The triglyceride-glucose index is inversely associated with atrial fibrillation in patients with hypertrophic cardiomyopathy: evidence from a cross-sectional study

The triglyceride-glucose index is inversely associated with atrial fibrillation in patients with hypertrophic cardiomyopathy: evidence from a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The triglyceride-glucose index is inversely associated with atrial fibrillation in patients with hypertrophic cardiomyopathy: evidence from a cross-sectional study Ke Zhang, Shengwei Wang, Changwei Ren, Xiaoping Zhang, Hao Cui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8042387/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background: The triglyceride-glucose (TyG) index is highly correlated with cardiovascular diseases, but the relationship between TyG index changes and hypertrophic cardiomyopathy (HCM) has rarely been reported. We aimed to investigate whether the level and the change of the TyG index were associated with HCM. Methods: The study retrospectively collected socio-demographic, medical, anthropometric, and laboratory data from patients with HCM who were continuously admitted to Beijing Anzhen Hospital between January 1, 2018, and December 31, 2022. Logistic regressions were used to determine the relationship between the TyG index, other HCM factors, and the incidence of atrial fibrillation (AF). After stratifying patients by TyG index, we compared inter-group characteristics and evaluated the association between the TyG index and AF using logistic regression, followed by subgroup analyses to examine the consistency of this association. Results: For the total HCM population, the prevalence of AF was 285/1284(22.20%). Patients with AF had greater age (61.8 ± 10.6 vs. 54.0 ± 14.2 years, P < 0.001), lower body mass index (BMI) (25.59 ± 3.27 vs. 26.47 ± 4.03, P < 0.001), larger left atrial diameter (LAD) (46.86 ± 7.11 vs. 40.39 ± 5.98, P < 0.001), and more moderate or severe mitral regurgitation (MR) (82.5% vs. 67.0%, P < 0.001). Patients with AF had significantly greater free fatty acids (FFA) level (0.50 ± 0.26 vs. 0.44 ± 0.22, P = 0.004), although their triglycerides (TG) (1.45 ± 0.95 vs. 1.77 ± 1.05, P < 0.001), TyG index (8.64 ± 0.64 VS. 8.84 ± 0.64, P < 0.001), total cholesterol (TC) (4.21 ± 1.07 vs. 4.53 ± 1.07, P < 0.001), and low-density lipoprotein cholesterol (LDL-C) (2.55 ± 0.91 vs. 2.75 ± 0.88, P = 0.001) levels were much lower. In the multivariate logistic regression model, we discovered that age, LAD, left ventricular ejection fraction (LVEF), and TyG index were independently linked with the occurrence of AF. Subgroup analysis of the study population showed no interaction between all subgroups after adjusting for confounders. Conclusion: In conclusion, our study demonstrates that a higher TyG index is inversely associated with key adverse features in HCM, including LAD, and the prevalence of AF and moderate-to-severe MR. Further investigations are warranted to elucidate the mechanisms underlying these associations and their impact on the long-term prognosis of HCM. the triglyceride-glucose (TyG) index hypertrophic cardiomyopathy insulin resistance Figures Figure 1 Figure 2 Figure 3 Introduction Hypertrophic cardiomyopathy (HCM) is the most common inherited myocardial disease 1 , caused by autosomal dominant sarcomeric gene mutations, which presents as left ventricular hypertrophy, myocardial over-contraction, diastolic dysfunction, myofibrillar disarray, and fibrosis 2 , 3 . Pathogenic mutations in sarcomeric genes can lead to hypercontractility, impaired relaxation, and increased cardiac energy consumption 4 , which in turn increases the risk of arrhythmia, heart failure, and even sudden death 5 , 6 . Deepening insights into the relationship between clinical phenotype and sarcomeric gene mutations have, in parallel, highlighted the critical role of impaired energy metabolism in the pathophysiology of.HCM 7 – 9 .Insulin resistance (IR) is well-established as a significant risk factor for incident cardiovascular diseases and all-cause mortality. 10 – 13 . Many techniques exist for measuring IR, with the hyperinsulinemic glucose clamp (HEC) being the most widely used method 14 . However, HEC necessitates several blood tests as well as intravenous infusion of glucose and insulin 15 . The TyG index is a straightforward substitute for IR that may be generated from clinical laboratory test data. However, the role of the TyG index in the pathophysiology of HCM remains unclear. Hence, this cross-sectional study sought to determine whether the TyG index is associated with the presence of AF and other structural or functional parameters in patients with HCM. Methods Study populations This cross-sectional study retrospectively screened Beijing Anzhen Hospital's electronic medical record system to identify all consecutive inpatients with a primary or secondary diagnosis of HCM between January 1, 2018, and December 31, 2022. These patients were admitted for various reasons, including heart failure evaluation, arrhythmia management, or scheduled HCM assessment. Diagnosis of HCM was established according to guidelines, requiring maximal LV wall thickness ≥ 15 mm in the absence of other causes 16 . Genetic testing was not systematically performed in this cohort and was not part of the current analysis, which centered on clinical and metabolic parameters. The Anzhen Hospitals Ethics Committee gave its approval for this study. All patients gave their informed consent in compliance with the principles of the Declaration of Helsinki. Data Collection All data for this study were retrospectively collected from the electronic medical records of Beijing Anzhen Hospital. This included baseline socio-demographic characteristics, detailed medical history, medication use, laboratory test results, and original echocardiographic reports and images. Echocardiography All patients had their hearts measured by one qualified echocardiologist using transthoracic echocardiography, which uses an ultrasonic device (PHILIP IE33), one to three days after they were brought to the hospital. Standardized two-dimensional and targeted M-mode images were obtained. End-diastolic measurements included the thickness of the left ventricular wall and the interventricular septum (IVS). Cardiac chamber dimensions were determined as the maximum anteroposterior diameters recorded throughout the cardiac cycle. The guidelines set forth by the American Society of Echocardiography were followed to measure the left ventricular ejection fraction and gather more precise data 17 . Laboratory measurements In the first fasting blood samples collected during hospitalization, which were obtained after a night of fasting, the following parameters were measured: brain natriuretic peptide (BNP), glucose, free fatty acid (FFA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatine (Cr), lipoprotein(a) (LPa), and effective glomerular filtration rate (eGFR). The TyG index was computed using the formula ln (fasting glucose [mg/dL] x fasting triglyceride [mg/dL]/2). After being collected, the samples were analyzed using the Beckman AU5400 (US) automated biochemical analyzer for 4–6 hours in order to assess lipid parameters and other indices. Additional blood analyses were performed using the Sysmex XE-2100, strictly adhering to the instructions provided by the manufacturer. Using blind quality control samples, these parameters were evaluated using a biochemical analyzer (Hitachi-7600, Tokyo, Japan). The corresponding coefficients of variation (CV) for intra- and inter-assays were 5% and 10%, respectively, according to previous studies 18 , 19 . Personal measurements Standard questionnaires were employed to gather data on demographics, lifestyle, medical history, and medication history. Hypertension was defined as a blood pressure measurement of 140/90 mmHg or above and the use of antihypertensive medications. Diabetes mellitus is diagnosed using the most recent guidelines 20 . Hyperlipidemia, also referred to as dyslipidemia, was described as having total cholesterol above 200 mg/dL, triglycerides above 150 mg/dL, LDL cholesterol above 130 mg/dL, HDL cholesterol below 40 mg/dL, and/or using lipid-lowering medications. Statistics analysis Statistical analyses were performed using SPSS 20.0. Categorical variables were expressed as numbers (percentages) and compared using the chi-square test. Continuous data were presented as mean ± standard deviation or median (interquartile range) based on normality assessed by the Shapiro–Wilk test, and compared using the Student’s t-test or Mann–Whitney U test, as appropriate. We prespecified the TyG index as the primary exposure and assessed its association with AF using binary logistic regression, adjusting for potential confounders. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis, with the optimal cut-off determined by maximizing Youden’s index. Subgroup analyses were conducted across strata of gender, BMI, hypertension, dyslipidemia, LDL-C, and eGFR. Interaction effects were tested using likelihood ratio tests. A two-sided p-value < 0.05 was considered statistically significant. Results The study included 1284 participants with a diagnosis of HCM. Each of them had laboratory tests for metabolic parameters and transthoracic echocardiography to diagnose heart hypertrophy and cardiac structural and functional characteristics. Table 1 displays the baseline characteristics of the entire population, categorized by AF. For the total HCM population, the prevalence of AF was 285/1284(22.20%). Patients with AF had greater age (61.8±10.6 vs. 54.0±14.2 years, P < 0.001), lower BMI (25.59±3.27 vs. 26.47±4.03, P < 0.001), larger LAD (46.86±7.11 vs. 40.39±5.98, P < 0.001), and more moderate or severe MR (82.5% vs. 67.0%, P < 0.001). Then let's concentrate on the indicator of serum metabolism. Patients with AF had significantly greater FFA (0.50±0.26 vs. 0.44±0.22, P = 0.004), levels than those without AF, although their TG (1.45±0.95 vs. 1.77±1.05, P < 0.001), TyG index (8.64±0.64 VS. 8.84±0.64, P < 0.001), TC (4.21±1.07 vs. 4.53±1.07, P < 0.001), and LDL-C (2.55±0.91 vs. 2.75±0.88, P = 0.001) levels were much lower. From a pathophysiological perspective, the lower circulating lipid levels in AF patients might reflect a compensatory metabolic shift: impaired myocardial fatty acid oxidation could lead to increased reliance on circulating FFAs, potentially resulting in reduced subsequent hepatic synthesis of TG and LDL-C. Otherwise, LVEF (59.45±10.39 vs. 63.81±8.75, P < 0.001), left ventricular fractional shortening (LVFS) (34.17±5.42 vs. 36.07±5.12, P < 0.001), eGFR (80.77±21.24 vs. 90.42±22.86, P < 0.001), and rates of HTN (48.1% vs. 60.3%, P < 0.001) and dyslipidemia (30.9% vs. 37.9%, P = 0.025) were also lower in patients with HCM and AF. Table 1. Baseline characteristics of the study population. Variable AF（285） non AF（999） p-value age(years) 61.8±10.6 54.0±14.2 ＜0.001 * Gender (male, n%) 176(61.8%) 664(66.5%) 0.148 Hypertension, n (%) 137(48.1%) 602(60.3) ＜0.001 * Dyslipidemia, n (%) 88(30.9%) 379(37.9%) 0.025 * Diabetes mellitus, n (%) 84(29.5%) 259(25.9%) 0.244 Coronary heart disease, n (%) 77(27.0%) 267(26.7%) 0.922 BMI(kg/m2) 25.59±3.27 26.47±4.03 ＜0.001 * SBP (mmHg) 125±20 133±21 ＜0.001 * DBP (mmHg) 74±12 79±16 ＜0.001 * LAD (mm) 46.86±7.11 40.39±5.98 ＜0.001 * IVST (mm) 15.40±4.58 15.86±5.04 0.158 LVEDD (mm) 47.42±7.76 46.04±6.96 0.009 * LVPWT (mm) 11.27±2.34 11.85±2.70 0.001 * LVM (g) 253.24±77.30 258.85±96.36 0.329 LVM index (g/m2) 142.65±45.16 143.29±54.09 0.854 LVEF (%) 59.45±10.39 63.81±8.75 ＜0.001 * LVFS (%) 34.17±5.42 36.07±5.12 ＜0.001 * Moderate or severe MR 235(82.5%) 669(67.0%) ＜0.001 * hs-CRP (mg/L) 2.58±3.85 2.48±3.54 0.686 TG (mmol/L) 1.45±0.95 1.77±1.05 ＜0.001 * TyG index 8.64±0.64 8.84±0.64 ＜0.001 * TC (mmol/L) 4.21±1.07 4.53±1.07 ＜0.001 * HDL-C (mmol/L) 1.10±0.29 1.10±0.28 0.821 LDL-C (mmol/L) 2.55±0.91 2.75±0.88 0.001 * Creatinine (ummol/L) 87.50±49.86 83.97±58.41 0.354 fasting glucose (mmol/L) 5.88±1.96 6.01±2.20 0.380 FFA (mmol/L) 0.50±0.26 0.44±0.22 0.004 * LPa (mmol/L) 24.92±42.56 26.50±53.59 0.712 BNP (pg/mL) 599.06±577.29 519.79±649.37 0.117 eGFR (mL/min/1.73m2) 80.77±21.24 90.42±22.86 ＜0.001 * AF, Atrial Fibrillation; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; LAD,Left Atrial Diameter; IVST, Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; hs-CRP, high-sensitivity C-Reactive Protein; TG, Triglyceride; TyG index, Triglyceride-Glucose index; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FFA, Free Fatty Acid; LPa, Lipoprotein(a); BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate The relationship between the aforementioned clinical factors and the prevalence of AF in HCM was examined with a focus on statistically significant inter-group variations. Table 2 presents the results of the univariate and multivariate logistic regression analyses to examine independent factors associated with AF. Supplemental Table 1 presents findings from collinear diagnosis among the variables in the regression model. In the multivariate logistic regression model, we discovered that age, LAD, LVEF, and TyG index were independently linked with the occurrence of AF when adjusted for BMI, diabetes mellitus, LVEDD, LVFS, LDL-C, eGFR, and FFA that were associated with AF in the univariable analysis. ROC curve analysis was performed to see whether the multivariate logistic regression model (Model 2) that was previously constructed could detect the presence of AF in patients with HCM. The findings are displayed in Figure 1. 0.809 (95% CI, 0.777-0.841, P <0.001) was the area under the curve. With a sensitivity of 0.772 and specificity of 0.729, the ideal cutoff value for discriminating AF in patients with HCM was 0.181. Table 2. Univariate and multivariate logistic regression analyses between variables and AF in patients with HCM. P-value OR 95%CI Univariate age(years) ＜0.001 1.048 1.036-1.060 * Hypertension, n (%) ＜0.001 1.638 1.257-2.134 * Dyslipidemia, n (%) 0.029 1.368 1.032-1.814 * Diabetes mellitus, n (%) 0.233 1.194 0.892-1.598 BMI(kg/m2) 0.001 0.940 0.906-0.976 * SBP (mmHg) ＜0.001 0.981 0.975-0.988 * DBP (mmHg) ＜0.001 0.975 0.966-0.985 * Left atrium diameter (mm) ＜0.001 1.160 1.133-1.187 * LVEDD (mm) 0.005 1.026 1.008-1.045 * LVPWT (mm) 0.002 0.914 0.864-0.967 * LVEF (%) ＜0.001 0.955 0.942-0.968 * LVFS (%) ＜0.001 0.933 0.907-0.961 * Moderate or severe MR ＜0.001 0.434 0.311-0.605 * TG (mmol/L) ＜0.001 0.714 0.607-0.839 * TyG index ＜0.001 0.602 0.484-0.748 * TC (mmol/L) ＜0.001 0.744 0.652-0.850 * LDL-C (mmol/L) 0.001 0.769 0.659-0.898 * FFA 0.005 2.692 1.356-5.343 * eGFR (mL/min/1.73m2) ＜0.001 0.982 0.977-0.988 * Multivariate Model 1 age(years) 1.020 - 1.065 * Hypertension, n (%) 0.423 - 1.111 Dyslipidemia, n (%) 0.521 - 1.440 Diabetes mellitus, n (%) 0.921 - 2.760 BMI(kg/m2) 0.946 - 1.073 Left atrium diameter (mm) 1.117 - 1.201 * LVEDD (mm) 0.937 - 1.004 LVPWT (mm) 0.849 - 1.005 LVEF (%) 0.933 - 0.981 * Moderate or severe MR 0.594 - 1.842 TyG index 0.361 - 0.813 * FFA 0.553 - 3.500 eGFR (mL/min/1.73m2) 0.977 - 0.999 * Model 2 age(years) <.001 1.050 1.036-1.063 * LAD <.001 1.150 1.122-1.179 * LVEF <.001 0.970 0.955-0.985 * TyG index 0.023 0.747 0.582-0.960 * AF, Atrial Fibrillation; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; LAD,Left Atrial Diameter; IVST, Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; TG, Triglyceride; TyG index, Triglyceride-Glucose index; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FFA, Free Fatty Acid; LPa, Lipoprotein(a); BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate; OR, Odds Ratio; CI, Confidence Interval Patients were classified into two groups based on their TyG index: the "high TyG index group" (≥8.79) and the "low TyG index group" (<8.79) based on the cut-off point determined by ROC curve analysis, shown as Supplemental Figure 1. Table 3 presents the initial characteristics of the whole population sorted by the optimal TyG index cut-off value. The patients in the high TyG index group had higher proportions of comorbidity history of HTN, dyslipidemia, diabetes mellitus, and CAD, and they were more likely to be younger and female than those in the low TyG index group. But it's important to note that the group with a high TyG index had significantly lower rates of moderate- to severe-grade MR (62.1% vs. 77.8%, P <0.001) and AF (16.7% vs. 27.1%, P <0.001). In addition, the high TyG index group had significantly higher BMI levels, but significantly lower levels of LVPTG, LVOT Vmax, E/A radio, LAD, IVST, LVM, LVM index, LVFS, BNP, and eGFR. When the TyG index was incorporated into model 2 as a binary classification variable, a similar regression relationship could be observed. The risk of AF in the high TyG index group was 0.603 (0.409-0.887) times higher than that in the low TyG index group, as shown in Table 4. Table 3. Baseline characteristics of the study population are sorted by the optimal TyG index cut-off value. Variable high TyG index group(n=604) low TyG index group(n=680) P value age(years) 54.3±13.9 57.02±13.8 ＜0.001 Gender (male, n%) 415(68.7%) 425(62.5) 0.019 AF, n (%) 101(16.7%) 184(27.1%) ＜0.001 Hypertension, n (%) 417(69.0%) 322(47.4%) ＜0.001 Dyslipidemia, n (%) 269(44.5%) 198(29.1%) ＜0.001 Diabetes mellitus, n (%) 261(43.2%) 82(12.1%) ＜0.001 Coronary heart disease, n (%) 180(29.8%) 164(24.1%) 0.022 BMI(kg/m2) 27.31±4.01 25.37±3.54 ＜0.001 LVOTG (mmHg) 51.64±54.99 60.57±58.36 0.016 LVOT Vmax (cm/s) 294.19±129.70 319.54±139.39 0.004 E/A ratio 0.97±0.50 1.05±0.59 0.024 LAD(mm) 40.84±6.45 42.62±6.97 ＜0.001 IVST (mm) 15.20±4.41 16.42±5.29 ＜0.001 LVEDD (mm) 46.50±6.84 46.20±7.44 0.451 LVESD (mm) 30.18±6.78 29.87±7.24 0.439 LVPWT (mm) 11.66±2.50 11.79±2.75 0.411 LVM (g) 248.72±89.12 265.58±95.02 0.001 LVM index (g/m2) 132.33±44.90 152.82±56.51 ＜0.001 LVEF (%) 62.77±8.69 62.96±9.82 0.705 LVFS (%) 35.28±4.96 36.10±5.44 0.010 Moderate or severe MR, n (%) 375(62.1%) 529(77.8%) ＜0.001 BNP (mmol/L) 397.26±497.49 647.60±701.78 ＜0.001 eGFR (mL/min/1.73m2) 86.86±24.00 89.58±21.74 0.035 TyG index, Triglyceride-Glucose index; AF, Atrial Fibrillation; BMI, Body Mass Index; LVOTG, Left Ventricular Outflow Tract Gradient;LVOT Vmax, maximum velocity across the left ventricular outflow tract; E/A ratio, Early to Late Diastolic Mitral Inflow Velocity Ratio; LAD,Left Atrial Diameter; IVST, Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; TyG index, Triglyceride-Glucose index; BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate; Table 4. Multivariate logistic regression analyses between variables and AF in patients with HCM. Model 3 P-value OR 95%CI age(years) ＜0.001 1.050 1.037-1.064 LAD ＜0.001 1.150 1.122-1.180 LVEF ＜0.001 0.970 0.955-0.985 TyG index group 0.017 0.680 0.495-0.933 LAD,Left Atrial Diameter; LVEF, Left Ventricular Ejection Fraction; TyG index, Triglyceride-Glucose index; OR, Odds Ratio; CI, Confidence Interval Subsequently, the RCS curve revealed a linear relationship between the TyG index and AF with the adjustment of Model 2 (p for non-linear = 0.121, p for overall < 0.001, shown in Figure 2. Subgroup analysis of the study population according to gender (female or male), BMI (< 24 or ≥ 24-28 kg/m2 or ≥ 28), hypertension (with or without), dyslipidemia (with or without), LDL-C (< 1.8 or ≥ 1.8 mmol/L), eGFR (< 60 or ≥ 60 mL/min/1.73m2) was performed to verify further the diagnostic value of the TyG index for AF in different subgroups. Figure 3 shows no interaction between all subgroups after adjusting for confounders with Model 2 (all p for interaction > 0.05). Discussion The present study is the first to assess the TyG index's associative value in HCM patients. Firstly, the TyG index was found to have a negative correlation with several HCM risk variables in our investigation. LVOTG, LVOT Vmax, and IVST are quantitative indicators that decreased while the TyG index increased. On the other hand, as the TyG index increased, so did the prevalence of MR and AF complications that had an impact on the prognosis of HCM patients. Secondly, after correcting for confounding variables, multivariate logistic regression analysis indicated that the TyG index was inversely associated with AF in HCM patients, regardless of whether it was a continuous or categorical variable. According to earlier research, compared to heart patients in good condition, people with HCM may have a lower rate of heart fatty acid oxidation (FAO) and a higher rate of glucose oxidation. With the progression of cell hypertrophy and myocardial fibrosis, microcirculation disorders lead to hypoxia of HCM cardiomyocytes, which may further encourage Randle's cycle to more oxygen-efficient glucose metabolism. Still, this transition further exacerbates the energy supply deficit and leads to lipid deposition in cardiomyocytes. Our previous studies have concluded that FFA accumulation harms HCM patients 18 . In this study cohort, higher circulating FFA levels were also observed in HCM patients with AF, lending a certain weight to the argument. According to an integrated omics study 21 , there was a significant accumulation of FFA in HCM tissues, which aligns with recent proteomic findings that indicate FAO is downregulated in HCM patients 22 , 23 . Heart failure in adults is characterized by reduction of FAO in favor of carbohydrate metabolism 24 , 25 . This adaptive strategy becomes maladaptive because energy supply decreases and lipid toxic deposition known to impair cardiac function are accumulating 26 , 27 . TyG index is a predictor of cardiovascular risk, but it has not been extensively studied in HCM. Our study reached the similar conclusion as previous studies that the TyG index is a potential protective factor for HCM patients without diabetes 28 . Concomitant AF patients in our study had a lower TyG index, and even after controlling for confounding variables, the logistic regression analysis supported an inverse association between the TyG index and the prevalence of AF in HCM patients. Many studies have reported that patients with elevated TyG index without known cardiovascular disease 29 or with type 2 diabetes 30 have a higher risk of AF; the cardiovascular risk is increased in the general population 31 or patients with aortic stenosis 32 with elevated TyG index. Investigating this differential result is interesting. IR increases the delivery of fatty acids (FA) to the heart, which increases FAO in the heart muscle and activates the peroxisome proliferator-activated receptor alpha-mediated transcriptional pathway, further enhancing the oxidative capacity of heart FA 33 . Elevated insulin stimulates cardiac tissue's phosphatidylinositol 3-kinase (PI3K) and protein kinase B (Akt). Akt activation then encourages CD36 translocation, which improves the heart's absorption of FA. Increased mitochondrial FA consumption is a result of this mechanism 24 , 34 . Excessive pressure load can cause marked cardiac hypertrophy and upregulation of insulin signaling in the heart, and either reducing insulin levels or inhibiting its downstream target can alleviate the left ventricular contractile dysfunction and LAD increase caused by excessive pressure load 35 . In Table 3 , When the TyG index is examined as a binary categorical variable, the relationship between TyG index and HCM risk variables reveals a trend toward a declining risk of LAD, AF, and moderate-to-severe MR as the TyG index rises (P < 0.001). These results could contribute to our understanding of HCM from the standpoint of metabolic anomalies and to our investigation of cardiac energy metabolism. The research's statistical power is increased by the study's high sample size of 1284 HOCM patients. This study investigated a new biomarker, TyG index, and its inverse relationship with AF in patients with HCM, which may have implications for further study of the role of IR in HCM energy metabolism, and research and development to delay or even reverse the progression of HCM lesions by improving the FAO of HCM cardiomyocytes to reverse Randle cycle. However, as a cross-sectional study, we cannot rule out the possibility that stricter dietary controls or medications in patients with AF might have influenced the TyG index. Therefore, the causal relationship between the TyG index and AF in HCM should be interpreted with caution. Additionally, our study did not differentiate between new-onset and pre-existing AF. Future prospective studies with longitudinal designs are needed to evaluate the correlation between the TyG index and the incidence of new-onset AF in patients with HCM. The usefulness of therapies aimed at increasing FAO in enhancing patient outcomes in individuals with HCM was not examined in this investigation. Conclusion The alterations in myocardial energy metabolism may influence disease progression and the development of adverse clinical outcomes in HCM. The TyG index, a marker of IR, is associated with poor clinical outcomes in some patients with cardiovascular disease. our study demonstrates that a higher TyG index is inversely associated with key adverse features in HCM, including left atrial diameter, and the prevalence of AF and moderate-to-severe MR. The characteristics of myocardial energy metabolism in HCM are complex and multifactorial, and further studies are needed to fully understand the underlying mechanisms of these changes and their impact on the pathophysiology and prognosis of HCM. Declarations Ethical Approval This study was approved by the Human Ethics Committee of the Ethics Committee at the Beijing Anzhen Hospital (Approval No.: 2025257X). Consent to Participate Informed consent was obtained from all individual participants included in the study. Consent to Publish All participants provided written informed consent for the publication of their data and any accompanying images. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 82100366). Author Contribution Z.K. performed the data analyses and wrote the manuscript. Z.K. and W.SW. prepared all the tables and figures. W.SW., C.H., and R.CW. helped perform the analysis with constructive discussions. Z.XP. and L.YQ. contributed to the conception of the study. All authors read and approved the final manuscript. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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R. et al. 2020 AHA/ACC Guideline for the Diagnosis and Treatment of Patients With Hypertrophic Cardiomyopathy: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 142 , e533-e557, doi:10.1161/cir.0000000000000938 (2020). Page, R. L. et al. 2015 ACC/AHA/HRS guideline for the management of adult patients with supraventricular tachycardia: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Heart Rhythm 13 , e136-221, doi:10.1016/j.hrthm.2015.09.019 (2016). Zhang, K. et al. The abnormalities of free fatty acid metabolism in patients with hypertrophic cardiomyopathy, a single-center retrospective observational study. BMC Cardiovasc Disord 24 , 312, doi:10.1186/s12872-024-03925-9 (2024). Yuan, Z. et al. Association between the Albumin-to-Globulin Ratio and Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy. Rev Cardiovasc Med 25 , 96, doi:10.31083/j.rcm2503096 (2024). ElSayed, N. A. et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes Care 46 , S19-s40, doi:10.2337/dc23-S002 (2023). Ranjbarvaziri, S. et al. Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy. Circulation 144 , 1714-1731, doi:10.1161/circulationaha.121.053575 (2021). Coats, C. J. et al. Proteomic Analysis of the Myocardium in Hypertrophic Obstructive Cardiomyopathy. Circ Genom Precis Med 11 , e001974, doi:10.1161/circgen.117.001974 (2018). Schuldt, M. et al. Proteomic and Functional Studies Reveal Detyrosinated Tubulin as Treatment Target in Sarcomere Mutation-Induced Hypertrophic Cardiomyopathy. Circ Heart Fail 14 , e007022, doi:10.1161/circheartfailure.120.007022 (2021). Lopaschuk, G. D., Ussher, J. R., Folmes, C. D., Jaswal, J. S. & Stanley, W. C. Myocardial fatty acid metabolism in health and disease. Physiol Rev 90 , 207-258, doi:10.1152/physrev.00015.2009 (2010). Sack, M. N. et al. Fatty acid oxidation enzyme gene expression is downregulated in the failing heart. Circulation 94 , 2837-2842, doi:10.1161/01.cir.94.11.2837 (1996). Fillmore, N. & Lopaschuk, G. D. Targeting mitochondrial oxidative metabolism as an approach to treat heart failure. Biochim Biophys Acta 1833 , 857-865, doi:10.1016/j.bbamcr.2012.08.014 (2013). Heggermont, W. A., Papageorgiou, A. P., Heymans, S. & van Bilsen, M. Metabolic support for the heart: complementary therapy for heart failure? Eur J Heart Fail 18 , 1420-1429, doi:10.1002/ejhf.678 (2016). Meng, X. et al. The triglyceride-glucose index as a potential protective factor for hypertrophic obstructive cardiomyopathy without diabetes: evidence from a two-center study. Diabetol Metab Syndr 15 , 143, doi:10.1186/s13098-023-01084-z (2023). Liu, X. et al. U-shaped association between the triglyceride-glucose index and atrial fibrillation incidence in a general population without known cardiovascular disease. Cardiovasc Diabetol 22 , 118, doi:10.1186/s12933-023-01777-9 (2023). Shi, W. et al. Usefulness of Triglyceride-glucose index for detecting prevalent atrial fibrillation in a type 2 diabetic population. Postgrad Med 134 , 820-828, doi:10.1080/00325481.2022.2124088 (2022). Wang, A. et al. Change in triglyceride-glucose index predicts the risk of cardiovascular disease in the general population: a prospective cohort study. Cardiovasc Diabetol 20 , 113, doi:10.1186/s12933-021-01305-7 (2021). Li, W. et al. Prognostic effect of the TyG index on patients with severe aortic stenosis following transcatheter aortic valve replacement: a retrospective cohort study. Cardiovasc Diabetol 23 , 312, doi:10.1186/s12933-024-02414-9 (2024). Stavinoha, M. A. et al. Evidence for mitochondrial thioesterase 1 as a peroxisome proliferator-activated receptor-alpha-regulated gene in cardiac and skeletal muscle. Am J Physiol Endocrinol Metab 287 , E888-895, doi:10.1152/ajpendo.00190.2004 (2004). Boudina, S. et al. Contribution of Impaired Myocardial Insulin Signaling to Mitochondrial Dysfunction and Oxidative Stress in the Heart. Circulation 119 , 1272-1283, doi:10.1161/CIRCULATIONAHA.108.792101 (2009). Issa, J. et al. Increased acylcarnitines in infant heart failure indicate fatty acid oxidation inhibition: towards therapeutic options? ESC Heart Fail 10 , 3114-3122, doi:10.1002/ehf2.14449 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.xlsx SupplementalFigure1.tif Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 11 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 23 Nov, 2025 Editor assigned by journal 09 Nov, 2025 Submission checks completed at journal 09 Nov, 2025 First submitted to journal 05 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":119484,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver-operator characteristic curve for the cutoff value for the discrimination probability based on the multivariate logistic regression model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8042387/v1/ba84a49e480eaff3778ec87f.png"},{"id":97120765,"identity":"2fc4e918-c487-422e-82da-64f61a662cb8","added_by":"auto","created_at":"2025-12-01 07:51:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72552,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines curve for the association of TyG index with AF.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8042387/v1/7e31994b10a01b11682efc37.png"},{"id":97141284,"identity":"6a090fa1-81af-4c95-92eb-2224dd20ab37","added_by":"auto","created_at":"2025-12-01 10:06:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":506488,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the impact of the TyG index on AF.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eBMI, Body Mass Index; eGFR, estimated Glomerular Filtration Rate; LDL-C, Low-Density Lipoprotein Cholesterol; HTN，Hypertension; OR, Odds Ratio\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8042387/v1/85da03dbd8685f25e5ab4f3b.png"},{"id":102785580,"identity":"f74fc4cb-a73c-4c67-be97-ab591a6971ad","added_by":"auto","created_at":"2026-02-16 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fibrosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Pathogenic mutations in sarcomeric genes can lead to hypercontractility, impaired relaxation, and increased cardiac energy consumption\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which in turn increases the risk of arrhythmia, heart failure, and even sudden death\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDeepening insights into the relationship between clinical phenotype and sarcomeric gene mutations have, in parallel, highlighted the critical role of impaired energy metabolism in the pathophysiology of.HCM\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.Insulin resistance (IR) is well-established as a significant risk factor for incident cardiovascular diseases and all-cause mortality.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Many techniques exist for measuring IR, with the hyperinsulinemic glucose clamp (HEC) being the most widely used method\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, HEC necessitates several blood tests as well as intravenous infusion of glucose and insulin\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The TyG index is a straightforward substitute for IR that may be generated from clinical laboratory test data. However, the role of the TyG index in the pathophysiology of HCM remains unclear. Hence, this cross-sectional study sought to determine whether the TyG index is associated with the presence of AF and other structural or functional parameters in patients with HCM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy populations\u003c/p\u003e\u003cp\u003eThis cross-sectional study retrospectively screened Beijing Anzhen Hospital's electronic medical record system to identify all consecutive inpatients with a primary or secondary diagnosis of HCM between January 1, 2018, and December 31, 2022. These patients were admitted for various reasons, including heart failure evaluation, arrhythmia management, or scheduled HCM assessment. Diagnosis of HCM was established according to guidelines, requiring maximal LV wall thickness\u0026thinsp;\u0026ge;\u0026thinsp;15 mm in the absence of other causes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Genetic testing was not systematically performed in this cohort and was not part of the current analysis, which centered on clinical and metabolic parameters. The Anzhen Hospitals Ethics Committee gave its approval for this study. All patients gave their informed consent in compliance with the principles of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eData Collection\u003c/p\u003e\u003cp\u003eAll data for this study were retrospectively collected from the electronic medical records of Beijing Anzhen Hospital. This included baseline socio-demographic characteristics, detailed medical history, medication use, laboratory test results, and original echocardiographic reports and images.\u003c/p\u003e\u003cp\u003eEchocardiography\u003c/p\u003e\u003cp\u003eAll patients had their hearts measured by one qualified echocardiologist using transthoracic echocardiography, which uses an ultrasonic device (PHILIP IE33), one to three days after they were brought to the hospital. Standardized two-dimensional and targeted M-mode images were obtained. End-diastolic measurements included the thickness of the left ventricular wall and the interventricular septum (IVS). Cardiac chamber dimensions were determined as the maximum anteroposterior diameters recorded throughout the cardiac cycle. The guidelines set forth by the American Society of Echocardiography were followed to measure the left ventricular ejection fraction and gather more precise data\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLaboratory measurements\u003c/p\u003e\u003cp\u003eIn the first fasting blood samples collected during hospitalization, which were obtained after a night of fasting, the following parameters were measured: brain natriuretic peptide (BNP), glucose, free fatty acid (FFA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatine (Cr), lipoprotein(a) (LPa), and effective glomerular filtration rate (eGFR). The TyG index was computed using the formula ln (fasting glucose [mg/dL] x fasting triglyceride [mg/dL]/2). After being collected, the samples were analyzed using the Beckman AU5400 (US) automated biochemical analyzer for 4\u0026ndash;6 hours in order to assess lipid parameters and other indices. Additional blood analyses were performed using the Sysmex XE-2100, strictly adhering to the instructions provided by the manufacturer. Using blind quality control samples, these parameters were evaluated using a biochemical analyzer (Hitachi-7600, Tokyo, Japan). The corresponding coefficients of variation (CV) for intra- and inter-assays were 5% and 10%, respectively, according to previous studies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePersonal measurements\u003c/p\u003e\u003cp\u003eStandard questionnaires were employed to gather data on demographics, lifestyle, medical history, and medication history. Hypertension was defined as a blood pressure measurement of 140/90 mmHg or above and the use of antihypertensive medications. Diabetes mellitus is diagnosed using the most recent guidelines\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Hyperlipidemia, also referred to as dyslipidemia, was described as having total cholesterol above 200 mg/dL, triglycerides above 150 mg/dL, LDL cholesterol above 130 mg/dL, HDL cholesterol below 40 mg/dL, and/or using lipid-lowering medications.\u003c/p\u003e\u003cp\u003eStatistics analysis\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using SPSS 20.0. Categorical variables were expressed as numbers (percentages) and compared using the chi-square test. Continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) based on normality assessed by the Shapiro\u0026ndash;Wilk test, and compared using the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test, as appropriate.\u003c/p\u003e\u003cp\u003eWe prespecified the TyG index as the primary exposure and assessed its association with AF using binary logistic regression, adjusting for potential confounders. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis, with the optimal cut-off determined by maximizing Youden\u0026rsquo;s index. Subgroup analyses were conducted across strata of gender, BMI, hypertension, dyslipidemia, LDL-C, and eGFR. Interaction effects were tested using likelihood ratio tests. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study included 1284 participants with a diagnosis of HCM. Each of them had laboratory tests for metabolic parameters and transthoracic echocardiography to diagnose heart hypertrophy and cardiac structural and functional characteristics. Table 1 displays the baseline characteristics of the entire population, categorized by AF. For the total HCM population, the prevalence of AF was 285/1284(22.20%). Patients with AF had greater age (61.8\u0026plusmn;10.6 vs. 54.0\u0026plusmn;14.2 years, P \u0026lt; 0.001), lower BMI (25.59\u0026plusmn;3.27 vs. 26.47\u0026plusmn;4.03, P \u0026lt; 0.001), larger LAD (46.86\u0026plusmn;7.11 vs. 40.39\u0026plusmn;5.98, P \u0026lt; 0.001), and more moderate or severe MR (82.5% vs. 67.0%, P \u0026lt; 0.001). Then let\u0026apos;s concentrate on the indicator of serum metabolism. Patients with AF had significantly greater FFA (0.50\u0026plusmn;0.26 vs. 0.44\u0026plusmn;0.22, P = 0.004), levels than those without AF, although their TG (1.45\u0026plusmn;0.95 vs. 1.77\u0026plusmn;1.05, P \u0026lt; 0.001), TyG index (8.64\u0026plusmn;0.64 VS. 8.84\u0026plusmn;0.64, P \u0026lt; 0.001), TC (4.21\u0026plusmn;1.07 vs. 4.53\u0026plusmn;1.07, P \u0026lt; 0.001), and LDL-C (2.55\u0026plusmn;0.91 vs. 2.75\u0026plusmn;0.88, P = 0.001) levels were much lower. From a pathophysiological perspective, the lower circulating lipid levels in AF patients might reflect a compensatory metabolic shift: impaired myocardial fatty acid oxidation could lead to increased reliance on circulating FFAs, potentially resulting in reduced subsequent hepatic synthesis of TG and LDL-C. Otherwise, LVEF (59.45\u0026plusmn;10.39 vs. 63.81\u0026plusmn;8.75, P \u0026lt; 0.001), left ventricular fractional shortening (LVFS) (34.17\u0026plusmn;5.42 vs. 36.07\u0026plusmn;5.12, P \u0026lt; 0.001), eGFR (80.77\u0026plusmn;21.24 vs. 90.42\u0026plusmn;22.86, P \u0026lt; 0.001), and rates of HTN (48.1% vs. 60.3%, P \u0026lt; 0.001) and dyslipidemia (30.9% vs. 37.9%, P = 0.025) were also lower in patients with HCM and AF.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of the study population.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 135px;\"\u003e\n \u003cp\u003eAF（285）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003enon AF（999）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.8\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.0\u0026plusmn;14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eGender (male, n%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176(61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e664(66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137(48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e602(60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88(30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e379(37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84(29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e259(25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.244\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCoronary heart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77(27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e267(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.922\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.59\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.47\u0026plusmn;4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125\u0026plusmn;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e133\u0026plusmn;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74\u0026plusmn;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79\u0026plusmn;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.86\u0026plusmn;7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.39\u0026plusmn;5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIVST (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.40\u0026plusmn;4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.86\u0026plusmn;5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.158\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.42\u0026plusmn;7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.04\u0026plusmn;6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVPWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.27\u0026plusmn;2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.85\u0026plusmn;2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVM (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253.24\u0026plusmn;77.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e258.85\u0026plusmn;96.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.329\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVM index (g/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142.65\u0026plusmn;45.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e143.29\u0026plusmn;54.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.854\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.45\u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.81\u0026plusmn;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLVFS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.17\u0026plusmn;5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.07\u0026plusmn;5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModerate or severe MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e235(82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e669(67.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.58\u0026plusmn;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.48\u0026plusmn;3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.686\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.45\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.77\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.64\u0026plusmn;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.84\u0026plusmn;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.21\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.53\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.821\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.55\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.75\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCreatinine (ummol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87.50\u0026plusmn;49.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.97\u0026plusmn;58.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.354\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003efasting glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.88\u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.01\u0026plusmn;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eFFA (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eLPa (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.92\u0026plusmn;42.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.50\u0026plusmn;53.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.712\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBNP (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e599.06\u0026plusmn;577.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e519.79\u0026plusmn;649.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.117\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.77\u0026plusmn;21.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.42\u0026plusmn;22.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAF, Atrial Fibrillation; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; LAD,Left Atrial Diameter; IVST,\u0026nbsp;Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; hs-CRP, high-sensitivity C-Reactive Protein; TG, Triglyceride; TyG index, Triglyceride-Glucose index; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FFA, Free Fatty Acid; LPa, Lipoprotein(a); BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003eThe relationship between the aforementioned clinical factors and the prevalence of AF in HCM was examined with a focus on statistically significant inter-group variations. Table 2 presents the results of the univariate and multivariate logistic regression analyses to examine independent factors associated with AF. Supplemental Table 1 presents findings from collinear diagnosis among the variables in the regression model. In the multivariate logistic regression model, we discovered that age, LAD, LVEF, and TyG index were independently linked with the occurrence of AF when adjusted for BMI, diabetes mellitus, LVEDD, LVFS, LDL-C, eGFR, and FFA that were associated with AF in the univariable analysis. ROC curve analysis was performed to see whether the multivariate logistic regression model (Model 2) that was previously constructed could detect the presence of AF in patients with HCM. The findings are displayed in Figure 1. 0.809 (95% CI, 0.777-0.841, P \u0026lt;0.001) was the area under the curve. With a sensitivity of 0.772 and specificity of 0.729, the ideal cutoff value for discriminating AF in patients with HCM was 0.181.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Univariate and multivariate\u0026nbsp;logistic regression analyses between variables and AF in patients with HCM.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"508\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.036-1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.638\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.257-2.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.368\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.032-1.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.233\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.892-1.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.906-0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.981\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.975-0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.975\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.966-0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLeft atrium diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.133-1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.026\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.008-1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVPWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.914\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.864-0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.955\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.942-0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVFS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.933\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.907-0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate or severe MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.434\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.311-0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.714\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.607-0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.602\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.484-0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.744\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.652-0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.769\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.659-0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eFFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.692\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.356-5.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.982\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.977-0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.020 - 1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.423 - 1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.521 - 1.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.921 - 2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.946 - 1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLeft atrium diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.117 - 1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.937 - 1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVPWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.849 - 1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.933 - 0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate or severe MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.594 - 1.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.361 - 0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eFFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.553 - 3.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.977 - 0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.036-1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.122-1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.955-0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.582-0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAF, Atrial Fibrillation; BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; LAD,Left Atrial Diameter; IVST,\u0026nbsp;Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; TG, Triglyceride; TyG index, Triglyceride-Glucose index; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FFA, Free Fatty Acid; LPa, Lipoprotein(a); BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate; OR, Odds Ratio; CI, Confidence Interval\u003c/p\u003e\n\u003cp\u003ePatients were classified into two groups based on their TyG index: the \u0026quot;high TyG index group\u0026quot; (\u0026ge;8.79) and the \u0026quot;low TyG index group\u0026quot; (\u0026lt;8.79) based on the cut-off point determined by ROC curve analysis, shown as Supplemental Figure 1. Table 3 presents the initial characteristics of the whole population sorted by the optimal TyG index cut-off value. The patients in the high TyG index group had higher proportions of comorbidity history of HTN, dyslipidemia, diabetes mellitus, and CAD, and they were more likely to be younger and female than those in the low TyG index group. But it\u0026apos;s important to note that the group with a high TyG index had significantly lower rates of moderate- to severe-grade MR (62.1% vs. 77.8%, P \u0026lt;0.001) and AF (16.7% vs. 27.1%, P \u0026lt;0.001). In addition, the high TyG index group had significantly higher BMI levels, but significantly lower levels of LVPTG, LVOT Vmax, E/A radio, LAD, IVST, LVM, LVM index, LVFS, BNP, and eGFR. When the TyG index was incorporated into model 2 as a binary classification variable, a similar regression relationship could be observed. The risk of AF in the high TyG index group was 0.603 (0.409-0.887) times higher than that in the low TyG index group, as shown in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 3. Baseline characteristics of the study population are\u0026nbsp;sorted by the optimal TyG index cut-off value.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ehigh TyG index group(n=604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003elow TyG index group(n=680)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e54.3\u0026plusmn;13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e57.02\u0026plusmn;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eGender (male, n%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e415(68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e425(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e101(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e184(27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e417(69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e322(47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e269(44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e198(29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e261(43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e82(12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCoronary heart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e180(29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e164(24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e27.31\u0026plusmn;4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e25.37\u0026plusmn;3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVOTG (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e51.64\u0026plusmn;54.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e60.57\u0026plusmn;58.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVOT\u0026nbsp;Vmax (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e294.19\u0026plusmn;129.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e319.54\u0026plusmn;139.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eE/A ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.05\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLAD(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e40.84\u0026plusmn;6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e42.62\u0026plusmn;6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eIVST (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e15.20\u0026plusmn;4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e16.42\u0026plusmn;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e46.50\u0026plusmn;6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e46.20\u0026plusmn;7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.451\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVESD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e30.18\u0026plusmn;6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e29.87\u0026plusmn;7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.439\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVPWT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e11.66\u0026plusmn;2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e11.79\u0026plusmn;2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.411\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVM (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e248.72\u0026plusmn;89.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e265.58\u0026plusmn;95.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVM index (g/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e132.33\u0026plusmn;44.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e152.82\u0026plusmn;56.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e62.77\u0026plusmn;8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e62.96\u0026plusmn;9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.705\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLVFS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e35.28\u0026plusmn;4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e36.10\u0026plusmn;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eModerate or severe MR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e375(62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e529(77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eBNP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e397.26\u0026plusmn;497.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e647.60\u0026plusmn;701.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e86.86\u0026plusmn;24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e89.58\u0026plusmn;21.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTyG index, Triglyceride-Glucose index; AF, Atrial Fibrillation; BMI, Body Mass Index; LVOTG, Left Ventricular Outflow Tract Gradient;LVOT Vmax, maximum velocity across the left ventricular outflow tract; E/A ratio, Early to Late Diastolic Mitral Inflow Velocity Ratio; LAD,Left Atrial Diameter; IVST, Interventricular Septal Thickness; LVEDD, Left Ventricular End-Diastolic Diameter; LVPWT, Left Ventricular Posterior Wall Thickness; LVM, Left Ventricular Mass; LVEF, Left Ventricular Ejection Fraction; LVFS, Left Ventricular Fractional Shortening; MR, Mitral Regurgitation; TyG index, Triglyceride-Glucose index; BNP, Brain Natriuretic Peptide; eGFR, estimated Glomerular Filtration Rate;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Multivariate logistic regression analyses between variables and AF in patients with HCM.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eage(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.037-1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.122-1.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.955-0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eTyG index group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.495-0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLAD,Left Atrial Diameter; LVEF, Left Ventricular Ejection Fraction; TyG index, Triglyceride-Glucose index; OR, Odds Ratio; CI, Confidence Interval\u003c/p\u003e\n\u003cp\u003eSubsequently, the RCS curve revealed a linear relationship between the TyG index and AF with the adjustment of Model 2 (p for non-linear\u0026thinsp;=\u0026thinsp;0.121, p for overall\u0026thinsp;\u0026lt; 0.001, shown in Figure 2.\u003c/p\u003e\n\u003cp\u003eSubgroup analysis of the study population according to gender (female or male), BMI (\u0026lt;\u0026thinsp;24 or \u0026ge;\u0026thinsp;24-28 kg/m2 or \u0026ge;\u0026thinsp;28), hypertension (with or without), dyslipidemia (with or without), LDL-C (\u0026lt;\u0026thinsp;1.8 or \u0026ge;\u0026thinsp;1.8 mmol/L), eGFR (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 mL/min/1.73m2) was performed to verify further the diagnostic value of the TyG index for AF in different subgroups. Figure 3 shows no interaction between all subgroups after adjusting for confounders with Model 2 (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study is the first to assess the TyG index's associative value in HCM patients. Firstly, the TyG index was found to have a negative correlation with several HCM risk variables in our investigation. LVOTG, LVOT Vmax, and IVST are quantitative indicators that decreased while the TyG index increased. On the other hand, as the TyG index increased, so did the prevalence of MR and AF complications that had an impact on the prognosis of HCM patients. Secondly, after correcting for confounding variables, multivariate logistic regression analysis indicated that the TyG index was inversely associated with AF in HCM patients, regardless of whether it was a continuous or categorical variable.\u003c/p\u003e\u003cp\u003eAccording to earlier research, compared to heart patients in good condition, people with HCM may have a lower rate of heart fatty acid oxidation (FAO) and a higher rate of glucose oxidation. With the progression of cell hypertrophy and myocardial fibrosis, microcirculation disorders lead to hypoxia of HCM cardiomyocytes, which may further encourage Randle's cycle to more oxygen-efficient glucose metabolism. Still, this transition further exacerbates the energy supply deficit and leads to lipid deposition in cardiomyocytes. Our previous studies have concluded that FFA accumulation harms HCM patients\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this study cohort, higher circulating FFA levels were also observed in HCM patients with AF, lending a certain weight to the argument. According to an integrated omics study\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, there was a significant accumulation of FFA in HCM tissues, which aligns with recent proteomic findings that indicate FAO is downregulated in HCM patients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Heart failure in adults is characterized by reduction of FAO in favor of carbohydrate metabolism\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This adaptive strategy becomes maladaptive because energy supply decreases and lipid toxic deposition known to impair cardiac function are accumulating\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTyG index is a predictor of cardiovascular risk, but it has not been extensively studied in HCM. Our study reached the similar conclusion as previous studies that the TyG index is a potential protective factor for HCM patients without diabetes\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Concomitant AF patients in our study had a lower TyG index, and even after controlling for confounding variables, the logistic regression analysis supported an inverse association between the TyG index and the prevalence of AF in HCM patients. Many studies have reported that patients with elevated TyG index without known cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e or with type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e have a higher risk of AF; the cardiovascular risk is increased in the general population\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e or patients with aortic stenosis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e with elevated TyG index.\u003c/p\u003e\u003cp\u003eInvestigating this differential result is interesting. IR increases the delivery of fatty acids (FA) to the heart, which increases FAO in the heart muscle and activates the peroxisome proliferator-activated receptor alpha-mediated transcriptional pathway, further enhancing the oxidative capacity of heart FA\u003csup\u003e33\u003c/sup\u003e. Elevated insulin stimulates cardiac tissue's phosphatidylinositol 3-kinase (PI3K) and protein kinase B (Akt). Akt activation then encourages CD36 translocation, which improves the heart's absorption of FA. Increased mitochondrial FA consumption is a result of this mechanism\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Excessive pressure load can cause marked cardiac hypertrophy and upregulation of insulin signaling in the heart, and either reducing insulin levels or inhibiting its downstream target can alleviate the left ventricular contractile dysfunction and LAD increase caused by excessive pressure load\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, When the TyG index is examined as a binary categorical variable, the relationship between TyG index and HCM risk variables reveals a trend toward a declining risk of LAD, AF, and moderate-to-severe MR as the TyG index rises (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results could contribute to our understanding of HCM from the standpoint of metabolic anomalies and to our investigation of cardiac energy metabolism.\u003c/p\u003e\u003cp\u003eThe research's statistical power is increased by the study's high sample size of 1284 HOCM patients. This study investigated a new biomarker, TyG index, and its inverse relationship with AF in patients with HCM, which may have implications for further study of the role of IR in HCM energy metabolism, and research and development to delay or even reverse the progression of HCM lesions by improving the FAO of HCM cardiomyocytes to reverse Randle cycle. However, as a cross-sectional study, we cannot rule out the possibility that stricter dietary controls or medications in patients with AF might have influenced the TyG index. Therefore, the causal relationship between the TyG index and AF in HCM should be interpreted with caution. Additionally, our study did not differentiate between new-onset and pre-existing AF. Future prospective studies with longitudinal designs are needed to evaluate the correlation between the TyG index and the incidence of new-onset AF in patients with HCM. The usefulness of therapies aimed at increasing FAO in enhancing patient outcomes in individuals with HCM was not examined in this investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe alterations in myocardial energy metabolism may influence disease progression and the development of adverse clinical outcomes in HCM. The TyG index, a marker of IR, is associated with poor clinical outcomes in some patients with cardiovascular disease. our study demonstrates that a higher TyG index is inversely associated with key adverse features in HCM, including left atrial diameter, and the prevalence of AF and moderate-to-severe MR. The characteristics of myocardial energy metabolism in HCM are complex and multifactorial, and further studies are needed to fully understand the underlying mechanisms of these changes and their impact on the pathophysiology and prognosis of HCM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Human Ethics Committee of the Ethics Committee at the Beijing Anzhen Hospital (Approval No.: 2025257X).\u003c/p\u003e\n\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish\u003c/h2\u003e\n\u003cp\u003eAll participants provided written informed consent for the publication of their data and any accompanying images.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 82100366).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZ.K. performed the data analyses and wrote the manuscript. Z.K. and W.SW. prepared all the tables and figures. W.SW., C.H., and R.CW. helped perform the analysis with constructive discussions. Z.XP. and L.YQ. contributed to the conception of the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSemsarian, C., Ingles, J., Maron, M. S. \u0026amp; Maron, B. J. New perspectives on the prevalence of hypertrophic cardiomyopathy. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, 1249-1254, doi:10.1016/j.jacc.2015.01.019 (2015).\u003c/li\u003e\n\u003cli\u003eMaron, B. J. \u0026amp; Maron, M. S. 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We aimed to investigate whether the level and the change of the TyG index were associated with HCM.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThe study retrospectively collected socio-demographic, medical, anthropometric, and laboratory data from patients with HCM who were continuously admitted to Beijing Anzhen Hospital between January 1, 2018, and December 31, 2022. Logistic regressions were used to determine the relationship between the TyG index, other HCM factors, and the incidence of atrial fibrillation (AF). After stratifying patients by TyG index, we compared inter-group characteristics and evaluated the association between the TyG index and AF using logistic regression, followed by subgroup analyses to examine the consistency of this association.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eFor the total HCM population, the prevalence of AF was 285/1284(22.20%). Patients with AF had greater age (61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 vs. 54.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower body mass index (BMI) (25.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27 vs. 26.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), larger left atrial diameter (LAD) (46.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11 vs. 40.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more moderate or severe mitral regurgitation (MR) (82.5% vs. 67.0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with AF had significantly greater free fatty acids (FFA) level (0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 vs. 0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22, P\u0026thinsp;=\u0026thinsp;0.004), although their triglycerides (TG) (1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95 vs. 1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TyG index (8.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 VS. 8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total cholesterol (TC) (4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 vs. 4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and low-density lipoprotein cholesterol (LDL-C) (2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 vs. 2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88, P\u0026thinsp;=\u0026thinsp;0.001) levels were much lower. In the multivariate logistic regression model, we discovered that age, LAD, left ventricular ejection fraction (LVEF), and TyG index were independently linked with the occurrence of AF. Subgroup analysis of the study population showed no interaction between all subgroups after adjusting for confounders.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eIn conclusion, our study demonstrates that a higher TyG index is inversely associated with key adverse features in HCM, including LAD, and the prevalence of AF and moderate-to-severe MR. Further investigations are warranted to elucidate the mechanisms underlying these associations and their impact on the long-term prognosis of HCM.\u003c/p\u003e","manuscriptTitle":"The triglyceride-glucose index is inversely associated with atrial fibrillation in patients with hypertrophic cardiomyopathy: evidence from a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:51:49","doi":"10.21203/rs.3.rs-8042387/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T07:22:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T04:28:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T14:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287171390948625538856115087834780834545","date":"2025-11-26T04:16:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217704503565773234752371987374884516083","date":"2025-11-25T06:11:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T04:00:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-09T14:59:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-09T14:57:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-11-06T01:01:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b0035bd-5f3d-493d-9dba-f655055d541a","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:05:46+00:00","versionOfRecord":{"articleIdentity":"rs-8042387","link":"https://doi.org/10.1186/s40001-026-04035-2","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-02-12 15:58:33","publishedOnDateReadable":"February 12th, 2026"},"versionCreatedAt":"2025-12-01 07:51:49","video":"","vorDoi":"10.1186/s40001-026-04035-2","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04035-2","workflowStages":[]},"version":"v1","identity":"rs-8042387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8042387","identity":"rs-8042387","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}