Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism

Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism | 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 Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism Ping Hu, Ao Zhong, Yuting Shen, Yinchen Yuan, Qing Jin, Quan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7813902/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 13 You are reading this latest preprint version Abstract Objective To develop a nomogram integrating serological markers and clinical parameters for predicting severe hepatic steatosis in patients with abnormal glucose metabolism. Methods This prospective study included 186 patients with abnormal glucose metabolism who underwent controlled attenuation parameter (CAP) measurement 和serological examination between February 2023 and May 2024. Patients were classified as severe (n = 56) or non-severe (n = 130) steatosis and randomly assigned to training and validation cohorts (7:3). least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression identified independent predictors. Restricted cubic splines (RCS) assessed nonlinear associations, and correlations with CAP were examined. A predictive nomogram was constructed and evaluated using receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA). Results Body mass index (BMI), triglycerides (TG), adiponectin (ADPN), and chemerin were independent predictors. RCS indicated nonlinear relationships for BMI, ADPN, and chemerin, and a positive linear trend for TG (all P < 0.01). CAP correlated positively with BMI, TG, and chemerin, and negatively with ADPN. The nomogram demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.862 (95% CI: 0.797–0.927) in the training cohort and 0.889 (95% CI: 0.800–0.978) in the validation cohort. Calibration and DCA confirmed its good performance and clinical utility. Conclusion The nomogram based on BMI, TG, ADPN, and chemerin accurately predicts severe hepatic steatosis in patients with abnormal glucose metabolism, providing a practical tool for individualized clinical decision-making. Abnormal glucose metabolism Severe hepatic steatosis Body mass index Serological markers Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Non-alcoholic fatty liver disease (NAFLD) is currently the most common chronic liver disease worldwide, with its prevalence rising sharply over recent decades, posing a significant global public health challenge 1 2 . Patients with abnormal glucose metabolism are at particularly high risk of developing NAFLD 3 . Studies have demonstrated that the severity of NAFLD is closely associated with liver fibrosis, with severe hepatic steatosis potentially progressing to fibrosis 4 5 . Early identification of severe hepatic steatosis in this population is crucial for timely intervention and prevention of disease progression. The controlled attenuation parameter (CAP), a novel noninvasive ultrasound-based technique, has shown considerable value in grading hepatic steatosis 6 7 . However, CAP is limited by the absence of real-time ultrasound image guidance, resulting in a relatively high failure rate 8 9 . Moreover, the technique requires specialized equipment that is not widely available in most community hospitals, which restricts its broader clinical application. Previous studies have also demonstrated that serological markers are closely linked to the development and progression of NAFLD 10 – 12 . Accordingly, the present study aimed to develop a nomogram integrating serological markers and clinical parameters to predict severe hepatic steatosis in patients with abnormal glucose metabolism. This predictive model is intended to facilitate rapid clinical identification of high-risk individuals and support early intervention strategies. Materials and methods Participants A total of 186 patients with abnormal glucose metabolism seeking medical advice in Kunshan Traditional Chinese Medicine Hospital from February 2023 and May 2024 were prospectively selected as the study subjects.The inclusion criteria were as follows: (1) Met the diagnostic criteria for abnormal glucose metabolism 13 ; (2) The patients’ age ranging from 40 to 70 years old. The exclusion criteria were: (1) With a long-term history of alcohol consumption, converted to the amount of ethanol consumption: males > 140 g/week, females > 70 g/week; (2) Patients in whom CAP measurement failed; (3) Patients with missing clinical indicators such as serology. This study has been approved by the Ethics Committee of our hospital. The approval number of the ethics committee for this study was KSZ2022-034-01. CAP Measurement The CAP was measured using FibroScan (Echosens, France). To improve the success rate of CAP assessment, the hepatic parenchymal region was first identified with the Mindray Resona R9 ultrasound system, and surface markers were placed to avoid intrahepatic vessels. All examinations were performed after an overnight fast, with patients in the supine position and the right arm elevated above the head while maintaining steady respiration. CAP measurements were considered valid only when the interquartile range-to-median ratio (IQR/MED) was ≤ 30%. After 10 valid acquisitions were obtained, the median value was recorded, and corresponding images were archived. All procedures were conducted by the same experienced senior sonographer. In this study, patients with a CAP value ≥ 292 dB/m were classified as having severe hepatic steatosis, whereas those with CAP values < 292 dB/m were classified as having non-severe hepatic steatosis, according to the cutoff threshold recommended by the manufacturer. Collection of clinical parameters Serological parameters collected included triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), urea (UREA), glomerular filtration rate (GFR), creatinine (CREA), uric acid (UA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), adiponectin (ADPN), chemerin, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and fasting insulin (FINS). Patient demographics, including sex and age, were recorded. Height and weight were measured, and body mass index (BMI) was calculated. Statistical methods Statistical analyses were performed using SPSS version 25.0 and R software (version 4.3.0, www.Medsta.cn ). Continuous variables conforming to a normal distribution were expressed as mean ± standard deviation (x̅±s), while non-normally distributed data were presented as median (interquartile range, M [Q1, Q3]). Categorical variables were compared using the χ² test or Fisher’s exact test, as appropriate. Lasso regression and univariate and multivariate logistic regression analyses were conducted to identify independent predictors. Restricted cubic splines (RCS) were applied to assess the relationships between independent predictors and severe hepatic steatosis. Spearman correlation analysis was performed to evaluate associations between independent predictors and CAP values. A nomogram was constructed based on the identified independent predictors. Its discriminative ability was assessed by the area under the receiver operating characteristic curve (AUC), calibration was evaluated using the Hosmer-Lemeshow test and calibration plots, and clinical utility was examined through decision curve analysis (DCA). A two-sided P value < 0.05 was considered statistically significant. RESULTS Baseline Characteristics of Training and Validation Cohorts and Comparison of Clinical and Serologica Parameters in the Training Cohort A total of 220 patients with abnormal glucose metabolism were initially enrolled in this study. Thirty-four patients were excluded due to failed CAP measurements or incomplete data, resulting in a final cohort of 186 patients, including 130 in the training cohort and 56 in the validation cohort. Table 1 presents a descriptive summary and statistical comparison of baseline characteristics in the training and validation cohorts, as well as clinical and biochemical parameters between patients with severe and non-severe hepatic steatosis in the training cohort. No statistically significant differences were observed between the training and validation cohorts across clinical or biochemical variables (all P > 0.05). Within the training cohort, patients with severe hepatic steatosis displayed significantly higher levels of BMI, UA, TG, ALT, FINS, and chemerin, as well as lower levels of HDL-C and ADPN, compared to those with non-severe hepatic steatosis (all P 0.05). Table 1 Baseline Characteristics of the Training and Validation Cohorts and Comparison of Clinical and Serologica Parameters Between Non-Severe and Severe Hepatic Steatosis in the Training Cohort variable training set (n = 130) validation set (n = 56) * P training set(n = 331) # P Non-severe hepatic steatosis (n = 92) Severe hepatic steatosis (n = 38) Sex[n(%)] 1.000 0.441 Male 65 (50.00) 28 (50.00) 44 (47.83) 21 (55.26) Female 65 (50.00) 28 (50.00) 48 (52.17) 17 (44.74) Age (years) 57.73 ± 7.42 58.41 ± 7.94 0.575 205(80.4) 15(19.7) 0.522 BMI (kg/m²) 24.82 ± 3.00 24.99 ± 3.27 0.737 50(19.6) 61(80.3) < 0.001 UREA (mmol/L) 5.72 ± 1.38 5.94 ± 1.35 0.327 5.72 ± 1.32 5.72 ± 1.53 0.990 TC (mmol/L) 5.17 ± 1.02 4.93 ± 1.11 0.166 5.20 ± 1.01 5.08 ± 1.06 0.559 LDL-C (mmol/L) 3.20 ± 0.73 3.00 ± 0.78 0.110 3.24 ± 0.74 3.08 ± 0.71 0.252 UA (µmol/L) 318.10 (270.20, 389.00) 330.30 (280.93, 377.45) 0.587 311.55(265.28,371.20) 340.25(305.72,415.30) 0.013 GFR(ml/min) 98.60 (86.57, 109.78) 100.70 (84.70, 111.80) 0.904 100.75 ± 19.93 97.38 ± 16.58 0.361 CREA (µmol/L) 61.95 (54.02, 73.22) 64.00 (52.20, 77.10) 0.757 61.65(54.45,71.08) 63.35(53.27,75.72) 0.506 TG (mmol/L) 1.50 (1.06, 2.25) 1.33 (0.90, 1.84) 0.139 1.30 (0.98,1.96) 2.08 (1.52,3.33) < 0.001 HDL-C (mmol/L) 1.25 (1.06, 1.41) 1.23 (1.07, 1.42) 0.674 1.29 (1.07,1.44) 1.17 (1.00,1.30) 0.022 ALT (U/L) 22.50 (17.07, 29.48) 22.90 (17.27, 35.18) 0.628 21.60 (15.40,27.55) 26.30 (21.33,38.18) 0.005 AST (U/L) 23.00 (19.33, 26.87) 22.85 (18.23, 28.20) 0.863 22.80 (18.70,26.65) 23.55 (20.52,27.27) 0.187 LDH(U/L) 195.20 (175.88, 213.30) 189.25 (166.55, 220.25) 0.823 194.25(173.30,213.60) 196.60(181.62,211.68) 0.695 ALP (U/L) 80.30 (65.55, 96.50) 78.80 (64.42, 97.97) 0.968 81.35 (66.32,97.90) 70.45 (65.48,89.28) 0.210 GGT (U/L) 23.10 (17.10, 41.08) 25.90 (17.12, 41.47) 0.837 21.30 (16.48,41.02) 27.85 (18.95,41.35) 0.138 TBIL(µmol) 13.70 (11.03, 17.20) 14.00 (11.47, 17.55) 0.782 13.85 (11.28,17.22) 13.45 (9.80,17.20) 0.441 DBIL(µmol) 2.30 (1.80, 3.00) 2.50 (1.87, 3.20) 0.448 2.30 (1.90,3.00) 2.25 (1.60,3.08) 0.304 IBIL(µmol) 11.45 (9.43, 14.20) 11.25 (9.67, 14.33) 0.849 11.80 (9.50,14.20) 11.05 (8.22,14.43) 0.521 ADPN(µɡ/mL) 7.82 (6.26, 10.17) 7.79 (6.06,10.10) 0.727 8.31 (6.39,11.15) 6.89 (5.63,8.55) 0.006 chemerin(Pg/Ml) 98.25 (79.47, 130.68) 99.85 (69.03, 125.49) 0.326 91.84 (75.12,125.95) 114.98 (96.99,171.65) 0.002 FINS (mU/L) 8.86 (5.25, 12.28) 8.71 (5.90, 12.28) 0.995 7.27 (4.94,10.55) 12.40 (8.65,17.58) < 0.001 FBG (mmol/L) 6.58 (5.80, 7.71) 6.70(6.08, 7.65) 0.668 6.48 (5.60, 7.68) 7.02 (6.26,7.94) 0.095 HbA1c(%) 6.25 (5.94, 6.96) 6.40(6.09, 7.04) 0.251 6.26 (5.94,7.06) 6.20 (5.95,6.61) 0.782 Selection of Clinical and Serologica Parameters Using Lasso Regression LASSO regression was applied to identify potential clinical and biochemical predictors of severe hepatic steatosis, as illustrated in Fig. 2 . The analysis revealed that TG (coefficient = 0.24), FINS (coefficient = 0.03), chemerin (coefficient = 0.01), and BMI (coefficient = 0.19) were positively associated with severe hepatic steatosis, whereas ADPN (coefficient = − 0.06) was negatively associated. Coefficients for all other variables were reduced to zero, indicating minimal predictive contribution. Univariate and Multivariate Logistic Regression Analyses Table 2 presents the results of univariate and multivariate logistic regression analyses. Univariate logistic regression indicated that BMI, TG, ADPN, FINS, and chemerin were candidate predictors of severe hepatic steatosis (all P < 0.05). These variables were then entered into a multivariate logistic regression model, which identified BMI, TG, ADPN, and chemerin as independent predictors of severe hepatic steatosis (all P < 0.05). Table 2 Results of Univariate and Multivariate Logistic Regression Analyses Variables Univariate Multivariate β S.E Z P OR (95%CI) β S.E Z P OR (95%CI) BMI 0.36 0.08 4.40 < 0.001 1.43 (1.22–1.68) 0.35 0.10 3.34 < 0.001 1.41 (1.15–1.73) TG 0.64 0.19 3.43 < 0.001 1.89 (1.31–2.73) 0.50 0.18 2.83 0.005 1.65 (1.17–2.33) ADPN -0.23 0.08 -2.79 0.005 0.79 (0.67–0.93) -0.24 0.11 -2.23 0.026 0.79 (0.64–0.97) FINS 0.13 0.04 3.42 < 0.001 1.14 (1.06–1.23) Chemerin 0.01 0.00 3.20 0.001 1.01 (1.01–1.02) 0.01 0.01 2.19 0.029 1.01 (1.01–1.02) Abbreviations: OR, Odds Ratio, CI, Confidence Interval, BMI, body mass index; TG, triglycerides; ADPN, adiponectin; RCS Analysis of the Associations Between BMI, TG, Chemerin, ADPN, and Severe Hepatic Steatosis In this study, RCS analysis combined with logistic regression was conducted to investigate the nonlinear relationships between BMI, TG, ADPN, chemerin, and severe hepatic steatosis, adjusting for sex as a covariate (Fig. 3 ). The results revealed a significant nonlinear association between BMI and severe hepatic steatosis (P < 0.01), with risk markedly increasing when BMI exceeded 27 kg/m², indicating a clear dose–response relationship. TG levels were approximately linearly and positively associated with severe hepatic steatosis risk (P < 0.01), with risk steadily rising as TG levels increased. ADPN exhibited a U-shaped relationship with severe hepatic steatosis (P < 0.01), with significantly higher risk observed when ADPN levels were below 5 µg/mL or above 15 µg/mL. Chemerin levels also showed a significant nonlinear association with severe hepatic steatosis risk (P < 0.01), with a sharp increase in risk once chemerin levels reached 100 pg/mL. Correlation Between BMI, TG, Chemerin, ADPN, and CAP Spearman correlation analysis was performed to assess the associations between BMI, TG, ADPN, chemerin, and the CAP in the training cohort of 130 participants(Fig. 4 ). The results demonstrated that BMI, TG, and chemerin were significantly positively correlated with CAP (r = 0.543, 0.471, and 0.333, respectively; all P < 0.001), whereas ADPN was significantly negatively correlated with CAP (r = -0.324, P < 0.001). Construction and Validation of the Predictive Nomogram A predictive model for severe hepatic steatosis in patients with abnormal glucose metabolism was developed using BMI, TG, ADPN, and chemerin, and visualized as a nomogram (Fig. 5 ). ROC curve analysis demonstrated that the nomogram achieved higher discriminative performance in both the training and validation cohorts compared with each individual predictor (BMI, TG, ADPN, or chemerin; all P < 0.05) (Figs. 6 A and 6 B). The predictive performance of the nomogram is shown in Table 3 . Calibration of the nomogram was assessed using the Hosmer-Lemeshow test (training cohort P = 0.945; validation cohort P = 0.436) and calibration plots, showing good agreement between predicted and observed probabilities (Figs. 6 C and 6 D). DCA indicated that the nomogram provided a wide range of net benefit thresholds, supporting its clinical utility (Figs. 6 E and 6 F). Table 3 Diagnostic performance of the nomogram in the training and validation cohorts nomogram AUC (95%CI) cut off Accuracy (95%CI) Sensitivity (95%CI) Specificity (95%CI) PPV (95%CI) NPV (95%CI) Train cohorts 0.862 (0.797–0.927) 0.22 0.754 (0.671–0.825) 0.895 (0.797–0.992) 0.696 (0.602–0.790) 0.548 (0.425–0.672) 0.941 (0.885–0.997) Test cohorts 0.889 (0.800-0.978) 0.22 0.821 (0.696–0.911) 0.889 (0.744–1.000) 0.789 (0.660–0.919) 0.667 (0.478–0.855) 0.938 (0.854–1.000) Abbreviations: AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; CI, confidence interval. DISCUSSION Liver biopsy continues to be the gold standard for evaluating hepatic steatosis 14 ; however, its invasive nature limits patient acceptance, and long-term dynamic follow-up is necessary to assess treatment efficacy. CAP offers a quantitative assessment of hepatic steatosis and is considered a relatively accurate method for evaluating the severity of steatosis 15 . Nevertheless, it has several limitations, including a relatively high failure rate and the requirement for dedicated equipment. In this study, we developed a predictive model based on clinical and serological parameters to estimate the risk of severe hepatic steatosis in patients with abnormal glucose metabolism, with the aim of facilitating early clinical identification and timely intervention in this population. LASSO regression analysis, a powerful method that combines feature selection with regularization, was systematically applied to identify potential predictors from a comprehensive set of clinical variables. The results identified BMI, TG, FINS, ADPN, and chemerin as candidate predictors of severe hepatic steatosis in patients with abnormal glucose metabolism. Among these, TG, FINS, chemerin, and BMI were positively correlated with severe hepatic steatosis, whereas ADPN was negatively correlated. Further univariate and multivariate logistic regression analyses demonstrated that BMI, TG, ADPN, and chemerin were independent predictors of severe hepatic steatosis. Obesity and hyperlipidemia are well-established risk factors for hepatic steatosis 16 – 18 . Consistent with the findings of Loomis et al. 19 , our study revealed that BMI increased progressively with the severity of steatosis. A potential explanation is that BMI is positively correlated with circulating fatty acid levels; in obese patients, elevated fatty acids exacerbate hepatic lipid deposition and fibrosis 20 21 . Elevated TG levels are often associated with insulin resistance, which promotes hepatic lipogenesis while inhibiting lipolysis, thereby aggravating lipid accumulation in hepatocytes 22 Pereira et al. 23 reported that patients with severe hepatic steatosis exhibited significantly higher TG levels (P < 0.05). Elevated serum TG increases the flux of free fatty acids into the liver, overwhelming hepatic oxidation and secretion capacity, leading to intrahepatic lipid accumulation, inflammatory responses, and hepatocellular injury, which in turn worsens steatosis. Chemerin is a multifunctional adipokine secreted by adipose tissue that regulates lipid metabolism and inflammatory processes via specific receptor-mediated signaling pathways 24 . Numerous clinical studies have demonstrated that elevated serum chemerin levels are closely associated with BMI, insulin resistance, and dyslipidemia 25 – 27 . In our study, chemerin was significantly positively correlated with CAP, suggesting that chemerin may contribute to the development of hepatic steatosis through mechanisms such as promoting hepatic lipid deposition, enhancing lipid peroxidation, and triggering chronic low-grade inflammation. In contrast, ADPN functions as a protective adipokine that improves glucose and lipid homeostasis by activating the AMPK signaling pathway to promote fatty acid oxidation in peripheral tissues and enhance insulin sensitivity 28 29 . Previous studies have shown that decreased adiponectin is an independent risk factor for hepatic steatosis 30 , which aligns with our findings. A nomogram is a graphical tool, based on regression models, that quantitatively predicts the probability of a clinical outcome 31 . In this study, four independent predictors—BMI, TG, ADPN, and chemerin—were incorporated into a nomogram to construct a visualized prediction model. ROC curve analysis revealed that the nomogram achieved an AUC of 0.862 (95% CI: 0.797–0.927) in the training cohort and 0.889 (95% CI: 0.800–0.978) in the validation cohort, demonstrating excellent discriminative performance. Additionally, calibration curve analysis confirmed good predictive accuracy, and decision curve analysis showed substantial clinical utility. Collectively, these results indicate that the nomogram not only provides reliable predictions but also holds significant clinical decision-making value. This study has several limitations. First, it was a single-center study with a relatively small sample size, which may limit the generalizability of the findings. Second, although CAP measurements in this study were obtained under ultrasound guidance, which improves accuracy and success rates, liver biopsy remains the gold standard for assessing steatosis and its absence may have influenced the results. Future multicenter studies incorporating liver biopsy as the reference standard are warranted to further optimize the model and expand its clinical applicability. Conclusion In summary, we developed a nomogram incorporating CAP, traditional risk factors, and adipokines to predict the risk of severe hepatic steatosis in patients with abnormal glucose metabolism. The model demonstrated robust predictive performance in both the training and validation cohorts, highlighting its potential utility in early risk stratification and clinical decision-making. This predictive tool may facilitate the early identification of high-risk patients with severe hepatic steatosis and help reduce the disease burden through timely intervention. Abbreviations ALP alkaline phosphatase ALT alanine aminotransferase AST aspartate aminotransferase ADPN adiponectin AUC Areas Under the Curve BMI Body mass index CAP controlled attenuation parameter CREA creatinine DCA ecision curve analysis DBIL direct bilirubin FBG fasting blood glucose FINS fasting insulin GGT glutamyl transpeptidase GFR glomerular filtration rate HDL-C high density lipoprotein cholesterol HbA1c glycated hemoglobin IBIL indirect bilirubin LDH lactate dehydrogenase LDL-C low density lipoprotein cholesterol NAFLD non-alcoholic fatty liver disease ROC Receiver Operating Characteristic RCS Restricted cubic splines TC total cholesterol TG triglyceride TBIL total bilirubin UA uric acid Lasso least absolute shrinkage and selection operator Declarations Ethics approval and consent to participate This study involving human participants was reviewed and approved by the Ethics Committee of Kunshan Hospital of Traditional Chinese Medical, Suzhou City, Jiangsu Province, China. The approval reference number is KSZ2022-034-01. Informed consent was obtained from all individual participants included in the study prior to data collection. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Consent for publication: Not applicable. All data presented in this manuscript are de-identified (no individual personal identifiers such as names, contact information, or unique medical records are included), and thus individual consent for publication is not required. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript. Authors' information Authors' information: Not applicable. Funding This work was supported by the Suzhou Science and Technology Development Program Guidance Project (Grant Number: SKYXD2022070) and the Guiding Project of Kunshan Key R&D Plan (Social Development) (Grant Number: KSZ2009). There are no other funding sources for this study. Author Contribution P.H. participated in study design, clinical data collection, preliminary data sorting and initial manuscript drafting; A.Z. conducted CAP data measurement & quality control, preliminary clinical parameter statistical analysis and manuscript revision; Y.S. collected & verified serological indicators, assisted in nomogram construction and checked table/figure data accuracy; Y.Y. participated in standardized ultrasound-guided CAP operation, sorted out subject inclusion/exclusion criteria and conducted preliminary baseline characteristic analysis.Q.J., corresponding author oversaw overall study design & supervision, guided statistical method selection, reviewed & finalized the manuscript and coordinated team collaboration;Q.C., corresponding author managed overall study quality control, guided nomogram validation & evaluation, addressed manuscript review academic questions and communicated with the ethics committee. Acknowledgments This work was supported by Suzhou Science and Technology Development Program Guidance Project (No. SKYXD2022070) and Guiding Project of Kunshan Key R&D Plan (Social Development) (No. KSZ2009). The authors would like to thank the staff of the Department of Ultrasonography, Kunshan Hospital of Traditional Chinese Medical, and Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical for their assistance in data collection and CAP measurements. Data Availability The data supporting the findings of this study are available from the corresponding author (Quan Chen, E-mail address: [email protected] ) upon reasonable request. All materials used in this study (e.g., serological detection kits, FibroScan equipment) are commercially available and do not involve proprietary materials. References Zhang Y, Li H, Jiang N et al. The Role of Traditional Chinese Medicine in the Management of Nonalcoholic Fatty Liver Disease: Targeting Gut Microbiome. Am J Chin Med 2025:1–34. 10.1142/s0192415x25500788 Zhou J, Zhou F, Wang W, et al. Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology (Baltimore MD). 2020;71(5):1851–64. 10.1002/hep.31150 . Muzica CM, Sfarti C, Trifan A, et al. Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: A Bidirectional Relationship. Can J Gastroenterol Hepatol. 2020;2020:6638306. 10.1155/2020/6638306 . Simon TG, Roelstraete B, Khalili H, et al. Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort. Gut. 2021;70(7):1375–82. 10.1136/gutjnl-2020-322786 . Harrison SA, Bashir MR, Guy CD, et al. Resmetirom (MGL-3196) for the treatment of non-alcoholic steatohepatitis: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet. 2019;394(10213):2012–24. 10.1016/s0140-6736(19)32517-6 . Kohira T, Oeda S, Eto E, et al. Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis. Sci Rep. 2025;15(1):31982. 10.1038/s41598-025-17396-1 . Lin X, Huang X, Yi Z, et al. Association of CALLY index with NAFLD in U.S. adults from NHANES 2017–2020 assessed by vibration-controlled transient elastography. Diabetol Metab Syndr. 2025;17(1):363. 10.1186/s13098-025-01926-y . Caussy C, Alquiraish MH, Nguyen P, et al. Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis. Hepatology (Baltimore MD). 2018;67(4):1348–59. 10.1002/hep.29639 . Hu YY, Dong NL, Qu Q, et al. The correlation between controlled attenuation parameter and metabolic syndrome and its components in middle-aged and elderly nonalcoholic fatty liver disease patients. Medicine. 2018;97(43):e12931. 10.1097/md.0000000000012931 . Francisco V, Sanz MJ, Real JT, et al. Adipokines in Non-Alcoholic Fatty Liver Disease: Are We on the Road toward New Biomarkers and Therapeutic Targets? Biology. 2022;11(8). 10.3390/biology11081237 . Orešič M, Hyötyläinen T, Kotronen A, et al. Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids. Diabetologia. 2013;56(10):2266–74. 10.1007/s00125-013-2981-2 . Moreno-Vedia J, Rosales R, Ozcariz E, et al. Triglyceride-Rich Lipoproteins and Glycoprotein A and B Assessed by 1H-NMR in Metabolic-Associated Fatty Liver Disease. Front Endocrinol. 2021;12:775677. 10.3389/fendo.2021.775677 . [Chinese expert consensus on comprehensive management. of cardiovascular risk in patients with cardiovascular diseases and abnormal glucose metabolism]. Zhonghua xin xue guan bing za zhi. 2021;49(7):656–72. 10.3760/cma.j.cn112148-20210114-00050 . Huang SC, Liu CJ. Chronic hepatitis B with concurrent metabolic dysfunction-associated fatty liver disease: Challenges and perspectives. Clin Mol Hepatol. 2023;29(2):320–31. 10.3350/cmh.2022.0422 . Alfadda NA, Aljuraiban GS, Awwad HM, et al. Higher carbohydrate intake in relation to non-alcoholic fatty liver disease in patients with type 2 diabetes. Front Nutr. 2022;9:996004. 10.3389/fnut.2022.996004 . Afonso-Alí A, Dicroce-Giacobini J, Teixido-Trujillo S, et al. Role of obesity and estrogen deficiency in non–alcoholic fatty liver disease: Insights from a mouse model. Mol Med Rep. 2025;32(4). 10.3892/mmr.2025.13629 . Shi Y, Feng R, Mao J, et al. Structural Characterization of Peptides From Huangjiu and Their Regulation of Hepatic Steatosis and Gut Microbiota Dysbiosis in Hyperlipidemia Mice. Front Pharmacol. 2021;12:689092. 10.3389/fphar.2021.689092 . Ullah MI, Tamanna S, Obesity. Clinical Impact, Pathophysiology, Complications, and Modern Innovations in Therapeutic Strategies. Med (Basel Switzerland). 2025;12(3). 10.3390/medicines12030019 . Loomis AK, Kabadi S, Preiss D, et al. Body Mass Index and Risk of Nonalcoholic Fatty Liver Disease: Two Electronic Health Record Prospective Studies. J Clin Endocrinol Metab. 2016;101(3):945–52. 10.1210/jc.2015-3444 . Wang TY, Wang RF, Bu ZY, et al. Association of metabolic dysfunction-associated fatty liver disease with kidney disease. Nat Rev Nephrol. 2022;18(4):259–68. 10.1038/s41581-021-00519-y . Wang W, Ren J, Zhou W, et al. Lean non-alcoholic fatty liver disease (Lean-NAFLD) and the development of metabolic syndrome: a retrospective study. Sci Rep. 2022;12(1):10977. 10.1038/s41598-022-14701-0 . Cariou B, Byrne CD, Loomba R, et al. Nonalcoholic fatty liver disease as a metabolic disease in humans: A literature review. Diabetes Obes Metab. 2021;23(5):1069–83. 10.1111/dom.14322 . Pouwels S, Sakran N, Graham Y, et al. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr disorders. 2022;22(1):63. 10.1186/s12902-022-00980-1 . Vorontsov O, Levitt L, Lilleri D, et al. Amniotic fluid biomarkers predict the severity of congenital cytomegalovirus infection. J Clin Investig. 2022;132(11). 10.1172/jci157415 . Ruszała M, Pilszyk A, Niebrzydowska M, et al. Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus 2.0. Int J Mol Sci. 2022;23(8). 10.3390/ijms23084364 . Kamiński M, Mierzyński R, Poniedziałek-Czajkowska E, et al. Comparative Evaluation of Adipokine Metrics for the Diagnosis of Gestational Diabetes Mellitus. Int J Mol Sci. 2023;25(1). 10.3390/ijms25010175 . Olszańska J, Pietraszek-Gremplewicz K, Nowak D. Melanoma Progression under Obesity: Focus on Adipokines. Cancers. 2021;13(9). 10.3390/cancers13092281 . Mukherjee S, Skrede S, Milbank E, et al. Understanding the Effects of Antipsychotics on Appetite Control. Front Nutr. 2021;8:815456. 10.3389/fnut.2021.815456 . Wang X, Karvonen-Gutierrez CA, Mukherjee B, et al. Urinary metals and adipokines in midlife women: The Study of Women's Health Across the nation (SWAN). Environ Res. 2021;196:110426. 10.1016/j.envres.2020.110426 . Luo K, Chen Y, Fang S, et al. Study on inflammation and fibrogenesis in MAFLD from 2000 to 2022: a bibliometric analysis. Front Endocrinol. 2023;14:1231520. 10.3389/fendo.2023.1231520 . Ghosh SK, Khandoker AH. A machine learning driven monogram for predicting chronic kidney disease stages 3–5. Sci Rep. 2023;13(1):21613. 10.1038/s41598-023-48815-w . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Revision requested 23 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers invited by journal 15 Nov, 2025 Editor assigned by journal 11 Oct, 2025 Submission checks completed at journal 11 Oct, 2025 First submitted to journal 09 Oct, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7813902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529158904,"identity":"c1df92a2-1bec-48bb-a024-5e125023d711","order_by":0,"name":"Ping Hu","email":"","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Hu","suffix":""},{"id":529158905,"identity":"5bf7d561-afe6-40df-b8fe-5c3cf68cc794","order_by":1,"name":"Ao Zhong","email":"","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Zhong","suffix":""},{"id":529158906,"identity":"7a656e27-9ae5-4e56-bdd5-cdf9a458771e","order_by":2,"name":"Yuting Shen","email":"","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Shen","suffix":""},{"id":529158907,"identity":"7a0d5443-9917-4e1e-b7dd-5f15e2b6b033","order_by":3,"name":"Yinchen Yuan","email":"","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":false,"prefix":"","firstName":"Yinchen","middleName":"","lastName":"Yuan","suffix":""},{"id":529158908,"identity":"296768b3-6346-4a58-9cfb-a83e74106935","order_by":4,"name":"Qing Jin","email":"","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Jin","suffix":""},{"id":529158909,"identity":"02c100a0-1679-44b0-846e-6cd8837f77c9","order_by":5,"name":"Quan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACefn3Dx9+MLDhkWdvIFKLYUMOs7FEQZqcYc8BYq05kMMmwfPhkDHDjQQidTA2nD0gIWFwILFx5uONNxhqbKIJamFn7EswKDC4k9gunVZswXAsLbeBoC3NDAYJEgbPEhtn55hJMDYcJqyF4RiDwQEeg8OJDTfPEKvlDI9hA1AL0Ps8RGoxnMGWzCxhAApkoF8SiPGLvATz8Z8f/oCi8vDGGx9qbIhwGBIwkEggRTlEC6k6RsEoGAWjYGQAADXVQTNBEm+AAAAAAElFTkSuQmCC","orcid":"","institution":"Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical","correspondingAuthor":true,"prefix":"","firstName":"Quan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-10-09 06:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7813902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7813902/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13098-026-02147-7","type":"published","date":"2026-03-19T15:59:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93971136,"identity":"866befa8-abcf-480f-ae67-9d16d9ba498e","added_by":"auto","created_at":"2025-10-20 21:21:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4849629,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTTEXT.docx","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/82df04aa411b5957c7d59bf8.docx"},{"id":93970344,"identity":"140d5f3c-13c0-4eed-be65-5c5d5acdeab0","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8377,"visible":true,"origin":"","legend":"","description":"","filename":"c7da5d0aeb38490cb0cb80e5a2c71fe9.json","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/50aa5a79f8f0d9d4ecfeee1d.json"},{"id":93970975,"identity":"cf1500b9-b3d0-4736-aa7c-c86567e95342","added_by":"auto","created_at":"2025-10-20 21:13:24","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116703,"visible":true,"origin":"","legend":"","description":"","filename":"c7da5d0aeb38490cb0cb80e5a2c71fe91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/f8551800592b0c0646d7856d.xml"},{"id":93970354,"identity":"e0c0d1b3-ecba-49ac-a020-5e285505b70d","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28877,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/74a7f44b2419558c893c0288.png"},{"id":93970356,"identity":"ea2a9efe-be08-4813-bdca-9ed0e65f40fc","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74648,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/020440dbaef9ec835fc85118.png"},{"id":93970357,"identity":"246ab57f-ffad-466d-a5c5-8d44a35b4a6a","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87490,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/4ad0ac745a01b3c352a4cdc3.png"},{"id":93970723,"identity":"965eab8b-bca3-4b54-b0c0-452f0aa3fbe8","added_by":"auto","created_at":"2025-10-20 21:05:24","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15536,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/3695f9a09bb93e7c4b199e60.png"},{"id":93970719,"identity":"7876d702-42e9-4f99-9a46-d98ce535125c","added_by":"auto","created_at":"2025-10-20 21:05:24","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65926,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/dcf3690f642486b64cf8a389.png"},{"id":93970355,"identity":"c6af3b27-e8aa-43f9-9903-11be4e549a47","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28007,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/e628c698b363b6a64883fde7.png"},{"id":93970359,"identity":"25b5c95d-1729-4112-8d46-c390a20ea8da","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113786,"visible":true,"origin":"","legend":"","description":"","filename":"c7da5d0aeb38490cb0cb80e5a2c71fe91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/e1908984ce6b6d105964ecfa.xml"},{"id":93970361,"identity":"631eaeab-3ff6-4682-a010-ef81db3ef2fd","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124690,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/2a99a837338efda015a8df49.html"},{"id":93970343,"identity":"35a99e07-c36c-4fd9-bd5e-a3b6b2f82ce1","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38177,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart of Research Design\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/97082b4221d636b3f538a1f4.png"},{"id":93970722,"identity":"608eb16f-04a6-434f-8367-c04f921f0afa","added_by":"auto","created_at":"2025-10-20 21:05:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180111,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Ten-fold cross-validation in LASSO regression for selecting the optimal regularization parameter. (B) LASSO coefficient profiles showing the variable selection process across different values of the regularization parameter (λ).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/a4dde0ae804bb8e533fb635c.jpeg"},{"id":93970353,"identity":"2ae3f120-fb17-461a-9f5a-6384f9b6d422","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293188,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the associations of BMI, TG, chemerin, and ADPN with severe hepatic steatosis.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; TG, triglycerides; ADPN, adiponectin; RCS, restricted cubic spline.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/8a5d104cdc474646c2a034e3.jpeg"},{"id":93970346,"identity":"63bc29b6-cc71-440e-b748-3867ceefbf32","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":306510,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of the correlations of BMI, TG, chemerin, and ADPN with CAP.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; TG, triglycerides; ADPN, adiponectin; CAP, controlled attenuation parameter.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/72f17666ba4432b2aafc5ad1.jpeg"},{"id":93970345,"identity":"b655dc61-01d0-4d8c-ae58-6c4355a9c374","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41756,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/64db6fbc9d323656fd53cda7.jpeg"},{"id":93970718,"identity":"aa880bb3-0aa7-4fe0-8e78-11ec6549e863","added_by":"auto","created_at":"2025-10-20 21:05:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342539,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) Receiver operating characteristic (ROC) curves for the training and validation cohorts, respectively; (C, D) Calibration plots for the training and validation cohorts, respectively\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/fecdbc9343ffc0a6edeb3239.jpeg"},{"id":93970348,"identity":"7fcfbdfb-d7c3-440d-8f1f-a21a57854cb8","added_by":"auto","created_at":"2025-10-20 20:57:24","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":141496,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6 (E, F). Decision curve analysis (DCA) for the training and validation cohorts, respectively.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/90f08ad69f67bb33d8c53927.jpeg"},{"id":105224355,"identity":"c431445a-2fdd-4bc3-bfd1-58f2a74c5e9e","added_by":"auto","created_at":"2026-03-23 16:14:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2403430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7813902/v1/9a029078-c790-454a-9ab5-beeae0308e4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is currently the most common chronic liver disease worldwide, with its prevalence rising sharply over recent decades, posing a significant global public health challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Patients with abnormal glucose metabolism are at particularly high risk of developing NAFLD\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated that the severity of NAFLD is closely associated with liver fibrosis, with severe hepatic steatosis potentially progressing to fibrosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Early identification of severe hepatic steatosis in this population is crucial for timely intervention and prevention of disease progression.\u003c/p\u003e\u003cp\u003eThe controlled attenuation parameter (CAP), a novel noninvasive ultrasound-based technique, has shown considerable value in grading hepatic steatosis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, CAP is limited by the absence of real-time ultrasound image guidance, resulting in a relatively high failure rate\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, the technique requires specialized equipment that is not widely available in most community hospitals, which restricts its broader clinical application. Previous studies have also demonstrated that serological markers are closely linked to the development and progression of NAFLD\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccordingly, the present study aimed to develop a nomogram integrating serological markers and clinical parameters to predict severe hepatic steatosis in patients with abnormal glucose metabolism. This predictive model is intended to facilitate rapid clinical identification of high-risk individuals and support early intervention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 186 patients with abnormal glucose metabolism seeking medical advice in Kunshan Traditional Chinese Medicine Hospital from February 2023 and May 2024 were prospectively selected as the study subjects.The inclusion criteria were as follows: (1) Met the diagnostic criteria for abnormal glucose metabolism\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e; (2) The patients\u0026rsquo; age ranging from 40 to 70 years old. The exclusion criteria were: (1) With a long-term history of alcohol consumption, converted to the amount of ethanol consumption: males\u0026thinsp;\u0026gt;\u0026thinsp;140 g/week, females\u0026thinsp;\u0026gt;\u0026thinsp;70 g/week; (2) Patients in whom CAP measurement failed; (3) Patients with missing clinical indicators such as serology. This study has been approved by the Ethics Committee of our hospital. The approval number of the ethics committee for this study was KSZ2022-034-01.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCAP Measurement\u003c/h3\u003e\n\u003cp\u003eThe CAP was measured using FibroScan (Echosens, France). To improve the success rate of CAP assessment, the hepatic parenchymal region was first identified with the Mindray Resona R9 ultrasound system, and surface markers were placed to avoid intrahepatic vessels. All examinations were performed after an overnight fast, with patients in the supine position and the right arm elevated above the head while maintaining steady respiration. CAP measurements were considered valid only when the interquartile range-to-median ratio (IQR/MED) was \u0026le;\u0026thinsp;30%. After 10 valid acquisitions were obtained, the median value was recorded, and corresponding images were archived. All procedures were conducted by the same experienced senior sonographer. In this study, patients with a CAP value\u0026thinsp;\u0026ge;\u0026thinsp;292 dB/m were classified as having severe hepatic steatosis, whereas those with CAP values\u0026thinsp;\u0026lt;\u0026thinsp;292 dB/m were classified as having non-severe hepatic steatosis, according to the cutoff threshold recommended by the manufacturer.\u003c/p\u003e\n\u003ch3\u003eCollection of clinical parameters\u003c/h3\u003e\n\u003cp\u003eSerological parameters collected included triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), urea (UREA), glomerular filtration rate (GFR), creatinine (CREA), uric acid (UA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), adiponectin (ADPN), chemerin, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and fasting insulin (FINS). Patient demographics, including sex and age, were recorded. Height and weight were measured, and body mass index (BMI) was calculated.\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 25.0 and R software (version 4.3.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.Medsta.cn\" target=\"_blank\"\u003ewww.Medsta.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.Medsta.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Continuous variables conforming to a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̅\u0026plusmn;s), while non-normally distributed data were presented as median (interquartile range, M [Q1, Q3]). Categorical variables were compared using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test, as appropriate. Lasso regression and univariate and multivariate logistic regression analyses were conducted to identify independent predictors. Restricted cubic splines (RCS) were applied to assess the relationships between independent predictors and severe hepatic steatosis. Spearman correlation analysis was performed to evaluate associations between independent predictors and CAP values. A nomogram was constructed based on the identified independent predictors. Its discriminative ability was assessed by the area under the receiver operating characteristic curve (AUC), calibration was evaluated using the Hosmer-Lemeshow test and calibration plots, and clinical utility was examined through decision curve analysis (DCA). A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eBaseline Characteristics of Training and Validation Cohorts and Comparison of Clinical and Serologica Parameters in the Training Cohort\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA total of 220 patients with abnormal glucose metabolism were initially enrolled in this study. Thirty-four patients were excluded due to failed CAP measurements or incomplete data, resulting in a final cohort of 186 patients, including 130 in the training cohort and 56 in the validation cohort.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a descriptive summary and statistical comparison of baseline characteristics in the training and validation cohorts, as well as clinical and biochemical parameters between patients with severe and non-severe hepatic steatosis in the training cohort. No statistically significant differences were observed between the training and validation cohorts across clinical or biochemical variables (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Within the training cohort, patients with severe hepatic steatosis displayed significantly higher levels of BMI, UA, TG, ALT, FINS, and chemerin, as well as lower levels of HDL-C and ADPN, compared to those with non-severe hepatic steatosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed for the remaining parameters (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of the Training and Validation Cohorts and Comparison of Clinical and Serologica Parameters Between Non-Severe and Severe Hepatic Steatosis in the Training Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003evariable\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003etraining set\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;130)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003evalidation set (n\u0026thinsp;=\u0026thinsp;56)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003csup\u003e*\u003c/sup\u003eP\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003etraining set(n\u0026thinsp;=\u0026thinsp;331)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003csup\u003e#\u003c/sup\u003eP\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003eNon-severe hepatic steatosis\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003eSevere hepatic steatosis\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSex[n(%)]\u003c/div\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.441\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e65 (50.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e28 (50.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e44 (47.83)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e21 (55.26)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e65 (50.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e28 (50.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e48 (52.17)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e17 (44.74)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAge (years)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e57.73\u0026thinsp;\u0026plusmn;\u0026thinsp;7.42\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e58.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.575\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e205(80.4)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e15(19.7)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.522\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eBMI (kg/m\u0026sup2;)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e24.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.737\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e50(19.6)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e61(80.3)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eUREA (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.327\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.990\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eTC (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.166\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.559\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eLDL-C (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.110\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.252\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eUA (\u0026micro;mol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e318.10 (270.20, 389.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e330.30 (280.93, 377.45)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.587\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e311.55(265.28,371.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e340.25(305.72,415.30)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.013\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eGFR(ml/min)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e98.60 (86.57, 109.78)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e100.70 (84.70, 111.80)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.904\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e100.75\u0026thinsp;\u0026plusmn;\u0026thinsp;19.93\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e97.38\u0026thinsp;\u0026plusmn;\u0026thinsp;16.58\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.361\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eCREA (\u0026micro;mol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e61.95 (54.02, 73.22)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e64.00 (52.20, 77.10)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.757\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e61.65(54.45,71.08)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e63.35(53.27,75.72)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.506\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eTG (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.50 (1.06, 2.25)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.33 (0.90, 1.84)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.139\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.30 (0.98,1.96)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.08 (1.52,3.33)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eHDL-C (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.25 (1.06, 1.41)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.23 (1.07, 1.42)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.674\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.29 (1.07,1.44)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.17 (1.00,1.30)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.022\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eALT (U/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e22.50 (17.07, 29.48)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e22.90 (17.27, 35.18)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.628\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e21.60 (15.40,27.55)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e26.30 (21.33,38.18)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.005\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAST (U/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e23.00 (19.33, 26.87)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e22.85 (18.23, 28.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.863\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e22.80 (18.70,26.65)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e23.55 (20.52,27.27)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.187\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eLDH(U/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e195.20 (175.88, 213.30)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e189.25 (166.55, 220.25)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.823\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e194.25(173.30,213.60)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e196.60(181.62,211.68)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.695\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eALP (U/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e80.30 (65.55, 96.50)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e78.80 (64.42, 97.97)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.968\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e81.35 (66.32,97.90)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e70.45 (65.48,89.28)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.210\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eGGT (U/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e23.10 (17.10, 41.08)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e25.90 (17.12, 41.47)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.837\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e21.30 (16.48,41.02)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e27.85 (18.95,41.35)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.138\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eTBIL(\u0026micro;mol)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e13.70 (11.03, 17.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e14.00 (11.47, 17.55)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.782\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e13.85 (11.28,17.22)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e13.45 (9.80,17.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.441\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eDBIL(\u0026micro;mol)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.30 (1.80, 3.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.50 (1.87, 3.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.448\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.30 (1.90,3.00)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.25 (1.60,3.08)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.304\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eIBIL(\u0026micro;mol)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e11.45 (9.43, 14.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e11.25 (9.67, 14.33)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.849\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e11.80 (9.50,14.20)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e11.05 (8.22,14.43)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.521\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eADPN(\u0026micro;ɡ/mL)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e7.82\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e(6.26, 10.17)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e7.79 (6.06,10.10)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.727\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e8.31 (6.39,11.15)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.89 (5.63,8.55)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.006\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003echemerin(Pg/Ml)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e98.25 (79.47, 130.68)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e99.85 (69.03, 125.49)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.326\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e91.84 (75.12,125.95)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e114.98 (96.99,171.65)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.002\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eFINS (mU/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e8.86 (5.25, 12.28)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e8.71 (5.90, 12.28)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.995\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e7.27 (4.94,10.55)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e12.40 (8.65,17.58)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eFBG (mmol/L)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.58 (5.80, 7.71)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.70(6.08, 7.65)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.668\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.48 (5.60, 7.68)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e7.02 (6.26,7.94)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.095\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eHbA1c(%)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.25 (5.94, 6.96)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.40(6.09, 7.04)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.251\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.26 (5.94,7.06)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e6.20 (5.95,6.61)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.782\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003cbr/\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSelection of Clinical and Serologica Parameters Using Lasso Regression\u003c/h2\u003e\u003cp\u003eLASSO regression was applied to identify potential clinical and biochemical predictors of severe hepatic steatosis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis revealed that TG (coefficient\u0026thinsp;=\u0026thinsp;0.24), FINS (coefficient\u0026thinsp;=\u0026thinsp;0.03), chemerin (coefficient\u0026thinsp;=\u0026thinsp;0.01), and BMI (coefficient\u0026thinsp;=\u0026thinsp;0.19) were positively associated with severe hepatic steatosis, whereas ADPN (coefficient = \u0026minus;\u0026thinsp;0.06) was negatively associated. Coefficients for all other variables were reduced to zero, indicating minimal predictive contribution.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Logistic Regression Analyses\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of univariate and multivariate logistic regression analyses. Univariate logistic regression indicated that BMI, TG, ADPN, FINS, and chemerin were candidate predictors of severe hepatic steatosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These variables were then entered into a multivariate logistic regression model, which identified BMI, TG, ADPN, and chemerin as independent predictors of severe hepatic steatosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Univariate and Multivariate Logistic Regression Analyses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\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\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.43 (1.22\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.41 (1.15\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.89 (1.31\u0026ndash;2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.65 (1.17\u0026ndash;2.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADPN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.79 (0.67\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.79 (0.64\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFINS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.14 (1.06\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemerin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: OR, Odds Ratio, CI, Confidence Interval, BMI, body mass index; TG, triglycerides; ADPN, adiponectin;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRCS Analysis of the Associations Between BMI, TG, Chemerin, ADPN, and Severe Hepatic Steatosis\u003c/h3\u003e\n\u003cp\u003eIn this study, RCS analysis combined with logistic regression was conducted to investigate the nonlinear relationships between BMI, TG, ADPN, chemerin, and severe hepatic steatosis, adjusting for sex as a covariate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results revealed a significant nonlinear association between BMI and severe hepatic steatosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with risk markedly increasing when BMI exceeded 27 kg/m\u0026sup2;, indicating a clear dose\u0026ndash;response relationship. TG levels were approximately linearly and positively associated with severe hepatic steatosis risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with risk steadily rising as TG levels increased. ADPN exhibited a U-shaped relationship with severe hepatic steatosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with significantly higher risk observed when ADPN levels were below 5 \u0026micro;g/mL or above 15 \u0026micro;g/mL. Chemerin levels also showed a significant nonlinear association with severe hepatic steatosis risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with a sharp increase in risk once chemerin levels reached 100 pg/mL.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Between BMI, TG, Chemerin, ADPN, and CAP\u003c/h2\u003e\u003cp\u003eSpearman correlation analysis was performed to assess the associations between BMI, TG, ADPN, chemerin, and the CAP in the training cohort of 130 participants(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results demonstrated that BMI, TG, and chemerin were significantly positively correlated with CAP (r\u0026thinsp;=\u0026thinsp;0.543, 0.471, and 0.333, respectively; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas ADPN was significantly negatively correlated with CAP (r = -0.324, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eConstruction and Validation of the Predictive Nomogram\u003c/h2\u003e\u003cp\u003eA predictive model for severe hepatic steatosis in patients with abnormal glucose metabolism was developed using BMI, TG, ADPN, and chemerin, and visualized as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). ROC curve analysis demonstrated that the nomogram achieved higher discriminative performance in both the training and validation cohorts compared with each individual predictor (BMI, TG, ADPN, or chemerin; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The predictive performance of the nomogram is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Calibration of the nomogram was assessed using the Hosmer-Lemeshow test (training cohort P\u0026thinsp;=\u0026thinsp;0.945; validation cohort P\u0026thinsp;=\u0026thinsp;0.436) and calibration plots, showing good agreement between predicted and observed probabilities (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). DCA indicated that the nomogram provided a wide range of net benefit thresholds, supporting its clinical utility (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\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\u003eDiagnostic performance of the nomogram in the training and validation cohorts\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enomogram\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecut off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrain\u003c/p\u003e\u003cp\u003ecohorts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.862 (0.797\u0026ndash;0.927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.754 (0.671\u0026ndash;0.825)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003cp\u003e(0.797\u0026ndash;0.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003cp\u003e(0.602\u0026ndash;0.790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003cp\u003e(0.425\u0026ndash;0.672)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003cp\u003e(0.885\u0026ndash;0.997)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest cohorts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.889 (0.800-0.978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821 (0.696\u0026ndash;0.911)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003cp\u003e(0.744\u0026ndash;1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003cp\u003e(0.660\u0026ndash;0.919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003cp\u003e(0.478\u0026ndash;0.855)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003cp\u003e(0.854\u0026ndash;1.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eLiver biopsy continues to be the gold standard for evaluating hepatic steatosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e; however, its invasive nature limits patient acceptance, and long-term dynamic follow-up is necessary to assess treatment efficacy. CAP offers a quantitative assessment of hepatic steatosis and is considered a relatively accurate method for evaluating the severity of steatosis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Nevertheless, it has several limitations, including a relatively high failure rate and the requirement for dedicated equipment. In this study, we developed a predictive model based on clinical and serological parameters to estimate the risk of severe hepatic steatosis in patients with abnormal glucose metabolism, with the aim of facilitating early clinical identification and timely intervention in this population.\u003c/p\u003e\u003cp\u003eLASSO regression analysis, a powerful method that combines feature selection with regularization, was systematically applied to identify potential predictors from a comprehensive set of clinical variables. The results identified BMI, TG, FINS, ADPN, and chemerin as candidate predictors of severe hepatic steatosis in patients with abnormal glucose metabolism. Among these, TG, FINS, chemerin, and BMI were positively correlated with severe hepatic steatosis, whereas ADPN was negatively correlated. Further univariate and multivariate logistic regression analyses demonstrated that BMI, TG, ADPN, and chemerin were independent predictors of severe hepatic steatosis.\u003c/p\u003e\u003cp\u003eObesity and hyperlipidemia are well-established risk factors for hepatic steatosis\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Consistent with the findings of Loomis et al.\u003csup\u003e19\u003c/sup\u003e, our study revealed that BMI increased progressively with the severity of steatosis. A potential explanation is that BMI is positively correlated with circulating fatty acid levels; in obese patients, elevated fatty acids exacerbate hepatic lipid deposition and fibrosis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Elevated TG levels are often associated with insulin resistance, which promotes hepatic lipogenesis while inhibiting lipolysis, thereby aggravating lipid accumulation in hepatocytes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Pereira et al.\u003csup\u003e23\u003c/sup\u003e reported that patients with severe hepatic steatosis exhibited significantly higher TG levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Elevated serum TG increases the flux of free fatty acids into the liver, overwhelming hepatic oxidation and secretion capacity, leading to intrahepatic lipid accumulation, inflammatory responses, and hepatocellular injury, which in turn worsens steatosis.\u003c/p\u003e\u003cp\u003eChemerin is a multifunctional adipokine secreted by adipose tissue that regulates lipid metabolism and inflammatory processes via specific receptor-mediated signaling pathways\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Numerous clinical studies have demonstrated that elevated serum chemerin levels are closely associated with BMI, insulin resistance, and dyslipidemia\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In our study, chemerin was significantly positively correlated with CAP, suggesting that chemerin may contribute to the development of hepatic steatosis through mechanisms such as promoting hepatic lipid deposition, enhancing lipid peroxidation, and triggering chronic low-grade inflammation. In contrast, ADPN functions as a protective adipokine that improves glucose and lipid homeostasis by activating the AMPK signaling pathway to promote fatty acid oxidation in peripheral tissues and enhance insulin sensitivity\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that decreased adiponectin is an independent risk factor for hepatic steatosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which aligns with our findings.\u003c/p\u003e\u003cp\u003eA nomogram is a graphical tool, based on regression models, that quantitatively predicts the probability of a clinical outcome\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In this study, four independent predictors\u0026mdash;BMI, TG, ADPN, and chemerin\u0026mdash;were incorporated into a nomogram to construct a visualized prediction model. ROC curve analysis revealed that the nomogram achieved an AUC of 0.862 (95% CI: 0.797\u0026ndash;0.927) in the training cohort and 0.889 (95% CI: 0.800\u0026ndash;0.978) in the validation cohort, demonstrating excellent discriminative performance. Additionally, calibration curve analysis confirmed good predictive accuracy, and decision curve analysis showed substantial clinical utility. Collectively, these results indicate that the nomogram not only provides reliable predictions but also holds significant clinical decision-making value.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, it was a single-center study with a relatively small sample size, which may limit the generalizability of the findings. Second, although CAP measurements in this study were obtained under ultrasound guidance, which improves accuracy and success rates, liver biopsy remains the gold standard for assessing steatosis and its absence may have influenced the results. Future multicenter studies incorporating liver biopsy as the reference standard are warranted to further optimize the model and expand its clinical applicability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we developed a nomogram incorporating CAP, traditional risk factors, and adipokines to predict the risk of severe hepatic steatosis in patients with abnormal glucose metabolism. The model demonstrated robust predictive performance in both the training and validation cohorts, highlighting its potential utility in early risk stratification and clinical decision-making. This predictive tool may facilitate the early identification of high-risk patients with severe hepatic steatosis and help reduce the disease burden through timely intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealkaline phosphatase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealanine aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003easpartate aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADPN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadiponectin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAreas Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econtrolled attenuation parameter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCREA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecreatinine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edirect bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting blood glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFINS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting insulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglutamyl transpeptidase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglomerular filtration rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglycated hemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eindirect bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elactate dehydrogenase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-alcoholic fatty liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic splines\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglyceride\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003euric acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLasso\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study involving human participants was reviewed and approved by the Ethics Committee of Kunshan Hospital of Traditional Chinese Medical, Suzhou City, Jiangsu Province, China. The approval reference number is KSZ2022-034-01. Informed consent was obtained from all individual participants included in the study prior to data collection. All procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eConsent for publication: Not applicable. All data presented in this manuscript are de-identified (no individual personal identifiers such as names, contact information, or unique medical records are included), and thus individual consent for publication is not required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors' information\u003c/h2\u003e\u003cp\u003eAuthors' information: Not applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Suzhou Science and Technology Development Program Guidance Project (Grant Number: SKYXD2022070) and the Guiding Project of Kunshan Key R\u0026amp;D Plan (Social Development) (Grant Number: KSZ2009). There are no other funding sources for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.H. participated in study design, clinical data collection, preliminary data sorting and initial manuscript drafting; A.Z. conducted CAP data measurement \u0026amp; quality control, preliminary clinical parameter statistical analysis and manuscript revision; Y.S. collected \u0026amp; verified serological indicators, assisted in nomogram construction and checked table/figure data accuracy; Y.Y. participated in standardized ultrasound-guided CAP operation, sorted out subject inclusion/exclusion criteria and conducted preliminary baseline characteristic analysis.Q.J., corresponding author oversaw overall study design \u0026amp; supervision, guided statistical method selection, reviewed \u0026amp; finalized the manuscript and coordinated team collaboration;Q.C., corresponding author managed overall study quality control, guided nomogram validation \u0026amp; evaluation, addressed manuscript review academic questions and communicated with the ethics committee.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by Suzhou Science and Technology Development Program Guidance Project (No. SKYXD2022070) and Guiding Project of Kunshan Key R\u0026amp;D Plan (Social Development) (No. KSZ2009). The authors would like to thank the staff of the Department of Ultrasonography, Kunshan Hospital of Traditional Chinese Medical, and Kunshan Branch of Jiangsu Clinical Research Institute of Traditional Chinese Medical for their assistance in data collection and CAP measurements.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author (Quan Chen, E-mail address: [email protected]) upon reasonable request. All materials used in this study (e.g., serological detection kits, FibroScan equipment) are commercially available and do not involve proprietary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Li H, Jiang N et al. The Role of Traditional Chinese Medicine in the Management of Nonalcoholic Fatty Liver Disease: Targeting Gut Microbiome. Am J Chin Med 2025:1\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1142/s0192415x25500788\u003c/span\u003e\u003cspan address=\"10.1142/s0192415x25500788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou J, Zhou F, Wang W, et al. Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology (Baltimore MD). 2020;71(5):1851\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.31150\u003c/span\u003e\u003cspan address=\"10.1002/hep.31150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuzica CM, Sfarti C, Trifan A, et al. Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: A Bidirectional Relationship. Can J Gastroenterol Hepatol. 2020;2020:6638306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2020/6638306\u003c/span\u003e\u003cspan address=\"10.1155/2020/6638306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon TG, Roelstraete B, Khalili H, et al. Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort. Gut. 2021;70(7):1375\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2020-322786\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2020-322786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison SA, Bashir MR, Guy CD, et al. Resmetirom (MGL-3196) for the treatment of non-alcoholic steatohepatitis: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet. 2019;394(10213):2012\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(19)32517-6\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(19)32517-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohira T, Oeda S, Eto E, et al. Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis. Sci Rep. 2025;15(1):31982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-17396-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-17396-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin X, Huang X, Yi Z, et al. Association of CALLY index with NAFLD in U.S. adults from NHANES 2017\u0026ndash;2020 assessed by vibration-controlled transient elastography. Diabetol Metab Syndr. 2025;17(1):363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-025-01926-y\u003c/span\u003e\u003cspan address=\"10.1186/s13098-025-01926-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaussy C, Alquiraish MH, Nguyen P, et al. Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis. Hepatology (Baltimore MD). 2018;67(4):1348\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.29639\u003c/span\u003e\u003cspan address=\"10.1002/hep.29639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu YY, Dong NL, Qu Q, et al. The correlation between controlled attenuation parameter and metabolic syndrome and its components in middle-aged and elderly nonalcoholic fatty liver disease patients. Medicine. 2018;97(43):e12931. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/md.0000000000012931\u003c/span\u003e\u003cspan address=\"10.1097/md.0000000000012931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrancisco V, Sanz MJ, Real JT, et al. Adipokines in Non-Alcoholic Fatty Liver Disease: Are We on the Road toward New Biomarkers and Therapeutic Targets? Biology. 2022;11(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biology11081237\u003c/span\u003e\u003cspan address=\"10.3390/biology11081237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrešič M, Hy\u0026ouml;tyl\u0026auml;inen T, Kotronen A, et al. Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids. Diabetologia. 2013;56(10):2266\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-013-2981-2\u003c/span\u003e\u003cspan address=\"10.1007/s00125-013-2981-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreno-Vedia J, Rosales R, Ozcariz E, et al. Triglyceride-Rich Lipoproteins and Glycoprotein A and B Assessed by 1H-NMR in Metabolic-Associated Fatty Liver Disease. Front Endocrinol. 2021;12:775677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2021.775677\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.775677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e[Chinese expert consensus on comprehensive management. of cardiovascular risk in patients with cardiovascular diseases and abnormal glucose metabolism]. Zhonghua xin xue guan bing za zhi. 2021;49(7):656\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.cn112148-20210114-00050\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.cn112148-20210114-00050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang SC, Liu CJ. Chronic hepatitis B with concurrent metabolic dysfunction-associated fatty liver disease: Challenges and perspectives. Clin Mol Hepatol. 2023;29(2):320\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3350/cmh.2022.0422\u003c/span\u003e\u003cspan address=\"10.3350/cmh.2022.0422\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlfadda NA, Aljuraiban GS, Awwad HM, et al. Higher carbohydrate intake in relation to non-alcoholic fatty liver disease in patients with type 2 diabetes. Front Nutr. 2022;9:996004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2022.996004\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.996004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAfonso-Al\u0026iacute; A, Dicroce-Giacobini J, Teixido-Trujillo S, et al. Role of obesity and estrogen deficiency in non\u0026ndash;alcoholic fatty liver disease: Insights from a mouse model. Mol Med Rep. 2025;32(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/mmr.2025.13629\u003c/span\u003e\u003cspan address=\"10.3892/mmr.2025.13629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi Y, Feng R, Mao J, et al. Structural Characterization of Peptides From Huangjiu and Their Regulation of Hepatic Steatosis and Gut Microbiota Dysbiosis in Hyperlipidemia Mice. Front Pharmacol. 2021;12:689092. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2021.689092\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2021.689092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUllah MI, Tamanna S, Obesity. Clinical Impact, Pathophysiology, Complications, and Modern Innovations in Therapeutic Strategies. Med (Basel Switzerland). 2025;12(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicines12030019\u003c/span\u003e\u003cspan address=\"10.3390/medicines12030019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoomis AK, Kabadi S, Preiss D, et al. Body Mass Index and Risk of Nonalcoholic Fatty Liver Disease: Two Electronic Health Record Prospective Studies. J Clin Endocrinol Metab. 2016;101(3):945\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2015-3444\u003c/span\u003e\u003cspan address=\"10.1210/jc.2015-3444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang TY, Wang RF, Bu ZY, et al. Association of metabolic dysfunction-associated fatty liver disease with kidney disease. Nat Rev Nephrol. 2022;18(4):259\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41581-021-00519-y\u003c/span\u003e\u003cspan address=\"10.1038/s41581-021-00519-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Ren J, Zhou W, et al. Lean non-alcoholic fatty liver disease (Lean-NAFLD) and the development of metabolic syndrome: a retrospective study. Sci Rep. 2022;12(1):10977. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-14701-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-14701-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCariou B, Byrne CD, Loomba R, et al. Nonalcoholic fatty liver disease as a metabolic disease in humans: A literature review. Diabetes Obes Metab. 2021;23(5):1069\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dom.14322\u003c/span\u003e\u003cspan address=\"10.1111/dom.14322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePouwels S, Sakran N, Graham Y, et al. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr disorders. 2022;22(1):63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-022-00980-1\u003c/span\u003e\u003cspan address=\"10.1186/s12902-022-00980-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVorontsov O, Levitt L, Lilleri D, et al. Amniotic fluid biomarkers predict the severity of congenital cytomegalovirus infection. J Clin Investig. 2022;132(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/jci157415\u003c/span\u003e\u003cspan address=\"10.1172/jci157415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuszała M, Pilszyk A, Niebrzydowska M, et al. Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus 2.0. Int J Mol Sci. 2022;23(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms23084364\u003c/span\u003e\u003cspan address=\"10.3390/ijms23084364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamiński M, Mierzyński R, Poniedziałek-Czajkowska E, et al. Comparative Evaluation of Adipokine Metrics for the Diagnosis of Gestational Diabetes Mellitus. Int J Mol Sci. 2023;25(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms25010175\u003c/span\u003e\u003cspan address=\"10.3390/ijms25010175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlszańska J, Pietraszek-Gremplewicz K, Nowak D. Melanoma Progression under Obesity: Focus on Adipokines. Cancers. 2021;13(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13092281\u003c/span\u003e\u003cspan address=\"10.3390/cancers13092281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukherjee S, Skrede S, Milbank E, et al. Understanding the Effects of Antipsychotics on Appetite Control. Front Nutr. 2021;8:815456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2021.815456\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.815456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Karvonen-Gutierrez CA, Mukherjee B, et al. Urinary metals and adipokines in midlife women: The Study of Women's Health Across the nation (SWAN). Environ Res. 2021;196:110426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envres.2020.110426\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2020.110426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo K, Chen Y, Fang S, et al. Study on inflammation and fibrogenesis in MAFLD from 2000 to 2022: a bibliometric analysis. Front Endocrinol. 2023;14:1231520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1231520\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1231520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhosh SK, Khandoker AH. A machine learning driven monogram for predicting chronic kidney disease stages 3\u0026ndash;5. Sci Rep. 2023;13(1):21613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-48815-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-48815-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Abnormal glucose metabolism, Severe hepatic steatosis, Body mass index, Serological markers, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7813902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7813902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo develop a nomogram integrating serological markers and clinical parameters for predicting severe hepatic steatosis in patients with abnormal glucose metabolism.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective study included 186 patients with abnormal glucose metabolism who underwent controlled attenuation parameter (CAP) measurement 和serological examination between February 2023 and May 2024. Patients were classified as severe (n\u0026thinsp;=\u0026thinsp;56) or non-severe (n\u0026thinsp;=\u0026thinsp;130) steatosis and randomly assigned to training and validation cohorts (7:3). least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression identified independent predictors. Restricted cubic splines (RCS) assessed nonlinear associations, and correlations with CAP were examined. A predictive nomogram was constructed and evaluated using receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBody mass index (BMI), triglycerides (TG), adiponectin (ADPN), and chemerin were independent predictors. RCS indicated nonlinear relationships for BMI, ADPN, and chemerin, and a positive linear trend for TG (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). CAP correlated positively with BMI, TG, and chemerin, and negatively with ADPN. The nomogram demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.862 (95% CI: 0.797\u0026ndash;0.927) in the training cohort and 0.889 (95% CI: 0.800\u0026ndash;0.978) in the validation cohort. Calibration and DCA confirmed its good performance and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe nomogram based on BMI, TG, ADPN, and chemerin accurately predicts severe hepatic steatosis in patients with abnormal glucose metabolism, providing a practical tool for individualized clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 20:57:19","doi":"10.21203/rs.3.rs-7813902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-23T08:43:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T08:31:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T09:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6179882619682592525724619924381281600","date":"2025-11-19T03:49:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35815475492306446520328427228604898780","date":"2025-11-18T11:43:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167484743443059073713258959718537018539","date":"2025-11-16T16:25:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233488276956108305399317246750617731147","date":"2025-11-16T10:05:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T09:27:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280546602731198577815823403778026985028","date":"2025-11-16T08:31:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-16T00:21:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-11T07:55:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-11T07:55:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-10-09T06:47:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5185c17a-d3df-4990-ae8c-a9e8da9db6d1","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:10:36+00:00","versionOfRecord":{"articleIdentity":"rs-7813902","link":"https://doi.org/10.1186/s13098-026-02147-7","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2026-03-19 15:59:04","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-10-20 20:57:19","video":"","vorDoi":"10.1186/s13098-026-02147-7","vorDoiUrl":"https://doi.org/10.1186/s13098-026-02147-7","workflowStages":[]},"version":"v1","identity":"rs-7813902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7813902","identity":"rs-7813902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}