Exploring Serum Bile Acids as Potential Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease

Exploring Serum Bile Acids as Potential Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Serum Bile Acids as Potential Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease Ashraf Abbass basuni, Ashraf Khalil, Dina Sweed, Mohamed Fathey Elgazzar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4896620/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Oct, 2024 Read the published version in Egyptian Liver Journal → Version 1 posted You are reading this latest preprint version Abstract Background: Bile acids are vital regulators of liver metabolism, and their dysregulation is closely linked with the progression of nonalcoholic fatty liver disease (NAFLD). Profiling these bile acids may provide valuable diagnostic and prognostic markers for these conditions. This study aimed to evaluate bile acid profiles in NAFLD patients and assess their potential as biomarkers for diagnosing and predicting disease progression. Serum levels of 14 bile acids were measured in 25 normal healthy controls (NHC), 35patients with nonalcoholic steatosis (NAST), and 40 patients with NASH, categorized by the NAFLD Activity Score (NAS). Quantification was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Results: Primary unconjugated bile acids, CA and CDCA, along with conjugated acids GCA, GCDCA, TCA, and TCDCA, were significantly elevated in both NAST and NASH compared to NHC (all p < 0.05). While levels increased progressively from NHC to NAST to NASH, no significant differences were observed between NAST and NASH except for GCA and TCA (P< 0.05). Similarly, secondary bile acids LCA, TLCA, GUDCA, and TUDCA were higher in NAST and NASH compared to NHC (all p < 0.05). Logistic regression identified CA (odds ratio = 2.05, p = 0.02), CDCA (odds ratio = 1.58, p = 0.04), GCA (odds ratio = 1.92, p = 0.03) and DCA (odds ratio = 2.06, p = 0.04) as significant predictors of fibrosis. For active inflammation, GCA (odds ratio = 2.04, p = 0.04), and TCA (odds ratio = 1.94, p = 0.04) were significant predictors. In steatosis, CA, CDCA, GCA, DCA, TDCA, TLCA, and UDCA were notable predictors, with high odds ratios. Conclusion: The study highlights significant alterations in bile acid profiles associated with NAFLD progression. Specific bile acids, such as CA, GCA, TCA, and TCDCA are strong predictors of disease severity, indicating their potential as biomarkers for NAFLD treatment and prognosis. Bile acids NAFLD Nonalcoholic fatty liver disease NASH Nonalcoholic steatohepatitis Liquid chromatography-mass spectrometry Figures Figure 1 Figure 2 Figure 3 Introduction Nonalcoholic fatty liver disease (NAFLD) includes a spectrum of liver disorders, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH). The mildest form, nonalcoholic fatty liver (NAFL), is characterized by lipid accumulation in hepatocytes, also known as hepatic steatosis. However, 20%–30% of NAFL cases may progress to NASH, marked by significant lobular inflammation and hepatocyte ballooning, potentially leading to fibrosis and cirrhosis[1]. NAFLD has emerged as the leading cause of chronic liver disease worldwide, with a prevalence estimated at 25% [2-4]. NASH, a severe form of NAFLD, affects about 1.5% to 6.45% of the population and can lead to serious complications such as cirrhosis, hepatocellular carcinoma, and liver-related mortality. The prevalence of NASH is projected to increase by 63% between 2015 and 2030, posing significant challenges to global healthcare [4-6]. The pathogenesis of NASH is complex and involves multiple mechanisms, including abnormal lipid accumulation and inflammation within hepatocytes or extrahepatic tissues, leading to hepatotoxic injuries[7]. NAFLD is strongly associated with metabolic dysfunctions, such as insulin resistance, obesity, and type 2 diabetes, particularly in Western populations. To better describe the metabolic basis of NAFLD, the term metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed [8, 9]. Bile acids are increasingly recognized as critical signaling molecules in NASH pathogenesis, facilitating communication between the liver, intestine, and other organs. These molecules, synthesized from cholesterol in the liver, play a vital role in emulsifying fats and in the digestion and absorption of lipids and fat-soluble vitamins. Primary bile acids, such as cholic acid and chenodeoxycholic acid, are transformed by intestinal bacteria into secondary bile acids, including lithocholic acid and deoxycholic acid[10, 11]. Besides their traditional roles, bile acids act as signaling molecules that regulate metabolic homeostasis and immune responses, primarily through receptors such as FXR and G protein-coupled bile acid receptor 1 (TGR5)[12]. Dysregulation of bile acid homeostasis is implicated in several metabolic diseases, including NASH, making bile acids and their receptors potential therapeutic targets [12, 13]. Despite advances in understanding NAFLD, predicting which patients are at risk for disease progression and complications remains challenging [14]. Liver biopsy is the gold standard for assessing inflammation and fibrosis in NAFLD but is invasive and carries risks such as bleeding and infection. Efforts to identify noninvasive biomarkers, particularly for inflammation and fibrosis, have produced mixed results, limiting their clinical utility [15-17]. Bile acids have emerged as promising biomarkers due to their roles in lipid absorption and regulation of hepatic glucose and lipid metabolism. High serum insulin levels can inhibit bile acid synthesis by suppressing CYP7A1, a key enzyme in bile acid biosynthesis, while elevated bile acids can reduce insulin secretion via glucagon-like peptide 1. This complex interplay highlights the connections between NAFLD, metabolic syndrome, and gut health[18-21]. The aim of this study is to comprehensively evaluate bile acid profiles as noninvasive biomarkers in NAFLD, with the goal of establishing these profiles as reliable diagnostic tools for assessing liver disease severity and predicting progression to NASH. Patients The study was conducted at the Department of Clinical Biochemistry and Molecular Diagnostics and the Department of Hepatobiliary and Gastroenterology from October 2022 to August 2024. The cohort included 75 biopsy-proven NAFLD patients, and a corresponding control group of 25 individuals, free from any liver impairment. The study received approval from the Ethics Committee of the National Liver Institute (IRB 00570/2024), and written consent was obtained from all participants. NAFLD diagnosis was established through abdominal ultrasound and liver biopsy but healthy controls were not subjected to liver biopsies[5]. The liver biopsy specimens were histologically assessed for steatosis, inflammation, and fibrosis according to the criteria established by the NAFLD Clinical Research Network [22]. The findings are illustrated in Figure 1. Patients with NAFLD were divided into two subgroups based on specific histological criteria into nonalcoholic steatosis (NAST, n=35) and nonalcoholic steatohepatitis (NASH, n=40). NAST patients included those with minimal or no fibrosis (F0-F1), no evidence of inflammation (A0-A1), but with varying degrees of steatosis; G0 (66%) of hepatocytes. NASH patients, besides having evidence of steatosis and or fibrosis, also showed evidence of inflammation greater than grade ≥ A2 The NAS was used to evaluate the severity of liver disease, ranging from 0 to 8. This score is calculated by summing the scores of steatoses (0-3), lobular inflammation (0-3), and hepatocyte ballooning (0-2). A NAS score of ≥ 5 strongly indicated NASH, while a score of ≤ 3 was associated with NAST [22]. Exclusion criteria encompassed patients with viral or autoimmune liver diseases, hepatotoxic drug use, iron overload, Wilson's disease, chronic cholestasis, and extrahepatic obstructive gall bladder diseases. Additionally, individuals presenting with liver diseases associated with severe renal or systemic conditions were excluded from the study. Serum Sample Collection and Bile Acid Measurement: Blood samples were obtained from both patients and controls following an overnight fast (8-12h). Three milliliters of blood were collected using sterile venipuncture techniques, and the extracted serum was stored at -80°C until analysis. Laboratory measurements, encompassing fasting blood glucose, HbA1c, lipid profile including total cholesterol, high density lipoprotein (HDL)-cholesterol, low density lipoprotein (LDL)-cholesterol, triglycerides; liver function tests AST, ALT, gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), direct and total bilirubin, albumin, and total proteins, AFP; kidney function tests as BUN, creatinine were conducted through standardized laboratory methods (Cobas 8000, Roche Diagnostics GmbH, Mannheim, Germany). Serum bile acids concentrations were measured in serum using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS), employing a reversed-phase (C18) column (1.7 µm, 100 mm × 2.1 mm internal dimensions) (Waters ACQUITY, Milford, MA) and a methanol/water gradient. The assay included a total of 14 bile acids, categorized into six primary and eight secondary bile acids. The primary bile acids consisted of unconjugated forms, including CA (cholic acid) and CDCA (chenodeoxycholic acid), and conjugated forms, including GCA (glycocholic acid), GCDCA (glycochenodeoxycholic acid), TCA (taurocholic acid), and TCDCA (taurochenodeoxycholic acid). The secondary bile acids included unconjugated forms, including DCA (deoxycholic acid) and LCA (lithocholic acid), and conjugated forms, including GDCA (glycodeoxycholic acid), TDCA (taurodeoxycholic acid), TLCA (taurolithocholic acid), UDCA (ursodeoxycholic acid), GUDCA (glycoursodeoxycholic acid), and TUDCA (tauroursodeoxycholic acid). Serum Sample Preparation and Bile Acid Detection: Serum bile acids underwent preparation for UPLC/MS/MS as previously described [13, 14]. Briefly, 100 µl of serum samples were treated with 400 µl of ice-cold 100% methanol, followed by centrifugation at 12,000 rpm for 20 min. Fifty microliters of the supernatant were mixed with 100 µl formic acid (1:1000), and 5 µl were injected into a C18 column at 50°C. Bile acids were eluted by gradient at a flow rate of 0.5 ml/min, with the mass spectrometer operating in the negative ion mode via Multiple Reactions Monitoring (MRM). UPLC-MS data were analyzed using MassLynx software version 4.1 (Waters Corp., Milford, MA, USA) to generate calibration equations and calculate the quantitative concentration of each bile acid in the sample. The UPLC-MS/MS analysis demonstrated robust performance including precision and accuracy at concentrations of 0.02 µMol/L, 0.2 µMol/L, and 2 µMol/L, exhibiting low relative standard deviation (RSD%) values for excellent precision. Relative error (RE%) values approximating 100% underscored the accuracy of quantitation. All the individual 14 bile acids were effectively resolved and quantified. The assay's linearity extended from 0.012 to 5x10 3 µMol/L for all bile acids, emphasizing its suitability across a wide concentration range. With a lower quantitation limit of 2 ng/mL, the UPLC-MS/MS assay demonstrated sensitivity, making it reliable for quantitative assessments in biomedical research. Statistical analysis Data were analyzed using SPSS 23 (SPSS Inc., CA, USA). The Kolmogorov-Smirnov test was employed to verify the normality of distribution. Numerical variables were presented as mean ± standard deviation or as medians and interquartile ranges (IQR), while categorical variables were expressed as numbers (percentages). Differences between groups were analyzed using the Kruskal-Wallis H test and the Mann-Whitney U test as appropriate. A P-value of < 0.05 was considered statistically significant for all analyses. Non-parametric Spearman correlation was used to assess the relationships between bile acids and other numerical variables, such as lipid profiles and blood chemistry parameters. Principal Component Analysis (PCA) was conducted to explore patterns in bile acid concentrations among the study cohort, which included individuals with NAFLD and control subjects. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory power of bile acids in distinguishing controls from NAFLD cases. Binary logistic regression was applied to assess the predictive value of bile acids for fibrosis, lobular inflammation, and steatosis in NAFLD patients. Results Demographic and Clinical Characteristics in Normal and NAFLD Groups The data presented in Table 1 summarize the clinical characteristics of the study population, divided into three groups: Control, NASH, and NAST. NASH and NAST groups had a significantly higher proportion of hyperlipidemic patients compared to the control group (χ² = 14.23, p = 0.001). The NASH and NAST groups demonstrated higher mean body mass index (BMI) values compared to the control group (p < 0.001), indicating a greater prevalence of obesity among patients with NASH and NAST. Furthermore, the presence of diabetes mellitus was more common in the NASH and NAST groups, with a higher percentage of patients being diagnosed with DM (χ² = 5.42, p = 0.067). Age differences across the groups were not statistically significant (t = 1.02, p = 0.313), suggesting that age is not a primary differentiating factor in the progression of NAFLD. Similarly, gender distribution did not differ significantly between the groups (χ² = 0.73, p = 0.695). Hypertension prevalence was assessed across the groups, but no significant differences were observed (χ² = 1.44, p = 0.487). However, the trend indicated a higher occurrence of hypertension in the NASH group compared to the control and NAST groups. There were significant differences in fibrosis and inflammation activity scores between the groups, with NASH patients having more advanced fibrosis (F2-F3) and higher activity (A2-A3) compared to the control group (p < 0.001). The distribution of steatosis grades and ultrasound findings also differed significantly among the groups, with the NASH and NAST groups showing more severe liver changes. These results highlight the progressive nature of liver disease in NAFLD and underscore the importance of early diagnosis and management. Biochemical and Hematological Parameters Across Control, NASH, and NAST Groups Table 2 details the biochemical and hematological parameters, showing significantly higher median FBS levels in NASH and NAST groups, indicating a tendency towards hyperglycemia. Direct bilirubin and GGT levels were elevated in NASH and NAST groups, suggesting liver dysfunction. Lipid profile analysis revealed higher total cholesterol levels in NASH and NAST groups, reflecting dyslipidemia. Hematological markers such as hemoglobin and platelet count remained stable across groups. Alterations in Bile Acid Across NAFLD Groups Table 3 summarizes the bile acid profiles among NHC, NAST, and nonalcoholic steatohepatitis (NASH). Primary unconjugated bile acids, CA and CDCA, were significantly elevated in both NAST and NASH compared to NHC (all p 0.05). Similarly, primary conjugated bile acids, including GCA, GCDCA, TCA, and TCDCA, were significantly higher in NASH and NAST compared to NHC (all p < 0.05). GCA and TCA levels were notably higher in NASH compared to NAST (all p 0.05). Secondary bile acids, LCA, TLCA, GUDCA, and TUDCA, were significantly elevated in NASH and NAST compared to NHC (all p 0.05). DCA, GDCA, TDCA, and UDCA did not show significant differences between either NAST or NASH and NHC (all p > 0.05). Bile Acids in Discriminating NAFLD Patients and Control ROC curves (Figure2) and Principal Component Analysis (PCA) (Figure3) were used to examine the capacity of bile acids in discriminating healthy from NAFLD subgroups NAST and NAST. Figure 2a-b summarize the ROC curve analysis for primary and secondary bile acids in differentiating healthy controls from NAFLD patients (NAST+NASH). The primary unconjugated bile acid CDCA showed high diagnostic potential with an AUC of 0.937, 100% specificity, and 74.7% sensitivity at a cutoff of 0.002. The primary conjugated bile acid GCDCA achieved an AUC of 0.877, 100% specificity, and 80% sensitivity at a cutoff of 2.48. The secondary bile acids collectively demonstrated moderate diagnostic performance. LCA had an AUC of 0.864, with 96% specificity and 74.7% sensitivity at a cutoff of 0.002, while TLCA had an AUC of 0.840, with 12% specificity and 80% sensitivity at a cutoff of 0.002. Other secondary bile acids, including TUDCA and GDCA, displayed varying degrees of diagnostic accuracy, with AUCs ranging from 0.784 to 0.420, reflecting different levels of specificity and sensitivity. Overall, primary bile acids exhibited superior diagnostic accuracy compared to secondary bile acids, underscoring their potential utility as biomarkers for distinguishing NAFLD patients from healthy controls. In the subsequent analysis comparing NAST and NASH, the ROC curves for both primary and secondary bile acids showed limited diagnostic utility, with AUC values generally indicating weak discrimination. Primary bile acids like CDCA and CA had AUCs of 0.596 and 0.575, respectively, demonstrating moderate accuracy. Sensitivity and specificity were inconsistent, with significant variability. Secondary bile acids exhibited similarly low AUC values, ranging from 0.466 for LCA to 0.571 for TLCA, indicating limited differentiation between the conditions (Figure 2c-d). These findings, suggest that the bile acids assessed are not robust biomarkers for distinguishing NAST from NASH. PCA of these bile acids across the studied groups. PCA demonstrated that the first two principal components (PC1 and PC2) accounted for 55.82% of the total variance, with eigenvalues of 5.27 and 2.54, respectively. Key bile acids, including GCDCA, CA, and CDCA, emerged as significant contributors, underscoring their potential as biomarkers for differentiating NAFLD from the control group (Figure 3). Impact of Gender, Diabetes Status, and Lipid Profile on Bile Acid Levels in NAST and NASH Patients Table 4 presents the analysis of bile acid levels in NAFLD (NAST and NASH) patients across gender, diabetes status, and lipid profile, highlighting several key differences. Significant differences were observed in TDCA and TUDCA levels between genders, with females showing higher levels (p = 0.033 and p = 0.037, respectively). For diabetes status, CA, CDCA, GCA, and TCDCA levels were significantly different, with the highest levels found in the DM group (p < 0.05 for all). Hyperlipidemic patients exhibit higher significant differences in bile acids CA, LCA, GCA and GUDCA than normolipidemic with p < 0.05 for all these bile acids, suggesting that lipid profile substantially impact bile acid metabolism in NAFLD. Further correlation analysis between bile acids and lipid profile in NAST and NASH revealed several significant associations between some bile acids and lipid profile parameters (Table5). In the NAST group, CA and CDCA had strong negative correlations with LDL (CA: r=-0.57**, p<0.01; CDCA: r=-0.53**, p<0.01) and positive correlations with HDL (CA: r=0.57**, p<0.01; CDCA: r=0.56**, p<0.01). LCA and GDCA were also negatively correlated with LDL (LCA: r=-0.69**, p<0.01; GDCA: r=-0.41*, p<0.05) and positively correlated with HDL (LCA: r=0.70**, p<0.01); GDCA: r= 0.39*, p<0.05). Conversely, other bile acids did not show significant correlations with lipid components. In the NASH group, unconjugated bile acids, CA was positively correlated with HDL (CA: r=0.42**, p<0.01) and negatively correlated with LDL (CA: r=-0.31*, p<0.05). Primary conjugated bile acids GCA, GCDCA and TCDCA were positively correlated with cholesterol (GCA: r=0.49**, p<0.01; GCDCA: r=0.38*, p<0.05; TCDCA: r=0.31*, p<0.05). Secondary bile acids, GDCA, and GDUCA were positively correlated with cholesterol (GDCA: r=0.39*, p<0.01; GDUCA: r=0.38*, p<0.05) and HDL (GDCA: r=0.46**, p<0.01; GUDCA: r=0.50**, p<0.01) and negatively correlated with LDL (GDCA: r=-0.44**, p<0.01; GDUCA: r=-0.41*, p<0.05). TUDCA positively correlated with cholesterol (r=0.37*, p<0.05) and HDL (r= 0.38*, p<0.05). Other bile acids did not show significant correlations with any lipid components. Bile Acid Levels Across Fibrosis, Inflammation, and Steatosis Grades in NAFLD Patients Table 6 presents a detailed comparison of bile acid levels across various stages of fibrosis, inflammation, and steatosis in patients with either NAST or NASH. In the context of fibrosis: CA, CDCA and GCA levels were significantly higher in the F2-F3 group compared to the F0-F1 group (all p < 0.05). No significant differences were found for, in the primary conjugated GCDCA, TCA, TCDCA, and all the secondaries uncogitated and conjugated bile acid DCA, LCA, GDCA, TDCA, TLCA, UDCA, GUDCA, and TUDCA (all p > 0.05). In the context of inflammation GCA, TCA and TLCA levels were significantly higher in the A2-A3 group compared to A0-A1 (p 0.05). No significant differences were found for CDCA, GCDCA, TCDCA, DCA, GDCA, TDCA, UDCA, GUDCA, and TUDCA (all p > 0.05). In the context of steatosis, CA, GCA, TCA, TCDCA, and TLCA levels were significantly elevated in the G2-G3 group compared to G0-G1 (CA: p = 0.003; GCA: p = 0.032). DCA levels were significantly higher in G0-G1 compared to G2-G3 (p 0.05). Predictive Value of Bile Acids for Fibrosis, Inflammation, and Steatosis in NAFLD: Logistic Regression Analysis Table 7 reveals the logistic regression analysis results for bile acids as predictors of fibrosis, active inflammation, and steatosis in NAFLD patients (NAST and NASH). For fibrosis the analysis identified several bile acids with significant associations with fibrosis, CA, CDCA and DCA were notable predictors, with CA showing an odds ratio of 2.05 (p = 0.02); CDCA had an odds ratio of (Exp(B) = 1.58, p = 0.04) and DCA an odds ratio of 2.06 (p = 0.04). In terms of active inflammation, GCA was a significant predictor (Exp(B) = 1.92, p = 0.03), along with TCA (Exp(B) = 1.94, p = 0.02, and TLCA (Exp(B) = 15.95, p = 0.03). CDCA did not show a significant association with inflammation (Exp(B) = 0.49, p = 0.66). In the context of steatosis, 1ry bile acids CA, CDCA, and GCA, were significant predictors (CA: Exp(B) = 2.62, p = 0.048; CDCA: Exp(B) = 1.25, p = 0.017; GCA: Exp(B) = 2.92, p = 0.041. Additionally,2ry Bile acid, DCA, TDCA, TLCA, UDCA showed a significant association and predictor of steatosis DCA: Exp(B) = 3.24, p = 0.042; TDCA: Exp(B) = 4.35, p = 0.04; TLCA: Exp(B) = 20, p = 0.03); and UDCA (Exp(B) = 20, p = 0.02). These results highlight the role of specific bile acids, such as CA, CDCA and GCA, in predicting NAFLD severity, with implications for both fibrosis and steatosis. The significant associations of DCA, TDCA, and TLCA with steatosis suggest their potential importance in hepatic fat accumulation. Additionally, the significant correlations of GCA, TCA, and TLCA with inflammation suggest their potential utility in predicting liver inflammation activity. Discussion This study provides a comprehensive analysis of bile acid profiles across the spectrum of NAFLD, from simple NAST to the more advanced hepatic inflammation in nonalcoholic steatohepatitis (NASH) and fibrosis. The findings underscore the critical role of bile acids as potential biomarkers in the progression of NAFLD, offering insights into the metabolic disturbances underlying the disease. Significant differences in clinical parameters, such as BMI, gender, and the prevalence of dyslipidemia and diabetes mellitus, were observed between the NASH and NAST groups compared to the NHC group. These differences align with existing literature that links metabolic disturbances, including obesity, dyslipidemia, and diabetes mellitus, to the severity of NAFLD, further emphasizing the role of these factors in NAFLD progression [3, 5]. The main finding of the current study is that bile acid profiles are significantly altered in NAFLD compared to healthy controls, with specific bile acids potentially serving as biomarkers for distinguishing between healthy individuals and NAFLD patients. Additionally, these bile acids may have predictive value for the progression of the disease, particularly in relation to fibrosis, inflammation, and steatosis. Both primary unconjugated and conjugated bile acids were significantly elevated in NAST and NASH compared to healthy controls, with a particular increase in CA, CDCA, GCA, and TCA levels. The minimal differences between NAST and NASH in many bile acids suggest that these metabolic alterations may occur early in the disease process and persist as NAFLD progresses. These results are in line with previous research indicating that bile acid metabolism is disrupted in NAFLD, likely due to impaired hepatic bile acid synthesis and secretion, as well as altered gut microbiota[23-27]. Secondary bile acids also showed significant changes across NAFLD groups. Elevated levels of secondary bile acids such as LCA, TLCA, GUDCA, and TUDCA were observed in both NASH and NAST compared to NHC, reflecting the disruption in bile acid metabolism. However, no significant differences were found between NAST and NASH, indicating that secondary bile acids might not provide robust differentiation between these subtypes. These findings align with Cassey et al., who reported higher total primary bile acids and lower secondary bile acids in NAFLD patients compared to controls [28]. Gillard et al. also noted variability in bile acid levels across NAFLD studies, with some reports showing elevated levels and others unchanged [28, 29]. Chen et al. (2020) emphasized the role of gut microbiota in altering bile acid profiles, which is consistent with the observed increase in primary bile acids and decrease in secondary bile acids in NASH[25]. Diagnostic assessments of the discriminative power of bile acids through ROC curve analysis and PCA further highlight the potential of certain bile acids, especially primary bile acid CA, CDCA, GCDCA, and TCDCA and secondary bile acids LCA and TLCA in distinguishing NAFLD patients from healthy individuals with moderate sensitivity and high specificity, however, the limited ability of these bile acids to differentiate between NAST and NASH, for both primary and secondary bile acids, suggests that while bile acids are useful in identifying the presence of NAFLD, they may not be as effective in distinguishing its subtypes. PCA also identified significant contributors, including GCDCA, CA, and CDCA, indicating their relevance in distinguishing NAFLD from healthy states. These results mirror findings from other studies that emphasize the metabolic alterations in bile acids during NAFLD progression[30]. Notably, both ROC analysis and PCA demonstrated limited effectiveness in differentiating between NAST and NASH, suggesting these bile acids alone are insufficient for precise NAFLD subtyping. This limitation is consistent with broader challenges in the field, where a need for more specific biomarkers has been frequently noted[31, 32]. The study demonstrated significant bile acid dysregulation in diabetic and pre-diabetic patients, particularly elevated levels of CA, CDCA, GCA, and TCDCA, with the highest levels observed in diabetic patients. These elevations suggest a progressive impairment in bile acid metabolism linked to worsening glucose regulation. Additionally, hyperlipidemic patients exhibited higher levels of CA, LCA, GCA, and GUDCA, highlighting the interplay between lipid metabolism and bile acid profiles in NAFLD. The presence of elevated bile acids in pre-diabetic individuals indicates early metabolic changes, underscoring their potential as early biomarkers for disease progression. These findings align with previous research, which has reported similar increases in unconjugated bile acids, particularly CA, in diabetic and pre-diabetic patients, and suggest a compensatory increase in conjugated bile acids as an adaptive response [33, 34] [26, 27, 35, 36]. The study also observed gender differences, with female patients exhibiting higher levels of TDCA and TUDCA. These findings align with the findings of Puri et al., who suggested that sex hormones significantly influence bile acid metabolism by impacting the enzymes involved in bile acid synthesis and clearance. These results underscore the complexity of bile acid metabolism and highlight the importance of considering factors such as diabetes status, lipid profile, and gender when interpreting bile acid levels in clinical practice [37]. Exploring the relationship between bile acid levels and the severity of steatosis, inflammation, and fibrosis in NAFLD patients revealed that high levels of CA, GCA, TCA, TCDCA, DCA and TLCA, were found to be significantly associated with of steatosis. Higher levels of GCA, TCA, and TLCA were associated with liver inflammation, however, CA, CDCA, and GCA were significantly elevated in patients with advanced fibrosis. The logistic regression analysis also identified several bile acids as significant predictors of either steatosis, inflammation or fibrosis. Elevated CA, CDCA, GCA, TDCA, DCA TLCA, and UDCA were found to be significant predictors of steatosis. GCA, TCA and TLCA were identified as significant predictors of inflammation. Similarly, CA, CDCA, GCA and DCA were identified as significant predictors of fibrosis, emphasizing their potential utility in monitoring disease progression and severity. Previous research partially or fully supports such associations[37-39]. For instance, Aranha et al. (2008) observed that elevated levels of CA, CDCA, and DCA in liver tissue correlated with steatosis and fibrosis in NASH patients[40]. Similarly, Puri et al. (2018) reported that increased serum levels of these bile acids were associated with steatosis and fibrosis in NAFLD patients, suggesting a role for these bile acids in the early detection and progression of the disease[37]. Furthermore, the elevated levels of GCA and DCA observed in this study align with earlier findings, indicating that these bile acids are involved in the pathogenesis of NAFLD and may reflect alterations in bile acid metabolism and liver function[25, 41, 42]. Chen et al. (2020) highlighted the importance of bile acids CA and CDCA in the progression from simple steatosis to more advanced stages of liver disease, emphasizing their role in the inflammatory and fibrotic processes[25]. TLCA, a secondary bile acid, has also been identified in previous studies as a marker of disease severity. Caussy et al. (2019) demonstrated significant changes in TLCA levels corresponding with advanced liver fibrosis, suggesting that TLCA could be a valuable marker for assessing the extent of liver damage [28]. Elevated levels of CA, CDCA, and TLCA in this study are consistent with observations from Khalil et al. (2022), who reported that elevated serum levels of these bile acids are associated with liver fibrosis and cirrhosis [43]. Additionally, Gottlieb and Canbay (2019) noted that increased conjugated bile acids such as TLCA are indicative of heightened inflammatory activity, which aligns with our findings that these bile acids are significant predictors of inflammation [41]. In summary, the study's findings that CA, CDCA, GCA, and DCA are predictors of steatosis, and CA, CDCA, TCA, and TLCA are predictors of fibrosis and inflammation, underscore the potential utility of these bile acids in monitoring the progression and severity of NAFLD. These observations are supported by existing literature, which highlights the role of these bile acids in the pathogenesis and progression of liver disease. The identification of these bile acids as significant predictors provides valuable insights into their potential use as biomarkers for early diagnosis, disease monitoring, and therapeutic targeting in NAFLD. This study has several limitations. The cross-sectional design limits the ability to establish causality between bile acid alterations and NAFLD progression. Additionally, the sample size, while sufficient for detecting significant differences, may limit the generalizability of the findings. The study also did not account for dietary intake or genetic factors, which could influence bile acid metabolism[30, 44-46]. In conclusion, this study highlights the significant alterations in bile acid profiles in NAFLD and their potential utility as biomarkers for disease presence and progression. The associations identified between specific bile acids such as (CA, GCA, TCA, TCDCA, DCA and TLCA), and key pathological features like steatosis, fibrosis, and inflammation, underscore their relevance as indicators of disease severity. The predictive value of CA, CDCA, and TLCA for fibrosis and inflammation further emphasizes their potential role in monitoring NAFLD progression. These findings are consistent with existing literature, which underscores the involvement of these bile acids in the pathogenesis and advancement of liver disease [38, 44]. The identification of these bile acids as significant predictors offers valuable insights into their potential application in early diagnosis, disease monitoring, and therapeutic targeting in NAFLD. Future research should focus on longitudinal studies to track changes in bile acid profiles over time and assess their relationship with NAFLD progression, as well as evaluate the impact of therapeutic interventions on these biomarkers[47]. Abbreviations NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH: Nonalcoholic steatohepatitis, NAFLD: Nonalcoholic fatty liver disease. CA: Cholic acid, CDCA: Chenodeoxycholic acid, GCA: Glycholic acid, GCDCA: Glycochenodeoxycholic acid, TCA: Taurocholic acid, TCDCA: Taurochenodeoxycholic acid, DCA: Deoxycholic acid, LCA: Lithocholic acid, GDCA: Glycodeoxycholic acid, TDCA: Taurodeoxycholic acid, TLCA: Taurolithocholic acid, UDCA: Ursodeoxycholic acid, GUDCA: Glycoursodeoxycholic acid, TUDCA: Tauroursodeoxycholic acid. 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary taurocongugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. AST: aspartate transaminase, ALT: alanine transaminase, GGT: Gamma-glutamyl transferase, ALP: alkaline phosphatase, TBil: total bilirubin, DBIL: direct bilirubin, TP: total protein, Alb: albumin, UA: uric acid, Chol: Cholesterol, LDL: low density lipoprotein, HDL: high density lipoprotein. TG: triglyceride. Hb: hemoglobin, WBCs: white blood cells. RBCs: red blood cells. Declarations Ethics approval and consent to participate: The research ethics committees of the National Liver Institute (IRB005700 -2024), Menoufia University, approved the research proposal and the protocols to comply with national research guidelines. Patients provided informed written consent for the use of tissue for research purposes. Consent for publication: obtained from all participants Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare they do not have any conflict of interest. Funding: The authors declare they do not have any financial disclosure. Authors' contributions: AAB : Conducted the UPLC/MS/MS analysis method. Contributed to the study concept and design, manuscript preparation. MFE: Patient recruitment and evaluation, collection of clinical data. DA : Conducted the pathological examination of the liver biopsy, staged and confirmed the diagnosis, and contributed to the concept and protocol development. AK : Corresponding author, study concept and design, analyzed the data, generated the result, wrote and edited the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgements: The authors would like to express their gratitude to Dr. Samah Elsheaty, Dr. Ghada Salah, Dr. Azza Elsheashaey, and Ms. Maryam Nagah for their dedicated efforts in patient care during sample collection and their outstanding work with HPLC at the National Liver Institute, Menoufia University. References Diehl, AM and C Day, Cause, Pathogenesis, and Treatment of Nonalcoholic Steatohepatitis. N Engl J Med, 2017. 377 (21): p. 2063-2072. Paik, JM, P Golabi, Y Younossi, A Mishraand ZM Younossi, Changes in the Global Burden of Chronic Liver Diseases From 2012 to 2017: The Growing Impact of NAFLD. Hepatology, 2020. 72 (5): p. 1605-1616. Younossi, Z, F Tacke, M Arrese, B Chander Sharma, I Mostafa, E Bugianesi. MB Vos, Global Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis. Hepatology, 2019. 69 (6): p. 2672-2682. Dulai, PS, S Singh, J Patel, M Soni, LJ Prokop, Z Younossi. R Loomba, Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis. Hepatology, 2017. 65 (5): p. 1557-1565. Chalasani, N, Z Younossi, JE Lavine, M Charlton, K Cusi, M Rinella. AJ Sanyal, The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology, 2018. 67 (1): p. 328-357. Estes, C, H Razavi, R Loomba, Z Younossiand AJ Sanyal, Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology, 2018. 67 (1): p. 123-133. Bessone, F, MV Razoriand MG Roma, Molecular pathways of nonalcoholic fatty liver disease development and progression. Cell Mol Life Sci, 2019. 76 (1): p. 99-128. Juanola, O, S Martinez-Lopez, R Francesand I Gomez-Hurtado, Non-Alcoholic Fatty Liver Disease: Metabolic, Genetic, Epigenetic and Environmental Risk Factors. Int J Environ Res Public Health, 2021. 18 (10). Eslam, M, PN Newsome, SK Sarin, QM Anstee, G Targher, M Romero-Gomez. J George, A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol, 2020. 73 (1): p. 202-209. Xue, H, S Fang, M Zheng, J Wu, H Li, M Zhang. Y Ma, Da-Huang-Xiao-Shi decoction protects against3, 5-diethoxycarbonyl-1,4-dihydroxychollidine-induced chronic cholestasis by upregulating bile acid metabolic enzymes and efflux transporters. J Ethnopharmacol, 2021. 269 : p. 113706. Dawson, PA, T Lanand A Rao, Bile acid transporters. J Lipid Res, 2009. 50 (12): p. 2340-57. Kawamata, Y, R Fujii, M Hosoya, M Harada, H Yoshida, M Miwa. M Fujino, A G protein-coupled receptor responsive to bile acids. J Biol Chem, 2003. 278 (11): p. 9435-40. Li, T and JYL Chiang, Bile acid-based therapies for non-alcoholic steatohepatitis and alcoholic liver disease. Hepatobiliary Surg Nutr, 2020. 9 (2): p. 152-169. Wegermann, K, C Howe, R Henao, Y Wang, CD Guy, MF Abdelmalek. CA Moylan, Serum Bile Acid, Vitamin E, and Serotonin Metabolites Are Associated With Future Liver-Related Events in Nonalcoholic Fatty Liver Disease. Hepatol Commun, 2021. 5 (4): p. 608-617. Pearce, SG, NC Thosaniand JJ Pan, Noninvasive biomarkers for the diagnosis of steatohepatitis and advanced fibrosis in NAFLD. Biomark Res, 2013. 1 (1): p. 7. Ajmera, V and R Loomba, Imaging biomarkers of NAFLD, NASH, and fibrosis. Mol Metab, 2021. 50 : p. 101167. Andersson, A, M Kelly, K Imajo, A Nakajima, JA Fallowfield, G Hirschfield. S Harrison, Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis. Clin Gastroenterol Hepatol, 2022. 20 (11): p. 2451-2461 e3. Chow, MD, YH Leeand GL Guo, The role of bile acids in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Mol Aspects Med, 2017. 56 : p. 34-44. Park, WH and YK Pak, Insulin-dependent suppression of cholesterol 7alpha-hydroxylase is a possible link between glucose and cholesterol metabolisms. Exp Mol Med, 2011. 43 (10): p. 571-9. Fuchs, C, T Claudeland M Trauner, Bile acid-mediated control of liver triglycerides. Semin Liver Dis, 2013. 33 (4): p. 330-42. Trauner, M, T Claudel, P Fickert, T Moustafaand M Wagner, Bile acids as regulators of hepatic lipid and glucose metabolism. Dig Dis, 2010. 28 (1): p. 220-4. Brunt, EM, DE Kleiner, LA Wilson, P Belt, BA Neuschwander-Tetriand NCR Network, Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology, 2011. 53 (3): p. 810-20. Zhou, Y, Z Liu, EC Lynch, L He, H Cheng, L Liu. L Xie, Osr1 regulates hepatic inflammation and cell survival in the progression of non-alcoholic fatty liver disease. Lab Invest, 2021. 101 (4): p. 477-489. Ferslew, BC, G Xie, CK Johnston, M Su, PW Stewart, W Jia. ASt Barritt, Altered Bile Acid Metabolome in Patients with Nonalcoholic Steatohepatitis. Dig Dis Sci, 2015. 60 (11): p. 3318-28. Chen, W, Y Wei, A Xiong, Y Li, H Guan, Q Wang. X Ma, Comprehensive Analysis of Serum and Fecal Bile Acid Profiles and Interaction with Gut Microbiota in Primary Biliary Cholangitis. Clin Rev Allergy Immunol, 2020. 58 (1): p. 25-38. Cai, J, B Rimal, C Jiang, JYL Chiangand AD Patterson, Bile acid metabolism and signaling, the microbiota, and metabolic disease. Pharmacol Ther, 2022. 237 : p. 108238. Jia, W, G Xieand W Jia, Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat Rev Gastroenterol Hepatol, 2018. 15 (2): p. 111-128. Caussy, C, C Hsu, S Singh, S Bassirian, J Kolar, C Faulkner. R Loomba, Serum bile acid patterns are associated with the presence of NAFLD in twins, and dose-dependent changes with increase in fibrosis stage in patients with biopsy-proven NAFLD. Aliment Pharmacol Ther, 2019. 49 (2): p. 183-193. Gillard, J and IA Leclercq, Biological tuners to reshape the bile acid pool for therapeutic purposes in non-alcoholic fatty liver disease. Clin Sci (Lond), 2023. 137 (1): p. 65-85. Powell, EE, VW Wongand M Rinella, Non-alcoholic fatty liver disease. Lancet, 2021. 397 (10290): p. 2212-2224. Kim, HY, Recent advances in nonalcoholic fatty liver disease metabolomics. Clin Mol Hepatol, 2021. 27 (4): p. 553-559. Paroutoglou, K, E Papadavid, GS Christodoulatosand M Dalamaga, Deciphering the Association Between Psoriasis and Obesity: Current Evidence and Treatment Considerations. Curr Obes Rep, 2020. 9 (3): p. 165-178. Haeusler, RA, B Astiarraga, S Camastra, D Acciliand E Ferrannini, Human insulin resistance is associated with increased plasma levels of 12alpha-hydroxylated bile acids. Diabetes, 2013. 62 (12): p. 4184-91. Puri, P, MM Wiest, O Cheung, F Mirshahi, C Sargeant, HK Min. AJ Sanyal, The plasma lipidomic signature of nonalcoholic steatohepatitis. Hepatology, 2009. 50 (6): p. 1827-38. Wang, M, C Wang, RH Hanand X Han, Novel advances in shotgun lipidomics for biology and medicine. Prog Lipid Res, 2016. 61 : p. 83-108. Al-Jebawi, AF, AH YoussefAgha, HS Al Suwaidi, MS Albadwawi, AS Al Marzooqi, AH Banihammad. MK Alkaabi, Attenuated PTH responsiveness to vitamin D deficiency among patients with type 2 diabetes and chronic hyperglycemia. Diabetes Res Clin Pract, 2017. 128 : p. 119-126. Puri, P, K Daita, A Joyce, F Mirshahi, PK Santhekadur, S Cazanave. AJ Sanyal, The presence and severity of nonalcoholic steatohepatitis is associated with specific changes in circulating bile acids. Hepatology, 2018. 67 (2): p. 534-548. Bertolini, A, R Fiorottoand M Strazzabosco, Bile acids and their receptors: modulators and therapeutic targets in liver inflammation. Semin Immunopathol, 2022. 44 (4): p. 547-564. Evangelakos, I, J Heeren, E Verkadeand F Kuipers, Role of bile acids in inflammatory liver diseases. Semin Immunopathol, 2021. 43 (4): p. 577-590. Aranha, MM, H Cortez-Pinto, A Costa, IB da Silva, ME Camilo, MC de Mouraand CM Rodrigues, Bile acid levels are increased in the liver of patients with steatohepatitis. Eur J Gastroenterol Hepatol, 2008. 20 (6): p. 519-25. Gottlieb, A and A Canbay, Why Bile Acids Are So Important in Non-Alcoholic Fatty Liver Disease (NAFLD) Progression. Cells, 2019. 8 (11). Xue, R, L Su, S Lai, Y Wang, D Zhao, J Fan. H Zhou, Bile Acid Receptors and the Gut-Liver Axis in Nonalcoholic Fatty Liver Disease. Cells, 2021. 10 (11). Khalil, A, A ElSheashaey, E Abdelsameea, M Obada, FFM Bayomyand H El-Said, Value of Bile Acids in Diagnosing Hepatitis C Virus-Induced Liver Cirrhosis and Hepatocellular Carcinoma. Br J Biomed Sci, 2022. 79 : p. 10191. Dong, S, ZY Zhan, HY Cao, C Wu, YQ Bian, JY Li. MY Sun, Urinary metabolomics analysis identifies key biomarkers of different stages of nonalcoholic fatty liver disease. World J Gastroenterol, 2017. 23 (15): p. 2771-2784. Appleby, RN, I Moghul, S Khan, M Yee, P Manousou, TD Nealand JRF Walters, Non-alcoholic fatty liver disease is associated with dysregulated bile acid synthesis and diarrhea: A prospective observational study. PLoS One, 2019. 14 (1): p. e0211348. Deng, Y, M Pan, H Nie, C Zheng, K Tang, Y Zhangand Q Yang, Lipidomic Analysis of the Protective Effects of Shenling Baizhu San on Non-Alcoholic Fatty Liver Disease in Rats. Molecules, 2019. 24 (21). Thibaut, MM and LB Bindels, Crosstalk between bile acid-activated receptors and microbiome in entero-hepatic inflammation. Trends Mol Med, 2022. 28 (3): p. 223-236. Tables Table 1: Clinical Characteristics of the Studied Groups Gender NHC (n=25) NAST (n=35) NASH (n=40) P-value - Female 16 (64%) 23 (65.7%) 23 (57.5%) χ² = 0.73, p = 0.695 - Male 9 (36%) 12 (34.3%) 17 (42.5%) Age (years) 50.32 ± 9.45 50.31 ± 8.56 48.13 ± 8.77 t = 1.02, p = 0.313 Weight (kg) 72.92 ± 5.90 81.26 ± 10.27 77.00 ± 10.71 t = 3.34, p = 0.001 Height (cm) 169.20 ± 8.97 162.69 ± 8.39 163.25 ± 9.81 t = 2.49, p = 0.015 BMI (kg/m²) 25.73 ± 3.88 30.91 ± 4.77 29.22 ± 5.64 t = 4.50, p < 0.001 Diabetes Mellitus - DM 4 (16%) 13 (37.1%) 12 (30%) χ² = 5.42, p = 0.067 - No DM 14 (56%) 13 (37.1%) 17 (42.5%) - Prediabetes (pDM) 7 (28%) 9 (25.7%) 11 (27.5%) Hypertension - Yes 7 (28%) 10 (28.6%) 16 (40%) χ² = 1.44, p = 0.487 - No 18 (72%) 25 (71.4%) 24 (60%) Lipid Status - Hyperlipidemic 6 (24%) 20 (57.1%) 26 (65%) χ² = 14.23, p = 0.001 - Normolipidemic 19 (76%) 15 (42.9%) 14 (35%) Fibrosis Stage - F0 25 (100%) 25 (71.4%) 0 (0%) χ² = 58.75, p < 0.001 - F1 0 (0%) 10 (28.6%) 0 (0%) - F2 0 (0%) 0 (0%) 16 (40%) - F3 0 (0%) 0 (0%) 24 (60%) Activity Grade - A0 25 (100%) 22 (62.9%) 0 (0%) χ² = 54.43, p < 0.001 - A1 0 (0%) 13 (37.1%) 0 (0%) - A2 0 (0%) 0 (0%) 16 (40%) - A3 0 (0%) 0 (0%) 24 (60%) Steatosis Grade - G0 25 (100%) 0 (0%) 0 (0%) χ² = 75.00, p < 0.001 - G1 0 (0%) 18 (51.4%) 0 (0%) - G2 0 (0%) 11 (31.4%) 16 (40%) - G3 0 (0%) 6 (17.1%) 24 (60%) Ultrasound Findings - Normal homogenous liver 25 (100%) 0 (0%) 0 (0%) χ² = 88.75, p < 0.001 - Fatty liver 0 (0%) 17 (48.6%) 0 (0%) - Mildly enlarged fatty liver 0 (0%) 8 (22.9%) 9 (22.5%) - Moderately enlarged fatty liver 0 (0%) 10 (28.6%) 31 (77.5%) Clinical characteristics of NHC, NAST, and NASH groups with statistical significance for demographic, anthropometric, metabolic, and histological variables. NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis, F0: No fibrosis F1: Mild fibrosis F2: Moderate fibrosis F3: Advanced fibrosis. Lobular inflammation Grade: A0: No lobular inflammation A1: Mild lobular inflammation A2: Moderate lobular inflammation, A3: Severe lobular inflammation. Steatosis Grade: G0: No steatosis (no fat accumulation in the liver), G1: Mild steatosis G2: Moderate steatosis G3: Severe steatosis. Ultrasound Findings: Normal homogeneous liver: Liver with normal echotexture. Fatty liver: Liver with increased echogenicity indicating fat accumulation. Mildly enlarged fatty liver: Slight increase in liver size with fatty infiltration. Moderately enlarged fatty liver: Moderate increase in liver size with more pronounced fatty infiltration. Table 2: Descriptive Statistics and Kruskal-Wallis Test Results for Biochemical and Hematological Parameters Across NHC, NASH, and NAST Groups Parameter NHC (n=25) NAST (n=35) NASH (n=40) P-value Blood Glucose & Hemoglobin FBS (mg/dL) 112 (21.5) 130 (71) (a) 143 (88.25) (b) 0.011 HA1C (%) 5.6 (0.35) 5.8 (0.9) 5.8 (1.475) 0.467 Renal Function Creatinine (mg/dL) 0.77 (0.205) 0.78 (0.15) 0.77 (0.255) 0.358 Urea (mg/dL) 23 (6.5) 23 (11) 23.5 (12.75) 0.364 UA (mg/dL) 5.3 (2.0) 5.3 (0.8) 5.3 (2.0) 0.668 Liver Function AST (U/L) 16 (8.5) 20 (31) 23 (41) 0.071 ALT (U/L) 21 (18.5) 24 (15) 30.5 (40.25) 0.136 TBIL (mg/dL) 0.9 (0.4) 0.98 (0.11) 0.9 (0.1) 0.215 DBIL (mg/dL) 0.11 (0.035) 0.13 (0.07) (a) 0.15(0.035) (b) 0.038 ALB (g/dL) 3.8 (0.5) 4.2 (1.1) 3.8 (0.775) 0.141 Glob (g/dL) 3.9 (0.9) 3.1 (1.3) (a) 3.7 (1.05) 0.016 TP (g/dL) 7.5 (0.45) 7.5 (0.7) 7.6 (0.55) 0.963 GGT (U/L) 12.4 (8.5) 23 (41) (a) 20 (12) (b) 0.002 Lipid Profile Chol (mg/dL) 188 (47) 214 (62) (a) 207 (45.5) (b) 0.014 LDL (mg/dL) 105 (8.5) 99 (45) 102 (28) 0.269 HDL (mg/dL) 56 (14) 65 (31) 64 (28.5) 0.174 TG (mg/dL) 123 (25) 116 (27) 123 (35.75) 0.294 Hematological Parameters Hb (g/dL) 11.7 (2.8) 12.0 (2.4) 12.8 (2.575) 0.489 RBC (10 6 /μL) 4.71 (1.125) 4.71 (0.83) 4.71 (0.98) 0.945 Platelets (10 3 /μL) 250 (40) 240 (41) 235 (53.75) 0.103 WBCs (10 3 /μL) 6.1 (2.7) 5.8 (2.7) 5.9 (4.15) 0.602 Comparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across NHC, NAST, and NASH Groups: IQR: Interquartile rang, K: Kruskal-Wallis test comparison among all groups, *P-value < 0.05 indicates significant. Mann-Whitney U test comparison between two groups. P-value < 0.05 indicates significant. a: comparing between NHC vs. NAST group. b: comparing between NHC vs. NASH group. c: comparing between NAST vs. NASH group. NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis. Table 3: Comparative Analysis of Bile Acid Profiles Across NHC, NAST, and NASH Groups Bile Acid Class NHC Median (IQR) NAST Median (IQR) NASH Median (IQR) K-Wallis P-value CA 1ryU 0.130 (0.12) 0.230 (1.55) a 0.410 (1.41) b 0.000 CDCA 1ryU 0.001 (0.000) 0.050 (3.298) a 0.830 (3.29) b 0.000 GCA 1rygc 0.190 (0.165) 0.400 (1.76) a, c 1.770 (4.83) b, c 0.031 GCDCA 1rygc 2.120 (0.3) 3.730 (1.97) a 4.220 (9.14) a 0.000 TCA 1rytc 0.002 (0.015) 0.130 (0.028) ac 0.310 (0.019) b, c 0.004 TCDCA 1rytc 0.002 (0.059) 3.205 (4.32) a 4.390 (4.40) b 0.000 DCA 2ryU 0.160 (0.06) 0.280 (0.51) 0.335 (0.56) 0.588 LCA 2ryU 0.001 (0.000) 0.030 (0.218) a 0.006 (0.206) b 0.000 GDCA 2rygc 0.060 (0.13) 0.110 (0.168) 0.105 (0.143) 0.447 TDCA 2rytc 0.002 (0.001) 0.002 (0.049) 0.002 (0.002) 0.218 TLCA 2rytc 0.001 (0.000) 0.003 (0.008) a 0.010 (0.468) b 0.000 UDCA 2ryU 0.030 (0.034) 0.002 (0.009) 0.002 (0.059) 0.506 GUDCA 2rygc 0.003 (0.001) 1.200 (1.698) a 1.300 (1.698) b 0.025 TUDCA 2rytg 0.001 (0.001) 0.005 (0.259) a 0.005 (0.039) b 0.000 Comparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across NHC, NAST, and NASH Groups: IQR: Interquartile rang, K: Kruskal-Wallis test comparison among all groups, *P-value < 0.05 indicates significant. Mann-Whitney U test comparison between two groups. P-value < 0.05 indicates significant. a: comparing between NHC vs. NAST group. b: comparing between NHC vs. NASH group. c: comparing between NAST vs. NASH group. NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis . 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. Table 4. Bile Acid Levels Across Gender, Diabetes Status, and Lipid Profile in NAFLD Patients Bile Acid Female (Median [IQR]) Male (Median [IQR]) P-value DM (Median (IQR) Pre-DM (Median (IQR) No DM Median (IQR) P-value Normolipidemic (Median [IQR]) Hyperlipidemic (Median [IQR]) Mann-Whitney U P-value CA 1ryU 0.900 (1.53) 0.320 (1.41) 0.952 1.06 (0.39) a 0.34 (0.44) 0.32 (0.66) 0.034 0.93 (1.38) 0.29 (1.59) 795.5 0.001 CDCA 1ryU 1.100 (3.32) 0.650 (3.33) 0.719 2.50 (0.75) a 1.98 (0.65) b 1.38 (3.32) 0.045 1.35 (3.53) 0.08 (3.25) 802.5 0.167 GCA 1rygc 1.765 (2.26) 0.280 (2.02) 0.116 2.80 (0.80) a 1.60 (0.59) b 1.09 (0.74) 0.039 1.23 (1.85) 1.78 (3.52) 582.5 0.016 GCDCA 1rygc 4.175 (4.21) 3.850 (1.94) 0.918 4.24 (9.36) 3.98 (3.43) 3.41 (2.04) 0.146 4.05 (1.75) 4.17 (6.73) 628.5 0.615 TCA 1rytc 0.006 (0.04) 0.002 (0.02) 0.991 0.002 (0.029) 0.01 (0.018) 0.002 (1.5) 0.892 0.00 (0.02) 0.01 (0.04) 541.0 0.139 TCDCA 1rytc 4.390 (4.36) 2.980 (4.39) 0.723 4.40 (4.16) a 3.65 (4.35) b 2.37 (4.32) 0.020* 4.06 (4.13) 4.22 (4.58) 667.5 0.935 DCA 2ryU 0.280 (0.50) 0.420 (0.61) 0.682 0.18 (0.495) 0.46 (0.495) 0.27 (0.54) 0.376 0.28 (0.52) 0.33 (0.55) 657.0 0.845 LCA 2ryU 0.030 (0.21) 0.002 (0.19) 0.235 0.002 (0.079) 0.007 (0.218) 0.11 (0.274) 0.616 0.04 (0.22) 0.08 (0.21) 713.5 0.023 GDCA 2rygc 0.110 (0.15) 0.110 (0.16) 0.934 0.01 (0.148) 0.12 (0.212) 0.11 (0.118) 0.115 0.11 (0.17) 0.11 (0.13) 753.5 0.392 TDCA 2rytc 0.004 (0.01) 0.002 (0.08) 0.033* 0.001 (0.001) 0.002 (0.079) 0.002 (0.001) 0.104 0.00 (0.00) 0.00 (0.04) 678.0 0.973 TLCA 2rytc 0.005 (0.18) 0.010 (0.01) 0.490 0.01 (0.008) 0.005 (0.177) 0.01 (0.458) 0.943 0.01 (0.02) 0.01 (0.01) 626.0 0.591 UDCA 2ryU 0.001 (0.01) 0.002 (0.06) 0.170 0.001 (0.030) 0.002 (0.059) 0.002 (0.009) 0.535 0.00 (0.04) 0.00 (0.05) 669.5 0.950 GUDCA 2rygc 0.245 (1.70) 1.200 (1.70) 0.755 0.10 (1.648) 1.20 (1.697) 1.20 (1.698) 0.439 0.77 (1.70) 1.20 (1.71) 644.0 0.036 TUDCA 2rytg 0.012 (0.24) 0.001 (0.17) 0.037* 0.003 (0.164) 0.005 (0.093) 0.006 (1.499) 0.871 0.01 (0.29) 0.01 (0.07) 683.5 0.926 Comparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across diabetic groups: IQR: Interquartile rang, *P-value < 0.05 indicates significant. Pairwise comparisons a: comparing between No DM vs. DM group. b: comparing between No DM vs. p-DM group, c: comparing between p-DM vs. DM group. Mann-Whitney U test comparison between two groups (gender, lipidemic state). P-value < 0.05 indicates significant. DM: diabetes mellitus, p-DM: pre-diabetic, No DM: no diabetes mellitus, 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary taurocongugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. Table 5: Correlation Analysis Between Bile Acids and Lipid Profile in NAST and NASH NAST, n=35 NASH, n=40 Bile Acid class Chol LDL HDL TG Chol LDL HDL TG CA 1ryU 0.22 -0.57** 0.57** -0.12 0.18 -0.31* 0.42** -0.11 CDCA 1ryU 0.15 -0.53** 0.56** -0.12 0.29 -0.29 0.25 0.01 GCA 1rygc 0.14 0.06 -0.07 -0.08 0.49** -0.19 0.28 -0.03 GCDCA 1rygc -0.08 0.00 -0.02 -0.07 0.38* -0.11 0.20 0.15 TCA 1rytc -0.18 0.21 -0.20 -0.29 0.16 -0.01 0.29 0.05 TCDCA 1rytc -0.32 0.32 -0.14 -0.13 0.31* -0.14 0.29 0.13 DCA 2ryU -0.13 -0.24 -0.24 -0.14 0.30 -0.22 0.20 -0.04 LCA 2ryU 0.14 -0.69** 0.70** -0.17 0.29 -0.14 0.32 0.11 GDCA 2rygc -0.09 -0.41* 0.39* -0.16 0.39* -0.44** 0.46** -0.13 TDCA 2rytc 0.27 -0.27 0.29 -0.05 -0.26 0.29 -0.18 -0.04 TLCA 2rytc -0.15 -0.15 0.07 -0.23 0.09 -0.09 0.07 0.05 UDCA 2ryU 0.00 0.28 -0.12 0.03 -0.08 0.08 -0.04 0.03 GUDCA 2rygc -0.13 -0.09 0.28 -0.11 0.38* -0.41* 0.50** -0.09 TUDCA 2rytg 0.15 -0.20 0.01 0.02 0.37* -0.23 0.38* 0.01 Spearman Rank Correlation Coefficients (r) Among Variables Across NHC, NAST, and NASH Groups. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH: Nonalcoholic steatohepatitis, 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. Table 6: Comparison of bile acid levels in NAFLD patients categorized by Fibrosis, Inflammation, and Steatosis Grades Fibrosis Inflammation Steatosis Bile Acid class F0-F1 F2-F3 P-value A0-A1 A2-A3 P-value G0-G1 G2-G3 P-value CA 1ryU 0.12 (1.05) 0.41 (1.40) 0.024 0.23 (1.55) 0.41 (1.40) 0.264 0.21 (0.54) 0.41 (0.42) 0.003 CDCA 1ryU 0.06 (3.30) 0.83 (0.29) 0.011 0.05 (3.30) 0.83 (3.29) 0.154 0.05 (3.14) 0.81 (3.32) 0.332 GCA 1rygc 1.57 (3.63) 0.40 (1.35) 0.041 1.77 (4.83) 0.40 (1.76) 0.048 1.27 (1.14) 2.76 (1.06) 0.032 GCDCA 1rygc 4.22 (9.14) 3.73 (1.97) 0.184 4.22 (9.14) 3.73 (1.97) 0.184 3.90 (3.20) 4.13 (3.20) 0.794 TCA 1rytc 0.01 (0.01) 0.00 (0.03) 0.803 0.01 (0.01) 0.04 (0.03) 0.033 0.02 (0.03) 0.04 (0.03) 0.025 TCDCA 1rytc 2.39 (4.11) 3.51 (4.32) 0.247 4.39 (4.30) 3.21 (4.32) 0.247 2.05 (4.35) 4.23 (4.38) 0.034 DCA 2ryU 0.21 (0.31) 0.24 (0.26) 0.890 0.28 (0.51) 0.34 (0.23) 0.890 0.40 (0.57) 0.28 (0.15) 0.047 LCA 2ryU 0.02 (0.29) 0.02 (0.23) 0.609 0.03 (0.19) 0.01 (0.20) 0.609 0.00 (0.19) 0.02 (0.20) 0.840 GDCA 2rygc 0.11 (0.04) 0.11 (0.05) 0.765 0.11 (0.06) 0.11 (0.04) 0.765 0.11 (0.15) 0.10 (0.15) 0.990 TDCA 2rytc 0.01 (0.00) 0.01 (0.00) 0.698 0.00 (0.00) 0.00 (0.00) 0.698 0.00 (0.08) 0.00 (0.00) 0.386 TLCA 2rytc 0.00 (0.42) 0.01 (0.46) 0.286 0.00 (0.02) 0.02 (0.46) 0.026 0.00 (0.01) 0.01 (0.60) 0.049 UDCA 2ryU 0.00 (0.04) 0.00 (0.06) 0.263 0.00 (0.01) 0.00 (0.06) 0.363 0.00 (0.06) 0.00 (0.02) 0.974 GUDCA 2rygc 1.28 (0.39) 1.34 (0.52) 0.673 1.20 (0.50) 1.30 (0.40) 0.673 1.20 (1.74) 0.34 (1.70) 0.950 TUDCA 2rytg 0.01 (0.37) 0.01 (0.14) 0.842 0.01 (0.26) 0.01 (0.04) 0.842 0.01 (0.13) 0.01 (0.24) 0.568 Comparison of bile acid levels in NAFLD patients categorized by fibrosis (F0-F1 vs. F2-F3), inflammation (A0-A1 vs. A2-A3), and steatosis (G0-G1 vs. G2-G3) grades. Mann-Whitney U test comparison between two groups. indicates significant , data are presented as median (IQR), with P-value < 0.05 indicates the statistical significance of differences between groups. 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. Table 7: Binary Logistic Regression Analysis for Predicting Fibrosis, Lobular Inflammation, and Steatosis in NAFLD Fibrosis F0-1 vs F2-3 Active inflammation A0-1 vs A2-3 Steatosis G0-1 vs G2-3 Variable B Wald Sig. Exp(B) B Wald Sig. Exp(B) B Wald Sig. Exp(B) CA 1ryU 0.72 1.29 0.02 2.05 0.09 0.02 0.90 1.10 0.96 0.50 0.048 2.62 CDCA 1ryU -0.55 0.93 0.04 1.58 -0.41 0.48 0.49 0.66 0.23 0.09 0.017 1.25 GCA 1rygc -0.08 0.45 0.03 1.92 0.04 0.02 0.04 2.04 -0.09 0.47 0.041 2.92 GCDCA 1rygc -0.20 1.67 0.20 0.82 -0.19 0.89 0.35 0.82 0.14 0.45 0.50 1.15 TCA 1rytc 0.18 1.11 0.29 1.20 -0.06 0.13 0.02 1.94 0.12 0.54 0.46 1.12 TCDCA 1rytc 0.12 0.54 0.47 1.12 0.16 0.98 0.32 1.17 -0.12 0.55 0.46 0.89 DCA 2ryU 0.72 0.31 0.04 2.06 0.37 0.16 0.69 1.45 -1.42 0.71 0.042 3.24 LCA 2ryU -3.63 0.51 0.48 0.03 -0.67 0.40 0.53 0.51 -4.42 0.34 0.56 0.01 GDCA 2rygc -1.66 0.63 0.43 0.19 -0.08 0.00 0.95 0.93 -4.11 3.81 0.05 0.02 TDCA 2rytc -28.9 2.54 0.11 0.00 -1.03 1.24 0.27 0.36 1.47 1.21 0.04 4.35 TLCA 2rytc 2.91 2.70 0.10 18.39 2.77 1.76 0.03 15.95 14.45 2.45 0.03 20 UDCA 2ryU 19.17 0.93 0.34 20 16.48 1.12 0.03 20 17.13 1.19 0.02 20 GUDCA 2rygc 0.31 0.54 0.47 1.37 -0.02 0.00 0.96 0.98 -0.60 1.22 0.27 0.55 TUDCA 2rytg 0.59 1.16 0.28 1.80 0.16 0.14 0.71 1.18 0.85 0.96 0.33 2.34 The binary logistic regression analysis results are presented for three comparisons: fibrosis (F0-1 vs. F2-3), active inflammation (A0-1 vs. A2-3), and steatosis (G0-1 vs. G2-3) in NAFLD patients, using various bile acids as predictors. 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2024 Read the published version in Egyptian Liver Journal → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4896620","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341763796,"identity":"14791724-e3e3-45ab-bd31-4166f7f65925","order_by":0,"name":"Ashraf Abbass basuni","email":"","orcid":"","institution":"National Liver Institute, Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Ashraf","middleName":"Abbass","lastName":"basuni","suffix":""},{"id":341763797,"identity":"9ac2c1aa-b895-4b15-8a1a-251d65543ff8","order_by":1,"name":"Ashraf Khalil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFACHmYGhgIJGX5mxoYDH4B8NnaitBhI8Ei2Nzc+nAHSwkycFgYegzPHm415QAKEtPD3nz1s8MPAgofhRmKbtM2vbfJ8zAyMHz7m4NYicSMvObEH6DDGGUAtuX23DduYGZglZ27DY80NHuMDPEAtzBIgLT23GYFa2Jh58WiRP3/G+OAfoBY2kBbLntv2BLUYHMgxTgbZwsNzsNmY4cftRIJaDG/kGBvLALVIsDc2PuxtuJ3cxszYjNcvckCHSb6pqJOzP8z+4MCPP7dt57c3H/zwEZ/3UQBjG5hsIFY9CPwhRfEoGAWjYBSMFAAA+/ZMDHJ8GvEAAAAASUVORK5CYII=","orcid":"","institution":"National Liver Institute, Menoufia University","correspondingAuthor":true,"prefix":"","firstName":"Ashraf","middleName":"","lastName":"Khalil","suffix":""},{"id":341763798,"identity":"1b529ca3-7c9e-4035-8c6b-22a54b8a5950","order_by":2,"name":"Dina Sweed","email":"","orcid":"","institution":"National Liver Institute, Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"","lastName":"Sweed","suffix":""},{"id":341763799,"identity":"07ea1ad3-216a-4d95-8538-57a25c2ff135","order_by":3,"name":"Mohamed Fathey Elgazzar","email":"","orcid":"","institution":"National Liver Institute, Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Fathey","lastName":"Elgazzar","suffix":""}],"badges":[],"createdAt":"2024-08-11 21:03:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4896620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4896620/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43066-024-00378-9","type":"published","date":"2024-10-09T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66123368,"identity":"d8904b66-6a6b-475b-8c12-d447cd15ecd4","added_by":"auto","created_at":"2024-10-08 02:26:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3630372,"visible":true,"origin":"","legend":"\u003cp\u003eActivity grade of the metabolic-associated fatty liver disease (MAFLD) cases: Fig. 1a shows mild steatosis limited to zone 3, Steatosis 1 (H\u0026amp;E 100x), Fig. 1b shows moderate steatosis extending to zone 2, Steatosis 2 (H\u0026amp;E 100x), Fig. 1c shows severe steatosis involving zone 3, 2 and 1, Steatosis 3 (H\u0026amp;E 100x), Fig. 1d shows mild lobular necroinflammation (arrows) without associated hepatocytes ballooning degeneration, Activity 2 (H\u0026amp;E 200x), Fig. 1e shows moderate lobular necroinflammation (arrows) with mild hepatocytes ballooning degeneration, Activity 3 (H\u0026amp;E 200x), Fig. 1f shows marked lobular necroinflammation (arrows) with prominent hepatocytes ballooning degeneration, Activity 4 (H\u0026amp;E 200x).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4896620/v1/23dfe0d24a95b4cfc1210079.png"},{"id":66124203,"identity":"0e9a5b1f-1c6d-40ac-ad29-d0fe5fca9273","added_by":"auto","created_at":"2024-10-08 02:34:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":837278,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for Primary and Secondary Bile \u0026nbsp;\u0026nbsp;Acids in distinguishing healthy controls from NAST and NASH groups (top) and \u0026nbsp;\u0026nbsp;NAST from NASH bottom). The AUC values indicate the diagnostic \u0026nbsp;\u0026nbsp;accuracy, with sensitivity and specificity providing additional details on \u0026nbsp;\u0026nbsp;the effectiveness of each bile acid as a biomarker. The optimal cutoffs are \u0026nbsp;\u0026nbsp;the thresholds that maximize the sum of sensitivity and specificity, \u0026nbsp;\u0026nbsp;indicating the most effective point for differentiating the conditions.\u003c/p\u003e\n\u003cp\u003eNHC: Normal \u0026nbsp;\u0026nbsp;healthy control, NAST: Nonalcoholic steatosis, NASH: Nonalcoholic \u0026nbsp;\u0026nbsp;steatohepatitis.\u003c/p\u003e\n\u003cp\u003e1ryU: primary \u0026nbsp;\u0026nbsp;unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, \u0026nbsp;\u0026nbsp;2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: \u0026nbsp;\u0026nbsp;secondary tauroconjugated.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4896620/v1/3f142a779d7747fa70d51281.png"},{"id":66123366,"identity":"fcf22147-7a2d-4a81-a4ce-febcea9ddede","added_by":"auto","created_at":"2024-10-08 02:26:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464586,"visible":true,"origin":"","legend":"\u003cp\u003eScore plot from Principal Component Analysis (PCA) \u0026nbsp;\u0026nbsp;illustrating the separation of bile acid profiles among NHC, NAST, and NASH \u0026nbsp;\u0026nbsp;groups. The x-axis represents the first principal component (PC1), which \u0026nbsp;\u0026nbsp;explains 37.65% of the variance, while the y-axis represents the second \u0026nbsp;\u0026nbsp;principal component (PC2), accounting for 18.17% of the variance. The plot \u0026nbsp;\u0026nbsp;shows the distinct clustering of the groups, highlighting the differential \u0026nbsp;\u0026nbsp;bile acid signatures that distinguish the groups. The percentage values \u0026nbsp;\u0026nbsp;indicate the proportion of total variance captured by each component. \u0026nbsp;NHC: Normal \u0026nbsp;\u0026nbsp;healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic \u0026nbsp;\u0026nbsp;steatohepatitis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4896620/v1/f6e76ce85ffd66de27aecdd0.png"},{"id":66597368,"identity":"af4e6675-bf25-4661-a8a6-c0c1b77e056d","added_by":"auto","created_at":"2024-10-14 16:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7328348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4896620/v1/d681fb21-3836-48b7-a940-1bb386f41aaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Serum Bile Acids as Potential Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) includes a spectrum of liver disorders, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH).\u0026nbsp;The mildest form, nonalcoholic fatty liver (NAFL), is characterized by lipid accumulation in hepatocytes, also known as hepatic steatosis. However, 20%–30% of NAFL cases may progress to NASH, marked by significant lobular inflammation and hepatocyte ballooning, potentially leading to fibrosis and cirrhosis[1]. NAFLD has emerged as the leading cause of chronic liver disease worldwide, with a prevalence estimated at 25%\u0026nbsp;[2-4]. NASH, a severe form of NAFLD, affects about 1.5% to 6.45% of the population and can lead to serious complications such as cirrhosis, hepatocellular carcinoma, and liver-related mortality. The prevalence of NASH is projected to increase by 63% between 2015 and 2030, posing significant challenges to global healthcare\u0026nbsp;[4-6].\u003c/p\u003e\n\u003cp\u003eThe pathogenesis of NASH is complex and involves multiple mechanisms, including abnormal lipid accumulation and inflammation within hepatocytes or extrahepatic tissues, leading to hepatotoxic injuries[7]. NAFLD is strongly associated with metabolic dysfunctions, such as insulin resistance, obesity, and type 2 diabetes, particularly in Western populations. To better describe the metabolic basis of NAFLD, the term metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed\u0026nbsp;[8, 9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBile acids are increasingly recognized as critical signaling molecules in NASH pathogenesis, facilitating communication between the liver, intestine, and other organs. These molecules, synthesized from cholesterol in the liver, play a vital role in emulsifying fats and in the digestion and absorption of lipids and fat-soluble vitamins. Primary bile acids, such as cholic acid and chenodeoxycholic acid, are transformed by intestinal bacteria into secondary bile acids, including lithocholic acid and deoxycholic acid[10, 11]. Besides their traditional roles, bile acids act as signaling molecules that regulate metabolic homeostasis and immune responses, primarily through receptors such as FXR and G protein-coupled bile acid receptor 1 (TGR5)[12]. Dysregulation of bile acid homeostasis is implicated in several metabolic diseases, including NASH, making bile acids and their receptors potential therapeutic targets\u0026nbsp;[12, 13].\u003c/p\u003e\n\u003cp\u003eDespite advances in understanding NAFLD, predicting which patients are at risk for disease progression and complications remains challenging\u0026nbsp;[14]. Liver biopsy is the gold standard for assessing inflammation and fibrosis in NAFLD but is invasive and carries risks such as bleeding and infection. Efforts to identify noninvasive biomarkers, particularly for inflammation and fibrosis, have produced mixed results, limiting their clinical utility\u0026nbsp;[15-17]. Bile acids have emerged as promising biomarkers due to their roles in lipid absorption and regulation of hepatic glucose and lipid metabolism. High serum insulin levels can inhibit bile acid synthesis by suppressing CYP7A1, a key enzyme in bile acid biosynthesis, while elevated bile acids can reduce insulin secretion via glucagon-like peptide 1. This complex interplay highlights the connections between NAFLD, metabolic syndrome, and gut health[18-21].\u003c/p\u003e\n\u003cp\u003eThe aim of this study is to comprehensively evaluate bile acid profiles as noninvasive biomarkers in NAFLD, with the goal of establishing these profiles as reliable diagnostic tools for assessing liver disease severity and predicting progression to NASH.\u003c/p\u003e\n\n\n\n\n\n\n\n"},{"header":"Patients","content":"\u003cp\u003eThe study was conducted at the Department of Clinical Biochemistry and Molecular Diagnostics and the Department of Hepatobiliary and Gastroenterology from October 2022 to August 2024. The cohort included 75 biopsy-proven NAFLD patients, and a corresponding control group of 25 individuals, free from any liver impairment. The study received approval from the Ethics Committee of the National Liver Institute (IRB 00570/2024), and written consent was obtained from all participants. NAFLD diagnosis was established through abdominal ultrasound and liver biopsy but healthy controls were not subjected to liver biopsies[5]. The liver biopsy specimens were histologically assessed for steatosis, inflammation, and fibrosis according to the criteria established by the NAFLD Clinical Research Network\u0026nbsp;[22].\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe findings are illustrated in Figure 1. Patients with NAFLD were divided into two subgroups based on specific histological criteria into nonalcoholic steatosis (NAST, n=35) and nonalcoholic steatohepatitis (NASH, n=40). NAST patients included those with minimal or no fibrosis (F0-F1), no evidence of inflammation (A0-A1), but with varying degrees of steatosis; G0 (\u0026lt;5%,), G1 (up to 33%,), G2 (33% to 66%,), and G3 (\u0026gt;66%) of hepatocytes. NASH patients, besides having evidence of steatosis and or fibrosis, also showed evidence of inflammation greater than grade ≥ A2 The NAS was used to evaluate the severity of liver disease, ranging from 0 to 8. This score is calculated by summing the scores of steatoses (0-3), lobular inflammation (0-3), and hepatocyte ballooning (0-2). A NAS score of ≥ 5 strongly indicated NASH, while a score of ≤ 3 was associated with NAST\u0026nbsp;[22].\u003c/p\u003e\u003cp\u003eExclusion criteria encompassed patients with viral or autoimmune liver diseases, hepatotoxic drug use, iron overload, Wilson's disease, chronic cholestasis, and extrahepatic obstructive gall bladder diseases. Additionally, individuals presenting with liver diseases associated with severe renal or systemic conditions were excluded from the study.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSerum Sample Collection and Bile Acid Measurement:\u0026nbsp;\u003c/strong\u003eBlood samples were obtained from both patients and controls following an overnight fast (8-12h). Three milliliters of blood were collected using sterile venipuncture techniques, and the extracted serum was stored at -80°C until analysis. Laboratory measurements, encompassing fasting blood glucose, HbA1c, lipid profile including total cholesterol, high density lipoprotein (HDL)-cholesterol, low density lipoprotein (LDL)-cholesterol, triglycerides; liver function tests AST, ALT, gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), direct and total bilirubin, albumin, and total proteins, AFP; kidney function tests as BUN, creatinine were conducted through standardized laboratory methods (Cobas 8000, Roche Diagnostics GmbH, Mannheim, Germany). Serum bile acids concentrations were measured in serum using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS), employing a reversed-phase (C18) column (1.7 µm, 100 mm × 2.1 mm internal dimensions) (Waters ACQUITY, Milford, MA) and a methanol/water gradient. The assay included a total of 14 bile acids, categorized into six primary and eight secondary bile acids. The primary bile acids consisted of unconjugated forms, including CA (cholic acid) and CDCA (chenodeoxycholic acid), and conjugated forms, including GCA (glycocholic acid), GCDCA (glycochenodeoxycholic acid), TCA (taurocholic acid), and TCDCA (taurochenodeoxycholic acid). The secondary bile acids included unconjugated forms, including DCA (deoxycholic acid) and LCA (lithocholic acid), and conjugated forms, including GDCA (glycodeoxycholic acid), TDCA (taurodeoxycholic acid), TLCA (taurolithocholic acid), UDCA (ursodeoxycholic acid), GUDCA (glycoursodeoxycholic acid), and TUDCA (tauroursodeoxycholic acid).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSerum Sample Preparation and Bile Acid Detection:\u003c/strong\u003e Serum bile acids underwent preparation for UPLC/MS/MS as previously described [13, 14]. Briefly, 100 µl of serum samples were treated with 400 µl of ice-cold 100% methanol, followed by centrifugation at 12,000 rpm for 20 min. Fifty microliters of the supernatant were mixed with 100 µl formic acid (1:1000), and 5 µl were injected into a C18 column at 50°C. Bile acids were eluted by gradient at a flow rate of 0.5 ml/min, with the mass spectrometer operating in the negative ion mode via Multiple Reactions Monitoring (MRM). UPLC-MS data were analyzed using MassLynx software version 4.1 (Waters Corp., Milford, MA, USA) to generate calibration equations and calculate the quantitative concentration of each bile acid in the sample. The UPLC-MS/MS analysis demonstrated robust performance including precision and accuracy at concentrations of 0.02 µMol/L, 0.2 µMol/L, and 2 µMol/L, exhibiting low relative standard deviation (RSD%) values for excellent precision. Relative error (RE%) values approximating 100% underscored the accuracy of quantitation. All the individual 14 bile acids were effectively resolved and quantified. The assay's linearity extended from 0.012 to 5x10\u003csup\u003e3\u003c/sup\u003e µMol/L for all bile acids, emphasizing its suitability across a wide concentration range. With a lower quantitation limit of 2 ng/mL, the UPLC-MS/MS assay demonstrated sensitivity, making it reliable for quantitative assessments in biomedical research.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eData were analyzed using SPSS 23 (SPSS Inc., CA, USA). The Kolmogorov-Smirnov test was employed to verify the normality of distribution. Numerical variables were presented as mean ± standard deviation or as medians and interquartile ranges (IQR), while categorical variables were expressed as numbers (percentages). Differences between groups were analyzed using the Kruskal-Wallis H test and the Mann-Whitney U test as appropriate. A P-value of \u0026lt; 0.05 was considered statistically significant for all analyses. Non-parametric Spearman correlation was used to assess the relationships between bile acids and other numerical variables, such as lipid profiles and blood chemistry parameters. Principal Component Analysis (PCA) was conducted to explore patterns in bile acid concentrations among the study cohort, which included individuals with NAFLD and control subjects. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory power of bile acids in distinguishing controls from NAFLD cases. Binary logistic regression was applied to assess the predictive value of bile acids for fibrosis, lobular inflammation, and steatosis in NAFLD patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics in Normal and NAFLD Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in Table 1 summarize the clinical characteristics of the study population, divided into three groups: Control, NASH, and NAST. NASH and NAST groups had a significantly higher proportion of hyperlipidemic patients compared to the control group (\u0026chi;\u0026sup2; = 14.23, p = 0.001). The NASH and NAST groups demonstrated higher mean body mass index (BMI) values compared to the control group (p \u0026lt; 0.001), indicating a greater prevalence of obesity among patients with NASH and NAST. Furthermore, the presence of diabetes mellitus was more common in the NASH and NAST groups, with a higher percentage of patients being diagnosed with DM (\u0026chi;\u0026sup2; = 5.42, p = 0.067).\u003c/p\u003e\n\u003cp\u003eAge differences across the groups were not statistically significant (t = 1.02, p = 0.313), suggesting that age is not a primary differentiating factor in the progression of NAFLD. Similarly, gender distribution did not differ significantly between the groups (\u0026chi;\u0026sup2; = 0.73, p = 0.695).\u003c/p\u003e\n\u003cp\u003eHypertension prevalence was assessed across the groups, but no significant differences were observed (\u0026chi;\u0026sup2; = 1.44, p = 0.487). However, the trend indicated a higher occurrence of hypertension in the NASH group compared to the control and NAST groups.\u003c/p\u003e\n\u003cp\u003eThere were significant differences in fibrosis and inflammation activity scores between the groups, with NASH patients having more advanced fibrosis (F2-F3) and higher activity (A2-A3) compared to the control group (p \u0026lt; 0.001). The distribution of steatosis grades and ultrasound findings also differed significantly among the groups, with the NASH and NAST groups showing more severe liver changes. These results highlight the progressive nature of liver disease in NAFLD and underscore the importance of early diagnosis and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical and Hematological Parameters Across Control, NASH, and NAST Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 details the biochemical and hematological parameters, showing significantly higher median FBS levels in NASH and NAST groups, indicating a tendency towards hyperglycemia. Direct bilirubin and GGT levels were elevated in NASH and NAST groups, suggesting liver dysfunction. Lipid profile analysis revealed higher total cholesterol levels in NASH and NAST groups, reflecting dyslipidemia. Hematological markers such as hemoglobin and platelet count remained stable across groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlterations in Bile Acid Across NAFLD Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e summarizes the bile acid profiles among NHC, NAST, and nonalcoholic steatohepatitis (NASH). Primary unconjugated bile acids, CA and CDCA, were significantly elevated in both NAST and NASH compared to NHC (all p \u0026lt; 0.05), with no significant differences between NAST and NASH (all p \u0026gt; 0.05). Similarly, primary conjugated bile acids, including GCA, GCDCA, TCA, and TCDCA, were significantly higher in NASH and NAST compared to NHC (all p \u0026lt; 0.05). GCA and TCA levels were notably higher in NASH compared to NAST (all p \u0026lt; 0.05), whereas GCDCA and TCDCA did not differ significantly between NAST and NASH (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eSecondary bile acids, LCA, TLCA, GUDCA, and TUDCA, were significantly elevated in NASH and NAST compared to NHC (all p \u0026lt; 0.05), with no significant differences between NAST and NASH (all p \u0026gt; 0.05). DCA, GDCA, TDCA, and UDCA did not show significant differences between either NAST or NASH and NHC (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBile Acids in Discriminating NAFLD Patients and Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves (Figure2) and Principal Component Analysis (PCA) (Figure3) were used to examine the capacity of bile acids in discriminating healthy from NAFLD subgroups NAST and NAST. Figure 2a-b\u0026nbsp;summarize the ROC curve analysis for primary and secondary bile acids in differentiating healthy controls from NAFLD patients (NAST+NASH). The primary unconjugated bile acid CDCA showed high diagnostic potential with an AUC of 0.937, 100% specificity, and 74.7% sensitivity at a cutoff of 0.002. The primary conjugated bile acid GCDCA achieved an AUC of 0.877, 100% specificity, and 80% sensitivity at a cutoff of 2.48.\u003c/p\u003e\n\u003cp\u003eThe secondary bile acids collectively demonstrated moderate diagnostic performance. LCA had an AUC of 0.864, with 96% specificity and 74.7% sensitivity at a cutoff of 0.002, while TLCA had an AUC of 0.840, with 12% specificity and 80% sensitivity at a cutoff of 0.002. Other secondary bile acids, including TUDCA and GDCA, displayed varying degrees of diagnostic accuracy, with AUCs ranging from 0.784 to 0.420, reflecting different levels of specificity and sensitivity.\u003c/p\u003e\n\u003cp\u003eOverall, primary bile acids exhibited superior diagnostic accuracy compared to secondary bile acids, underscoring their potential utility as biomarkers for distinguishing NAFLD patients from healthy controls. In the subsequent analysis comparing NAST and NASH, the ROC curves for both primary and secondary bile acids showed limited diagnostic utility, with AUC values generally indicating weak discrimination. Primary bile acids like CDCA and CA had AUCs of 0.596 and 0.575, respectively, demonstrating moderate accuracy. Sensitivity and specificity were inconsistent, with significant variability. Secondary bile acids exhibited similarly low AUC values, ranging from 0.466 for LCA to 0.571 for TLCA, indicating limited differentiation between the conditions (Figure 2c-d). These findings, suggest that the bile acids assessed are not robust biomarkers for distinguishing NAST from NASH. \u0026nbsp;PCA of these bile acids across the studied groups. PCA demonstrated that the first two principal components (PC1 and PC2) accounted for 55.82% of the total variance, with eigenvalues of 5.27 and 2.54, respectively. Key bile acids, including GCDCA, CA, and CDCA, emerged as significant contributors, underscoring their potential as biomarkers for differentiating NAFLD from the control group (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of Gender, Diabetes Status, and Lipid Profile on Bile Acid Levels in NAST and NASH Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the analysis of bile acid levels in NAFLD (NAST and NASH) patients across gender, diabetes status, and lipid profile, highlighting several key differences. Significant differences were observed in TDCA and TUDCA levels between genders, with females showing higher levels (p = 0.033 and p = 0.037, respectively). For diabetes status, CA, CDCA, GCA, and TCDCA levels were significantly different, with the highest levels found in the DM group (p \u0026lt; 0.05 for all). Hyperlipidemic patients exhibit higher significant differences in bile acids CA, LCA, GCA and GUDCA than\u0026nbsp;normolipidemic with\u0026nbsp;p \u0026lt; 0.05 for all these bile acids, suggesting that lipid profile substantially impact bile acid metabolism in NAFLD. \u0026nbsp;Further\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecorrelation analysis between bile acids and lipid profile in NAST and NASH revealed several significant associations between some bile acids and lipid profile parameters (Table5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the NAST group, CA and CDCA had strong negative correlations with LDL (CA: r=-0.57**, p\u0026lt;0.01; CDCA: r=-0.53**, p\u0026lt;0.01) and positive correlations with HDL (CA: r=0.57**, p\u0026lt;0.01; CDCA: r=0.56**, p\u0026lt;0.01). LCA and GDCA were also negatively correlated with LDL (LCA: r=-0.69**, p\u0026lt;0.01; GDCA: r=-0.41*, p\u0026lt;0.05) and positively correlated with HDL (LCA: r=0.70**, p\u0026lt;0.01); GDCA: r= 0.39*, p\u0026lt;0.05). Conversely, other bile acids did not show significant correlations with lipid components.\u003c/p\u003e\n\u003cp\u003eIn the NASH group, unconjugated bile acids, CA was positively correlated with HDL (CA: r=0.42**, p\u0026lt;0.01) and negatively correlated with LDL (CA: r=-0.31*, p\u0026lt;0.05). Primary conjugated bile acids GCA, GCDCA and TCDCA were positively correlated with cholesterol (GCA: r=0.49**, p\u0026lt;0.01; GCDCA: r=0.38*, p\u0026lt;0.05; TCDCA: r=0.31*, p\u0026lt;0.05). Secondary bile acids, GDCA, and GDUCA were positively correlated with cholesterol (GDCA: r=0.39*, p\u0026lt;0.01; GDUCA: r=0.38*, p\u0026lt;0.05) and HDL (GDCA: r=0.46**, p\u0026lt;0.01; GUDCA: r=0.50**, p\u0026lt;0.01) and negatively correlated with LDL (GDCA: r=-0.44**, p\u0026lt;0.01; GDUCA: r=-0.41*, p\u0026lt;0.05). TUDCA positively correlated with cholesterol (r=0.37*, p\u0026lt;0.05) and HDL (r= 0.38*, p\u0026lt;0.05). Other bile acids did not show significant correlations with any lipid components.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBile Acid Levels Across Fibrosis, Inflammation, and Steatosis Grades in NAFLD Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 presents a detailed comparison of bile acid levels across various stages of fibrosis, inflammation, and steatosis in patients with either NAST or NASH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of fibrosis: CA, CDCA and GCA levels were significantly higher in the F2-F3 group compared to the F0-F1 group (all p \u0026lt; 0.05). No significant differences were found for, in the primary conjugated GCDCA, TCA, TCDCA, and all the secondaries uncogitated and conjugated bile acid DCA, LCA, GDCA, TDCA, TLCA, UDCA, GUDCA, and TUDCA (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn the context of inflammation GCA, TCA and TLCA levels were significantly higher in the A2-A3 group compared to A0-A1 (p \u0026lt; 0.05). In contrast, LCA levels were notably higher in A0-A1 compared to A2-A3 but did not reach the level of significant (p \u0026gt; 0.05). No significant differences were found for CDCA, GCDCA, TCDCA, DCA, GDCA, TDCA, UDCA, GUDCA, and TUDCA (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn the context of steatosis, CA, GCA, TCA, TCDCA, and TLCA levels were significantly elevated in the G2-G3 group compared to G0-G1 (CA: p = 0.003; GCA: p = 0.032). DCA levels were significantly higher in G0-G1 compared to G2-G3 (p \u0026lt; 0.05). No significant differences were observed for CDCA, GCDCA, LCA, GDCA, TDCA, UDCA, GUDCA, and TUDCA (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Value of Bile Acids for Fibrosis, Inflammation, and Steatosis in NAFLD: Logistic Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 reveals the logistic regression analysis results for bile acids as predictors of fibrosis, active inflammation, and steatosis in NAFLD patients (NAST and NASH). For fibrosis the analysis identified several bile acids with significant associations with fibrosis, CA, CDCA and DCA were notable predictors, with CA showing an odds ratio of 2.05 (p = 0.02); CDCA had an odds ratio of (Exp(B) = 1.58, p = 0.04) and DCA an odds ratio of 2.06 (p = 0.04).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of active inflammation, GCA was a significant predictor (Exp(B) = 1.92, p = 0.03), along with TCA (Exp(B) = 1.94, p = 0.02, and TLCA (Exp(B) = 15.95, p = 0.03). CDCA did not show a significant association with inflammation (Exp(B) = 0.49, p = 0.66).\u003c/p\u003e\n\u003cp\u003eIn the context of steatosis, 1ry bile acids CA, CDCA, and GCA, were significant predictors (CA: Exp(B) = 2.62, p = 0.048; CDCA: Exp(B) = 1.25, p = 0.017; GCA: Exp(B) = 2.92, p = 0.041. Additionally,2ry Bile acid, DCA, TDCA, TLCA, UDCA showed a significant association and predictor of steatosis DCA: Exp(B) = 3.24, p = 0.042; TDCA: Exp(B) = 4.35, p = 0.04; TLCA: Exp(B) = 20, p = 0.03); and UDCA (Exp(B) = 20, p = 0.02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese results highlight the role of specific bile acids, such as CA, CDCA and GCA, in predicting NAFLD severity, with implications for both fibrosis and steatosis. The significant associations of DCA, TDCA, and TLCA with steatosis suggest their potential importance in hepatic fat accumulation. Additionally, the significant correlations of GCA, TCA, and TLCA with inflammation suggest their potential utility in predicting liver inflammation activity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive analysis of bile acid profiles across the spectrum of NAFLD, from simple NAST to the more advanced hepatic inflammation in nonalcoholic steatohepatitis (NASH) and fibrosis. The findings underscore the critical role of bile acids as potential biomarkers in the progression of NAFLD, offering insights into the metabolic disturbances underlying the disease. Significant differences in clinical parameters, such as BMI, gender, and the prevalence of dyslipidemia and diabetes mellitus, were observed between the NASH and NAST groups compared to the NHC group. These differences align with existing literature that links metabolic disturbances, including obesity, dyslipidemia, and diabetes mellitus, to the severity of NAFLD, further emphasizing the role of these factors in NAFLD progression\u0026nbsp;[3, 5].\u003c/p\u003e\n\u003cp\u003eThe main finding of the current study is that bile acid profiles are significantly altered in NAFLD compared to healthy controls, with specific bile acids potentially serving as biomarkers for distinguishing between healthy individuals and NAFLD patients. Additionally, these bile acids may have predictive value for the progression of the disease, particularly in relation to fibrosis, inflammation, and steatosis.\u003c/p\u003e\n\u003cp\u003eBoth primary unconjugated and conjugated bile acids were significantly elevated in NAST and NASH compared to healthy controls, with a particular increase in CA, CDCA, GCA, and TCA levels. The minimal differences between NAST and NASH in many bile acids suggest that these metabolic alterations may occur early in the disease process and persist as NAFLD progresses. These results are in line with previous research indicating that bile acid metabolism is disrupted in NAFLD, likely due to impaired hepatic bile acid synthesis and secretion, as well as altered gut microbiota[23-27].\u003c/p\u003e\n\u003cp\u003eSecondary bile acids also showed significant changes across NAFLD groups. Elevated levels of secondary bile acids such as LCA, TLCA, GUDCA, and TUDCA were observed in both NASH and NAST compared to NHC, reflecting the disruption in bile acid metabolism. However, no significant differences were found between NAST and NASH, indicating that secondary bile acids might not provide robust differentiation between these subtypes. These findings align with Cassey et al., who reported higher total primary bile acids and lower secondary bile acids in NAFLD patients compared to controls\u0026nbsp;[28]. Gillard et al. also noted variability in bile acid levels across NAFLD studies, with some reports showing elevated levels and others unchanged\u0026nbsp;[28, 29]. Chen et al. (2020) emphasized the role of gut microbiota in altering bile acid profiles, which is consistent with the observed increase in primary bile acids and decrease in secondary bile acids in NASH[25].\u003c/p\u003e\n\u003cp\u003eDiagnostic assessments of the discriminative power of bile acids through ROC curve analysis and PCA further highlight the potential of certain bile acids, especially primary bile acid CA, CDCA, GCDCA, and TCDCA and secondary bile acids LCA and TLCA in distinguishing NAFLD patients from healthy individuals with moderate sensitivity and high specificity, however, the limited ability of these bile acids to differentiate between NAST and NASH, for both primary and secondary bile acids, suggests that while bile acids are useful in identifying the presence of NAFLD, they may not be as effective in distinguishing its subtypes. PCA also identified significant contributors, including GCDCA, CA, and CDCA, indicating their relevance in distinguishing NAFLD from healthy states. These results mirror findings from other studies that emphasize the metabolic alterations in bile acids during NAFLD progression[30]. Notably, both ROC analysis and PCA demonstrated limited effectiveness in differentiating between NAST and NASH, suggesting these bile acids alone are insufficient for precise NAFLD subtyping. This limitation is consistent with broader challenges in the field, where a need for more specific biomarkers has been frequently noted[31, 32].\u003c/p\u003e\n\u003cp\u003eThe study demonstrated significant bile acid dysregulation in diabetic and pre-diabetic patients, particularly elevated levels of CA, CDCA, GCA, and TCDCA, with the highest levels observed in diabetic patients. These elevations suggest a progressive impairment in bile acid metabolism linked to worsening glucose regulation. Additionally, hyperlipidemic patients exhibited higher levels of CA, LCA, GCA, and GUDCA, highlighting the interplay between lipid metabolism and bile acid profiles in NAFLD. The presence of elevated bile acids in pre-diabetic individuals indicates early metabolic changes, underscoring their potential as early biomarkers for disease progression. These findings align with previous research, which has reported similar increases in unconjugated bile acids, particularly CA, in diabetic and pre-diabetic patients, and suggest a compensatory increase in conjugated bile acids as an adaptive response\u0026nbsp;[33, 34]\u0026nbsp;[26, 27, 35, 36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study also observed gender differences, with female patients exhibiting higher levels of TDCA and TUDCA. These findings align with the findings of Puri et al., who suggested that sex hormones significantly influence bile acid metabolism by impacting the enzymes involved in bile acid synthesis and clearance. These results underscore the complexity of bile acid metabolism and highlight the importance of considering factors such as diabetes status, lipid profile, and gender when interpreting bile acid levels in clinical practice\u0026nbsp;[37].\u003c/p\u003e\n\u003cp\u003eExploring the relationship between bile acid levels and the severity of steatosis, inflammation, and fibrosis in NAFLD patients revealed that high levels of CA, GCA, TCA, TCDCA, DCA and TLCA, were found to be significantly associated with of steatosis. Higher levels of GCA, TCA, and TLCA were associated with liver inflammation, however, CA, CDCA, and GCA were significantly elevated in patients with advanced fibrosis. The logistic regression analysis also identified several bile acids as significant predictors of either steatosis, inflammation or fibrosis. Elevated CA, CDCA, GCA, TDCA, DCA TLCA, and UDCA were found to be significant predictors of steatosis. GCA, TCA and TLCA were identified as significant predictors of inflammation. Similarly, CA, CDCA, GCA and DCA were identified as significant predictors of fibrosis, emphasizing their potential utility in monitoring disease progression and severity.\u003c/p\u003e\n\u003cp\u003ePrevious research partially or fully supports such associations[37-39]. For instance, Aranha et al. (2008) observed that elevated levels of CA, CDCA, and DCA in liver tissue correlated with steatosis and fibrosis in NASH patients[40]. Similarly, Puri et al. (2018) reported that increased serum levels of these bile acids were associated with steatosis and fibrosis in NAFLD patients, suggesting a role for these bile acids in the early detection and progression of the disease[37]. Furthermore, the elevated levels of GCA and DCA observed in this study align with earlier findings, indicating that these bile acids are involved in the pathogenesis of NAFLD and may reflect alterations in bile acid metabolism and liver function[25, 41, 42].\u003c/p\u003e\n\u003cp\u003eChen et al. (2020) highlighted the importance of bile acids CA and CDCA in the progression from simple steatosis to more advanced stages of liver disease, emphasizing their role in the inflammatory and fibrotic processes[25]. TLCA, a secondary bile acid, has also been identified in previous studies as a marker of disease severity. Caussy et al. (2019) demonstrated significant changes in TLCA levels corresponding with advanced liver fibrosis, suggesting that TLCA could be a valuable marker for assessing the extent of liver damage\u0026nbsp;[28]. Elevated levels of CA, CDCA, and TLCA in this study are consistent with observations from Khalil et al. (2022), who reported that elevated serum levels of these bile acids are associated with liver fibrosis and cirrhosis\u0026nbsp;[43]. Additionally, Gottlieb and Canbay (2019) noted that increased conjugated bile acids such as TLCA are indicative of heightened inflammatory activity, which aligns with our findings that these bile acids are significant predictors of inflammation\u0026nbsp;[41].\u003c/p\u003e\n\u003cp\u003eIn summary, the study\u0026apos;s findings that CA, CDCA, GCA, and DCA are predictors of steatosis, and CA, CDCA, TCA, and TLCA are predictors of fibrosis and inflammation, underscore the potential utility of these bile acids in monitoring the progression and severity of NAFLD. These observations are supported by existing literature, which highlights the role of these bile acids in the pathogenesis and progression of liver disease. The identification of these bile acids as significant predictors provides valuable insights into their potential use as biomarkers for early diagnosis, disease monitoring, and therapeutic targeting in NAFLD.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. The cross-sectional design limits the ability to establish causality between bile acid alterations and NAFLD progression. Additionally, the sample size, while sufficient for detecting significant differences, may limit the generalizability of the findings. The study also did not account for dietary intake or genetic factors, which could influence bile acid metabolism[30, 44-46].\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study highlights the significant alterations in bile acid profiles in NAFLD and their potential utility as biomarkers for disease presence and progression. The associations identified between specific bile acids such as (CA, GCA, TCA, TCDCA, DCA and TLCA), and key pathological features like steatosis, fibrosis, and inflammation, underscore their relevance as indicators of disease severity. The predictive value of CA, CDCA, and TLCA for fibrosis and inflammation further emphasizes their potential role in monitoring NAFLD progression. These findings are consistent with existing literature, which underscores the involvement of these bile acids in the pathogenesis and advancement of liver disease [38, 44]. The identification of these bile acids as significant predictors offers valuable insights into their potential application in early diagnosis, disease monitoring, and therapeutic targeting in NAFLD. Future research should focus on longitudinal studies to track changes in bile acid profiles over time and assess their relationship with NAFLD progression, as well as evaluate the impact of therapeutic interventions on these biomarkers[47].\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH: Nonalcoholic steatohepatitis, NAFLD: Nonalcoholic fatty liver disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCA: Cholic acid, CDCA: Chenodeoxycholic acid, GCA: Glycholic acid, GCDCA: Glycochenodeoxycholic acid, TCA: Taurocholic acid, TCDCA: Taurochenodeoxycholic acid, DCA: Deoxycholic acid, LCA: Lithocholic acid, GDCA: Glycodeoxycholic acid, TDCA: Taurodeoxycholic acid, TLCA: Taurolithocholic acid, UDCA: Ursodeoxycholic acid, GUDCA: Glycoursodeoxycholic acid, TUDCA: Tauroursodeoxycholic acid. 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary taurocongugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n\u003cp\u003eAST: aspartate transaminase, ALT: alanine transaminase, GGT: Gamma-glutamyl transferase, ALP: alkaline phosphatase, TBil: total bilirubin, DBIL: direct bilirubin, TP: total protein, Alb: albumin, UA: uric acid, Chol: Cholesterol, LDL: low density lipoprotein, HDL: high density lipoprotein. TG: triglyceride. Hb: hemoglobin, WBCs: white blood cells. RBCs: red blood cells.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe research ethics committees of the National Liver Institute (IRB005700 -2024), Menoufia University, approved the research proposal and the protocols to comply with national research guidelines. Patients provided informed written consent for the use of tissue for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e obtained from all participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare they do not have any conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare they do not have any financial disclosure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions: AAB\u003c/strong\u003e: Conducted the UPLC/MS/MS analysis method. Contributed to the study concept and design, manuscript preparation. \u003cstrong\u003eMFE:\u003c/strong\u003e Patient recruitment and evaluation, collection of clinical data. \u003cstrong\u003eDA\u003c/strong\u003e: Conducted the pathological examination of the liver biopsy, staged and confirmed the diagnosis, and contributed to the concept and protocol development. \u003cstrong\u003eAK\u003c/strong\u003e: Corresponding author, study concept and design, analyzed the data, generated the result, wrote and edited the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors would like to express their gratitude to Dr. Samah Elsheaty, Dr. Ghada Salah, Dr. Azza Elsheashaey, and Ms. Maryam Nagah for their dedicated efforts in patient care during sample collection and their outstanding work with HPLC at the National Liver Institute, Menoufia University.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiehl, AM and C Day, \u003cem\u003eCause, Pathogenesis, and Treatment of Nonalcoholic Steatohepatitis.\u003c/em\u003e N Engl J Med, 2017. \u003cstrong\u003e377\u003c/strong\u003e(21): p. 2063-2072.\u003c/li\u003e\n\u003cli\u003ePaik, JM, P Golabi, Y Younossi, A Mishraand ZM Younossi, \u003cem\u003eChanges in the Global Burden of Chronic Liver Diseases From 2012 to 2017: The Growing Impact of NAFLD.\u003c/em\u003e Hepatology, 2020. \u003cstrong\u003e72\u003c/strong\u003e(5): p. 1605-1616.\u003c/li\u003e\n\u003cli\u003eYounossi, Z, F Tacke, M Arrese, B Chander Sharma, I Mostafa, E Bugianesi. MB Vos, \u003cem\u003eGlobal Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis.\u003c/em\u003e Hepatology, 2019. \u003cstrong\u003e69\u003c/strong\u003e(6): p. 2672-2682.\u003c/li\u003e\n\u003cli\u003eDulai, PS, S Singh, J Patel, M Soni, LJ Prokop, Z Younossi. R Loomba, \u003cem\u003eIncreased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis.\u003c/em\u003e Hepatology, 2017. \u003cstrong\u003e65\u003c/strong\u003e(5): p. 1557-1565.\u003c/li\u003e\n\u003cli\u003eChalasani, N, Z Younossi, JE Lavine, M Charlton, K Cusi, M Rinella. AJ Sanyal, \u003cem\u003eThe diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases.\u003c/em\u003e Hepatology, 2018. \u003cstrong\u003e67\u003c/strong\u003e(1): p. 328-357.\u003c/li\u003e\n\u003cli\u003eEstes, C, H Razavi, R Loomba, Z Younossiand AJ Sanyal, \u003cem\u003eModeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease.\u003c/em\u003e Hepatology, 2018. \u003cstrong\u003e67\u003c/strong\u003e(1): p. 123-133.\u003c/li\u003e\n\u003cli\u003eBessone, F, MV Razoriand MG Roma, \u003cem\u003eMolecular pathways of nonalcoholic fatty liver disease development and progression.\u003c/em\u003e Cell Mol Life Sci, 2019. \u003cstrong\u003e76\u003c/strong\u003e(1): p. 99-128.\u003c/li\u003e\n\u003cli\u003eJuanola, O, S Martinez-Lopez, R Francesand I Gomez-Hurtado, \u003cem\u003eNon-Alcoholic Fatty Liver Disease: Metabolic, Genetic, Epigenetic and Environmental Risk Factors.\u003c/em\u003e Int J Environ Res Public Health, 2021. \u003cstrong\u003e18\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eEslam, M, PN Newsome, SK Sarin, QM Anstee, G Targher, M Romero-Gomez. J George, \u003cem\u003eA new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement.\u003c/em\u003e J Hepatol, 2020. \u003cstrong\u003e73\u003c/strong\u003e(1): p. 202-209.\u003c/li\u003e\n\u003cli\u003eXue, H, S Fang, M Zheng, J Wu, H Li, M Zhang. Y Ma, \u003cem\u003eDa-Huang-Xiao-Shi decoction protects against3, 5-diethoxycarbonyl-1,4-dihydroxychollidine-induced chronic cholestasis by upregulating bile acid metabolic enzymes and efflux transporters.\u003c/em\u003e J Ethnopharmacol, 2021. \u003cstrong\u003e269\u003c/strong\u003e: p. 113706.\u003c/li\u003e\n\u003cli\u003eDawson, PA, T Lanand A Rao, \u003cem\u003eBile acid transporters.\u003c/em\u003e J Lipid Res, 2009. \u003cstrong\u003e50\u003c/strong\u003e(12): p. 2340-57.\u003c/li\u003e\n\u003cli\u003eKawamata, Y, R Fujii, M Hosoya, M Harada, H Yoshida, M Miwa. M Fujino, \u003cem\u003eA G protein-coupled receptor responsive to bile acids.\u003c/em\u003e J Biol Chem, 2003. \u003cstrong\u003e278\u003c/strong\u003e(11): p. 9435-40.\u003c/li\u003e\n\u003cli\u003eLi, T and JYL Chiang, \u003cem\u003eBile acid-based therapies for non-alcoholic steatohepatitis and alcoholic liver disease.\u003c/em\u003e Hepatobiliary Surg Nutr, 2020. \u003cstrong\u003e9\u003c/strong\u003e(2): p. 152-169.\u003c/li\u003e\n\u003cli\u003eWegermann, K, C Howe, R Henao, Y Wang, CD Guy, MF Abdelmalek. CA Moylan, \u003cem\u003eSerum Bile Acid, Vitamin E, and Serotonin Metabolites Are Associated With Future Liver-Related Events in Nonalcoholic Fatty Liver Disease.\u003c/em\u003e Hepatol Commun, 2021. \u003cstrong\u003e5\u003c/strong\u003e(4): p. 608-617.\u003c/li\u003e\n\u003cli\u003ePearce, SG, NC Thosaniand JJ Pan, \u003cem\u003eNoninvasive biomarkers for the diagnosis of steatohepatitis and advanced fibrosis in NAFLD.\u003c/em\u003e Biomark Res, 2013. \u003cstrong\u003e1\u003c/strong\u003e(1): p. 7.\u003c/li\u003e\n\u003cli\u003eAjmera, V and R Loomba, \u003cem\u003eImaging biomarkers of NAFLD, NASH, and fibrosis.\u003c/em\u003e Mol Metab, 2021. \u003cstrong\u003e50\u003c/strong\u003e: p. 101167.\u003c/li\u003e\n\u003cli\u003eAndersson, A, M Kelly, K Imajo, A Nakajima, JA Fallowfield, G Hirschfield. S Harrison, \u003cem\u003eClinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis.\u003c/em\u003e Clin Gastroenterol Hepatol, 2022. \u003cstrong\u003e20\u003c/strong\u003e(11): p. 2451-2461 e3.\u003c/li\u003e\n\u003cli\u003eChow, MD, YH Leeand GL Guo, \u003cem\u003eThe role of bile acids in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis.\u003c/em\u003e Mol Aspects Med, 2017. \u003cstrong\u003e56\u003c/strong\u003e: p. 34-44.\u003c/li\u003e\n\u003cli\u003ePark, WH and YK Pak, \u003cem\u003eInsulin-dependent suppression of cholesterol 7alpha-hydroxylase is a possible link between glucose and cholesterol metabolisms.\u003c/em\u003e Exp Mol Med, 2011. \u003cstrong\u003e43\u003c/strong\u003e(10): p. 571-9.\u003c/li\u003e\n\u003cli\u003eFuchs, C, T Claudeland M Trauner, \u003cem\u003eBile acid-mediated control of liver triglycerides.\u003c/em\u003e Semin Liver Dis, 2013. \u003cstrong\u003e33\u003c/strong\u003e(4): p. 330-42.\u003c/li\u003e\n\u003cli\u003eTrauner, M, T Claudel, P Fickert, T Moustafaand M Wagner, \u003cem\u003eBile acids as regulators of hepatic lipid and glucose metabolism.\u003c/em\u003e Dig Dis, 2010. \u003cstrong\u003e28\u003c/strong\u003e(1): p. 220-4.\u003c/li\u003e\n\u003cli\u003eBrunt, EM, DE Kleiner, LA Wilson, P Belt, BA Neuschwander-Tetriand NCR Network, \u003cem\u003eNonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings.\u003c/em\u003e Hepatology, 2011. \u003cstrong\u003e53\u003c/strong\u003e(3): p. 810-20.\u003c/li\u003e\n\u003cli\u003eZhou, Y, Z Liu, EC Lynch, L He, H Cheng, L Liu. L Xie, \u003cem\u003eOsr1 regulates hepatic inflammation and cell survival in the progression of non-alcoholic fatty liver disease.\u003c/em\u003e Lab Invest, 2021. \u003cstrong\u003e101\u003c/strong\u003e(4): p. 477-489.\u003c/li\u003e\n\u003cli\u003eFerslew, BC, G Xie, CK Johnston, M Su, PW Stewart, W Jia. ASt Barritt, \u003cem\u003eAltered Bile Acid Metabolome in Patients with Nonalcoholic Steatohepatitis.\u003c/em\u003e Dig Dis Sci, 2015. \u003cstrong\u003e60\u003c/strong\u003e(11): p. 3318-28.\u003c/li\u003e\n\u003cli\u003eChen, W, Y Wei, A Xiong, Y Li, H Guan, Q Wang. X Ma, \u003cem\u003eComprehensive Analysis of Serum and Fecal Bile Acid Profiles and Interaction with Gut Microbiota in Primary Biliary Cholangitis.\u003c/em\u003e Clin Rev Allergy Immunol, 2020. \u003cstrong\u003e58\u003c/strong\u003e(1): p. 25-38.\u003c/li\u003e\n\u003cli\u003eCai, J, B Rimal, C Jiang, JYL Chiangand AD Patterson, \u003cem\u003eBile acid metabolism and signaling, the microbiota, and metabolic disease.\u003c/em\u003e Pharmacol Ther, 2022. \u003cstrong\u003e237\u003c/strong\u003e: p. 108238.\u003c/li\u003e\n\u003cli\u003eJia, W, G Xieand W Jia, \u003cem\u003eBile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis.\u003c/em\u003e Nat Rev Gastroenterol Hepatol, 2018. \u003cstrong\u003e15\u003c/strong\u003e(2): p. 111-128.\u003c/li\u003e\n\u003cli\u003eCaussy, C, C Hsu, S Singh, S Bassirian, J Kolar, C Faulkner. R Loomba, \u003cem\u003eSerum bile acid patterns are associated with the presence of NAFLD in twins, and dose-dependent changes with increase in fibrosis stage in patients with biopsy-proven NAFLD.\u003c/em\u003e Aliment Pharmacol Ther, 2019. \u003cstrong\u003e49\u003c/strong\u003e(2): p. 183-193.\u003c/li\u003e\n\u003cli\u003eGillard, J and IA Leclercq, \u003cem\u003eBiological tuners to reshape the bile acid pool for therapeutic purposes in non-alcoholic fatty liver disease.\u003c/em\u003e Clin Sci (Lond), 2023. \u003cstrong\u003e137\u003c/strong\u003e(1): p. 65-85.\u003c/li\u003e\n\u003cli\u003ePowell, EE, VW Wongand M Rinella, \u003cem\u003eNon-alcoholic fatty liver disease.\u003c/em\u003e Lancet, 2021. \u003cstrong\u003e397\u003c/strong\u003e(10290): p. 2212-2224.\u003c/li\u003e\n\u003cli\u003eKim, HY, \u003cem\u003eRecent advances in nonalcoholic fatty liver disease metabolomics.\u003c/em\u003e Clin Mol Hepatol, 2021. \u003cstrong\u003e27\u003c/strong\u003e(4): p. 553-559.\u003c/li\u003e\n\u003cli\u003eParoutoglou, K, E Papadavid, GS Christodoulatosand M Dalamaga, \u003cem\u003eDeciphering the Association Between Psoriasis and Obesity: Current Evidence and Treatment Considerations.\u003c/em\u003e Curr Obes Rep, 2020. \u003cstrong\u003e9\u003c/strong\u003e(3): p. 165-178.\u003c/li\u003e\n\u003cli\u003eHaeusler, RA, B Astiarraga, S Camastra, D Acciliand E Ferrannini, \u003cem\u003eHuman insulin resistance is associated with increased plasma levels of 12alpha-hydroxylated bile acids.\u003c/em\u003e Diabetes, 2013. \u003cstrong\u003e62\u003c/strong\u003e(12): p. 4184-91.\u003c/li\u003e\n\u003cli\u003ePuri, P, MM Wiest, O Cheung, F Mirshahi, C Sargeant, HK Min. AJ Sanyal, \u003cem\u003eThe plasma lipidomic signature of nonalcoholic steatohepatitis.\u003c/em\u003e Hepatology, 2009. \u003cstrong\u003e50\u003c/strong\u003e(6): p. 1827-38.\u003c/li\u003e\n\u003cli\u003eWang, M, C Wang, RH Hanand X Han, \u003cem\u003eNovel advances in shotgun lipidomics for biology and medicine.\u003c/em\u003e Prog Lipid Res, 2016. \u003cstrong\u003e61\u003c/strong\u003e: p. 83-108.\u003c/li\u003e\n\u003cli\u003eAl-Jebawi, AF, AH YoussefAgha, HS Al Suwaidi, MS Albadwawi, AS Al Marzooqi, AH Banihammad. MK Alkaabi, \u003cem\u003eAttenuated PTH responsiveness to vitamin D deficiency among patients with type 2 diabetes and chronic hyperglycemia.\u003c/em\u003e Diabetes Res Clin Pract, 2017. \u003cstrong\u003e128\u003c/strong\u003e: p. 119-126.\u003c/li\u003e\n\u003cli\u003ePuri, P, K Daita, A Joyce, F Mirshahi, PK Santhekadur, S Cazanave. AJ Sanyal, \u003cem\u003eThe presence and severity of nonalcoholic steatohepatitis is associated with specific changes in circulating bile acids.\u003c/em\u003e Hepatology, 2018. \u003cstrong\u003e67\u003c/strong\u003e(2): p. 534-548.\u003c/li\u003e\n\u003cli\u003eBertolini, A, R Fiorottoand M Strazzabosco, \u003cem\u003eBile acids and their receptors: modulators and therapeutic targets in liver inflammation.\u003c/em\u003e Semin Immunopathol, 2022. \u003cstrong\u003e44\u003c/strong\u003e(4): p. 547-564.\u003c/li\u003e\n\u003cli\u003eEvangelakos, I, J Heeren, E Verkadeand F Kuipers, \u003cem\u003eRole of bile acids in inflammatory liver diseases.\u003c/em\u003e Semin Immunopathol, 2021. \u003cstrong\u003e43\u003c/strong\u003e(4): p. 577-590.\u003c/li\u003e\n\u003cli\u003eAranha, MM, H Cortez-Pinto, A Costa, IB da Silva, ME Camilo, MC de Mouraand CM Rodrigues, \u003cem\u003eBile acid levels are increased in the liver of patients with steatohepatitis.\u003c/em\u003e Eur J Gastroenterol Hepatol, 2008. \u003cstrong\u003e20\u003c/strong\u003e(6): p. 519-25.\u003c/li\u003e\n\u003cli\u003eGottlieb, A and A Canbay, \u003cem\u003eWhy Bile Acids Are So Important in Non-Alcoholic Fatty Liver Disease (NAFLD) Progression.\u003c/em\u003e Cells, 2019. \u003cstrong\u003e8\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eXue, R, L Su, S Lai, Y Wang, D Zhao, J Fan. H Zhou, \u003cem\u003eBile Acid Receptors and the Gut-Liver Axis in Nonalcoholic Fatty Liver Disease.\u003c/em\u003e Cells, 2021. \u003cstrong\u003e10\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eKhalil, A, A ElSheashaey, E Abdelsameea, M Obada, FFM Bayomyand H El-Said, \u003cem\u003eValue of Bile Acids in Diagnosing Hepatitis C Virus-Induced Liver Cirrhosis and Hepatocellular Carcinoma.\u003c/em\u003e Br J Biomed Sci, 2022. \u003cstrong\u003e79\u003c/strong\u003e: p. 10191.\u003c/li\u003e\n\u003cli\u003eDong, S, ZY Zhan, HY Cao, C Wu, YQ Bian, JY Li. MY Sun, \u003cem\u003eUrinary metabolomics analysis identifies key biomarkers of different stages of nonalcoholic fatty liver disease.\u003c/em\u003e World J Gastroenterol, 2017. \u003cstrong\u003e23\u003c/strong\u003e(15): p. 2771-2784.\u003c/li\u003e\n\u003cli\u003eAppleby, RN, I Moghul, S Khan, M Yee, P Manousou, TD Nealand JRF Walters, \u003cem\u003eNon-alcoholic fatty liver disease is associated with dysregulated bile acid synthesis and diarrhea: A prospective observational study.\u003c/em\u003e PLoS One, 2019. \u003cstrong\u003e14\u003c/strong\u003e(1): p. e0211348.\u003c/li\u003e\n\u003cli\u003eDeng, Y, M Pan, H Nie, C Zheng, K Tang, Y Zhangand Q Yang, \u003cem\u003eLipidomic Analysis of the Protective Effects of Shenling Baizhu San on Non-Alcoholic Fatty Liver Disease in Rats.\u003c/em\u003e Molecules, 2019. \u003cstrong\u003e24\u003c/strong\u003e(21).\u003c/li\u003e\n\u003cli\u003eThibaut, MM and LB Bindels, \u003cem\u003eCrosstalk between bile acid-activated receptors and microbiome in entero-hepatic inflammation.\u003c/em\u003e Trends Mol Med, 2022. \u003cstrong\u003e28\u003c/strong\u003e(3): p. 223-236.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Clinical Characteristics of the Studied Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"86%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eNHC (n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eNAST (n=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eNASH (n=40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e16 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e23 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e23 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 0.73, p = 0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e9 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e12 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e17 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e50.32 \u0026plusmn; 9.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e50.31 \u0026plusmn; 8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e48.13 \u0026plusmn; 8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003et = 1.02, p = 0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e72.92 \u0026plusmn; 5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e81.26 \u0026plusmn; 10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e77.00 \u0026plusmn; 10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003et = 3.34, p = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e169.20 \u0026plusmn; 8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e162.69 \u0026plusmn; 8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e163.25 \u0026plusmn; 9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003et = 2.49, p = 0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25.73 \u0026plusmn; 3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e30.91 \u0026plusmn; 4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e29.22 \u0026plusmn; 5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003et = 4.50, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e4 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e13 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e12 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 5.42, p = 0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- No DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e14 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e13 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e17 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Prediabetes (pDM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e7 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e9 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e11 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e7 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e10 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e16 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 1.44, p = 0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e18 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e24 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eLipid Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Hyperlipidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e6 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e20 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e26 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 14.23, p = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Normolipidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e19 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e15 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e14 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eFibrosis Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- F0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 58.75, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e10 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e16 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e24 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eActivity Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- A0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e22 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 54.43, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e13 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e16 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e24 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eSteatosis Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- G0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 75.00, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- G1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e18 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e11 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e16 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e6 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e24 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003eUltrasound Findings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Normal homogenous liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e25 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 88.75, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Fatty liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e17 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Mildly enlarged fatty liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e8 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e9 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" valign=\"top\"\u003e\n \u003cp\u003e- Moderately enlarged fatty liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e10 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e31 (77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eClinical characteristics of NHC, NAST, and NASH groups with statistical significance for demographic, anthropometric, metabolic, and histological variables. NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis, F0: No fibrosis F1: Mild fibrosis F2: Moderate fibrosis F3: Advanced fibrosis. Lobular inflammation Grade: A0: No lobular inflammation A1: Mild lobular inflammation A2: Moderate lobular inflammation, A3: Severe lobular inflammation. Steatosis Grade: G0: No steatosis (no fat accumulation in the liver), G1: Mild steatosis G2: Moderate steatosis G3: Severe steatosis. Ultrasound Findings: Normal homogeneous liver: Liver with normal echotexture. Fatty liver: Liver with increased echogenicity indicating fat accumulation. Mildly enlarged fatty liver: Slight increase in liver size with fatty infiltration. Moderately enlarged fatty liver: Moderate increase in liver size with more pronounced fatty infiltration.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Descriptive Statistics and Kruskal-Wallis Test Results for Biochemical and Hematological Parameters Across NHC, NASH, and NAST Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eNHC (n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eNAST (n=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eNASH (n=40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eBlood Glucose \u0026amp; Hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eFBS (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e112 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e130 (71) (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e143 (88.25)\u0026nbsp;(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.6 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.8 (1.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eRenal Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.78 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eUrea (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e23 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e23 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e23.5 (12.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eUA (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.3 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.3 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eLiver Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e16 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e20 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e23 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e21 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e24 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e30.5 (40.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTBIL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eDBIL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.13 (0.07) (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e0.15(0.035)\u0026nbsp;(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eALB (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e3.8 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e4.2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e3.8 (0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eGlob (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e3.9 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e3.1 (1.3)\u0026nbsp;(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e3.7 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTP (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e7.5 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e7.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e7.6 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e12.4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e23 (41)\u0026nbsp;(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e20 (12)\u0026nbsp;(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eLipid Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eChol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e188 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e214 (62) (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e207 (45.5)\u0026nbsp;(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e105 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e99 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e102 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e56 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e65 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e64 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e123 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e116 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e123 (35.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHematological Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHb (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e11.7 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e12.0 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e12.8 (2.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eRBC (10\u003csup\u003e6\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e4.71 (1.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e4.71 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e4.71 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e250 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e240 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e235 (53.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eWBCs (10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e6.1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.8 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e5.9 (4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eComparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across NHC, NAST, and NASH Groups: IQR: Interquartile rang, K: Kruskal-Wallis test comparison among all groups, *P-value \u0026lt; 0.05 indicates significant. Mann-Whitney U test comparison between two groups. P-value \u0026lt; 0.05 indicates significant. a: comparing between NHC vs. NAST group. b: comparing between NHC vs. NASH group. c: comparing between NAST vs. NASH group.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 3: Comparative Analysis of Bile Acid Profiles Across NHC, NAST, and NASH Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eBile Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNHC\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNAST\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNASH\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eK-Wallis P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.130 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.230 (1.55) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.410 (1.41) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.050 (3.298) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.830 (3.29) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.190 (0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.400 (1.76) a, c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.770 (4.83) b, c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eGCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e2.120 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3.730 (1.97) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.220 (9.14) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.130 (0.028) ac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.310 (0.019) b, c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eTCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3.205 (4.32) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.390 (4.40) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.160 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.280 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.335 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.030 (0.218) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.006 (0.206) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eGDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.060 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.110 (0.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.105 (0.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eTDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eTLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.003 (0.008) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.010 (0.468) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.030 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eGUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.003 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.200 (1.698) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.300 (1.698) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eTUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.005 (0.259) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.005 (0.039) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eComparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across NHC, NAST, and NASH Groups: IQR: Interquartile rang, K: Kruskal-Wallis test comparison among all groups, *P-value \u0026lt; 0.05 indicates significant. Mann-Whitney U test comparison between two groups. P-value \u0026lt; 0.05 indicates significant. a: comparing between NHC vs. NAST group. b: comparing between NHC vs. NASH group. c: comparing between NAST vs. NASH group.\u003c/p\u003e\n \u003cp\u003eNHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH, Nonalcoholic steatohepatitis\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 4. Bile Acid Levels Across Gender, Diabetes Status, and Lipid Profile in NAFLD Patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003eBile Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (Median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003eMale (Median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003ePre-DM\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003eNo DM Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003eNormolipidemic (Median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003eHyperlipidemic (Median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.900 (1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.320 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.39) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.32 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.29 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e795.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.100 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.650 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e2.50 (0.75) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.98 (0.65) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.35 (3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.08 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e802.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.765 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.280 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e2.80 (0.80) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.60 (0.59) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.78 (3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e582.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eGCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.175 (4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e3.850 (1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e4.24 (9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e3.98 (3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e3.41 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.05 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.17 (6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e628.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.006 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e541.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eTCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.390 (4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e2.980 (4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e4.40 (4.16) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e3.65 (4.35) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e2.37 (4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.06 (4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e4.22 (4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e667.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.280 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.420 (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.46 (0.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.27 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.28 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.33 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e657.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.030 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.007 (0.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.08 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e713.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eGDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.110 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.110 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.12 (0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e753.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eTDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.004 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e678.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eTLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.005 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.010 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.005 (0.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e626.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e669.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eGUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.245 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.200 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.10 (1.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e644.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.022187004754358%\" valign=\"top\"\u003e\n \u003cp\u003eTUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.54675118858954%\" valign=\"top\"\u003e\n \u003cp\u003e2rytg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.012 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.037*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.003 (0.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.005 (0.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508716323296355%\" valign=\"top\"\u003e\n \u003cp\u003e0.006 (1.499)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863708399366086%\" valign=\"top\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.033280507131538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6561014263074485%\" valign=\"top\"\u003e\n \u003cp\u003e683.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3882725832012675%\" valign=\"top\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eComparative analysis of bile acid profiles using Kruskal-Wallis and Pairwise comparisons across diabetic groups: IQR: Interquartile rang, *P-value \u0026lt; 0.05 indicates significant. Pairwise comparisons a: comparing between No DM vs. DM group. b: comparing between No DM vs. p-DM group, c: comparing between p-DM vs. DM group. Mann-Whitney U test comparison between two groups (gender, lipidemic state). P-value \u0026lt; 0.05 indicates significant.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDM: diabetes mellitus, p-DM: pre-diabetic, No DM: no diabetes mellitus, 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary taurocongugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 5: Correlation Analysis Between Bile Acids and Lipid Profile in NAST and NASH\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.37373737373738%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNAST, n=35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNASH, n=40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eBile Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eclass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eChol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eChol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eGCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.38*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.70**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eGDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eGUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.38*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eTUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2rytg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.38*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSpearman Rank Correlation Coefficients (r) Among Variables Across NHC, NAST, and NASH Groups.\u003c/p\u003e\n \u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e* Correlation is significant at the 0.05 level (2-tailed). NHC: Normal healthy control, NAST: Nonalcoholic steatosis, NASH: Nonalcoholic steatohepatitis, 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 6: Comparison of bile acid levels in NAFLD patients categorized by Fibrosis, Inflammation, and Steatosis Grades\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eInflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSteatosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBile Acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eclass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eF0-F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eF2-F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eA0-A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eA2-A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eG0-G1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eG2-G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.12 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.23 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.06 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.81 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.57 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.40 (1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.77 (4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.40 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.27 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.76 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eGCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.22 (9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.73 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.22 (9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.73 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.90 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.13 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eTCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.39 (4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.51 (4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.39 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.21 (4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.05 (4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.23 (4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.24 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.28 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.40 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.28 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.03 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eGDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.10 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eTDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eTLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eGUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.28 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.34 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.30 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003eTUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2rytg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"21\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eComparison of bile acid levels in NAFLD patients categorized by fibrosis (F0-F1 vs. F2-F3), inflammation (A0-A1 vs. A2-A3), and steatosis (G0-G1 vs. G2-G3) grades. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMann-Whitney U test comparison between two groups. indicates significant\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003edata are presented as median (IQR), with P-value \u0026lt; 0.05 indicates the statistical significance of differences between groups. 1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Binary Logistic Regression Analysis for Predicting Fibrosis, Lobular Inflammation, and Steatosis in NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFibrosis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eF0-1 vs F2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eActive inflammation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA0-1 vs A2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSteatosis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eG0-1 vs G2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWald\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExp(B)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWald\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExp(B)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWald\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExp(B)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2rytc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2ryU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2rygc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTUDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2rytg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe binary logistic regression analysis results are presented for three comparisons: fibrosis (F0-1 vs. F2-3), active inflammation (A0-1 vs. A2-3), and steatosis (G0-1 vs. G2-3) in NAFLD patients, using various bile acids as predictors.\u0026nbsp;1ryU: primary unconjugated, 1rygc: primary glycoconjugated, 1rytc: primary tauroconjugated, 2ryU: secondary unconjugated, 2rygc: secondary glycoconjugated, 2rytc: secondary tauroconjugated.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bile acids, NAFLD, Nonalcoholic fatty liver disease, NASH, Nonalcoholic steatohepatitis, Liquid chromatography-mass spectrometry","lastPublishedDoi":"10.21203/rs.3.rs-4896620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4896620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBile acids are vital regulators of liver metabolism, and their dysregulation is closely linked with the progression of nonalcoholic fatty liver disease (NAFLD). Profiling these bile acids may provide valuable diagnostic and prognostic markers for these conditions. This study aimed to evaluate bile acid profiles in NAFLD patients and assess their potential as biomarkers for diagnosing and predicting disease progression. Serum levels of 14 bile acids were measured in 25 normal healthy controls (NHC), 35patients with nonalcoholic steatosis (NAST), and 40 patients with NASH, categorized by the NAFLD Activity Score (NAS). Quantification was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003ePrimary unconjugated bile acids, CA and CDCA, along with conjugated acids GCA, GCDCA, TCA, and TCDCA, were significantly elevated in both NAST and NASH compared to NHC (all p \u0026lt; 0.05). While levels increased progressively from NHC to NAST to NASH, no significant differences were observed between NAST and NASH except for GCA and TCA (P\u0026lt; 0.05). Similarly, secondary bile acids LCA, TLCA, GUDCA, and TUDCA were higher in NAST and NASH compared to NHC (all p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eLogistic regression identified CA (odds ratio = 2.05, p = 0.02), CDCA (odds ratio = 1.58, p = 0.04), GCA (odds ratio = 1.92, p = 0.03) and DCA (odds ratio = 2.06, p = 0.04) as significant predictors of fibrosis. For active inflammation, GCA (odds ratio = 2.04, p = 0.04), and TCA (odds ratio = 1.94, p = 0.04) were significant predictors. In steatosis, CA, CDCA, GCA, DCA, TDCA, TLCA, and UDCA were notable predictors, with high odds ratios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study highlights significant alterations in bile acid profiles associated with NAFLD progression. Specific bile acids, such as CA, GCA, TCA, and TCDCA are strong predictors of disease severity, indicating their potential as biomarkers for NAFLD treatment and prognosis.\u003c/p\u003e","manuscriptTitle":"Exploring Serum Bile Acids as Potential Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 02:26:19","doi":"10.21203/rs.3.rs-4896620/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7bb84cd-7c35-40de-b0c3-e9694185e072","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-4896620","link":"https://doi.org/10.1186/s43066-024-00378-9","journal":{"identity":"egyptian-liver-journal","isVorOnly":false,"title":"Egyptian Liver Journal"},"publishedOn":"2024-10-09 15:57:37","publishedOnDateReadable":"October 9th, 2024"},"versionCreatedAt":"2024-10-08 02:26:19","video":"","vorDoi":"10.1186/s43066-024-00378-9","vorDoiUrl":"https://doi.org/10.1186/s43066-024-00378-9","workflowStages":[]},"version":"v1","identity":"rs-4896620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4896620","identity":"rs-4896620","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}