Analysis of TM6SF2, PNPLA3, and ATG16L1 Genetic Variants in MASLD and MASH: An Egyptian Case-control Study

Analysis of TM6SF2, PNPLA3, and ATG16L1 Genetic Variants in MASLD and MASH: An Egyptian Case-control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of TM6SF2, PNPLA3, and ATG16L1 Genetic Variants in MASLD and MASH: An Egyptian Case-control Study Asmaa Mohamed Fteah, Khaled Mabrouk, Ali Abdel Rahim, Mohamed A Elrefaiy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9203531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Metabolic dysfunction associated steatotic liver disease (MASLD) and metabolic dysfunction associated steatohepatitis (MASH), represent complex metabolic liver diseases and a rapidly growing public health burden, particularly in Egypt. Genetic susceptibility plays a pivotal role in modulating disease progression. Variants in Patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2) and Autophagy Related 16-Like 1 (ATG16L1) are among the most relevant genetic determinants implicated in hepatic lipid metabolism, autophagy regulation, and hepatocellular injury. The interplay between these genetic variants and metabolic stress regulators is increasingly recognized as central to disease heterogeneity. Aims This case-control study aimed to provide an integrative evaluation of these genetic variants, elucidating their biochemical associations, and mechanistic roles in MASLD and MASH. Methodology: 150 patients with fibroscan-confirmed MASLD, 150 with MASH and 150 healthy controls were genotyped for PNPLA3 (rs738409), TM6SF2 (rs58542926), and ATG16L1 (rs2241880) using real-time TaqMan assays. Genotypic data were correlated with liver injury biomarkers, lipid profiles, and insulin resistance indices. Results TM6SF2 (rs58542926) TT and CT genotypes, as well as PNPLA3 (rs738409) CG genotype were strongly associated with exacerbated hepatic steatosis and elevated biochemical markers of liver injury. The G allele of ATG16L1 demonstrated modulatory effects on autophagy-related inflammatory pathways. PNPLA3 and ATG16L1 exert complementary and additive effects on hepatic fat accumulation and metabolic derangements. Conclusions The investigated genetic variants showed exhibited biochemical signatures influencing hepatic fat accumulation, autophagy activity, inflammatory response, and fibrosis progression among Egyptian patients. Functional insights were integrated to illuminate how these variants shape disease pathophysiology. Clinical trial registration Not applicable. MAFLD MASH Genetics TM6SF2 PNPLA3 ATG16L1 autophagy Introduction In 2023, three multinational liver associations proposed the term metabolic dysfunction-associated steatotic liver disease (MASLD) to replace the old term non-alcoholic fatty liver disease (NAFLD), aiming to provide a more accurate and inclusive definition of this highly prevalent condition by emphasizing the central role of metabolic dysfunction in disease pathogenesis. This updated nomenclature reflects a paradigm shift in understanding fatty liver disease as a systemic metabolic disorder rather than a diagnosis of exclusion [ 1 ] and [ 2 ]. Recent data from the Global Burden of Disease (GBD) study highlight the rapidly increasing health and economic burden of MASLD worldwide, with substantial implications for both liver-related and extrahepatic morbidity [ 3 ] MASLD encompasses a broad disease spectrum ranging from isolated steatotic liver conditions to metabolic dysfunction-associated steatohepatitis (MASH), which may further progress to advanced fibrosis, cirrhosis, and hepatocellular carcinoma [ 4 ]. Beyond liver-specific outcomes, MASLD is increasingly recognized as a multisystem disease associated with an elevated risk of cardiovascular disease, chronic kidney disease, and extrahepatic malignancies [ 5 ]. Notably, the global prevalence of MASLD has risen from approximately 25.3% during 1990–2006 to 38.2% during 2016–2019, representing an almost 50% increase over the past three decades [ 6 ]. Although metabolic risk factors such as obesity, insulin resistance, and type 2 diabetes mellitus are well-established drivers of MASLD, substantial interindividual variability in disease severity and progression cannot be fully explained by environmental or metabolic factors alone. Accumulating evidence underscores a pivotal role for genetic susceptibility in modulating hepatic fat accumulation, inflammatory responses, and fibrogenesis. In this context, genetic variants in patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and autophagy-related 16-like 1 (ATG16L1) have emerged as key determinants influencing lipid metabolism, hepatocellular stress responses, and disease progression. The PNPLA3 gene, particularly the rs738409 C > G polymorphism which encodes an isoleucine-to-methionine substitution at position 148 (I148M), represents the most consistently replicated associated genetic determinant of MASLD and MASH. Mechanistically, the I148M variant impairs triglyceride hydrolysis within hepatic lipid droplets, leading to intracellular triglyceride accumulation and altered lipid remodeling, despite paradoxically lower serum fasting triglyceride levels and alters polyunsaturated fatty acid handling interfering with lipoprotein secretion and alters lipid droplet remodeling, thereby exacerbating lipotoxic stress and promoting fibrogenesis [ 7 ]. Similarly, the TM6SF2 gene located on chromosome 19, has been strongly implicated in the pathogenesis of progressive steatotic liver disease. The rs58542926 C > T variant (E167K) is associated with impaired hepatic very-low-density lipoprotein secretion, resulting in increased intrahepatic lipid retention and accelerated fibrotic progression, while concurrently reducing circulating lipid levels [ 8 ]. These dual metabolic effects underscore the complex role of TM6SF2 as a modifier of both hepatic and systemic lipid homeostasis. ‏Autophagy has emerged as a critical cellular process in maintaining hepatocyte homeostasis under metabolic stress. ATG16L1 plays a central role in autophagosome formation, regulation of lipid turnover, and control of inflammatory responses. Genetic variants in ATG16L1 may disrupt autophagic flux, thereby exacerbating lipotoxicity, oxidative stress, and inflammatory signaling within the liver. Emerging evidence suggests that ATG16L1 polymorphisms may act as disease progression modifiers, particularly influencing the transition from simple steatosis to steatohepatitis and fibrotic remodeling [ 9 ]. Materials and Methods Study design and participants After exclusion of 23 participants (not meeting inclusion/exclusion criteria or incomplete data), a total of 450 subjects were included in the final analysis and categorized into three groups: 150 adult patients (≥ 18 years) with a confirmed diagnosis of MASLD, 150 patients with clinically suspected MASH. Patients presenting to the liver clinic were initially screened using abdominal ultrasonography for evidence of hepatic steatosis. The operational distinction between MASLD and clinically suspected MASH was established using non-invasive criteria aligned with the current guidance of the American Association for the Study of Liver Diseases [ 10 ], including: Imaging-confirmed steatosis; with pre-d efined cut-off values used for steatosis grading and fibrosis staging, based on Controlled Attenuation Parameter (CAP) measurements obtained by transient elastography (FibroScan). Biochemical evidence of liver injury (elevated liver enzymes). Presence of metabolic risk factors, consistent with established diagnostic criteria. And as a normal control group; 150 apparently healthy subjects were included in the study, who were free from history of fatty liver, diabetes and hypertension, with normal clinical and biochemical profiles. Excluded patients were those testing positive for viral markers for hepatitis B and C, individuals with significant alcohol consumption (≥ 30 g/day in men and ≥ 20 g/day in women), those with recreational drug abuse, patients with confirmed autoimmune or cholestatic liver diseases, other secondary causes of steatosis, and those with history of past malignancies or recurrent/secondary tumors. 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 [ 11 ]. 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 [ 12 ]. 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 Genomic DNA was extracted from peripheral blood leukocytes using the GeneJET whole blood genomic DNA purification mini-kit (ThermoFisher Scientific Inc., CA, USA), following the manufacturer’s instructions. Extracted DNA samples were stored at − 20°C until further analysis. The DNA concentration and purity were determined using qubit fluorometric quantification assays (ThermoFisher Scientific Inc., CA, USA). To ensure consistency across all downstream genotyping assays, and samples were adjusted to a final concentration of 20 ng/µL, and no samples were excluded due to poor DNA quality. Genotyping of PNPLA3 (rs738409) (Assay ID: C_7241_10), TM6SF2 (rs58542926) (Assay ID: C_89463510_10), and ATG16L1 (rs2241880) (Assay ID: C_9095577_20) was performed for all participants using predesigned allele-specific TaqMan quantitative real-time polymerase chain reaction (qPCR) assays (ThermoFisher Scientific Inc., CA, USA). Quantitative PCR amplification and allelic discrimination were performed using the Applied Biosystems Step One Real-Time PCR System (ABI 7500, Applied Biosystems, Foster City, CA, USA) was achieved using sequence-specific fluorescent taqman probes, according to the protocol described by [ 13 ]. Quality control measures included the incorporation of negative controls in each PCR run to ensure assay accuracy and exclude possible cross-contamination or genotyping bias. The overall genotyping call rate exceeded 95% for both SNPs. Samples that showed unsuccessful amplification or ambiguous genotype calls were reanalyzed and successfully resolved upon retesting, with no persistent genotyping failures. Statistical analysis Data were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Continuous variables expressed as 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 [ 14 ]. Categorical variables compared with chi-square (χ2) test. Exact test was used instead when the expected frequency is less than 5 [ 15 ]. Multivariate regression for genotype-phenotype correlations, gene-gene interactions, and biochemical associations. Odds ratio (OR) with 95% confidence intervals were calculated. Data was double checked for normality using normality plots and Shapiro Wilk test and Post-hoc Bonferroni test. A p-value more than 0.05 was considered indicative of equilibrium. Correlations between quantitative variables were done using Spearman correlation coefficient [ 16 ]. P-values less than 0.05 were considered as statistically significant. 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 clinically suspected 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 clinically suspected 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 patients with MASLD and clinically suspected MASH showed significantly higher levels of fasting insulin, HbA1C %, HOMA-IR, aminotransferases, γ-glutamyl transferase, and adverse lipid parameters, with the most pronounced alterations ob1served in patients with clinically suspected 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) * 8.89 ± 0.31 8.71 ± 0.39 7.33 ± 0.55 < 0.001 Albumin (g/dl) * 4.57 ± 0.47 4.51 ± 0.30 4.27 ± 0.32 < 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 the TM6SF2 rs58542926 (C/T) polymorphism and its clinical correlates A significant difference in genotype distribution of TM6SF2 was observed across the three studied groups (P < 0.001); with higher frequency distribution of TT genotype in MASH patients. When comparing controls to MASLD patients, both the heterozygous CT and homozygous TT genotypes were significantly associated with increased risk of MASLD (CT: OR = 9.956, 95% CI: 4.373–22.665; TT: OR = 18.667, 95% CI: 5.537–62.936; both P < 0.001). Allelic analysis further confirmed a significant enrichment of the T allele in the MASLD group compared to controls (OR = 6.311, 95% CI: 3.649–10.915; P < 0.001). While in the comparison between controls and MASH patients, the TT genotype again showed a strong association with disease presence (OR = 14.84, 95% CI: 4.843–45.523; P < 0.001). As well the T allele frequency was significantly higher in MASH patients compared to controls, with an odds ratio of 5.824 (95% CI: 3.369–10.070; P < 0.001) (table 2). As described in table (3), waist circumference differed significantly between TM6SF2 rs58542926 (C/T) genotypes (P = 0.036), with the TT group displaying a slightly higher mean value compared to other groups. Serum insulin levels showed a marked elevation in the TT genotype compared with other genotypes (P = 0.001). Regarding liver functions, ALT levels and total protein concentrations were markedly elevated in the TT group compared with CT and CC groups. Similarly, serum uric acid and urea levels were significantly higher among TT carriers compared with CT and CC (P = 0.002 and 0.004 respectively). Collectively, these data suggest that the TT genotype is associated with higher insulin levels, total protein, and liver and renal function markers, indicating a possible metabolic burden linked to this genetic variant. Table (2) Genotype and Allele distribution of TM6SF2 rs58542926 genotypic variants and their odd’s ratios among the studied groups Genotype Control (n = 150) MASLD (n = 150) MASH (n = 150) P value CC 112 (74.7%) 30 (20.0%) 66 (44.0%) < 0.001 CT 30 (20.0%) 80 (53.3%) 14 (9.3%) TT 8 (5.3%) 40 (26.7%) 70 (46.7%) Genotype Control (n = 150) MASLD (n = 150) P value OR 95% CI CC 112 (74.7%) 30 (20.0%) Reference CT 30 (20.0%) 80 (53.3%) < 0.001 9.956 4.373–22.665 TT 8 (5.3%) 40 (26.7%) < 0.001 18.667 5.537–62.936 Alleles Control (n = 300) MASLD (n = 300) P value OR 95% CI C 254 (84.7%) 140 (46.7%) Reference T 46 (15.3%) 160 (53.3%) < 0.001 6.311 3.649–10.915 Genotype Control (n = 150) MASH (n = 150) P value OR 95% CI CC 112 (74.7%) 66 (44.0%) Reference CT 30 (20.0%) 14 (9.3%) 0.646 0.792 0.293–2.142 TT 8 (5.3%) 70 (46.7%) < 0.001 14.848 4.843–45.523 Alleles Control (n = 300) MASH (n = 300) P value OR 95% CI C 254 (84.7%) 146 (48.7%) Reference T 46 (15.3%) 154 (51.3%) < 0.001 5.824 3.369–10.070 Genotype MASLD (n = 150) MASH (n = 150) P value OR 95% CI CC 30 (20.0%) 66 (44.0%) Reference CT 80 (53.3%) 14 (9.3%) < 0.001 0.080 0.029–0.218 TT 40 (26.7%) 70 (46.7%) 0.585 0.795 0.350–1.808 Alleles MASLD (n = 300) MASH (n = 300) P value OR 95% CI C 140 (46.7%) 146 (48.7%) Reference T 160 (53.3%) 154 (51.3%) 0.729 0.923 0.587–1.452 Data are presented as numbers (percentage). Table (3) Comparison between TM6SF2 rs58542926 (A/T) genetic variants in MASLD & MASH patients regarding the laboratory data Variable represented as mean ± SD CC genotype CT genotype TT genotype P value Age (Years) 48.10 ± 8.23 48.19 ± 10.18 48.65 ± 8.53 0.945 Waist circumference 95.40 ± 7.05 91.68 ± 6.63 93.33 ± 7.18 0.036 Length (cm) 171.58 ± 5.16 170.49 ± 6.05 171.04 ± 5.60 0.638 Weight (kg) 92.15 ± 8.17 88.40 ± 9.58 89.87 ± 8.56 0.115 BMI (kg/m²) 31.31 ± 2.56 30.45 ± 3.39 30.74 ± 2.74 0.345 Hemoglobin (g/dl) 11.25 ± 1.47 11.56 ± 1.28 11.30 ± 1.50 0.513 TLC (×10⁹/L) 5.02 ± 1.61 4.93 ± 1.46 5.31 ± 1.55 0.423 Platelet count (×10³/µL) 177.40 ± 29.13 167.49 ± 22.85 178.56 ± 32.89 0.118 FBS (mg/dl) 89.35 ± 24.64 90.79 ± 17.97 92.11 ± 30.47 0.858 HbA1C (%) 5.36 ± 0.74 5.26 ± 0.63 5.51 ± 1.06 0.308 Insulin (µU/ml) 19.88 ± 7.66 15.45 ± 7.16 21.76 ± 9.74 0.001 HOMA-IR 4.67 ± 3.36 3.72 ± 3.00 5.54 ± 5.21 0.081 Total protein (g/dl) 8.83 ± 0.28 8.66 ± 0.40 8.90 ± 0.38 0.003 Albumin (g/dl) 4.62 ± 0.40 4.46 ± 0.36 4.53 ± 0.42 0.156 ALT (IU/L) 31.28 ± 10.92 41.76 ± 14.63 42.83 ± 14.08 < 0.001 AST (IU/L) 31.04 ± 11.38 30.72 ± 7.78 32.56 ± 11.26 0.626 Total blirubin (mg/dl) 1.04 ± 0.10 1.02 ± 0.09 1.02 ± 0.11 0.502 Direct blirubin (mg/dl) 0.56 ± 0.10 0.54 ± 0.12 0.52 ± 0.11 0.274 Uric acid (mg/dl) 5.26 ± 1.06 4.90 ± 1.01 5.72 ± 1.31 0.002 GGT (IU/L) 47.10 ± 15.66 43.51 ± 11.24 45.69 ± 17.10 0.503 Urea (mg/dl) 37.98 ± 6.60 34.11 ± 7.48 38.33 ± 6.21 0.004 Creatinine (mg/dl) 1.07 ± 0.15 1.02 ± 0.18 1.06 ± 0.15 0.184 Total cholesterol (mg/dl) 200.83 ± 37.93 202.13 ± 39.27 201.82 ± 48.35 0.988 HDL-C (mg/dl) 38.65 ± 9.90 41.30 ± 10.03 38.85 ± 9.76 0.346 LDL-C (mg/dl) 106.83 ± 15.02 108.87 ± 9.22 109.05 ± 15.32 0.667 Triglycerides (mg/dl) 148.96 ± 24.12 156.30 ± 24.35 151.31 ± 26.12 0.344 Frequency distribution of the Patatin-like phospholipase domain-containing protein 3 (PNPLA3) (C > G) Table 4 elucidates the genotypic and allelic distribution regarding PNPLA3 (rs738409) C>G between MASH and other studied groups; suggesting that G allele is significantly associated with higher susceptibility to MASH development among the studied groups (p=0.019). Specifically, as the CG genotype and G allele were significantly increased in patients with MASH than normal control group [OR = 2.11, 95% CI = 1.039-4.282] and [OR = 1.683, 95% CI = 0.999-2.834] respectively. Table 5 demonstrates the genotypes’ distribution in MASLD and MASH groups and their relation with the laboratory data, showing that PNPLA3 I148M risk genotype carriers had significantly higher LDL-C and triglycerides levels compared to non-GG carriers (P = 0.047 and 0.017) respectively. Table (4): Genotype and Allele distribution of PNPLA3 rs738409 (C/G) variants and their odd’s ratios among the studied groups Genotypes Control (= 150) MASLD (n = 150) MASH (n = 150) P value CC 96 (65.3%) 74 (49.3%) 72 (48.0%) 0.225 CG 40 (26.7%) 60 (40.0%) 62 (41.3%) GG 14 (8.0%) 16 (10.7%) 16 (10.7%) Alleles Control (n = 300) MASLD (n = 300) MASH (n = 300) P value C allele 236 (78.7%) 208 (69.3%) 206 (68.7%) 0.019 G allele 64 (21.3%) 92 (30.7%) 94 (31.3%) Genotypes Control (= 150) MASLD (n = 150) OR (95% CI) P value CC 96 (65.3%) 74 (49.3%) Reference CG 40 (26.7%) 60 (40.0%) 1.986 (0.978–4.035) 0.058 GG 14 (8.0%) 16 (10.7%) 1.766 (0.564–5.529) 0.329 Alleles Control (n = 300) MASLD (n = 300) OR (95% CI) P value C 236 (78.7%) 208 (69.3%) Reference G 64 (21.3%) 92 (30.7%) 1.631 (0.967–2.750) 0.067 Genotypes Control (= 150) MASH (n = 150) OR (95% CI) P value CC 96 (65.3%) 72 (48.0%) Reference CG 40 (26.7%) 62 (41.3%) 2.110 (1.039–4.282) 0.039 GG 14 (8.0%) 16 (10.7%) 1.815 (0.579–5.689) 0.307 Alleles Control (n = 300) MASH (n = 300) OR (95% CI) P value C 236 (78.7%) 206 (68.7%) Reference G 64 (21.3%) 94 (31.3%) 1.683 (0.999–2.834) 0.050 Genotypes MASLD (n = 150) MASH (n = 150) OR (95% CI) P value CC 74 (49.3%) 72 (48.0%) Reference CG 60 (40.0%) 62 (41.3%) 1.062 (0.538–2.096) 0.862 GG 16 (10.7%) 16 (10.7%) 1.028 (0.348–3.033) 0.960 Alleles MASLD (n = 300) MASH (n = 300) OR (95% CI) P value C 208 (69.3%) 206 (68.7%) Reference G 92 (30.7%) 94 (31.3%) 1.032 (0.632–1.683) 0.901 Data are presented as numbers (percentage). Table (5) Comparison between PNPLA3 rs738409 (C/G) genetic variants in MASLD & MASH patients regarding the laboratory data Variable represented as mean ± SD CC genotype CG genotype GG genotype P value Age (Years) 49.00 ± 9.01 48.21 ± 9.01 45.75 ± 8.30 0.418 Waist circumference 92.95 ± 6.32 93.48 ± 7.74 95.88 ± 7.72 0.327 Length (cm) 170.45 ± 4.76 171.77 ± 6.31 170.94 ± 6.24 0.399 Weight (kg) 89.23 ± 7.32 90.33 ± 9.85 93.56 ± 10.75 0.204 BMI (kg/m²) 30.75 ± 2.77 30.62 ± 3.06 31.29 ± 2.85 0.234 Hemoglobin (g/dl) 11.48 ± 1.50 11.26 ± 1.38 11.22 ± 1.26 0.604 TLC (×10⁹/L) 5.21 ± 1.07 4.98 ± 1.45 5.06 ± 1.23 0.680 Platelet count (×10³/µL) 173.99 ± 30.11 175.36 ± 27.13 175.63 ± 33.11 0.956 FBS (mg/dl) 88.85 ± 21.59 93.41 ± 24.17 89.88 ± 31.20 0.573 HbA1C (%) 5.31 ± 0.84 5.43 ± 0.89 5.53 ± 0.96 0.556 Insulin (µU/ml) 18.58 ± 3.87 19.99 ± 9.06 18.88 ± 9.00 0.643 HOMA-IR 4.34 ± 0.39 5.09 ± 4.86 4.74 ± 4.79 0.578 Total protein (g/dl) 8.50 ± 0.42 8.79 ± 0.38 8.81 ± 0.46 0.974 Albumin (g/dl) 4.81 ± 0.33 4.60 ± 0.35 4.43 ± 0.46 0.259 ALT (IU/L) 37.82 ± 15.24 39.84 ± 14.19 39.50 ± 9.47 0.706 AST (IU/L) 32.55 ± 10.73 30.67 ± 10.11 29.88 ± 9.03 0.464 Total blirubin (mg/dl) 1.02 ± 0.10 1.03 ± 0.11 1.02 ± 0.10 0.675 Direct blirubin (mg/dl) 0.54 ± 0.11 0.53 ± 0.11 0.53 ± 0.12 0.773 Uric acid (mg/dl) 5.29 ± 1.26 5.28 ± 1.15 5.38 ± 0.79 0.952 GGT (IU/L) 44.29 ± 13.71 45.77 ± 15.40 49.63 ± 18.79 0.428 Urea (mg/dl) 36.19 ± 8.09 37.49 ± 5.75 37.69 ± 5.79 0.502 Creatinine (mg/dl) 1.04 ± 0.17 1.06 ± 0.15 1.08 ± 0.14 0.518 Total cholesterol (mg/dl) 198.42 ± 40.80 200.41 ± 42.25 220.63 ± 45.64 0.156 HDL-C (mg/dl) 40.29 ± 10.05 39.18 ± 10.06 37.63 ± 8.72 0.581 LDL-C (mg/dl) 106.62 ± 12.80 108.31 ± 12.73 115.81 ± 17.68 0.047 Triglycerides (mg/dl) 138.63 ± 16.84 149.77 ± 24.20 157.04 ± 25.93 0.017 Frequency distribution of the ATG rs2642438 (A/T) SNP No statistically significant differences were found in genotype distribution among the three groups (P = 0.374). Comparing controls to MASLD, neither genotypic nor allelic frequency analysis revealed a significant association with disease risk. Although the differences in genotype frequencies were not statistically significant when comparing MASH to controls, the G allele was significantly more prevalent in MASH (40.7%) than in controls (29.3%) with a P value of 0.040 (OR = 1.651, 95% CI: 1.022–2.666), indicating a possible association of the G allele with increased risk of MASH (table 6). Table (7) summarized the relationship between ATG16L1 rs2241880 (A/G) genetic variants and various clinical and biochemical parameters including glycemic markers and lipid profile components. No statistically significant differences were observed among the three genotypic groups in the study population; with the exception of a marginally significant association with weight (P = 0.044). Table (6) Genotype and Allele distribution of ATG16L1 rs2642438 (A/T) genotypic variants and their odd’s ratios among the studied groups Genotype Control (n = 150) MASLD (n = 150) MASH (n = 150) P value AA 84 (56.0%) 66 (44.0%) 60 (40.0%) 0.374 AG 44 (29.3%) 56 (17.3%) 58 (38.7%) GG 22 (14.7%) 28 (18.7%) 32 (21.3%) Genotype Control (n = 150) MASLD (n = 150) P value OR 95% CI AA 84 (56.0%) 66 (44.0%) Reference AG 44 (29.3%) 56 (17.3%) 0.190 1.620 0.788–3.331 GG 22 (14.7%) 28 (18.7%) 0.300 1.620 0.651–4.032 Alleles Control (n = 300) MASLD (n = 300) P value OR 95% CI A 212 (70.7%) 188 (62.7%) Reference G 66 (29.3%) 112 (37.3%) 0.142 1.435 0.886–2.326 Genotype Control (n = 150) MASH (n = 150) P value OR 95% CI AA 84 (56.0%) 60 (40.0%) Reference AG 44 (29.3%) 58 (38.7%) 0.098 1.845 0.893–3.813 GG 22 (14.7%) 32 (21.3%) 0.121 2.036 0.828–5.005 Alleles Control (n = 300) MASH (n = 300) P value OR 95% CI A 212 (70.7%) 178 (59.3%) Reference G 66 (29.3%) 122 (40.7%) 0.040 1.651 1.022–2.666 Genotype MASLD (n = 150) MASH (n = 150) P value OR 95% CI AA 66 (44.0%) 60 (40.0%) Reference AG 56 (17.3%) 58 (38.7%) 0.722 1.139 0.556–2.334 GG 28 (18.7%) 32 (21.3%) 0.607 1.257 0.526–3.004 Alleles MASLD (n = 300) MASH (n = 300) A 188 (62.7%) 178 (59.3%) Reference G 112 (37.3%) 122 (40.7%) 0.554 1.150 0.723–1.830 Data are presented as numbers (percentage). Table (7) Comparison between ATG rs2642438 (A/T) genetic variants in MASLD & MASH patients regarding the laboratory data Variable represented as mean ± SD AA genotype AG genotype GG genotype P value Age (Years) 48.76 ± 9.48 48.40 ± 9.00 47.30 ± 7.71 0.762 Waist circumference 93.16 ± 6.78 92.98 ± 7.43 95.07 ± 7.05 0.386 Length (cm) 170.46 ± 4.52 171.04 ± 6.24 172.27 ± 6.29 0.349 Weight (kg) 89.25 ± 7.19 89.23 ± 9.33 93.73 ± 10.32 0.044 BMI (kg/m²) 30.77 ± 2.84 30.52 ± 3.04 31.56 ± 2.75 0.279 Hemoglobin (g/dl) 11.43 ± 1.46 11.28 ± 1.39 11.38 ± 1.45 0.835 TLC (×10⁹/L) 5.02 ± 1.61 5.15 ± 1.58 5.17 ± 1.32 0.871 Platelet count (×10³/µL) 173.67 ± 30.49 174.60 ± 28.04 177.17 ± 28.80 0.864* FBS (mg/dl) 92.08 ± 26.60 92.25 ± 24.16 85.43 ± 23.53 0.424 HbA1C (%) 5.41 ± 0.97 5.39 ± 0.77 5.33 ± 0.72 0.920 Insulin (µU/ml) 19.29 ± 9.36 19.54 ± 8.74 18.27 ± 7.30 0.805 HOMA-IR 4.76 ± 4.15 4.81 ± 4.31 3.60 ± 0.852 0.852 Total protein (g/dl) 8.80 ± 0.32 8.77 ± 0.38 8.84 ± 0.43 0.723 Albumin (g/dl) 4.50 ± 0.41 4.62 ± 0.37 4.47 ± 0.42 0.147 ALT (IU/L) 37.97 ± 15.47 40.33 ± 14.85 37.73 ± 10.00 0.597 AST (IU/L) 33.24 ± 10.40 31.18 ± 10.58 28.47 ± 9.04 0.108 Total blirubin (mg/dl) 1.02 ± 0.10 1.03 ± 0.10 1.03 ± 0.11 0.631 Direct blirubin (mg/dl) 0.53 ± 0.11 0.53 ± 0.11 0.56 ± 0.12 0.368 Uric acid (mg/dl) 5.25 ± 1.24 5.22 ± 1.10 5.55 ± 1.16 0.418 GGT (IU/L) 43.48 ± 12.93 47.02 ± 14.99 46.67 ± 18.63 0.386 Urea (mg/dl) 35.87 ± 7.79 37.61 ± 6.15 37.60 ± 6.64 0.325 Creatinine (mg/dl) 1.03 ± 0.17 1.07 ± 0.15 1.07 ± 0.15 0.418 Total cholesterol (mg/dl) 201.83 ± 41.97 201.40 ± 41.30 201.50 ± 45.51 0.998 HDL-C (mg/dl) 40.48 ± 9.63 39.54 ± 10.48 37.63 ± 9.32 0.435 LDL-C (mg/dl) 107.19 ± 13.25 107.98 ± 12.55 111.17 ± 15.87 0.410 Triglycerides (mg/dl) 157.24 ± 25.57 149.58 ± 23.04 146.20 ± 25.91 0.084 Table (8) illustrates the distribution of ATG16L1 rs2241880 (A/G) genotypes according to PNPLA3 rs738409 (C/G) variants among the studied groups. A highly significant association was observed between the two genetic loci across all studied groups (P < 0.001). In the control group, the PNPLA3 CC genotype carriers predominantly exhibited the ATG16L1 AA genotype (85.7%). Similarly, within the MASLD group, the PNPLA3 CC genotype was strongly associated with ATG16L1 AA (89.2%), whereas the PNPLA3 CG variant was exclusively linked to ATG16L1 AG (93.3%). A comparable trend was observed in the MASH group, among subjects with PNPLA3 CC, the ATG16L1 AA genotype was most prevalent (75%), Those carrying the PNPLA3 CG variant were mainly heterozygous for ATG16L1 (AG) (77.4%), whereas all individuals with the PNPLA3 GG genotype exhibited ATG16L1 GG. These consistent findings across the three groups indicate a strong genetic linkage or potential epistatic interaction between ATG16L1 and PNPLA3 loci, which may contribute to the molecular pathogenesis and progression from simple steatosis to MASH. Table (8) Correlation between genetic variants of ATG rs2642438 (A/T) and PNPLA3 rs738409 (C/G) A) Controls PNPLA3 rs738409 (C/G) CC CG GG P value ATG16L1 rs2241880 (A/G) AA 84 (85.7%) 0 (0.0%) 0 (0.0%) < 0.001 AG 4 (4.1%) 40 (100.0%) 0 (0.0%) GG 10 (10.2%) 0 (0.0%) 12 (100.0%) B) MASLD PNPLA3 rs738409 (C/G) CC CG GG P value ATG16L1 rs2241880 (A/G) AA 66 (89.2%) 0 (0.0%) 0 (0.0%) < 0.001 AG 0 (0.0%) 56 (93.3%) 0 (0.0%) GG 8 (10.8%) 4 (6.7%) 16 (100.0%) C)MASH PNPLA3 rs738409 (C/G) CC CG GG P value ATG16L1 rs2241880 (A/G) AA 54 (75.0%) 6 (9.7%) 0 (0.0%) < 0.001 AG 10 (13.9%) 48 (77.4%) 0 (0.0%) GG 8 (11.1%) 8 (12.9%) 16 (100.0%) Discussion The present study provides an integrative evaluation of key genetic variants involved in lipid metabolism and autophagy and their association with MASLD and MASH in an Egyptian population. Our findings demonstrate that variants in TM6SF2, PNPLA3, and ATG16L1 exert distinct yet complementary effects on hepatic steatosis severity, metabolic derangements, inflammatory activity, and disease progression. Collectively, these results support the concept that genetic susceptibility modulates both the biochemical phenotype and clinical trajectory of MASLD beyond conventional metabolic risk factors. Among the investigated variants, TM6SF2 rs58542926 emerged as the strongest determinant of disease progression and severity. The higher prevalence of the TT and CT genotypes among patients with MASH, along with their association with elevated liver enzymes and insulin resistance indices, underscores the pivotal role of TM6SF2 in promoting hepatocellular lipid retention and liver injury. Mechanistically, the E167K substitution impairs very-low-density lipoprotein secretion, leading to intracellular lipid accumulation and enhanced susceptibility to oxidative stress and fibrogenic signaling. These findings are consistent with previous reports by [ 8 ] and [ 17 ] who linked TM6SF2 variants to progressive steatotic liver disease and advanced fibrosis despite a relatively favorable circulating lipid profile. From a metabolic standpoint, our biochemical analyses provide important functional insight into the consequences of this genetic variant. Collectively, our data suggest that the TT genotype is associated with higher mean values regarding waist circumference, higher insulin levels, dysregulated liver function markers (ALT and total protein) and elevated renal function markers (serum uric acid and urea levels) compared to other groups, indicating a possible metabolic burden linked to this genetic variant; which aligns with findings of studies by [ 18 ]. ‏The PNPLA3 rs738409 polymorphism also demonstrated a significant association with MASH susceptibility, particularly through the CG and GG genotypes. Carriers of the risk G allele exhibited pronounced dyslipidemia and biochemical features indicative of metabolic stress. The I148M substitution compromises triglyceride hydrolysis within hepatic lipid droplets, resulting in lipid droplet expansion, lipotoxicity, and activation of inflammatory pathways. Our findings align with extensive evidence identifying PNPLA3 as a central genetic modifier of steatosis severity and fibrotic progression across diverse ethnic populations [ 19 ] and [ 20 ]. Patients harboring the I148M risk genotypes exhibited significantly higher levels of LDL-C and triglycerides, aligning with the mechanistic role of PNPLA3 dysfunction in impaired lipid remodeling and abnormal hepatic lipid droplet metabolism. The elevated aminotransferase levels and increased disease severity observed in carriers reinforce the pathogenic role of impaired lipolysis and altered triglyceride metabolism. These findings are aligned with international research by [ 21 ] and [ 22 ]. but also provide population-specific evidence supporting the integration of PNPLA3 genotyping into risk assessment models for precision hepatology. ‏With respect to autophagy-related pathways, the G allele of ATG16L1 rs2241880 showed modest independent effects but was significantly enriched among patients with MASH. This observation supports a role for ATG16L1 as a disease progression modifier rather than a primary driver of steatosis. Given its essential function in autophagosome formation and cellular stress regulation, genetic alterations in ATG16L1 may exacerbate hepatocellular vulnerability under conditions of lipid overload, thereby facilitating the transition from simple steatosis to inflammatory and fibrotic disease phenotypes. These findings go hand by hand with findings by [ 23 ]. A notable finding of the present study is the observed association between PNPLA3 and ATG16L1 genotypes, suggesting a potential epistatic interaction between lipid metabolism and autophagy pathways. This interaction may amplify hepatocellular stress responses by coupling impaired lipid remodeling with defective autophagic clearance, thereby promoting inflammation and disease progression. Although the precise molecular mechanisms underlying this interaction require further investigation, our results highlight the importance of considering combined genetic effects rather than isolated variants in MASLD and MASH pathogenesis. The clinical relevance of these findings lies in the potential utility of genetic profiling for improved risk stratification. Identifying individuals harboring high-risk genetic combinations may facilitate earlier intervention, targeted monitoring, and the development of precision-based therapeutic strategies addressing lipid handling and autophagy dysfunction. This approach may be particularly valuable in regions with high metabolic disease prevalence, such as Egypt. Conclusion The present study demonstrates that TM6SF2, PNPLA3, and ATG16L1 genetic variants contribute synergistically to the heterogeneity of MASLD and MASH. Their combined effects on lipid metabolism, autophagy, and inflammatory responses underscore the multifactorial nature of disease progression and support the incorporation of genetic insights into future precision hepatology frameworks. Identifying individuals harboring high-risk alleles may facilitate early risk stratification, guide surveillance strategies, and inform the development of precision-based therapeutics aimed at restoring lipid balance, enhancing autophagy, and suppressing fibrogenic signaling. The study’s strengths include its focus on a genetically understudied population and the integration of both biochemical and functional analyses. However, limitations such as sample size, lack of longitudinal follow-up, and the absence of liver histology for all patients should be considered when interpreting the findings. Future studies incorporating larger cohorts and mechanistic investigations are needed to confirm the functional consequences of these variants and to explore their potential use in clinical decision-making. Abbreviations 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 MASLD Metabolic dysfunction–associated steatotic liver disease MASH Metabolic dysfunction–associated steatohepatitis PNPLA3 Patatin-like phospholipase domain-containing protein 3 TM6SF2 Transmembrane 6 superfamily member 2 ATG16L1 Autophagy Related 16-Like 1 Declarations Ethical approval The study was conducted in accordance with the ethical principles outlined by the Declaration of Helsinki (1975) and its subsequent amendments. Ethical approval for the study protocol was obtained from the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under No. PT 784. Participants were recruited between October 2023 and September 2024 from specialized hepatology and gastroenterology inpatient departments, as well as outpatient clinics at TBRI and signed written informed consents were obtained from all participants prior to enrollment 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. Competing interests The authors declare that they have no competing interests. Consent for publication All authors agree to publish this article and the article contains no data that need approval from other authors. 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. Author Contribution Asmaa Mohamed Fteah (AMF), Doaa Mamdouh Aly (DMA) and Khaled Mabrouk (KM)conceived and designed the study. Mohamed A Elrefaiy (ME) and Ali Abdel Rahim (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. Acknowledgement The authors extend their appreciation to all study participants for their invaluable contribution and they would like to formally acknowledge Professor Dr. Nihal M. El Assaly for her invaluable support and significant contributions to this research. Her continuous guidance, insightful scientific feedback, and commitment throughout all stages of the study were instrumental in achieving the final outcomes of this work. The authors deeply appreciate her efforts and professional dedication, which greatly enhanced the quality and integrity of the research. Data Availability The data is available upon reasonable request from the corresponding author. References Huang DQ, Singal AG, Kono Y, Tan DJH, El-Serag HB, Loomba R. Changing global epidemiology of liver cancer from 2010 to 2019: NASH is the fastest growing cause. Cell Metab. 2022;34(7):969–e9772. Hagström H, Vessby J, Ekstedt M, Shang Y. Ninety-nine percent of patients with NAFLD meet MASLD criteria and natural history is therefore identical. J Hepatol. 2024;80(2):e76–7. Golabi P, Paik JM, AlQahtani S, Tuncer G, Younossi ZM. Burden of NAFLD in Asia, the Middle East and North Africa: Data from Global Burden of Disease 2009–2019. J Hepatol. 2021;75(4):795–809. Deprince A, Haas JT, Staels B. Dysregulated lipid metabolism links NAFLD to cardiovascular disease. Mol Metab. 2020;42:101092. Wei S, Song J, Xie Y, Huang J, Yang J. Metabolic dysfunction-associated fatty liver disease increases the risk of chronic kidney disease in adults with type 2 diabetes. Diabetes Res Clin Pract. 2023;197:110563. Miao L, Targher G, Byrne C, Cao YY, Zheng MH. Current status and future trends of the global burden of MASLD. Trends Endocrinol Metabolism. 2024;35(8):697–707. Mikaeeli S, Cohen D. Loss or gain of function: the functional complexity of the PNPLA3 I148M variant. J Hepatol. 2025;82(5):778–80. Balcar L, Scheiner B, Urheu M, Weinberger P, Paternostro R, Simbrunner B, et al. The impact of TM6SF2 rs58542926 on liver-related events in patients with advanced chronic liver disease. Dig Liver Dis. 2023;55(8):1072–80. Wei F, Qin N, Chen Y, Liu Z, Zhao X, Yu X, et al. ATG16L1 membrane recruitment in autophagy. Biochem Mol Biol. 2025;60(1–3):107–22. Cusi K, Isaacs S, Barb D, Rinella ME, Vos MB, Younossi Z. American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of nonalcoholic fatty liver disease in Primary Care and Endocrinology Clinical Settings. Endocr Pract. 2022;28(5):528–62. Potter AW, Chin GC, Looney DP, Friedl KE. Defining overweight and obesity by percent body fat instead of body mass index. J Clin Endocrinol Metabolism. 2025;110:e1103–7. Horáková D, Štěpánek L, Janout V, Janoutová J, Pastucha D, Kollárová H, et al. Optimal HOMA-IR cut-off values: a cross-sectional study in the Czech population. Med (Kaunas). 2019;55(5):158. Kristensen V, Kelefiotis D, Kristensen T, et al. High-throughput methods for detection of genetic variation. Biotechniques. 2001;30:318–24. Chan YH. Biostatistics102: Quantitative Data – Parametric & Non-parametric Tests. Singap Med J. 2003a;44(8):391–6. Chan YH. Biostatistics 103: Qualitative Data –Tests of Independence. Singap Med J. 2003b;44(10):498–503. Chan YH. Biostatistics 104: Correlational Analysis. Singap Med J. 2003c;44(12):614–9. Huang H, Vitali C, Zhang D, Hand NJ, Phillips MC, Creasy KT, et al. Deep metabolic phenotyping of humans with protein-altering variants in TM6SF2 using a genome-first approach. JHEP Rep. 2025;7:101243. Chen LZ, Wang YR, Zhao ZZ, Zhao SL, Min CC. Intestinal depletion of TM6SF2 exacerbates high fat diet-induced metabolic dysfunction–associated steatotic liver disease through the gut–liver axis. J Clin Translational Hepatol. 2025;13:443–55. Lee J, Cha J. The PNPLA3 I148M variant is associated with immune cell infiltration and advanced fibrosis in MASLD: a prospective genotype–phenotype study. J Gastroenterol. 2025;60:1284–95. Kozlitina J, Sookoian S. Global epidemiological impact of PNPLA3 I148M on liver disease. Liver Int. 2025;45:e16123. Cavalcante L, Porto J, Mazo D, Longatto-Filho A, Stefano JT, Lyra AC, et al. African genetic ancestry is associated with lower frequency of PNPLA3 G allele in non-alcoholic fatty liver disease in an admixed population. Ann Hepatol. 2022;27:100728. Petta S, Armandi A, Bugianesi E. Impact of PNPLA3 I148M on clinical outcomes in patients with MASLD. Liver Int. 2025;45:e16133. Yu J. Macrophage ATG16L1 expression suppresses metabolic dysfunction-associated steatohepatitis progression by promoting lipophagy. Clin Mol Hepatol. 2024;30:721–3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9203531","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618222170,"identity":"12c6047e-877e-4466-ada8-b4c687f5beec","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":618222171,"identity":"75551cd9-e9b5-49cf-82c5-e20d0c4633dd","order_by":1,"name":"Khaled Mabrouk","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Mabrouk","suffix":""},{"id":618222172,"identity":"1c499acb-dac2-4773-8a50-f929f3141348","order_by":2,"name":"Ali Abdel Rahim","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Abdel","lastName":"Rahim","suffix":""},{"id":618222179,"identity":"9342a25c-8a5e-4e6a-aa38-6275649c48ae","order_by":3,"name":"Mohamed A Elrefaiy","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"A","lastName":"Elrefaiy","suffix":""},{"id":618222181,"identity":"28ec8a8b-fedc-4461-8bc0-63d884d93b37","order_by":4,"name":"Doaa Mamdouh Aly","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Doaa","middleName":"Mamdouh","lastName":"Aly","suffix":""}],"badges":[],"createdAt":"2026-03-23 17:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9203531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9203531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109454642,"identity":"9c3274b3-e690-4477-9262-37754ae1ba88","added_by":"auto","created_at":"2026-05-18 09:41:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672930,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9203531/v1/e54c1169-70f6-4fba-8a3b-8a108627efd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of TM6SF2, PNPLA3, and ATG16L1 Genetic Variants in MASLD and MASH: An Egyptian Case-control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2023, three multinational liver associations proposed the term metabolic dysfunction-associated steatotic liver disease (MASLD) to replace the old term non-alcoholic fatty liver disease (NAFLD), aiming to provide a more accurate and inclusive definition of this highly prevalent condition by emphasizing the central role of metabolic dysfunction in disease pathogenesis. This updated nomenclature reflects a paradigm shift in understanding fatty liver disease as a systemic metabolic disorder rather than a diagnosis of exclusion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] \u003cb\u003eand\u003c/b\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent data from the Global Burden of Disease (GBD) study highlight the rapidly increasing health and economic burden of MASLD worldwide, with substantial implications for both liver-related and extrahepatic morbidity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] MASLD encompasses a broad disease spectrum ranging from isolated steatotic liver conditions to metabolic dysfunction-associated steatohepatitis (MASH), which may further progress to advanced fibrosis, cirrhosis, and hepatocellular carcinoma [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Beyond liver-specific outcomes, MASLD is increasingly recognized as a multisystem disease associated with an elevated risk of cardiovascular disease, chronic kidney disease, and extrahepatic malignancies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, the global prevalence of MASLD has risen from approximately 25.3% during 1990\u0026ndash;2006 to 38.2% during 2016\u0026ndash;2019, representing an almost 50% increase over the past three decades [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although metabolic risk factors such as obesity, insulin resistance, and type 2 diabetes mellitus are well-established drivers of MASLD, substantial interindividual variability in disease severity and progression cannot be fully explained by environmental or metabolic factors alone. Accumulating evidence underscores a pivotal role for genetic susceptibility in modulating hepatic fat accumulation, inflammatory responses, and fibrogenesis. In this context, genetic variants in patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and autophagy-related 16-like 1 (ATG16L1) have emerged as key determinants influencing lipid metabolism, hepatocellular stress responses, and disease progression.\u003c/p\u003e \u003cp\u003eThe PNPLA3 gene, particularly the rs738409 C\u0026thinsp;\u0026gt;\u0026thinsp;G polymorphism which encodes an isoleucine-to-methionine substitution at position 148 (I148M), represents the most consistently replicated associated genetic determinant of MASLD and MASH. Mechanistically, the I148M variant impairs triglyceride hydrolysis within hepatic lipid droplets, leading to intracellular triglyceride accumulation and altered lipid remodeling, despite paradoxically lower serum fasting triglyceride levels and alters polyunsaturated fatty acid handling interfering with lipoprotein secretion and alters lipid droplet remodeling, thereby exacerbating lipotoxic stress and promoting fibrogenesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, the TM6SF2 gene located on chromosome 19, has been strongly implicated in the pathogenesis of progressive steatotic liver disease. The rs58542926 C\u0026thinsp;\u0026gt;\u0026thinsp;T variant (E167K) is associated with impaired hepatic very-low-density lipoprotein secretion, resulting in increased intrahepatic lipid retention and accelerated fibrotic progression, while concurrently reducing circulating lipid levels [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These dual metabolic effects underscore the complex role of TM6SF2 as a modifier of both hepatic and systemic lipid homeostasis.\u003c/p\u003e \u003cp\u003e\u0026rlm;Autophagy has emerged as a critical cellular process in maintaining hepatocyte homeostasis under metabolic stress. ATG16L1 plays a central role in autophagosome formation, regulation of lipid turnover, and control of inflammatory responses. Genetic variants in ATG16L1 may disrupt autophagic flux, thereby exacerbating lipotoxicity, oxidative stress, and inflammatory signaling within the liver. Emerging evidence suggests that ATG16L1 polymorphisms may act as disease progression modifiers, particularly influencing the transition from simple steatosis to steatohepatitis and fibrotic remodeling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eAfter exclusion of 23 participants (not meeting inclusion/exclusion criteria or incomplete data), a total of 450 subjects were included in the final analysis and categorized into three groups: 150 adult patients (\u0026ge;\u0026thinsp;18 years) with a confirmed diagnosis of MASLD, 150 patients with clinically suspected MASH. Patients presenting to the liver clinic were initially screened using abdominal ultrasonography for evidence of hepatic steatosis. The operational distinction between MASLD and clinically suspected MASH was established using non-invasive criteria aligned with the current guidance of the American Association for the Study of Liver Diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], including:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImaging-confirmed steatosis; with pre-d\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eefined cut-off values used for steatosis grading and fibrosis staging, based on Controlled Attenuation Parameter (CAP) measurements obtained by transient elastography (FibroScan).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBiochemical evidence of liver injury (elevated liver enzymes).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePresence of metabolic risk factors, consistent with established diagnostic criteria.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAnd as a normal control group; 150 apparently healthy subjects were included in the study, who were free from history of fatty liver, diabetes and hypertension, with normal clinical and biochemical profiles.\u003c/p\u003e \u003cp\u003eExcluded patients were those testing positive for viral markers for hepatitis B and C, individuals with significant alcohol consumption (\u0026ge;\u0026thinsp;30 g/day in men and \u0026ge;\u0026thinsp;20 g/day in women), those with recreational drug abuse, patients with confirmed autoimmune or cholestatic liver diseases, other secondary causes of steatosis, and those with history of past malignancies or recurrent/secondary tumors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical and Biochemical Assessment\u003c/h3\u003e\n\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=\"CR11\" class=\"CitationRef\"\u003e11\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=\"CR12\" class=\"CitationRef\"\u003e12\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\n\u003ch3\u003eGenetic variants and Biomarker Analyses\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from peripheral blood leukocytes using the GeneJET whole blood genomic DNA purification mini-kit (ThermoFisher Scientific Inc., CA, USA), following the manufacturer\u0026rsquo;s instructions. Extracted DNA samples were stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further analysis. The DNA concentration and purity were determined using qubit fluorometric quantification assays (ThermoFisher Scientific Inc., CA, USA). To ensure consistency across all downstream genotyping assays, and samples were adjusted to a final concentration of 20 ng/\u0026micro;L, and no samples were excluded due to poor DNA quality. Genotyping of PNPLA3 (rs738409) (Assay ID: C_7241_10), TM6SF2 (rs58542926) (Assay ID: C_89463510_10), and ATG16L1 (rs2241880) (Assay ID: C_9095577_20) was performed for all participants using predesigned allele-specific TaqMan quantitative real-time polymerase chain reaction (qPCR) assays (ThermoFisher Scientific Inc., CA, USA). Quantitative PCR amplification and allelic discrimination were performed using the Applied Biosystems Step One Real-Time PCR System (ABI 7500, Applied Biosystems, Foster City, CA, USA) was achieved using sequence-specific fluorescent taqman probes, according to the protocol described by [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Quality control measures included the incorporation of negative controls in each PCR run to ensure assay accuracy and exclude possible cross-contamination or genotyping bias. The overall genotyping call rate exceeded 95% for both SNPs. Samples that showed unsuccessful amplification or ambiguous genotype calls were reanalyzed and successfully resolved upon retesting, with no persistent genotyping failures.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Continuous variables expressed as 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=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Categorical variables compared with chi-square (χ2) test. Exact test was used instead when the expected frequency is less than 5 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Multivariate regression for genotype-phenotype correlations, gene-gene interactions, and biochemical associations. Odds ratio (OR) with 95% confidence intervals were calculated. Data was double checked for normality using normality plots and Shapiro Wilk test and Post-hoc Bonferroni test. A p-value more than 0.05 was considered indicative of equilibrium. Correlations between quantitative variables were done using Spearman correlation coefficient [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. P-values less than 0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e\n\u003cp\u003eA total of 450 participants were included and categorized into three groups: healthy controls, patients with MASLD, and patients with clinically suspected 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 clinically suspected 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 patients with MASLD and clinically suspected MASH showed significantly higher levels of fasting insulin, HbA1C %, HOMA-IR, aminotransferases, \u0026gamma;-glutamyl transferase, and adverse lipid parameters, with the most pronounced alterations ob1served in patients with clinically suspected MASH. These findings confirm the close association between metabolic dysfunction, hepatic injury, and disease severity (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic data, clinical, laboratory and radiological data in the MASLD, MASH and normal controls\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\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 colspan=\"2\" 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\u003e46.51\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 rowspan=\"2\" align=\"left\"\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 rowspan=\"2\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"6\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" 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\u003e170.20\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"6\" align=\"left\"\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\u003e94.09\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\u003e8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\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\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\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 colspan=\"2\" 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\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\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\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\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 colspan=\"2\" 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\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 colspan=\"2\" 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\u003e21.99\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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"2\" 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\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 colspan=\"6\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 colspan=\"6\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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 rowspan=\"3\" 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 colspan=\"2\" align=\"left\"\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 colspan=\"2\" align=\"left\"\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\u003c/div\u003e\n\u003ch3\u003eFrequency distribution of the TM6SF2 rs58542926 (C/T) polymorphism and its clinical correlates\u003c/h3\u003e\n\u003cp\u003eA significant difference in genotype distribution of TM6SF2 was observed across the three studied groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); with higher frequency distribution of TT genotype in MASH patients. When comparing controls to MASLD patients, both the heterozygous CT and homozygous TT genotypes were significantly associated with increased risk of MASLD (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; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Allelic analysis further confirmed a significant enrichment of the T allele in the MASLD group compared to controls (OR\u0026thinsp;=\u0026thinsp;6.311, 95% CI: 3.649\u0026ndash;10.915; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While in the comparison between controls and MASH patients, the TT genotype again showed a strong association with disease presence (OR\u0026thinsp;=\u0026thinsp;14.84, 95% CI: 4.843\u0026ndash;45.523; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As well the T allele frequency was significantly higher in MASH patients compared to controls, with an odds ratio of 5.824 (95% CI: 3.369\u0026ndash;10.070; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (table 2). As described in table (3), waist circumference differed significantly between TM6SF2 rs58542926 (C/T) genotypes (P\u0026thinsp;=\u0026thinsp;0.036), with the TT group displaying a slightly higher mean value compared to other groups. Serum insulin levels showed a marked elevation in the TT genotype compared with other genotypes (P\u0026thinsp;=\u0026thinsp;0.001). Regarding liver functions, ALT levels and total protein concentrations were markedly elevated in the TT group compared with CT and CC groups. Similarly, serum uric acid and urea levels were significantly higher among TT carriers compared with CT and CC (P\u0026thinsp;=\u0026thinsp;0.002 and 0.004 respectively). Collectively, these data suggest that the TT genotype is associated with higher insulin levels, total protein, and liver and renal function markers, indicating a possible metabolic burden linked to this genetic variant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotype and Allele distribution of TM6SF2 rs58542926 genotypic variants and their odd\u0026rsquo;s ratios among the studied groups\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMASH (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e112 (74.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e66 (44.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" 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\"\u003e\n\u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e80 (53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e14 (9.3%)\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e70 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003e112 (74.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003e30 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (53.3%)\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\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.956\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4.373\u0026ndash;22.665\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003e8 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\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\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.537\u0026ndash;62.936\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003e254 (84.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" 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\u003e46 (15.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160 (53.3%)\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\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.649\u0026ndash;10.915\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" 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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e112 (74.7%)\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 colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30 (20.0%)\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\u003e0.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.293\u0026ndash;2.142\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (46.7%)\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\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4.843\u0026ndash;45.523\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 colspan=\"2\" 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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e254 (84.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146 (48.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e46 (15.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154 (51.3%)\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\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.369\u0026ndash;10.070\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" 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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30 (20.0%)\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 colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e80 (53.3%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.029\u0026ndash;0.218\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.795\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.350\u0026ndash;1.808\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 colspan=\"2\" 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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e140 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146 (48.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReference\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e160 (53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154 (51.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.587\u0026ndash;1.452\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\u0026nbsp;(3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison between TM6SF2 rs58542926 (A/T) genetic variants in MASLD \u0026amp; MASH patients regarding the laboratory data\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\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\u003eP value\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\u003e48.10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\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\u003e95.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\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\u003eLength (cm)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.638\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\u003e92.15\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\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\u003e31.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.345\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\u003e11.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\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\u003eTLC (\u0026times;10⁹/L)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.423\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\u003e177.40\u0026thinsp;\u0026plusmn;\u0026thinsp;29.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e167.49\u0026thinsp;\u0026plusmn;\u0026thinsp;22.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178.56\u0026thinsp;\u0026plusmn;\u0026thinsp;32.89\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\u003eFBS (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.35\u0026thinsp;\u0026plusmn;\u0026thinsp;24.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.79\u0026thinsp;\u0026plusmn;\u0026thinsp;17.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.11\u0026thinsp;\u0026plusmn;\u0026thinsp;30.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.858\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\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308\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\u003e19.88\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.45\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\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\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\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\u003e8.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\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\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.156\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\u003e31.28\u0026thinsp;\u0026plusmn;\u0026thinsp;10.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.83\u0026thinsp;\u0026plusmn;\u0026thinsp;14.08\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\"\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\u003e31.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\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\u003eTotal blirubin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\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\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.502\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;0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.274\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\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\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\u003e47.10\u0026thinsp;\u0026plusmn;\u0026thinsp;15.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.51\u0026thinsp;\u0026plusmn;\u0026thinsp;11.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.503\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\u003e37.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\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\u003eCreatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\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\u003e0.184\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\u003e200.83\u0026thinsp;\u0026plusmn;\u0026thinsp;37.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202.13\u0026thinsp;\u0026plusmn;\u0026thinsp;39.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201.82\u0026thinsp;\u0026plusmn;\u0026thinsp;48.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988\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\u003e38.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.30\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.346\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\u003e106.83\u0026thinsp;\u0026plusmn;\u0026thinsp;15.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.667\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\u003e148.96\u0026thinsp;\u0026plusmn;\u0026thinsp;24.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.30\u0026thinsp;\u0026plusmn;\u0026thinsp;24.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.31\u0026thinsp;\u0026plusmn;\u0026thinsp;26.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.344\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eFrequency distribution of the Patatin-like phospholipase domain-containing protein 3 (PNPLA3) (C\u0026thinsp;\u0026gt;\u0026thinsp;G)\u003c/h3\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003eTable 4 elucidates the genotypic and allelic distribution regarding PNPLA3 (rs738409) C\u0026gt;G between MASH and other studied groups; suggesting that G allele is significantly associated with higher susceptibility to MASH development among the studied groups (p=0.019). Specifically, as the CG genotype and G allele were significantly increased in patients with MASH than normal control group [OR = 2.11, 95% CI = 1.039-4.282] and [OR = 1.683, 95% CI = 0.999-2.834] respectively. Table 5 demonstrates the genotypes\u0026rsquo; distribution in MASLD and MASH groups and their relation with the laboratory data, showing that PNPLA3 I148M risk genotype carriers had significantly higher LDL-C and triglycerides levels compared to non-GG carriers (P = 0.047 and 0.017) respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4): \u003c/strong\u003eGenotype and Allele distribution of PNPLA3 rs738409 (C/G) variants and their odd\u0026rsquo;s ratios among the studied groups\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" 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 (=\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\u003eP value\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\u003e96 (65.3%)\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\u003e72 (48.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" 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\u003eCG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\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\u003e62 (41.3%)\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\u003e14 (8.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.7%)\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC allele\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e236 (78.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208 (69.3%)\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 rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.019\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\u003e64 (21.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (30.7%)\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\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 (=\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\u003eP value\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\u003e96 (65.3%)\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\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\u003e1.986 (0.978\u0026ndash;4.035)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.058\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\u003e14 (8.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.766 (0.564\u0026ndash;5.529)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.329\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\u003eP value\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\u003e236 (78.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208 (69.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (21.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (30.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.631 (0.967\u0026ndash;2.750)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\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 (=\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\u003eP value\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\u003e96 (65.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (48.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (41.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.110 (1.039\u0026ndash;4.282)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\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\u003e14 (8.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.815 (0.579\u0026ndash;5.689)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.307\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\u003eP value\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\u003e236 (78.7%)\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (21.3%)\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\u003e1.683 (0.999\u0026ndash;2.834)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\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\u003eP value\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\u003e74 (49.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (48.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCG\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\u003e62 (41.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.062 (0.538\u0026ndash;2.096)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.862\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\u003e16 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.028 (0.348\u0026ndash;3.033)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.960\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\u003eP value\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\u003e208 (69.3%)\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 colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (30.7%)\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\u003e1.032 (0.632\u0026ndash;1.683)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.901\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\u0026nbsp;(5)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison between PNPLA3 rs738409 (C/G) genetic variants in MASLD \u0026amp; MASH patients regarding the laboratory data\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabd\" 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\u003eCG genotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGG genotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\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\u003e49.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.418\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\u003e92.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.88\u0026thinsp;\u0026plusmn;\u0026thinsp;7.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.327\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\u003e170.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.399\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\u003e89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.204\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\u003e30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.234\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\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.604\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\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.680\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\u003e173.99\u0026thinsp;\u0026plusmn;\u0026thinsp;30.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175.36\u0026thinsp;\u0026plusmn;\u0026thinsp;27.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175.63\u0026thinsp;\u0026plusmn;\u0026thinsp;33.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.956\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\u003e88.85\u0026thinsp;\u0026plusmn;\u0026thinsp;21.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.41\u0026thinsp;\u0026plusmn;\u0026thinsp;24.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.88\u0026thinsp;\u0026plusmn;\u0026thinsp;31.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.573\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\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.556\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\u003e18.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.99\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.643\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\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.578\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\u003e8.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.974\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\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.259\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\u003e37.82\u0026thinsp;\u0026plusmn;\u0026thinsp;15.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.706\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\u003e32.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.464\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;0.10\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\u003e0.675\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\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\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\u003e0.773\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\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952\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\u003e44.29\u0026thinsp;\u0026plusmn;\u0026thinsp;13.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.77\u0026thinsp;\u0026plusmn;\u0026thinsp;15.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.63\u0026thinsp;\u0026plusmn;\u0026thinsp;18.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.428\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\u003e36.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.502\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\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\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\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.518\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\u003e198.42\u0026thinsp;\u0026plusmn;\u0026thinsp;40.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200.41\u0026thinsp;\u0026plusmn;\u0026thinsp;42.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220.63\u0026thinsp;\u0026plusmn;\u0026thinsp;45.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.156\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\u003e40.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.581\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\u003e106.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\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\u003e138.63\u0026thinsp;\u0026plusmn;\u0026thinsp;16.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.77\u0026thinsp;\u0026plusmn;\u0026thinsp;24.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157.04\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eFrequency distribution of the ATG rs2642438 (A/T) SNP\u003c/h2\u003e\n\u003cp\u003eNo statistically significant differences were found in genotype distribution among the three groups (P\u0026thinsp;=\u0026thinsp;0.374). Comparing controls to MASLD, neither genotypic nor allelic frequency analysis revealed a significant association with disease risk. Although the differences in genotype frequencies were not statistically significant when comparing MASH to controls, the G allele was significantly more prevalent in MASH (40.7%) than in controls (29.3%) with a P value of 0.040 (OR\u0026thinsp;=\u0026thinsp;1.651, 95% CI: 1.022\u0026ndash;2.666), indicating a possible association of the G allele with increased risk of MASH (table 6).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(7) summarized the relationship between ATG16L1 rs2241880 (A/G) genetic variants and various clinical and biochemical parameters including glycemic markers and lipid profile components. No statistically significant differences were observed among the three genotypic groups in the study population; with the exception of a marginally significant association with weight (P\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(6)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotype and Allele distribution of ATG16L1 rs2642438 (A/T) genotypic variants and their odd\u0026rsquo;s ratios among the studied groups\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGenotype\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 colspan=\"2\" 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\u003eP value\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 (56.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e66 (44.0%)\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 rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.374\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\u003e44 (29.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e56 (17.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (38.7%)\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\u003e22 (14.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" 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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95% CI\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 (56.0%)\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 colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\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\u003e44 (29.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (17.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.788\u0026ndash;3.331\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\u003e22 (14.7%)\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\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.651\u0026ndash;4.032\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212 (70.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188 (62.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (29.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (37.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.886\u0026ndash;2.326\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95% CI\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 (56.0%)\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 colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\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\u003e44 (29.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (38.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.893\u0026ndash;3.813\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\u003e22 (14.7%)\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\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.828\u0026ndash;5.005\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212 (70.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 (59.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (29.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (40.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.022\u0026ndash;2.666\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\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\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95% CI\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\u003e66 (44.0%)\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 colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\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\u003e56 (17.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58 (38.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.556\u0026ndash;2.334\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\u003e0.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.526\u0026ndash;3.004\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188 (62.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 (59.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (37.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (40.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.723\u0026ndash;1.830\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\u0026nbsp;(7)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison between ATG rs2642438 (A/T) genetic variants in MASLD \u0026amp; MASH patients regarding the laboratory data\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\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 mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAA genotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAG genotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGG genotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\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\u003e48.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.762\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\u003e93.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.386\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\u003e170.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172.27\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.349\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\u003e89.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.73\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\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\u003e30.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.279\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\u003e11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.835\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\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.871\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\u003e173.67\u0026thinsp;\u0026plusmn;\u0026thinsp;30.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174.60\u0026thinsp;\u0026plusmn;\u0026thinsp;28.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177.17\u0026thinsp;\u0026plusmn;\u0026thinsp;28.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.864*\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\u003e92.08\u0026thinsp;\u0026plusmn;\u0026thinsp;26.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.25\u0026thinsp;\u0026plusmn;\u0026thinsp;24.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.43\u0026thinsp;\u0026plusmn;\u0026thinsp;23.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.424\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;0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.920\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\u003e19.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.805\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\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.852\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\u003e8.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.723\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\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.147\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\u003e37.97\u0026thinsp;\u0026plusmn;\u0026thinsp;15.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.73\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.597\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\u003e33.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.108\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;0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\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\u003e0.631\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\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\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\u003e0.368\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\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.418\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\u003e43.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.02\u0026thinsp;\u0026plusmn;\u0026thinsp;14.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.67\u0026thinsp;\u0026plusmn;\u0026thinsp;18.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.386\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\u003e35.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.325\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\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.418\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\u003e201.83\u0026thinsp;\u0026plusmn;\u0026thinsp;41.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201.40\u0026thinsp;\u0026plusmn;\u0026thinsp;41.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201.50\u0026thinsp;\u0026plusmn;\u0026thinsp;45.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.998\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\u003e40.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.435\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\u003e107.19\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.410\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\u003e157.24\u0026thinsp;\u0026plusmn;\u0026thinsp;25.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.58\u0026thinsp;\u0026plusmn;\u0026thinsp;23.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146.20\u0026thinsp;\u0026plusmn;\u0026thinsp;25.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.084\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\u003eTable\u0026nbsp;(8) illustrates the distribution of ATG16L1 rs2241880 (A/G) genotypes according to PNPLA3 rs738409 (C/G) variants among the studied groups. A highly significant association was observed between the two genetic loci across all studied groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the control group, the PNPLA3 CC genotype carriers predominantly exhibited the ATG16L1 AA genotype (85.7%). Similarly, within the MASLD group, the PNPLA3 CC genotype was strongly associated with ATG16L1 AA (89.2%), whereas the PNPLA3 CG variant was exclusively linked to ATG16L1 AG (93.3%). A comparable trend was observed in the MASH group, among subjects with PNPLA3 CC, the ATG16L1 AA genotype was most prevalent (75%), Those carrying the PNPLA3 CG variant were mainly heterozygous for ATG16L1 (AG) (77.4%), whereas all individuals with the PNPLA3 GG genotype exhibited ATG16L1 GG. These consistent findings across the three groups indicate a strong genetic linkage or potential epistatic interaction between ATG16L1 and PNPLA3 loci, which may contribute to the molecular pathogenesis and progression from simple steatosis to MASH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(8)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation between genetic variants of ATG rs2642438 (A/T) and PNPLA3 rs738409 (C/G)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabg\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eA) Controls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePNPLA3 rs738409 (C/G)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eATG16L1 rs2241880 (A/G)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\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 (85.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" 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\"\u003e\n\u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (4.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (100.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\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\u003e10 (10.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (100.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eB) MASLD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePNPLA3 rs738409 (C/G)\u003c/strong\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\u003e\u003cstrong\u003eCG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\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\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eATG16L1 rs2241880 (A/G)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\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\u003e66 (89.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" 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\"\u003e\n\u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (93.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\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\u003e8 (10.8%)\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\u003e16 (100.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC)MASH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePNPLA3 rs738409 (C/G)\u003c/strong\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\u003e\u003cstrong\u003eCG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\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\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eATG16L1 rs2241880 (A/G)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\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\u003e54 (75.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" 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\"\u003e\n\u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (13.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (77.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\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\u003e8 (11.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (12.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (100.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides an integrative evaluation of key genetic variants involved in lipid metabolism and autophagy and their association with MASLD and MASH in an Egyptian population. Our findings demonstrate that variants in TM6SF2, PNPLA3, and ATG16L1 exert distinct yet complementary effects on hepatic steatosis severity, metabolic derangements, inflammatory activity, and disease progression. Collectively, these results support the concept that genetic susceptibility modulates both the biochemical phenotype and clinical trajectory of MASLD beyond conventional metabolic risk factors.\u003c/p\u003e \u003cp\u003eAmong the investigated variants, TM6SF2 rs58542926 emerged as the strongest determinant of disease progression and severity. The higher prevalence of the TT and CT genotypes among patients with MASH, along with their association with elevated liver enzymes and insulin resistance indices, underscores the pivotal role of TM6SF2 in promoting hepatocellular lipid retention and liver injury. Mechanistically, the E167K substitution impairs very-low-density lipoprotein secretion, leading to intracellular lipid accumulation and enhanced susceptibility to oxidative stress and fibrogenic signaling. These findings are consistent with previous reports by [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] \u003cb\u003eand\u003c/b\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] who linked TM6SF2 variants to progressive steatotic liver disease and advanced fibrosis despite a relatively favorable circulating lipid profile. From a metabolic standpoint, our biochemical analyses provide important functional insight into the consequences of this genetic variant. Collectively, our data suggest that the TT genotype is associated with higher mean values regarding waist circumference, higher insulin levels, dysregulated liver function markers (ALT and total protein) and elevated renal function markers (serum uric acid and urea levels) compared to other groups, indicating a possible metabolic burden linked to this genetic variant; which aligns with findings of studies by [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e\u0026rlm;The PNPLA3 rs738409 polymorphism also demonstrated a significant association with MASH susceptibility, particularly through the CG and GG genotypes. Carriers of the risk G allele exhibited pronounced dyslipidemia and biochemical features indicative of metabolic stress. The I148M substitution compromises triglyceride hydrolysis within hepatic lipid droplets, resulting in lipid droplet expansion, lipotoxicity, and activation of inflammatory pathways. Our findings align with extensive evidence identifying PNPLA3 as a central genetic modifier of steatosis severity and fibrotic progression across diverse ethnic populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] \u003cb\u003eand\u003c/b\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients harboring the I148M risk genotypes exhibited significantly higher levels of LDL-C and triglycerides, aligning with the mechanistic role of PNPLA3 dysfunction in impaired lipid remodeling and abnormal hepatic lipid droplet metabolism. The elevated aminotransferase levels and increased disease severity observed in carriers reinforce the pathogenic role of impaired lipolysis and altered triglyceride metabolism. These findings are aligned with international research by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] \u003cb\u003eand\u003c/b\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. but also provide population-specific evidence supporting the integration of PNPLA3 genotyping into risk assessment models for precision hepatology.\u003c/p\u003e \u003cp\u003e\u0026rlm;With respect to autophagy-related pathways, the G allele of ATG16L1 rs2241880 showed modest independent effects but was significantly enriched among patients with MASH. This observation supports a role for ATG16L1 as a disease progression modifier rather than a primary driver of steatosis. Given its essential function in autophagosome formation and cellular stress regulation, genetic alterations in ATG16L1 may exacerbate hepatocellular vulnerability under conditions of lipid overload, thereby facilitating the transition from simple steatosis to inflammatory and fibrotic disease phenotypes. These findings go hand by hand with findings by [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA notable finding of the present study is the observed association between PNPLA3 and ATG16L1 genotypes, suggesting a potential epistatic interaction between lipid metabolism and autophagy pathways. This interaction may amplify hepatocellular stress responses by coupling impaired lipid remodeling with defective autophagic clearance, thereby promoting inflammation and disease progression. Although the precise molecular mechanisms underlying this interaction require further investigation, our results highlight the importance of considering combined genetic effects rather than isolated variants in MASLD and MASH pathogenesis.\u003c/p\u003e \u003cp\u003eThe clinical relevance of these findings lies in the potential utility of genetic profiling for improved risk stratification. Identifying individuals harboring high-risk genetic combinations may facilitate earlier intervention, targeted monitoring, and the development of precision-based therapeutic strategies addressing lipid handling and autophagy dysfunction. This approach may be particularly valuable in regions with high metabolic disease prevalence, such as Egypt.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrates that TM6SF2, PNPLA3, and ATG16L1 genetic variants contribute synergistically to the heterogeneity of MASLD and MASH. Their combined effects on lipid metabolism, autophagy, and inflammatory responses underscore the multifactorial nature of disease progression and support the incorporation of genetic insights into future precision hepatology frameworks. Identifying individuals harboring high-risk alleles may facilitate early risk stratification, guide surveillance strategies, and inform the development of precision-based therapeutics aimed at restoring lipid balance, enhancing autophagy, and suppressing fibrogenic signaling.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s strengths include its focus on a genetically understudied population and the integration of both biochemical and functional analyses. However, limitations such as sample size, lack of longitudinal follow-up, and the absence of liver histology for all patients should be considered when interpreting the findings. Future studies incorporating larger cohorts and mechanistic investigations are needed to confirm the functional consequences of these variants and to explore their potential use in clinical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\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\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\u003ePNPLA3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatatin-like phospholipase domain-containing protein 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTM6SF2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransmembrane 6 superfamily member 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eATG16L1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutophagy Related 16-Like 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the ethical principles outlined by the Declaration of Helsinki (1975) and its subsequent amendments. Ethical approval for the study protocol was obtained from the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under No. PT 784. Participants were recruited between October 2023 and September 2024 from specialized hepatology and gastroenterology inpatient departments, as well as outpatient clinics at TBRI and signed written informed consents were obtained from all participants prior to enrollment\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e 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.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eAll authors agree to publish this article and the article contains no data that need approval from other authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \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.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAsmaa Mohamed Fteah (AMF), Doaa Mamdouh Aly (DMA) and Khaled Mabrouk (KM)conceived and designed the study. Mohamed A Elrefaiy (ME) and Ali Abdel Rahim (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\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their appreciation to all study participants for their invaluable contribution and they would like to formally acknowledge Professor Dr. Nihal M. El Assaly for her invaluable support and significant contributions to this research. Her continuous guidance, insightful scientific feedback, and commitment throughout all stages of the study were instrumental in achieving the final outcomes of this work. The authors deeply appreciate her efforts and professional dedication, which greatly enhanced the quality and integrity of the research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data is available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang DQ, Singal AG, Kono Y, Tan DJH, El-Serag HB, Loomba R. Changing global epidemiology of liver cancer from 2010 to 2019: NASH is the fastest growing cause. Cell Metab. 2022;34(7):969\u0026ndash;e9772.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagstr\u0026ouml;m H, Vessby J, Ekstedt M, Shang Y. Ninety-nine percent of patients with NAFLD meet MASLD criteria and natural history is therefore identical. J Hepatol. 2024;80(2):e76\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolabi P, Paik JM, AlQahtani S, Tuncer G, Younossi ZM. Burden of NAFLD in Asia, the Middle East and North Africa: Data from Global Burden of Disease 2009\u0026ndash;2019. J Hepatol. 2021;75(4):795\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeprince A, Haas JT, Staels B. Dysregulated lipid metabolism links NAFLD to cardiovascular disease. Mol Metab. 2020;42:101092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei S, Song J, Xie Y, Huang J, Yang J. Metabolic dysfunction-associated fatty liver disease increases the risk of chronic kidney disease in adults with type 2 diabetes. Diabetes Res Clin Pract. 2023;197:110563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao L, Targher G, Byrne C, Cao YY, Zheng MH. Current status and future trends of the global burden of MASLD. Trends Endocrinol Metabolism. 2024;35(8):697\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikaeeli S, Cohen D. Loss or gain of function: the functional complexity of the PNPLA3 I148M variant. J Hepatol. 2025;82(5):778\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalcar L, Scheiner B, Urheu M, Weinberger P, Paternostro R, Simbrunner B, et al. The impact of TM6SF2 rs58542926 on liver-related events in patients with advanced chronic liver disease. Dig Liver Dis. 2023;55(8):1072\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei F, Qin N, Chen Y, Liu Z, Zhao X, Yu X, et al. ATG16L1 membrane recruitment in autophagy. Biochem Mol Biol. 2025;60(1\u0026ndash;3):107\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCusi K, Isaacs S, Barb D, Rinella ME, Vos MB, Younossi Z. American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of nonalcoholic fatty liver disease in Primary Care and Endocrinology Clinical Settings. Endocr Pract. 2022;28(5):528\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotter AW, Chin GC, Looney DP, Friedl KE. Defining overweight and obesity by percent body fat instead of body mass index. J Clin Endocrinol Metabolism. 2025;110:e1103\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHor\u0026aacute;kov\u0026aacute; D, Štěp\u0026aacute;nek L, Janout V, Janoutov\u0026aacute; J, Pastucha D, Koll\u0026aacute;rov\u0026aacute; H, et al. Optimal HOMA-IR cut-off values: a cross-sectional study in the Czech population. Med (Kaunas). 2019;55(5):158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristensen V, Kelefiotis D, Kristensen T, et al. High-throughput methods for detection of genetic variation. Biotechniques. 2001;30:318\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan YH. Biostatistics102: Quantitative Data \u0026ndash; Parametric \u0026amp; Non-parametric Tests. Singap Med J. 2003a;44(8):391\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan YH. Biostatistics 103: Qualitative Data \u0026ndash;Tests of Independence. Singap Med J. 2003b;44(10):498\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan YH. Biostatistics 104: Correlational Analysis. Singap Med J. 2003c;44(12):614\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Vitali C, Zhang D, Hand NJ, Phillips MC, Creasy KT, et al. Deep metabolic phenotyping of humans with protein-altering variants in TM6SF2 using a genome-first approach. JHEP Rep. 2025;7:101243.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen LZ, Wang YR, Zhao ZZ, Zhao SL, Min CC. Intestinal depletion of TM6SF2 exacerbates high fat diet-induced metabolic dysfunction\u0026ndash;associated steatotic liver disease through the gut\u0026ndash;liver axis. J Clin Translational Hepatol. 2025;13:443\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Cha J. The PNPLA3 I148M variant is associated with immune cell infiltration and advanced fibrosis in MASLD: a prospective genotype\u0026ndash;phenotype study. J Gastroenterol. 2025;60:1284\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozlitina J, Sookoian S. Global epidemiological impact of PNPLA3 I148M on liver disease. Liver Int. 2025;45:e16123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavalcante L, Porto J, Mazo D, Longatto-Filho A, Stefano JT, Lyra AC, et al. African genetic ancestry is associated with lower frequency of PNPLA3 G allele in non-alcoholic fatty liver disease in an admixed population. Ann Hepatol. 2022;27:100728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetta S, Armandi A, Bugianesi E. Impact of PNPLA3 I148M on clinical outcomes in patients with MASLD. Liver Int. 2025;45:e16133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu J. Macrophage ATG16L1 expression suppresses metabolic dysfunction-associated steatohepatitis progression by promoting lipophagy. Clin Mol Hepatol. 2024;30:721\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MAFLD, MASH, Genetics, TM6SF2, PNPLA3, ATG16L1, autophagy","lastPublishedDoi":"10.21203/rs.3.rs-9203531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9203531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMetabolic dysfunction associated steatotic liver disease (MASLD) and metabolic dysfunction associated steatohepatitis (MASH), represent complex metabolic liver diseases and a rapidly growing public health burden, particularly in Egypt. Genetic susceptibility plays a pivotal role in modulating disease progression. Variants in Patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2) and Autophagy Related 16-Like 1 (ATG16L1) are among the most relevant genetic determinants implicated in hepatic lipid metabolism, autophagy regulation, and hepatocellular injury. The interplay between these genetic variants and metabolic stress regulators is increasingly recognized as central to disease heterogeneity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAims\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis case-control study aimed to provide an integrative evaluation of these genetic variants, elucidating their biochemical associations, and mechanistic roles in MASLD and MASH.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology:\u003c/b\u003e\u003c/p\u003e \u003cp\u003e150 patients with fibroscan-confirmed MASLD, 150 with MASH and 150 healthy controls were genotyped for PNPLA3 (rs738409), TM6SF2 (rs58542926), and ATG16L1 (rs2241880) using real-time TaqMan assays. Genotypic data were correlated with liver injury biomarkers, lipid profiles, and insulin resistance indices.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTM6SF2 (rs58542926) TT and CT genotypes, as well as PNPLA3 (rs738409) CG genotype were strongly associated with exacerbated hepatic steatosis and elevated biochemical markers of liver injury. The G allele of ATG16L1 demonstrated modulatory effects on autophagy-related inflammatory pathways. PNPLA3 and ATG16L1 exert complementary and additive effects on hepatic fat accumulation and metabolic derangements.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe investigated genetic variants showed exhibited biochemical signatures influencing hepatic fat accumulation, autophagy activity, inflammatory response, and fibrosis progression among Egyptian patients. Functional insights were integrated to illuminate how these variants shape disease pathophysiology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial registration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Analysis of TM6SF2, PNPLA3, and ATG16L1 Genetic Variants in MASLD and MASH: An Egyptian Case-control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:10:07","doi":"10.21203/rs.3.rs-9203531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83ad4fe2-338e-4ece-a00f-f1c635f8a3bb","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T09:27:09+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"76970537699786859213987391977577945745","date":"2026-05-17T05:50:46+00:00","index":81,"fulltext":""},{"type":"reviewerAgreed","content":"230617376278852939247908080601427997783","date":"2026-05-16T14:50:23+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"128829954142545496855977360616610347081","date":"2026-05-16T13:43:58+00:00","index":78,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-16T09:36:22+00:00","index":77,"fulltext":""},{"type":"reviewerAgreed","content":"298280141350677003722243966853682833944","date":"2026-05-14T05:31:34+00:00","index":76,"fulltext":""},{"type":"reviewerAgreed","content":"322004081198498120449765692050999325160","date":"2026-05-13T15:07:59+00:00","index":75,"fulltext":""},{"type":"reviewerAgreed","content":"172842315877771063352281006337733170280","date":"2026-05-13T05:36:50+00:00","index":74,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:15:15+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"184663554765149589234776925781693602610","date":"2026-05-12T08:39:27+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"173960926515197964366836516353745085382","date":"2026-05-11T14:38:42+00:00","index":70,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9203531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9203531","identity":"rs-9203531","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}