Association between left ventricular mass and heart failure in chronic kidney disease: a cross-sectional study

Association between left ventricular mass and heart failure in chronic kidney disease: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between left ventricular mass and heart failure in chronic kidney disease: a cross-sectional study Ruo-nan Wang, Dan Bai, Fan Zhao, Wen-jia Shi, Rui Zhang, Bang Du, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5917579/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Left ventricular mass (LVM) is an indicator of left ventricular hypertrophy (LVH), and has been studied in a variety of diseases, but the relationship between LVH and its occurrence in heart failure (HF) in patients with chronic kidney disease (CKD) is currently unknown. Methods In this cross-sectional study, we investigated the association between LVM and HF in 2354 patients with CKD using stratified analyses, restricted cubic spline, and subgroup analyses by the Gansu Provincial People’s Hospital Medical Record Bank. P < 0.05 was considered statistically significant. Results There was a significant difference in LVM between chronic kidney disease patients with and without heart failure ( P 1, P < 0.001). A threshold effect analysis after restricted cubic spline revealed an inflexion point of LVM and a different trend in the prevalence of HF before and after the inflexion point with the increasing of LVM. Subgroup analysis showed a clear positive correlation between LVM and HF at ages greater than 55 years ( P = 0.018). Conclusions In patients with CKD, higher LVM is significantly associated with the development of heart failure, and this association is pronounced in older patients. Enhanced monitoring of left ventricular mass in patients with CKD can help in early recognition and prevention of heart failure. Left ventricular mass Heart failure left ventricular hypertrophy chronic kidney disease Figures Figure 1 Figure 2 Introduction Chronic kidney disease (CKD) is characterized by persistent renal damage; the global burden is high and increasing, with approximately 10 percent of the world’s adult population suffering from some form of chronic kidney disease, which is projected to become the fifth leading cause of death worldwide by 2040[ 1 – 3 ]. CKD is accompanied by impairment of systemic homeostasis and damage to various body systems, and the progression of the disease is associated with a variety of complications[ 4 ], of which cardiovascular disease and the onset of heart failure are particularly important[ 5 , 6 ]. Numerous studies have shown that uremia-induced cardiomyopathy is characterized by diastolic dysfunction, LVH and myocardial fibrosis[ 7 , 8 ]. LVH is present in approximately 40% of patients with kidney disease. LVH is a common marker of cardiovascular risk in patients with CKD and an important prognostic indicator in patients with uremia[ 9 , 10 ]. LVH is an adaptive mechanism in the development of CKD; persistent cardiac hypertrophy can lead to heart failure, arrhythmias and sudden death, resulting in a poor prognosis for patients[ 11 – 13 ]. Hypertension, the most common comorbidity in CKD patients, is a central pathologic factor driving LVH. Chronic hypertension leads to myocardial remodeling and fibrosis by increasing cardiac afterload, activating the renin-angiotensin-aldosterone system (RAAS), and inducing endothelial dysfunction[ 9 , 14 ]. In addition, hypertension and CKD form a vicious circle, with CKD-associated sodium and water retention and RAAS dysregulation exacerbating elevated blood pressure, while elevated blood pressure accelerates the deterioration of renal function through glomerular hyperperfusion, which collectively promotes the conversion of LVH to HF[ 15 , 16 ]. In this context, echocardiography as an imaging tool for quantitative assessment of left ventricular mass (LVM) in patients can accurately assess LVM and left ventricular contractility in patients with CKD[ 17 – 19 ]. The thickness of the posterior wall of the left ventricle (LVPW), the interventricular septum (IVSD) and the anteroposterior diameter of the left ventricle (LVD) were obtained from patients by M-mode ultrasound, and the left ventricular mass of the patients was calculated by the formula: LVM(g) = 0.8[1.04(LVPW + IVSD + LVD) 3 -LVD 3 ] + 0.6[ 19 , 20 ]. In this study, we retrospectively collected patients who attended the Department of Nephrology of Gansu Provincial People's Hospital with a diagnosis of CKD during the period of 2018–2024, and all of them underwent echocardiography to assess the left ventricular mass as well as cardiac function, and their clinical information was collected in order to investigate the correlation between the left ventricular mass and the occurrence of heart failure in the patients. Study design and population This study was a retrospective investigation that included patient data collected between January 2018 and January 2024. The patients’ medical data were obtained from the medical record database of Gansu Provincial People’s Hospital in Lanzhou, China. The inclusion criteria were as follows: (1) CKD was diagnosed in the nephrology department; (2) blood tests and urinalysis had been performed; (3) echocardiographic evaluation was performed. The exclusion criteria included: (1) nonhypertensive causes of LVH (including hypertrophic cardiomyopathy and moderate to severe valvular heart disease); (2) cardiac arrhythmias (atrial fibrillation, atrial flutter, sick sinus node syndrome, and second-degree or third-degree atrioventricular block); (3) pregnancy or breastfeeding; (4) estimated glomerular filtration rate (eGFR) fluctuating by more than 30% over the past 3 months; (5) cardiovascular disease within the past 3 months and unstable clinical status; (6) aggressive malignancy. Therefore, 2354 participants were finally included in this analysis. The study protocol was approved by the ethics committee and institutional review board of our hospital. (Fig. 1 ) The exposure and outcome variables’ definition All echocardiograms were performed independently by experienced physicians, and the sonographers performed the exams according to the 205 ASE guidelines, which were not clear to the patients at the screening[ 21 ]. The exposure variable was LVM measured by echocardiography and calculated as LVM (g) = 0.8 [1.04 (LVPW + IVSD + LVD) 3 -LVD 3 ] + 0.6. According to the 2013 ACCF/AHA guideline for the management of heart failure, the diagnosis of heart failure relies on a comprehensive assessment by the clinician based on various indicators of the patient: (1) Clinical presentation: including dyspnea, fatigue, and decreased exercise tolerance; (2) History and physical examination: including body weight, jugular venous pressure, and peripheral edema; (3) Biomarkers: brain natriuretic peptide (BNP) or its amino-terminal precursor (NT-proBNP); (4) Imaging studies, including Two-dimensional echocardiography is used to assess ejection fraction, size, wall thickness, wall motion, and valve function; (5) Ruling out other diseases: the diagnosis of heart failure requires the exclusion of other noncardiac diseases that may cause similar symptoms[ 22 ]. Covariates The covariates were general personal information of the population, echocardiographic data, blood biochemical data and urine data. General personal information of the population included sex, age, height, weight, disease stage, and etiology (including diabetes mellitus, hypertension, IgA nephropathy, systemic lupus erythematosus, ANCA-associated vasculitis, nephrotic syndrome and chronic glomerulonephritis). Echocardiographic data included anteroposterior left atrial diameter, anteroposterior left ventricular diameter, interventricular septal thickness, left ventricular mass, Left ventricular mass index. Blood biochemical data included NT-proBNP, Urea, Creatinine and Uric acid. Renal function indicators included estimated glomerular filtration rate (eGFR), Urea, Urinary creatinine, urinary microalbumin/urine creatinine, endogenous creatinine clearance and Urinary albumin excretion rate. Statistical analysis Continuous variables were expressed as mean ± standard error if it obeyed the normal distribution, those not obeying normal distribution were expressed as quartiles [M (Q1,Q3)] and categorical variables were expressed as percentages. T-test or Mann-Whitney U test was used for between-group comparisons for continuous variables, Chi-square test was used for between groups of non-normally distributed continuous variables and Fisher’s exact test for categorical variables. One-way logistic regression was used to screen for factors associated with the development of heart failure in patients and was included as covariates in subsequent multifactorial regression models for adjustment. Multiple linear regression models were used to investigate the relationship between LVM and HF levels. Three models were constructed as follows: model 1, unadjusted for covariates; model 2, minimum-adjusted model for age, sex and BMI; model 3 adjusted for potential confounders. To assess the nonlinear properties of the LVM and HF, smooth curve fitting (restricted cubic splines) was used. When a nonlinear correlation was found between LVM and HF, a two-segment linear regression model was used to calculate the inflection point of LVM on HF. Likelihood ratio tests were used for nonlinearity. Subgroup analyses were performed using stratified linear regression analysis. All tests were two-sided, with statistical significance at P < 0.05. All analyses were performed using Empower States ( www.empowerstats.com ) and IBM SPSS Statistics 25.0. A two-sided P value < 0.05 was considered statistically significant. Results Baseline characteristics of the participants with or without heart failure A total of 2354 participants were included in our study and the data of baseline profile are represented in Table1. They were 64.02% male and 35.98% female, their age level was 54.25±15.15 years, 72.77% of the patients were suffering from hypertension and 36.66% were suffering from diabetes mellitus, there was no significant difference in BMI, blood creatinine, uric acid, urinary creatinine in patients with heart failure as compared to patients who were not suffering from heart failure, while for echocardiographic indices, there were significant differences between the two groups in left ventricular, posterior left ventricular wall thickness and septal thickness. For blood indices, there were clear differences between the two groups in NT-proBNP, urea and serum creatinine. For the analysis of urine, the estimated glomerular filtration rate, urinary microalbumin/creatinine ratio and urinary albumin excretion rate indices showed significant differences between the two groups. Association between LVM and HF Firstly, univariate logistic regression analysis was used to analyzed the associations between the collected variables and HF in Table2. There are several variables, including Dialysis, DM, systolic blood pressure, dia­stolic blood pressure, LVM, LVMI, NT-proBNP were significantly positively associated with HF. In addition, female, age≥55years, eGFR, and BMI were significantly negative associated with HF. Multivariate logistic regression models were used to explore the association between LVM and HF as continuous and categorical variables (Table 3). When LVM was analyzed as a continuous variable, we found that an increase in LVM was significantly and positively associated with the risk of HF (Model 1: OR = 3.64, 95% CI: 3.11-4.25; Model 2: OR = 2.71, 95% CI: 1.90 -3.87; all P < 0.001). In the fully adjusted Model 3, the results showed that the risk of HF increased by 119% for each unit increase in LVM (OR = 2.19, 95% CI: 1.51-3.18, P < 0.001). In sensitivity analyses, validation was performed using different LVM quartiles, the multivariable-adjusted OR for quartile Q2 (reference quartile Q1) was 1.38 (95% CI: 0.91-2.10; P = 0.131), for quartile Q3 it was OR 1.93 (95% CI: 1.19-3.12; P = 0.008), and for quartile Q4 it was OR 2.66 (95% CI: 1.33-5.33; P = 0.006), indicating a stable positive association between increased risk of LVM and increased risk of HF ( P for trend = 0.010). Nonlinear results of LVM and HF Smoothed curve fitting (restricted cubic splines) after adjusting for confounders in model 3 showed that the association between LVM and HF was nonlinear over the entire range of LVM ( P for nonlinear = 0.003) (Figure 2). We further found that the threshold effect of LVM was 117.91, and the prevalence of heart failure increased slowly as LVM was below the threshold and then increased significantly above the threshold. Subgroup analysis We performed subgroup analyses stratified by sex, age, BMI, hypertension, and diabetes mellitus to further explore the association between left ventricular mass and heart failure in different populations by means of stratification-weighted multivariate regression analyses and to test for interactions (Table 4). Firstly, for all patients, there was a 2.64-fold increase in the risk of HF for every 1 g increase in LVM. Regarding the correlation between LVM and heart failure, the interaction test showed a significant interaction between LVM and heart failure and age after stratifying age by median ( P = 0.018), which also suggests that we are more likely to develop LVH and thus faster progression to heart failure in patients with chronic kidney disease who are older than 55 years. However, gender, BMI, hypertension, and diabetes mellitus had no significant effect on this positive correlation (interaction P > 0.05). These results suggest that the positive correlation between left ventricular mass and heart failure is similar across gender, BMI, hypertension status, and diabetes mellitus status. Discussion In this study, we used a large sample of chronic kidney disease patients treated in Gansu Provincial People’s Hospital, we investigated the relationship between left ventricular mass and the occurrence of heart failure in the patients, we found a prevalence of HF of 27% in CKD patients, which is consistent with current epidemiologic findings[ 23 ], and we found a positive correlation between left ventricular mass and heart failure. Furthermore, this significant positive association remained after adjusting for confounders between them. Based on the restricted cubic spline model, we found an inflection point of 117.91, and the prevalence of heart failure showed different increasing trends above and below this threshold, suggesting that clinicians should strengthen cardiac function monitoring, echocardiography every 6–12 months, optimize blood pressure and volume management in CKD patients with significantly increased LVM, and formulate an individualized treatment plan to slow down patients' disease progression and improve quality of life. In addition, when performing subgroup analyses and testing for interactions, we found the strongest significance between left ventricular mass and the occurrence of heart failure in patients when their age was greater than 55 years. Our findings are consistent with the established literature and support the validity of LVM as a predictor of cardiovascular complications, because previous studies have shown that LVH is a significant independent risk factor for cardiovascular disease in patients with CKD[ 24 – 26 ]. However, the research further revealed a nonlinear relationship between LVM and HF and identified 117.91(g) as a critical value for LVM, above which the risk of HF is significantly increased. Although it has been emphasized to us in previous studies that deterioration of renal function significantly increases the risk of left ventricular hypertrophy as well as heart failure, no relevant threshold effect analysis has been performed to specifically explain the turning point of this association[ 27 ]. But this finding provides a specific reference index for clinical practice and helps to identify high-risk patients more precisely. Hypertension is the most common comorbidity and etiology in patients with CKD, long-term hypertension directly drives glomerulosclerosis and interstitial fibrosis through high glomerular pressure, microvascular endothelial damage, and sustained activation of the renin-angiotensin-aldosterone system (RAAS), leading to the development and progression of CKD[ 28 , 29 ]. At the same time, the interaction between hypertension and CKD further exacerbates cardiac damage: on the one hand, patients with CKD develop a vicious cycle of hypertension-renal disease due to sodium and water retention, dysregulation of the RAAS, and accumulation of uremic toxins; on the other hand, elevated blood pressure forces compensatory hypertrophy of the left ventricle by increasing cardiac afterload [ 30 , 31 ]. However, this adaptive response gradually evolves into pathological remodeling, including myocardial fibrosis, abnormal energy metabolism and impaired diastolic function, which eventually leads to heart failure with preserved ejection fraction (HFpEF)[ 32 ]. In addition, CKD-associated anemia, chronic inflammation, and disturbances in calcium and phosphorus metabolism further impair myocardial oxygen supply and promote vascular calcification and myocardial fibrosis, which together drive the conversion of LVH to heart failure with reduced ejection fraction (HFrEF)[ 31 , 33 ]. LVH is not only an adaptive response of the heart to hypertension, but also an independent risk factor for cardiovascular events and heart failure[ 34 ]. Imbalance of oxygen supply and demand in hypertrophied myocardium induces myocardial ischemia and promotes contractile dysfunction[ 35 ]. In addition, CKD exacerbates myocardial fibrosis and directly impairs cardiac function through mechanisms such as anemia, disturbances in calcium and phosphorus metabolism, and chronic inflammation[ 36 , 37 ]. The interaction between heart failure and CKD is reflected in the “cardiorenal syndrome,” including decreased cardiac output leads to insufficient renal perfusion, which activates the sympathetic nerves and the RAAS system, further raising blood pressure and aggravating the burden on the heart; while the deterioration of renal function triggers volume overload and electrolyte disorders, forming the basis for the treatment of difficult cardiac conditions, the deterioration of renal function leads to volume overload and electrolyte disorders, creating a therapeutic dilemma[ 35 , 38 ]. A retrospective cohort study tells us that an important predictor of cardiovascular mortality and morbidity in dialysis patients is LVH[ 39 ]. In this study, we use echocardiography to calculate the LVM of the patients and explore the relationship between it and heart failure. Furthermore, some studies have also found that heart failure is often present in patients with chronic kidney disease, as CKD can increase the risk of heart failure, a proportion that can reach 50%. And in a study on heart failure it was also found that impaired kidney function can be found in 45%-63% of heart failure patients[ 40 ].Therefore, the data we obtained only support this association and cannot be applied to clinical prediction and diagnosis, it is also impossible to infer the timing relationship between LVM and HF[ 13 , 17 ]. There are several limitations in this study: it is a retrospective analysis with some selection bias and the causality of the results needs to be interpreted with caution. Secondly, the study data originated from a single center and the sample may lack diversity, limiting the general applicability of the results. Thirdly, time-to-event analysis was not feasible due to the lack of longitudinal data, and we can further ensure the accuracy of the results by conducting cohort studies in subsequent studies.Fourthly, because the data were derived from a retrospective medical record bank, some potential confounders such as medication use, lifestyle, and indicators of RAAS activation and inflammation were not routinely documented and therefore were not included in the analysis, and consideration should be given to incorporating additional variables in future studies to improve the accuracy of the findings. Finally, the measurement of LVM relies on the technical level of echocardiography and is subject to some measurement error. Conclusion This study found that higher LVM was significantly associated with the development of heart failure in CKD patients, and this association was more pronounced in older patients. These findings emphasize the importance of regular monitoring of LVM in the early identification and prevention of heart failure, especially in older patients with CKD, and the assessment and management of cardiac function should be enhanced to improve prognosis. Declarations Ethics statement This study involving human participants were reviewed and approved by the Ethics Committee of Gansu Provincial Hospital (Approval Number: 2024-762).Patients were exempted from informed consent because of the use of a retrospective medical record database. Funding This work was supported by In-Hospital Cultivation Fund of Gansu Provincial Hospital (project number 19SYPYB-2), the Central Guidance Fund for Local Science and Technology Development Reserve Project (project number 24ZYQA029). Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data availability The datasets generated and/or analysed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author on reasonable request. Acknowledgments We thank all the volunteers for the participation. Consent for publication This study does not include any materials revealing personal details and contains no images related to participant privacy. Author Contribution RNW, WJS, RZ, BD, HYS and HYT were responsible for the study concept and RNW for study design. Data extraction was undertaken by WJS,HYS,RZ and BD were responsible for data analysis. Drafting of the manuscript: RNW, HYT and AAC. Critical revision of the manuscript for important intellectual content: RNW, WJS, RZ, BD, HYS, HYT. DB Revised manuscript with data analysis and language .All authors read and approved the fnal manuscript. 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Total （ n=2354 ） No-HF (n=1708) HF (n=646) P Sex (%) <0.001 Male 64.02 31.71 70.12 Female 35.98 38.29 29.88 Age (years) 54.25±15.15 54.62±14.90 53.26±15.77 0.052 BMI (kg/m 2 ) 22.73±3.60 22.81±3.64 22.50±3.48 0.064 BSA (m 2 ) 1.67±0.15 1.66±0.18 1.67±0.18 0.169 CKD classification (%) <0.001 G1 3.19 3.98 1.08 G2 2.55 3.22 0.77 G3 5.23 6.21 2.63 G4 4.63 5.09 3.41 G5 84.41 81.50 92.11 Dialysis (%) <0.001 No 19.81 22.60 12.87 Yes 80.19 77.40 87.13 Altitude（m） 1685.60±483.22 1687.25±478.36 1681.08±496.70 0.805 SBP (mmHg) 145.14±25.38 143.88±25.30 148.48±25.29 <0.001 DBP (mmHg) 81.84±15.89 81.05±15.51 83.93±16.67 <0.001 EF (%) 58.03±9.78 62.56±4.81 46.06±9.48 <0.001 LVPW (mm) 10.64±1.63 10.49±1.60 11.04±1.64 <0.001 LV (mm) 50.84±6.84 49.23±5.62 55.11±7.88 <0.001 IVS (mm) 10.71±1.84 10.55±1.77 11.13±1.94 <0.001 LVM (g) 209.55±66.65 194.62±59.17 249.03±69.18 <0.001 LVMI (g/m 2 ) 323.33±111.21 300.96±100.33 382.46±116.81 <0.001 NT-proBNP (pg/ml) 15350.02±14977.29 11637.38±14064.42 24208.26±13285.71 <0.001 eGFR(ml/min/1.73) 13.55±21.50 15.29±23.56 8.93±13.73 <0.001 Urea(mmol/L) 21.19±11.08 20.93±11.21 21.87±10.71 0.071 Scr (umol/L) 644.73±382.04 637.09±391.11 664.95±356.45 0.104 Ucr (umol/L) 5726.20±2918.19 5753.00±3025.45 5645.97±2577.70 0.689 UAER (ug/min) 1536.80±1470.91 1473.89±1729.45 1718.49±1766.75 0.136 Uric acid (umol/L) 376.00(295.00,465.29) 380.00(301.00,461.15) 366.04(283.07,470.53) 0.294 UACR (mg/g) 264.33(84.08,497.53) 226.14(66.49,465.32) 317.29(165.59,582.02) <0.001 Ccr (mL/min/1.73m 2 ) 11.98(5.47,35.20) 14.49(5.70,42.48) 8.64(5.22,18.58) <0.001 DM (%) 0.005 No 63.34 65.05 58.82 Yes 36.66 34.95 41.18 IgA kidney (%) 1.000 No 98.99 99.0 98.95 Yes 1.01 1.00 1.05 SLE (%) 0.906 No 98.48 98.46 98.54 Yes 1.52 1.54 1.46 AAV (%) 0.161 No 97.97 97.69 98.74 Yes 2.03 2.31 1.26 NS (%) 0.008 No 98.20 97.69 97.74 Yes 2.03 2.31 1.26 CGN (%) 0.394 No 99.44 99.31 99.79 Yes 0.56 0.69 0.21 Hypertension (%) 0.007 No 27.23 28.75 23.22 Yes 72.77 71.25 76.78 Abbreviations: LVM, Left ventricular mass; HF, Heart Failure; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; BMI body mass index; SBP, systolic blood pressure; DBP, dia­stolic blood pressure; LVMI, Left ventricular mass index; BSA, body surface area; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Scr, serum creatinine; Ucr, urinary creatinine; OR, odds ratio; UACR, Urinary Albumin Creatinine Ratio; CI, confidence interval; CI confidence interval, OR odds ratio; BMI = weight (kg) / height (m)²; BSA (m²) = 0.007184 × height (cm)^0.725 × weight (kg)^0.425; EF = (EDV - ESV) / EDV × 100%; Table2. Univariate logistic regression for variables association with HF. OR (95%) P Sex (%) Female 1.00 (Reference) Male 0.69 (0.57-0.83) <0.001 Age (years) <55 1.00 (Reference) ≥55 0.82 (0.69-0.99) 0.037 Dialysis (%) No 1.00 (Reference) Yes 1.98 (1.49-2.62) <0.001 DM (%) No 1.00 (Reference) Yes 1.30 (1.08-1.57) 0.005 eGFR (ml/min/1.73m 2 ) 0.98 (0.97-0.99) <0.001 BMI (kg/m 2 ) 0.95 (0.89-1.05) <0.001 SBP (mmHg) 2.05 (1.43-2.93) <0.001 DBP (mmHg) 3.11 (1.76-5.48) <0.001 LVM (g) 3.64 (3.11-4.25) <0.001 LVMI (g/m 2 ) 1.97 (1.80-2.16) <0.001 BSA (m 2 ) 1.42 (0.86-2.35) 0.169 NT-proB (pg/ml) 1.69 (1.58-1.80) <0.001 Urea(mmol/L) 1.08 (0.99-1.07) 0.071 Scr (umol/L 1.02 (1.00-1.04) 0.104 Uric acid (umol/L) 0.99 (0.93-1.05) 0.727 Ucr (umol/L) 0.99 (0.93-1.05) 0.689 UACR (mg/g) 1.01 (0.99-1.03) 0.408 Abbreviations: LVM, Left ventricular mass; HF, Heart Failure; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; BMI body mass index; SBP, systolic blood pressure; DBP, dia­stolic blood pressure; LVMI, Left ventricular mass index; BSA, body surface area; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Scr, serum creatinine; Ucr, urinary creatinine; OR, odds ratio; UACR, Urinary Albumin Creatinine Ratio; CI, confidence interval; CI confidence interval, OR odds ratio; Table3. Adjusted odds ratios (95%CI) for association between LVM and the prevalence of HF. Model1 Model2 Model OR (95%CI) P OR (95%CI) P OR (95%CI) P LVM 3.64 (3.11 ~ 4.25) <0.001 2.71 (1.90 ~ 3.87) <0.001 2.19 (1.51 ~ 3.18) <0.001 LVM (quantile) 1 1.00(Reference) <0.001 1.00(Reference) 1.00(Reference) 2 1.95 (1.38 ~ 2.76) <0.001 1.36 (0.92 ~ 1.99) 0.119 1.38 (0.91 ~ 2.10) 0.131 3 4.02 (2.91 ~ 5.55) <0.001 1.84 (1.18 ~ 2.88) 0.008 1.93 (1.19 ~ 3.12) 0.008 4 9.39 (6.86 ~ 12.87) <0.001 2.20 (1.15 ~ 4.20) 0.017 2.66 (1.33 ~ 5.33) 0.006 P for trend <0.001 0.012 0.007 Model 1: crude model; Model 2: adjusted for Sex, Age and BMI; Model 3: adjusted for Sex, Age, BMI, Dialysis, DM, SBP, DBP, eGFR; Abbreviations: LVM, Left ventricular mass; HF, Heart Failure; BMI body mass index; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, dia­stolic blood pressure; eGFR, estimated-glomerular filtration rate; OR, odds ratio; CI, confidence interval. Table 4. Subgroup analyses of the association between LVM and HF in chronic kidney disease. All presented covariates were adjusted (as Model 3) except the corresponding stratification variable. Abbreviations: BMI, body mass index; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; OR, odds ratio; CI, confidence interval Subgroups n (%) OR (95%CI) P P for interaction All patients 2354 (100.00) 3.64 (3.11 ~ 4.25) <0.001 Sex 0.968 Male 1507 (64.02) 3.77 (3.11 ~ 4.57) <0.001 Female 847 (35.98) 3.74 (2.73 ~ 5.13) <0.001 Age (years) 0.018 <55 1113 (47.28) 3.09 (2.53 ~ 3.78) <0.001 ≥55 1241 (52.72) 4.55 (3.54 ~ 5.86) <0.001 DM 0.055 No 1491 (63.34) 3.26 (2.70 ~ 3.92) <0.001 Yes 863 (36.66) 4.54 (3.41 ~ 6.05) <0.001 BMI (kg/m 2 ) 0.115 <19 334 (14.19) 3.13 (2.07 ~ 4.75) <0.001 19-24 1223 (51.95) 4.48 (3.55 ~ 5.65) <0.001 ≥24 797 (33.86) 3.22 (2.49 ~ 4.17) <0.001 Hypertension 0.162 No 641 (27.23) 3.42 (2.85 ~ 4.10) <0.001 Yes 1713 (72.77) 4.46 (3.20 ~ 6.20) <0.001 Additional Declarations No competing interests reported. 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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-5917579","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445315408,"identity":"45ad3ea3-85f7-4be9-ae55-a73e034e1cb1","order_by":0,"name":"Ruo-nan Wang","email":"","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruo-nan","middleName":"","lastName":"Wang","suffix":""},{"id":445315409,"identity":"e1377593-07a3-4e58-8372-5f66d94d558c","order_by":1,"name":"Dan Bai","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Bai","suffix":""},{"id":445315410,"identity":"9388c8fa-df63-4d9e-a532-6981221391b0","order_by":2,"name":"Fan Zhao","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhao","suffix":""},{"id":445315411,"identity":"e379e6dc-e90b-4a6e-af1f-6c808fea28f5","order_by":3,"name":"Wen-jia Shi","email":"","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen-jia","middleName":"","lastName":"Shi","suffix":""},{"id":445315412,"identity":"5c7868f7-a8a8-487d-883d-78eb7478bc19","order_by":4,"name":"Rui Zhang","email":"","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":445315413,"identity":"a9c20b0a-8784-4016-9e0f-62634e8c10da","order_by":5,"name":"Bang Du","email":"","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bang","middleName":"","lastName":"Du","suffix":""},{"id":445315414,"identity":"438cd1ec-ec65-4eeb-935e-6c685d3ca115","order_by":6,"name":"Hong-yan Sun","email":"","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong-yan","middleName":"","lastName":"Sun","suffix":""},{"id":445315415,"identity":"dc573387-6a48-4ddf-a725-4eb3c303cd1a","order_by":7,"name":"Haiyang Tang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Tang","suffix":""},{"id":445315416,"identity":"d0cd73b4-263b-4d33-89ad-ad1fef9065cf","order_by":8,"name":"Ai-ai Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACZgaGA4wNDHJs7M0HSNNizMdzLIEEm4BaEudJ5CgQp5q/nfnhgZ87Dqe3MeQwMPyo2EZYi8RhNoODvWcO57YxnD3A2HPmNhHWHGYwOMzYBtTC2JfAzNhGhBb5w+wfQFrS2Zh5DIjTYnCYB2xLAhsbsVoMD/MUHOxtSzds42FLOEiUX+TOH9/84Webtbz8/McHH/yoIMb7yOAAiepHwSgYBaNgFOACAGG/PSkqQRfdAAAAAElFTkSuQmCC","orcid":"","institution":"The First Clinical Medical School of Gansu University of Chinese Medicine, Gansu Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ai-ai","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2025-01-28 09:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5917579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5917579/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81541987,"identity":"f468d861-0bc1-4d69-b426-b1bb8bb589c0","added_by":"auto","created_at":"2025-04-28 11:17:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38258,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5917579/v1/507e5429c33a28f31af485c4.jpg"},{"id":81541988,"identity":"ae111da1-4da0-4ae3-9042-a797beb18cbe","added_by":"auto","created_at":"2025-04-28 11:17:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24995,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between LVM and the risk of HF. Adjusted odds ratio of total HF from a restricted cubic spline logistic regression model with knots at the 5th, 35th, 65th and 95th percentiles. Data are ORs (red line) and 95% CIs (shadow area) from multivariate logistic regression analysis with restricted cubic splines. Abbreviations: LVM, Left ventricular mass; HF, Heart Failure; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5917579/v1/e1a6251de5d62c9457bb61ca.jpg"},{"id":83213530,"identity":"942dfa8e-2474-4e31-8550-03179f72218a","added_by":"auto","created_at":"2025-05-21 08:47:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":963715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5917579/v1/6cf16a08-b61a-4853-b36a-9cdb34760be4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between left ventricular mass and heart failure in chronic kidney disease: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is characterized by persistent renal damage; the global burden is high and increasing, with approximately 10 percent of the world’s adult population suffering from some form of chronic kidney disease, which is projected to become the fifth leading cause of death worldwide by 2040[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. CKD is accompanied by impairment of systemic homeostasis and damage to various body systems, and the progression of the disease is associated with a variety of complications[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], of which cardiovascular disease and the onset of heart failure are particularly important[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Numerous studies have shown that uremia-induced cardiomyopathy is characterized by diastolic dysfunction, LVH and myocardial fibrosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLVH is present in approximately 40% of patients with kidney disease. LVH is a common marker of cardiovascular risk in patients with CKD and an important prognostic indicator in patients with uremia[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. LVH is an adaptive mechanism in the development of CKD; persistent cardiac hypertrophy can lead to heart failure, arrhythmias and sudden death, resulting in a poor prognosis for patients[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hypertension, the most common comorbidity in CKD patients, is a central pathologic factor driving LVH. Chronic hypertension leads to myocardial remodeling and fibrosis by increasing cardiac afterload, activating the renin-angiotensin-aldosterone system (RAAS), and inducing endothelial dysfunction[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, hypertension and CKD form a vicious circle, with CKD-associated sodium and water retention and RAAS dysregulation exacerbating elevated blood pressure, while elevated blood pressure accelerates the deterioration of renal function through glomerular hyperperfusion, which collectively promotes the conversion of LVH to HF[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, echocardiography as an imaging tool for quantitative assessment of left ventricular mass (LVM) in patients can accurately assess LVM and left ventricular contractility in patients with CKD[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The thickness of the posterior wall of the left ventricle (LVPW), the interventricular septum (IVSD) and the anteroposterior diameter of the left ventricle (LVD) were obtained from patients by M-mode ultrasound, and the left ventricular mass of the patients was calculated by the formula: LVM(g) = 0.8[1.04(LVPW + IVSD + LVD)\u003csup\u003e3\u003c/sup\u003e-LVD\u003csup\u003e3\u003c/sup\u003e] + 0.6[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we retrospectively collected patients who attended the Department of Nephrology of Gansu Provincial People's Hospital with a diagnosis of CKD during the period of 2018–2024, and all of them underwent echocardiography to assess the left ventricular mass as well as cardiac function, and their clinical information was collected in order to investigate the correlation between the left ventricular mass and the occurrence of heart failure in the patients.\u003c/p\u003e"},{"header":"Study design and population","content":"\u003cp\u003eThis study was a retrospective investigation that included patient data collected between January 2018 and January 2024. The patients’ medical data were obtained from the medical record database of Gansu Provincial People’s Hospital in Lanzhou, China. The inclusion criteria were as follows: (1) CKD was diagnosed in the nephrology department; (2) blood tests and urinalysis had been performed; (3) echocardiographic evaluation was performed. The exclusion criteria included: (1) nonhypertensive causes of LVH (including hypertrophic cardiomyopathy and moderate to severe valvular heart disease); (2) cardiac arrhythmias (atrial fibrillation, atrial flutter, sick sinus node syndrome, and second-degree or third-degree atrioventricular block); (3) pregnancy or breastfeeding; (4) estimated glomerular filtration rate (eGFR) fluctuating by more than 30% over the past 3 months; (5) cardiovascular disease within the past 3 months and unstable clinical status; (6) aggressive malignancy. Therefore, 2354 participants were finally included in this analysis. The study protocol was approved by the ethics committee and institutional review board of our hospital. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eThe exposure and outcome variables’ definition\u003c/h2\u003e\u003cp\u003eAll echocardiograms were performed independently by experienced physicians, and the sonographers performed the exams according to the 205 ASE guidelines, which were not clear to the patients at the screening[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The exposure variable was LVM measured by echocardiography and calculated as LVM (g) = 0.8 [1.04 (LVPW + IVSD + LVD)\u003csup\u003e3\u003c/sup\u003e-LVD\u003csup\u003e3\u003c/sup\u003e] + 0.6. According to the 2013 ACCF/AHA guideline for the management of heart failure, the diagnosis of heart failure relies on a comprehensive assessment by the clinician based on various indicators of the patient: (1) Clinical presentation: including dyspnea, fatigue, and decreased exercise tolerance; (2) History and physical examination: including body weight, jugular venous pressure, and peripheral edema; (3) Biomarkers: brain natriuretic peptide (BNP) or its amino-terminal precursor (NT-proBNP); (4) Imaging studies, including Two-dimensional echocardiography is used to assess ejection fraction, size, wall thickness, wall motion, and valve function; (5) Ruling out other diseases: the diagnosis of heart failure requires the exclusion of other noncardiac diseases that may cause similar symptoms[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003eCovariates\u003c/h3\u003e\u003cp\u003eThe covariates were general personal information of the population, echocardiographic data, blood biochemical data and urine data. General personal information of the population included sex, age, height, weight, disease stage, and etiology (including diabetes mellitus, hypertension, IgA nephropathy, systemic lupus erythematosus, ANCA-associated vasculitis, nephrotic syndrome and chronic glomerulonephritis). Echocardiographic data included anteroposterior left atrial diameter, anteroposterior left ventricular diameter, interventricular septal thickness, left ventricular mass, Left ventricular mass index. Blood biochemical data included NT-proBNP, Urea, Creatinine and Uric acid. Renal function indicators included estimated glomerular filtration rate (eGFR), Urea, Urinary creatinine, urinary microalbumin/urine creatinine, endogenous creatinine clearance and Urinary albumin excretion rate.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were expressed as mean ± standard error if it obeyed the normal distribution, those not obeying normal distribution were expressed as quartiles [M (Q1,Q3)] and categorical variables were expressed as percentages. T-test or Mann-Whitney U test was used for between-group comparisons for continuous variables, Chi-square test was used for between groups of non-normally distributed continuous variables and Fisher’s exact test for categorical variables. One-way logistic regression was used to screen for factors associated with the development of heart failure in patients and was included as covariates in subsequent multifactorial regression models for adjustment. Multiple linear regression models were used to investigate the relationship between LVM and HF levels. Three models were constructed as follows: model 1, unadjusted for covariates; model 2, minimum-adjusted model for age, sex and BMI; model 3 adjusted for potential confounders. To assess the nonlinear properties of the LVM and HF, smooth curve fitting (restricted cubic splines) was used. When a nonlinear correlation was found between LVM and HF, a two-segment linear regression model was used to calculate the inflection point of LVM on HF. Likelihood ratio tests were used for nonlinearity. Subgroup analyses were performed using stratified linear regression analysis.\u003c/p\u003e\u003cp\u003eAll tests were two-sided, with statistical significance at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. All analyses were performed using Empower States (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and IBM SPSS Statistics 25.0. A two-sided \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the participants with or without heart failure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2354 participants were included in our study and the data of baseline profile are represented in Table1. They were 64.02% male and 35.98% female, their age level was 54.25\u0026plusmn;15.15 years, 72.77% of the patients were suffering from hypertension and 36.66% were suffering from diabetes mellitus, there was no significant difference in BMI, blood creatinine, uric acid, urinary creatinine in patients with heart failure as compared to patients who were not suffering from heart failure, while for echocardiographic indices, there were significant differences between the two groups in left ventricular, posterior left ventricular wall thickness and septal thickness. For blood indices, there were clear differences between the two groups in NT-proBNP, urea and serum creatinine. For the analysis of urine, the estimated glomerular filtration rate, urinary microalbumin/creatinine ratio and urinary albumin excretion rate indices showed significant differences between the two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between LVM and HF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, univariate logistic regression analysis was used to analyzed the associations between the collected variables and HF in Table2. There are several variables, including Dialysis, DM, systolic blood pressure, dia\u0026shy;stolic blood pressure, LVM, LVMI, NT-proBNP were significantly positively associated with HF. In addition, female, age\u0026ge;55years, eGFR, and BMI were significantly negative associated with HF.\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression models were used to explore the association between LVM and HF as continuous and categorical variables (Table 3). When LVM was analyzed as a continuous variable, we found that an increase in LVM was significantly and positively associated with the risk of HF (Model 1: OR = 3.64, 95% CI: 3.11-4.25; Model 2: OR = 2.71, 95% CI: 1.90 -3.87; all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In the fully adjusted Model 3, the results showed that the risk of HF increased by 119% for each unit increase in LVM (OR = 2.19, 95% CI: 1.51-3.18, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In sensitivity analyses, validation was performed using different LVM quartiles, the multivariable-adjusted OR for quartile Q2 (reference quartile Q1) was 1.38 (95% CI: 0.91-2.10; \u003cem\u003eP\u003c/em\u003e = 0.131), for quartile Q3 it was OR 1.93 (95% CI: 1.19-3.12; \u003cem\u003eP\u003c/em\u003e = 0.008), and for quartile Q4 it was OR 2.66 (95% CI: 1.33-5.33; \u003cem\u003eP\u003c/em\u003e = 0.006), indicating a stable positive association between increased risk of LVM and increased risk of HF (\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor trend = 0.010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNonlinear results of LVM and HF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmoothed curve fitting (restricted cubic splines) after adjusting for confounders in model 3 showed that the association between LVM and HF was nonlinear over the entire range of LVM (\u003cem\u003eP\u003c/em\u003e for nonlinear = 0.003) (Figure 2). We further found that the threshold effect of LVM was 117.91, and the prevalence of heart failure increased slowly as LVM was below the threshold and then increased significantly above the threshold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed subgroup analyses stratified by sex, age, BMI, hypertension, and diabetes mellitus to further explore the association between left ventricular mass and heart failure in different populations by means of stratification-weighted multivariate regression analyses and to test for interactions (Table 4). Firstly, for all patients, there was a 2.64-fold increase in the risk of HF for every 1 g increase in LVM. Regarding the correlation between LVM and heart failure, the interaction test showed a significant interaction between LVM and heart failure and age after stratifying age by median (\u003cem\u003eP\u003c/em\u003e = 0.018), which also suggests that we are more likely to develop LVH and thus faster progression to heart failure in patients with chronic kidney disease who are older than 55 years. However, gender, BMI, hypertension, and diabetes mellitus had no significant effect on this positive correlation (interaction \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). These results suggest that the positive correlation between left ventricular mass and heart failure is similar across gender, BMI, hypertension status, and diabetes mellitus status.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used a large sample of chronic kidney disease patients treated in Gansu Provincial People\u0026rsquo;s Hospital, we investigated the relationship between left ventricular mass and the occurrence of heart failure in the patients, we found a prevalence of HF of 27% in CKD patients, which is consistent with current epidemiologic findings[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and we found a positive correlation between left ventricular mass and heart failure. Furthermore, this significant positive association remained after adjusting for confounders between them. Based on the restricted cubic spline model, we found an inflection point of 117.91, and the prevalence of heart failure showed different increasing trends above and below this threshold, suggesting that clinicians should strengthen cardiac function monitoring, echocardiography every 6\u0026ndash;12 months, optimize blood pressure and volume management in CKD patients with significantly increased LVM, and formulate an individualized treatment plan to slow down patients' disease progression and improve quality of life. In addition, when performing subgroup analyses and testing for interactions, we found the strongest significance between left ventricular mass and the occurrence of heart failure in patients when their age was greater than 55 years. Our findings are consistent with the established literature and support the validity of LVM as a predictor of cardiovascular complications, because previous studies have shown that LVH is a significant independent risk factor for cardiovascular disease in patients with CKD[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the research further revealed a nonlinear relationship between LVM and HF and identified 117.91(g) as a critical value for LVM, above which the risk of HF is significantly increased. Although it has been emphasized to us in previous studies that deterioration of renal function significantly increases the risk of left ventricular hypertrophy as well as heart failure, no relevant threshold effect analysis has been performed to specifically explain the turning point of this association[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. But this finding provides a specific reference index for clinical practice and helps to identify high-risk patients more precisely.\u003c/p\u003e \u003cp\u003eHypertension is the most common comorbidity and etiology in patients with CKD, long-term hypertension directly drives glomerulosclerosis and interstitial fibrosis through high glomerular pressure, microvascular endothelial damage, and sustained activation of the renin-angiotensin-aldosterone system (RAAS), leading to the development and progression of CKD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. At the same time, the interaction between hypertension and CKD further exacerbates cardiac damage: on the one hand, patients with CKD develop a vicious cycle of hypertension-renal disease due to sodium and water retention, dysregulation of the RAAS, and accumulation of uremic toxins; on the other hand, elevated blood pressure forces compensatory hypertrophy of the left ventricle by increasing cardiac afterload [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, this adaptive response gradually evolves into pathological remodeling, including myocardial fibrosis, abnormal energy metabolism and impaired diastolic function, which eventually leads to heart failure with preserved ejection fraction (HFpEF)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, CKD-associated anemia, chronic inflammation, and disturbances in calcium and phosphorus metabolism further impair myocardial oxygen supply and promote vascular calcification and myocardial fibrosis, which together drive the conversion of LVH to heart failure with reduced ejection fraction (HFrEF)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLVH is not only an adaptive response of the heart to hypertension, but also an independent risk factor for cardiovascular events and heart failure[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Imbalance of oxygen supply and demand in hypertrophied myocardium induces myocardial ischemia and promotes contractile dysfunction[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, CKD exacerbates myocardial fibrosis and directly impairs cardiac function through mechanisms such as anemia, disturbances in calcium and phosphorus metabolism, and chronic inflammation[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The interaction between heart failure and CKD is reflected in the \u0026ldquo;cardiorenal syndrome,\u0026rdquo; including decreased cardiac output leads to insufficient renal perfusion, which activates the sympathetic nerves and the RAAS system, further raising blood pressure and aggravating the burden on the heart; while the deterioration of renal function triggers volume overload and electrolyte disorders, forming the basis for the treatment of difficult cardiac conditions, the deterioration of renal function leads to volume overload and electrolyte disorders, creating a therapeutic dilemma[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A retrospective cohort study tells us that an important predictor of cardiovascular mortality and morbidity in dialysis patients is LVH[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, we use echocardiography to calculate the LVM of the patients and explore the relationship between it and heart failure. Furthermore, some studies have also found that heart failure is often present in patients with chronic kidney disease, as CKD can increase the risk of heart failure, a proportion that can reach 50%. And in a study on heart failure it was also found that impaired kidney function can be found in 45%-63% of heart failure patients[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].Therefore, the data we obtained only support this association and cannot be applied to clinical prediction and diagnosis, it is also impossible to infer the timing relationship between LVM and HF[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several limitations in this study: it is a retrospective analysis with some selection bias and the causality of the results needs to be interpreted with caution. Secondly, the study data originated from a single center and the sample may lack diversity, limiting the general applicability of the results. Thirdly, time-to-event analysis was not feasible due to the lack of longitudinal data, and we can further ensure the accuracy of the results by conducting cohort studies in subsequent studies.Fourthly, because the data were derived from a retrospective medical record bank, some potential confounders such as medication use, lifestyle, and indicators of RAAS activation and inflammation were not routinely documented and therefore were not included in the analysis, and consideration should be given to incorporating additional variables in future studies to improve the accuracy of the findings. Finally, the measurement of LVM relies on the technical level of echocardiography and is subject to some measurement error.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that higher LVM was significantly associated with the development of heart failure in CKD patients, and this association was more pronounced in older patients. These findings emphasize the importance of regular monitoring of LVM in the early identification and prevention of heart failure, especially in older patients with CKD, and the assessment and management of cardiac function should be enhanced to improve prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants were reviewed and approved by the Ethics Committee of Gansu Provincial Hospital (Approval Number: 2024-762).Patients were exempted from informed consent because of the use of a retrospective medical record database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by In-Hospital Cultivation Fund of Gansu Provincial Hospital (project number 19SYPYB-2), the Central Guidance Fund for Local Science and Technology Development Reserve Project (project number 24ZYQA029).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the volunteers for the participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not include any materials revealing personal details and contains no images related to participant privacy.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRNW, WJS, RZ, BD, HYS and HYT were responsible for the study concept and RNW for study design. Data extraction was undertaken by WJS,HYS,RZ and BD were responsible for data analysis. Drafting of the manuscript: RNW, HYT and AAC. Critical revision of the manuscript for important intellectual content: RNW, WJS, RZ, BD, HYS, HYT. DB Revised manuscript with data analysis and language .All authors read and approved the fnal manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. 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J Hypertens. 2002;20(7):1295\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHickson LJ, et al. Echocardiography Criteria for Structural Heart Disease in Patients With End-Stage Renal Disease Initiating Hemodialysis. J Am Coll Cardiol. 2016;67(10):1173\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee L, et al. Normal Values of Left Ventricular Mass by Two-Dimensional and Three-Dimensional Echocardiography: Results from the World Alliance Societies of Echocardiography Normal Values Study. J Am Soc Echocardiogr. 2023;36(5):533\u0026ndash;e5421.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyerson SG, et al. Left ventricular mass: reliability of M-mode and 2-dimensional echocardiographic formulas. Hypertension. 2002;40(5):673\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarkness A, et al. Normal reference intervals for cardiac dimensions and function for use in echocardiographic practice: a guideline from the British Society of Echocardiography. Echo Res Pract. 2020;7(1):G1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLang RM, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015;28(1):1\u0026ndash;e3914.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYancy CW, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2013;128(16):e240\u0026ndash;327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushita K, et al. Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease. Nat Rev Nephrol. 2022;18(11):696\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaesler N, et al. Mapping cardiac remodeling in chronic kidney disease. Sci Adv. 2023;9(47):eadj4846.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu X, et al. Association of Nighttime Masked Uncontrolled Hypertension With Left Ventricular Hypertrophy and Kidney Function Among Patients with Chronic Kidney Disease Not Receiving Dialysis. JAMA Netw Open. 2022;5(5):e2214460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayer MK, et al. Defining Myocardial Abnormalities Across the Stages of Chronic Kidney Disease: A Cardiac Magnetic Resonance Imaging Study. Volume 13. JACC Cardiovasc Imaging; 2020. pp. 2357\u0026ndash;67. 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtounousi E et al. The bidirectional link between left ventricular hypertrophy and chronic kidney disease. A cross lagged analysis. J Hypertens, 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKu E, et al. Hypertension in CKD: Core Curriculum 2019. Am J Kidney Dis. 2019;74(1):120\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaffer CL, et al. New Insights Into the Renin-Angiotensin System in Chronic Kidney Disease. Circ Res. 2020;127(5):607\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu TW, et al. Renoprotective effect of renin-angiotensin-aldosterone system blockade in patients with predialysis advanced chronic kidney disease, hypertension, and anemia. JAMA Intern Med. 2014;174(3):347\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrexler YR, Bomback AS. Definition, identification and treatment of resistant hypertension in chronic kidney disease patients. Nephrol Dial Transpl. 2014;29(7):1327\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePugliese NR, Masi S, Taddei S. The renin-angiotensin-aldosterone system: a crossroad from arterial hypertension to heart failure. Heart Fail Rev. 2020;25(1):31\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira JP, Rossignol P, Zannad F. Renin-angiotensin-aldosterone system and kidney interactions in heart failure. J Renin Angiotensin Aldosterone Syst. 2019;20(4):1470320319889415.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArtham SM, et al. Clinical impact of left ventricular hypertrophy and implications for regression. Prog Cardiovasc Dis. 2009;52(2):153\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoulikakos D, et al. Left ventricular hypertrophy and endothelial dysfunction in chronic kidney disease. Eur Heart J Cardiovasc Imaging. 2014;15(1):56\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCullough PA, et al. Intensive Hemodialysis, Left Ventricular Hypertrophy, and Cardiovascular Disease. Am J Kidney Dis. 2016;68(5s1):S5\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonstam MA, et al. Left ventricular remodeling in heart failure: current concepts in clinical significance and assessment. JACC Cardiovasc Imaging. 2011;4(1):98\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYogasundaram H, et al. Cardiorenal Syndrome and Heart Failure-Challenges and Opportunities. Can J Cardiol. 2019;35(9):1208\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao CT, Liao MT, Wu CK. Left Ventricular Hypertrophy Geometry and Vascular Calcification Co-Modify the Risk of Cardiovascular Mortality in Patients with End-Stage Kidney Disease: A Retrospective Cohort Study. J Atheroscler Thromb. 2023;30(9):1242\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonco C, Lullo LD. Cardiorenal Syndrome in Western Countries: Epidemiology, Diagnosis and Management Approaches. Kidney Dis (Basel). 2017;2(4):151\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1.\u003c/strong\u003e Characteristics of study participants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=2354\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo-HF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=1708)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=646)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e64.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e31.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e70.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e35.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e38.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e29.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e54.25\u0026plusmn;15.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e54.62\u0026plusmn;14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e53.26\u0026plusmn;15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e22.73\u0026plusmn;3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e22.81\u0026plusmn;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e22.50\u0026plusmn;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBSA (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.67\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.66\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.67\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCKD classification (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eG5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e84.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e81.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e92.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDialysis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e22.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e12.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e80.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e77.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e87.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAltitude（m）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1685.60\u0026plusmn;483.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1687.25\u0026plusmn;478.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1681.08\u0026plusmn;496.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e145.14\u0026plusmn;25.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e143.88\u0026plusmn;25.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e148.48\u0026plusmn;25.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e81.84\u0026plusmn;15.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e81.05\u0026plusmn;15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e83.93\u0026plusmn;16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e58.03\u0026plusmn;9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e62.56\u0026plusmn;4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e46.06\u0026plusmn;9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLVPW (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10.64\u0026plusmn;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10.49\u0026plusmn;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e11.04\u0026plusmn;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLV (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e50.84\u0026plusmn;6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e49.23\u0026plusmn;5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e55.11\u0026plusmn;7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eIVS (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10.71\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10.55\u0026plusmn;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e11.13\u0026plusmn;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLVM (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e209.55\u0026plusmn;66.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e194.62\u0026plusmn;59.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e249.03\u0026plusmn;69.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLVMI (g/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e323.33\u0026plusmn;111.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e300.96\u0026plusmn;100.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e382.46\u0026plusmn;116.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNT-proBNP (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e15350.02\u0026plusmn;14977.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e11637.38\u0026plusmn;14064.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e24208.26\u0026plusmn;13285.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eeGFR(ml/min/1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e13.55\u0026plusmn;21.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e15.29\u0026plusmn;23.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e8.93\u0026plusmn;13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUrea(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e21.19\u0026plusmn;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e20.93\u0026plusmn;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e21.87\u0026plusmn;10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eScr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e644.73\u0026plusmn;382.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e637.09\u0026plusmn;391.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e664.95\u0026plusmn;356.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUcr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5726.20\u0026plusmn;2918.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5753.00\u0026plusmn;3025.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e5645.97\u0026plusmn;2577.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUAER (ug/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1536.80\u0026plusmn;1470.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1473.89\u0026plusmn;1729.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1718.49\u0026plusmn;1766.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUric acid (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e376.00(295.00,465.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e380.00(301.00,461.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e366.04(283.07,470.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e264.33(84.08,497.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e226.14(66.49,465.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e317.29(165.59,582.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCcr (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e11.98(5.47,35.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e14.49(5.70,42.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e8.64(5.22,18.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e63.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e65.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e58.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e36.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e34.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e41.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eIgA kidney (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e98.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e99.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e98.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSLE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e98.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e98.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e98.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAAV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e97.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e97.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e98.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e98.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e97.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e97.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCGN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e99.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e99.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e99.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e27.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e28.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e23.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e72.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e71.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e76.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LVM, Left ventricular mass; HF, Heart Failure; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; BMI body mass index; SBP, systolic blood pressure; DBP, dia\u0026shy;stolic blood pressure; LVMI, Left ventricular mass index; BSA, body surface area; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Scr, serum creatinine; Ucr, urinary creatinine; OR, odds ratio; UACR, Urinary Albumin Creatinine Ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; CI confidence interval, OR odds ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI = weight (kg) / height (m)\u0026sup2;;\u003c/p\u003e\n\u003cp\u003eBSA (m\u0026sup2;) = 0.007184 \u0026times; height (cm)^0.725 \u0026times; weight (kg)^0.425;\u003c/p\u003e\n\u003cp\u003eEF = (EDV - ESV) / EDV \u0026times; 100%;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2.\u003c/strong\u003eUnivariate logistic regression for variables association with HF.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.69 (0.57-0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026ge;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.82 (0.69-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eDialysis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.98 (1.49-2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eDM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.30 (1.08-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.98 (0.97-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.95 (0.89-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2.05 (1.43-2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e3.11 (1.76-5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eLVM (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e3.64 (3.11-4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eLVMI (g/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.97 (1.80-2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eBSA (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.42 (0.86-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eNT-proB (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.69 (1.58-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eUrea(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.08 (0.99-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eScr (umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.02 (1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eUric acid (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.99 (0.93-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eUcr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.99 (0.93-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.01 (0.99-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LVM, Left ventricular mass; HF, Heart Failure; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; BMI body mass index; SBP, systolic blood pressure; DBP, dia\u0026shy;stolic blood pressure; LVMI, Left ventricular mass index; BSA, body surface area; NT-proBNP, N-terminal pro-B-type natriuretic peptide; Scr, serum creatinine; Ucr, urinary creatinine; OR, odds ratio; UACR, Urinary Albumin Creatinine Ratio; CI, confidence interval; CI confidence interval, OR odds ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3.\u0026nbsp;\u003c/strong\u003eAdjusted odds ratios (95%CI) for association between LVM and the prevalence of HF.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.64 (3.11 ~ 4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.71 (1.90 ~ 3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.19 (1.51 ~ 3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eLVM (quantile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.95 (1.38 ~ 2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.36 (0.92 ~ 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.38 (0.91 ~ 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.02 (2.91 ~ 5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.84 (1.18 ~ 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.93 (1.19 ~ 3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e9.39 (6.86 ~ 12.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.20 (1.15 ~ 4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.66 (1.33 ~ 5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: crude model;\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for Sex, Age and BMI;\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted for Sex, Age, BMI, Dialysis, DM, SBP, DBP, eGFR;\u003c/p\u003e\n\u003cp\u003eAbbreviations: LVM, Left ventricular mass; HF, Heart Failure; BMI body mass index; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, dia\u0026shy;stolic blood pressure; eGFR, estimated-glomerular filtration rate; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eSubgroup analyses of the association between LVM and HF in chronic kidney disease. All presented covariates were adjusted (as Model 3) except the corresponding stratification variable. Abbreviations: BMI, body mass index; DM, diabetes mellitus; eGFR, estimated-glomerular filtration rate; OR, odds ratio; CI, confidence interval\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003efor interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2354 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.64 (3.11 ~ 4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1507 (64.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.77 (3.11 ~ 4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e847 (35.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.74 (2.73 ~ 5.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1113 (47.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.09 (2.53 ~ 3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1241 (52.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4.55 (3.54 ~ 5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1491 (63.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.26 (2.70 ~ 3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e863 (36.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4.54 (3.41 ~ 6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e334 (14.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.13 (2.07 ~ 4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp; 19-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1223 (51.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4.48 (3.55 ~ 5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e797 (33.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.22 (2.49 ~ 4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e641 (27.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.42 (2.85 ~ 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1713 (72.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4.46 (3.20 ~ 6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Left ventricular mass, Heart failure, left ventricular hypertrophy, chronic kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-5917579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5917579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eLeft ventricular mass (LVM) is an indicator of left ventricular hypertrophy (LVH), and has been studied in a variety of diseases, but the relationship between LVH and its occurrence in heart failure (HF) in patients with chronic kidney disease (CKD) is currently unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, we investigated the association between LVM and HF in 2354 patients with CKD using stratified analyses, restricted cubic spline, and subgroup analyses by the Gansu Provincial People\u0026rsquo;s Hospital Medical Record Bank. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was a significant difference in LVM between chronic kidney disease patients with and without heart failure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for several covariates, there was a positive correlation between LVM and HF (OR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A threshold effect analysis after restricted cubic spline revealed an inflexion point of LVM and a different trend in the prevalence of HF before and after the inflexion point with the increasing of LVM. Subgroup analysis showed a clear positive correlation between LVM and HF at ages greater than 55 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn patients with CKD, higher LVM is significantly associated with the development of heart failure, and this association is pronounced in older patients. Enhanced monitoring of left ventricular mass in patients with CKD can help in early recognition and prevention of heart failure.\u003c/p\u003e","manuscriptTitle":"Association between left ventricular mass and heart failure in chronic kidney disease: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:17:08","doi":"10.21203/rs.3.rs-5917579/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac72093a-aad7-4bfd-9588-41be3c5c8dcf","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-21T08:39:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 11:17:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5917579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5917579","identity":"rs-5917579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}