Development and Validation of an Online Dynamic Nomogram for Predicting Nonobese Metabolic Dysfunction-associated fatty liver disease Based on Body Composition Analysis

Development and Validation of an Online Dynamic Nomogram for Predicting Nonobese Metabolic Dysfunction-associated fatty liver disease Based on Body Composition Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of an Online Dynamic Nomogram for Predicting Nonobese Metabolic Dysfunction-associated fatty liver disease Based on Body Composition Analysis Tingting Zheng, Haizhen Yang, Sijia Wang, Xiaoqin Shi, Qingxiu Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7174767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background The factors influencing nonobese Metabolic dysfunction-associated fatty liver disease are discussed, and an online dynamic nomogram model is constructed. Methods A retrospective review was conducted on medical history data from 216 patients diagnosed with nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) and 322 nonobese normal individuals at the Second Affiliated Hospital of Soochow University. An automated random grouping procedure employing statistical software allocated the 538 subjects into training and verification cohorts at a 7:3 ratio. Initial screening of relevant indicators employed univariate and correlation analyses. Subsequently, significant potential independent risk factors (P < 0.05) were identified through the Lasso regression method, followed by cross-validation. These statistically significant indicators were further analyzed using binary logistic regression. Their discriminative capacity was evaluated using receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC). Finally, a dynamic online diagnostic nomogram was constructed. The nomogram's predictive performance was assessed via AUC, while its calibration was evaluated using a calibration plot. Results Six independent risk factors, namely, past history (odds ratio [OR]: 2.399, P = 0.0008), TG (OR: 1.176, P = 0.008), HDL-C (OR: 0.173, P = 0.014), LDL-C (OR: 3.916, P = 0.001), fat (OR: 4.299, P = 0.0009) and dPhaseAngle(OR: 3.174, P = 0.022), were screened from the results of Lasso regression method and a binary logistic regression analysis of the training cohort and included in the nonobese MAFLD online diagnostic nomogram. The nomogram predicted nonobese MAFLD with AUC values of 0.863 in the training cohort, and 0.83 in the validation cohort. The calibration curve also revealed that the nomogram predicted outcomes were close to the ideal curve, and that the predicted outcomes were consistent with the real outcomes. Conclusion An online dynamic nomogram for nonobese MAFLD patients with good predictive performance was constructed, which can be used as a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors and can assist physicians in making personalized diagnoses and treatments for patients. nonobese MAFLD body composition analysis dynamic nomogram risk factors LASSO regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Metabolic Dysfunction-associated fatty liver disease(MAFLD) is a disease which is closely related to the liver, and its onset is closely linked to metabolic abnormalities.The disease, formerly known as nonalcoholic fatty liver disease (NAFLD), is known to be associated with insulin resistance, overnutrition and genetic susceptibility, and is a chronic metabolic stress liver disease. In recent years, with the change of lifestyle and the improvement of life quality, the incidence of fatty liver has been increasing year afer year [ 1 ] .A recent meta-analysis provides more specifc prevalence rates for diferent regions as follows: South Asia 34% (23–47%), South-East Asia 33% (19–51%), East Asia 30% (26–34%), and the Pacifc region 28% (25 − 32%) [ 2 ] .In China, the prevalence of MAFLD is 26%, and there is an increasing trend [ 3 ] .Traditionally, MAFLD has been conventionally regarded as being associated primarily with obesity; however, approximately one-third of affected individuals possess a normal body mass index (BMI) [ 4 ] . The prevalence of nonobese MAFLD within China ranges from 4.2–19.33%, constituting 15–56% of the total MAFLD patient population [ 5 ] . Compared to their obese counterparts, nonobese MAFLD patients typically exhibit a BMI within the normative range, a standard physique, and a relatively balanced body fat distribution, presenting an ostensibly healthy appearance. Nevertheless, they may manifest similar metabolic dysfunctions, potentially attributable to factors such as visceral adipose tissue accumulation and genetic predispositions, albeit distinct from the pathological profile observed in obese MAFLD patients [ 6 ] . Consequently, nonobese patients are frequently overlooked, resulting in underdiagnosis. This oversight leads to disease progression and the forfeiture of optimal therapeutic intervention. Therefore, identifying non-invasive, accurate, and straightforward serological diagnostic approaches assumes particular importance. The objective of this study is to identify predictive indicators for nonobese MAFLD and construct corresponding diagnostic models, thereby providing a scientific foundation for the early screening of MAFLD in individuals possessing a normal body mass index. MATERIALS AND METHODS Patient Data This study was conducted in the Physical examination Center of the Second Affiliated Hospital of Soochow University from January 2024 to April 2025. A total of 1720 eligible participants with BMI≤24kg/m 2 were investigated, among which 219 cases of MAFLD patients and 1501 cases of non-MAFLD patients were found. Of these participants,538 underwent body composition analysis, including 216 with MAFLD disease and 322 without.The diagnosis of MAFLD was made by two senior attending physicians based on the results of ultrasound and imaging examinations.The subjects included: (1) patients diagnosed with MAFLD,(2) patients with complete clinical data, (3) patients who had signed an informed consent form and agreed to collect their medical data.The following patients were excluded from the study: (1) those with other liver diseases, such as viral hepatitis, drug-induced liver disease, autoimmune liver disease, cirrhosis, or liver cancer,(2) patients with other major organic diseases or those requiring medication that could affect the study results,(3)patients with incomplete general information and medical history.This study has been approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (Ethics number: LK2024111) and informed consent has been obtained from patients. Data Collection Data collection covered the following items: age, height, weight, blood pressure, and past history, including metabolic diseases such as hypertension and diabetes, and other history. For statistical analysis, the history of metabolic diseases such as hypertension and diabetes was classified as metabolic disease history, and the rest were classified as other disease history. Laboratory tests were carried out by the staff from 7 a.m. to 10 a.m. and fasting blood samples of participants were collected by vein and sent to the Laboratory Testing Center of the Second Affiliated Hospital of Soochow University for testing. The test items include blood routine, fasting blood glucose (FBG),total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT) and other indicators.The human body composition analysis was analyzed by DBA-610 to measure various indicators, including intracellular fluid (inw), extracellular fluid (exw), total body water (tbw), protein (pro), muscle mass (mus), fat free mass (ffm), fat mass (fat), percent body fat (pbf), visceral fat area (vfa), waist-to-hip ratio (whr), basal metabolic rate (bmr), phase angle (PhA), and limb skeletal muscle index (dASMI) and so on.The operation is carried out by two professional technicians according to the manual, and the temperature is maintained at 20 to 25 degrees celsius. The examinee should fast, empty his or her bladder and bowels, take off shoes, hats and heavy clothes, stand on the scale, keep even breathing, and the height and weight are accurately recorded by professionals.The abdominal ultrasound examination was performed using GE-VIVid T9 color ultrasonic diagnostic instrument (probe frequency 5.0MHz) to observe the liver echo intensity, intrahepatic duct structure and deep attenuation.The examination was performed by a professional physician. The examinee should be fasting and lie in a supine position. The liver area was repeatedly explored in the frontal and lateral positions, and whether fatty liver was diagnosed according to relevant guidelines.The CAP(controlled attenuation parameter) value was measured by FibroTouch-pro5000 to complete the quantitative detection of liver steatosis, which was expressed as CAP value with units of dB/m.The operation is performed by two professional doctors according to the user manual,The detection point is located at the right axillary line to the 7th and 8th intercostal or 8th and 9th intercostal lines of the axillary line. The effective detection should be recorded continuously for 10 times, and the median is taken as the final measurement result. The success rate of the final detection should be greater than 60%, and the interquartile distance should be less than 1/3 of the median of the measured value. Statistical Analysis The study population was stratified into two cohorts based on the presence or absence of MAFLD. A descriptive analysis of baseline characteristics for both cohorts was conducted. Continuous variables underwent assessment for normality utilizing the Kolmogorov-Smirnov test. Variables adhering to a normal distribution were expressed as mean ±standard deviation, with group differences evaluated using Student's t-tests. Variables deviating from normality were described using the median and interquartile range, and group differences were assessed employing the Mann-Whitney U test. Categorical variables were summarized by frequency and percentage，comparisons between the two groups' baseline data utilized either the chi-square test or Fisher's exact test, as appropriate. Screening of predictive variables was performed utilizing R software (version 4.2.0) and associated packages, including glmnet and Matrix. The collected dataset was randomly partitioned by statistical software into training and validation cohorts at a 7:3 ratio, followed by variable comparison. Variables demonstrating statistically significant differences in univariate analysis were incorporated into Lasso regression analysis. The optimal lambda parameter was determined via 10-fold cross-validation to identify independent risk factors, enabling the construction of a predictive online dynamic nomogram for MAFLD diagnosis.The nomogram's performance was evaluated through receiver operating characteristic (ROC) curve analysis and calibration curve assessment. The area under the ROC curve (AUC), ranging from 0.5 (indicating no discriminatory capacity) to 1.0 (denoting perfect discrimination), quantified predictive accuracy. All statistical analyses within this study were executed using SPSS version 25.0 and R version 4.2.0. Two-tailed tests were applied, and a p-value less than 0.05 was deemed statistically significant. RESULT Patient Features The detailed flow diagram is presented in Figure 1 . A total of 216 patients with MAFLD and 322 patients without MAFLD were included in this study from January 2024 to April 2025, all of whom were diagnosed in the Physical examination Center of the Second Affiliated Hospital of Soochow University. All the patients included in the study met the established inclusion and exclusion criteria.Of these patients, 70% were selected for the training cohort and the remaining 30% for the validation cohort.The clinicopathological characteristics of the patients are summarized in Table 1 .The Wilcoxon test and chi-square test were used to compare the indices between the nonobese MAFLD and non-MAFLD groups. In the training cohort,some significant indicators (P<0.05, Table 2 ) were selected, including gender(P<0.001),history(P<0.001),age (P<0.001),waist(P<0.001),ALT(P<0.001), AST(P<0.001),GGT (P<0.001), FBG(P<0.001),TC(P<0.001),TG(P<0.001),HDL-C(P<0.001),LDL-C(P<0.001),inw(P<0.001),exw(P<0.001),tbw(P<0.001),pro(P<0.001),mus(P<0.001),ffm(P<0.001),fat(P<0.001),vfa(P<0.001),whr(P<0.001),bmr(P<0.001),dPhaseAngle(P<0.001) and dASMI (P<0.001) were statistically significan.The percentage of pbf (P=0.601) was not statistically significant. TABLE 1 |Patient demographics and clinical characteristics Characteristics Training Cohort Validation Cohort nonobese MAFLD Non MAFLD P nonobese MAFLD Non MAFLD P N 147 229 69 93 Gender(%) <0.001 <0.001 male 109 (74) 100 (44) 51(73.91) 39(41.93) female 38 (26) 129 (56) 18(26.09) 54(58.07) History (Metabolic disease) <0.001 0.005 yes 35(23.81) 19(8.30) 16(23.19) 7(7.53) no 112(76.19) 210(91.70) 53(76.81) 86(92.47) Age /year(Median (Q1,Q3)) 51(42, 58.5) 42 (36,54) <0.001 50.54± 9.95 45.24 ± 12.30 0.003 Waist/cm(Median (Q1,Q3)) 85 (79, 88) 76 (70, 83) <0.001 84.32± 6.31 75.69 ± 7.98 <0.001 ALT U/L(Median (Q1,Q3)) 23 (17, 31) 14 (11, 21) <0.001 21(18,31) 14(12,20) <0.001 AST U/L(Median (Q1,Q3)) 19(16, 24) 17(15, 20) <0.001 20(16,230 18(15,21) 0.061 GGT U/L(Median (Q1,Q3)) 27 (19.5, 41) 17 (12, 25) <0.001 30(21,49) 16(12,24) <0.001 FBG mmol/l(Median (Q1,Q3)) 5.13 (4.81, 5.64) 4.87 (4.56, 5.18) 0.011 5.07(4.64,5.83) 4.9(4.61,5.22) 0.005 TC mmol/L(Mean ± SD) 5.244 ± 1.038 4.861± 0.884 <0.001 5.32(4.52,5.85) 4.72(4.12,5.35) 0.002 TG mmol/L(Median (Q1,Q3)) 1.69 (1.26, 2.53) 1.04 (0.77, 1.48) <0.001 1.89(1.54,;2.73) 0.92(0.72,1.37) <0.001 HDL-C mmol/l(Median (Q1,Q3)) 1.18 (1.06, 1.36) 1.47 (1.23, 1.76) <0.001 1.22(1.06,1.38) 1.44(1.27,1.68) <0.001 LDL-C mmol/L(Mean ± SD) 3.345 ± 0.941 2.965 ± 0.787 <0.001 3.31± 0.92 2.91± 0.89 0.006 Inw kg(Median (Q1,Q3)) 23.3 (19.6, 25.25) 19 (17.2, 23.4) <0.001 23.4(20.3,25.4) 19.6(17.2,23.8) <0.001 Exw kg(Median (Q1,Q3)) 14(12.1, 15.1) 11.7 (10.6, 14.3) <0.001 14(12.3,15.4) 12.2(10.6,14.3) <0.001 Tbw %(Median (Q1,Q3)) 37.2 (31.75, 40.15) 30.9 (27.8, 37.5) <0.001 37.5(32.2,40.7) 31.7(27.9,38.3) <0.001 Pro kg(Median (Q1,Q3)) 10.1 (8.45, 10.9) 8.2 (7.4, 10.1) <0.001 10.1(8.8,11) 8.5(7.4,10.3) <0.001 Mus kg(Median (Q1,Q3)) 47.6(40.45, 51.45) 39.3(35.3, 47.9) <0.001 47.9(41.3,51.9) 40.4(35.5,48.9) <0.001 Ffm kg(Median (Q1,Q3)) 50.8 (43.2, 54.85) 42.1 (38, 51.1) <0.001 51(44.2,55.4) 43.2(38.0,52.2) <0.001 Fat %(Median (Q1,Q3)) 17(15.2, 18.8) 15.3 (13.1, 17.7) <0.001 17.48±3.17 15.09±3.55 <0.001 Pbf %(Mean ± SD) 26.14± 5.63 25.81± 5.89 0.984 26.16± 4.66 25.46±6.01 0.401 Vfa cm 2 (Mean ± SD) 76.7 ± 18.23 67.69 ± 19.9 <0.001 78.26± 17.38 65.84±19.84 <0.001 whr(Median (Q1,Q3)) 0.9 (0.88, 0.93) 0.87 (0.84, 0.89) <0.001 0.91±0.04 0.87 ±0.04 <0.001 Bmr kcal/day(Median (Q1,Q3)) 1467.2 (1304, 1554.45) 1278.6 (1191.9, 1473.7) <0.001 1471.3 (1324.5,1567.2) 1303 (1190.6,1497.7) <0.001 dPhaseAngle ° (Mean ± SD) 5.91± 0.67 5.53± 0.66 <0.001 5.97±0.59 5.54±0.69 <0.001 dASMI kg/m 2 (Median (Q1,Q3)) 7.37(6.64, 7.78) 6.46 (5.88, 7.35) <0.001 7.325(6.7,7.68) 6.52(5.92,7.43) <0.001 Factor Selection for the Predictive Model, Calibration, and Validation of the Nomogram The aforementioned variables were integrated into the baseline model and subsequently narrowed to eleven potential predictors through LASSO regression analysis applied to the training cohort, with cross-validation of the LASSO model ( Figure 2A ).A cross-validated error plot of the LASSO regression model is shown in Figure 2B .This figure demonstrates that the most regularized and parsimonious model, achieving a cross-validated error within one standard error of the minimum, incorporated eleven variables. The standard error of the minimum distance corresponded to λ=0.031. The variables selected for this model comprised: history, age, waist, ALT, GGT, FBG, TG, HDL-C, LDL-C, fat, and dPhaseAngle. As demonstrated in Table 2 and Figure 3 , the receiver operating characteristic (ROC) analysis of the specified diagnostic indicators reveals that the area under the curve (AUC) values exceed 0.5 for the following parameters, history (AUC=0.578), age (AUC=0.632), waist circumference(AUC=0.764), alanine aminotransferase (ALT) (AUC=0.738), gamma-glutamyl transferase (GGT) (AUC=0.746), fasting blood glucose (FBG) (AUC=0.64), triglycerides (TG) (AUC=0.793), high-density lipoprotein cholesterol (HDL-C) (AUC=0.738), low-density lipoprotein cholesterol (LDL-C) (AUC=0.63), fat content (AUC=0.659), and dPhaseAngle (AUC=0.672). The corresponding critical values, sensitivity, and specificity for these parameters are detailed in Table 2, with the ROC curve analysis of candidate diagnostic indicators presented in Figure 3 . TABLE 2 | The Results of receiver operating characteristic (ROC) curve Characteristics AUC Cut-off value Sensitivity Specificity Youden index 95%CI P history 0.578 1.0 0.236 0.919 0.155 0.555-0.617 0.012 age 0.632 46.0 0.671 0.568 0.24 0.589-0.681 0.000 waist 0.764 78.0 0.838 0.578 0.416 0.722-0.799 0.000 ALT 0.738 17.0 0.796 0.602 0.399 0.696-0.765 0.000 GGT 0.746 20.0 0.759 0.643 0.402 0.713-0.786 0.000 FBG 0.640 5.0 0.606 0.621 0.228 0.597-0.686 0.000 TG 0.793 1.38 0.741 0.72 0.461 0.760-0.828 0.000 HDL-C 0.738 1.33 0.738 0.668 0.376 0.704-0.778 0.000 LDL-C 0.630 3.39 0.514 0.739 0.253 0.575-0.672 0.000 fat 0.659 15.1 0.792 0.463 0.254 0.594-0.711 0.000 dPhaseAngle 0.672 5.589 0.741 0.571 0.312 0.623-0.717 0.000 In order to determine whether the above eleven variables were independent risk factors for MAFLD, a binary logistic regression analysis was used to evaluate the effects of history, age, waist, ALT, GGT, FBG, TG, HDL-C, LDL-C, fat and dPhaseAngle. The AUC of the training Cohort data was 0.85, and the prediction effect of the model was good. The analysis results showed in Table3 and history, TG, HDL-C, LDL-C, fat and dPhaseAngle were statistically significant and were included in the model (Figures 4A) . Similar results were obtained in validation cohorts (Figures 4B) .Results of the correlation analysis showed that the six factors( history, TG, HDL-C, LDL-C, fat and dPhaseAngle ) were all linearly correlated with each other, while HDL-C was negatively correlated with other indices (Figure 5) . TABLE 3 | The Results of Logistic regression analysis of prediction indicators Predictor Estimate SE OR (95%CI) Z p history 0.875 0.338 2.399(1.247 - 4.716) 2.587 0.0008 age 0.023 0.011 1.023(1.001 - 1.046) 2.074 0.279 ALT 0.024 0.009 1.024(1.006 - 1.044) 2.548 0.130 waist 0.033 0.021 1.034(0.992 - 1.077) 1.569 0.119 GGT 0.004 0.003 1.004(0.998 - 1.011) 1.35 0.328 FBG 0.196 0.089 1.216(1.012 - 1.470) 2.204 0.278 TG 0.162 0.12 1.176(0.951 - 1.526) 1.352 0.008 HDL-C -1.757 0.456 0.173(0.069 - 0.414) -3.853 0.014 LDL-C 0.552 0.141 1.737(1.323 - 2.302) 3.916 0.001 fat 0.175 0.041 1.191(1.102 - 1.292) 4.299 0.0009 dPhaseAngle 0.729 0.23 2.073(1.329 - 3.278) 3.174 0.022 The final logistic model included six independent predictors( history, TG, HDL-C, LDL-C, fat and dPhaseAngle )and was developed as a simple-to-use nomogram, which is illustrated in Figure 6A and available online ( https://stop15.shinyapps.io/DynNomapp/ ) and presented in Figure 6B . For example,one person, whose history is no, TG is 0.79mmol/L, HDL-C is 1.47mmol/L, LDL-C is 2.7mmol/L, fat is 16.3 %and dPhaseAngle is 5.153°. The total score of the predicted score was 112, indicating a probability of 9.52% for MAFLD( Figure 6A ). As demonstrated in Figures 7A-C , the area under the curve values for the model were 0.863 in the training cohort and 0.83 in the validation cohort, indicating robust predictive capability. These findings suggest that the Nomogram could serve as a valuable tool for the early screening of nonobese metabolic dysfunction-associated fatty liver disease. Discussion nonobese Metabolic dysfunction-associated fatty liver disease(MAFLD) has become a main cause of chronic liver disease globally and is one of the most common type in China [7] .This disease is not only the main cause of cryptogenic cirrhosis,but is also closely related to insulin resistance,metabolic disorders,and cardiovascular diseases. As the disease progresses,The disease burden of patients will be significantly increased and the quality of life will be significantly decreased [8] .At present, MAFLD has become an important public health problem. nonobese MAFLD is asymptomatic in the early stage, with low clinical laboratory indicators and normal BMI value, which makes it difficult to attract people's attention and easy to be missed [9] .However, liver biopsy, as the gold standard for diagnosing fatty liver [5] , is an invasive and invasive procedure that is not readily accepted by the population.Ultrasonic and fibroscan are not sensitive to mild fatty liver, and detect more than 20%~30% of fatty degeneration [10] . In addition, these examinations all depend on the level of operators, which is highly subjective, and it is difficult to popularize in economically backward areas.The new expert consensus affirms the value of blood biomarkers in diagnosing MAFLD, but there is still a lack of effective predictive models [11] .The incidence of nonobese MAFLD in the physical examination population was 40.15% in our study, which was higher than that in existing studies (4.2%~19.33%) [8] .Therefore, the development of prediction tools for nonobese population is very important for disease prevention and management, and it is also one of the hot research topics in recent years. The body composition mainly discusses the composition data of human body, the detection methods and the influence of internal and external factors on the quantitative relationship of each component, which reflects the response process of the body to different changes in the inside and outside of the body at the molecular, cellular and tissue levels.The measurement of human body can not only accurately reflect the content of body fat, muscle and water [12] ,It can also reflect the visceral fat area (VFA) ,waist-to-hip ratio (WHR) and so on, so as to judge whether the body composition is normal,which has certain screening value for metabolic diseases [13] .Changes in body composition are associated with the occurrence and development of many diseases [14] .With the progress of society and the improvement of people's living standards, obesity and the diseases caused by it are attracting more and more attention, becoming an important factor that has a serious impact on health. The results of Logistic regression in our study showed that the history of metabolic disease, TG, HDL-C, LDL-C, fat and phase Angle were the predictive indicators for the diagnosis of nonobese MAFLD, which were consistent with most of the previous studies [9] .At present, it has been shown that the incidence of MAFLD in nonobese people is often associated with nutritional status, metabolic factors and genetic inheritance [9] .MAFLD is a hereditary disease, and a large number of studies have shown that the presence of patatin-like phospholipase domain protein 3 rs738409 gene is closely associated with it [15] .In addition,abnormal blood lipid is an important factor in the occurrence and development of MAFLD. Dyslipidemia is usually caused by increased lipolysis in liver tissue, activation of new fat production, and excessive accumulation of fat due to high-fat diet mechanisms [16] .nonobese MAFLD patients have more visceral fat accumulation, and visceral adipose tissue has stronger adipolysis activity than subcutaneous adipose tissue, leading to the increase of high levels of free fatty acids (FFA) in the liver, which aggravates the TG burden of the liver [17] .High TG, especially the increase of ceramide and diacylglycerol, can aggravate insulin resistance and induce inflammatory response of liver fibrosis, which further aggravates the disease [16] .HDL-C is a protective factor for nonobese MAFLD, cardiovascular and cerebrovascular diseases, and type 2 diabetes (T2DM).It delays the progression of nonobese MAFLD by activating the adenylate activated protein kinase signaling pathway and regulating lipid metabolism [18] . Fat in body composition is also an independent risk factor for nonobese MAFLD. People with high visceral fat were more likely to develop MAFLD, which was consistent with previous studies [13] .dPhaseAngle is a common indicator to evaluate the nutritional health level of patients in clinical practice [19] ,A high value reflects good cell health, while low of it may indicate damage to the cell membrane or reduced cell integrity, often associated with disease or malnutrition [20] .In this study, an online dynamic nomogram for Predicting MAFLD in nonobese patients was constructed for the first time by computer software. The AUC values in training and verification queues were 0.863 and 0.83 respectively, showing good predictive ability and helping to screen MAFLD in early stage. However, our study also has some limitations: (1) The diagnosis of MAFLD is based on ultrasound rather than biopsy, which may lead to misclassification. Future studies should consider liver biopsy as the diagnostic standard whenever possible to ensure accuracy; (2) Due to the cross-sectional design, the study may be biased due to participants' high concern for their health. Future studies could expand to include multi-center designs and long-term follow-up data to further refine the research; (3)Although the line graph performed well in the training set and internal validation set, its generalization ability needs to be evaluated in larger external validation sets and prospective studies. In summary, this research established an online diagnostic model, predicated on body composition and biochemical blood indicators, for the detection of nonobese metabolic dysfunction-associated fatty liver disease (MAFLD). This model represents the first instance utilizing metabolic indicators as its core foundation, providing an intuitive visual interface amenable to widespread implementation. It further demonstrates robust discrimination and high calibration accuracy. Given the current absence of specific pharmacological interventions for nonobese MAFLD, this model effectively identifies potential patients within the nonobese population, thereby assisting clinicians in the early identification of high-risk individuals. Furthermore, the model facilitates the adoption of preventative measures—such as dietary and lifestyle modifications—among physical examination participants, thereby mitigating disease progression and alleviating the associated socioeconomic and healthcare burdens. CONCLUSION This study was the first to develop and validate online nomograms based on the independent risk factors to dynamically predict diagnosis in individuals with nonobese MAFLD. This model demonstrated superior performance and discriminative capability, which can used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors. Abbreviations MAFLDmetabolic dysfunction-associated fatty liver NAFLD nonalcoholic fatty liver disease ROC receiver operating characteristic curve AUC area under the curve OR odds ratio BMI body mass index FBG fasting blood glucose TC total cholesterol TG triglyceride LDL-C low density lipoprotein cholesterol HDL-C high density lipoprotein cholesterol ALT alanine aminotransferase AST aspartate aminotransferase GGT γ-glutamyl transferase Inw intracellular fluid Exw extracellular fluid Tbw total body water Pro protein Mus muscle mass Ffm fat free mass Fat fat mass Pbf percent body fat Vfa visceral fat area Whr waist-to-hip ratio Bmr basal metabolic rate PhA phase angle dASMI limb skeletal muscle index CAP controlled attenuation parameter Declarations Ethical approval This study has been approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (Ethics number: LK2024111) and informed consent has been obtained from patients. Research on human data must comply with the Helsinki Declaration. Consent for publication Not applicable. Availability of data and materials We are willing to share the raw data. Data has been provided within the manuscript or supplementary information files. Competing interests The authors declare no competing interests. Funding This project was supported by National Natural Science Fund (82371218) and by pre-research fund project of Second Affiliated Hospital of Soochow University(SDFEYJC2330). Authors’ contributions All the authors worked together to complete the paper. ZW, as the corresponding authors guided the design of the entire. XS and QZ contributed to collecting ultrasound data and body composition data.TZ and JW performed the experiments and contributed to the acquisition and analysis of data.The manuscript was drafted by TZ and HZ. All authors read and approved the final manuscript. Acknowledgements Thank you to the Zhongmin Wen professor for meticulous guidance and valuable suggestions during the topic selection, research approach, methodology, and article writing. I also thank the colleagues at Health examination center for their selfless assistance and valuable advice in data collection and experimental operations. Additionally, I am grateful to all the authors of the cited literature for laying a crucial foundation for this study through their prior work. Author details 1 Physical Examination Center Research Center,The Second Affiliated Hospital of Soochow University, Suzhou 215004, P.R. China. 2 Department of Public Health, Second Affiliated Hospital of Soochow University, Suzhou 215004, Jiangsu Province, China. References ZOBAIR M. YOUNOSSI, PEGAH GOLABI, JAMES M. PAIK, et al. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review[J]. Hepatology: Official Journal of the American Association for the Study of Liver Diseases,2023,77(4):1335-1347. DOI:10.1097/HEP.0000000000000004. RINELLA ME, LAZARUS JV,RATZIU V. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. Ann Hepatol,2024,29(1):101133. DOI:10.1016/j.aohep.2023.101133. CHEN YL, LI H, LI S, XU Z, TIAN S, Wu J, et al. Prevalence of and risk factors for metabolic associated fatty liver disease in an urban population in China: a cross-sectional comparative study. BMC Gastroenterol,2021;21(1):212.DOI: 10.1186/s12876-021-01782-w. YE Q, ZOU B, YEO YH, LI J, HUANG DQ, WU Y, et al. Global prevalence, incidence, and outcomes of nonobese or lean nonalcoholic fatty liver disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol,2020;5(8):739-752.doi: 10.1016/S2468-1253(20)30077-7. Epub 2020 May 12. YOUNOSSI, ZOBAIR, ANSTEE, QUENTIN M., MARIETTI, MILENA, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention[J].Nature reviews. Gastroenterology & hepatology, 2018,15(1):11-20. DOI:10.1038/nrgastro.2017.109. KAYA E，YILMAZ Y. Metabolic-associated fatty liver disease(MAFLD):a multi-systemic disease beyond the liver[J].Clin Transl Hepatol, 2022,10(2)329-338. DOI:10.14218/jcth.2021.00178. TRINELLA M E, LAZARUS J V , RATZIU V, et al.A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. Ann Hepatol,2024,29(1):101133. DOI:10.1016/j.aohep.2023.101133. Chinese Medical Association Hepatology Branch.Guidelines for the prevention and treatment of metabolic dysfunction associated(non-alcoholic)fatty liver disease(Version 2024)[J]1.Chinese Journal of Hepatology,2024, 32(5):418-434. DOI:10.3760／cma.j.cn501 1 13-20240327-00163． CHEN HT，ZHOU YJ．Diagnosis and therapeutic strategies for nonobese type of non-alcoholic fatty liver diseases[J].Chinese Journal of Hepatology,2020,28(3):203-207. DOI：10.3760／cma.j.cn501113-20191226-00480． FEDCHUK,L., NASCIMBENI,F., PAIS,R., et al. Performance and limitations of steatosis biomarkers in patients with nonalcoholic fatty liver disease[J]. Alimentary Pharmacology and Therapeutics,2014,40(10):1209-1222. DOI:10.1111/apt.12963. ZHAO JH，ZHANG J. Advances in serological non-invasive diagnosis for metabolic dysfunction--associated fatty liver disease[J]．Chinese Journal of Hepatology,2023,31(12):1235-1239. DOE 10.3760/cnla.J.cn501 1 13-20230831-00085． MSSLENNIKOV R, IVASHKIN V, ALIEVA A, et al. Gut dysbiosis and body composition in cirrhosis[J]. World Hepatol,2022,14(6):1210-1225. DOI :10.4254/wjh.v14.i6.1210. LIU JING, LI YAN JU , RAO ZHI YONG. Correlation analysis of FibroScan measurements and body composition measurements in patients with nonalcoholic fatty liver disease[J]. Chinese Journal of Integrated Traditional and Western Medicine on Liver Diseases, 2021,31(12):1108-1111. DOI:10.3969/j. issn. 1005-0264.2021.12. 013. ALEJANDRA CARRETERO-KRUG, NATALIA ÚBEDA, CARLOS VELASCO, et al. Hydration status, body composition, and anxiety status in aeronautical military personnel from Spain: a cross-sectional study[J].Journal of shandong university(health sciences),2022,9(2):184-192. DOI:10.6040/j.issn.1671-7554.0.2024.0895. LIN H P, -H WONG G L, WHATLING C, et al.Association of genetic variations with NAFLD in lean individuals[J].Liver Int,2022,42(1):149-160. DOI:10.1111/liv.15078 CARLI F,DELLA PEPA G,SABATINI S,et al. Lipid metabolism in MASLD and MASH：from mechanism to the clinic[J]. JHEP Rep,2024,6(12):101185. DOI:10.1016/j.jhepr.2024.101185. HSU C L, WU F Z, LIN K H, et al. Role of fatty liver index and metabolic factors in the prediction of nonalcoholic fatty liver disease in a lean population receiving health checkup[J]. Clin Transl Gastroenterol, 2019,10(5):1-8. DOI:10.14309/ctg.0000000000000042. CAI XT, CHEN M，WANG MR，et a1. Relationship between HDL-C level and nonalcoholic fatty liver disease in non obese population:a prospective cohort study[J]. Journal of Medical Research, 2021,50(10):64-68,74.DOI:10.1 1969/j.issn.1673-548X.2021.10,014. PRAGET-BRACAMONTES S, GONZALEZ-ARELLANES R, AGUILAR-SALINAS CA, et al. Phase Angle as a Potential Screening Tool in Adults with Melabo1ic Diseases in Clinical Practice：A Systematic Review[J]. Int J Environ Res Public Health, 2023,20(2):1608.DOI: 10.3390/ijerph20021608. HENRY C.,LUKASKI, ANTONIO,TALLURI. Phase angle as an index of physiological status: validating bioelectrical assessments of hydration and cell mass in health and disease[J]. Reviews in endocrine & metabolic disorders,2023,24(3):371-379. DOI:10.1007/s11154-022-09764-3. Additional Declarations No competing interests reported. Supplementary Files mydata.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Oct, 2025 Reviewers invited by journal 03 Sep, 2025 Editor invited by journal 02 Sep, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 08 Aug, 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-7174767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510241780,"identity":"9147840b-9c2c-4af3-a704-8eb5c985f838","order_by":0,"name":"Tingting Zheng","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zheng","suffix":""},{"id":510241782,"identity":"ece4e6f5-b1cf-4930-b0a8-9a1a23e0d985","order_by":1,"name":"Haizhen Yang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Haizhen","middleName":"","lastName":"Yang","suffix":""},{"id":510241783,"identity":"32b1634e-21ac-4a6e-a15a-95a955c6f388","order_by":2,"name":"Sijia Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Wang","suffix":""},{"id":510241784,"identity":"36bfadae-0e24-4cfa-a6d4-553a12f35d74","order_by":3,"name":"Xiaoqin Shi","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Shi","suffix":""},{"id":510241785,"identity":"198bd78b-42d0-478e-8efc-425e97bec154","order_by":4,"name":"Qingxiu Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Qingxiu","middleName":"","lastName":"Zhang","suffix":""},{"id":510241786,"identity":"61fe453b-d3b0-4040-9c36-66882ddf616f","order_by":5,"name":"Zhongmin Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJACZgRZISHHT6KWMxbGkg1EawEBxraKxA2EtBgc7z38uaDijt3adt7DH3/Ok2DcwMD88NENfFrOnEswnnHmWfK2w3xpEpLbJJjNGdiMjXPwaDG7kWOQzNt2ONnsMI8Zg+E2CTbLBh42abxa7r8xOAzVYvwhcY4Ej8EBQlpu8Bg2A7XYAbUYSBxskJAgqMX+TI4xM8+Zwwkgh0k2HJMwkGwm4BfJ9jPGn3kqDtubnT9j/PFHTV19P3vzw8f4tMBAYgOcyYxbFaoDiVQ3CkbBKBgFIxEAAJeLSczW7d/6AAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Zhongmin","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2025-07-21 08:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7174767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7174767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91073355,"identity":"75f0eb87-2527-458c-90d0-7963ab118bc0","added_by":"auto","created_at":"2025-09-11 10:58:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83418,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/f6efada43d8ae1d73371e5b1.png"},{"id":91077113,"identity":"ded45d6c-da17-4488-813a-6ce9306d1519","added_by":"auto","created_at":"2025-09-11 11:14:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52926,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the LASSO regression analysis. (A) Plot of the LASSO coefficient profiles. (B) Tuning parameter (λ) selection cross-validation error curve.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/a0cf4e9192670dae9c88fcba.png"},{"id":91075229,"identity":"d1f0671c-b7c9-4c59-8bdd-e364e963eac7","added_by":"auto","created_at":"2025-09-11 11:06:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109079,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of candidate diagnostic indicators\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/678b92d14f8acf85ec344972.png"},{"id":91075231,"identity":"52d7e35c-507b-4dc7-bd16-6e105afa2d21","added_by":"auto","created_at":"2025-09-11 11:06:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69205,"visible":true,"origin":"","legend":"\u003cp\u003eForest maps of the logistic regression analysis of the training cohort (A), Validation cohort (B),\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/6266067914c31a90603ed921.png"},{"id":91077112,"identity":"3e7a1a5a-fb24-4532-9548-d2fb68ed7ddf","added_by":"auto","created_at":"2025-09-11 11:14:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84277,"visible":true,"origin":"","legend":"\u003cp\u003eLinear correlation analysis of the eight indicators. The number in the right of the plot was the correlation coefficient.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/8f1e079c8c162293ef8a3ef1.png"},{"id":91073364,"identity":"34a24f4e-2f0c-4bc8-a2d5-e3ab14b4a8b6","added_by":"auto","created_at":"2025-09-11 10:58:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105767,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model for nonobese MAFLD diagnosis. (A) Established nomogram in the training cohort by incorporating the following six parameters.(B) Online dynamic nomogram accessible at https://stop15.shinyapps.io/DynNomapp/\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/8fbd730b92a0ca8fcb9afdcf.png"},{"id":91073385,"identity":"02b4109e-15af-47b0-b61b-a0d253f8538d","added_by":"auto","created_at":"2025-09-11 10:58:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51210,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of validity and reliability of the model.ROC curves of the nomogram prediction model in the training cohort (A), validation cohortt (B),\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/e5d5681abced6803877e67c5.png"},{"id":91080090,"identity":"bddbf881-8947-495c-8bff-e724755f612d","added_by":"auto","created_at":"2025-09-11 11:30:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1373162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/84150df1-1b5c-496f-b7b2-d66ff5264a00.pdf"},{"id":91075228,"identity":"b6e06d59-5c8b-4d09-8ece-2220ea91738b","added_by":"auto","created_at":"2025-09-11 11:06:10","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":110625,"visible":true,"origin":"","legend":"","description":"","filename":"mydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7174767/v1/8027b63562f2088c3bf63ac8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of an Online Dynamic Nomogram for Predicting Nonobese Metabolic Dysfunction-associated fatty liver disease Based on Body Composition Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMetabolic Dysfunction-associated fatty liver disease(MAFLD) is a disease which is closely related to the liver, and its onset is closely linked to metabolic abnormalities.The disease, formerly known as nonalcoholic fatty liver disease (NAFLD), is known to be associated with insulin resistance, overnutrition and genetic susceptibility, and is a chronic metabolic stress liver disease. In recent years, with the change of lifestyle and the improvement of life quality, the incidence of fatty liver has been increasing year afer year\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.A recent meta-analysis provides more specifc prevalence rates for diferent regions as follows: South Asia 34% (23\u0026ndash;47%), South-East Asia 33% (19\u0026ndash;51%), East Asia 30% (26\u0026ndash;34%), and the Pacifc region 28% (25\u0026thinsp;\u0026minus;\u0026thinsp;32%) \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.In China, the prevalence of MAFLD is 26%, and there is an increasing trend\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Traditionally, MAFLD has been conventionally regarded as being associated primarily with obesity; however, approximately one-third of affected individuals possess a normal body mass index (BMI)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The prevalence of nonobese MAFLD within China ranges from 4.2\u0026ndash;19.33%, constituting 15\u0026ndash;56% of the total MAFLD patient population\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Compared to their obese counterparts, nonobese MAFLD patients typically exhibit a BMI within the normative range, a standard physique, and a relatively balanced body fat distribution, presenting an ostensibly healthy appearance. Nevertheless, they may manifest similar metabolic dysfunctions, potentially attributable to factors such as visceral adipose tissue accumulation and genetic predispositions, albeit distinct from the pathological profile observed in obese MAFLD patients\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Consequently, nonobese patients are frequently overlooked, resulting in underdiagnosis. This oversight leads to disease progression and the forfeiture of optimal therapeutic intervention. Therefore, identifying non-invasive, accurate, and straightforward serological diagnostic approaches assumes particular importance. The objective of this study is to identify predictive indicators for nonobese MAFLD and construct corresponding diagnostic models, thereby providing a scientific foundation for the early screening of MAFLD in individuals possessing a normal body mass index.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003ePatient Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in the Physical examination Center of the Second Affiliated Hospital of Soochow University from January 2024 to April 2025. A total of 1720 eligible participants with BMI\u0026le;24kg/m\u003csup\u003e2\u003c/sup\u003e were investigated, among which 219 cases of MAFLD patients and 1501 cases of non-MAFLD patients were found. Of these participants,538 underwent body composition analysis, including 216 with MAFLD disease and 322 without.The diagnosis of MAFLD was made by two senior attending physicians based on the results of ultrasound and imaging examinations.The subjects included: (1) patients diagnosed with MAFLD,(2) patients with complete clinical data, (3) patients who had signed an informed consent form and agreed to collect their medical data.The following patients were excluded from the study: (1) those with other liver diseases, such as viral hepatitis, drug-induced liver disease, autoimmune liver disease, cirrhosis, or liver cancer,(2) patients with other major organic diseases or those requiring medication that could affect the study results,(3)patients with incomplete general information and medical history.This study has been approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (Ethics number: LK2024111) and informed consent has been obtained from patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection covered the following items: age, height, weight, blood pressure, and past history, including metabolic diseases such as hypertension and diabetes, and other history. For statistical analysis, the history of metabolic diseases such as hypertension and diabetes was classified as metabolic disease history, and the rest were classified as other disease history. Laboratory tests were carried out by the staff from 7 a.m. to 10 a.m. and fasting blood samples of participants were collected by vein and sent to the Laboratory Testing Center of the Second Affiliated Hospital of Soochow University for testing. The test items include blood routine, fasting blood glucose (FBG),total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST),\u0026nbsp;\u0026gamma;-glutamyl transferase (GGT) and other indicators.The human body composition analysis was analyzed by DBA-610 to measure various indicators, including intracellular fluid (inw), extracellular fluid (exw), total body water (tbw), protein (pro), muscle mass (mus), fat free mass (ffm), fat mass (fat), percent body fat (pbf), visceral fat area (vfa), waist-to-hip ratio (whr), basal metabolic rate (bmr), phase angle (PhA), and limb skeletal muscle index (dASMI) and so on.The operation is carried out by two professional technicians according to the manual, and the temperature is maintained at 20 to 25 degrees celsius. The examinee should fast, empty his or her bladder and bowels, take off shoes, hats and heavy clothes, stand on the scale, keep even breathing, and the height and weight are accurately recorded by professionals.The abdominal ultrasound examination was performed using GE-VIVid T9 color ultrasonic diagnostic instrument (probe frequency 5.0MHz) to observe the liver echo intensity, intrahepatic duct structure and deep attenuation.The examination was performed by a professional physician. The examinee should be fasting and lie in a supine position. The liver area was repeatedly explored in the frontal and lateral positions, and whether fatty liver was diagnosed according to relevant guidelines.The CAP(controlled attenuation parameter) value was measured by FibroTouch-pro5000 to complete the quantitative detection of liver steatosis, which was expressed as CAP value with units of dB/m.The operation is performed by two professional doctors according to the user manual,The detection point is located at the right axillary line to the 7th and 8th intercostal or 8th and 9th intercostal lines of the axillary line. The effective detection should be recorded continuously for 10 times, and the median is taken as the final measurement result. The success rate of the final detection should be greater than 60%, and the interquartile distance should be less than 1/3 of the median of the measured value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population was stratified into two cohorts based on the presence or absence of MAFLD. A descriptive analysis of baseline characteristics for both cohorts was conducted. Continuous variables underwent assessment for normality utilizing the Kolmogorov-Smirnov test. Variables adhering to a normal distribution were expressed as mean \u0026plusmn;standard deviation, with group differences evaluated using Student\u0026apos;s t-tests. Variables deviating from normality were described using the median and interquartile range, and group differences were assessed employing the Mann-Whitney U test. Categorical variables were summarized by frequency and percentage，comparisons between the two groups\u0026apos; baseline data utilized either the chi-square test or Fisher\u0026apos;s exact test, as appropriate. Screening of predictive variables was performed utilizing R software (version 4.2.0) and associated packages, including glmnet and Matrix. The collected dataset was randomly partitioned by statistical software into training and validation cohorts at a 7:3 ratio, followed by variable comparison. Variables demonstrating statistically significant differences in univariate analysis were incorporated into Lasso regression analysis. The optimal lambda parameter was determined via 10-fold cross-validation to identify independent risk factors, enabling the construction of a predictive online dynamic nomogram for MAFLD diagnosis.The nomogram\u0026apos;s performance was evaluated through receiver operating characteristic (ROC) curve analysis and calibration curve assessment. The area under the ROC curve (AUC), ranging from 0.5 (indicating no discriminatory capacity) to 1.0 (denoting perfect discrimination), quantified predictive accuracy. All statistical analyses within this study were executed using SPSS version 25.0 and R version 4.2.0. Two-tailed tests were applied, and a p-value less than 0.05 was deemed statistically significant.\u003c/p\u003e"},{"header":"RESULT","content":"\u003cp\u003e\u003cstrong\u003ePatient Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed flow diagram is presented in \u003cstrong\u003eFigure 1\u003c/strong\u003e. A total of 216 patients with MAFLD and 322 patients without MAFLD were included in this study from January 2024 to April 2025, all of whom were diagnosed in the Physical examination Center of the Second Affiliated Hospital of Soochow University. All the patients included in the study met the established inclusion and exclusion criteria.Of these patients, 70% were selected for the training cohort and the remaining 30% for the validation cohort.The clinicopathological characteristics of the patients are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e.The Wilcoxon test and chi-square test were used to compare the indices between the nonobese MAFLD and non-MAFLD groups. In the training cohort,some significant indicators (P\u0026lt;0.05, \u003cstrong\u003eTable 2\u003c/strong\u003e) were selected, including gender(P\u0026lt;0.001),history(P\u0026lt;0.001),age (P\u0026lt;0.001),waist(P\u0026lt;0.001),ALT(P\u0026lt;0.001), AST(P\u0026lt;0.001),GGT (P\u0026lt;0.001),\u0026nbsp;FBG(P\u0026lt;0.001),TC(P\u0026lt;0.001),TG(P\u0026lt;0.001),HDL-C(P\u0026lt;0.001),LDL-C(P\u0026lt;0.001),inw(P\u0026lt;0.001),exw(P\u0026lt;0.001),tbw(P\u0026lt;0.001),pro(P\u0026lt;0.001),mus(P\u0026lt;0.001),ffm(P\u0026lt;0.001),fat(P\u0026lt;0.001),vfa(P\u0026lt;0.001),whr(P\u0026lt;0.001),bmr(P\u0026lt;0.001),dPhaseAngle(P\u0026lt;0.001) and\u0026nbsp;dASMI\u0026nbsp;(P\u0026lt;0.001) were statistically significan.The percentage of pbf (P=0.601) was not statistically significant.\u003c/p\u003e\n\u003cp\u003eTABLE 1 |Patient demographics and clinical characteristics\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 225px;\"\u003e\n \u003cp\u003eTraining Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003eValidation Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003enonobese MAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNon MAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003enonobese MAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNon MAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eGender(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e109\u0026nbsp;(74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100\u0026nbsp;(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e51(73.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e39(41.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e38\u0026nbsp;(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e129\u0026nbsp;(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18(26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e54(58.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eHistory\u003c/p\u003e\n \u003cp\u003e(Metabolic disease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e35(23.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e19(8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16(23.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7(7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e112(76.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e210(91.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e53(76.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e86(92.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eAge /year(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e51(42,\u0026nbsp;58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e42\u0026nbsp;(36,54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e50.54\u0026plusmn;\u0026nbsp;9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e45.24\u0026nbsp;\u0026plusmn;\u0026nbsp;12.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eWaist/cm(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e85\u0026nbsp;(79,\u0026nbsp;88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e76\u0026nbsp;(70,\u0026nbsp;83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e84.32\u0026plusmn;\u0026nbsp;6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e75.69\u0026nbsp;\u0026plusmn;\u0026nbsp;7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eALT U/L(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e23\u0026nbsp;(17,\u0026nbsp;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14\u0026nbsp;(11,\u0026nbsp;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e21(18,31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14(12,20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eAST U/L(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e19(16,\u0026nbsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17(15,\u0026nbsp;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20(16,230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e18(15,21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eGGT U/L(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e27\u0026nbsp;(19.5,\u0026nbsp;41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17\u0026nbsp;(12,\u0026nbsp;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e30(21,49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e16(12,24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eFBG mmol/l(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.13\u0026nbsp;(4.81,\u0026nbsp;5.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e4.87\u0026nbsp;(4.56,\u0026nbsp;5.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.07(4.64,5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e4.9(4.61,5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eTC mmol/L(Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.244\u0026nbsp;\u0026plusmn;\u0026nbsp;1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e4.861\u0026plusmn;\u0026nbsp;0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.32(4.52,5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e4.72(4.12,5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eTG mmol/L(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.69\u0026nbsp;(1.26,\u0026nbsp;2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.04\u0026nbsp;(0.77,\u0026nbsp;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.89(1.54,;2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.92(0.72,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eHDL-C mmol/l(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.18\u0026nbsp;(1.06,\u0026nbsp;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.47\u0026nbsp;(1.23,\u0026nbsp;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.22(1.06,1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.44(1.27,1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eLDL-C mmol/L(Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3.345\u0026nbsp;\u0026plusmn;\u0026nbsp;0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.965\u0026nbsp;\u0026plusmn;\u0026nbsp;0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3.31\u0026plusmn;\u0026nbsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.91\u0026plusmn;\u0026nbsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eInw kg(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e23.3\u0026nbsp;(19.6,\u0026nbsp;25.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19\u0026nbsp;(17.2,\u0026nbsp;23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23.4(20.3,25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19.6(17.2,23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eExw kg(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e14(12.1,\u0026nbsp;15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11.7\u0026nbsp;(10.6,\u0026nbsp;14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14(12.3,15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e12.2(10.6,14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eTbw %(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e37.2\u0026nbsp;(31.75,\u0026nbsp;40.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e30.9\u0026nbsp;(27.8,\u0026nbsp;37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e37.5(32.2,40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e31.7(27.9,38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003ePro kg(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e10.1\u0026nbsp;(8.45,\u0026nbsp;10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8.2\u0026nbsp;(7.4,\u0026nbsp;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e10.1(8.8,11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8.5(7.4,10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eMus kg(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e47.6(40.45,\u0026nbsp;51.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e39.3(35.3,\u0026nbsp;47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e47.9(41.3,51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e40.4(35.5,48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eFfm kg(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e50.8\u0026nbsp;(43.2,\u0026nbsp;54.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e42.1\u0026nbsp;(38,\u0026nbsp;51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e51(44.2,55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e43.2(38.0,52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eFat %(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e17(15.2,\u0026nbsp;18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e15.3\u0026nbsp;(13.1,\u0026nbsp;17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.48\u0026plusmn;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e15.09\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003ePbf %(Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e26.14\u0026plusmn;\u0026nbsp;5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25.81\u0026plusmn;\u0026nbsp;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e26.16\u0026plusmn;\u0026nbsp;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25.46\u0026plusmn;6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eVfa cm\u003csup\u003e2\u003c/sup\u003e(Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e76.7\u0026nbsp;\u0026plusmn;\u0026nbsp;18.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e67.69\u0026nbsp;\u0026plusmn;\u0026nbsp;19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e78.26\u0026plusmn;\u0026nbsp;17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e65.84\u0026plusmn;19.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003ewhr(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.9\u0026nbsp;(0.88,\u0026nbsp;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.87\u0026nbsp;(0.84,\u0026nbsp;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.91\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eBmr kcal/day(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1467.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1304, 1554.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1278.6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1191.9, 1473.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1471.3\u003c/p\u003e\n \u003cp\u003e(1324.5,1567.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1303\u003c/p\u003e\n \u003cp\u003e(1190.6,1497.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003edPhaseAngle \u0026deg; (Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.91\u0026plusmn;\u0026nbsp;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;5.53\u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.97\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.54\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003edASMI kg/m\u003csup\u003e2\u003c/sup\u003e(Median\u0026nbsp;(Q1,Q3))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e7.37(6.64,\u0026nbsp;7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.46\u0026nbsp;(5.88,\u0026nbsp;7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7.325(6.7,7.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.52(5.92,7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFactor Selection for the Predictive Model, Calibration, and Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aforementioned variables were integrated into the baseline model and subsequently narrowed to eleven potential predictors through LASSO regression analysis applied to the training cohort, with cross-validation of the LASSO model (\u003cstrong\u003eFigure 2A\u003c/strong\u003e).A cross-validated error plot of the LASSO regression model is shown in \u003cstrong\u003eFigure 2B\u003c/strong\u003e.This figure demonstrates that the most regularized and parsimonious model, achieving a cross-validated error within one standard error of the minimum, incorporated eleven variables. The standard error of the minimum distance corresponded to \u0026lambda;=0.031. The variables selected for this model comprised: history, age, waist, ALT, GGT, FBG, TG, HDL-C, LDL-C, fat, and dPhaseAngle.\u003c/p\u003e\n\u003cp\u003eAs demonstrated in \u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e, the receiver operating characteristic (ROC) analysis of the specified diagnostic indicators reveals that the area under the curve (AUC) values exceed 0.5 for the following parameters, history (AUC=0.578), age (AUC=0.632), waist circumference(AUC=0.764), alanine aminotransferase (ALT) (AUC=0.738), gamma-glutamyl transferase (GGT) (AUC=0.746), fasting blood glucose (FBG) (AUC=0.64), triglycerides (TG) (AUC=0.793), high-density lipoprotein cholesterol (HDL-C) (AUC=0.738), low-density lipoprotein cholesterol (LDL-C) (AUC=0.63), fat content (AUC=0.659), and dPhaseAngle (AUC=0.672). The corresponding critical values, sensitivity, and specificity for these parameters are detailed in \u003cstrong\u003eTable 2,\u003c/strong\u003e with the ROC curve analysis of candidate diagnostic indicators presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTABLE 2 | The Results of receiver operating characteristic (ROC) curve\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"124%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eYouden index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ehistory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.555-0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.589-0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ewaist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.722-0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.696-0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.713-0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.597-0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.760-0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.704-0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.575-0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003efat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.594-0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003edPhaseAngle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.623-0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;In order to determine whether the above eleven variables were independent risk factors for MAFLD, a binary logistic regression analysis was used to evaluate the effects of history, age, waist, ALT, GGT, FBG, TG, HDL-C, LDL-C, fat and dPhaseAngle. The AUC of the training Cohort data was 0.85, and the prediction effect of the model was good. The analysis results showed in \u003cstrong\u003eTable3\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003ehistory, TG, HDL-C, LDL-C, fat and dPhaseAngle\u003c/em\u003e\u0026nbsp;\u003c/strong\u003ewere statistically significant and were included in the model\u003cstrong\u003e(Figures 4A)\u003c/strong\u003e. Similar results were obtained in validation cohorts\u003cstrong\u003e\u0026nbsp;(Figures 4B)\u003c/strong\u003e.Results of the correlation analysis showed that the six factors(\u003cstrong\u003e\u003cem\u003ehistory, TG, HDL-C, LDL-C, fat and dPhaseAngle\u003c/em\u003e)\u003c/strong\u003e were all linearly correlated with each other, while HDL-C was negatively correlated with other indices\u003cstrong\u003e\u0026nbsp;(Figure 5)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTABLE 3 | The Results of Logistic regression analysis of prediction indicators\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ehistory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e2.399(1.247 - 4.716)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.023(1.001 - 1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.024(1.006 - 1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ewaist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.034(0.992 - 1.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.004(0.998 - 1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.216(1.012 - 1.470)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.176(0.951 - 1.526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e0.173(0.069 - 0.414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.737(1.323 - 2.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003efat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e1.191(1.102 - 1.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003edPhaseAngle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e2.073(1.329 - 3.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The final logistic model included six independent predictors(\u003cstrong\u003e\u003cem\u003ehistory, TG, HDL-C, LDL-C, fat and dPhaseAngle\u003c/em\u003e\u003c/strong\u003e)and was developed as a simple-to-use nomogram, which is illustrated in\u003cstrong\u003e\u0026nbsp;Figure 6A\u003c/strong\u003e and available online (\u003cstrong\u003e\u003cem\u003ehttps://stop15.shinyapps.io/DynNomapp/\u003c/em\u003e)\u003c/strong\u003e and presented in \u003cstrong\u003eFigure 6B\u003c/strong\u003e. For example,one person, whose history is no, TG is 0.79mmol/L, HDL-C is 1.47mmol/L, LDL-C is 2.7mmol/L, fat is 16.3 %and dPhaseAngle is 5.153\u0026deg;. The total score of the predicted score was 112, indicating a probability of 9.52% for MAFLD(\u003cstrong\u003eFigure 6A\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAs demonstrated in \u003cstrong\u003eFigures 7A-C\u003c/strong\u003e, the area under the curve values for the model were 0.863 in the training cohort and 0.83 in the validation cohort, indicating robust predictive capability. These findings suggest that the Nomogram could serve as a valuable tool for the early screening of nonobese metabolic dysfunction-associated fatty liver disease.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003enonobese Metabolic dysfunction-associated fatty liver disease(MAFLD) has become a main cause of chronic liver disease globally and is one of the most common type in China\u003csup\u003e[7]\u003c/sup\u003e.This disease is \u0026nbsp;not only the main cause of cryptogenic cirrhosis,but is also closely related to insulin resistance,metabolic disorders,and cardiovascular diseases. As the disease progresses,The disease burden of patients will be significantly increased and the quality of life will be significantly decreased\u003csup\u003e[8]\u003c/sup\u003e.At present, MAFLD has become an important public health problem. nonobese MAFLD is asymptomatic in the early stage, with low clinical laboratory indicators and normal BMI value, which makes it difficult to attract people's attention and easy to be missed\u003csup\u003e[9]\u003c/sup\u003e.However, liver biopsy, as the gold standard for diagnosing fatty liver\u003csup\u003e[5]\u003c/sup\u003e, is an invasive and invasive procedure that is not readily accepted by the population.Ultrasonic and fibroscan are not sensitive to mild fatty liver, and detect more than 20%~30% of fatty degeneration\u003csup\u003e[10]\u003c/sup\u003e. In addition, these examinations all depend on the level of operators, which is highly subjective, and it is difficult to popularize in economically backward areas.The new expert consensus affirms the value of blood biomarkers in diagnosing MAFLD, but there is still a lack of effective predictive models\u003csup\u003e[11]\u003c/sup\u003e.The incidence of nonobese MAFLD in the physical examination population was 40.15% in our study, which was higher than that in existing studies (4.2%~19.33%)\u003csup\u003e[8]\u003c/sup\u003e.Therefore, the development of prediction tools for nonobese population is very important for disease prevention and management, and it is also one of the hot research topics in recent years.\u003c/p\u003e\n\u003cp\u003eThe body composition mainly discusses the composition data of human body, the detection methods and the influence of internal and external factors on the quantitative relationship of each component, which reflects the response process of the body to different changes in the inside and outside of the body at the molecular, cellular and tissue levels.The measurement of human body can not only accurately reflect the content of body fat, muscle and water \u003csup\u003e[12]\u003c/sup\u003e,It can also reflect the visceral fat area (VFA) ,waist-to-hip ratio (WHR) and so on, so as to judge whether the body composition is normal,which has certain screening value for metabolic diseases\u003csup\u003e[13]\u003c/sup\u003e.Changes in body composition are associated with the occurrence and development of many diseases\u003csup\u003e[14]\u003c/sup\u003e.With the progress of society and the improvement of people's living standards, obesity and the diseases caused by it are attracting more and more attention, becoming an important factor that has a serious impact on health.\u003c/p\u003e\n\u003cp\u003eThe results of Logistic regression in our study showed that the history of metabolic disease, TG, HDL-C, LDL-C, fat and phase Angle were the predictive indicators for the diagnosis of nonobese MAFLD, which were consistent with most of the previous studies\u003csup\u003e[9]\u003c/sup\u003e.At present, it has been shown that the incidence of MAFLD in nonobese people is often associated with nutritional status, metabolic factors and genetic inheritance\u003csup\u003e[9]\u003c/sup\u003e.MAFLD is a hereditary disease, and a large number of studies have shown that the presence of patatin-like phospholipase domain protein 3 rs738409 gene is closely associated with it\u003csup\u003e[15]\u003c/sup\u003e.In addition,abnormal blood lipid is an important factor in the occurrence and development of MAFLD. Dyslipidemia is usually caused by increased lipolysis in liver tissue, activation of new fat production, and excessive accumulation of fat due to high-fat diet mechanisms\u003csup\u003e[16]\u003c/sup\u003e.nonobese MAFLD patients have more visceral fat accumulation, and visceral adipose tissue has stronger adipolysis activity than subcutaneous adipose tissue, leading to the increase of high levels of free fatty acids (FFA) in the liver, which aggravates the TG burden of the liver\u003csup\u003e[17]\u003c/sup\u003e.High TG, especially the increase of ceramide and diacylglycerol, can aggravate insulin resistance and induce inflammatory response of liver fibrosis, which further aggravates the disease\u003csup\u003e[16]\u003c/sup\u003e.HDL-C is a protective factor for nonobese MAFLD, cardiovascular and cerebrovascular diseases, and type 2 diabetes (T2DM).It delays the progression of nonobese MAFLD by activating the adenylate activated protein kinase signaling pathway and regulating lipid metabolism\u003csup\u003e[18]\u003c/sup\u003e. Fat in body composition is also an independent risk factor for nonobese MAFLD. People with high visceral fat were more likely to develop MAFLD, which was consistent with previous studies\u003csup\u003e[13]\u003c/sup\u003e.dPhaseAngle is a common indicator to evaluate the nutritional health level of patients in clinical practice\u003csup\u003e[19]\u003c/sup\u003e,A high value reflects good cell health, while low of it may indicate damage to the cell membrane or reduced cell integrity, often associated with disease or malnutrition\u003csup\u003e[20]\u003c/sup\u003e.In this study, an online dynamic nomogram for Predicting MAFLD in nonobese patients was constructed for the first time by computer software. The AUC values in training and verification queues were 0.863 and 0.83 respectively, showing good predictive ability and helping to screen MAFLD in early stage.\u003c/p\u003e\n\u003cp\u003eHowever, our study also has some limitations: (1) The diagnosis of MAFLD is based on ultrasound rather than biopsy, which may lead to misclassification. Future studies should consider liver biopsy as the diagnostic standard whenever possible to ensure accuracy; (2) Due to the cross-sectional design, the study may be biased due to participants' high concern for their health. Future studies could expand to include multi-center designs and long-term follow-up data to further refine the research; (3)Although the line graph performed well in the training set and internal validation set, its generalization ability needs to be evaluated in larger external validation sets and prospective studies.\u003c/p\u003e\n\u003cp\u003eIn summary, this research established an online diagnostic model, predicated on body composition and biochemical blood indicators, for the detection of nonobese metabolic dysfunction-associated fatty liver disease (MAFLD). This model represents the first instance utilizing metabolic indicators as its core foundation, providing an intuitive visual interface amenable to widespread implementation. It further demonstrates robust discrimination and high calibration accuracy. Given the current absence of specific pharmacological interventions for nonobese MAFLD, this model effectively identifies potential patients within the nonobese population, thereby assisting clinicians in the early identification of high-risk individuals. Furthermore, the model facilitates the adoption of preventative measures—such as dietary and lifestyle modifications—among physical examination participants, thereby mitigating disease progression and alleviating the associated socioeconomic and healthcare burdens.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study was the first to develop and validate online nomograms based on the independent risk factors to dynamically predict diagnosis in individuals with nonobese MAFLD. This model demonstrated superior performance and discriminative capability, which can used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMAFLDmetabolic dysfunction-associated fatty liver\u003c/p\u003e\n\u003cp\u003eNAFLD\u0026nbsp; \u0026nbsp;nonalcoholic fatty liver disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; receiver operating characteristic curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp;\u0026nbsp; \u0026nbsp;area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp; \u0026nbsp; \u0026nbsp;odds ratio\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; body mass index\u003c/p\u003e\n\u003cp\u003eFBG\u0026nbsp; \u0026nbsp; \u0026nbsp;fasting blood glucose\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;triglyceride\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL-C\u0026nbsp; \u0026nbsp;low density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; high density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; alanine aminotransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp;aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eGGT \u0026nbsp; \u0026nbsp;γ-glutamyl transferase\u003c/p\u003e\n\u003cp\u003eInw \u0026nbsp; \u0026nbsp; \u0026nbsp; intracellular fluid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExw \u0026nbsp; \u0026nbsp; \u0026nbsp;extracellular fluid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTbw \u0026nbsp; \u0026nbsp; total body water\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePro \u0026nbsp; \u0026nbsp; \u0026nbsp;protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMus \u0026nbsp; \u0026nbsp; muscle mass\u003c/p\u003e\n\u003cp\u003eFfm \u0026nbsp; \u0026nbsp; fat free mass\u003c/p\u003e\n\u003cp\u003eFat \u0026nbsp; \u0026nbsp; \u0026nbsp;fat mass\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePbf \u0026nbsp; \u0026nbsp; \u0026nbsp;percent body fat\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVfa \u0026nbsp; \u0026nbsp; \u0026nbsp; visceral fat area\u003c/p\u003e\n\u003cp\u003eWhr \u0026nbsp; \u0026nbsp; \u0026nbsp;waist-to-hip ratio\u003c/p\u003e\n\u003cp\u003eBmr \u0026nbsp; \u0026nbsp; basal metabolic rate\u003c/p\u003e\n\u003cp\u003ePhA \u0026nbsp; \u0026nbsp; phase angle\u003c/p\u003e\n\u003cp\u003edASMI \u0026nbsp; limb skeletal muscle index\u003c/p\u003e\n\u003cp\u003eCAP \u0026nbsp; \u0026nbsp; \u0026nbsp;controlled attenuation parameter\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (Ethics number: LK2024111) and informed consent has been obtained from patients. Research on human data must comply with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are willing to share the raw data. Data has been provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by National Natural Science Fund (82371218) and by pre-research fund project of Second Affiliated Hospital of Soochow University(SDFEYJC2330).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors worked together to complete the paper. ZW, as the corresponding authors guided the design of the entire. XS and QZ contributed to collecting ultrasound data and body composition data.TZ and JW performed the experiments and contributed to the acquisition and analysis of data.The manuscript was drafted by TZ and HZ. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you to the Zhongmin Wen professor for meticulous guidance and valuable suggestions during the topic selection, research approach, methodology, and article writing. I also thank the colleagues at Health examination center for their selfless assistance and valuable advice in data collection and experimental operations. Additionally, I am grateful to all the authors of the cited literature for laying a crucial foundation for this study through their prior work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003ePhysical Examination Center Research Center,The Second Affiliated Hospital of Soochow University, Suzhou 215004, P.R. China.\u003csup\u003e2\u003c/sup\u003eDepartment of Public Health, Second Affiliated Hospital of Soochow University, Suzhou 215004, Jiangsu Province, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZOBAIR M. YOUNOSSI, PEGAH GOLABI, JAMES M. PAIK, et al. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review[J]. Hepatology: Official Journal of the American Association for the Study of Liver Diseases,2023,77(4):1335-1347. DOI:10.1097/HEP.0000000000000004.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRINELLA ME, LAZARUS JV,RATZIU V. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. Ann Hepatol,2024,29(1):101133. DOI:10.1016/j.aohep.2023.101133.\u003c/li\u003e\n \u003cli\u003eCHEN YL, LI H, LI S, XU Z, TIAN S, Wu J, et al. Prevalence of and risk factors for metabolic associated fatty liver disease in an urban population in China: a cross-sectional comparative study. BMC Gastroenterol,2021;21(1):212.DOI: 10.1186/s12876-021-01782-w.\u003c/li\u003e\n \u003cli\u003eYE Q, ZOU B, YEO YH, LI J, HUANG DQ, WU Y, et\u0026nbsp;al. Global prevalence, incidence, and outcomes of nonobese or lean nonalcoholic fatty liver disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol,2020;5(8):739-752.doi: 10.1016/S2468-1253(20)30077-7. Epub 2020 May 12.\u003c/li\u003e\n \u003cli\u003eYOUNOSSI, ZOBAIR, ANSTEE, QUENTIN M., MARIETTI, MILENA, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention[J].Nature reviews. Gastroenterology \u0026amp; hepatology, 2018,15(1):11-20. DOI:10.1038/nrgastro.2017.109.\u003c/li\u003e\n \u003cli\u003eKAYA E，YILMAZ Y. Metabolic-associated fatty liver disease(MAFLD):a multi-systemic disease beyond the liver[J].Clin Transl Hepatol, 2022,10(2)329-338. DOI:10.14218/jcth.2021.00178.\u003c/li\u003e\n \u003cli\u003eTRINELLA M E, LAZARUS J V , RATZIU V, et al.A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. Ann Hepatol,2024,29(1):101133. DOI:10.1016/j.aohep.2023.101133.\u003c/li\u003e\n \u003cli\u003eChinese Medical Association Hepatology Branch.Guidelines for the prevention and treatment of metabolic dysfunction associated(non-alcoholic)fatty liver disease(Version 2024)[J]1.Chinese Journal of Hepatology,2024, 32(5):418-434. DOI:10.3760／cma.j.cn501 1 13-20240327-00163．\u003c/li\u003e\n \u003cli\u003eCHEN HT，ZHOU YJ．Diagnosis and therapeutic strategies for nonobese type of non-alcoholic fatty liver diseases[J].Chinese Journal of Hepatology,2020,28(3):203-207. DOI：10.3760／cma.j.cn501113-20191226-00480．\u003c/li\u003e\n \u003cli\u003eFEDCHUK,L., NASCIMBENI,F., PAIS,R., et al. Performance and limitations of steatosis biomarkers in patients with nonalcoholic fatty liver disease[J]. Alimentary Pharmacology and Therapeutics,2014,40(10):1209-1222. DOI:10.1111/apt.12963.\u003c/li\u003e\n \u003cli\u003eZHAO JH，ZHANG J. Advances in serological non-invasive diagnosis for metabolic dysfunction--associated fatty liver disease[J]．Chinese Journal of Hepatology,2023,31(12):1235-1239. DOE 10.3760/cnla.J.cn501 1 13-20230831-00085．\u003c/li\u003e\n \u003cli\u003eMSSLENNIKOV R, IVASHKIN V, ALIEVA A, et al. Gut dysbiosis and body composition in cirrhosis[J]. World Hepatol,2022,14(6):1210-1225. DOI :10.4254/wjh.v14.i6.1210.\u003c/li\u003e\n \u003cli\u003eLIU JING, LI YAN JU , RAO ZHI YONG. Correlation analysis of FibroScan measurements and body composition measurements in patients with nonalcoholic fatty liver disease[J]. Chinese Journal of Integrated Traditional and Western Medicine on Liver Diseases, 2021,31(12):1108-1111. DOI:10.3969/j. issn. 1005-0264.2021.12. 013.\u003c/li\u003e\n \u003cli\u003eALEJANDRA CARRETERO-KRUG, NATALIA \u0026Uacute;BEDA, CARLOS VELASCO, et al. Hydration status, body composition, and anxiety status in aeronautical military personnel from Spain: a cross-sectional study[J].Journal of shandong university(health sciences),2022,9(2):184-192. DOI:10.6040/j.issn.1671-7554.0.2024.0895.\u003c/li\u003e\n \u003cli\u003eLIN H P, -H WONG G L, WHATLING C, et al.Association of genetic variations with NAFLD in lean individuals[J].Liver Int,2022,42(1):149-160. DOI:10.1111/liv.15078\u003c/li\u003e\n \u003cli\u003eCARLI F,DELLA PEPA G,SABATINI S,et al. Lipid metabolism in MASLD and MASH：from mechanism to the clinic[J]. JHEP Rep,2024,6(12):101185. DOI:10.1016/j.jhepr.2024.101185.\u003c/li\u003e\n \u003cli\u003eHSU C L, WU F Z, LIN K H, et al. Role of fatty liver index and metabolic factors in the prediction of nonalcoholic fatty liver disease in a lean population receiving health checkup[J]. Clin Transl Gastroenterol, 2019,10(5):1-8. DOI:10.14309/ctg.0000000000000042.\u003c/li\u003e\n \u003cli\u003eCAI XT, CHEN M，WANG MR，et a1. Relationship between HDL-C level and nonalcoholic fatty liver disease in non obese population:a prospective cohort study[J]. Journal of Medical Research, 2021,50(10):64-68,74.DOI:10.1 1969/j.issn.1673-548X.2021.10,014.\u003c/li\u003e\n \u003cli\u003ePRAGET-BRACAMONTES S, GONZALEZ-ARELLANES R, AGUILAR-SALINAS CA, et al. Phase Angle as a Potential Screening Tool in Adults with Melabo1ic Diseases in Clinical Practice：A Systematic Review[J]. Int J Environ Res Public Health, 2023,20(2):1608.DOI: 10.3390/ijerph20021608.\u003c/li\u003e\n \u003cli\u003eHENRY C.,LUKASKI, ANTONIO,TALLURI. Phase angle as an index of physiological status: validating bioelectrical assessments of hydration and cell mass in health and disease[J]. Reviews in endocrine \u0026amp; metabolic disorders,2023,24(3):371-379. DOI:10.1007/s11154-022-09764-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"nonobese MAFLD, body composition analysis, dynamic nomogram, risk factors, LASSO regression","lastPublishedDoi":"10.21203/rs.3.rs-7174767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7174767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe factors influencing nonobese Metabolic dysfunction-associated fatty liver disease are discussed, and an online dynamic nomogram model is constructed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e A retrospective review was conducted on medical history data from 216 patients diagnosed with nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) and 322 nonobese normal individuals at the Second Affiliated Hospital of Soochow University. An automated random grouping procedure employing statistical software allocated the 538 subjects into training and verification cohorts at a 7:3 ratio. Initial screening of relevant indicators employed univariate and correlation analyses. Subsequently, significant potential independent risk factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified through the Lasso regression method, followed by cross-validation. These statistically significant indicators were further analyzed using binary logistic regression. Their discriminative capacity was evaluated using receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC). Finally, a dynamic online diagnostic nomogram was constructed. The nomogram's predictive performance was assessed via AUC, while its calibration was evaluated using a calibration plot.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSix independent risk factors, namely, past history (odds ratio [OR]: 2.399, P\u0026thinsp;=\u0026thinsp;0.0008), TG (OR: 1.176, P\u0026thinsp;=\u0026thinsp;0.008), HDL-C (OR: 0.173, P\u0026thinsp;=\u0026thinsp;0.014), LDL-C (OR: 3.916, P\u0026thinsp;=\u0026thinsp;0.001), fat (OR: 4.299, P\u0026thinsp;=\u0026thinsp;0.0009) and dPhaseAngle(OR: 3.174, P\u0026thinsp;=\u0026thinsp;0.022), were screened from the results of Lasso regression method and a binary logistic regression analysis of the training cohort and included in the nonobese MAFLD online diagnostic nomogram. The nomogram predicted nonobese MAFLD with AUC values of 0.863 in the training cohort, and 0.83 in the validation cohort. The calibration curve also revealed that the nomogram predicted outcomes were close to the ideal curve, and that the predicted outcomes were consistent with the real outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn online dynamic nomogram for nonobese MAFLD patients with good predictive performance was constructed, which can be used as a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors and can assist physicians in making personalized diagnoses and treatments for patients.\u003c/p\u003e","manuscriptTitle":"Development and Validation of an Online Dynamic Nomogram for Predicting Nonobese Metabolic Dysfunction-associated fatty liver disease Based on Body Composition Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:58:05","doi":"10.21203/rs.3.rs-7174767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-24T05:26:35+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T02:50:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T10:03:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T14:09:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T08:35:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-08-08T08:32:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7309858-e43f-4542-b7ed-b610aa362453","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T05:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 10:58:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7174767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7174767","identity":"rs-7174767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}