ABSTRACT
The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings.
ABSTRACT
Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new term that no longer excludes patients that consume alcohol or present other liver diseases, unlike nonalcoholic fatty liver disease (NAFLD). The aim of this study was to evaluate the role of different biomarkers as predictors of MAFLD in patients with type 2 diabetes mellitus (T2DM). In this regard, a cross-sectional, non-interventional study was conducted over a period of 8 months in patients with T2DM. Liver steatosis displayed by abdominal ultrasound certified the MAFLD diagnosis. A percentage of 49.5% of the studied patients presented MAFLD. Through logistic regression adjusted for gender, age, T2DM duration, lipid-lowering therapy, smoking status, nutritional status, we demonstrated that elevated triglycerides (TG) levels, high non-high-density-lipoprotein (HDL)-cholesterol-to-HDL-cholesterol (non-HDL/HDL) ratio, high atherogenic index of plasma (AIP), and increased Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) had predictive value for MAFLD in patients with T2DM. Furthermore, we calculated the optimal cut-off values for these biomarkers (184 mg/dL for TG, 0.615 for AIP, 3.9 for the non-HDL/HDL ratio, and 2.01 for HOMA-IR) which can predict the presence of MAFLD in patients with T2DM. To our knowledge, this is the first study to assess the predictive value of the non-HDL/HDL ratio for MAFLD in patients with T2DM.
ABSTRACT
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is regarded as a component of metabolic syndrome, which involves insulin resistance (IR) as the primary physiopathological event. The aim of this study was to establish the association between IR, assessed using the triglyceride and glucose index (TyG), and histopathological features of NAFLD lesions. METHODS: The study included 113 patients with metabolic syndrome. Fasting plasma glucose (FPG), fasting lipid profiles and liver enzymes were measured. IR was assessed by the TyG index. Liver biopsy was performed for assessment steatosis and fibrosis. RESULTS: the TyG index had a mean value of 8.93 ± 1.45, with a higher value in the patients with overweight (p = 0.002) and obesity (p = 0.004) characteristics than in the patients with normal weight. The TyG index mean value was 8.78 ± 0.65 in subjects without NASH, 8.91 ± 0.57 in patients with borderline NASH and 9.13 ± 0.55 in patients with definite NASH. A significant difference was found between subjects without NASH and the ones with definite NASH (p = 0.004), as well as in patients with early fibrosis vs. those with significant fibrosis. The analysis of the area under the ROC curve proved that the TyG index is a predictor of NASH (p = 0.043). CONCLUSION: the TyG index is a facile tool that can be used to identify individuals at risk for NAFLD.
