Development and validation of a prediction model for risk of atrial fibrillation in elderly patients with nonalcoholic fatty liver disease

Development and validation of a prediction model for risk of atrial fibrillation in elderly patients with nonalcoholic fatty liver disease | 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 Development and validation of a prediction model for risk of atrial fibrillation in elderly patients with nonalcoholic fatty liver disease Lijuan Yu, Shuai Zhang, Hao Liang, Jia You, Jun Liu, Ye Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7054945/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objectives To develop a prediction model for risk of atrial fibrillation(AF) in elderly patients with nonalcoholic fatty liver disease (NAFLD). Methods This study collected data from 2035 elderly patients over 65 years old diagnosed with NAFLD at Northern Jiangsu People's Hospital. Using a 7:3 ratio, participants were separated into two groups through random assignment: a model development cohort (n = 1424) and an internal verification cohort (n = 611). 511 elderly NAFLD patients from The First Affiliated Hospital of Soochow University were collected as an external validation set to further assess the predictive capability of the model. Using multivariate logistic regression analysis, we developed a predictive model for AF and visualized it through a nomogram. The discrimination, calibration, and clinical application value of the model were comprehensively evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results In this study, 2035 elderly patients with NAFLD, 116 cases (5.7%) were found to have AF. The baseline features between validation and training groups exhibited no significant statistical differences( P > 0.05). Direct bilirubin (OR = 1.405, 95% CI: 1.281–1.549), body mass index (BMI) (OR = 1.168, 95% CI: 1.087–1.255), low-density lipoprotein cholesterol (LDL-c) (OR = 0.494, 95% CI: 0.377–0.643), albumin (OR = 0.804, 95% CI: 0.735–0.878), and age (OR = 1.075, 95% CI: 1.039–1.112) were all independent risk factors for AF in elderly NAFLD patients ( P < 0.001). The combined prediction model composed of these five indicators had an AUC value of 0.829 (95% CI: 0.774–0.884) in the training group, 0.855 (95% CI: 0.794–0.916) in the internal validation group, and 0.826 (95% CI: 0.742–0.910) in the external validation set. The consistency of the prediction model was effectively confirmed through its calibration curve. The DCA and CIC showed that the risk threshold probabilities for AF in the training and validation groups were 5%-79.5% and 5%-90%, respectively. Conclusions The nomogram model constructed in this study has good predictive efficacy and clinical application value for the risk of AF in elderly NAFLD patients. NAFLD atrial fibrillation risk factors Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders marked by abnormal fat buildup, often occurring in conjunction with various metabolic dysfunctions. A meta-analysis of 72 studies revealed that approximately 32% of the global population is affected by NAFLD 1 , exceeding the prevalence of obesity and diabetes significantly 2 . In the NAFLD patient population, cardiovascular disease (CVD) is the leading cause of death worldwide 3 . Furthermore, studies have shown that people diagnosed with NAFLD, especially those who have progressed to the fibrotic stage, face a notably higher risk of developing cardiovascular complications 4 . This suggests that NAFLD is a major risk factor for increased cardiovascular events and their adverse outcomes. The incidence of Atrial fbrillation (AF) represents an escalating challenge to public healthcare systems 5 . Factors that contribute to increase risks of AF often include excess body weight, older age, coronary artery disease and so on. Furthermore, a cohort study showed that NAFLD was associated with an increased risk of prevalent AF in a middle-aged population 6 . Given that the elderly population has a high prevalence of both NAFLD and AF, elderly patients diagnosed with AF face risks of complications such as heart failure, stroke, greater emphasis ought to be placed on identifying and addressing the potential risk of AF among older individuals suffering from NAFLD 7 . At present, most nomogram studies focus on predictive models for new-onset AF after cardiopulmonary surgery 8 , with limited attention paid to the general population undergoing physical examinations, particularly NAFLD patients over the age of 65. Furthermore, whether the association between NAFLD and AF also holds true in the elderly population remains uncertain. Therefore, a cross-sectional analysis was carried out aiming to establish a predictive framework for AF among older individuals diagnosed with NAFLD within the Chinese elderly population. Methods Study population Patients over 65 years old diagnosed with NAFLD at the Health Check-up Center of Northern Jiangsu People's Hospital and The First Affiliated Hospital of Soochow University from January 1, 2014, to December 31, 2023, were included in this study. Information was gathered through the outpatient and inpatient systems of the Hospital Information System (HIS). Patients diagnosed with AF by 12-lead electrocardiogram (ECG) or with a history of AF were considered AF patients. Based on the presence or absence of AF, participants were classified into corresponding groups. Exclusion criteria included: (1) coronary heart disease, rheumatic, congenital, and valvular heart diseases, patients who had undergone biological or mechanical valve replacement, hyperthyroidism, or connective tissue diseases that could lead to AF; (2) viral, drug-induced, autoimmune, or other chronic liver diseases; (3) excessive alcohol consumption (alcohol equivalent to ethanol, exceeding 70g weekly for women and 140g weekly for men); (4) severe renal insufficiency; (5) acute infection; (6) lack of clinical data. The diagnosis of NAFLD was made by abdominal color Doppler ultrasound examination performed by physicians at least five years of clinical experience, and the results were reviewed by experts with a deputy senior or higher professional title to issue the final health examination report. In these individuals, hepatic steatosis could not be attributed to any other known causes, including long-term use of medication with hepatotoxicity, alcohol abuse, or previous viral liver infection 9 . AF was determined through the criteria outlined below: standard 12-lead ECG or ≥ 30s single-lead ECG recording of ECG events, irregular RR intervals, and no identifiable P waves (without atrioventricular block) 10 , 11 . Ethical approval for this study was obtained from both the Ethics Committee of Northern Jiangsu People's Hospital (Approval No. 2024ky284) and the First Affiliated Hospital of Soochow University (Approval No. 228). Given that this investigation was retrospective in design, the Ethics Committees waived the need for informed consent. Data Collection Information on demographic factors, including age, sex and body dimensions (such as height and weight), smoking status, hypertension and diabetes were extracted through a thorough review of the patients' medical records. Laboratory parameters comprising white blood cell count (WBC), neutrophil count (NE#), hemoglobin (HB), alanine aminotransferase (ALT), urea (Scr), uric acid (UA), albumin (ALB) were also collected. The BMI was determined by dividing the weight in kilograms by the square of height in meters (kg/m²). Statistical analysis Graphs were created using R-4.4.1 software, while statistical analysis was carried out utilizing SPSS version 27.0. Normally distributed quantitative variables were expressed as means with standard deviations (mean ± SD), and comparisons between groups were performed using independent samples t-tests. Categorical variables were described in terms of proportions, and group differences were assessed via the chi-square (χ²) test. In cases where quantitative data did not meet the assumption of normality, results were reported using the interquartile range [M (Q1, Q3)], and the Mann–Whitney U test was applied for intergroup comparisons. Participants from Northern Jiangsu People's Hospital were divided into a training set to construct the prediction model and a testing set to validate the model's performance. Samples from Northern Jiangsu People's Hospital served as the internal validation set, while samples from the First Affiliated Hospital of Soochow University served as the external validation set to assess the model's predictive ability. LASSO (least absolute shrinkage and selection operator) regression analysis was used to identify statistically significant predictors, and the random forest algorithm was employed to rank the importance of the selected variables. To identify the optimal predictive factors and establish a nomogram, we performed multivariate logistic regression analysis incorporating the variables selected through LASSO regression. Model prediction accuracy was evaluated by analyzing its receiver operating characteristic (ROC) and calibration curves. Additionally, we employed decision curve analysis (DCA) and clinical impact curve (CIC) analyses to determine the clinical value and practical utility of the prediction model. A P -value of less than 0.05 was considered statistically significant. Results Baseline Characteristics This research consisted of 2,035 elderly individuals diagnosed with NAFLD. Female participants constituted 38.1% (n = 776) of the study population, with a mean age of 71 years. AF was present in 116 subjects, representing 5.7% of the cohort.The baseline characteristics of the modeling group and the validation group showed no notable variation( P > 0.05, as shown in Table 1 ) Table 1 Baseline characteristics between the modeling group and the validation group Characteristic Overall(n = 2035) Modeling Group(n = 1424) Validation group(n = 611) P Basic Information Gender, n(%) 0.841 Male 1259(61.9%) 883(62.0%) 376(61.5%) Smoking History Yes/n(%) 351(17.2%) 251(17.6%) 100(16.4%) 0.485 Age, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S})\) 71.98 ± 5.82 71.89 ± 5.78 72.19 ± 5.91 0.296 BMI, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\mathbf{k}\mathbf{g}/\mathbf{m}\) 2) 26.12 ± 2.72 26.16 ± 2.72 26.04 ± 2.71 0.321 Laboratory Indicators TP, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 73.72 ± 4.19 73.73 ± 4.23 73.68 ± 4.08 0.791 ALB, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 45.65 ± 2.33 45.69 ± 2.35 45.56 ± 2.29 0.236 TBIL, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 12.85 ± 5.47 12.87 ± 5.50 12.81 ± 5.40 0.814 DBIL, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 4.58 ± 1.73 4.58 ± 1.71 4.57 ± 1.79 0.913 GGT, [M(P25, P75, U/L)] 25(19, 38) 25(19, 38) 26(19, 38.5) 0.519 AST, [M(P25, P75, U/L)] 21(18, 26) 21(18, 26) 21(18, 26) 0.219 ALT, [M(P25, P75, U/L)] 19(15, 28) 20(15, 27) 19(14, 28) 0.343 Urea, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 5.66 ± 1.51 5.66 ± 1.49 5.65 ± 1.52 0.904 Cre, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 86.17 ± 19.14 85.46 ± 17.69 86.47 ± 19.72 0.251 TG, [M(P25, P75, mmol/l)] 1.74(1.29, 2.44) 0.642 LDL-c, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) mmol/l) 2.93 ± 0.88 2.93 ± 0.86 2.93 ± 0.93 0.951 RBC, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) ×1012/l) 4.76 ± 0.45 4.77 ± 0.46 4.74 ± 0.43 0.081 HCT, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) %) 43.14 ± 3.61 43.18 ± 3.67 43.05 ± 3.47 0.414 RDW, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) %) 12.75 ± 0.78 12.76 ± 0.82 12.74 ± 0.69 0.618 HB, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 146.73 ± 13.28 146.91 ± 13.38 146.31 ± 13.05 0.349 WBC, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 6.42 ± 1.56 6.44 ± 1.61 6.39 ± 1.44 0.556 NE#, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 1.97 ± 0.62 1.97 ± 0.64 1.95 ± 0.59 0.501 Comorbid conditions Hyperuricemia Yes/n(%) 607(29.8%) 428(30.1%) 179(29.3%) 0.731 Diabetes Yes/n(%) 485(23.8%) 346(24.3%) 139(22.7%) 0.449 Hypertension Yes/n(%) 582(28.6%) 429(30.1%) 153(25.0%) 0.052 In the modeling group, consisting of 1,424 patients, 72 patients (5.1%) were in the AF group, while 1,352 patients (94.9%) were in the non-AF group. Elderly patients with NAFLD who also had AF demonstrated notably elevated values in several clinical parameters, including age,BMI, total and direct bilirubin, γ-glutamyl transferase (γ-GGT), serum creatinine, hematocrit, red blood cell distribution width (RDW), and white blood cell count, in comparison to those without AF. In contrast, there was a marked decrease in the concentrations of total protein, albumin, ALT and low-density lipoprotein cholesterol (LDL-c)( P < 0.05). Additionally, elderly NAFLD patients with hyperuricemia were more likely to develop AF( P < 0.05, as shown in Table 2 ). Table 2 Baseline Characteristics Between the AF and the Non-AF Group in the Modeling Cohort Characteristic Non-AF(n = 1352) AF(n = 72) t/z/x 2 P Basic Information Gender, n(%) 1.177 0.278 Male 834(61.7%) 49(68.1%) Smoking History Yes/n(%) 233(17.2%) 18(25.0%) 2.841 0.092 Age, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S})\) 71.77 ± 5.78 75.57 ± 5.41 -6.909 <0.001 BMI, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\mathbf{k}\mathbf{g}/\mathbf{m}\) 2) 26.04 ± 2.69 27.41 ± 2.83 -5.296 <0.001 Laboratory Indicators TP, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 73.79 ± 4.09 72.52 ± 5.38 3.176 0.014 ALB, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 45.74 ± 2.31 44.19 ± 2.26 7.019 <0.001 TBIL, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 12.56 ± 5.02 17.65 ± 9.18 -9.951 <0.001 DBIL, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 4.47 ± 1.58 6.40 ± 2.84 -12.061 <0.001 GGT, [M(P25, P75, U/L)] 25(18, 38) 30.5(22, 45.5) -4.069 <0.001 AST, [M(P25, P75, U/L)] 21(18, 26) 21(18, 27) -0.003 0.998 ALT, [M(P25, P75, U/L)] 19(15, 28) 16(12.93, 23.13) -3.665 <0.001 Urea, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 5.65 ± 1.50 5.79 ± 1.66 -1.046 0.296 Cre, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) g/l) 85.69 ± 18.75 94.05 ± 23.45 -4.591 <0.001 TG, [M(P25, P75, mmol/l)] 1.74(1.30, 2.44) 1.75(1.19, 2.41) -0.608 0.544 LDL-c, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) mmol/l) 2.97 ± 0.87 2.31 ± 0.76 7.995 <0.001 RBC, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) ×1012/l) 4.76 ± 0.45 4.74 ± 0.48 0.598 0.598 HCT, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) %) 43.07 ± 3.58 44.28 ± 3.89 -3.503 <0.001 RDW, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S},\:\) %) 12.74 ± 0.78 12.97 ± 0.74 -3.127 <0.001 HB, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 146.60 ± 13.23 148.92 ± 14.03 -1.832 0.067 WBC, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 6.39 ± 1.53 6.94 ± 1.92 -3.662 0.003 NE#, ( \(\:\stackrel{-}{\varvec{X}}\pm\:\mathbf{S}\) , g/l) 1.96 ± 0.62 2.07 ± 0.67 -1.875 0.061 Comorbid conditions Hyperuricemia Yes/n(%) 398(29.4%) 30(41.7%) 4.863 0.027 Diabetes Yes/n(%) 322(23.8%) 24(33.3%) 3.366 0.067 Hypertension Yes/n(%) 410(30.3%) 29(40.3%) 3.175 0.075 Screening of Independent Predictors A total of elderly individuals diagnosed with NAFLD were randomly allocated into two separate groups following a 7:3 ratio: 1,424 cases in the training cohort and 611 in the validation cohort. In the training group, we used the occurrence of AF as the dependent variable and 24 feature variables as independent variables for LASSO regression, as shown in Fig. 1 . The results indicated that two vertical lines correspond to Lambda.min (λ = 0.001) and Lambda.1se (λ = 0.019), respectively. At Lambda.min, 21 predictor variables were selected, while at Lambda.1se, 5 predictor variables were identified. Considering the simplicity of the model, we chose to construct the model using the 5 predictors selected at Lambda.1se, including age, BMI, direct bilirubin, LDL-c, and albumin. Subsequently, the importance of each variable was assessed using the random forest algorithm by calculating the mean decrease in Gini value. The variables were ranked in descending order of their effect size as follows: direct bilirubin, BMI, LDL-c, albumin and age, as shown in Fig. 2 . Multivariate logistic regression model for predicting the risk of AF in elderly patients with NAFLD Multivariate logistic regression analysis was performed using the variables identified as relevant, as shown in Table 3 . It was found that age (OR = 1.075, 95% CI: 1.039–1.112), BMI (OR = 1.168, 95% CI: 1.087–1.255), albumin (OR = 0.804, 95% CI: 0.735–0.878), direct bilirubin (OR = 1.405, 95% CI: 1.281–1.549), and LDL-c (OR = 0.494, 95% CI: 0.377–0.643) were independent risk factors for AF in elderly NAFLD patients. Additionally, the variance inflation factor (VIF) values were all less than 2, indicating that the independent variables in the model were relatively uncorrelated. The final multivariate logistic regression equation for predicting the risk of AF in elderly NAFLD patients was as follows: Logit(P) = ln[P/(1-P)] = -2.359 + 0.072×Age + 0.155×BMI + 0.34 ×Direct Bilirubin − 0.218×Albumin − 0.706 × LDL-c. Table 3 Multivariate Logistic Regression Analysis and Variance Inflation Factor (VIF) for AF Risk in Elderly NAFLD Patients Variable β SE Wald P OR 95%Cl VIF Age 0.072 0.017 17.908 <0.001 1.075 1.039–1.112 1.044 BMI 0.155 0.037 18.032 <0.001 1.168 1.087–1.255 1.009 ALB -0.218 0.045 23.137 <0.001 0.804 0.735–0.878 1.052 DBIL 0.34 0.048 49.404 <0.001 1.405 1.281–1.549 1.049 LDL-c -0.706 0.136 26.939 <0.001 0.494 0.377–0.643 1.047 Internal Validation of Predictive Models Based on the area under the ROC curve (AUC), the combined predictive model comprising five indicators in the training group achieved an AUC value of 0.829 (95% CI: 0.774–0.884), which was significantly superior to other predictive factors ( P < 0.001). The AUC value in the validation group was 0.855 (95% CI: 0.794–0.916), indicating that the predictive model possesses excellent discriminatory ability, as shown in Fig. 3 . Furthermore, we conducted internal validation of the model within the training set using the Bootstrapping method, yielding a C-index of 0.829 (95% CI: 0.774–0.884). This demonstrates that the model has good discriminatory capability in predicting the risk of AF in elderly NAFLD patients. Additionally, the calibration plots showed that the models in both the training and validation groups achieved well-fitted curves, as shown in Fig. 4 . Visualization and Clinical Applicability Analysis of the Prediction Model To enhance the intuitiveness of our prediction model, we visualized it using a nomogram, as shown in Fig. 5 . By summing the scores of each feature variable, a total score can be obtained. A higher total score indicates a greater risk of AF in elderly NAFLD patients. The yellow region in the chart illustrates the distribution range of most data and probabilities. The DCA indicates that the model provides greater clinical net benefit than individual factors when the threshold probability is between 0.05 and 0.795 in the training group and between 0.05 and 0.9 in the validation group, as shown in Fig. 6 . The CIC analysis of our model reveals that, in both the training and validation cohorts, when the risk threshold probability exceeds 50% of the total predicted score probability, the population identified by the model is highly consistent with the actual population. This result demonstrates that the nomogram model has promising clinical application prospects. External Data Validation To further enhance the reliability and credibility of the Nomogram model, we conducted external validation using data from 511 elderly NAFLD patients undergoing health examinations in Suzhou region, among whom 24 had AF (prevalence rate: 4.70%). External validation analysis demonstrated a predictive accuracy with an AUC of 0.826(95% CI: 0.742–0.910), which was consistent with the AUC value of 0.829 (95% CI: 0.774–0.884) obtained during model development. Additionally, the calibration curve demonstrated that the model can accurately predict the probability of endpoint events (Hosmer-Lemeshow Test: X² = 7.926, P = 0.441), proving that the model has good generalizability. Discussion In our study, we selected 2,035 NAFLD patients aged 65 years and older from Yangzhou, Jiangsu Province of China, dividing them into a model construction group and an internal validation group. Additionally, we selected an external validation cohort of 511 elderly NAFLD patients from Suzhou, Jiangsu Province, China. Ultimately, the nomogram model demonstrated strong discriminatory and calibration capabilities in both the internal and external validation cohorts. Furthermore, the DCA and CIC curves in the internal validation cohort also indicated that the model exhibited excellent clinical utility. BMI is often considered a surrogate marker for obesity and research has demonstrated that obesity functions as a contributing factor to AF 12 . A nationwide cohort study found that compared to a normal BMI, being overweight (25.0 ≤ BMI < 30.0, hazard ratio [HR] = 1.123) and being obese (BMI ≥ 30.0, HR = 1.327) showed a strong positive correlation with an elevated risk of developing new-onset AF 13 . Furthermore, another study found that individuals who met the criteria for obesity but did not have accompanying metabolic abnormalities such as diabetes, hyperlipidemia, or insulin resistance also exhibited an increased risk of AF. This finding further supports the independent association between obesity and elevated AF risk 14 . A Mendelian randomization study demonstrated a causal relationship between higher BMI and increased AF risk 15 . Obesity may contribute to AF development through mechanisms such as changes in epicardial adipose tissue, atrial remodeling and inflammation 16 . Therefore, controlling BMI and obesity may help prevent the occurrence of AF. In our nomogram model, which targets elderly NAFLD patients who generally have higher BMIs, we incorporated BMI as a continuous variable to clearly illustrate the significant association between changes in BMI and AF risk. As individuals aged over 65 grow older, the risks of hypertension and heart failure increase significantly. This might explain the significant positive correlation between age and AF observed in our study. A multicenter epidemiological study 17 in Europe indicated that by 2060, the number of AF patients aged 65 and above in the European Union will increase by 89%. Notably, in 2016, AF patients aged 80 and above accounted for 51.2% of the total AF patient population, and this proportion is expected to rise to 65.2% by 2060. Furthermore, the risks of cardiovascular events and mortality associated with AF are higher among elderly individuals 18 . Albumin is a primary protein in human plasma with various biochemical properties. Hypoalbuminemia, typically defined as ALB < 35 g/L, is considered an important factor in inflammation, malnutrition, and cachexia, and serves as a useful biomarker for diagnosing cardiovascular diseases 19 . Our study confirmed a negative correlation between ALB and AF risk in elderly patients with NAFLD and incorporated it as an important variable in the predictive model. This finding aligns with many previous studies. A large-scale prospective epidemiological and Mendelian randomization study demonstrated a nearly linear negative correlation between ALB levels and AF risk 20 . A dose-response meta-analysis found a significant negative linear association between serum ALB and AF risk, showing that for every 10 g/L increase in serum albumin levels, the risk of AF decreased by 36% 21 . Inflammation and oxidative stress are two key factors in the process of atrial remodeling, significantly influencing the electrophysiological and structural changes associated with AF 22 . ALB exhibits anti-inflammatory properties 23 and due to its abundant thiol groups, accounts for 80% of the total thiol-based reactive oxygen and nitrogen species scavenging in plasma. Additionally, it can carry nitric oxide (NO), thereby demonstrating strong antioxidant stress capacity 24 . These properties may explain the protective role of albumin in reducing the risk of AF. Bilirubin, a bile pigment, is a well-known metabolite of heme breakdown. Currently, multiple observational studies have revealed a significant positive correlation between bilirubin levels and the risk of AF. A study involving 437 patients with thyrotoxicosis who underwent radioactive iodine therapy indicated that DBIL levels are an independent risk factor for predicting AF in hyperthyroidism patients 25 . Another study of 212 patients with paroxysmal AF after catheter ablation revealed that elevated bilirubin levels were significantly associated with postoperative AF recurrence 26 . Our study consisted of elderly patients with NAFLD, who generally had higher DBIL levels. Additionally, we found that DBIL had a superior effect in predicting AF risk in elderly NAFLD patients compared to other characteristic variables. However, the pathophysiological mechanisms that contribute to this connection between DBIL and AF are still not clearly defined. LDL-c is composed of lipids and proteins. It is synthesized in the liver and is responsible for transporting cholesterol from the liver to other parts of the body. LDL-c levels are usually positively correlated with the occurrence of NAFLD 27 . Elderly NAFLD patients often exhibit higher LDL-c levels, and LDL-c can lead to coronary heart disease, which is a risk factor for AF 28 . Interestingly, evidence from several studies 29 , 30 suggest that in individuals suffering from conditions such as chronic kidney disease, hypertension, or acute myocardial infarction, reduced LDL-c levels may be linked to a heightened likelihood of developing AF. Our study also confirmed a negative correlation between LDL-c levels and AF. The underlying mechanism is likely unrelated to traditional factors such as obesity and alcohol consumption, which are known to elevate LDL-c levels, but rather involves other pathways. One possible reason is that cholesterol may contribute to the development of AF by influencing the structure of ion channels in cell membranes 31 . Additionally, LDL-c might play a role in reducing inflammatory responses and oxidative stress, consequently affecting the development of AF 32 . This research has several limitations. To begin with, due to its cross-sectional design, it is unable to establish causal relationships between AF and related variables in elderly individuals with NAFLD effectively. Second, since our research focuses on a health check-up population, it lacks data on indicators such as NT-proBNP, cardiac troponin as well as information on coronary artery disease and stroke history. Finally, the sample size for external validation is relatively small and further multicenter larger population and studies are needed to validate our model. In this study, a successful predictive model of AF risk in elderly patients with NAFLD was constructed with indicators including age, BMI, albumin, direct bilirubin and LDL-c. The predictive model demonstrated a strong capacity to distinguish between different outcome categories and might provide a tool to identify patients with NAFLD who were at high risk of AF. Declarations Acknowledgments We thank the medical staff at the Northern Jiangsu People’s Hospital and The First Affiliated Hospital of Soochow University for support in this study. Ethics and Clinical trial statement The portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki. The study has been approved by the Ethics Committee of Northern Jiangsu People Hospital (Approval Number: 2024ky284) and Affiliated Hospital of Soochow University(Approval Number:228) . Clinical trial number: not applicable. Author Contributions YZ, JL and LJ Y designed and wrote the article; H L and S Z collected the clinical data. All authors contributed to the article and approved the submitted version. Declaration of conflicting interest The authors declare that there is no conflict of interest. Funding information This work was supported by The National Natural Science Foundation of China (81800250), China Postdoctoral Science Foundation (2022M711417), Jiangsu Province Traditional Chinese Medicine Science and Technology Development Program Project (MS2023137), Xuzhou Medical University Affiliated Hospital Development Fund Project (XYFY202401), Yangzhou Municipal Basic Research Program Project (2024-2-12), Clinical Trials from Northern Jiangsu People’s Hospital(SBLC23002). Data availability No datasets were generated or analysed during the current study. References Riazi K, Azhari H, Charette JH, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851–61. Ruze R, Liu T, Zou X, et al. Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments. Front Endocrinol (Lausanne). 2023;14:1161521. Jiang H, Wang H, Guo Y, et al. Characterization of the hepatic flora and metabolome in nonalcoholic fatty liver disease. Front Microbiol. 2024;15:1528258. Xiao J, Ng CH, Chan KE, et al. Hepatic, Extra-hepatic Outcomes and Causes of Mortality in NAFLD - An Umbrella Overview of Systematic Review of Meta-Analysis. J Clin Exp Hepatol. 2023;13:656–65. Messori A, Mamone D, Rivano M, Romeo MR, Vaiani M, Trippoli S. Pulsed-field ablation for paroxysmal atrial fibrillation: An indirect comparison of effectiveness among three proprietary devices conducted in the absence of randomized trials. Int J Cardiol. 2024;406:132025. Cho EJ, Chung GE, Yoo JJ, et al. Association of nonalcoholic fatty liver disease with new-onset atrial fibrillation stratified by age groups. Cardiovasc Diabetol. 2024;23:340. Vaz K, Kemp W, Majeed A, et al. NAFLD and MAFLD independently increase the risk of major adverse cardiovascular events (MACE): a 20-year longitudinal follow-up study from regional Australia. Hepatol Int. 2024;18:1135–43. Nayak SS, Kuriyakose D, Polisetty LD, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23:310. Chalasani N, Younossi Z, Lavine JE, et al. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67(1):328–57. US Preventive Services Task Force, Davidson KW, Barry MJ, et al. Screening for Atrial Fibrillation: US Preventive Services Task Force Recommendation Statement. JAMA. 2022;327(4):360–7. Gou R, Dou D, Tian M, et al. Association between triglyceride glucose index and hyperuricemia: a new evidence from China and the United States. Front Endocrinol (Lausanne). 2024;15:1403858. Published 2024 Jul 1. Hu Y, Zhao Y, Zhang J, Li C. The association between triglyceride glucose-body mass index and all-cause mortality in critically ill patients with atrial fibrillation: a retrospective study from MIMIC-IV database. Cardiovasc Diabetol. 2024;23:64. Kim YG, Han KD, Choi JI, et al. The impact of body weight and diabetes on new-onset atrial fibrillation: a nationwide population based study. Cardiovasc Diabetol. 2019;18:128. Palatini P, Saladini F, Mos L, et al. Healthy overweight and obesity in the young: Prevalence and risk of major adverse cardiovascular events. Nutr Metab Cardiovasc Dis. 2024;34:783–91. Ma M, Zhi H, Yang S, Yu EY, Wang L. Body Mass Index and the Risk of Atrial Fibrillation: A Mendelian Randomization Study. Nutrients 2022;14. Javed S, Gupta D, Lip GYH. Obesity and atrial fibrillation: making inroads through fat. Eur Heart J Cardiovasc Pharmacother. 2021;7:59–67. Di Carlo A, Bellino L, Consoli D, et al. Prevalence of atrial fibrillation in the Italian elderly population and projections from 2020 to 2060 for Italy and the European Union: the FAI Project. Europace. 2019;21:1468–75. Ikeda S, Hiasa KI, Inoue H, et al. Clinical outcomes and anticoagulation therapy in elderly non-valvular atrial fibrillation and heart failure patients. ESC Heart Fail. 2024;11:902–13. Ronit A, Kirkegaard-Klitbo DM, Dohlmann TL, et al. Plasma Albumin and Incident Cardiovascular Disease: Results From the CGPS and an Updated Meta-Analysis. Arterioscler Thromb Vasc Biol. 2020;40:473–82. Liao LZ, Zhang SZ, Li WD, et al. Serum albumin and atrial fibrillation: insights from epidemiological and mendelian randomization studies. Eur J Epidemiol. 2020;35:113–22. Wang Y, Du P, Xiao Q, et al. Relationship Between Serum Albumin and Risk of Atrial Fibrillation: A Dose-Response Meta-Analysis. Front Nutr. 2021;8:728353. Liu X, Zhang W, Luo J, et al. TRIM21 deficiency protects against atrial inflammation and remodeling post myocardial infarction by attenuating oxidative stress. Redox Biol. 2023;62:102679. Song G, Zhang Y, Wang X, et al. An inflammatory prognostic scoring system to predict the risk for adults with acute coronary syndrome undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2024;24:728. Son BK, Lyu W, Tanaka T, Yoshizawa Y, Akishita M, Iijima K. Impact of the anti-inflammatory diet on serum high-sensitivity C-Reactive protein and new-onset frailty in community-dwelling older adults: A 7-year follow-up of the Kashiwa cohort study. Geriatr Gerontol Int. 2024;24(Suppl 1):189–95. Creeden JF, Gordon DM, Stec DE, Hinds TD. Jr. Bilirubin as a metabolic hormone: the physiological relevance of low levels. Am J Physiol Endocrinol Metab. 2021;320:E191–207. Lee HA, Jung JY, Lee YS, et al. Direct Bilirubin Is More Valuable than Total Bilirubin for Predicting Prognosis in Patients with Liver Cirrhosis. Gut Liver. 2021;15:599–605. Lu S, Xie Q, Kuang M, et al. Lipid metabolism, BMI and the risk of nonalcoholic fatty liver disease in the general population: evidence from a mediation analysis. J Transl Med. 2023;21:192. Borén J, Chapman MJ, Krauss RM, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41:2313–30. Guan M, Hu H, Qi D, Qin X, Wan Q. Inverse relationship between LDL-C/HDL-C ratio and atrial fibrillation in chronic kidney disease patients. Sci Rep. 2024;14:17721. Liu L, Liu X, Ding X, Chen H, Li W, Li H. Lipid Levels and New-Onset Atrial Fibrillation in Patients with Acute Myocardial Infarction. J Atheroscler Thromb. 2023;30:515–30. Ding WY, Protty MB, Davies IG, Lip GYH. Relationship between lipoproteins, thrombosis, and atrial fibrillation. Cardiovasc Res. 2022;118:716–31. Guan B, Wang A, Xu H. Causal associations of remnant cholesterol with cardiometabolic diseases and risk factors: a mendelian randomization analysis. Cardiovasc Diabetol. 2023;22:207. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor invited by journal 13 Aug, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 17 Jul, 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7054945","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510000308,"identity":"ec23e556-c414-4a49-b058-02b36354d70b","order_by":0,"name":"Lijuan Yu","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Yu","suffix":""},{"id":510000313,"identity":"0e0d5a4d-a1d5-44d3-b498-8ddedc985767","order_by":1,"name":"Shuai Zhang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhang","suffix":""},{"id":510000318,"identity":"26ef8512-adae-43ce-9fb2-3d65b56edd49","order_by":2,"name":"Hao Liang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Liang","suffix":""},{"id":510000322,"identity":"fbe7d62b-b9b0-486b-a62f-b039494eea17","order_by":3,"name":"Jia You","email":"","orcid":"","institution":"Yangzhou University Medical College Affiliated Yangzhou Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"You","suffix":""},{"id":510000326,"identity":"a454c513-33d3-4934-abff-af5d2e11eedd","order_by":4,"name":"Jun Liu","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""},{"id":510000332,"identity":"5b5b7ced-a269-4e9f-9060-2c6cbe9f96c7","order_by":5,"name":"Ye Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACeWb+j4//GNTYsbE3EKnFsL3B2ICn4FgyH88BYq05c8BMgucDM+M8iQQidTDOSEiTkDBgY2aTfLzxBkONTTRBLewSCYctDAxk+Nik04otGI6l5TYQtiWx8UYCyBbpHDMJxobDhLUw3EhmkDhgwMzYJnmGWC1njjFJNoC0SPAQqcWwvYfZmMHgWDIbD9AvCcT4RZ6Zh/Exw58aO/n2wxtvfKixIcJhSMCA6KhB0kKqjlEwCkbBKBgZAADbpDiZLn/+4wAAAABJRU5ErkJggg==","orcid":"","institution":"Northern Jiangsu People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-07-05 21:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7054945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7054945/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91073662,"identity":"4e8dd7d1-ab7d-46f8-b649-96a64e99adef","added_by":"auto","created_at":"2025-09-11 11:00:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO Diagram for Feature Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Variable selection path diagram of LASSO regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) 10-fold cross-validation diagram of LASSO regression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/451c09e54d5d25f193a8972b.png"},{"id":91073663,"identity":"9792ada6-f434-4dc3-8c47-3981ddfd1e73","added_by":"auto","created_at":"2025-09-11 11:00:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Feature Importance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/2ad89a1410a2b1c170842405.png"},{"id":91073666,"identity":"492df9ea-31f1-40be-9f7b-e0eb8ec346f4","added_by":"auto","created_at":"2025-09-11 11:00:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) ROC Curve of the Training Set (b) ROC Curve of the Validation Set\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/a96703879c531400a2536206.png"},{"id":91073668,"identity":"764faf0b-3734-421d-b7e8-54531c83aec4","added_by":"auto","created_at":"2025-09-11 11:00:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration Curve for Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Calibration curve for the training set (b) Calibration curve for the validation set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The diagonal blue dashed line represents the perfect prediction of the ideal model. The solid red line indicates the performance of the predictive model; the closer it fits to the diagonal dashed line, the better the predictive performance.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/5b29e678c842a4096f8b0921.png"},{"id":91075480,"identity":"f8f8882f-35c7-4fb7-9f98-30a8f1a7008d","added_by":"auto","created_at":"2025-09-11 11:08:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram Prediction Model for the Risk of AF in Elderly Patients with NAFLD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/545aad10b34d57a1271695c7.png"},{"id":91077385,"identity":"2227406d-f458-46e0-a9dd-2f1c924ebdd5","added_by":"auto","created_at":"2025-09-11 11:16:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA Curve for Model Evaluation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) DCA curve for the training set (b) DCA curve for the validation set\u003c/p\u003e\n\u003cp\u003eNote: The thin black horizontal line represents the net benefit when no one receives treatment, while the gray diagonal line represents the net benefit when everyone receives treatment. The other colored curves represent the net benefits of different variables and the nomogram model at various thresholds. DCA: Decision Curve Analysis.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/32b971128a8abb9ac910eea4.png"},{"id":91075483,"identity":"322aa179-03dc-4d23-8d74-704277d007eb","added_by":"auto","created_at":"2025-09-11 11:08:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":61932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCIC Curve for Model Evaluation. (a) CIC curve for the training set (b) CIC curve for the validation set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The solid line represents the number of individuals predicted by the nomogram model to experience AF at various threshold probabilities. The dashed line represents the actual number of individuals who experienced AF at the same threshold probabilities.\u003c/p\u003e\n\u003cp\u003eAF: Atrial fibrillation; CIC: Clinical impact curve analysis.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/164adaca56a553d6cb2f91f7.png"},{"id":91073678,"identity":"b87fa77f-147e-4be4-a18f-25e5179462e7","added_by":"auto","created_at":"2025-09-11 11:00:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":91808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExternal Data Evaluation Model. (a) ROC Curve of External Data (b) Calibration Curve of External Data.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/c15f64a9ad871425fc3a8246.png"},{"id":91079579,"identity":"8c778883-987c-4ea9-85ba-04f37f0c9886","added_by":"auto","created_at":"2025-09-11 11:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2742273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7054945/v1/13670e53-9d3c-4072-ba36-84a52f9da6c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a prediction model for risk of atrial fibrillation in elderly patients with nonalcoholic fatty liver disease","fulltext":[{"header":"Background","content":"\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders marked by abnormal fat buildup, often occurring in conjunction with various metabolic dysfunctions. A meta-analysis of 72 studies revealed that approximately 32% of the global population is affected by NAFLD\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, exceeding the prevalence of obesity and diabetes significantly\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In the NAFLD patient population, cardiovascular disease (CVD) is the leading cause of death worldwide\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, studies have shown that people diagnosed with NAFLD, especially those who have progressed to the fibrotic stage, face a notably higher risk of developing cardiovascular complications\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This suggests that NAFLD is a major risk factor for increased cardiovascular events and their adverse outcomes.\u003c/p\u003e\u003cp\u003eThe incidence of Atrial fbrillation (AF) represents an escalating challenge to public healthcare systems\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Factors that contribute to increase risks of AF often include excess body weight, older age, coronary artery disease and so on. Furthermore, a cohort study showed that NAFLD was associated with an increased risk of prevalent AF in a middle-aged population\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Given that the elderly population has a high prevalence of both NAFLD and AF, elderly patients diagnosed with AF face risks of complications such as heart failure, stroke, greater emphasis ought to be placed on identifying and addressing the potential risk of AF among older individuals suffering from NAFLD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. At present, most nomogram studies focus on predictive models for new-onset AF after cardiopulmonary surgery\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, with limited attention paid to the general population undergoing physical examinations, particularly NAFLD patients over the age of 65. Furthermore, whether the association between NAFLD and AF also holds true in the elderly population remains uncertain. Therefore, a cross-sectional analysis was carried out aiming to establish a predictive framework for AF among older individuals diagnosed with NAFLD within the Chinese elderly population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients over 65 years old diagnosed with NAFLD at the Health Check-up Center of Northern Jiangsu People's Hospital and The First Affiliated Hospital of Soochow University from January 1, 2014, to December 31, 2023, were included in this study. Information was gathered through the outpatient and inpatient systems of the Hospital Information System (HIS). Patients diagnosed with AF by 12-lead electrocardiogram (ECG) or with a history of AF were considered AF patients. Based on the presence or absence of AF, participants were classified into corresponding groups.\u003c/p\u003e\u003cp\u003eExclusion criteria included: (1) coronary heart disease, rheumatic, congenital, and valvular heart diseases, patients who had undergone biological or mechanical valve replacement, hyperthyroidism, or connective tissue diseases that could lead to AF; (2) viral, drug-induced, autoimmune, or other chronic liver diseases; (3) excessive alcohol consumption (alcohol equivalent to ethanol, exceeding 70g weekly for women and 140g weekly for men); (4) severe renal insufficiency; (5) acute infection; (6) lack of clinical data. The diagnosis of NAFLD was made by abdominal color Doppler ultrasound examination performed by physicians at least five years of clinical experience, and the results were reviewed by experts with a deputy senior or higher professional title to issue the final health examination report. In these individuals, hepatic steatosis could not be attributed to any other known causes, including long-term use of medication with hepatotoxicity, alcohol abuse, or previous viral liver infection\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. AF was determined through the criteria outlined below: standard 12-lead ECG or \u0026ge;\u0026thinsp;30s single-lead ECG recording of ECG events, irregular RR intervals, and no identifiable P waves (without atrioventricular block) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Ethical approval for this study was obtained from both the Ethics Committee of Northern Jiangsu People's Hospital (Approval No. 2024ky284) and the First Affiliated Hospital of Soochow University (Approval No. 228). Given that this investigation was retrospective in design, the Ethics Committees waived the need for informed consent.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInformation on demographic factors, including age, sex and body dimensions (such as height and weight), smoking status, hypertension and diabetes were extracted through a thorough review of the patients' medical records. Laboratory parameters comprising white blood cell count (WBC), neutrophil count (NE#), hemoglobin (HB), alanine aminotransferase (ALT), urea (Scr), uric acid (UA), albumin (ALB) were also collected. The BMI was determined by dividing the weight in kilograms by the square of height in meters (kg/m\u0026sup2;).\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eGraphs were created using R-4.4.1 software, while statistical analysis was carried out utilizing SPSS version 27.0. Normally distributed quantitative variables were expressed as means with standard deviations (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and comparisons between groups were performed using independent samples t-tests. Categorical variables were described in terms of proportions, and group differences were assessed via the chi-square (χ\u0026sup2;) test. In cases where quantitative data did not meet the assumption of normality, results were reported using the interquartile range [M (Q1, Q3)], and the Mann\u0026ndash;Whitney U test was applied for intergroup comparisons. Participants from Northern Jiangsu People's Hospital were divided into a training set to construct the prediction model and a testing set to validate the model's performance. Samples from Northern Jiangsu People's Hospital served as the internal validation set, while samples from the First Affiliated Hospital of Soochow University served as the external validation set to assess the model's predictive ability.\u003c/p\u003e\u003cp\u003eLASSO (least absolute shrinkage and selection operator) regression analysis was used to identify statistically significant predictors, and the random forest algorithm was employed to rank the importance of the selected variables. To identify the optimal predictive factors and establish a nomogram, we performed multivariate logistic regression analysis incorporating the variables selected through LASSO regression. Model prediction accuracy was evaluated by analyzing its receiver operating characteristic (ROC) and calibration curves. Additionally, we employed decision curve analysis (DCA) and clinical impact curve (CIC) analyses to determine the clinical value and practical utility of the prediction model.\u003c/p\u003e\u003cp\u003eA \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research consisted of 2,035 elderly individuals diagnosed with NAFLD. Female participants constituted 38.1% (n\u0026thinsp;=\u0026thinsp;776) of the study population, with a mean age of 71 years. AF was present in 116 subjects, representing 5.7% of the cohort.The baseline characteristics of the modeling group and the validation group showed no notable variation(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics between the modeling group and the validation group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;2035)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModeling Group(n\u0026thinsp;=\u0026thinsp;1424)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation group(n\u0026thinsp;=\u0026thinsp;611)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Information\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1259(61.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e883(62.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e376(61.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking History\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e351(17.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e251(17.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e100(16.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.485\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e71.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e71.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e72.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.91\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.296\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\mathbf{k}\\mathbf{g}/\\mathbf{m}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e26.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e26.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.321\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory Indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTP, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e73.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e73.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.791\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALB, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e45.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e45.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e45.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.236\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTBIL, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e12.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.814\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBIL, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.913\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGGT, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e25(19, 38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e25(19, 38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e26(19, 38.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.519\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21(18, 26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e21(18, 26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e21(18, 26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.219\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e19(15, 28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e20(15, 27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e19(14, 28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.343\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrea, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.904\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCre, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e86.17\u0026thinsp;\u0026plusmn;\u0026thinsp;19.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e85.46\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e86.47\u0026thinsp;\u0026plusmn;\u0026thinsp;19.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.251\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG, [M(P25, P75, mmol/l)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.74(1.29, 2.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.642\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003emmol/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.951\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBC, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e\u0026times;1012/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.081\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHCT, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e43.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e43.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e43.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.414\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRDW, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e12.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e12.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.618\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHB, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e146.73\u0026thinsp;\u0026plusmn;\u0026thinsp;13.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e146.91\u0026thinsp;\u0026plusmn;\u0026thinsp;13.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e146.31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.349\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.556\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNE#, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.501\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbid conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyperuricemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e607(29.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e428(30.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e179(29.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.731\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e485(23.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e346(24.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e139(22.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.449\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e582(28.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e429(30.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e153(25.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.052\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the modeling group, consisting of 1,424 patients, 72 patients (5.1%) were in the AF group, while 1,352 patients (94.9%) were in the non-AF group. Elderly patients with NAFLD who also had AF demonstrated notably elevated values in several clinical parameters, including age,BMI, total and direct bilirubin, γ-glutamyl transferase (γ-GGT), serum creatinine, hematocrit, red blood cell distribution width (RDW), and white blood cell count, in comparison to those without AF. In contrast, there was a marked decrease in the concentrations of total protein, albumin, ALT and low-density lipoprotein cholesterol (LDL-c)(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, elderly NAFLD patients with hyperuricemia were more likely to develop AF(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics Between the AF and the Non-AF Group in the Modeling Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-AF(n\u0026thinsp;=\u0026thinsp;1352)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAF(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et/z/x\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Information\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.177\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e834(61.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e49(68.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking History\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e233(17.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e18(25.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.841\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.092\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e71.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e75.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-6.909\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\mathbf{k}\\mathbf{g}/\\mathbf{m}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e27.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-5.296\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory Indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTP, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e72.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.176\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALB, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e45.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTBIL, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e17.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-9.951\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBIL, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-12.061\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGGT, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e25(18, 38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e30.5(22, 45.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-4.069\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21(18, 26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e21(18, 27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.998\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT, [M(P25, P75, U/L)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e19(15, 28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e16(12.93, 23.13)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-3.665\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrea, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-1.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.296\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCre, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e85.69\u0026thinsp;\u0026plusmn;\u0026thinsp;18.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e94.05\u0026thinsp;\u0026plusmn;\u0026thinsp;23.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-4.591\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG, [M(P25, P75, mmol/l)]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.74(1.30, 2.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.75(1.19, 2.41)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.608\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.544\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003emmol/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7.995\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBC, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e\u0026times;1012/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.598\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.598\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHCT, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e43.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-3.503\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRDW, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S},\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-3.127\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHB, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e146.60\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e148.92\u0026thinsp;\u0026plusmn;\u0026thinsp;14.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-1.832\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.067\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-3.662\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNE#, (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{X}}\\pm\\:\\mathbf{S}\\)\u003c/span\u003e\u003c/span\u003e, \u003cb\u003eg/l)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-1.875\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.061\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbid conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyperuricemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e398(29.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e30(41.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.863\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e322(23.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e24(33.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.366\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.067\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes/n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e410(30.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e29(40.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.175\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.075\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eScreening of Independent Predictors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of elderly individuals diagnosed with NAFLD were randomly allocated into two separate groups following a 7:3 ratio: 1,424 cases in the training cohort and 611 in the validation cohort. In the training group, we used the occurrence of AF as the dependent variable and 24 feature variables as independent variables for LASSO regression, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results indicated that two vertical lines correspond to Lambda.min (λ\u0026thinsp;=\u0026thinsp;0.001) and Lambda.1se (λ\u0026thinsp;=\u0026thinsp;0.019), respectively. At Lambda.min, 21 predictor variables were selected, while at Lambda.1se, 5 predictor variables were identified. Considering the simplicity of the model, we chose to construct the model using the 5 predictors selected at Lambda.1se, including age, BMI, direct bilirubin, LDL-c, and albumin.\u003c/p\u003e\u003cp\u003eSubsequently, the importance of each variable was assessed using the random forest algorithm by calculating the mean decrease in Gini value. The variables were ranked in descending order of their effect size as follows: direct bilirubin, BMI, LDL-c, albumin and age, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate logistic regression model for predicting the risk of AF in elderly patients with NAFLD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultivariate logistic regression analysis was performed using the variables identified as relevant, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It was found that age (OR\u0026thinsp;=\u0026thinsp;1.075, 95% CI: 1.039\u0026ndash;1.112), BMI (OR\u0026thinsp;=\u0026thinsp;1.168, 95% CI: 1.087\u0026ndash;1.255), albumin (OR\u0026thinsp;=\u0026thinsp;0.804, 95% CI: 0.735\u0026ndash;0.878), direct bilirubin (OR\u0026thinsp;=\u0026thinsp;1.405, 95% CI: 1.281\u0026ndash;1.549), and LDL-c (OR\u0026thinsp;=\u0026thinsp;0.494, 95% CI: 0.377\u0026ndash;0.643) were independent risk factors for AF in elderly NAFLD patients. Additionally, the variance inflation factor (VIF) values were all less than 2, indicating that the independent variables in the model were relatively uncorrelated. The final multivariate logistic regression equation for predicting the risk of AF in elderly NAFLD patients was as follows: Logit(P)\u0026thinsp;=\u0026thinsp;ln[P/(1-P)] = -2.359\u0026thinsp;+\u0026thinsp;0.072\u0026times;Age\u0026thinsp;+\u0026thinsp;0.155\u0026times;BMI\u0026thinsp;+\u0026thinsp;0.34 \u0026times;Direct Bilirubin \u0026minus;\u0026thinsp;0.218\u0026times;Albumin \u0026minus;\u0026thinsp;0.706 \u0026times; LDL-c.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Logistic Regression Analysis and Variance Inflation Factor (VIF) for AF Risk in Elderly NAFLD Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%Cl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.908\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.039\u0026ndash;1.112\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.032\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.168\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.087\u0026ndash;1.255\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.009\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.218\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e23.137\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.804\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.735\u0026ndash;0.878\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.052\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBIL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e49.404\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.405\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.281\u0026ndash;1.549\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.706\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.136\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e26.939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.494\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.377\u0026ndash;0.643\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternal Validation of Predictive Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the area under the ROC curve (AUC), the combined predictive model comprising five indicators in the training group achieved an AUC value of 0.829 (95% CI: 0.774\u0026ndash;0.884), which was significantly superior to other predictive factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUC value in the validation group was 0.855 (95% CI: 0.794\u0026ndash;0.916), indicating that the predictive model possesses excellent discriminatory ability, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, we conducted internal validation of the model within the training set using the Bootstrapping method, yielding a C-index of 0.829 (95% CI: 0.774\u0026ndash;0.884). This demonstrates that the model has good discriminatory capability in predicting the risk of AF in elderly NAFLD patients. Additionally, the calibration plots showed that the models in both the training and validation groups achieved well-fitted curves, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVisualization and Clinical Applicability Analysis of the Prediction Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo enhance the intuitiveness of our prediction model, we visualized it using a nomogram, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. By summing the scores of each feature variable, a total score can be obtained. A higher total score indicates a greater risk of AF in elderly NAFLD patients. The yellow region in the chart illustrates the distribution range of most data and probabilities.\u003c/p\u003e\u003cp\u003eThe DCA indicates that the model provides greater clinical net benefit than individual factors when the threshold probability is between 0.05 and 0.795 in the training group and between 0.05 and 0.9 in the validation group, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe CIC analysis of our model reveals that, in both the training and validation cohorts, when the risk threshold probability exceeds 50% of the total predicted score probability, the population identified by the model is highly consistent with the actual population. This result demonstrates that the nomogram model has promising clinical application prospects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExternal Data Validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further enhance the reliability and credibility of the Nomogram model, we conducted external validation using data from 511 elderly NAFLD patients undergoing health examinations in Suzhou region, among whom 24 had AF (prevalence rate: 4.70%). External validation analysis demonstrated a predictive accuracy with an AUC of 0.826(95% CI: 0.742\u0026ndash;0.910), which was consistent with the AUC value of 0.829 (95% CI: 0.774\u0026ndash;0.884) obtained during model development. Additionally, the calibration curve demonstrated that the model can accurately predict the probability of endpoint events (Hosmer-Lemeshow Test: X\u0026sup2; = 7.926, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.441), proving that the model has good generalizability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we selected 2,035 NAFLD patients aged 65 years and older from Yangzhou, Jiangsu Province of China, dividing them into a model construction group and an internal validation group. Additionally, we selected an external validation cohort of 511 elderly NAFLD patients from Suzhou, Jiangsu Province, China. Ultimately, the nomogram model demonstrated strong discriminatory and calibration capabilities in both the internal and external validation cohorts. Furthermore, the DCA and CIC curves in the internal validation cohort also indicated that the model exhibited excellent clinical utility.\u003c/p\u003e\u003cp\u003eBMI is often considered a surrogate marker for obesity and research has demonstrated that obesity functions as a contributing factor to AF\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A nationwide cohort study found that compared to a normal BMI, being overweight (25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0, hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.123) and being obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0, HR\u0026thinsp;=\u0026thinsp;1.327) showed a strong positive correlation with an elevated risk of developing new-onset AF\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, another study found that individuals who met the criteria for obesity but did not have accompanying metabolic abnormalities such as diabetes, hyperlipidemia, or insulin resistance also exhibited an increased risk of AF. This finding further supports the independent association between obesity and elevated AF risk \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A Mendelian randomization study demonstrated a causal relationship between higher BMI and increased AF risk\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Obesity may contribute to AF development through mechanisms such as changes in epicardial adipose tissue, atrial remodeling and inflammation \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, controlling BMI and obesity may help prevent the occurrence of AF. In our nomogram model, which targets elderly NAFLD patients who generally have higher BMIs, we incorporated BMI as a continuous variable to clearly illustrate the significant association between changes in BMI and AF risk.\u003c/p\u003e\u003cp\u003eAs individuals aged over 65 grow older, the risks of hypertension and heart failure increase significantly. This might explain the significant positive correlation between age and AF observed in our study. A multicenter epidemiological study\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e in Europe indicated that by 2060, the number of AF patients aged 65 and above in the European Union will increase by 89%. Notably, in 2016, AF patients aged 80 and above accounted for 51.2% of the total AF patient population, and this proportion is expected to rise to 65.2% by 2060. Furthermore, the risks of cardiovascular events and mortality associated with AF are higher among elderly individuals\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlbumin is a primary protein in human plasma with various biochemical properties. Hypoalbuminemia, typically defined as ALB\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L, is considered an important factor in inflammation, malnutrition, and cachexia, and serves as a useful biomarker for diagnosing cardiovascular diseases \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Our study confirmed a negative correlation between ALB and AF risk in elderly patients with NAFLD and incorporated it as an important variable in the predictive model. This finding aligns with many previous studies. A large-scale prospective epidemiological and Mendelian randomization study demonstrated a nearly linear negative correlation between ALB levels and AF risk\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A dose-response meta-analysis found a significant negative linear association between serum ALB and AF risk, showing that for every 10 g/L increase in serum albumin levels, the risk of AF decreased by 36%\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInflammation and oxidative stress are two key factors in the process of atrial remodeling, significantly influencing the electrophysiological and structural changes associated with AF\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. ALB exhibits anti-inflammatory properties\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and due to its abundant thiol groups, accounts for 80% of the total thiol-based reactive oxygen and nitrogen species scavenging in plasma. Additionally, it can carry nitric oxide (NO), thereby demonstrating strong antioxidant stress capacity\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These properties may explain the protective role of albumin in reducing the risk of AF.\u003c/p\u003e\u003cp\u003eBilirubin, a bile pigment, is a well-known metabolite of heme breakdown. Currently, multiple observational studies have revealed a significant positive correlation between bilirubin levels and the risk of AF. A study involving 437 patients with thyrotoxicosis who underwent radioactive iodine therapy indicated that DBIL levels are an independent risk factor for predicting AF in hyperthyroidism patients \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Another study of 212 patients with paroxysmal AF after catheter ablation revealed that elevated bilirubin levels were significantly associated with postoperative AF recurrence \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our study consisted of elderly patients with NAFLD, who generally had higher DBIL levels. Additionally, we found that DBIL had a superior effect in predicting AF risk in elderly NAFLD patients compared to other characteristic variables. However, the pathophysiological mechanisms that contribute to this connection between DBIL and AF are still not clearly defined.\u003c/p\u003e\u003cp\u003eLDL-c is composed of lipids and proteins. It is synthesized in the liver and is responsible for transporting cholesterol from the liver to other parts of the body. LDL-c levels are usually positively correlated with the occurrence of NAFLD\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Elderly NAFLD patients often exhibit higher LDL-c levels, and LDL-c can lead to coronary heart disease, which is a risk factor for AF\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Interestingly, evidence from several studies \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003esuggest that in individuals suffering from conditions such as chronic kidney disease, hypertension, or acute myocardial infarction, reduced LDL-c levels may be linked to a heightened likelihood of developing AF. Our study also confirmed a negative correlation between LDL-c levels and AF. The underlying mechanism is likely unrelated to traditional factors such as obesity and alcohol consumption, which are known to elevate LDL-c levels, but rather involves other pathways. One possible reason is that cholesterol may contribute to the development of AF by influencing the structure of ion channels in cell membranes\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, LDL-c might play a role in reducing inflammatory responses and oxidative stress, consequently affecting the development of AF\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This research has several limitations. To begin with, due to its cross-sectional design, it is unable to establish causal relationships between AF and related variables in elderly individuals with NAFLD effectively. Second, since our research focuses on a health check-up population, it lacks data on indicators such as NT-proBNP, cardiac troponin as well as information on coronary artery disease and stroke history. Finally, the sample size for external validation is relatively small and further multicenter larger population and studies are needed to validate our model.\u003c/p\u003e\u003cp\u003eIn this study, a successful predictive model of AF risk in elderly patients with NAFLD was constructed with indicators including age, BMI, albumin, direct bilirubin and LDL-c. The predictive model demonstrated a strong capacity to distinguish between different outcome categories and might provide a tool to identify patients with NAFLD who were at high risk of AF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the medical staff at the Northern Jiangsu People\u0026rsquo;s Hospital and The First Affiliated Hospital of Soochow University for support in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Clinical trial statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki. The study has been approved by the Ethics Committee of Northern Jiangsu People Hospital (Approval Number: 2024ky284) and Affiliated Hospital of Soochow University(Approval Number:228) . Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ, JL and LJ Y designed and wrote the article; H L and S Z collected the clinical data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The National Natural Science Foundation of China (81800250), China Postdoctoral Science Foundation (2022M711417), Jiangsu Province Traditional Chinese Medicine Science and Technology Development Program Project (MS2023137), Xuzhou Medical University Affiliated Hospital Development Fund Project (XYFY202401), Yangzhou Municipal Basic Research Program Project (2024-2-12), Clinical Trials from Northern Jiangsu People\u0026rsquo;s Hospital(SBLC23002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRiazi K, Azhari H, Charette JH, et al. 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Arterioscler Thromb Vasc Biol. 2020;40:473\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao LZ, Zhang SZ, Li WD, et al. Serum albumin and atrial fibrillation: insights from epidemiological and mendelian randomization studies. Eur J Epidemiol. 2020;35:113\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Du P, Xiao Q, et al. Relationship Between Serum Albumin and Risk of Atrial Fibrillation: A Dose-Response Meta-Analysis. Front Nutr. 2021;8:728353.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Zhang W, Luo J, et al. TRIM21 deficiency protects against atrial inflammation and remodeling post myocardial infarction by attenuating oxidative stress. Redox Biol. 2023;62:102679.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong G, Zhang Y, Wang X, et al. An inflammatory prognostic scoring system to predict the risk for adults with acute coronary syndrome undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2024;24:728.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSon BK, Lyu W, Tanaka T, Yoshizawa Y, Akishita M, Iijima K. Impact of the anti-inflammatory diet on serum high-sensitivity C-Reactive protein and new-onset frailty in community-dwelling older adults: A 7-year follow-up of the Kashiwa cohort study. Geriatr Gerontol Int. 2024;24(Suppl 1):189\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreeden JF, Gordon DM, Stec DE, Hinds TD. Jr. Bilirubin as a metabolic hormone: the physiological relevance of low levels. Am J Physiol Endocrinol Metab. 2021;320:E191\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HA, Jung JY, Lee YS, et al. Direct Bilirubin Is More Valuable than Total Bilirubin for Predicting Prognosis in Patients with Liver Cirrhosis. Gut Liver. 2021;15:599\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu S, Xie Q, Kuang M, et al. Lipid metabolism, BMI and the risk of nonalcoholic fatty liver disease in the general population: evidence from a mediation analysis. J Transl Med. 2023;21:192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBor\u0026eacute;n J, Chapman MJ, Krauss RM, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41:2313\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan M, Hu H, Qi D, Qin X, Wan Q. Inverse relationship between LDL-C/HDL-C ratio and atrial fibrillation in chronic kidney disease patients. Sci Rep. 2024;14:17721.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu L, Liu X, Ding X, Chen H, Li W, Li H. Lipid Levels and New-Onset Atrial Fibrillation in Patients with Acute Myocardial Infarction. J Atheroscler Thromb. 2023;30:515\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing WY, Protty MB, Davies IG, Lip GYH. Relationship between lipoproteins, thrombosis, and atrial fibrillation. Cardiovasc Res. 2022;118:716\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan B, Wang A, Xu H. Causal associations of remnant cholesterol with cardiometabolic diseases and risk factors: a mendelian randomization analysis. Cardiovasc Diabetol. 2023;22:207.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NAFLD, atrial fibrillation, risk factors, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7054945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7054945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo develop a prediction model for risk of atrial fibrillation(AF) in elderly patients with nonalcoholic fatty liver disease (NAFLD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study collected data from 2035 elderly patients over 65 years old diagnosed with NAFLD at Northern Jiangsu People's Hospital. Using a 7:3 ratio, participants were separated into two groups through random assignment: a model development cohort (n\u0026thinsp;=\u0026thinsp;1424) and an internal verification cohort (n\u0026thinsp;=\u0026thinsp;611). 511 elderly NAFLD patients from The First Affiliated Hospital of Soochow University were collected as an external validation set to further assess the predictive capability of the model. Using multivariate logistic regression analysis, we developed a predictive model for AF and visualized it through a nomogram. The discrimination, calibration, and clinical application value of the model were comprehensively evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, 2035 elderly patients with NAFLD, 116 cases (5.7%) were found to have AF. The baseline features between validation and training groups exhibited no significant statistical differences(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Direct bilirubin (OR\u0026thinsp;=\u0026thinsp;1.405, 95% CI: 1.281\u0026ndash;1.549), body mass index (BMI) (OR\u0026thinsp;=\u0026thinsp;1.168, 95% CI: 1.087\u0026ndash;1.255), low-density lipoprotein cholesterol (LDL-c) (OR\u0026thinsp;=\u0026thinsp;0.494, 95% CI: 0.377\u0026ndash;0.643), albumin (OR\u0026thinsp;=\u0026thinsp;0.804, 95% CI: 0.735\u0026ndash;0.878), and age (OR\u0026thinsp;=\u0026thinsp;1.075, 95% CI: 1.039\u0026ndash;1.112) were all independent risk factors for AF in elderly NAFLD patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The combined prediction model composed of these five indicators had an AUC value of 0.829 (95% CI: 0.774\u0026ndash;0.884) in the training group, 0.855 (95% CI: 0.794\u0026ndash;0.916) in the internal validation group, and 0.826 (95% CI: 0.742\u0026ndash;0.910) in the external validation set. The consistency of the prediction model was effectively confirmed through its calibration curve. The DCA and CIC showed that the risk threshold probabilities for AF in the training and validation groups were 5%-79.5% and 5%-90%, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe nomogram model constructed in this study has good predictive efficacy and clinical application value for the risk of AF in elderly NAFLD patients.\u003c/p\u003e","manuscriptTitle":"Development and validation of a prediction model for risk of atrial fibrillation in elderly patients with nonalcoholic fatty liver disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:00:04","doi":"10.21203/rs.3.rs-7054945/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-17T03:28:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172800022935198300916784727324792719619","date":"2025-09-14T14:25:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T04:35:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T07:57:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T09:41:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T23:22:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-17T22:17:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3832a0b8-e871-480d-a07c-4a630c865885","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T11:00:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:00:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7054945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7054945","identity":"rs-7054945","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}