progression of LVH is a crucial strategy for mitigating CMD.\u003c/p\u003e","manuscriptTitle":"D-SPECT-derived myocardial perfusion imaging and coronary blood flow reserve in the clinical diagnosis of hypertensive patients with INOCA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:54:09","doi":"10.21203/rs.3.rs-7272519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T11:54:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T07:04:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T06:36:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160538080378547765343820537578360423022","date":"2025-09-03T06:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71040998443114823210487719300577361317","date":"2025-09-01T06:56:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272993682560694384049929685142519778463","date":"2025-09-01T05:31:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-01T05:26:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T05:22:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-01T05:05:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T01:23:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-25T01:18:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d498fb67-b74e-4085-8b13-5f2ad8762e58","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":54315844,"name":"Health sciences/Cardiology"},{"id":54315845,"name":"Health sciences/Diseases"},{"id":54315846,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-09-15T12:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:54:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7272519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7272519","identity":"rs-7272519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}