GCKR genetic variants and circulating FGF21 define a metabolic risk signature in Metabolic-associated Steatotic Liver disease

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Interindividual variability in disease onset and progression reflects a complex interplay between metabolic burden and inherited susceptibility. The present study investigated the combined impact of genetic variants in the glucokinase regulatory protein gene (GCKR) and fibroblast growth factor 21 (FGF21), together with circulating FGF21 concentrations, on susceptibility to MASLD and its progression to MASH. Methods: This case-control study enrolled 450 age- and sex-matched participants: 150 patients with MASLD, 150 with fibroscan-confirmed MASH, and 150 healthy controls. Genotyping of GCKR rs1260326 and FGF21 rs838133 polymorphisms was performed using real-time polymerase chain reaction, while serum FGF21 levels were quantified by enzyme-linked immunosorbent assay. Associations with metabolic characteristics, liver function indices, and fibrosis severity were examined using correlation analyses and multivariate logistic regression models. Results : The GCKR rs1260326 TT genotype was significantly overrepresented among patients with MASH (p=0.005) and was associated with higher alanine aminotransferase levels and reduced markers of hepatic synthetic capacity. In parallel, carriers of the FGF21 rs838133 G allele exhibited an increased likelihood of MASLD and a higher propensity for progression to MASH, accompanied by greater insulin resistance and unfavorable lipid profiles. Circulating FGF21 concentrations demonstrated a stepwise increase from controls to MASLD and MASH groups and showed strong diagnostic performance in identifying advanced disease stages. Multivariate analysis confirmed that both serum FGF21 levels and GCKR genetic variation independently predicted the risk of MASH. Conclusions: Genetic variation in GCKR and FGF21, together with altered hepatokine signaling, contributes substantially to metabolic dysregulation and liver disease severity. Integrating genetic profiling with circulating biomarkers may offer a refined strategy for identifying individuals at high risk of MASLD progression and advancing precision-based approaches in metabolic liver disease. MASLD MASH GCKR FGF21 genetic susceptibility Figures Figure 1 Figure 2 Figure 3 1. Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), has emerged as one of the most prevalent chronic liver disorders worldwide. Its rapid rise parallels the global increase in obesity, insulin resistance, and type 2 diabetes, positioning MASLD as a major contributor to liver-related morbidity and mortality. Current estimates indicate that nearly one-third of the adult population is affected by hepatic steatosis, with a substantial proportion at risk of progressing to the inflammatory and fibrotic stage known as metabolic dysfunction–associated steatohepatitis (MASH), previously termed nonalcoholic steatohepatitis (NASH) [ 1 ]. This progressive form carries a markedly higher likelihood of cirrhosis, hepatocellular carcinoma, and liver-related death, underscoring the urgent need for improved strategies in risk stratification and early detection [ 2 ]. Despite its clinical relevance, MASLD is far from a homogeneous condition. While many individuals remain in a relatively benign steatotic stage, others experience rapid disease progression driven by a convergence of metabolic stressors and intrinsic biological susceptibility [ 3 ]. Traditional clinical tools, including liver enzyme measurements and imaging, offer limited accuracy in distinguishing simple steatosis from active steatohepatitis. Although liver biopsy remains the diagnostic gold standard for MASH, its invasiveness and limited feasibility in large populations have fueled growing interest in noninvasive biomarkers and molecular determinants that better reflect disease activity and prognosis [ 4 ]. In this context, genetic predisposition and hepatokine signaling have gained attention as complementary contributors to disease heterogeneity [ 5 ]. In this context, host genetic factors have gained increasing recognition as key modulators of hepatic lipid handling and metabolic homeostasis. Genome-wide association studies have consistently implicated variants within the glucokinase regulatory protein (GCKR) gene as influential determinants of hepatic glucose flux and de novo lipogenesis. The common rs1260326 (P446L) variant reduces the inhibitory effect of GCKR on glucokinase, thereby enhancing glycolytic throughput and triglyceride synthesis within hepatocytes [ 6 ]. Although this variant is often associated with favorable glycemic traits, it paradoxically predisposes to hypertriglyceridemia and hepatic fat accumulation, suggesting a complex role in metabolic liver disease. Whether this genetic background also contributes to the transition from MASLD to MASH remains an important area of investigation [ 7 ]. Alongside genetic susceptibility, hepatokines have emerged as pivotal mediators linking hepatic metabolism with systemic energy balance. Fibroblast growth factor 21 (FGF21) is a liver-derived hormone that orchestrates adaptive responses to metabolic stress by promoting fatty acid oxidation, ketogenesis, and energy expenditure while restraining lipogenic pathways [ 8 ]. Circulating FGF21 levels are consistently elevated in obesity and fatty liver disease, a pattern widely interpreted as a compensatory response to metabolic overload. However, chronic elevation may also reflect a state of impaired tissue responsiveness, commonly described as FGF21 resistance [ 9 ]. Beyond its circulating concentrations, genetic variation within the FGF21 locus particularly the rs838133 polymorphism has been associated with adverse metabolic traits and altered dietary preferences, raising the possibility that inherited differences in FGF21 signaling influence susceptibility to hepatic steatosis and disease progression [ 10 ]. Despite growing evidence implicating both genetic susceptibility and hepatokine dysregulation in MASLD, these factors are often investigated in isolation. GCKR polymorphisms influence hepatic glucose–lipid flux, while FGF21 reflects adaptive metabolic stress; however, their combined clinical relevance in MASLD remains poorly defined. This study identifies a combined genetic–hepatokine signature involving GCKR variants and circulating FGF21 levels that characterizes metabolic risk stratification in MASLD beyond single-marker approaches. Unlike previous reports that evaluated these markers independently, the present work highlights their integrated contribution to disease-associated metabolic phenotypes in a real-world clinical study. 2. Methods Study design and participants This case-control study included 150 adult patients (≥ 18 years) with a confirmed diagnosis of MASLD, 150 patients with its progressive form MASH. Patients presenting to the liver clinic were initially screened using abdominal ultrasonography for evidence of hepatic steatosis. These patients underwent fibroscan on liver to assess level of steatosis and fibrosis according to the American association guideline of MASH [ 11 ]. Considering patients positive for viral markers for hepatitis B and C, significant alcohol intake (≥ 30 g/day in men and ≥ 20 g/day in women), recreational drug abuse, confirmed autoimmune or cholestatic liver diseases, other secondary causes of steatosis, and those with history of past malignancies or recurrent/secondary tumors, excluded from the study. And as a normal control group; 150 apparently healthy subjects free from history of fatty liver and had normal clinical and biochemical profiles were included in the study. Clinical and Biochemical Assessment All participants underwent an initial screening visit that included medical history, physical examination and standardized anthropometric measures (height, weight, body mass index (BMI) and waist circumference), obesity was defined by the World Health Organization as BMI over 30 kg/m², with morbid obesity defined as a BMI of 40 kg/m² or higher [ 12 ]. Relevant comorbidities such as type 2 diabetes mellitus, arterial hypertension and dyslipidemia were carefully documented. Routine laboratory analysis was performed for all participants; including liver and kidney function tests in the form of serum bilirubin level, alanine aminotransferase (ALT) activity, aspartate aminotransaminase (AST) activity, total protein, albumin, alkaline phosphatase (ALP), ᵞ-glutamyl transferase (GGT), urea and creatinine along with total cholesterol, high-density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C) and triglycerides. All biochemical analyses were carried out on the same day using the Beckman Coulter AU 480 chemistry analyzer (Beckman Coulter Ireland Inc., Brea, CA, USA) in the clinical chemistry department of our facility. Insulin resistance was assessed using the homeostatic model assessment of insulin resistance (HOMA-IR), calculated according to the formula: fasting plasma glucose (mmol/L) Ⅹ fasting serum insulin (mIU/L) / 22.5 [ 13 ]. Viral serology for hepatitis B and C viruses, as well as insulin level, was determined using a chemiluminescence immunoassay kit (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA). Complete blood count was performed using an automated hematology analyzer on Beckman Coulter AcT Diff cell counter (Beckman Coulter Ireland Inc., Brea, CA, USA). Genetic variants and Biomarker Analyses All molecular analyses and allelic discrimination assays were conducted within the chemical pathology department, Cairo university hospitals, under controlled laboratory conditions adhering to standardized molecular diagnostics protocols to ensure data reproducibility and analytical precision. The genomic deoxyribonucleic acid (DNA) was meticulously extracted using the GeneJET whole blood genomic DNA purification mini-kit (ThermoFisher Scientific Inc., CA, USA), strictly following the manufacturer’s standardized protocol. Genotyping of the candidate genetic variants was determined in all patients using predesigned assays with quantitative real-time polymerase chain reaction (qPCR) employing taqman genotyping assays (ThermoFisher Scientific Inc., CA, USA). Allelic discrimination of genetic variants of GCKR rs1260326 (C > T, P446L) (Assay ID: C_2862880_1) and FGF21 rs838133 (G > A) (Assay ID: C_8832415_10) was achieved using sequence-specific fluorescent taqman probes, according to the protocol proposed by [ 14 ] on the software of step one real-time PCR ABI-7500 instrument (Applied Biosystems, Foster City, CA, USA). The DNA concentration and purity were determined using qubit fluorometric quantification assays (ThermoFisher Scientific Inc., CA, USA). All samples were normalized to a working concentration of 20 ng/µL to ensure analytical consistency across downstream genotyping assays. Rigorous quality control procedures were implemented throughout the workflow, including parallel processing of negative controls on each plate to preclude possible cross-contamination or genotyping bias. Evaluation of Circulating FGF21 Levels Venous blood samples were obtained from all participants following an overnight fast under standardized conditions. Immediately after collection, samples were centrifuged at 2500 × g for 10 minutes at 4°C and the resulting supernatant was carefully aliquoted into pre-labeled tubes and promptly stored at − 80°C until subsequent biochemical assessment to maintain analyte integrity and prevent degradation. Serum FGF21 concentrations were determined using a commercially available human enzyme-linked immunosorbent assay (ELISA) kit validated for clinical research; (BT LAB, China) catalogue number: E1983Hu. The serum FGF21 was detected following the manufacturer’s guidelines. No samples exhibited FGF21 concentrations below the assay’s lower limit of quantification. Statistical analysis Data were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Data was summarized using mean and standard deviation for normally distributed quantitative variables or median and interquartile range for non-normally distributed quantitative variables and frequencies (number of cases) and relative frequencies (percentages) for categorical variables. Comparisons between groups were done using analysis of variance (ANOVA) with multiple comparisons post-hoc test in normally distributed quantitative variables while non-parametric Kruskal-Wallis test and Mann-Whitney test were used for non-normally distributed quantitative variables [ 15 ]. For comparing categorical data, Chi square (χ2) test was performed. Exact test was used instead when the expected frequency is less than 5 [ 16 ]. Genotype frequencies were compared between the disease and the control groups using logistic regression. Odds ratio (OR) with 95% confidence intervals were calculated. Correlations between quantitative variables were done using Spearman correlation coefficient [ 17 ]. ROC curve was constructed with area under curve analysis performed to detect best cutoff value of FGF-21 for detection of diseased liver. Logistic regression was done to detect independent predictors of diseased liver [ 18 ]. P values less than 0.05 were considered as statistically significant. 3. Results Baseline characteristics of the study population A total of 450 participants were included and categorized into three groups: healthy controls, patients with MASLD, and patients with MASH. No statistically significant differences were observed among the groups with respect to age or sex distribution, indicating appropriate matching. In contrast, marked differences were evident in anthropometric and metabolic parameters. Patients with MASLD and particularly those with MASH exhibited significantly higher body mass index, waist circumference, and prevalence of metabolic comorbidities, including type 2 diabetes mellitus and hypertension (p < 0.001 for all). Biochemical profiling further demonstrated a progressive deterioration in metabolic and hepatic indices across the disease spectrum. Compared with controls, both MASLD and MASH groups showed significantly higher levels of fasting insulin, HbA1C %, HOMA-IR, aminotransferases, γ-glutamyl transferase, and adverse lipid parameters, with the most pronounced alterations observed in patients with MASH. These findings confirm the close association between metabolic dysfunction, hepatic injury, and disease severity (Table 1). Table (1) Demographic data, clinical, laboratory and radiological data in the MASLD, MASH and normal controls Covariate Controls (n = 150) MASLD (n = 150) MASH (n = 150) P value Age (Years) * 46.51 ± 6.98 48.41 ± 10.43 48.25 ± 7.20 0.302 Sex Male † 30 (20%) 22 (14.7%) 36 (24%) 0.352 Female † 120 (80%) 128 (85.3%) 114 (76%) Diabetes (Yes/No) 0/150 24/126 24/126 < 0.001 Hypertension (Yes/No) 0/150 48/102 78/72 < 0.001 Anthropometric measurements Waist circumference (cm) * 86.15 ± 5.71 90.67 ± 6.45 96.28 ± 6.59 < 0.001 Length (cm) * 170.20 ± 3.64 170.56 ± 5.99 171.52 ± 5.16 0.253 Weight (kg) * 69.48 ± 3.36 87.80 ± 9.14 92.48 ± 7.93 < 0.001 BMI (kg/m²) * 23.99 ± 0.95 30.22 ± 3.30 31.44 ± 2.32 < 0.001 Laboratories Hemoglobin (g/dl) * 14.08 ± 0.91 11.61 ± 1.33 11.12 ± 1.47 < 0.001 TLC (×10⁹/L) * 6.48 ± 1.65 5.00 ± 1.39 5.20 ± 1.68 < 0.001 Platelet count (×10³/µL) * 281.01 ± 73.07 166.27 ± 18.05 183.17 ± 35.09 < 0.001 FBS (mg/dl) * 94.09 ± 7.05 88.68 ± 8.21 92.95 ± 34.49 0.249 HbA1C (%) * 4.85 ± 0.57 5.15 ± 0.47 5.62 ± 1.05 < 0.001 Insulin (µU/ml) * 10.21 ± 3.56 13.79 ± 3.50 24.57 ± 9.02 < 0.001 HOMA-IR * 2.33 ± 0.88 3.05 ± 0.93 6.33 ± 5.23 < 0.001 Total protein (g/dl) * 7.33 ± 0.55 8.71 ± 0.39 8.89 ± 0.31 < 0.001 Albumin (g/dl) * 4.27 ± 0.32 4.51 ± 0.30 4.57 ± 0.47 < 0.001 ALT (IU/L) * 22.47 ± 8.72 29.61 ± 8.59 48.03 ± 12.80 < 0.001 AST (IU/L) * 21.99 ± 6.19 28.87 ± 7.44 34.13 ± 12.02 < 0.001 Total Bilirubin (mg/dl) * 0.81 ± 0.18 1.00 ± 0.09 1.05 ± 0.10 < 0.001 Direct Bilirubin (mg/dl) * 0.16 ± 0.05 0.55 ± 0.13 0.52 ± 0.09 < 0.001 Uric acid (mg/dl) * 3.72 ± 0.59 4.97 ± 0.99 5.62 ± 1.25 < 0.001 GGT (IU/L) * 25.75 ± 7.00 41.84 ± 10.65 49.08 ± 17.66 < 0.001 Urea (mg/dl) * 23.65 ± 7.21 33.09 ± 7.77 40.67 ± 2.95 < 0.001 Creatinine (mg/dl) * 0.80 ± 0.13 0.99 ± 0.17 1.11 ± 0.12 < 0.001 Total cholesterol (mg/dl) * 151.61 ± 22.00 205.67 ± 37.58 197.53 ± 46.18 < 0.001 HDL-C (mg/dl) * 51.80 ± 4.79 42.24 ± 8.73 36.87 ± 10.31 < 0.001 LDL-C (mg/dl) 75.89 ± 21.23 109.05 ± 8.33 107.52 ± 17.29 < 0.001 Triglycerides (mg/dl) * 119.28 ± 25.93 157.47 ± 24.33 146.77 ± 24.58 < 0.001 Abdominal US Findings † Fatty liver 0 (0%) 92 (62.7%) 50 (33.3%) < 0.001 Hepatomegaly 0 (0%) 46 (30.7%) 100 (66.7%) < 0.001 Fibroscan Findings † Fibroscan S1 0 (0%) 42 (28.0%) 0 (0%) < 0.001 Fibroscan S2 0 (0%) 94 (62.7%) 56 (37.3%) Fibroscan S3 0 (0%) 14 (9.3%) 94 (62.7%) * Data are represented as mean ± SD. † Data are represented as a number (Percent). Frequency distribution of GCKR rs1260326 polymorphism and its clinical correlates Our study revealed a significant difference in the distribution of GCKR rs1260326 variants across the three study groups. The TT genotype and the T allele were markedly enriched in the MASH group compared with both MASLD patients and healthy controls. When MASLD patients were compared with those with MASH, carriers of the TT genotype exhibited more than a fivefold increased risk of disease progression, while the T allele conferred a more than twofold higher risk (Table 2). Individuals carrying the TT genotype demonstrated significantly higher mean alanine aminotransferase levels and lower total protein concentrations compared with CC and CT carriers ( p = 0.009 and p = 0.034 , respectively). These findings suggest that the rs1260326 risk allele is not only associated with susceptibility to advanced disease but also correlates with markers of hepatocellular injury and reduced synthetic capacity (Table 3). Table (2) Genotype and Allele distribution of rs1260326 SNP genotypic variants and their odd’s ratios among the studied groups Genotypes Control (n = 150) MASLD (n = 150) MASH (n = 150) P value CC 32 (21.3%) 60 (40.0%) 26 (17.3%) 0.005 CT 74 (49.3%) 66 (44.0%) 68 (45.3%) TT 44 (29.3%) 24 (16.0%) 56 (37.3%) Alleles Control (n = 300) MASLD (n = 300) MASH (n = 300) P value C 138 (46.0%) 186 (62.0%) 120 (40%) 0.010 T 162 (54.0%) 114 (38.0%) 180 (60%) Genotypes Control (n = 150) MASLD (n = 150) OR (95% CI) P value CC 32 (21.3%) 60 (40.0%) Reference CT 74 (49.3%) 66 (44.0%) 0.476 (0.221–1.024) 0.058 TT 44 (29.3%) 24 (16.0%) 0.291 (0.115–0.737) 0.009 Alleles Control (n = 300) MASLD (n = 300) OR (95% CI) P value C 138 (46.0%) 186 (62.0%) Reference T 162 (54.0%) 114 (38.0%) 0.522 (0.330–0.827) 0.006 Genotypes Control (n = 150) MASH (n = 150) OR (95% CI) P value CC 32 (21.3%) 26 (17.3%) Reference CT 74 (49.3%) 68 (45.3%) 1.131 (0.475–2.693) 0.781 TT 44 (29.3%) 56 (37.3%) 1.566 (0.624–3.933) 0.339 Alleles Control (n = 300) MASH (n = 300) OR (95% CI) P value C 138 (46.0%) 120 (40%) Reference T 162 (54.0%) 180 (60%) 1.278 (0.808–2.020) 0.294 Genotypes MASLD (n = 150) MASH (n = 150) OR (95% CI) P value CC 60 (40.0%) 26 (17.3%) Reference CT 66 (44.0%) 68 (45.3%) 2.378 (1.060–5.334) 0.036 TT 24 (16.0%) 56 (37.3%) 5.385 (2.106–13.764) < 0.001 Alleles MASLD (n = 300) MASH (n = 300) OR (95% CI) P value C 186 (62.0%) 120 (40%) Reference T 114 (38.0%) 180 (60%) 2.447 (1.539–3.893) < 0.001 Data are presented as numbers (percentage). Table (3) Comparison between GCKR rs1260326 (C/T) genetic variants in MASLD and MASH patients regarding the laboratory data Variable represented as mean ± SD CC genotype CT genotype TT genotype P value Age (Years) 49.47 ± 9.58 48.34 ± 9.41 47.10 ± 7.28 0.486 Waist circumference 97.41 ± 6.48 94.34 ± 7.61 93.88 ± 6.60 0.157 Length (cm) 170.33 ± 6.92 171.34 ± 5.57 171.30 ± 3.82 0.614 Weight (kg) 87.81 ± 9.38 91.40 ± 9.38 90.53 ± 6.83 0.110 BMI (kg/m²) 30.27 ± 2.91 31.16 ± 3.13 30.88 ± 2.47 0.299 Hemoglobin (g/dl) 11.46 ± 1.50 11.24 ± 1.23 11.47 ± 1.64 0.628 TLC (×10⁹/L) 5.34 ± 1.60 4.90 ± 1.45 5.17 ± 1.62 0.326 Platelet count (×10³/µL) 167.74 ± 20.99 175.03 ± 31.70 181.70 ± 30.82 0.091 FBS (mg/dl) 94.56 ± 28.16 89.39 ± 22.47 89.18 ± 25.93 0.513 HbA1C (%) 5.41 ± 0.92 5.41 ± 0.81 5.31 ± 0.83 0.809 Insulin (µU/ml) 18.35 ± 8.10 19.28 ± 9.21 19.90 ± 8.60 0.716 HOMA-IR 4.60 ± 3.99 4.62 ± 4.24 5.50 ± 4.5 0.932 Total protein (g/dl) 8.88 ± 0.35 8.53 ± 0.33 6.68 ± 0.41 0.034 Albumin (g/dl) 4.51 ± 0.35 4.55 ± 0.41 4.54 ± 0.44 0.850 ALT (IU/L) 34.16 ± 9.35 38.88 ± 14.23 43.72 ± 17.07 0.009 AST (IU/L) 29.02 ± 8.62 32.25 ± 10.06 32.90 ± 12.02 0.167 Total blirubin (mg/dl) 1.02 ± 0.11 1.02 ± 0.09 1.04 ± 0.11 0.469 Direct blirubin (mg/dl) 0.56 ± 0.12 0.53 ± 0.11 0.53 ± 0.10 0.217 Uric acid (mg/dl) 5.01 ± 0.99 5.41 ± 1.22 5.41 ± 1.24 0.167 GGT (IU/L) 43.26 ± 10.97 46.13 ± 15.17 46.70 ± 18.18 0.515 Urea (mg/dl) 36.35 ± 7.64 37.00 ± 7.08 37.25 ± 6.18 0.829 Creatinine (mg/dl) 1.05 ± 0.17 1.05 ± 0.16 1.06 ± 0.15 0.889 Total cholesterol (mg/dl) 206.74 ± 45.52 199.18 ± 41.01 200.13 ± 40.88 0.637 HDL-C (mg/dl) 41.33 ± 9.42 39.84 ± 10.10 37.17 ± 9.83 0.154 LDL-C (mg/dl) 109.84 ± 12.64 106.94 ± 13.51 108.87 ± 14.61 0.525 Triglycerides (mg/dl) 149.02 ± 22.88 153.60 ± 24.12 152.98 ± 28.57 0.627 Frequency distribution of FGF21 rs838133 polymorphism and metabolic associations Genotypic analysis of the FGF21 rs838133 variant showed a striking shift in allele distribution across the disease spectrum. The GG genotype and G allele were significantly more frequent in both MASLD and MASH patients than in controls, with the highest prevalence observed in the MASH group (p < 0.001); with the GG genotype conferring more than a threefold increased risk of MASH compared with MASLD (Table 4). In patients with MASH, the GG genotype was independently associated with higher LDL-cholesterol levels and greater insulin resistance, as reflected by increased HOMA-IR values ( p = 0.035 and p = 0.004 , respectively; Table 5). Among patients with MASLD, ultrasonographic assessment revealed a significantly higher prevalence of hepatic steatosis in GG carriers compared with those harboring the AA or AG genotypes (p = 0.049) , supporting a role for this variant in early disease susceptibility. Table (4) Genotype and Allele distribution of rs838133 SNP genotypic variants and their odd’s ratios among the studied groups Genotypes Control (n = 150) MASLD (n = 150) MASH (n = 150) P value AA 84 (56%) 30 (20%) 22 (14.7%) < 0.001 AG 38 (25.3%) 88 (58.7%) 46 (30.7%) GG 28 (18.7%) 32 (21.3%) 82 (54.7%) Alleles Control (n = 300) MASLD (n = 300) MASH (n = 300) P value A allele 206 (68.7%) 148 (49.3%) 90 (30%) < 0.001 G allele 94 (31.3%) 152 (50.7%) 210 (70%) Genotypes MASH (n = 150) MASLD (n = 150) OR (95% CI) P value AA 22 (14.7%) 30 (20%) Reference AG 46 (30.7%) 88 (58.7%) 0.713 (0.282–1.802) 0.474 GG 82 (54.7%) 32 (21.3%) 3.494 (1.326–9.209) 0.011 Alleles MASH (n = 300) MASLD (n = 300) OR (95% CI) P value A allele 90 (30%) 148 (49.3%) Reference G allele 210 (70%) 152 (50.7%) 2.272 (1.415–3.649) < 0.001 Genotypes Control (n = 150) MASH (n = 150) OR (95% CI) P value AA 84 (56%) 22 (14.7%) Reference AG 38 (25.3%) 46 (30.7%) 4.622 (1.879–11.368) < 0.001 GG 28 (18.7%) 82 (54.7%) 11.182 (4.549–27.484) < 0.001 Alleles Control (n = 300) MASH (n = 300) OR (95% CI) P value A allele 206 (68.7%) 90 (30%) Reference G allele 94 (31.3%) 210 (70%) 5.113 (3.130–8.354) < 0.001 Genotypes Control (n = 150) MASLD (n = 150) OR (95% CI) P value AA 84 (56%) 30 (20%) Reference AG 38 (25.3%) 88 (58.7%) 6.484 (12.919–14.404) < 0.001 GG 28 (18.7%) 32 (21.3%) 3.200 (1.265–8.098) 0.014 Alleles Control (n = 300) MASLD (n = 300) OR (95% CI) P value A allele 206 (68.7%) 148 (49.3%) Reference G allele 94 (31.3%) 152 (50.7%) 2.251 (1.406–3.603) < 0.001 Data are presented as numbers (percentage). Table (5) Comparison between FGF21 rs838133 (A/G) genetic variants in MASLD and MASH patients regarding the laboratory data Variable represented as mean ± SD AA (Mean ± SD) AG (Mean ± SD) GG (Mean ± SD) P value Age (Years) 46.09 ± 10.31 47.58 ± 8.52 49.99 ± 8.26 0.089 Waist circumference 91.79 ± 8.48 94.60 ± 7.11 93.51 ± 6.19 0.209 Length (cm) 170.62 ± 9.54 171.69 ± 9.10 170.72 ± 5.64 0.626 Weight (kg) 87.67 ± 8.94 91.73 ± 9.10 90.28 ± 7.95 0.114 BMI (kg/m²) 30.08 ± 3.45 31.13 ± 2.96 30.99 ± 2.64 0.225 Hemoglobin (g/dl) 12.90 ± 1.87 13.22 ± 1.49 13.26 ± 1.22 0.444 TLC (×10⁹/L) 4.64 ± 1.25 5.41 ± 1.64 5.11 ± 1.56 0.086 Platelet count (×10³/µL) 173.76 ± 28.88 178.41 ± 28.74 173.37 ± 29.75 0.756 FBS (mg/dl) 97.44 ± 10.33 87.10 ± 10.26 90.12 ± 12.55 0.114 HbA1C (%) 5.55 ± 1.14 5.27 ± 0.77 5.39 ± 0.71 0.341 Insulin (µU/ml) 19.47 ± 11.72 20.02 ± 7.73 18.44 ± 7.60 0.618 HOMA-IR 4.46 ± 3.26 4.66 ± 3.62 5.68 ± 4.75 0.004 Total protein (g/dl) 8.02 ± 0.32 8.37 ± 0.37 8.76 ± 0.39 0.447 Albumin (g/dl) 4.51 ± 0.38 4.57 ± 0.35 4.57 ± 0.36 0.691 ALT (IU/L) 35.62 ± 13.58 39.96 ± 16.10 39.62 ± 13.14 0.330 AST (IU/L) 21.49 ± 10.52 26.14 ± 11.20 32.91 ± 9.57 0.118 Total blirubin (mg/dl) 1.03 ± 0.11 1.02 ± 0.10 1.01 ± 0.10 0.815 Direct blirubin (mg/dl) 0.52 ± 0.36 0.49 ± 0.31 0.53 ± 0.12 0.826 Uric acid (mg/dl) 5.16 ± 1.03 5.47 ± 1.31 5.23 ± 0.89 0.314 GGT (IU/L) 44.24 ± 13.85 45.94 ± 16.62 45.74 ± 14.47 0.683 Urea (mg/dl) 34.30 ± 8.62 35.43 ± 6.35 35.16 ± 8.02 0.514 Creatinine (mg/dl) 1.02 ± 0.19 1.08 ± 0.14 1.07 ± 0.16 0.383 Total cholesterol (mg/dl) 195.97 ± 39.85 198.35 ± 41.27 208.75 ± 48.02 0.133 HDL-C (mg/dl) 40.68 ± 8.70 40.49 ± 9.76 37.44 ± 10.71 0.199 LDL-C (mg/dl) 104.88 ± 8.81 106.33 ± 11.91 111.37 ± 9.35 0.035 Triglycerides (mg/dl) 149.03 ± 24.79 153.68 ± 22.81 155.40 ± 26.60 0.370 Interaction between GCKR and FGF21 genetic variants To assess potential gene–gene interactions, the distribution of GCKR rs1260326 and FGF21 rs838133 genotypes was examined jointly. A statistically significant association was identified between the two loci (p = 0.036; Table 6). Notably, individuals carrying the TT genotype of GCKR were more frequently co-classified with the AG genotype of FGF21, whereas the CT genotype of GCKR was most commonly observed alongside the GG genotype of FGF21. This non-random distribution suggests a possible synergistic effect between the two genes that may amplify disturbances in glucose and lipid metabolism, thereby contributing to the heterogeneity of disease progression. Table (6) Association between FGF-21 rs838133 (A/G) and GCKR rs1260326 (C/T) genotypes Variable represented as frequency (%) FGF-21 (AA) genotype FGF-21 (AG) genotype FGF-21 (GG) genotype P value GCKR (CC) genotype 8 (33%) 4 (6.7%) 14 (21.2%) 0.036 GCKR (CT) genotype 12 (50%) 22 (36.6%) 34 (51.5%) GCKR (TT) genotype 4 (17%) 34 (56.7%) 18 (27.3%) Data are presented as numbers (percentage). Circulating FGF21 levels across disease stages Serum FGF21 concentrations increased progressively across the three study groups, with the lowest levels observed in healthy controls, intermediate levels in MASLD patients, and the highest concentrations in those with MASH (p < 0.001). Median FGF21 levels rose nearly threefold from controls to MASLD and more than sixfold in patients with MASH, reflecting a strong association between circulating FGF21 and disease severity (Table 7). Receiver operating characteristic (ROC) analysis demonstrated that serum FGF21 possessed substantial diagnostic value. The biomarker showed good discriminatory performance in differentiating MASLD from controls (AUC = 0.821) and excellent accuracy in identifying MASH among healthy individuals (AUC = 0.929). Although sensitivity was moderate, specificity consistently exceeded 95%, underscoring the potential utility of FGF21 as a confirmatory marker for advanced disease rather than a screening tool (Fig. 1–3). Table (7) Median and interquartile range (IQR) of circulating FGF-21 levels in pg/mL among the three studied groups The median and IQR of FGF-21 concentration in pg/mL Control (n = 150) MASLD (n = 150) MASH (n = 150) P value 37.0 (21.0–64.0) 107.0 (61.0-156.0) 227.0 (121.0-369.0) < 0.001 Control (n = 150) MASLD (n = 150) P value 37.0 (21.0–64.0) 107.0 (61.0-156.0) < 0.001 Control (n = 150) MASH (n = 150) P value 37.0 (21.0–64.0) 227.0 (121.0-369.0) < 0.001 MASLD (n = 150) MASH (n = 150) P value 107.0 (61.0-156.0) 227.0 (121.0-369.0) < 0.001 Area Under the Curve P value 95% Confidence Interval Cut off Sensitivity % Specificity % 0.821 < 0.001 0.753–0.890 98.5 56 97.3 Area Under the Curve P value 95% Confidence Interval Cut off Sensitivity % Specificity % 0.929 < 0.001 0.886–0.971 106 77.3 100 Area Under the Curve P value 95% Confidence Interval Cut off Sensitivity % Specificity % 0.799 < 0.001 0.724–0.875 189.5 66.7 96 Correlation analysis of serum FGF21 with metabolic and hepatic parameters Distinct correlation patterns were observed across study groups. In healthy controls, serum FGF21 levels exhibited a weak but significant positive correlation with γ-glutamyl transferase (r = 0.279, p = 0.015), suggesting that even subtle variations in hepatic metabolic activity may influence FGF21 secretion under physiological conditions. No other significant associations were detected in this group. Among patients with MASLD, FGF21 concentrations showed no meaningful correlations with most metabolic or biochemical indices, with the exception of a weak inverse relationship with hemoglobin levels (r = -0.227, p = 0.050). In contrast, a different profile emerged in patients with MASH, in whom elevated FGF21 levels were positively correlated with insulin resistance, as reflected by HOMA-IR values (r = 0.244, p = 0.035). Although trends toward positive associations with fasting insulin and HbA1c were noted, these did not reach statistical significance. Collectively, these findings suggest that the clinical relevance of circulating FGF21 becomes more pronounced with increasing disease severity. 4. Discussion The present study provides integrative evidence that disease progression is shaped by the convergence of metabolic burden, inherited genetic susceptibility, and dysregulated hepatokine signaling. By combining genetic variants in GCKR and FGF21 with circulating FGF21 concentrations, our findings move beyond single-marker approaches and offer a more refined framework for risk stratification in MASLD. A principal finding of this study is the strong association between the GCKR rs1260326 variant and progression from MASLD to MASH. The enrichment of the T allele, particularly the TT genotype, among patients with MASH underscores the role of altered hepatic glucose handling in amplifying liver injury. Functionally, this variant reduces the inhibitory effect of GCKR on glucokinase, resulting in enhanced glycolytic flux and increased de novo lipogenesis. Although this metabolic shift may confer favorable glycemic effects, it simultaneously promotes hepatic lipid accumulation and lipotoxic stress. Similarly, Samarasinghe et al. , [ 19 ] found that the T allele exhibited a higher grade of hepatic steatosis in Indian patients. The biochemical signature observed in TT carriers characterized by higher aminotransferase levels and reduced total protein concentrations, suggests that this variant contributes not only to steatosis but also to impaired hepatocellular function as disease advances [ 20 ]. These findings are consistent with previous reports by Nisar et al. , [ 21 ] linking GCKR rs1260326 to hepatic fat accumulation and more severe metabolic phenotypes, reinforcing its role as a genetic modifier of disease severity rather than mere disease presence. In parallel, our data highlight a substantial contribution of FGF21 rs838133 genetic variability to MASLD susceptibility and progression. Carriers of the GG genotype exhibited a markedly increased risk of both MASLD and MASH, accompanied by greater insulin resistance and adverse lipid profiles, particularly elevated LDL-cholesterol levels. While FGF21 is widely recognized as a hepatoprotective hormone induced under metabolic stress, genetic perturbations at the FGF21 locus may compromise the effectiveness of this adaptive response. The higher prevalence of hepatic steatosis among GG carriers within the MASLD group further supports a role for this variant early in disease development, potentially predisposing individuals to metabolic dysregulation before overt inflammation or fibrosis becomes evident. These observations align with emerging genetic and experimental data by Ramne et al. , [ 22 ] indicating that impaired FGF21 signaling may contribute to metabolic inflexibility and hepatic lipid overload. A novel aspect of the present study is the demonstration of a gene–gene interaction between GCKR and FGF21 variants. The non-random co-distribution of high-risk genotypes suggests a synergistic effect that may amplify metabolic disturbances beyond the impact of either variant alone. From a mechanistic standpoint, enhanced hepatic glycolytic flux driven by GCKR variation could increase lipid synthesis and metabolic stress, thereby stimulating FGF21 secretion. If this compensatory pathway is genetically compromised, the imbalance between lipid accumulation and adaptive capacity may accelerate hepatocellular injury and fibrogenesis; such findings support the integrated genetic model identified by Singh et al. , [ 23 ]. This multilocus interaction provides a plausible explanation for the marked interindividual variability observed in MASLD progression and supports polygenic models of disease susceptibility. Beyond genetic determinants, circulating FGF21 concentrations emerged as a robust biochemical marker of disease severity. Serum FGF21 levels increased progressively from healthy controls to MASLD and MASH, reflecting escalating hepatic and systemic metabolic stress. Importantly, receiver operating characteristic analyses demonstrated high specificity for advanced disease, indicating that elevated FGF21 levels may be particularly useful for identifying patients with established MASH rather than for population-wide screening. This pattern supports the concept of “FGF21 resistance,” whereby chronically elevated hormone levels reflect an insufficient compensatory response to ongoing metabolic injury. Filimidou et al. , [ 24 ] demonstrated a pattern which aligns with our findings, in which serum FGF21 exhibits strong discriminatory capacity for identify patients with fatty liver disease compared with healthy subjects (AUC ≈ 0.83). Gallego-Durán et al. , [ 10 ] further demonstrated that both hepatic and circulating FGF21 levels were significantly elevated in MASH patients, in Huh7.5 cells exposed to free fatty acids, and in CDA-HFD animal model, corroborating our observations. Multivariate logistic regression analysis reinforced the multifactorial nature of MASH. Both circulating FGF21 levels and GCKR genetic variation independently predicted disease risk after adjustment for conventional biochemical markers, including alanine aminotransferase. These findings suggest that molecular markers capture dimensions of disease biology not fully reflected by routine clinical tests. Moreover, the concurrent influence of additional lipid-related genetic variants reported in the same population supports a polygenic framework in which multiple modest-effect alleles collectively shape susceptibility to progressive liver disease. Moreover, the combined contribution of additional lipid-related genetic variants (MARC1 rs2642438 A > T and TM6SF2 rs58542926 C > T) concurrently reported in other study on same population, highlights the polygenic framework underlying MASH susceptibility, in which multiple modest-effect alleles collectively shape individual trajectories toward advanced liver disease. The analysis revealed that serum FGF21 levels were a significant positive predictor of MASH (B = 0.011, p = 0.001 , OR = 1.011, 95% CI: 1.005–1.018). Similarly, individuals carrying the GCKR rs1260326 (TT + CT) genotype exhibited a markedly higher risk of developing MASH (B = 1.690, p = 0.039 , OR = 5.420, 95% CI: 1.085–27.062). As well the TT + CT genotype of TM6SF2 rs58542926 showed a strong association with disease presence (B = 2.127, both p < 0.001 , CT: OR = 9.956, 95% CI: 4.373–22.665; TT: OR = 18.667, 95% CI: 5.537–62.936-). Conversely, carriers of the MARC1 rs2642438 (TT) genotypes demonstrated a protective effect against MASH (B = − 2.289, p = 0.013 , OR = 0.101, 95% CI: 0.017–0.614). Additionally, ALT levels were found to be positively correlated with MASH risk (B = 0.530, p < 0.001 , OR = 1.699, 95% CI: 1.304–2.213), whereas cholesterol levels demonstrated a modest but significant inverse association (B = − 0.030, p = 0.002 , OR = 0.971, 95% CI: 0.952–0.989). The final regression equation for MASH prediction was expressed as: ‏Logit (P) = 0.011 × FGF21 protein + 1.690 × GCKR rs1260326 (TT + CT) − 2.289 × MARC1 rs2642438 (TT) − 2.127 × TM6SF2 rs58542926 (TT + CT) + 0.530 × ALT − 0.030 × Chol − 15.071. Clinically, these results advocate for a shift toward precision-based risk stratification in MASLD. Integrating genetic profiling with circulating hepatokines may allow earlier identification of individuals at high risk for progression, enabling targeted lifestyle interventions and prioritization for emerging pharmacotherapies. Such an approach moves beyond the traditional reliance on liver enzymes and imaging, offering a more nuanced understanding of disease biology. Several limitations should be acknowledged. The case–control design limits causal inference, and validation in larger, multiethnic cohorts is warranted to confirm generalizability. In addition, functional studies are required to elucidate the precise molecular mechanisms underlying the observed gene–gene interactions. Nevertheless, the strength of this study lies in its integrative design, combining genetic, biochemical, and clinical data to better characterize disease heterogeneity. Conclusion Progression from MASLD to MASH appears to be driven by the interplay between metabolic stress, inherited susceptibility, and adaptive hepatokine responses. Variants in GCKR and FGF21 not only influence individual metabolic phenotypes but may also interact to shape the hepatic response to chronic metabolic overload. Circulating FGF21 levels further reflect this interaction, serving as a marker of disease burden and a potential tool for clinical risk assessment. Collectively, these findings support the development of personalized strategies for the management of metabolic liver disease. Abbreviations MASLD Metabolic dysfunction–associated steatotic liver disease MASH Metabolic dysfunction–associated steatohepatitis GCKR Glucokinase regulatory protein FGF21 Fibroblast growth factor 21 ALT Alanine aminotransferase AST Aspartate aminotransaminase ALP Alkaline phosphatase GGT ᵞ-glutamyl transferase HDL-C High‐density lipoprotein cholesterol LDL-C Low‐density lipoprotein cholesterol HOMA-IR Homeostatic model assessment of insulin resistance BMI Body mass index IRB Institutional Review Board ELISA Enzyme-linked immunosorbent assay q-PCR quantitative polymerase chain reaction AUC Area under the curve ROC Receiver operating characteristic Declarations Ethics approval and consent to participate Prior to initiation of our case control study; the study protocol was reviewed and approved by the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under approval number PT 784, following the ethical principles described by the 1975 Declaration of Helsinki and its later amendments. Consent to participate All participants voluntarily provided informed written consents prior to participation between October 2023 and September 2024 from specialized hepatology and gastroenterology inpatient departments, and outpatient clinics at TBRI. Clinical trial registration Not applicable. Data availability statement The data is available upon reasonable request from the corresponding author. Competing interests The authors declare that they have no competing interests. Authors’ contributions AMF and DMA conceived and designed the study. ME and AA collected the clinical data. AMF and DMA performed the laboratory analyses and conducted the statistical analysis. AMF interpreted the data. AMF and ME drafted the manuscript. All authors critically revised the manuscript and approved the final version. Funding This work was conducted as part of an internally funded research project under project number (127) at Theodor Bilharz Research Institute and this study was supported and fully financed by the Institute. References Younossi Z, Golabi P, Paik J, et al (2023a) Global epidemiology of metabolic dysfunction–associated steatotic liver disease and steatohepatitis. Hepatology;77:1335–1347. Paik J, Henry L, Younossi Z, et al (2023) The burden of metabolic dysfunction–associated steatotic liver disease is rapidly growing worldwide from 1990 to 2019. Hepatol Commun;7:e0251. Rinella ME, Lazarus JV, Ratziu V, et al (2023) A multisociety Delphi consensus on new fatty liver disease nomenclature. Ann Hepatol;29(1):101133. doi:10.1016/j.aohep.2023.101133 Perumpail BJ, Manikat R, Wijarnpreecha K, et al (2024) The prevalence and predictors of metabolic dysfunction-associated steatotic liver disease and fibrosis/cirrhosis among adolescents/young adults. J Pediatr Gastroenterol Nutr;79(1):110–118. Zhang H, Su W, Xu H, Zhang X, and Guan Y (2022) HSD17B13: A potential therapeutic target for MASLD. Front Mol Biosci;8:824776. doi:10.3389/fmolb.2021.824776. Younossi Z, Wong G, Anstee Q, et al (2023b) The global burden of liver disease. Clin Gastroenterol Hepatol.;21:1978–1991. Huang D, Wilson L, Behling C, et al (2023) Fibrosis progression in biopsy-proven NAFLD among patients with and without diabetes. Gastroenterology;165:463–472.e5. Younossi Z and Henry L (2024) Epidemiology of MASLD—focus on diabetes. Diabetes Res Clin Pract.;210:111648. Harrison S, Bedossa P, Guy CD, et al (2024) MAESTRO-MASH Investigators A phase 3, randomized, controlled trial of resmetiromin MASH with liver fibrosis. N Engl J Med.;390:497–509. Gallego-Durán R, Ampuero J, Maya-Miles D, et al (2024) Fibroblast growth factor-21 in MASLD progression. United European Gastroenterol J.;12(8):1056–1068. doi: 10.1002/ueg2.12534. Cusi K, Isaacs S, Barb D, et al (2022) American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of NAFLD in Primary Care and Endocrinology Clinical Settings. Endocr Pract.;28(5):528–562. Potter AW, Chin GC, Looney DP, Friedl KE (2025) Defining overweight and obesity by percent body fat instead of BMI. J Clin Endocrinol Metab.;110(4):e1103–e1107. doi:10.1210/clinem/dgae341 Horáková D, Štěpánek L, Janout V, et al (2019) Optimal HOMA-IR cut-offs in the Czech population. Medicina (Kaunas);55(5):158. doi:10.3390/medicina55050158 Kristensen V, Kelefiotis D, Kristensen T, et al (2001) High-throughput methods for detection of genetic variation. Biotechniques;30:318–322. Chan YH (2003a) Biostatistics 102: Quantitative data – parametric & non-parametric tests. Singapore Med J.;44(8):391–396. Chan YH (2003b) Biostatistics 103: Qualitative data – tests of independence. Singapore Med J.;44(10):498–503. Chan YH (2003c) Biostatistics 104: Correlational analysis. Singapore Med J.;44(12):614–619. Chan YH (2004) Biostatistics 202: Logistic regression analysis. Singapore Med J.;45(4):149–153. Samarasinghe SM, Anselmi L, Kumar R, et al (2023) Genetic and metabolic aspects of non-alcoholic fatty liver disease: implications of GCKR rs1260326 association with steatosis severity in Indian patients. J Med Hist Genet.; Article Collection 2023. doi:10.1186/s43042-023-00433-x. Zhang M, Li X, Zhao Y, et al (2024) Association between weight-adjusted waist index and non-alcoholic fatty liver disease: a population-based study. BMC Endocr Disord.;24(1):15. doi:10.1186/s12902-024-01554-z. Nisar T, Ahmad S, Riaz H, et al (2023) Prevalence of GCKR rs1260326 variant in subjects with obesity-associated NAFLD and T2DM: a case-control study in South Punjab, Pakistan. J Obes.;2023:6661858. doi:10.1155/2023/6661858. Ramne S, et al (2024) Genetic variants at the FGF21 locus influence circulating FGF21 and metabolic traits. medRxiv. doi:10.1101/2024.01.01. Singh C, Xiao Y, Fernández-Hernando C, et al (2024) ChREBP is activated by reductive stress and mediates GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerols. Cell Metabolism;36(3):xxx–xxx. doi:10.1016/j.cmet.2024.02.012. Filimidou I, et al (2025) Circulating Fibroblast Growth Factor-21 in Patients with Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis. Curr Obes Rep.;[Epub ahead of print]. (meta-analysis indicating higher FGF21 in NAFLD/NASH). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8643484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587895268,"identity":"791d49bf-7cd5-4c8a-bb19-5a0d0a20d6f9","order_by":0,"name":"Asmaa Mohamed Fteah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPACOSDmYXzAwHCAaC3GEkAtzAYka2GTIEoLf//pxA8/GAzq+NnPHqvmqbkjx8/A/PDRDTxaJG7kbpbsYTCQkOzJS7vNc+yZsWQDm7FxDj5rbvBukOBh+CNhcCDH7DYP2+HEDQd42KTxaZE/f3bzzz9AW+zPvzEr5vlHhBaDA7nbpHmAWgwkcsyYeduI0GJ4I3ebtYyBgeSMG2+MJef2HTaWbCbgFzmgw26+qTDg5+/PMfzw5tthOX725oeP8Xof4jwIxcQDIpkJKkcCjD9IUT0KRsEoGAUjBgAA4O1J6xGsFr4AAAAASUVORK5CYII=","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Asmaa","middleName":"Mohamed","lastName":"Fteah","suffix":""},{"id":587895269,"identity":"d3ad9b3a-5bb7-4467-bebd-b8331eeac4a8","order_by":1,"name":"Doaa Mamdouh Aly","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Doaa","middleName":"Mamdouh","lastName":"Aly","suffix":""},{"id":587895270,"identity":"75d17383-5f78-46ff-ab2c-343a9d03e0cc","order_by":2,"name":"Mohamed A Elrefaiy","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"A","lastName":"Elrefaiy","suffix":""},{"id":587895271,"identity":"9728ebd5-c4a1-44f3-a777-ebdae17297f8","order_by":3,"name":"Ali Abdel Rahim","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Abdel","lastName":"Rahim","suffix":""}],"badges":[],"createdAt":"2026-01-20 00:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8643484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8643484/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12876-026-04767-9","type":"published","date":"2026-04-13T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297629,"identity":"2b084051-5674-448c-8d56-e1b3eb470562","added_by":"auto","created_at":"2026-02-10 10:28:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44085,"visible":true,"origin":"","legend":"\u003cp\u003eROC for FGF-21 best cutoff to diagnose MASLD from control\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8643484/v1/3a855b4ec5f9dcf61890de61.png"},{"id":102212819,"identity":"9343e916-55d3-4bdd-b0d7-67a9bd96a8ee","added_by":"auto","created_at":"2026-02-09 12:36:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39211,"visible":true,"origin":"","legend":"\u003cp\u003eROC for FGF-21 best cutoff to diagnose MASH from control\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643484/v1/69f873cea69cd4b94c7d5d50.png"},{"id":102297466,"identity":"f6add6fd-1706-4411-8dca-8f0cc382a3d2","added_by":"auto","created_at":"2026-02-10 10:27:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45058,"visible":true,"origin":"","legend":"\u003cp\u003eROC for FGF-21 best cutoff to diagnose MASH from MASLD\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643484/v1/d841c5fb368912640b0e0519.png"},{"id":107352944,"identity":"c690aa93-3abb-4ef3-a01b-6f5a17b03f15","added_by":"auto","created_at":"2026-04-20 16:23:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1861283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643484/v1/efd97393-f802-46e5-b46f-3fa4b11c8eab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GCKR genetic variants and circulating FGF21 define a metabolic risk signature in Metabolic-associated Steatotic Liver disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), has emerged as one of the most prevalent chronic liver disorders worldwide. Its rapid rise parallels the global increase in obesity, insulin resistance, and type 2 diabetes, positioning MASLD as a major contributor to liver-related morbidity and mortality. Current estimates indicate that nearly one-third of the adult population is affected by hepatic steatosis, with a substantial proportion at risk of progressing to the inflammatory and fibrotic stage known as metabolic dysfunction\u0026ndash;associated steatohepatitis (MASH), previously termed nonalcoholic steatohepatitis (NASH) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This progressive form carries a markedly higher likelihood of cirrhosis, hepatocellular carcinoma, and liver-related death, underscoring the urgent need for improved strategies in risk stratification and early detection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its clinical relevance, MASLD is far from a homogeneous condition. While many individuals remain in a relatively benign steatotic stage, others experience rapid disease progression driven by a convergence of metabolic stressors and intrinsic biological susceptibility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditional clinical tools, including liver enzyme measurements and imaging, offer limited accuracy in distinguishing simple steatosis from active steatohepatitis. Although liver biopsy remains the diagnostic gold standard for MASH, its invasiveness and limited feasibility in large populations have fueled growing interest in noninvasive biomarkers and molecular determinants that better reflect disease activity and prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this context, genetic predisposition and hepatokine signaling have gained attention as complementary contributors to disease heterogeneity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this context, host genetic factors have gained increasing recognition as key modulators of hepatic lipid handling and metabolic homeostasis. Genome-wide association studies have consistently implicated variants within the glucokinase regulatory protein (GCKR) gene as influential determinants of hepatic glucose flux and de novo lipogenesis. The common \u003cem\u003ers1260326\u003c/em\u003e (P446L) variant reduces the inhibitory effect of GCKR on glucokinase, thereby enhancing glycolytic throughput and triglyceride synthesis within hepatocytes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although this variant is often associated with favorable glycemic traits, it paradoxically predisposes to hypertriglyceridemia and hepatic fat accumulation, suggesting a complex role in metabolic liver disease. Whether this genetic background also contributes to the transition from MASLD to MASH remains an important area of investigation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlongside genetic susceptibility, hepatokines have emerged as pivotal mediators linking hepatic metabolism with systemic energy balance. Fibroblast growth factor 21 (FGF21) is a liver-derived hormone that orchestrates adaptive responses to metabolic stress by promoting fatty acid oxidation, ketogenesis, and energy expenditure while restraining lipogenic pathways [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Circulating FGF21 levels are consistently elevated in obesity and fatty liver disease, a pattern widely interpreted as a compensatory response to metabolic overload. However, chronic elevation may also reflect a state of impaired tissue responsiveness, commonly described as FGF21 resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Beyond its circulating concentrations, genetic variation within the FGF21 locus particularly the \u003cem\u003ers838133\u003c/em\u003e polymorphism has been associated with adverse metabolic traits and altered dietary preferences, raising the possibility that inherited differences in FGF21 signaling influence susceptibility to hepatic steatosis and disease progression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite growing evidence implicating both genetic susceptibility and hepatokine dysregulation in MASLD, these factors are often investigated in isolation. GCKR polymorphisms influence hepatic glucose\u0026ndash;lipid flux, while FGF21 reflects adaptive metabolic stress; however, their combined clinical relevance in MASLD remains poorly defined. This study identifies a combined genetic\u0026ndash;hepatokine signature involving GCKR variants and circulating FGF21 levels that characterizes metabolic risk stratification in MASLD beyond single-marker approaches. Unlike previous reports that evaluated these markers independently, the present work highlights their integrated contribution to disease-associated metabolic phenotypes in a real-world clinical study.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e \u003cb\u003eStudy design and participants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis case-control study included 150 adult patients (\u0026ge;\u0026thinsp;18 years) with a confirmed diagnosis of MASLD, 150 patients with its progressive form MASH. Patients presenting to the liver clinic were initially screened using abdominal ultrasonography for evidence of hepatic steatosis. These patients underwent fibroscan on liver to assess level of steatosis and fibrosis according to the American association guideline of MASH [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering patients positive for viral markers for hepatitis B and C, significant alcohol intake (\u0026ge;\u0026thinsp;30 g/day in men and \u0026ge;\u0026thinsp;20 g/day in women), recreational drug abuse, confirmed autoimmune or cholestatic liver diseases, other secondary causes of steatosis, and those with history of past malignancies or recurrent/secondary tumors, excluded from the study. And as a normal control group; 150 apparently healthy subjects free from history of fatty liver and had normal clinical and biochemical profiles were included in the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical and Biochemical Assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll participants underwent an initial screening visit that included medical history, physical examination and standardized anthropometric measures (height, weight, body mass index (BMI) and waist circumference), obesity was defined by the World Health Organization as BMI over 30 kg/m\u0026sup2;, with morbid obesity defined as a BMI of 40 kg/m\u0026sup2; or higher [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Relevant comorbidities such as type 2 diabetes mellitus, arterial hypertension and dyslipidemia were carefully documented.\u003c/p\u003e \u003cp\u003eRoutine laboratory analysis was performed for all participants; including liver and kidney function tests in the form of serum bilirubin level, alanine aminotransferase (ALT) activity, aspartate aminotransaminase (AST) activity, total protein, albumin, alkaline phosphatase (ALP), ᵞ-glutamyl transferase (GGT), urea and creatinine along with total cholesterol, high-density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C) and triglycerides. All biochemical analyses were carried out on the same day using the Beckman Coulter AU 480 chemistry analyzer (Beckman Coulter Ireland Inc., Brea, CA, USA) in the clinical chemistry department of our facility.\u003c/p\u003e \u003cp\u003eInsulin resistance was assessed using the homeostatic model assessment of insulin resistance (HOMA-IR), calculated according to the formula: fasting plasma glucose (mmol/L) Ⅹ fasting serum insulin (mIU/L) / 22.5 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Viral serology for hepatitis B and C viruses, as well as insulin level, was determined using a chemiluminescence immunoassay kit (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA).\u003c/p\u003e \u003cp\u003eComplete blood count was performed using an automated hematology analyzer on Beckman Coulter AcT Diff cell counter (Beckman Coulter Ireland Inc., Brea, CA, USA).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenetic variants and Biomarker Analyses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll molecular analyses and allelic discrimination assays were conducted within the chemical pathology department, Cairo university hospitals, under controlled laboratory conditions adhering to standardized molecular diagnostics protocols to ensure data reproducibility and analytical precision. The genomic deoxyribonucleic acid (DNA) was meticulously extracted using the GeneJET whole blood genomic DNA purification mini-kit (ThermoFisher Scientific Inc., CA, USA), strictly following the manufacturer\u0026rsquo;s standardized protocol. Genotyping of the candidate genetic variants was determined in all patients using predesigned assays with quantitative real-time polymerase chain reaction (qPCR) employing taqman genotyping assays (ThermoFisher Scientific Inc., CA, USA). Allelic discrimination of genetic variants of GCKR \u003cem\u003ers1260326\u003c/em\u003e (C\u0026thinsp;\u0026gt;\u0026thinsp;T, P446L) (Assay ID: C_2862880_1) and FGF21 \u003cem\u003ers838133\u003c/em\u003e (G\u0026thinsp;\u0026gt;\u0026thinsp;A) (Assay ID: C_8832415_10) was achieved using sequence-specific fluorescent taqman probes, according to the protocol proposed by [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] on the software of step one real-time PCR ABI-7500 instrument (Applied Biosystems, Foster City, CA, USA).\u003c/p\u003e \u003cp\u003eThe DNA concentration and purity were determined using qubit fluorometric quantification assays (ThermoFisher Scientific Inc., CA, USA). All samples were normalized to a working concentration of 20 ng/\u0026micro;L to ensure analytical consistency across downstream genotyping assays.\u003c/p\u003e \u003cp\u003eRigorous quality control procedures were implemented throughout the workflow, including parallel processing of negative controls on each plate to preclude possible cross-contamination or genotyping bias.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of Circulating FGF21 Levels\u003c/b\u003e \u003c/p\u003e \u003cp\u003eVenous blood samples were obtained from all participants following an overnight fast under standardized conditions. Immediately after collection, samples were centrifuged at 2500 \u0026times; g for 10 minutes at 4\u0026deg;C and the resulting supernatant was carefully aliquoted into pre-labeled tubes and promptly stored at \u0026minus;\u0026thinsp;80\u0026deg;C until subsequent biochemical assessment to maintain analyte integrity and prevent degradation. Serum FGF21 concentrations were determined using a commercially available human enzyme-linked immunosorbent assay (ELISA) kit validated for clinical research; (BT LAB, China) catalogue number: E1983Hu. The serum FGF21 was detected following the manufacturer\u0026rsquo;s guidelines. No samples exhibited FGF21 concentrations below the assay\u0026rsquo;s lower limit of quantification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Data was summarized using mean and standard deviation for normally distributed quantitative variables or median and interquartile range for non-normally distributed quantitative variables and frequencies (number of cases) and relative frequencies (percentages) for categorical variables. Comparisons between groups were done using analysis of variance (ANOVA) with multiple comparisons post-hoc test in normally distributed quantitative variables while non-parametric Kruskal-Wallis test and Mann-Whitney test were used for non-normally distributed quantitative variables [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For comparing categorical data, Chi square (χ2) test was performed. Exact test was used instead when the expected frequency is less than 5 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Genotype frequencies were compared between the disease and the control groups using logistic regression. Odds ratio (OR) with 95% confidence intervals were calculated. Correlations between quantitative variables were done using Spearman correlation coefficient [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. ROC curve was constructed with area under curve analysis performed to detect best cutoff value of FGF-21 for detection of diseased liver. Logistic regression was done to detect independent predictors of diseased liver [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. P values less than 0.05 were considered as statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 450 participants were included and categorized into three groups: healthy controls, patients with MASLD, and patients with MASH. No statistically significant differences were observed among the groups with respect to age or sex distribution, indicating appropriate matching. In contrast, marked differences were evident in anthropometric and metabolic parameters. Patients with MASLD and particularly those with MASH exhibited significantly higher body mass index, waist circumference, and prevalence of metabolic comorbidities, including type 2 diabetes mellitus and hypertension \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all).\u003c/em\u003e Biochemical profiling further demonstrated a progressive deterioration in metabolic and hepatic indices across the disease spectrum. Compared with controls, both MASLD and MASH groups showed significantly higher levels of fasting insulin, HbA1C %, HOMA-IR, aminotransferases, \u0026gamma;-glutamyl transferase, and adverse lipid parameters, with the most pronounced alterations observed in patients with MASH. These findings confirm the close association between metabolic dysfunction, hepatic injury, and disease severity (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (1)\u0026nbsp;\u003c/strong\u003eDemographic data, clinical, laboratory and radiological data in the MASLD, MASH and normal controls\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.41\u0026thinsp;\u0026plusmn;\u0026thinsp;10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (Yes/No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension (Yes/No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78/72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnthropometric measurements\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference (cm) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength (cm) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLC (\u0026times;10⁹/L) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026micro;L) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.01\u0026thinsp;\u0026plusmn;\u0026thinsp;73.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.27\u0026thinsp;\u0026plusmn;\u0026thinsp;18.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.17\u0026thinsp;\u0026plusmn;\u0026thinsp;35.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBS (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.95\u0026thinsp;\u0026plusmn;\u0026thinsp;34.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1C (%) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin (\u0026micro;U/ml) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHOMA-IR *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal protein (g/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (g/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (IU/L) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.61\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (IU/L) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Bilirubin (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Bilirubin (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUric acid (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT (IU/L) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.75\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.61\u0026thinsp;\u0026plusmn;\u0026thinsp;22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.67\u0026thinsp;\u0026plusmn;\u0026thinsp;37.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197.53\u0026thinsp;\u0026plusmn;\u0026thinsp;46.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.87\u0026thinsp;\u0026plusmn;\u0026thinsp;10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.89\u0026thinsp;\u0026plusmn;\u0026thinsp;21.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.52\u0026thinsp;\u0026plusmn;\u0026thinsp;17.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides (mg/dl) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.28\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.47\u0026thinsp;\u0026plusmn;\u0026thinsp;24.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.77\u0026thinsp;\u0026plusmn;\u0026thinsp;24.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbdominal US Findings\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatty liver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatomegaly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibroscan Findings\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibroscan S1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibroscan S2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibroscan S3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Data are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Data are represented as a number (Percent).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency distribution of GCKR\u003c/strong\u003e \u003cstrong\u003ers1260326\u003c/strong\u003e \u003cstrong\u003epolymorphism and its clinical correlates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study revealed a significant difference in the distribution of GCKR \u003cem\u003ers1260326\u003c/em\u003e variants across the three study groups. The TT genotype and the T allele were markedly enriched in the MASH group compared with both MASLD patients and healthy controls. When MASLD patients were compared with those with MASH, carriers of the TT genotype exhibited more than a fivefold increased risk of disease progression, while the T allele conferred a more than twofold higher risk (Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eIndividuals carrying the TT genotype demonstrated significantly higher mean alanine aminotransferase levels and lower total protein concentrations compared with CC and CT carriers (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.009\u003c/em\u003e and \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.034\u003c/em\u003e, respectively). These findings suggest that the \u003cem\u003ers1260326\u003c/em\u003e risk allele is not only associated with susceptibility to advanced disease but also correlates with markers of hepatocellular injury and reduced synthetic capacity (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (2)\u0026nbsp;\u003c/strong\u003eGenotype and Allele distribution of \u003cem\u003ers1260326\u003c/em\u003e SNP genotypic variants and their odd\u0026rsquo;s ratios among the studied groups\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476 (0.221\u0026ndash;1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.058\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.291 (0.115\u0026ndash;0.737)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.522 (0.330\u0026ndash;0.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.131 (0.475\u0026ndash;2.693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.781\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.566 (0.624\u0026ndash;3.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.339\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.278 (0.808\u0026ndash;2.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.294\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.378 (1.060\u0026ndash;5.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.036\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.385 (2.106\u0026ndash;13.764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.447 (1.539\u0026ndash;3.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as numbers (percentage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (3)\u0026nbsp;\u003c/strong\u003eComparison between GCKR \u003cem\u003ers1260326\u003c/em\u003e (C/T) genetic variants in MASLD and MASH patients regarding the laboratory data\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003erepresented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCC genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCT genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTT genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.81\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLC (\u0026times;10⁹/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.74\u0026thinsp;\u0026plusmn;\u0026thinsp;20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175.03\u0026thinsp;\u0026plusmn;\u0026thinsp;31.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.70\u0026thinsp;\u0026plusmn;\u0026thinsp;30.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBS (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.56\u0026thinsp;\u0026plusmn;\u0026thinsp;28.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.39\u0026thinsp;\u0026plusmn;\u0026thinsp;22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.18\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1C (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin (\u0026micro;U/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.28\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.90\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHOMA-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal protein (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.16\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.88\u0026thinsp;\u0026plusmn;\u0026thinsp;14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.72\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.90\u0026thinsp;\u0026plusmn;\u0026thinsp;12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal blirubin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect blirubin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUric acid (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.70\u0026thinsp;\u0026plusmn;\u0026thinsp;18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.35\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206.74\u0026thinsp;\u0026plusmn;\u0026thinsp;45.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199.18\u0026thinsp;\u0026plusmn;\u0026thinsp;41.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200.13\u0026thinsp;\u0026plusmn;\u0026thinsp;40.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.84\u0026thinsp;\u0026plusmn;\u0026thinsp;12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.94\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.02\u0026thinsp;\u0026plusmn;\u0026thinsp;22.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.60\u0026thinsp;\u0026plusmn;\u0026thinsp;24.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.98\u0026thinsp;\u0026plusmn;\u0026thinsp;28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency distribution of FGF21\u003c/strong\u003e \u003cstrong\u003ers838133\u003c/strong\u003e \u003cstrong\u003epolymorphism and metabolic associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotypic analysis of the FGF21 \u003cem\u003ers838133\u003c/em\u003e variant showed a striking shift in allele distribution across the disease spectrum. The GG genotype and G allele were significantly more frequent in both MASLD and MASH patients than in controls, with the highest prevalence observed in the MASH group \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001);\u003c/em\u003e with the GG genotype conferring more than a threefold increased risk of MASH compared with MASLD (Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003eIn patients with MASH, the GG genotype was independently associated with higher LDL-cholesterol levels and greater insulin resistance, as reflected by increased HOMA-IR values (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.035\u003c/em\u003e and \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e, respectively; Table 5). Among patients with MASLD, ultrasonographic assessment revealed a significantly higher prevalence of hepatic steatosis in GG carriers compared with those harboring the AA or AG genotypes \u003cem\u003e(p\u0026thinsp;=\u0026thinsp;0.049)\u003c/em\u003e, supporting a role for this variant in early disease susceptibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4)\u0026nbsp;\u003c/strong\u003eGenotype and Allele distribution of \u003cem\u003ers838133\u003c/em\u003e SNP genotypic variants and their odd\u0026rsquo;s ratios among the studied groups\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713 (0.282\u0026ndash;1.802)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.474\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.494 (1.326\u0026ndash;9.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.272 (1.415\u0026ndash;3.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.622 (1.879\u0026ndash;11.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.182 (4.549\u0026ndash;27.484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.113 (3.130\u0026ndash;8.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.484 (12.919\u0026ndash;14.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.200 (1.265\u0026ndash;8.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.014\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.251 (1.406\u0026ndash;3.603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as numbers (percentage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (5)\u0026nbsp;\u003c/strong\u003eComparison between FGF21 \u003cem\u003ers838133\u003c/em\u003e (A/G) genetic variants in MASLD and MASH patients regarding the laboratory data\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003erepresented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAA (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAG (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGG (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.09\u0026thinsp;\u0026plusmn;\u0026thinsp;10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.79\u0026thinsp;\u0026plusmn;\u0026thinsp;8.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.69\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLC (\u0026times;10⁹/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.76\u0026thinsp;\u0026plusmn;\u0026thinsp;28.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.41\u0026thinsp;\u0026plusmn;\u0026thinsp;28.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.37\u0026thinsp;\u0026plusmn;\u0026thinsp;29.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBS (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1C (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin (\u0026micro;U/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.47\u0026thinsp;\u0026plusmn;\u0026thinsp;11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.02\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHOMA-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal protein (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.62\u0026thinsp;\u0026plusmn;\u0026thinsp;13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.96\u0026thinsp;\u0026plusmn;\u0026thinsp;16.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.62\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal blirubin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect blirubin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUric acid (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.94\u0026thinsp;\u0026plusmn;\u0026thinsp;16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.74\u0026thinsp;\u0026plusmn;\u0026thinsp;14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.97\u0026thinsp;\u0026plusmn;\u0026thinsp;39.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.35\u0026thinsp;\u0026plusmn;\u0026thinsp;41.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208.75\u0026thinsp;\u0026plusmn;\u0026thinsp;48.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.03\u0026thinsp;\u0026plusmn;\u0026thinsp;24.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.68\u0026thinsp;\u0026plusmn;\u0026thinsp;22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.40\u0026thinsp;\u0026plusmn;\u0026thinsp;26.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between GCKR and FGF21 genetic variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess potential gene\u0026ndash;gene interactions, the distribution of GCKR \u003cem\u003ers1260326\u003c/em\u003e and FGF21 \u003cem\u003ers838133\u003c/em\u003e genotypes was examined jointly. A statistically significant association was identified between the two loci (p\u0026thinsp;=\u0026thinsp;0.036; Table 6). Notably, individuals carrying the TT genotype of GCKR were more frequently co-classified with the AG genotype of FGF21, whereas the CT genotype of GCKR was most commonly observed alongside the GG genotype of FGF21. This non-random distribution suggests a possible synergistic effect between the two genes that may amplify disturbances in glucose and lipid metabolism, thereby contributing to the heterogeneity of disease progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (6)\u0026nbsp;\u003c/strong\u003eAssociation between FGF-21 \u003cem\u003ers838133\u003c/em\u003e (A/G) and GCKR rs1260326 (C/T) genotypes\u003c/p\u003e\n\u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003erepresented as frequency (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFGF-21 (AA) genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFGF-21 (AG) genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFGF-21 (GG) genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCKR (CC) genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCKR (CT) genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCKR (TT) genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as numbers (percentage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCirculating FGF21 levels across disease stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum FGF21 concentrations increased progressively across the three study groups, with the lowest levels observed in healthy controls, intermediate levels in MASLD patients, and the highest concentrations in those with MASH \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/em\u003e Median FGF21 levels rose nearly threefold from controls to MASLD and more than sixfold in patients with MASH, reflecting a strong association between circulating FGF21 and disease severity (Table 7). Receiver operating characteristic (ROC) analysis demonstrated that serum FGF21 possessed substantial diagnostic value. The biomarker showed good discriminatory performance in differentiating MASLD from controls (AUC\u0026thinsp;=\u0026thinsp;0.821) and excellent accuracy in identifying MASH among healthy individuals (AUC\u0026thinsp;=\u0026thinsp;0.929). Although sensitivity was moderate, specificity consistently exceeded 95%, underscoring the potential utility of FGF21 as a confirmatory marker for advanced disease rather than a screening tool (Fig. 1\u0026ndash;3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (7)\u0026nbsp;\u003c/strong\u003eMedian and interquartile range (IQR) of circulating FGF-21 levels in pg/mL among the three studied groups\u003c/p\u003e\n\u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eThe median and IQR of FGF-21\u003c/p\u003e\n \u003cp\u003econcentration in pg/mL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.0 (21.0\u0026ndash;64.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e107.0 (61.0-156.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e227.0 (121.0-369.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e37.0 (21.0\u0026ndash;64.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e107.0 (61.0-156.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e37.0 (21.0\u0026ndash;64.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e227.0 (121.0-369.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e107.0 (61.0-156.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e227.0 (121.0-369.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Under the Curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.753\u0026ndash;0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Under the Curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.929\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.886\u0026ndash;0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Under the Curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.799\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.724\u0026ndash;0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of serum FGF21 with metabolic and hepatic parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistinct correlation patterns were observed across study groups. In healthy controls, serum FGF21 levels exhibited a weak but significant positive correlation with \u0026gamma;-glutamyl transferase (r\u0026thinsp;=\u0026thinsp;0.279, p\u0026thinsp;=\u0026thinsp;0.015), suggesting that even subtle variations in hepatic metabolic activity may influence FGF21 secretion under physiological conditions. No other significant associations were detected in this group. Among patients with MASLD, FGF21 concentrations showed no meaningful correlations with most metabolic or biochemical indices, with the exception of a weak inverse relationship with hemoglobin levels (r = -0.227, p\u0026thinsp;=\u0026thinsp;0.050). In contrast, a different profile emerged in patients with MASH, in whom elevated FGF21 levels were positively correlated with insulin resistance, as reflected by HOMA-IR values (r\u0026thinsp;=\u0026thinsp;0.244, p\u0026thinsp;=\u0026thinsp;0.035). Although trends toward positive associations with fasting insulin and HbA1c were noted, these did not reach statistical significance. Collectively, these findings suggest that the clinical relevance of circulating FGF21 becomes more pronounced with increasing disease severity.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study provides integrative evidence that disease progression is shaped by the convergence of metabolic burden, inherited genetic susceptibility, and dysregulated hepatokine signaling. By combining genetic variants in GCKR and FGF21 with circulating FGF21 concentrations, our findings move beyond single-marker approaches and offer a more refined framework for risk stratification in MASLD.\u003c/p\u003e \u003cp\u003eA principal finding of this study is the strong association between the GCKR rs1260326 variant and progression from MASLD to MASH. The enrichment of the T allele, particularly the TT genotype, among patients with MASH underscores the role of altered hepatic glucose handling in amplifying liver injury. Functionally, this variant reduces the inhibitory effect of GCKR on glucokinase, resulting in enhanced glycolytic flux and increased de novo lipogenesis. Although this metabolic shift may confer favorable glycemic effects, it simultaneously promotes hepatic lipid accumulation and lipotoxic stress. Similarly, \u003cb\u003eSamarasinghe et al.\u003c/b\u003e, [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found that the T allele exhibited a higher grade of hepatic steatosis in Indian patients. The biochemical signature observed in TT carriers characterized by higher aminotransferase levels and reduced total protein concentrations, suggests that this variant contributes not only to steatosis but also to impaired hepatocellular function as disease advances [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings are consistent with previous reports by \u003cb\u003eNisar et al.\u003c/b\u003e, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] linking GCKR rs1260326 to hepatic fat accumulation and more severe metabolic phenotypes, reinforcing its role as a genetic modifier of disease severity rather than mere disease presence.\u003c/p\u003e \u003cp\u003eIn parallel, our data highlight a substantial contribution of FGF21 rs838133 genetic variability to MASLD susceptibility and progression. Carriers of the GG genotype exhibited a markedly increased risk of both MASLD and MASH, accompanied by greater insulin resistance and adverse lipid profiles, particularly elevated LDL-cholesterol levels. While FGF21 is widely recognized as a hepatoprotective hormone induced under metabolic stress, genetic perturbations at the FGF21 locus may compromise the effectiveness of this adaptive response. The higher prevalence of hepatic steatosis among GG carriers within the MASLD group further supports a role for this variant early in disease development, potentially predisposing individuals to metabolic dysregulation before overt inflammation or fibrosis becomes evident. These observations align with emerging genetic and experimental data by \u003cb\u003eRamne et al.\u003c/b\u003e, [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] indicating that impaired FGF21 signaling may contribute to metabolic inflexibility and hepatic lipid overload.\u003c/p\u003e \u003cp\u003eA novel aspect of the present study is the demonstration of a gene\u0026ndash;gene interaction between GCKR and FGF21 variants. The non-random co-distribution of high-risk genotypes suggests a synergistic effect that may amplify metabolic disturbances beyond the impact of either variant alone. From a mechanistic standpoint, enhanced hepatic glycolytic flux driven by GCKR variation could increase lipid synthesis and metabolic stress, thereby stimulating FGF21 secretion. If this compensatory pathway is genetically compromised, the imbalance between lipid accumulation and adaptive capacity may accelerate hepatocellular injury and fibrogenesis; such findings support the integrated genetic model identified by \u003cb\u003eSingh et al.\u003c/b\u003e, [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This multilocus interaction provides a plausible explanation for the marked interindividual variability observed in MASLD progression and supports polygenic models of disease susceptibility.\u003c/p\u003e \u003cp\u003eBeyond genetic determinants, circulating FGF21 concentrations emerged as a robust biochemical marker of disease severity. Serum FGF21 levels increased progressively from healthy controls to MASLD and MASH, reflecting escalating hepatic and systemic metabolic stress. Importantly, receiver operating characteristic analyses demonstrated high specificity for advanced disease, indicating that elevated FGF21 levels may be particularly useful for identifying patients with established MASH rather than for population-wide screening. This pattern supports the concept of \u0026ldquo;FGF21 resistance,\u0026rdquo; whereby chronically elevated hormone levels reflect an insufficient compensatory response to ongoing metabolic injury. \u003cb\u003eFilimidou et al.\u003c/b\u003e, [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] demonstrated a pattern which aligns with our findings, in which serum FGF21 exhibits strong discriminatory capacity for identify patients with fatty liver disease compared with healthy subjects (AUC\u0026thinsp;\u0026asymp;\u0026thinsp;0.83). \u003cb\u003eGallego-Dur\u0026aacute;n et al.\u003c/b\u003e, [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] further demonstrated that both hepatic and circulating FGF21 levels were significantly elevated in MASH patients, in Huh7.5 cells exposed to free fatty acids, and in CDA-HFD animal model, corroborating our observations.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis reinforced the multifactorial nature of MASH. Both circulating FGF21 levels and GCKR genetic variation independently predicted disease risk after adjustment for conventional biochemical markers, including alanine aminotransferase. These findings suggest that molecular markers capture dimensions of disease biology not fully reflected by routine clinical tests. Moreover, the concurrent influence of additional lipid-related genetic variants reported in the same population supports a polygenic framework in which multiple modest-effect alleles collectively shape susceptibility to progressive liver disease.\u003c/p\u003e \u003cp\u003eMoreover, the combined contribution of additional lipid-related genetic variants (MARC1 \u003cem\u003ers2642438\u003c/em\u003e A\u0026thinsp;\u0026gt;\u0026thinsp;T and TM6SF2 \u003cem\u003ers58542926\u003c/em\u003e C\u0026thinsp;\u0026gt;\u0026thinsp;T) concurrently reported in other study on same population, highlights the polygenic framework underlying MASH susceptibility, in which multiple modest-effect alleles collectively shape individual trajectories toward advanced liver disease. The analysis revealed that serum FGF21 levels were a significant positive predictor of MASH (B\u0026thinsp;=\u0026thinsp;0.011, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e, OR\u0026thinsp;=\u0026thinsp;1.011, 95% CI: 1.005\u0026ndash;1.018). Similarly, individuals carrying the GCKR \u003cem\u003ers1260326\u003c/em\u003e (TT\u0026thinsp;+\u0026thinsp;CT) genotype exhibited a markedly higher risk of developing MASH (B\u0026thinsp;=\u0026thinsp;1.690, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.039\u003c/em\u003e, OR\u0026thinsp;=\u0026thinsp;5.420, 95% CI: 1.085\u0026ndash;27.062). As well the TT\u0026thinsp;+\u0026thinsp;CT genotype of TM6SF2 \u003cem\u003ers58542926\u003c/em\u003e showed a strong association with disease presence (B\u0026thinsp;=\u0026thinsp;2.127, both \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e, CT: OR\u0026thinsp;=\u0026thinsp;9.956, 95% CI: 4.373\u0026ndash;22.665; TT: OR\u0026thinsp;=\u0026thinsp;18.667, 95% CI: 5.537\u0026ndash;62.936-). Conversely, carriers of the MARC1 \u003cem\u003ers2642438\u003c/em\u003e (TT) genotypes demonstrated a protective effect against MASH (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.289, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.013\u003c/em\u003e, OR\u0026thinsp;=\u0026thinsp;0.101, 95% CI: 0.017\u0026ndash;0.614). Additionally, ALT levels were found to be positively correlated with MASH risk (B\u0026thinsp;=\u0026thinsp;0.530, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e, OR\u0026thinsp;=\u0026thinsp;1.699, 95% CI: 1.304\u0026ndash;2.213), whereas cholesterol levels demonstrated a modest but significant inverse association (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.030, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e, OR\u0026thinsp;=\u0026thinsp;0.971, 95% CI: 0.952\u0026ndash;0.989).\u003c/p\u003e \u003cp\u003eThe final regression equation for MASH prediction was expressed as:\u003c/p\u003e \u003cp\u003e\u0026rlm;Logit (P)\u0026thinsp;=\u0026thinsp;0.011 \u0026times; FGF21 protein\u0026thinsp;+\u0026thinsp;1.690 \u0026times; GCKR rs1260326 (TT\u0026thinsp;+\u0026thinsp;CT)\u0026thinsp;\u0026minus;\u0026thinsp;2.289 \u0026times; MARC1 rs2642438 (TT)\u0026thinsp;\u0026minus;\u0026thinsp;2.127 \u0026times; TM6SF2 rs58542926 (TT\u0026thinsp;+\u0026thinsp;CT)\u0026thinsp;+\u0026thinsp;0.530 \u0026times; ALT\u0026thinsp;\u0026minus;\u0026thinsp;0.030 \u0026times; Chol\u0026thinsp;\u0026minus;\u0026thinsp;15.071.\u003c/p\u003e \u003cp\u003eClinically, these results advocate for a shift toward precision-based risk stratification in MASLD. Integrating genetic profiling with circulating hepatokines may allow earlier identification of individuals at high risk for progression, enabling targeted lifestyle interventions and prioritization for emerging pharmacotherapies. Such an approach moves beyond the traditional reliance on liver enzymes and imaging, offering a more nuanced understanding of disease biology.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The case\u0026ndash;control design limits causal inference, and validation in larger, multiethnic cohorts is warranted to confirm generalizability. In addition, functional studies are required to elucidate the precise molecular mechanisms underlying the observed gene\u0026ndash;gene interactions. Nevertheless, the strength of this study lies in its integrative design, combining genetic, biochemical, and clinical data to better characterize disease heterogeneity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eProgression from MASLD to MASH appears to be driven by the interplay between metabolic stress, inherited susceptibility, and adaptive hepatokine responses. Variants in GCKR and FGF21 not only influence individual metabolic phenotypes but may also interact to shape the hepatic response to chronic metabolic overload. Circulating FGF21 levels further reflect this interaction, serving as a marker of disease burden and a potential tool for clinical risk assessment. Collectively, these findings support the development of personalized strategies for the management of metabolic liver disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMASLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction\u0026ndash;associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMASH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction\u0026ndash;associated steatohepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGCKR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucokinase regulatory protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFGF21\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFibroblast growth factor 21\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGGT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eᵞ-glutamyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh‐density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow‐density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostatic model assessment of insulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eELISA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eq-PCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to initiation\u0026nbsp;of our case control study; the study protocol was reviewed and approved by the Institutional Review Board\u0026nbsp;(IRB)\u0026nbsp;of Theodor\u0026nbsp;Bilharz Research Institute (TBRI) under approval number PT 784, following the ethical principles described by the 1975 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants voluntarily provided informed written consents prior to participation between October 2023 and September 2024 from specialized hepatology and gastroenterology inpatient departments, and outpatient clinics at TBRI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data is available upon reasonable request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAMF and DMA conceived and designed the study. ME and AA collected the clinical data. AMF and DMA performed the laboratory analyses and conducted the statistical analysis. AMF interpreted the data. AMF and ME drafted the manuscript. All authors critically revised the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was conducted as part of an internally funded research project under project number (127) at Theodor Bilharz Research Institute and this study was supported and fully financed by the Institute.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYounossi Z, Golabi P, Paik J, et al (2023a) Global epidemiology of metabolic dysfunction\u0026ndash;associated steatotic liver disease and steatohepatitis. Hepatology;77:1335\u0026ndash;1347. \u003c/li\u003e\n\u003cli\u003ePaik J, Henry L, Younossi Z, et al (2023) The burden of metabolic dysfunction\u0026ndash;associated steatotic liver disease is rapidly growing worldwide from 1990 to 2019. Hepatol Commun;7:e0251. \u003c/li\u003e\n\u003cli\u003eRinella ME, Lazarus JV, Ratziu V, et al (2023) A multisociety Delphi consensus on new fatty liver disease nomenclature. Ann Hepatol;29(1):101133. doi:10.1016/j.aohep.2023.101133 \u003c/li\u003e\n\u003cli\u003ePerumpail BJ, Manikat R, Wijarnpreecha K, et al (2024) The prevalence and predictors of metabolic dysfunction-associated steatotic liver disease and fibrosis/cirrhosis among adolescents/young adults. J Pediatr Gastroenterol Nutr;79(1):110\u0026ndash;118. \u003c/li\u003e\n\u003cli\u003eZhang H, Su W, Xu H, Zhang X, and Guan Y (2022) HSD17B13: A potential therapeutic target for MASLD. Front Mol Biosci;8:824776. doi:10.3389/fmolb.2021.824776.\u003c/li\u003e\n\u003cli\u003eYounossi Z, Wong G, Anstee Q, et al (2023b) The global burden of liver disease. Clin Gastroenterol Hepatol.;21:1978\u0026ndash;1991. \u003c/li\u003e\n\u003cli\u003eHuang D, Wilson L, Behling C, et al (2023) Fibrosis progression in biopsy-proven NAFLD among patients with and without diabetes. Gastroenterology;165:463\u0026ndash;472.e5. \u003c/li\u003e\n\u003cli\u003eYounossi Z and Henry L (2024) Epidemiology of MASLD\u0026mdash;focus on diabetes. Diabetes Res Clin Pract.;210:111648. \u003c/li\u003e\n\u003cli\u003eHarrison S, Bedossa P, Guy CD, et al (2024) MAESTRO-MASH Investigators A phase 3, randomized, controlled trial of resmetiromin MASH with liver fibrosis. N Engl J Med.;390:497\u0026ndash;509. \u003c/li\u003e\n\u003cli\u003eGallego-Dur\u0026aacute;n R, Ampuero J, Maya-Miles D, et al (2024) Fibroblast growth factor-21 in MASLD progression. United European Gastroenterol J.;12(8):1056\u0026ndash;1068. doi: 10.1002/ueg2.12534.\u003c/li\u003e\n\u003cli\u003eCusi K, Isaacs S, Barb D, et al (2022) American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of NAFLD in Primary Care and Endocrinology Clinical Settings. Endocr Pract.;28(5):528\u0026ndash;562. \u003c/li\u003e\n\u003cli\u003ePotter AW, Chin GC, Looney DP, Friedl KE (2025) Defining overweight and obesity by percent body fat instead of BMI. J Clin Endocrinol Metab.;110(4):e1103\u0026ndash;e1107. doi:10.1210/clinem/dgae341 \u003c/li\u003e\n\u003cli\u003eHor\u0026aacute;kov\u0026aacute; D, \u0026Scaron;těp\u0026aacute;nek L, Janout V, et al (2019) Optimal HOMA-IR cut-offs in the Czech population. Medicina (Kaunas);55(5):158. doi:10.3390/medicina55050158 \u003c/li\u003e\n\u003cli\u003eKristensen V, Kelefiotis D, Kristensen T, et al (2001) High-throughput methods for detection of genetic variation. Biotechniques;30:318\u0026ndash;322. \u003c/li\u003e\n\u003cli\u003eChan YH (2003a) Biostatistics 102: Quantitative data \u0026ndash; parametric \u0026amp; non-parametric tests. Singapore Med J.;44(8):391\u0026ndash;396. \u003c/li\u003e\n\u003cli\u003eChan YH (2003b) Biostatistics 103: Qualitative data \u0026ndash; tests of independence. Singapore Med J.;44(10):498\u0026ndash;503. \u003c/li\u003e\n\u003cli\u003eChan YH (2003c) Biostatistics 104: Correlational analysis. Singapore Med J.;44(12):614\u0026ndash;619. \u003c/li\u003e\n\u003cli\u003eChan YH (2004) Biostatistics 202: Logistic regression analysis. Singapore Med J.;45(4):149\u0026ndash;153.\u003c/li\u003e\n\u003cli\u003eSamarasinghe SM, Anselmi L, Kumar R, et al (2023) Genetic and metabolic aspects of non-alcoholic fatty liver disease: implications of GCKR rs1260326 association with steatosis severity in Indian patients. J Med Hist Genet.; Article Collection 2023. doi:10.1186/s43042-023-00433-x.\u003c/li\u003e\n\u003cli\u003eZhang M, Li X, Zhao Y, et al (2024) Association between weight-adjusted waist index and non-alcoholic fatty liver disease: a population-based study. BMC Endocr Disord.;24(1):15. doi:10.1186/s12902-024-01554-z.\u003c/li\u003e\n\u003cli\u003eNisar T, Ahmad S, Riaz H, et al (2023) Prevalence of GCKR rs1260326 variant in subjects with obesity-associated NAFLD and T2DM: a case-control study in South Punjab, Pakistan. J Obes.;2023:6661858. doi:10.1155/2023/6661858.\u003c/li\u003e\n\u003cli\u003eRamne S, et al (2024) Genetic variants at the FGF21 locus influence circulating FGF21 and metabolic traits. medRxiv. doi:10.1101/2024.01.01.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eSingh C, Xiao Y, Fern\u0026aacute;ndez-Hernando C, et al (2024) ChREBP is activated by reductive stress and mediates GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerols. Cell Metabolism;36(3):xxx\u0026ndash;xxx. doi:10.1016/j.cmet.2024.02.012.\u003c/li\u003e\n\u003cli\u003eFilimidou I, et al (2025) Circulating Fibroblast Growth Factor-21 in Patients with Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis. Curr Obes Rep.;[Epub ahead of print]. (meta-analysis indicating higher FGF21 in NAFLD/NASH).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MASLD, MASH, GCKR, FGF21, genetic susceptibility","lastPublishedDoi":"10.21203/rs.3.rs-8643484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u0026amp; Aims: \u003c/strong\u003eMetabolic dysfunction–associated steatotic liver disease (MASLD) represents a broad clinicopathological spectrum, extending from isolated hepatic steatosis to its progressive inflammatory stage, metabolic dysfunction–associated steatohepatitis (MASH). Interindividual variability in disease onset and progression reflects a complex interplay between metabolic burden and inherited susceptibility. The present study investigated the combined impact of genetic variants in the glucokinase regulatory protein gene (GCKR) and fibroblast growth factor 21 (FGF21), together with circulating FGF21 concentrations, on susceptibility to MASLD and its progression to MASH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis case-control study enrolled 450 age- and sex-matched participants: 150 patients with MASLD, 150 with fibroscan-confirmed MASH, and 150 healthy controls. Genotyping of GCKR \u003cem\u003ers1260326\u003c/em\u003e and FGF21 \u003cem\u003ers838133\u003c/em\u003epolymorphisms was performed using real-time polymerase chain reaction, while serum FGF21 levels were quantified by enzyme-linked immunosorbent assay. Associations with metabolic characteristics, liver function indices, and fibrosis severity were examined using correlation analyses and multivariate logistic regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The GCKR \u003cem\u003ers1260326\u003c/em\u003e TT genotype was significantly overrepresented among patients with MASH \u003cem\u003e(p=0.005)\u003c/em\u003e and was associated with higher alanine aminotransferase levels and reduced markers of hepatic synthetic capacity. In parallel, carriers of the FGF21 \u003cem\u003ers838133\u003c/em\u003e G allele exhibited an increased likelihood of MASLD and a higher propensity for progression to MASH, accompanied by greater insulin resistance and unfavorable lipid profiles. Circulating FGF21 concentrations demonstrated a stepwise increase from controls to MASLD and MASH groups and showed strong diagnostic performance in identifying advanced disease stages. Multivariate analysis confirmed that both serum FGF21 levels and GCKR genetic variation independently predicted the risk of MASH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eGenetic variation in GCKR and FGF21, together with altered hepatokine signaling, contributes substantially to metabolic dysregulation and liver disease severity. Integrating genetic profiling with circulating biomarkers may offer a refined strategy for identifying individuals at high risk of MASLD progression and advancing precision-based approaches in metabolic liver disease.\u003c/p\u003e","manuscriptTitle":"GCKR genetic variants and circulating FGF21 define a metabolic risk signature in Metabolic-associated Steatotic Liver disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:36:42","doi":"10.21203/rs.3.rs-8643484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T09:56:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T06:48:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T20:12:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T15:42:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278154077964226591188113544487800380958","date":"2026-02-09T14:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222369755170640534637157841195928941525","date":"2026-02-09T02:48:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105473534001409794524328578476521524811","date":"2026-02-09T01:45:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232100137558362168438636809824749661239","date":"2026-02-08T23:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T02:03:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-22T12:22:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T11:32:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T11:31:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-01-20T00:17:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83ad4fe2-338e-4ece-a00f-f1c635f8a3bb","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:22:37+00:00","versionOfRecord":{"articleIdentity":"rs-8643484","link":"https://doi.org/10.1186/s12876-026-04767-9","journal":{"identity":"bmc-gastroenterology","isVorOnly":false,"title":"BMC Gastroenterology"},"publishedOn":"2026-04-13 15:57:00","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2026-02-09 12:36:42","video":"","vorDoi":"10.1186/s12876-026-04767-9","vorDoiUrl":"https://doi.org/10.1186/s12876-026-04767-9","workflowStages":[]},"version":"v1","identity":"rs-8643484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643484","identity":"rs-8643484","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}