Studies characterizing Nonalcoholic Fatty Liver Disease (NAFLD) are limited in Oman. This study aims to assess the prevalence of NAFLD in obese Omani patients and find its relationship with other obesity-related medical conditions. Fifty-five adult obese patients were evaluated via medical history, clinical examination, and laboratory tests. Patients with any possible risk of liver injury were excluded. Diagnosis of NAFLD relied on ultrasonography criteria. Data were entered and analyzed in SPSS (version 22). The prevalence of NAFLD was calculated using frequency and percentages. Out of the total patient population, 37 (67.3%) have had NAFLD. Most of the patients (81%) were below 50 years of age. Systolic hypertension was present in 45.9%, while diastolic hypertension was present in 43.2%. AST and ALT levels were significantly increased (p<0.001) in most patients; 81.1% and 73%, respectively. Fasting blood sugar (FBS) and glycosylated hemoglobin (HbA1c) levels were higher in NAFLD patients (p<0.01). Mean levels of LDL, uric acid, and total cholesterol were significantly higher in the NAFLD patients (p<0.01). In conclusion, NAFLD and related metabolic complications are prevalent in obese Omani individuals. Keywords. NAFLD, Obesity, Oman

Background/Objectives: Differentiating Type 1 from Type 2 diabetes mellitus (T1DM vs. T2DM) remains clinically challenging, especially in early-onset cases with overlapping features. This study assessed the diagnostic utility of diabetes-related autoantibodies in an Omani cohort and evaluated their predictive performance using machine learning. Methods: Clinical and laboratory data from 448 patients (aged ≥ 2 years) in Al Batinah North, Oman, were retrospectively analyzed. We assessed autoantibody positivity (anti-GAD, anti-islet, anti-TPO, anti-tissue), age, sex, and HbA1c. Receiver operating characteristic (ROC) curves and a neural network model were used to evaluate diagnostic accuracy. Results: Anti-GAD and anti-islet antibodies were significantly more prevalent in T1DM (69.0% and 64.1%) than T2DM (7.4% and 3.8%; p < 0.0001). HbA1c was elevated in both subtypes but lacked discriminatory specificity. Nearly half (48.5%) of T1DM patients showed multiple antibody positivity, especially in younger age groups. Anti-TPO and anti-tissue antibodies were more frequently detected in T1DM, suggesting broader autoimmunity. ROC analysis showed strong predictive value for anti-islet (AUC = 0.835) and anti-GAD (AUC = 0.827). Neural network modeling identified anti-GAD, anti-islet, and age as the most informative predictors, achieving over 92% classification accuracy. Importantly, antibody positivity in a subset of insulin-treated T2DM patients suggested potential latent autoimmune diabetes (LADA) misclassification. Conclusions: This is the first study in Oman to combine autoantibody screening with AI-based modeling to refine diabetes classification. Our findings highlight the value of immunological profiling in early diagnosis, uncover possible misclassification, and support AI integration to guide individualized management.