Effect of heart rate on B-type natriuretic peptide in sinus rhythm

Effect of heart rate on B-type natriuretic peptide in sinus rhythm | 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 Effect of heart rate on B-type natriuretic peptide in sinus rhythm Keisuke Fukushima, Kazuo Ogawa, Makoto Kawai, Michihiro Yoshimura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4580756/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract B-type natriuretic peptide (BNP) levels accurately reflect the degree of cardiac overload in heart failure. Considering cardiac morphology and intracardiac pressure, including the left ventricular end-systolic volume index (LVESVI) and left ventricular end-diastolic volume index (LVEDVI), is essential for cardiac overload assessment. These indexes influence plasma BNP levels, and an elevated heart rate affects cardiac morphology. However, the direct relationship between elevated heart rate and plasma BNP levels remains unknown. In this study, we simultaneously measured various hemodynamic parameters and BNP levels during cardiac catheterization in 5,429 inpatients with sinus rhythm at our hospital. Furthermore, we examined how heart rate affects cardiac morphology, intracardiac pressure, and plasma BNP levels via regression analysis and structure equation modeling (SEM). Univariate regression analysis revealed a significant positive correlation between heart rate and log BNP levels. The path model with SEM revealed significant positive relationships of heart rate and LVESVI with left ventricular end-diastolic pressure, in addition to a significant negative relationship of heart rate and LVEDVI with log BNP. Collectively, these findings suggest no positive relationship (rather, a negative relationship) between heart rate and log BNP and that elevated heart rate indirectly increases plasma BNP levels by altering cardiac morphology and intracardiac pressure. Health sciences/Cardiology Health sciences/Medical research/Biomarkers Cardiac intracardiac pressure Cardiac morphology Structure equation modeling Heart rate Natriuretic peptides Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction During heart failure, the synthesis and secretion of both A-type natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) are enhanced, as reflected by an increase in their blood levels [ 1 – 3 ]. However, distinct mechanisms are responsible for ANP and BNP secretion. ANP is stored in granules within atrial myocytes and is immediately secreted from these upon stimulation via atrial stretch. This secretion mode is called the regulatory pathway. In contrast, BNP is primarily synthesized by stimulating the mechanical stretch of the myocardium. Owing to the absence of granules in the ventricular muscle, the protein is synthesized and directly secreted without storage [ 4 – 6 ]. This secretion mode is called the constitutive pathway. BNP has a slower stimulation-to-secretion time than ANP. However, because of the characteristics of its gene sequence containing an AU-rich sequence including several repeat units of AUUUA motif in the 3’-untranslated region of BNP mRNA, the rate of BNP synthesis is considerably faster than that of ANP and other proteins [ 4 ]. ANP is secreted from the atria and is easily affected by heart rate [ 7 – 10 ]. In contrast, BNP is primarily secreted from the ventricles and may be less affected by heart rate [ 10 ]. However, only a few studies have comprehensively investigated these aspects, with clinical studies in human being insufficient, which limits our understanding of the matter. Elevated heart rate is a poor prognostic factor for heart failure, increasing the risk of cardiovascular events and heart failure hospitalization [ 11 – 13 ]. Elevated heart rate may affect cardiac morphology and intracardiac pressure, in addition to leading to various other pathophysiological changes. For example, an elevated heart rate is accompanied by a sustained increase in catecholamine levels, decreasing cardiac function [ 14 ]. Furthermore, elevated heart rate increases p66shc levels, resulting in oxidative stress and promoting myocardial apoptosis [ 15 ]. In our previous structure equation modeling (SEM), we reported a positive correlation between left ventricular end-systolic volume index (LVESVI) and plasma BNP levels as well as a negative correlation between left ventricular end-diastolic volume index (LVEDVI) and plasma BNP levels [ 16 ]. Univariate analysis revealed a positive correlation between LVEDVI and plasma BNP levels. However, when simultaneously considering LVESVI, LVEDVI was negatively correlated with plasma BNP levels. In other words, LVEDVI and plasma BNP levels exhibit a direct negative relationship. Meanwhile, several studies, including ours, have revealed a positive correlation between left ventricular end-diastolic pressure (LVEDP) and plasma BNP levels, which is easy to understand [ 2 , 3 , 17 – 19 ]. Studies have not explored the direct relationship between heart rate and plasma BNP levels in humans. An elevated heart rate may indirectly alter the cardiac morphology and intracardiac pressure, which could in turn increase plasma BNP levels. In other words, the relationship among (1) heart rate, (2) cardiac morphology and intracardiac pressure, and (3) plasma BNP levels cannot be verified without conducting a comprehensive study, particularly when assessing the direct relationship between heart rate and plasma BNP levels. Herein, we determined the effects of heart rate on cardiac morphology, intracardiac pressure, and plasma BNP levels in patients with sinus rhythm. In other words, we verified whether elevated heart rate directly or indirectly affects plasma BNP levels via changes in cardiac morphology and intracardiac pressure. Results Clinical characteristics of the patients Table 1 summarizes the clinical characteristics of the 5,429 patients included in this study. Table 1 Clinical characteristics of the patients Number (%) or Mean ± SD, median [upper and lower quartiles] Total number of patients 5,429 Male (sex) 4,457 (82.1%) Age (years) 65.71 ± 12.02, 67.00 [58.00, 75.00] BMI (kg/m 2 ) 24.30 ± 4.00, 24.00 [21.80, 26.50] Smoking history 3,565 (65.7%) Heart rate (bpm) 72.13 ± 13.71, 71.00 [63.00, 80.00] LVESVI (ml/m 2 ) 30.11 ± 18.53, 24.50 [18.80, 34.40] LVEDVI (ml/m 2 ) 66.47 ± 22.57, 62.10 [51.70, 75.10] LVEDP (mmHg) 14.51 ± 6.75, 14.00 [10.00, 18.00] LVEF (%) 56.81 ± 11.87, 59.80 [50.65, 65.10] Underlying disease Acute myocardial infarction 441 (8.1%) Old myocardial infarction 78 (1.4%) Angina pectoris 3582 (66.0%) Coronary spasm angina 171 (3.1%) Valvular heart disease 406 (7.5%) Cardiomyopathy 445 (8.2%) Hypertension 4,081 (75.2%) Diabetes mellitus 2,148 (39.6%) Hemodialysis 494 (9.1%) Blood routine BNP (pg/mL) 182.76 ± 452.93,47.60 [18.20, 144.50] Cr (mg/dL) 1.60 ± 2.33, 0.86 [0.73, 1.05] eGFR (ml/min/1.73m 2 ) 63.37 ± 26.55, 67.00 [52.20, 79.80] Hb (g/dL) 13.20 ± 2.02, 13.50 [11.90, 14.70] HbA1c (%) 6.23 ± 0.98, 6.00 [5.60, 6.70] Medication Beta-blocker 2,351 (43.3%) Calcium channel blocker 2,820 (51.9%) SGLT-2 inhibitor 126 (2.3%) Antiarrhythmics 239 (4.4%) Digoxin 22 (0.4%) Inotropic drug 35 (0.6%) BMI, body mass index; BNP, B-type natriuretic peptide; Cr, creatinine; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; HbA1c, hemoglobin A1c; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVEF, left ventricular ejection fraction; LVESVI, left ventricular end-systolic volume index; SD, standard deviation. Univariate regression analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP Figure 1 illustrates a scatter plot and Table 2 summarizes the coefficients of univariate regression analysis results between heart rate and log BNP, LVESVI, LVEDVI, and LVEDP. Significant correlations were observed between heart rate and log BNP, LVESVI, LVEDVI, and LVEDP, respectively (Fig. 1 -a, b, c, d). Furthermore, Fig. 1 shows the significant correlations among factors other than heart rate in all combinations (Fig. 1 -e, f, g, h, i, j). Table 2 Results of univariate regression analysis of log BNP Clinical factor Regression coefficientβ Standardizedβ Coefficient of determination R 2 P-value Male −0.262 −0.155 0.024 < 0.001 Age 0.018 0.322 0.111 < 0.001 BMI −0.040 −0.249 0.062 < 0.001 eGFR −0.013 −0.528 0.279 < 0.001 LVEF −0.025 −0.489 0.239 < 0.001 LVESVI 0.016 0.500 0.250 < 0.001 LVEDVI 0.011 0.417 0.174 < 0.001 LVEDP 0.033 0.343 0.118 < 0.001 Heart rate 0.004 0.090 0.008 < 0.001 BMI, body mass index; BNP, B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVEF, left ventricular ejection fraction; LVESVI, left ventricular end-systolic volume index. Multivariate regression analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP Table 3 summarizes the results of multivariate regression analysis, which was performed for a more comprehensive evaluation. A significant correlation was observed between LVESVI and log BNP. However, no significant association was observed between heart rate and log BNP. Table 3 Results of multivariate regression analysis of log BNP Coefficient of determination R 2 0.505 Clinical factor Partial regression coefficientβ Standardized β P-value VIF Male −0.144 −0.089 < 0.001 1.082 Age 0.011 0.211 < 0.001 1.255 BMI −0.019 −0.123 < 0.001 1.177 eGFR −0.007 −0.303 < 0.001 1.161 LVESVI 0.013 0.397 < 0.001 1.210 LVEDP 0.019 0.1992 < 0.001 1.167 Heart rate −0.001 −0.016 0.196 1.040 BMI, body mass index; BNP, B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; LVEDP, left ventricular end-diastolic pressure; LVESVI, left ventricular end-systolic volume index. Structural equation modeling (SEM) of heart rate to log BNP considering cardiac morphology and intracardiac pressure Table 4 summarizes the results of the SEM of heart rate, cardiac morphology, intracardiac pressure, and log BNP. Figure 2 describes the path model. Table 4 Results of structural equation modeling using heart rate, cardiac morphology, intracardiac pressure, and log BNP Clinical factor Standardized estimate Standard error Test statistic P-value Correlation coefficient Heart rate ---> Log BNP −0.046 0.001 −3.403 Log BNP 0.650 0.001 19.672 Log BNP −0.225 0.001 −6.625 Log BNP 0.241 0.001 16.499 LVESVI 0.087 0.023 5.294 LVEDVI −0.061 0.028 −3.659 LVEDP 0.078 0.008 4.760 < 0.001 e1 e2 0.909 e2 e3 0.353 e1 e3 0.328 BNP, B-type natriuretic peptide; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index. A significant positive correlation was observed between heart rate and LVESVI and LVEDP. However, a significant negative relationship was observed between heart rate and log BNP and LVEDVI. Furthermore, a significant positive relationship was observed for LVESVI, LVEDVI, and LVEDP with log BNP. Bayesian inference analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP As shown in Fig. 3 , the effects of LVESVI and LVEDP on log BNP (Fig. 3 a, c) and those of heart rate on LVESVI and LVEDP (Fig. 3 d, f) were in the positive range from zero on the horizontal axis. However, the effect of heart rate on LVEDVI (Fig. 3 e) was in the negative range from zero on the horizontal axis. Furthermore, the effect of heart rate on log BNP was in the negative range from zero on the vertical axis (Fig. 3 a, b, c). A positive or negative range indicated a positive or negative relationship, respectively. Examining the heart rate at which the effect on log BNP is minimized Figure 4 demonstrates the relationship between heart rate and Log BNP by using a scatter plot and a quadratic curve. The heart rate for log BNP at the top of the quadratic curve was approximately 77 bpm. Discussion In this study, univariate regression analysis revealed a positive correlation for heart rate with LVESVI and LVEDP as well as a negative correlation between heart rate and LVEDVI. In contrast, heart rate positively correlated with log BNP. Multivariate regression analysis revealed that the relationship of heart rate with LVESVI and LVEDP was similar to that observed in univariate regression analysis. However, the relationship between heart rate and log BNP was not significantly different. Furthermore, SEM was performed to evaluate the relationship of heart rate with LVESVI, LVEDVI, LVEDP, and log BNP. These results demonstrated a similar relationship to that obtained via multivariate analysis; however, heart rate and log BNP exhibited a significant negative relationship. LVESVI and log BNP exhibited a positive correlation, whereas LVEDVI and log BNP exhibited a negative correlation, which is consistent with the findings of our previous report [ 16 ]. Moreover, the results of Bayesian inference were similar to those of SEM. In other words, in the present study group with sinus rhythm, no direct positive relationship was noted between heart rate and log BNP, while a negative relationship was observed. This suggests that an elevated heart rate indirectly increases plasma BNP levels via changes in cardiac morphology and intracardiac pressure. In the present study, SEM revealed a negative relationship between heart rate and plasma BNP levels. This may be due to many patients in the present study having mild cardiac overload caused by mild bradycardia. However, as revealed in this study, elevated heart rate increases cardiac overload via changes in cardiac morphology and intracardiac pressure, thus increasing plasma BNP levels. Considering this, we attempted to calculate the heart rate at which plasma BNP levels were the lowest in the present study group. As shown in Fig. 4 , the heart rate at which plasma BNP levels were the lowest was 77 bpm. Although this is only a reference value, it does not significantly deviate from the findings of previous studies in which the optimal heart rate was examined [ 12 , 20 ]. In this study, all patients only had sinus rhythm. Patients with atrial fibrillation and other arrhythmias were excluded. Although BNP is primarily secreted via a constitutive pathway in the ventricles, some BNP may actually be secreted from the atria via a regulated pathway, similarly to ANP [ 6 , 21 ]. Therefore, unlike sinus rhythm, plasma BNP and ANP levels may be elevated in patients with atrial fibrillation. In other words, atrial fibrillation may not only affect ventricular morphology and intracardiac pressure but could also directly affect elevated plasma BNP levels. Study limitations The present study had certain limitations. First, although 5,429 patients were included, which represent a fairly large cohort, the study was conducted at a single institution. Therefore, multicenter studies are necessary to confirm our findings. Second, although only patients with sinus rhythm were included in this study, it was the rhythm at the time of cardiac catheterization and not at other times. Third, SEM was applied in this study; however, it does not indicate the true causal relationship between each factor, but merely an association between these. Therefore, caution should be exercised when interpreting the results. Last, the path model devised in this study is relatively simple and is the best one that can be considered at present. However, exploring the possibility that different results may be obtained if another path model is devised and analyzed is warranted. Conclusion In the present study, we noted that elevated heart rate in sinus rhythm does not directly affect increased plasma BNP levels. Elevated heart rate may indirectly increase plasma BNP levels by affecting cardiac morphology and intracardiac pressure. The results of this study suggest that taking into account changes in cardiac morphology and intracardiac pressure is essential when considering the increase in BNP with elevated heart rate. In contrast, it was also suggested that these cardiac overloads could be assessed by BNP. Methods Patients and collection methods The study included 5,429 patients with sinus rhythm who were admitted to the Department of Cardiology, Jikei University Hospital, for cardiovascular diseases. These patients underwent cardiac catheterization between February 2012 and August 2021. Patient information, namely, age, sex, height, weight, body mass index (BMI), smoking history, presence of ischemic heart disease, presence of valvular heart disease, presence of cardiomyopathy, presence of hypertension, presence of diabetes mellitus, presence of hemodialysis, and medications were compiled in database and retrospectively analyzed. Routinely tested blood parameters, namely, plasma BNP levels, estimated glomerular filtration rate, hemoglobin A1c, and hemoglobin levels, as well as cardiac catheterization results, including left ventricular ejection fraction (LVEF) by left ventriculogram (LVG), LVESVI, and LVEDVI were also recorded and analyzed. All data were anonymized. This study was approved by the Ethics Committee of The Jikei University School of Medicine for Biomedical Research (study protocol: 24–355(7121)). We complied with the routine ethical regulations of our institution. All clinical investigations were conducted in accordance with the principles set forth in the Declaration of Helsinki. As this was a retrospective study, instead of obtaining informed consent from each patient, we posted a notice about the study design and contact information according to our routine ethical regulations on the official website of our institution ( https://jikei.bvits.com/rinri/publish.aspx ). In this public notification, we ensured that patients had the opportunity to refuse to participate (opt-out) in the study. LVG during cardiac catheterization During cardiac catheterization, LVG images were obtained from end-systolic and end-diastolic LVG tracings. Then, LVESVI was calculated from end-systolic tracings and divided by the body surface area. Similarly, LVEDVI was calculated from end-diastolic tracings and divided by the body surface area. Single-plane cineangiograms were used to calculate LVEF, LVESVI, and LVEDVI using the area-length formula from the QAngio XA V.7.1 (Medis Medical Imaging Systems, Leiden, The Netherlands) semiautomatic tracing method. Area-length was calculated from a single-plane cineangiogram using a semi-automated tracking method. Statistical analysis Continuous variables are expressed as mean ± standard deviation or median [upper and lower quartiles]. Categorical variables are expressed as percentages of the population. Univariate regression analysis and Pearson’s product-moment correlation coefficient analysis were performed to compare two datasets of continuous variables. The Kolmogorov-Smirnov test was performed to determine whether LVESVI, LVEDVI, and plasma BNP levels were normally distributed. Plasma BNP levels were log-transformed for normal distribution and log BNP was used for analysis. A BNP value of less than 4 (measurement lower limit value) was assumed to be 4 pg/mL. Path models based on SEM were utilized to determine the relationships among clinical factors in this study, particularly with respect to plasma BNP levels. Similar to previous studies [ 16 , 22 , 23 ], the effect of the independent variable on the dependent variable was depicted as a one-way arrow. When a covariate relationship was observed between two variables, it was depicted as a two-way arrow. The dependent variable was affected and defined by the independent variable and other factors (e), and standardized estimates (β) were determined. SPSS statistical software (version 27.0, IBM Corp., Armonk, NY, USA) and IBM SPSS AMOS statistical software (version 27, Amos Development Corporation, Meadville, PA, USA) were used to perform statistical analysis. A P-value of < 0.05 was considered statistically significant. Bayesian inference with SEM was performed using a program contained in IBM SPSS AMOS (version 27.0). Bayesianism allows for uncertainty despite limited information, and the Bayesian approach allows the incorporation of background knowledge into the analysis. Additional validation by Bayesian estimation seemed reasonable to re-evaluate our data by using different statistical methods. In IBM SPSS AMOS, a window summary table is available for Bayesian inference with SEM. Frequency distribution polygons were constructed using the surrounding posterior distribution of the estimates. The selected two-dimensional contour lines comprised three colors from the center of the figure: black, dark gray, and light gray. The black, dark gray, and light gray colors represents 95%, 90%, and 50% confidence intervals, respectively. Two-dimensional contours were used in this study because they can be easily visualized [ 22 – 24 ]. Declarations Competing interests The authors declare no competing interests. Author Contribution K.O., M.K., and M.Y. conceived and designed the study. K.O. and M.K. acquired the data. K.F., K.O., M.K., and M.Y. performed data analysis, wrote the manuscript, and produced the figures and tables. All the authors have reviewed the manuscript. Acknowledgement This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP22K08113 (M.Y.). We thank all trial physicians and nurses from the participating hospitals for their important contributions to this study. We would like to thank ELSEVIER Language Service (https://webshop.elsevier.com/language-editing/) for English language editing. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Mukoyama, M. et al. Brain natriuretic peptide as a novel cardiac hormone in humans. Evidence for an exquisite dual natriuretic peptide system, atrial natriuretic peptide and brain natriuretic peptide. J. Clin. Invest. 87, 1402–1412 (1991). 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Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviews received at journal 21 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers invited by journal 13 Jul, 2024 Editor assigned by journal 13 Jul, 2024 Editor invited by journal 07 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 14 Jun, 2024 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4580756","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329720473,"identity":"2ae8148c-0cf8-4f45-b908-0245f3d402f4","order_by":0,"name":"Keisuke Fukushima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3QMWrDMBTG8c8Y4uVB1xcMyRUUtHQpuYqCwVkNXTqUIii4Sw6gEMgZmqWzTMFZfICCl5pC9+K1lCqBQCfF3QrVfxBC8EN6AkKhP1iiR4ghAE60qjT4eCp8hOyJkP0VcTErRHrIw4jzrC+Kp+l4/dpV5uYSFw8W14Wf1KkR7WyTKlU9NgxuFKTxkDkvy5hEG20PpCvdLC+AJO8ty/vekfl27MbvvhjT8yS3qSOLDUNVO80QZwm95weSrVfuYaZmmjUL7Z2Fklz29NlemX2Tfaxu7yaT/XMtfT/2U6vjCkSlHCaQ2NMufhtIQqFQ6H/0DSihTIzGScvLAAAAAElFTkSuQmCC","orcid":"","institution":"The Jikei University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"Fukushima","suffix":""},{"id":329720474,"identity":"952c3a57-edc0-4c5d-bba4-e71c2a8c223f","order_by":1,"name":"Kazuo Ogawa","email":"","orcid":"","institution":"The Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kazuo","middleName":"","lastName":"Ogawa","suffix":""},{"id":329720476,"identity":"4d1ed96c-d05e-438d-93aa-6d0bddd6d990","order_by":2,"name":"Makoto Kawai","email":"","orcid":"","institution":"The Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Kawai","suffix":""},{"id":329720480,"identity":"b73ac915-c3b9-4daa-bae5-f98c7ab29d83","order_by":3,"name":"Michihiro Yoshimura","email":"","orcid":"","institution":"The Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Michihiro","middleName":"","lastName":"Yoshimura","suffix":""}],"badges":[],"createdAt":"2024-06-14 08:51:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4580756/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4580756/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-81922-w","type":"published","date":"2024-12-30T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61313570,"identity":"57ff87a6-a77d-455f-b159-b266d7f9f70e","added_by":"auto","created_at":"2024-07-29 11:35:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346142,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots obtained from univariate regression analysis.\u003c/p\u003e\n\u003cp\u003eUnivariate regression analysis of (a) log BNP-dependent heart rate, (b) LVESVI-dependent heart rate, (c) LVEDVI-dependent heart rate, (d) LVEDP-dependent heart rate, (e) log BNP-dependent LVESVI, (f) log BNP-dependent LVEDVI, (g) log BNP-dependent LVEDP, (h) LVEDVI-dependent LVESVI, (i) LVEDP-dependent LVESVI, (j) LVEDP-dependent LVEDVI. Regression lines were established for significantly related variables. R\u003csup\u003e2\u003c/sup\u003e represents the predictive accuracy of the regression lines.\u003c/p\u003e\n\u003cp\u003eBNP, B-type natriuretic peptide; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index; R\u003csup\u003e2\u003c/sup\u003e, coefficient of determination.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4580756/v1/ac81536f2b036db867b46b94.png"},{"id":61313569,"identity":"1a83cd09-761a-4953-b702-c7d6876fcca9","added_by":"auto","created_at":"2024-07-29 11:35:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":651025,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram illustrating the effects of heart rate, LVESVI, LVEDVI, and LVEDP on log BNP based on the structural equation modeling (SEM).\u003c/p\u003e\n\u003cp\u003eUnidirectional arrows drawn from heart rate (independent variable) to LVESVI, LVEDVI, LVEDP, and log BNP (dependent variables) represent positive or negative effects, whereas bidirectional arrows represent the relationship between the two variables. The dependent variable is accompanied by an error variable. The square value of the multiple correlation coefficient is presented in the upper-right corner of the dependent variable. The estimated standardized coefficient is displayed above the unidirectional arrows, with values ranging from −1.0 to 1.0, indicating a positive or negative effect, respectively. The higher the positive or negative value, the stronger the effect. Bidirectional arrows are accompanied by an estimate of the correlation coefficient.\u003c/p\u003e\n\u003cp\u003eBNP, B-type natriuretic peptide; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4580756/v1/f851b0d629dee992d7609360.png"},{"id":61313568,"identity":"21d813ca-107c-4478-a16a-8123f425ba23","added_by":"auto","created_at":"2024-07-29 11:35:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":416206,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian inference with structural equation modeling (SEM).\u003c/p\u003e\n\u003cp\u003eThe figures are based on Bayesian estimates. A circular distribution of three colors is present in the center of each figure. The colors change from the center to the outside: black, dark gray, and light gray. Black, dark gray, and light gray indicate 95%, 90%, and 50% confidence intervals, respectively.\u003c/p\u003e\n\u003cp\u003eEffect of heart rate on log BNP \u003cem\u003eversus\u003c/em\u003e LVESVI on log BNP (a), heart rate on log BNP \u003cem\u003eversus\u003c/em\u003e LVEDVI on log BNP (b), heart rate on log BNP \u003cem\u003eversus\u003c/em\u003e LVEDP on log BNP (c), LVESVI on log BNP \u003cem\u003eversus\u003c/em\u003e heart rate on LVESVI (d), LVEDVI on log BNP \u003cem\u003eversus\u003c/em\u003e heart rate on LVEDVI (e), and LVEDP on log BNP \u003cem\u003eversus\u003c/em\u003eheart rate on LVEDP (f).\u003c/p\u003e\n\u003cp\u003eBNP, B-type natriuretic peptide; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4580756/v1/f8dfa69a076ed16083099a86.png"},{"id":61313566,"identity":"615b2724-cc6f-401a-9692-46872e5d24a4","added_by":"auto","created_at":"2024-07-29 11:35:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1557708,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots and quadratic curves of log BNP-dependent heart rate.\u003c/p\u003e\n\u003cp\u003eBNP, B-type natriuretic peptide; R\u003csup\u003e2\u003c/sup\u003e, coefficient of determination.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4580756/v1/99ab2e7b7573cab09d409095.png"},{"id":73093505,"identity":"2556c52a-3890-48dd-88ac-eb1d140700f3","added_by":"auto","created_at":"2025-01-06 16:20:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4168010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4580756/v1/e2570c8d-f94e-4be7-a498-2d6172d49f02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of heart rate on B-type natriuretic peptide in sinus rhythm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDuring heart failure, the synthesis and secretion of both A-type natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) are enhanced, as reflected by an increase in their blood levels [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, distinct mechanisms are responsible for ANP and BNP secretion. ANP is stored in granules within atrial myocytes and is immediately secreted from these upon stimulation via atrial stretch. This secretion mode is called the regulatory pathway. In contrast, BNP is primarily synthesized by stimulating the mechanical stretch of the myocardium. Owing to the absence of granules in the ventricular muscle, the protein is synthesized and directly secreted without storage [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This secretion mode is called the constitutive pathway. BNP has a slower stimulation-to-secretion time than ANP. However, because of the characteristics of its gene sequence containing an AU-rich sequence including several repeat units of AUUUA motif in the 3\u0026rsquo;-untranslated region of BNP mRNA, the rate of BNP synthesis is considerably faster than that of ANP and other proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eANP is secreted from the atria and is easily affected by heart rate [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, BNP is primarily secreted from the ventricles and may be less affected by heart rate [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, only a few studies have comprehensively investigated these aspects, with clinical studies in human being insufficient, which limits our understanding of the matter.\u003c/p\u003e \u003cp\u003eElevated heart rate is a poor prognostic factor for heart failure, increasing the risk of cardiovascular events and heart failure hospitalization [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Elevated heart rate may affect cardiac morphology and intracardiac pressure, in addition to leading to various other pathophysiological changes. For example, an elevated heart rate is accompanied by a sustained increase in catecholamine levels, decreasing cardiac function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, elevated heart rate increases p66shc levels, resulting in oxidative stress and promoting myocardial apoptosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our previous structure equation modeling (SEM), we reported a positive correlation between left ventricular end-systolic volume index (LVESVI) and plasma BNP levels as well as a negative correlation between left ventricular end-diastolic volume index (LVEDVI) and plasma BNP levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Univariate analysis revealed a positive correlation between LVEDVI and plasma BNP levels. However, when simultaneously considering LVESVI, LVEDVI was negatively correlated with plasma BNP levels. In other words, LVEDVI and plasma BNP levels exhibit a direct negative relationship. Meanwhile, several studies, including ours, have revealed a positive correlation between left ventricular end-diastolic pressure (LVEDP) and plasma BNP levels, which is easy to understand [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have not explored the direct relationship between heart rate and plasma BNP levels in humans. An elevated heart rate may indirectly alter the cardiac morphology and intracardiac pressure, which could in turn increase plasma BNP levels. In other words, the relationship among (1) heart rate, (2) cardiac morphology and intracardiac pressure, and (3) plasma BNP levels cannot be verified without conducting a comprehensive study, particularly when assessing the direct relationship between heart rate and plasma BNP levels.\u003c/p\u003e \u003cp\u003eHerein, we determined the effects of heart rate on cardiac morphology, intracardiac pressure, and plasma BNP levels in patients with sinus rhythm. In other words, we verified whether elevated heart rate directly or indirectly affects plasma BNP levels via changes in cardiac morphology and intracardiac pressure.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the patients\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical characteristics of the 5,429 patients included in this study.\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\u003eClinical characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%) or\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median [upper and lower quartiles]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (sex)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,457 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.02, 67.00 [58.00, 75.00]\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\u003e24.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00, 24.00 [21.80, 26.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,565 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.71, 71.00 [63.00, 80.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESVI (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.11\u0026thinsp;\u0026plusmn;\u0026thinsp;18.53, 24.50 [18.80, 34.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDVI (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.47\u0026thinsp;\u0026plusmn;\u0026thinsp;22.57, 62.10 [51.70, 75.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.75, 14.00 [10.00, 18.00]\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\u003e56.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.87, 59.80 [50.65, 65.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e441 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngina pectoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3582 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary spasm angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValvular heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,081 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,148 (39.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemodialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e494 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood routine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182.76\u0026thinsp;\u0026plusmn;\u0026thinsp;452.93,47.60 [18.20, 144.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33, 0.86 [0.73, 1.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.37\u0026thinsp;\u0026plusmn;\u0026thinsp;26.55, 67.00 [52.20, 79.80]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02, 13.50 [11.90, 14.70]\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\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98, 6.00 [5.60, 6.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,351 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium channel blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,820 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT-2 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigoxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInotropic drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eBMI, body mass index; BNP, B-type natriuretic peptide; Cr, creatinine; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; HbA1c, hemoglobin A1c; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVEF, left ventricular ejection fraction; LVESVI, left ventricular end-systolic volume index; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate regression analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates a scatter plot and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the coefficients of univariate regression analysis results between heart rate and log BNP, LVESVI, LVEDVI, and LVEDP. Significant correlations were observed between heart rate and log BNP, LVESVI, LVEDVI, and LVEDP, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-a, b, c, d). Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the significant correlations among factors other than heart rate in all combinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-e, f, g, h, i, j).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of univariate regression analysis of log BNP\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eClinical\u003c/p\u003e \u003cp\u003efactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003cp\u003ecoefficientβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardizedβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient of\u003c/p\u003e \u003cp\u003edetermination\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; BNP, B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVEF, left ventricular ejection fraction; LVESVI, left ventricular end-systolic volume index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate regression analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the results of multivariate regression analysis, which was performed for a more comprehensive evaluation. A significant correlation was observed between LVESVI and log BNP. However, no significant association was observed between heart rate and log BNP.\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\u003eResults of multivariate regression analysis of log BNP\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCoefficient of determination R\u003csup\u003e2\u003c/sup\u003e 0.505\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial regression coefficientβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.082\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\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; BNP, B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; LVEDP, left ventricular end-diastolic pressure; LVESVI, left ventricular end-systolic volume index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStructural equation modeling (SEM) of heart rate to log BNP considering cardiac morphology and intracardiac pressure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the results of the SEM of heart rate, cardiac morphology, intracardiac pressure, and log BNP. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the path model.\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\u003eResults of structural equation modeling using heart rate, cardiac morphology, intracardiac pressure, and log BNP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eClinical factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003cp\u003eestimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003ecoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;3.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVESVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVEDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;3.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVEDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ee2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ee3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ee3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBNP, B-type natriuretic peptide; LVEDP, left ventricular end-diastolic pressure; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA significant positive correlation was observed between heart rate and LVESVI and LVEDP. However, a significant negative relationship was observed between heart rate and log BNP and LVEDVI. Furthermore, a significant positive relationship was observed for LVESVI, LVEDVI, and LVEDP with log BNP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBayesian inference analysis of heart rate, cardiac morphology, intracardiac pressure, and log BNP\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the effects of LVESVI and LVEDP on log BNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, c) and those of heart rate on LVESVI and LVEDP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, f) were in the positive range from zero on the horizontal axis. However, the effect of heart rate on LVEDVI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) was in the negative range from zero on the horizontal axis. Furthermore, the effect of heart rate on log BNP was in the negative range from zero on the vertical axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c). A positive or negative range indicated a positive or negative relationship, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExamining the heart rate at which the effect on log BNP is minimized\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the relationship between heart rate and Log BNP by using a scatter plot and a quadratic curve. The heart rate for log BNP at the top of the quadratic curve was approximately 77 bpm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, univariate regression analysis revealed a positive correlation for heart rate with LVESVI and LVEDP as well as a negative correlation between heart rate and LVEDVI. In contrast, heart rate positively correlated with log BNP. Multivariate regression analysis revealed that the relationship of heart rate with LVESVI and LVEDP was similar to that observed in univariate regression analysis. However, the relationship between heart rate and log BNP was not significantly different. Furthermore, SEM was performed to evaluate the relationship of heart rate with LVESVI, LVEDVI, LVEDP, and log BNP. These results demonstrated a similar relationship to that obtained via multivariate analysis; however, heart rate and log BNP exhibited a significant negative relationship. LVESVI and log BNP exhibited a positive correlation, whereas LVEDVI and log BNP exhibited a negative correlation, which is consistent with the findings of our previous report [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, the results of Bayesian inference were similar to those of SEM. In other words, in the present study group with sinus rhythm, no direct positive relationship was noted between heart rate and log BNP, while a negative relationship was observed. This suggests that an elevated heart rate indirectly increases plasma BNP levels via changes in cardiac morphology and intracardiac pressure.\u003c/p\u003e \u003cp\u003eIn the present study, SEM revealed a negative relationship between heart rate and plasma BNP levels. This may be due to many patients in the present study having mild cardiac overload caused by mild bradycardia. However, as revealed in this study, elevated heart rate increases cardiac overload via changes in cardiac morphology and intracardiac pressure, thus increasing plasma BNP levels. Considering this, we attempted to calculate the heart rate at which plasma BNP levels were the lowest in the present study group. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the heart rate at which plasma BNP levels were the lowest was 77 bpm. Although this is only a reference value, it does not significantly deviate from the findings of previous studies in which the optimal heart rate was examined [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, all patients only had sinus rhythm. Patients with atrial fibrillation and other arrhythmias were excluded. Although BNP is primarily secreted via a constitutive pathway in the ventricles, some BNP may actually be secreted from the atria via a regulated pathway, similarly to ANP [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, unlike sinus rhythm, plasma BNP and ANP levels may be elevated in patients with atrial fibrillation. In other words, atrial fibrillation may not only affect ventricular morphology and intracardiac pressure but could also directly affect elevated plasma BNP levels.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eThe present study had certain limitations. First, although 5,429 patients were included, which represent a fairly large cohort, the study was conducted at a single institution. Therefore, multicenter studies are necessary to confirm our findings. Second, although only patients with sinus rhythm were included in this study, it was the rhythm at the time of cardiac catheterization and not at other times. Third, SEM was applied in this study; however, it does not indicate the true causal relationship between each factor, but merely an association between these. Therefore, caution should be exercised when interpreting the results. Last, the path model devised in this study is relatively simple and is the best one that can be considered at present. However, exploring the possibility that different results may be obtained if another path model is devised and analyzed is warranted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, we noted that elevated heart rate in sinus rhythm does not directly affect increased plasma BNP levels. Elevated heart rate may indirectly increase plasma BNP levels by affecting cardiac morphology and intracardiac pressure. The results of this study suggest that taking into account changes in cardiac morphology and intracardiac pressure is essential when considering the increase in BNP with elevated heart rate. In contrast, it was also suggested that these cardiac overloads could be assessed by BNP.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003ePatients and collection methods\u003c/h2\u003e \u003cp\u003eThe study included 5,429 patients with sinus rhythm who were admitted to the Department of Cardiology, Jikei University Hospital, for cardiovascular diseases. These patients underwent cardiac catheterization between February 2012 and August 2021.\u003c/p\u003e \u003cp\u003ePatient information, namely, age, sex, height, weight, body mass index (BMI), smoking history, presence of ischemic heart disease, presence of valvular heart disease, presence of cardiomyopathy, presence of hypertension, presence of diabetes mellitus, presence of hemodialysis, and medications were compiled in database and retrospectively analyzed. Routinely tested blood parameters, namely, plasma BNP levels, estimated glomerular filtration rate, hemoglobin A1c, and hemoglobin levels, as well as cardiac catheterization results, including left ventricular ejection fraction (LVEF) by left ventriculogram (LVG), LVESVI, and LVEDVI were also recorded and analyzed.\u003c/p\u003e \u003cp\u003eAll data were anonymized. This study was approved by the Ethics Committee of The Jikei University School of Medicine for Biomedical Research (study protocol: 24\u0026ndash;355(7121)). We complied with the routine ethical regulations of our institution. All clinical investigations were conducted in accordance with the principles set forth in the Declaration of Helsinki. As this was a retrospective study, instead of obtaining informed consent from each patient, we posted a notice about the study design and contact information according to our routine ethical regulations on the official website of our institution (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jikei.bvits.com/rinri/publish.aspx\u003c/span\u003e\u003cspan address=\"https://jikei.bvits.com/rinri/publish.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this public notification, we ensured that patients had the opportunity to refuse to participate (opt-out) in the study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLVG during cardiac catheterization\u003c/h2\u003e \u003cp\u003eDuring cardiac catheterization, LVG images were obtained from end-systolic and end-diastolic LVG tracings. Then, LVESVI was calculated from end-systolic tracings and divided by the body surface area. Similarly, LVEDVI was calculated from end-diastolic tracings and divided by the body surface area. Single-plane cineangiograms were used to calculate LVEF, LVESVI, and LVEDVI using the area-length formula from the QAngio XA V.7.1 (Medis Medical Imaging Systems, Leiden, The Netherlands) semiautomatic tracing method. Area-length was calculated from a single-plane cineangiogram using a semi-automated tracking method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median [upper and lower quartiles]. Categorical variables are expressed as percentages of the population. Univariate regression analysis and Pearson\u0026rsquo;s product-moment correlation coefficient analysis were performed to compare two datasets of continuous variables. The Kolmogorov-Smirnov test was performed to determine whether LVESVI, LVEDVI, and plasma BNP levels were normally distributed. Plasma BNP levels were log-transformed for normal distribution and log BNP was used for analysis. A BNP value of less than 4 (measurement lower limit value) was assumed to be 4 pg/mL.\u003c/p\u003e \u003cp\u003ePath models based on SEM were utilized to determine the relationships among clinical factors in this study, particularly with respect to plasma BNP levels.\u003c/p\u003e \u003cp\u003eSimilar to previous studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the effect of the independent variable on the dependent variable was depicted as a one-way arrow. When a covariate relationship was observed between two variables, it was depicted as a two-way arrow. The dependent variable was affected and defined by the independent variable and other factors (e), and standardized estimates (β) were determined.\u003c/p\u003e \u003cp\u003eSPSS statistical software (version 27.0, IBM Corp., Armonk, NY, USA) and IBM SPSS AMOS statistical software (version 27, Amos Development Corporation, Meadville, PA, USA) were used to perform statistical analysis. A P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eBayesian inference with SEM was performed using a program contained in IBM SPSS AMOS (version 27.0). Bayesianism allows for uncertainty despite limited information, and the Bayesian approach allows the incorporation of background knowledge into the analysis. Additional validation by Bayesian estimation seemed reasonable to re-evaluate our data by using different statistical methods. In IBM SPSS AMOS, a window summary table is available for Bayesian inference with SEM. Frequency distribution polygons were constructed using the surrounding posterior distribution of the estimates. The selected two-dimensional contour lines comprised three colors from the center of the figure: black, dark gray, and light gray. The black, dark gray, and light gray colors represents 95%, 90%, and 50% confidence intervals, respectively. Two-dimensional contours were used in this study because they can be easily visualized [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.O., M.K., and M.Y. conceived and designed the study. K.O. and M.K. acquired the data. K.F., K.O., M.K., and M.Y. performed data analysis, wrote the manuscript, and produced the figures and tables. All the authors have reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP22K08113 (M.Y.). We thank all trial physicians and nurses from the participating hospitals for their important contributions to this study. We would like to thank ELSEVIER Language Service (https://webshop.elsevier.com/language-editing/) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMukoyama, M. \u003cem\u003eet al.\u003c/em\u003e Brain natriuretic peptide as a novel cardiac hormone in humans. Evidence for an exquisite dual natriuretic peptide system, atrial natriuretic peptide and brain natriuretic peptide. J. Clin. Invest. 87, 1402\u0026ndash;1412 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshimura, M. \u003cem\u003eet al.\u003c/em\u003e Different secretion patterns of atrial natriuretic peptide and brain natriuretic peptide in patients with congestive heart failure. 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ESC Heart Fail. 10, 955\u0026ndash;964 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Cardiac intracardiac pressure, Cardiac morphology, Structure equation modeling, Heart rate, Natriuretic peptides","lastPublishedDoi":"10.21203/rs.3.rs-4580756/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4580756/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eB-type natriuretic peptide (BNP) levels accurately reflect the degree of cardiac overload in heart failure. Considering cardiac morphology and intracardiac pressure, including the left ventricular end-systolic volume index (LVESVI) and left ventricular end-diastolic volume index (LVEDVI), is essential for cardiac overload assessment. These indexes influence plasma BNP levels, and an elevated heart rate affects cardiac morphology. However, the direct relationship between elevated heart rate and plasma BNP levels remains unknown. In this study, we simultaneously measured various hemodynamic parameters and BNP levels during cardiac catheterization in 5,429 inpatients with sinus rhythm at our hospital. Furthermore, we examined how heart rate affects cardiac morphology, intracardiac pressure, and plasma BNP levels via regression analysis and structure equation modeling (SEM). Univariate regression analysis revealed a significant positive correlation between heart rate and log BNP levels. The path model with SEM revealed significant positive relationships of heart rate and LVESVI with left ventricular end-diastolic pressure, in addition to a significant negative relationship of heart rate and LVEDVI with log BNP. Collectively, these findings suggest no positive relationship (rather, a negative relationship) between heart rate and log BNP and that elevated heart rate indirectly increases plasma BNP levels by altering cardiac morphology and intracardiac pressure.\u003c/p\u003e","manuscriptTitle":"Effect of heart rate on B-type natriuretic peptide in sinus rhythm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 11:35:01","doi":"10.21203/rs.3.rs-4580756/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-11T06:11:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T08:30:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263289237984640144444906784216279763476","date":"2024-10-25T15:01:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-21T11:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191070591977113653591786126736450388927","date":"2024-07-13T12:18:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-13T10:56:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-13T10:51:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-07T05:45:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T10:45:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-14T08:47:56+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":"b3798294-b504-4554-a940-d36f0a6afa35","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34902371,"name":"Health sciences/Cardiology"},{"id":34902372,"name":"Health sciences/Medical research/Biomarkers"}],"tags":[],"updatedAt":"2025-01-06T16:05:24+00:00","versionOfRecord":{"articleIdentity":"rs-4580756","link":"https://doi.org/10.1038/s41598-024-81922-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-30 15:57:33","publishedOnDateReadable":"December 30th, 2024"},"versionCreatedAt":"2024-07-29 11:35:01","video":"","vorDoi":"10.1038/s41598-024-81922-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81922-w","workflowStages":[]},"version":"v1","identity":"rs-4580756","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4580756","identity":"rs-4580756","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}