Triglyceride glucose-body mass index is a risk factor for adverse clinical outcomes in patients undergoing septal myectomy: a retrospective study

Triglyceride glucose-body mass index is a risk factor for adverse clinical outcomes in patients undergoing septal myectomy: a retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Triglyceride glucose-body mass index is a risk factor for adverse clinical outcomes in patients undergoing septal myectomy: a retrospective study Changpeng Song, Xinxin Zheng, Jingang Cui, Qiulan Yang, Shuiyun MD, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8068895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study investigated whether triglyceride‑glucose‑body mass index (TyG-BMI) is associated with clinical outcomes among individuals undergoing septal myectomy. Methods The retrospective cohort study was conducted at Fuwai Hospital, enrolling 1302 individuals with hypertrophic obstructive cardiomyopathy (HOCM) who received septal myectomy. According to the tertile distribution of TyG-BMI, participants were classified into 3 groups (T1-T3). The primary endpoint consisted of hospitalization attributable to heart failure and overall death. Results Mean age of the participants was 49.2 ± 11.8 years, of whom 61% (791) were men. During the 4.7-year follow-up, both composite endpoints and overall mortality were significantly different across the three groups on the K-M curve (Log-rank P < 0.05). Cox analysis showed patients in group T3 exhibited the greatest incidence rates of primary endpoint (adjusted hazard ratio (HR) = 2.18, 95% confidence interval (CI) 1.24–3.83, P = 0.007), as well as the highest risk of overall death (adjusted HR = 3.67, 95% CI 1.41–9.58, P = 0.008) compared to those in group T1. When treated as a continuous variable, TyG-BMI remained significantly linked to an elevated risk of primary endpoint (each 1-SD increment, adjusted HR = 1.39, 95%CI 1.12–1.71, P = 0.003) and increased overall mortality (each 1-SD increment, adjusted HR = 1.72, 95%CI 1.29–2.30, P < 0.001). Conclusions The observations emphasize the role of TyG-BMI in the prediction of adverse outcomes after septal myectomy in HOCM. TyG-BMI Hypertrophic cardiomyopathy Insulin resistance Septal myectomy Figures Figure 1 Figure 2 Introduction Hypertrophic cardiomyopathy (HCM) can lead to myocardial ischemia, sudden cardiac death, and heart failure [ 1 – 2 ]. For patients with symptoms refractory to medical management and a left ventricular outflow tract (LVOT) gradient of 50mmHg or higher, surgical myectomy remains the gold standard therapeutic intervention to relieve obstruction and improve long-term survival [ 1 – 3 ]. Although surgical techniques are continuously improving, in addition to recent advancement in the drug therapy [ 4 ], the long-term postoperative prognosis remains an important concern for clinicians. Identifying potential risk factors for adverse outcomes and implementing early intervention are of great importance. Metabolic dysfunction is an important factor influencing myocardial structure and function. Insulin resistance (IR) refers to diminished tissue sensitivity to normal insulin concentrations, which is linked to higher incidence of type 2 diabetes mellitus (DM) and cardiovascular disorders [ 5 ]. There are multiple methods to assess IR [ 6 ]. The triglyceride glucose index (TyG) is demonstrated to serve as a simple and reliable indicator of IR, irrespective of diabetes status [ 7 ]. Several parameters derived from TyG were reported in recent studies [ 7 ]. Triglyceride‑glucose‑body mass index (TyG-BMI) shows excellent representativeness and is not inferior to HOMA-IR for assessment of IR [ 8 ]. Furthermore, elevated Tyg-BMI have been acknowledged as a risk factor for adverse outcomes in many cardiovascular disorders [ 7 , 9 – 10 ]. However, evidence regarding the relationship between TyG-BMI and clinical outcomes after septal myectomy in HCM is scarce. Thereby, this current investigation is performed to assess the relationship between TyG-BMI and postoperative adverse outcome in the individuals undergoing septal myectomy. Materials and methods Participants and design of study This study was conducted at Fuwai Hospital and is designed as a retrospective longitudinal analysis. This study screened 1416 patients who diagnosed hypertrophic obstructive cardiomyopathy (HOCM) and underwent surgical therapy at our center between January 2013 and December 2018. The diagnosis of HOCM and the selection of candidates for septal myectomy were based on established guideline criteria. Exclusion criteria included age younger than 20 years, prior septal reduction therapy, infective endocarditis, left ventricular aneurysm, and lacking value of triglycerides, and fasting blood glucose data. Baseline demographics and clinical characteristics was extracted. Finally, for the current analysis, 1302 patients were enrolled (Fig. 1 ). TyG-BMI calculation TyG-BMI was derived through the calculation using the formula “TyG multiplied by BMI”. BMI was a computed value as weight/height 2 (kg/m 2 ). TyG was equal to ln [fasting glucose (mg/dl) ×fasting TG (mg/dl)]/2. In accordance to tertiles of TyG-BMI, the cohort was stratified into 3 groups: group T1 (n = 434, TyG-BMI 233.30). Definition of adverse outcomes The primary endpoint encompassed hospitalization due to heart failure together with overall death, while overall mortality by itself serving as the secondary endpoint. Follow-up data were obtained via outpatient clinic evaluation or telephone interviews. Statistical analysis For continuous variables, based on the distributional normality assessment, data were presented as median (interquartile) or mean ± SD. ANOVA test and Mann–Whitney U test were conducted to test differences across groups accordingly. Categorical variables were expressed as number (percentage). X2 test or Fisher’s exact test were performed to test the differences across groups appropriately. We performed a comparison of incident rates of adverse outcomes among 3 groups using Kaplan–Meier curve analysis. Cox hazards models were constructed to evaluate the prognostic value of TyG-BMI on clinical outcomes. Model 2 adjusted for gender and age, whereas model 3 adjusted for gender, age, New York Heart Association (NYHA) III/IV, hypertension, DM, and atrial fibrillation, left ventricular ejection fraction (LVEF), maximum wall thickness, left atrial dimension (LAD), left ventricular end-diastolic dimension (LVEDD), and moderate to severe mitral regurgitation. In both adjusted models, group T1 was used as the reference. Additionally, stratified Cox hazards models were performed for subgroup assessment. A two-tailed P-value < 0.05 was recognized as statistically significant. GraphPad Prism 9.0 (GraphPad) and SPSS 25.0 (IBM Corp) were used in this study Results Baseline Data of Study Population 1302 individuals with HOCM undergoing septal myectomy were enrolled for analysis. The average age was 49.2 ± 11.8 years. 61% (791) of participants were male. Mean BMI was 25.6 ± 3.5 kg/m2. Significant differences of several parameters across the 3 groups were detected, comprising age, sex, BMI, triglycerides, fasting blood glucose, hypertension, diabetes mellitus, systolic and diastolic blood pressure, LAD, LVEDD, LVEF, posterior wall thickness, right ventricular dimension, and moderate to severe mitral regurgitation. (Details shown in Table 1 ). Table 1 Demographic and Clinical Characteristics of Study Participants Characteristics T1 ( 233.30) P Value n 434 434 434 Age (years) 46.9 ± 13.1 50.7 ± 11.5 50.1 ± 10.4 < 0.001 Sex (n male/n female) 221/213 281/153 289/145 < 0.001 Heart beats (bpm) 72.4 ± 10.0 72.1 ± 9 .8 72.3 ± 9.4 0.898 Systolic blood pressure (mmHg) 117.7 ± 14.4 123.0 ± 15.6 124.9 ± 15.8 < 0.001 Dilated blood pressure (mmHg) 69.9 ± 9.6 73.4 ± 9.8 74.5 ± 9.7 < 0.001 BMI (kg/m2) 22.1 ± 2.1 25.7 ± 1.4 29.1 ± 2.5 < 0.001 Fasting glucose (mg/dl) 83.2 ± 12.5 88.3 ± 14.2 97.6 ± 58.0 < 0.001 Triglyceride (mg/dl) 97.1 ± 40.0 127.4 ± 52.1 182.4 ± 122.3 < 0.001 TyG index 8.2 ± 0.4 8.5 ± 0.4 8.9 ± 0.5 < 0.001 Hypertension, n (%) 86 (20) 136 (31) 191 (44) < 0.001 Atrial fibrillation, n (%) 66 (15) 58 (13) 69 (16) 0.572 Diabetes mellitus, n (%) 10 (2) 20 (5) 47 (11) < 0.001 NYHA III/IV, n (%) 312 (72) 314 (72) 317 (73) 0.936 Echocardiographic data LAD (mm) 43.2 ± 7.3 45.1 ± 6.9 45.6 ± 6.9 < 0.001 LVEDD (mm) 40.7 ± 5.3 43.3 ± 5.2 44.0 ± 5.0 < 0.001 LVEF (%) 71.1 ± 6.2 69.9 ± 6.0 69.6 ± 5.8 < 0.001 MWT (mm) 22.7 ± 4.9 22.5 ± 5.0 22.5 ± 4.8 0.755 PWT 11.6 ± 2.8 11.8 ± 2.4 12.2 ± 2.5 0.005 Maximum LVOTG (mmHg) 85.8 ± 30.2 85.9 ± 29.5 81.8 ± 27.8 0.056 RVD 20.2 ± 3.2 21.2 ± 3.2 21.6 ± 2.9 < 0.001 MR 258 (59) 235 (54) 214 (49) 0.011 BMI = Body Mass Index; LAD = Left Atrial Dimension; LVEDD = Left Ventricular End-Diastolic Dimension; LVEF = Left Ventricular Ejection Fraction; LVOTG = Left Ventricular Outflow Tract Gradient; MR = Mitral Regurgitation; NYHA = New York Heart Association; PWT = Posterior Wall Thickness; RVD = Right Ventricular Dimension; Intraoperative and Postoperative Outcomes Concomitant procedures were common in patients who underwent septal myectomy, including coronary artery bypass grafting (CABG), valvular procedures, myocardial unroofing, and surgical ablation. The percentages of CABG and tricuspid valve procedure were differed among the 3 groups. Higher TyG-BMI was in relation with shorter durations of ventilator use, intensive care unit stays, and postoperative hospitalizations (all P value < 0.05). (Table 2 ). Table 2 Intraoperative and Postoperative Outcomes Characteristic T1 (n = 434) T2 (n = 434) T3 (n = 434) P Value Aortic Valve Procedure 8(2) 12(3) 9(2) 0.632 Mitral Valve Procedure 62(14) 53(12) 48(11) 0.347 Tricuspid Valve Procedure 4(1) 23(5) 10(2) 0.000 Myocardial Unroofing 20(5) 22(5) 16(4) 0.603 CABG 48(11) 67(15) 86(20) 0.002 Maze ablation 25(6) 27(6) 26(6) 0.960 CPB time, min 90(75–115) 94(76–120) 94(78–122) 0.429 Aortic clamp time, min 60(49–81) 62(50–81) 62(50–81) 0.406 Ventilation, h 18(14–20) 17(14–20) 16(13–19) 0.000 ICU stay, h 48(24–72) 46(24–72) 30(24–60) 0.008 Post-operative hospital stays, d 7(6–9) 7(7–9) 7(6–8) 0.015 Note: CABG = Coronary Artery Bypass Grafting; CPB = Cardiopulmonary Bypass; ICU = Intensive Care Unit; Study population prognosis Across a follow-up duration of 4.7 years, K-M analysis showed distinct incidence of primary endpoint and overall mortality across the three groups (Fig. 2 ). In comparison with those in Group T1, patients in Group T3 with higher TyG-BMI suffered highest rate of primary endpoint and overall death (log-rank P = 0.019 for primary endpoint and P = 0.023 for overall death). Predictive value of TyG-BMI on adverse prognosis We performed Cox regression analyses to investigate the influence of TyG-BMI on adverse prognosis. After adjusting for age, gender, LAD, LVEDD, LVEF, maximum wall thickness, moderate to severe mitral regurgitation, NYHA III/IV, hypertension, diabetes mellitus, and atrial fibrillation (Model 3), patients in group T3 had the highest incident rate of primary endpoint [hazard ratio (HR) = 2.18, 95% confidence interval (CI) 1.24–3.83, P = 0.007] as well as overall mortality (HR = 3.67, 95%CI 1.41–9.58, P = 0.008), compared to those in group T1. When TyG-BMI was incorporated into the analysis as a continuous parameter, elevated TyG-BMI remained significantly correlated with increased risk of primary endpoint (each 1 SD increase, HR = 1.39, 95%CI 1.12–1.71, P = 0.003) and overall mortality (each 1 SD increase, HR = 1.72, 95%CI 1.29–2.30, P < 0.001) even after adjustment for the potential confounding variables. (Table 3 ). Table 3 Univariable and adjusted Cox regression analysis of the relation between TyG-BMI index and clinical outcomes Model 1 Model 2 Model 3 HR (95% CI) P value HR (95% CI) P value HR (95%CI) P value Composite endpoints TyG-BMI (Per SD increase) 1.31(1.09–1.57) 0.004 1.28(1.06–1.56) 0.012 1.39(1.12–1.71) 0.003 T1 Reference Reference Reference T2 1.70(0.98–2.97) 0.060 1.54(0.88–2.71) 0.133 1.62(0.91–2.89) 0.101 T3 2.12(1.24–3.61) 0.006 1.97(1.15–3.38) 0.014 2.18(1.24–3.83) 0.007 All-cause death TyG-BMI (Per SD increase) 1.56(1.21–2.02) 0.001 1.56(1.19–2.04) 0.001 1.72(1.29–2.30) 0.000 T1 Reference Reference Reference T2 2.59(1.01–6.68) 0.049 2.31(0.89–6.02) 0.086 2.61(0.98–6.95) 0.055 T3 3.37(1.35–8.38) 0.009 3.06(1.22–7.71) 0.017 3.67(1.41–9.58) 0.008 Note: a Adjusted for age and sex. b Adjusted for age, gender, left atrial dimension, left ventricular dimension, left ventricular ejection fraction, maximum wall thickness, moderate to severe mitral regurgitation, New York Heart Association (NYHA) III/IV, hypertension, diabetes mellitus, and atrial fibrillation. HR = Hazard Ratio; 95% CI = 95% Confidence Interval. Subgroup analyses To evaluate the prognostic relationship across different subgroups, Cox proportional hazards models with stratification by age, gender, BMI, DM, hypertension, LAD, and maximum wall thickness were constructed. TyG-BMI was related with composite endpoints in most strata, with a statistically significant interaction emerged between TyG-BMI and hypertension (P for interaction < 0.05). Likewise, in majority of strata, TyG-BMI was related with overall mortality, but significant interactions were detected in the age, BMI, hypertension, and maximum wall thickness status subgroups (all P value for interaction < 0.05). (Table 4 ). Table 4 Subgroup analyses of the association between the Tyg-BMI index and composite endpoints in patients undergoing septal myectomy. Characteristics Composite endpoints All-cause death Number of participants HR (95%CI) P value P for interaction HR (95%CI) P value P for interaction Age (years) 0.329 0.047 < 60 1016 1.009(1.003–1.015) 0.003 1.016(1.009–1.023) 0.000 ≥ 60 286 1.003(0.993–1.013) 0.561 0.998(0.982–1.015) 0.830 Gender 0.068 0.054 Male 791 1.003(0.996–1.010) 0.358 1.012(1.003–1.020) 0.010 Female 511 1.013(1.005–1.020) 0.001 1.013(1.001–1.025) 0.028 BMI 0.762 0.030 < 30 1170 1.011(1.003–1.018) 0.005 1.019(1.007–1.031) 0.002 ≥ 30 132 1.006(0.99–1.023) 0.452 1.021(1.003–1.039) 0.024 Diabetes mellitus 0.457 0.123 No 1225 1.009(1.003–1.014) 0.002 1.016(1.008–1.024) 0.000 Yes 77 1.002(0.983–1.021) 0.833 0.993(0.965–1.021) 0.623 Hypertension 0.047 0.026 No 889 1.011(1.005–1.017) < 0.001 1.017(1.010–1.024) 0.000 Yes 413 0.999(0.990–1.009) 0.892 0.998(0.982–1.013) 0.770 Left atrial dimension 0.378 0.770 < 45 669 1.009(1.003–1.016) 0.006 1.013(1.004–1.023) 0.007 ≥ 45 633 1.005(0.997–1.012) 0.227 1.011(1.000-1.021) 0.041 Maximum wall thickness 0.109 0.006 < 30mm 1186 1.005(1.000-1.011) 0.063 1.006(0.998–1.015) 0.159 ≥ 30mm 116 1.015(1.005–1.024) 0.002 1.026(1.014–1.039) 0.000 HR = Hazard Ratio; 95% CI = 95% Confidence Interval. Discussion In this investigation, the predictive role of TyG-BMI on adverse prognostic events was evaluated in individuals with HOCM who underwent septal myectomy. The results reveal that higher TyG-BMI is strongly related to higher rates of primary endpoint and overall death. IR is a typical indicator of metabolic disturbance and associated with progression and poor outcomes of cardiovascular disorders [ 9 – 11 ]. Previous data showed that IR is independently associated with elevated rates of mortality and hospitalization in heart failure population [ 10 ]. Metabolic disturbance could have a significant impact in this process, as it can result in myocardial energy metabolism impairment, endothelial dysfunction, and increased inflammation. These long-standing pathophysiological changes subsequently cause myocardial injury and cardiac remodeling [ 12 – 13 ]. Considering HCM is distinguished by significant myocardial hypertrophy, the impact of IR on the promotion of myocardial hypertrophy and fibrosis could be more striking. Moreover, patients with both HF and HCM and comorbid IR are more likely to suffer arrhythmias, SCD, and heart failure progression [ 14 – 15 ]. There are many measurements to evaluate IR. The index is widely regarded as a robust marker reflecting IR, which is highly correlated to HOMA-IR. The TyG-BMI incorporates effect of both TyG and BMI, which can provide a more comprehensive assessment of metabolic status [ 6 – 7 ]. This combined index has shown a better predictive value for cardiovascular diseases than TyG alone. While there is limited research directly linking TyG-BMI to clinical features and prognosis among patients with HCM, existing studies on TyG index and BMI provide valuable insights. A previous study showed that TyG index was in a positive association with LVEDD in HCM [ 16 ]. In this cohort, TyG-BMI was also in a positive relation to LVEDD. Additionally, left atrial dimension, posterior wall thickness, LVEF, and right ventricular dimension were also significantly different among the 3 groups. Considering that obesity is associated with IR [ 17 ] and a significant factor influencing cardiac structure and function in HCM [ 18 ], the discrepancy between TyG index and TyG-BMI regarding echocardiographic characteristics might be partially attributed to the additional effect of BMI. Thereby, TyG-BMI, by accounting for both metabolic and adiposity factors, may provide a more insightful evaluation of the factors influencing cardiac structure in HCM patients. In terms of prognosis, a prior study reported an inverse association between TyG and clinical outcomes in individuals with HOCM treated with septal reduction therapy [ 19 ]. However, our investigation observed that patients with higher TyG-BMI values experienced worse long-term outcomes. Notedly, the previous study excluded the individuals with DM [ 19 ], whereas such patients were included in the present analysis. Many large-scared studies have reported that DM exerts a significant influence on cardiac remodeling and long-term survival in HCM [ 20 – 21 ]. This could partially explain the different results between our study and that previous study. Furthermore, as noted previously, the TyG-BMI incorporates BMI-related effects. Obesity is well established as a major risk factor in various cardiovascular diseases including HCM [ 18 , 22 ]. This could be another potential reason. The exact mechanisms underlying these observations are not yet fully understood, but several potential pathways may contribute. The TyG-BMI incorporates both metabolic and adiposity factors, providing a more in-depth evaluation of IR and leading to a better prognostic prediction of adverse outcome in HCM. Additionally, obesity contributes to IR and increases cardiovascular risk. The TyG-BMI incorporate the obesity factor into the assessment of IR. This combination may increase the predictive accuracy for worse outcomes in HCM patients, as obesity contribute to the progression of HCM to some extent. In addtion, insulin resistance and obesity are proved to be linked to inflammation and altered metabolism, which are also associated with myocardial fibrosis and hypertrophy [ 23 – 24 ]. Our study not only highlights the significance of TyG-BMI in preoperative risk evaluation for HCM but also underscores the significance of metabolic factors in postoperative long-term management. The elevation in the TyG-BMI corresponded to worse clinical prognoses. Given its significant relationship with clinical outcomes, TyG-BMI appears to be a reliable biomarker for risk assessment for patients undergoing septal myectomy. Furthermore, our findings highlight the potential benefit of persistent management of IR and metabolism in this population. This finding is consistent with another study which reported that metabolic alterations can influence cardiac function and overall prognosis in HCM [ 25 ]. Study limitations Several limitations should be noted. Firstly, the instinctive limitation is selection bias. Secondly, although adjusted for many variables, we believe that there a range of other factors, such as genetic factors and lifestyle factors, may also influence the observed associations. Third, patients in this study predominantly are those undergoing septal myectomy, this limitation may restrict the application of the conclusions to overall HOCM patients. Conclusion In conclusion, TyG-BMI appears to be a promising predictor of clinical prognosis after septal myectomy in patients with HOCM. These observations emphasize its role in preoperative risk stratification and the necessity of modifying metabolic dysfunction after surgery. Declarations Acknowledgement We thank the participants in the study. Author contributions SCP design of this study. SCP, CJG, and ZXX collected the data; SCP conducted the statistical analysis; SCP write the draft; HXH, YQL, and WSY reviewed the manuscript. All the authors approved to submit the manuscript. Funding This work was supported by CAMS Innovation Fund for Medical Sciences (2023-I2M-1-001), the National High Level Hospital Clinical Research Funding of China (grant number 2022-GSP-TS-8; 2022-GSP-GG-29), and National Natural Science Foundation of China (82570466). Availability of data and materials Data is provided upon reasonable requests. Ethics approval and consent to participate This study was approved by the Ethics Committee of Fuwai Hospital (2022-1892) in accordance to the Declaration of Helsinki. Due to its retrospective design, the informed consent to participate was waived. Consent for publication All the authors approved to the publication. Competing interests: None. 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Front Endocrinol (Lausanne). 2025 Aug 26;16:1653927. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8068895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560519616,"identity":"117af553-edfb-4eaf-b22b-1d530b82d132","order_by":0,"name":"Changpeng Song","email":"","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Changpeng","middleName":"","lastName":"Song","suffix":""},{"id":560519617,"identity":"eaa6abef-adc0-4962-a5a2-620efad541fa","order_by":1,"name":"Xinxin Zheng","email":"","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Zheng","suffix":""},{"id":560519622,"identity":"df768428-d380-4bac-b3a6-537c8daf0012","order_by":2,"name":"Jingang Cui","email":"","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Jingang","middleName":"","lastName":"Cui","suffix":""},{"id":560519625,"identity":"b932c8ad-62c1-4b41-a3e7-77c011c1971f","order_by":3,"name":"Qiulan Yang","email":"","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Qiulan","middleName":"","lastName":"Yang","suffix":""},{"id":560519629,"identity":"82e90062-3b9d-41ff-8b17-9066a463f4eb","order_by":4,"name":"Shuiyun MD","email":"","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Shuiyun","suffix":"MD"},{"id":560519630,"identity":"e63a0826-d0b2-4e07-8fa6-a23efce61d2f","order_by":5,"name":"Xiaohong Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACAwh1gB9MfjCwsSNai2QDkDw4oyAtmTQtzDwfDjE2ENJiLpH87OHXtjsSBjeSHx62MTjAzMB++OgGfFosZ6SZG8uceQbUkmZwOMfgDh8DT1raDbwOu5FgJi1RcbjO4HYCSMszZgYJHjMCWtK/SUsYHJYwuJ3+4bCFwWHGBsJacswkP1SAtOQYHGYgSsuZN2XSDGcOS0jef1NwsMcgLZmNoF+Op2+T/Nl2WILvzPHNH378sbHjZz98DK8WEGDmQeaxEVIOAow/iFE1CkbBKBgFIxcAABMPU0yRmTKoAAAAAElFTkSuQmCC","orcid":"","institution":"Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China","correspondingAuthor":true,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-11-09 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1","display":"","copyAsset":false,"role":"figure","size":149829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Chart of patient selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8068895/v1/50848d0b09404d0dc1a1873c.png"},{"id":98751409,"identity":"a0f29745-c0ab-4216-a8a6-98c77fc97b8a","added_by":"auto","created_at":"2025-12-22 09:10:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Kaplan-Meier Curve in patients undergoing septal myectomy. (A) Composite endpoints (All-cause mortality or Heart failure hospitalization), and(B) All-cause mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8068895/v1/72789385d44e3a718d077c64.png"},{"id":102297471,"identity":"bdafc05e-0665-4d7b-a7c0-a517e898fedd","added_by":"auto","created_at":"2026-02-10 10:27:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1940120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8068895/v1/b3a75e5d-3825-4b1a-966d-4f9ae141584b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triglyceride glucose-body mass index is a risk factor for adverse clinical outcomes in patients undergoing septal myectomy: a retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertrophic cardiomyopathy (HCM) can lead to myocardial ischemia, sudden cardiac death, and heart failure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For patients with symptoms refractory to medical management and a left ventricular outflow tract (LVOT) gradient of 50mmHg or higher, surgical myectomy remains the gold standard therapeutic intervention to relieve obstruction and improve long-term survival [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although surgical techniques are continuously improving, in addition to recent advancement in the drug therapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the long-term postoperative prognosis remains an important concern for clinicians. Identifying potential risk factors for adverse outcomes and implementing early intervention are of great importance.\u003c/p\u003e \u003cp\u003eMetabolic dysfunction is an important factor influencing myocardial structure and function. Insulin resistance (IR) refers to diminished tissue sensitivity to normal insulin concentrations, which is linked to higher incidence of type 2 diabetes mellitus (DM) and cardiovascular disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. There are multiple methods to assess IR [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The triglyceride glucose index (TyG) is demonstrated to serve as a simple and reliable indicator of IR, irrespective of diabetes status [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several parameters derived from TyG were reported in recent studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Triglyceride‑glucose‑body mass index (TyG-BMI) shows excellent representativeness and is not inferior to HOMA-IR for assessment of IR [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, elevated Tyg-BMI have been acknowledged as a risk factor for adverse outcomes in many cardiovascular disorders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, evidence regarding the relationship between TyG-BMI and clinical outcomes after septal myectomy in HCM is scarce. Thereby, this current investigation is performed to assess the relationship between TyG-BMI and postoperative adverse outcome in the individuals undergoing septal myectomy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and design of study\u003c/h2\u003e \u003cp\u003eThis study was conducted at Fuwai Hospital and is designed as a retrospective longitudinal analysis. This study screened 1416 patients who diagnosed hypertrophic obstructive cardiomyopathy (HOCM) and underwent surgical therapy at our center between January 2013 and December 2018. The diagnosis of HOCM and the selection of candidates for septal myectomy were based on established guideline criteria. Exclusion criteria included age younger than 20 years, prior septal reduction therapy, infective endocarditis, left ventricular aneurysm, and lacking value of triglycerides, and fasting blood glucose data. Baseline demographics and clinical characteristics was extracted. Finally, for the current analysis, 1302 patients were enrolled (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTyG-BMI calculation\u003c/h3\u003e\n\u003cp\u003eTyG-BMI was derived through the calculation using the formula \u0026ldquo;TyG multiplied by BMI\u0026rdquo;. BMI was a computed value as weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e). TyG was equal to ln [fasting glucose (mg/dl) \u0026times;fasting TG (mg/dl)]/2. In accordance to tertiles of TyG-BMI, the cohort was stratified into 3 groups: group T1 (n\u0026thinsp;=\u0026thinsp;434, TyG-BMI\u0026thinsp;\u0026lt;\u0026thinsp;205.40), group T2 [n\u0026thinsp;=\u0026thinsp;434, TyG-BMI (205.40-233.30)], and group T3 (n\u0026thinsp;=\u0026thinsp;434, TyG-BMI\u0026thinsp;\u0026gt;\u0026thinsp;233.30).\u003c/p\u003e\n\u003ch3\u003eDefinition of adverse outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint encompassed hospitalization due to heart failure together with overall death, while overall mortality by itself serving as the secondary endpoint. Follow-up data were obtained via outpatient clinic evaluation or telephone interviews.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables, based on the distributional normality assessment, data were presented as median (interquartile) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. ANOVA test and Mann\u0026ndash;Whitney U test were conducted to test differences across groups accordingly. Categorical variables were expressed as number (percentage). \u003csup\u003e\u003cb\u003eX2\u003c/b\u003e\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test were performed to test the differences across groups appropriately. We performed a comparison of incident rates of adverse outcomes among 3 groups using Kaplan\u0026ndash;Meier curve analysis. Cox hazards models were constructed to evaluate the prognostic value of TyG-BMI on clinical outcomes. Model 2 adjusted for gender and age, whereas model 3 adjusted for gender, age, New York Heart Association (NYHA) III/IV, hypertension, DM, and atrial fibrillation, left ventricular ejection fraction (LVEF), maximum wall thickness, left atrial dimension (LAD), left ventricular end-diastolic dimension (LVEDD), and moderate to severe mitral regurgitation. In both adjusted models, group T1 was used as the reference. Additionally, stratified Cox hazards models were performed for subgroup assessment. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was recognized as statistically significant. GraphPad Prism 9.0 (GraphPad) and SPSS 25.0 (IBM Corp) were used in this study\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Data of Study Population\u003c/h2\u003e \u003cp\u003e1302 individuals with HOCM undergoing septal myectomy were enrolled for analysis. The average age was 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years. 61% (791) of participants were male. Mean BMI was 25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 kg/m2. Significant differences of several parameters across the 3 groups were detected, comprising age, sex, BMI, triglycerides, fasting blood glucose, hypertension, diabetes mellitus, systolic and diastolic blood pressure, LAD, LVEDD, LVEF, posterior wall thickness, right ventricular dimension, and moderate to severe mitral regurgitation. (Details shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics of Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;205.40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003e(205.40-233.30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;233.30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (n male/n female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221/213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289/145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart beats (bpm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9 .8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDilated blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting glucose (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.6\u0026thinsp;\u0026plusmn;\u0026thinsp;58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.4\u0026thinsp;\u0026plusmn;\u0026thinsp;52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182.4\u0026thinsp;\u0026plusmn;\u0026thinsp;122.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAtrial fibrillation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNYHA III/IV, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLAD (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVEDD (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVEF (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMWT (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePWT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum LVOTG (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.8\u0026thinsp;\u0026plusmn;\u0026thinsp;30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.9\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRVD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; LAD\u0026thinsp;=\u0026thinsp;Left Atrial Dimension; LVEDD\u0026thinsp;=\u0026thinsp;Left Ventricular End-Diastolic Dimension; LVEF\u0026thinsp;=\u0026thinsp;Left Ventricular Ejection Fraction; LVOTG\u0026thinsp;=\u0026thinsp;Left Ventricular Outflow Tract Gradient; MR\u0026thinsp;=\u0026thinsp;Mitral Regurgitation; NYHA\u0026thinsp;=\u0026thinsp;New York Heart Association; PWT\u0026thinsp;=\u0026thinsp;Posterior Wall Thickness; RVD\u0026thinsp;=\u0026thinsp;Right Ventricular Dimension;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntraoperative and Postoperative Outcomes\u003c/h3\u003e\n\u003cp\u003eConcomitant procedures were common in patients who underwent septal myectomy, including coronary artery bypass grafting (CABG), valvular procedures, myocardial unroofing, and surgical ablation. The percentages of CABG and tricuspid valve procedure were differed among the 3 groups. Higher TyG-BMI was in relation with shorter durations of ventilator use, intensive care unit stays, and postoperative hospitalizations (all P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntraoperative and Postoperative Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAortic Valve Procedure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMitral Valve Procedure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTricuspid Valve Procedure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyocardial Unroofing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCABG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaze ablation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCPB time, min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(75\u0026ndash;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(76\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(78\u0026ndash;122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAortic clamp time, min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(49\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(50\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62(50\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVentilation, h\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(14\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(14\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(13\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU stay, h\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(24\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(24\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(24\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-operative hospital stays, d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(6\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(7\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: CABG\u0026thinsp;=\u0026thinsp;Coronary Artery Bypass Grafting; CPB\u0026thinsp;=\u0026thinsp;Cardiopulmonary Bypass; ICU\u0026thinsp;=\u0026thinsp;Intensive Care Unit;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eStudy population prognosis\u003c/h3\u003e\n\u003cp\u003eAcross a follow-up duration of 4.7 years, K-M analysis showed distinct incidence of primary endpoint and overall mortality across the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In comparison with those in Group T1, patients in Group T3 with higher TyG-BMI suffered highest rate of primary endpoint and overall death (log-rank P\u0026thinsp;=\u0026thinsp;0.019 for primary endpoint and P\u0026thinsp;=\u0026thinsp;0.023 for overall death).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictive value of TyG-BMI on adverse prognosis\u003c/h2\u003e \u003cp\u003eWe performed Cox regression analyses to investigate the influence of TyG-BMI on adverse prognosis. After adjusting for age, gender, LAD, LVEDD, LVEF, maximum wall thickness, moderate to severe mitral regurgitation, NYHA III/IV, hypertension, diabetes mellitus, and atrial fibrillation (Model 3), patients in group T3 had the highest incident rate of primary endpoint [hazard ratio (HR)\u0026thinsp;=\u0026thinsp;2.18, 95% confidence interval (CI) 1.24\u0026ndash;3.83, P\u0026thinsp;=\u0026thinsp;0.007] as well as overall mortality (HR\u0026thinsp;=\u0026thinsp;3.67, 95%CI 1.41\u0026ndash;9.58, P\u0026thinsp;=\u0026thinsp;0.008), compared to those in group T1. When TyG-BMI was incorporated into the analysis as a continuous parameter, elevated TyG-BMI remained significantly correlated with increased risk of primary endpoint (each 1 SD increase, HR\u0026thinsp;=\u0026thinsp;1.39, 95%CI 1.12\u0026ndash;1.71, P\u0026thinsp;=\u0026thinsp;0.003) and overall mortality (each 1 SD increase, HR\u0026thinsp;=\u0026thinsp;1.72, 95%CI 1.29\u0026ndash;2.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) even after adjustment for the potential confounding variables. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and adjusted Cox regression analysis of the relation between TyG-BMI index and clinical outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eComposite endpoints\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI (Per SD increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31(1.09\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28(1.06\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.39(1.12\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70(0.98\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54(0.88\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.62(0.91\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12(1.24\u0026ndash;3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97(1.15\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.18(1.24\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI (Per SD increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56(1.21\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56(1.19\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.72(1.29\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59(1.01\u0026ndash;6.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31(0.89\u0026ndash;6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.61(0.98\u0026ndash;6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37(1.35\u0026ndash;8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06(1.22\u0026ndash;7.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.67(1.41\u0026ndash;9.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for age and sex.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Adjusted for age, gender, left atrial dimension, left ventricular dimension, left ventricular ejection fraction, maximum wall thickness, moderate to severe mitral regurgitation, New York Heart Association (NYHA) III/IV, hypertension, diabetes mellitus, and atrial fibrillation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eHR\u0026thinsp;=\u0026thinsp;Hazard Ratio; 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic relationship across different subgroups, Cox proportional hazards models with stratification by age, gender, BMI, DM, hypertension, LAD, and maximum wall thickness were constructed. TyG-BMI was related with composite endpoints in most strata, with a statistically significant interaction emerged between TyG-BMI and hypertension (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Likewise, in majority of strata, TyG-BMI was related with overall mortality, but significant interactions were detected in the age, BMI, hypertension, and maximum wall thickness status subgroups (all P value for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analyses of the association between the Tyg-BMI index and composite endpoints in patients undergoing septal myectomy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eComposite endpoints\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAll-cause death\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.009(1.003\u0026ndash;1.015)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.016(1.009\u0026ndash;1.023)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e286\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.003(0.993\u0026ndash;1.013)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.561\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.998(0.982\u0026ndash;1.015)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.830\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.068\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.054\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.003(0.996\u0026ndash;1.010)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.358\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.012(1.003\u0026ndash;1.020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e511\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.013(1.005\u0026ndash;1.020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.013(1.001\u0026ndash;1.025)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.762\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1170\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.011(1.003\u0026ndash;1.018)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.019(1.007\u0026ndash;1.031)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e132\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.006(0.99\u0026ndash;1.023)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.452\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.021(1.003\u0026ndash;1.039)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.457\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.123\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1225\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.009(1.003\u0026ndash;1.014)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.016(1.008\u0026ndash;1.024)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.002(0.983\u0026ndash;1.021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.833\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.993(0.965\u0026ndash;1.021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.623\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.011(1.005\u0026ndash;1.017)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.017(1.010\u0026ndash;1.024)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e413\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.999(0.990\u0026ndash;1.009)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.892\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.998(0.982\u0026ndash;1.013)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.770\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft atrial dimension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.378\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.770\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e669\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.009(1.003\u0026ndash;1.016)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.013(1.004\u0026ndash;1.023)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e633\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.005(0.997\u0026ndash;1.012)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.227\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.011(1.000-1.021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum wall thickness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.109\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;30mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1186\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.005(1.000-1.011)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.006(0.998\u0026ndash;1.015)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.159\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;30mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e116\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.015(1.005\u0026ndash;1.024)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.026(1.014\u0026ndash;1.039)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eHR\u0026thinsp;=\u0026thinsp;Hazard Ratio; 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this investigation, the predictive role of TyG-BMI on adverse prognostic events was evaluated in individuals with HOCM who underwent septal myectomy. The results reveal that higher TyG-BMI is strongly related to higher rates of primary endpoint and overall death.\u003c/p\u003e \u003cp\u003eIR is a typical indicator of metabolic disturbance and associated with progression and poor outcomes of cardiovascular disorders [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous data showed that IR is independently associated with elevated rates of mortality and hospitalization in heart failure population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Metabolic disturbance could have a significant impact in this process, as it can result in myocardial energy metabolism impairment, endothelial dysfunction, and increased inflammation. These long-standing pathophysiological changes subsequently cause myocardial injury and cardiac remodeling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Considering HCM is distinguished by significant myocardial hypertrophy, the impact of IR on the promotion of myocardial hypertrophy and fibrosis could be more striking. Moreover, patients with both HF and HCM and comorbid IR are more likely to suffer arrhythmias, SCD, and heart failure progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are many measurements to evaluate IR. The index is widely regarded as a robust marker reflecting IR, which is highly correlated to HOMA-IR. The TyG-BMI incorporates effect of both TyG and BMI, which can provide a more comprehensive assessment of metabolic status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This combined index has shown a better predictive value for cardiovascular diseases than TyG alone. While there is limited research directly linking TyG-BMI to clinical features and prognosis among patients with HCM, existing studies on TyG index and BMI provide valuable insights. A previous study showed that TyG index was in a positive association with LVEDD in HCM [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this cohort, TyG-BMI was also in a positive relation to LVEDD. Additionally, left atrial dimension, posterior wall thickness, LVEF, and right ventricular dimension were also significantly different among the 3 groups. Considering that obesity is associated with IR [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and a significant factor influencing cardiac structure and function in HCM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the discrepancy between TyG index and TyG-BMI regarding echocardiographic characteristics might be partially attributed to the additional effect of BMI. Thereby, TyG-BMI, by accounting for both metabolic and adiposity factors, may provide a more insightful evaluation of the factors influencing cardiac structure in HCM patients.\u003c/p\u003e \u003cp\u003eIn terms of prognosis, a prior study reported an inverse association between TyG and clinical outcomes in individuals with HOCM treated with septal reduction therapy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, our investigation observed that patients with higher TyG-BMI values experienced worse long-term outcomes. Notedly, the previous study excluded the individuals with DM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], whereas such patients were included in the present analysis. Many large-scared studies have reported that DM exerts a significant influence on cardiac remodeling and long-term survival in HCM [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This could partially explain the different results between our study and that previous study. Furthermore, as noted previously, the TyG-BMI incorporates BMI-related effects. Obesity is well established as a major risk factor in various cardiovascular diseases including HCM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This could be another potential reason.\u003c/p\u003e \u003cp\u003eThe exact mechanisms underlying these observations are not yet fully understood, but several potential pathways may contribute. The TyG-BMI incorporates both metabolic and adiposity factors, providing a more in-depth evaluation of IR and leading to a better prognostic prediction of adverse outcome in HCM. Additionally, obesity contributes to IR and increases cardiovascular risk. The TyG-BMI incorporate the obesity factor into the assessment of IR. This combination may increase the predictive accuracy for worse outcomes in HCM patients, as obesity contribute to the progression of HCM to some extent. In addtion, insulin resistance and obesity are proved to be linked to inflammation and altered metabolism, which are also associated with myocardial fibrosis and hypertrophy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study not only highlights the significance of TyG-BMI in preoperative risk evaluation for HCM but also underscores the significance of metabolic factors in postoperative long-term management. The elevation in the TyG-BMI corresponded to worse clinical prognoses. Given its significant relationship with clinical outcomes, TyG-BMI appears to be a reliable biomarker for risk assessment for patients undergoing septal myectomy. Furthermore, our findings highlight the potential benefit of persistent management of IR and metabolism in this population. This finding is consistent with another study which reported that metabolic alterations can influence cardiac function and overall prognosis in HCM [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be noted. Firstly, the instinctive limitation is selection bias. Secondly, although adjusted for many variables, we believe that there a range of other factors, such as genetic factors and lifestyle factors, may also influence the observed associations. Third, patients in this study predominantly are those undergoing septal myectomy, this limitation may restrict the application of the conclusions to overall HOCM patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, TyG-BMI appears to be a promising predictor of clinical prognosis after septal myectomy in patients with HOCM. These observations emphasize its role in preoperative risk stratification and the necessity of modifying metabolic dysfunction after surgery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSCP design of this study. SCP, CJG, and ZXX collected the data; SCP conducted the statistical analysis; SCP write the draft; HXH, YQL, and WSY reviewed the manuscript. All the authors approved to submit the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by CAMS Innovation Fund for Medical Sciences (2023-I2M-1-001), the National High Level Hospital Clinical Research Funding of China (grant number 2022-GSP-TS-8; 2022-GSP-GG-29), and National Natural Science Foundation of China (82570466).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided upon reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Fuwai Hospital (2022-1892) in accordance to the Declaration of Helsinki. Due to its retrospective design, the informed consent to participate was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors approved to the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOmmen SR, Ho CY, Asif IM, Balaji S, Burke MA, Day SM, Dearani JA, Epps KC, Evanovich L, Ferrari VA, Joglar JA, Khan SS, Kim JJ, Kittleson MM, Krittanawong C, Martinez MW, Mital S, Naidu SS, Saberi S, Semsarian C, Times S, Waldman CB; Peer Review Committee Members. 2024 AHA/ACC/AMSSM/HRS/PACES/SCMR Guideline for the Management of Hypertrophic Cardiomyopathy: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2024 Jun 4;149(23):e1239-e1311.\u003c/li\u003e\n \u003cli\u003eArbelo E, Protonotarios A, Gimeno JR, Arbustini E, Barriales-Villa R, Basso C, Bezzina CR, Biagini E, Blom NA, de Boer RA, De Winter T, Elliott PM, Flather M, Garcia-Pavia P, Haugaa KH, Ingles J, Jurcut RO, Klaassen S, Limongelli G, Loeys B, Mogensen J, Olivotto I, Pantazis A, Sharma S, Van Tintelen JP, Ware JS, Kaski JP; ESC Scientific Document Group. 2023 ESC Guidelines for the management of cardiomyopathies. Eur Heart J. 2023 Oct 1;44(37):3503-3626.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBraunwald E. Hypertrophic Cardiomyopathy. N Engl J Med. 2025 Sep 11;393(10):1004-1015.\u003c/li\u003e\n \u003cli\u003eSaberi S, Kramer CM, Oręziak A, Masri A, Barriales-Villa R, Abraham TP, Lakdawala NK, Wang A, Choudhury L, Rader F, Havakuk O, Stendahl JC, Cardim N, Seidler T, Sherrid M, Hegde SM, Kwong RY, Jerosch-Herold M, Balaratnam G, Kurio G, Fox S, Olivotto I, Owens A. 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Association between triglyceride glucose-body mass index and long-term adverse outcomes of heart failure patients with coronary heart disease. Cardiovasc Diabetol. 2024 May 9;23(1):162.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007-2010 Korean National Health and Nutrition Examination Survey. PLoS One. 2019 Mar 7;14(3):e0212963.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCheng Y, Fang Z, Zhang X, Wen Y, Lu J, He S, Xu B. Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023 Mar 30;22(1):75.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCheng J, Cheng Q, Wu Y, Yin J, He F. Association between triglyceride-glucose-body mass index and adverse prognosis in elderly patients with severe heart failure and type 2 diabetes: a retrospective study based on the MIMIC-IV database. Cardiovasc Diabetol. 2025 Jul 24;24(1):299.\u003c/li\u003e\n \u003cli\u003ePark K, Ahn CW, Lee SB, Kang S, Nam JS, Lee BK, Kim JH, Park JS. Elevated TyG Index Predicts Progression of Coronary Artery Calcification. Diabetes Care. 2019 Aug;42(8):1569-1573.\u003c/li\u003e\n \u003cli\u003eAccili D, Deng Z, Liu Q. Insulin resistance in type 2 diabetes mellitus. Nat Rev Endocrinol. 2025 Jul;21(7):413-426.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCaprio S, Perry R, Kursawe R. Adolescent Obesity and Insulin Resistance: Roles of Ectopic Fat Accumulation and Adipose Inflammation. Gastroenterology. 2017 May;152(7):1638-1646.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMurakami K, Shigematsu Y, Hamada M, Higaki J. Insulin resistance in patients with hypertrophic cardiomyopathy. Circ J. 2004 Jul;68(7):650-5.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003evan Hoek I, Hodgkiss-Geere H, Bode EF, Hamilton-Elliott J, M\u0026otilde;tsk\u0026uuml;la P, Palermo V, Pereira YM, Culshaw GJ, Ivanova A, Dukes-McEwan J. Associations among echocardiography, cardiac biomarkers, insulin metabolism, morphology, and inflammation in cats with asymptomatic hypertrophic cardiomyopathy. J Vet Intern Med. 2020 Mar;34(2):591-599.\u003c/li\u003e\n \u003cli\u003eWei Z, Zhu E, Ren C, Dai J, Li J, Lai Y. Triglyceride-Glucose Index Independently Predicts New-Onset Atrial Fibrillation After Septal Myectomy for Hypertrophic Obstructive Cardiomyopathy Beyond the Traditional Risk Factors. Front Cardiovasc Med. 2021 Jul 23;8:692511.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZheng ZG, Xu YY, Liu WP, Zhang Y, Zhang C, Liu HL, Zhang XY, Liu RZ, Zhang YP, Shi MY, Yang H, Li P. Discovery of a potent allosteric activator of DGKQ that ameliorates obesity-induced insulin resistance via the sn-1,2-DAG-PKC\u0026epsilon; signaling axis. Cell Metab. 2025 May 6;37(5):1235-1236.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang J, Zhu C, Nie C, Song C, Zhang Y, Huang M, Zheng X, Lu J, Wang S, Huang X. Impact of Body Mass Index on Postoperative Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy Undergoing Septal Myectomy. J Am Heart Assoc. 2022 Feb;11(3):e023152.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMeng X, Gao J, Zhang K, Jun W, Wang JJ, Wang XL, Wang YG, Zheng JL, Liu YP, Song JJ, Yang J, Zheng YT, Li C, Wang WY, Shao C, Tang YD. The triglyceride-glucose index as a potential protective factor for hypertrophic obstructive cardiomyopathy without diabetes: evidence from a two-center study. Diabetol Metab Syndr. 2023 Jun 29;15(1):143.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang S, Cui H, Ji K, Song C, Ren C, Guo H, Zhu C, Wang S, Lai Y. Impact of type 2 diabetes mellitus on mid-term mortality for hypertrophic cardiomyopathy patients who underwent septal myectomy. Cardiovasc Diabetol. 2020 May 13;19(1):64.\u003c/li\u003e\n \u003cli\u003eWasserstrum Y, Barriales-Villa R, Fern\u0026aacute;ndez-Fern\u0026aacute;ndez X, Adler Y, Lotan D, Peled Y, Klempfner R, Kuperstein R, Shlomo N, Sabbag A, Freimark D, Monserrat L, Arad M. The impact of diabetes mellitus on the clinical phenotype of hypertrophic cardiomyopathy. Eur Heart J. 2019 Jun 1;40(21):1671-1677.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFumagalli C, Maurizi N, Day SM, Ashley EA, Michels M, Colan SD, Jacoby D, Marchionni N, Vincent-Tompkins J, Ho CY, Olivotto I; SHARE Investigators. Association of Obesity With Adverse Long-term Outcomes in Hypertrophic Cardiomyopathy. JAMA Cardiol. 2020 Jan 1;5(1):65-72.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGuo X, Zhang J, Huang M, Song C, Nie C, Zheng X, Wang S, Huang X. Systemic inflammation is associated with myocardial fibrosis in patients with obstructive hypertrophic cardiomyopathy. ESC Heart Fail. 2025 Feb;12(1):582-591.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDonath MY, Drucker DJ. Obesity, diabetes, and inflammation: Pathophysiology and clinical implications. Immunity. 2025 Oct 3:S1074-7613(25)00422-4.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePeng X, Huo H, Zhao Z, Cai Q, Tian J, Yang D, Song Y, Huang Y, Li Z, Gao J. Impact of glucose metabolism on myocardial fibrosis and inflammation in hypertrophic cardiomyopathy: a cardiac MR study. Front Endocrinol (Lausanne). 2025 Aug 26;16:1653927. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TyG-BMI, Hypertrophic cardiomyopathy, Insulin resistance, Septal myectomy","lastPublishedDoi":"10.21203/rs.3.rs-8068895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8068895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigated whether triglyceride‑glucose‑body mass index (TyG-BMI) is associated with clinical outcomes among individuals undergoing septal myectomy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe retrospective cohort study was conducted at Fuwai Hospital, enrolling 1302 individuals with hypertrophic obstructive cardiomyopathy (HOCM) who received septal myectomy. According to the tertile distribution of TyG-BMI, participants were classified into 3 groups (T1-T3). The primary endpoint consisted of hospitalization attributable to heart failure and overall death.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMean age of the participants was 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years, of whom 61% (791) were men. During the 4.7-year follow-up, both composite endpoints and overall mortality were significantly different across the three groups on the K-M curve (Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Cox analysis showed patients in group T3 exhibited the greatest incidence rates of primary endpoint (adjusted hazard ratio (HR)\u0026thinsp;=\u0026thinsp;2.18, 95% confidence interval (CI) 1.24\u0026ndash;3.83, P\u0026thinsp;=\u0026thinsp;0.007), as well as the highest risk of overall death (adjusted HR\u0026thinsp;=\u0026thinsp;3.67, 95% CI 1.41\u0026ndash;9.58, P\u0026thinsp;=\u0026thinsp;0.008) compared to those in group T1. When treated as a continuous variable, TyG-BMI remained significantly linked to an elevated risk of primary endpoint (each 1-SD increment, adjusted HR\u0026thinsp;=\u0026thinsp;1.39, 95%CI 1.12\u0026ndash;1.71, P\u0026thinsp;=\u0026thinsp;0.003) and increased overall mortality (each 1-SD increment, adjusted HR\u0026thinsp;=\u0026thinsp;1.72, 95%CI 1.29\u0026ndash;2.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe observations emphasize the role of TyG-BMI in the prediction of adverse outcomes after septal myectomy in HOCM.\u003c/p\u003e","manuscriptTitle":"Triglyceride glucose-body mass index is a risk factor for adverse clinical outcomes in patients undergoing septal myectomy: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:08:43","doi":"10.21203/rs.3.rs-8068895/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":"ad2333f9-12ad-4d41-8e9b-0988c5684281","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T15:56:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:08:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8068895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8068895","identity":"rs-8068895","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}