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Introduction and Objectives: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in children and it is more prevalent in Hispanic males. The gender differences can be explained by body fat distribution, lifestyle, or sex hormone metabolism. We evaluated anthropometric and metabolic differences by gender in children with and without NAFLD. Methods: We included 194 participants (eutrophic, overweight, and individuals with obesity). The presence of NAFLD was determined using ultrasonography, and we evaluated the association between this disease with metabolic and anthropometric variables by gender. Results: The mean age was 10.64 ± 2.54 years. The frequency of NAFLD in boys was 24.51% and in girls was 11.96% (OR = 2.39; 95%CI = 1.10–5.19; p = 0.025). For girls, NAFLD was significantly associated with triglycerides (p = 0.012), homeostatic model assessment of insulin resistance (HOMA-IR) (p = 0.048), and the visceral adiposity index (VAI) (p = 0.024). The variables related to NAFLD in a gender-specific manner were body mass index (BMI) (p = 0.001), waist circumference (WC) (p

Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance.137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI25 with absence of NAFLD (G1), n = 82; BMI30 with absence of NAFLD (G2), n = 24; and BMI30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size0.2), but did predict obesity phenotype.Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.

Background Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance. Methods/Results 137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size

Objective To evaluate the utility of predicting nonalcoholic fatty liver disease (NAFLD) and obesity by retinal vascular changes (RVC) found in fundoscopy and to determine whether this is related to a low-grade inflammatory process. Methods We carried out a cross-sectional analysis that included 152 (ages 18-45 years) patients divided into four groups: NAFLD and BMI less than 25, absence of NAFLD and BMI less than 25, NAFLD and BMI more than 30, and absence of NAFLD and BMI more than 30. Retinal fundoscopy, hepatic ultrasound, metabolic profile, and cytokine measurement were performed. We calculated odds ratio [95% confidence interval (CI)], performed diagnostic utility tests, and carried out a 2 factorial analysis. Results Obesity was associated with RVC (odds ratio: 21.25; 95% CI: 8.79-51.4) and NAFLD [25 (9.07-72.6)]. NAFLD was associated with RVC [11.24 (4.98-26.48)], and the prediction of NAFLD showed a sensitivity of 75% (95% CI: 68-82) and a specificity of 81% (75-86); when RVC-obesity were combined, sensitivity increased to 90% (88-91.7), with a specificity of 85% (84-85.8). C-reactive protein was associated with the three factors, suggesting an independent contribution. Thin patients with RVC and NAFLD had higher concentrations of interleukin-2, interleukin-6, tumor necrosis factor-α, and interferon-γ. Conclusion NAFLD in patients with obesity without diabetes or hypertension can be predicted by RVC, a noninvasive technique carried out by eye fundoscopy. NAFLD alone can drive inflammatory conditions in the absence of obesity that manifests as RVC.