Association of Endothelial Dysfunction and Microalbuminuria with Fibrosis Severity in Patients with MASLD

Assessment of Severity of Metabolic Dysfunction–Associated Steatotic Liver Disease and Cardiovascular Risk in Individuals with and without Diabetes

Liver fibrosis risk: a silent threat in type 1 diabetes mellitus.

NON-INVASIVE RISK STRATIFICATION IN METABOLIC DYSFUNCTION-ASSOCIATED STEATOTIC LIVER DISEASE: INTEGRATING FIBROSIS AND KIDNEY RISK

Patients with obesity, type 2 diabetes, hypertension, dyslipidaemia and chronic kidney disease (CKD) are commonly observed with metabolic dysfunction-associated steatotic liver disease (MASLD). It is very important not only to detect the presence of steatosis but also to be able to identify advanced fibrosis and a definition of kidney and cardiometabolic risk. Objectives: To review the available evidence regarding sequential non-invasive fibrosis assessment and to suggest a liver-kidney risk-stratification strategy that could be integrated for general clinical practice. Methods: A focused diagnostic evidence review was performed in PubMed/MEDLINE, the Cochrane Library, as well as official resources from EASL, AASLD, AGA and KDIGO. The eleven guidelines, consensus documents, metaanalyses, and large prospective studies were synthesized narratively without attempting to perform a statistical pooling because there was heterogeneity of tests, thresholds, reference standards, and outcomes. Results: Vibration-controlled transient elastography was more accurate at detecting advanced fibrosis than FIB-4 (area under the receiver operating characteristic curve 0.85 vs. 0.76, respectively) in an individual-patient-data meta-analysis. A sequential FIB-4 and elastography thresholds of 66% sensitivity and 86% specificity were achieved, leaving 33% of patients as candidates for biopsy [6]. Higher all-cause mortality was found to be associated with more severe liver fibrosis (F4 fibrosis) (HR 3.9, 95% CI 1.8-8.4) [7]. In observational cohorts, separately, fatty liver disease was found to be associated with incident CKD (HR 1.43, 95% CI 1.33-1.54) [9]. Discussion: FIB-4 should be used as a triage test, not as a diagnosis. The interpretation of secondary fibrosis testing, eGFR and UACR should be used in combination to inform the level of referral, surveillance and organ protection management.

Correlation of hepatic steatosis and fibrosis with microvascular complications among obese and non-obese individuals with T2D and MASLD.

A simple clinical model using waist circumference and cholesterol predicts liver fibrosis in MAFLD

Metabolic dysfunction-associated fatty liver disease (MAFLD) involves chronic low-grade inflammation; however, the utility of inflammatory markers for fibrosis risk stratification remains unclear. We aimed to investigate the relationships between inflammatory cytokines, steatosis, and fibrosis and to develop a simple predictive model for liver stiffness. We enrolled 49 patients with MAFLD who underwent anthropometric measurements, laboratory testing (including Interleukin-6 (IL-6) and High-sensitivity of C-reactive protein (hs-CRP), and vibration-controlled transient elastography (FibroScan) for steatosis ( controlled attenuation parameter, CAP) and fibrosis (liver stiffness measurement, LSM). Correlations between inflammatory markers, metabolic parameters, and liver outcomes were analyzed. Logistic regression identified fibrosis predictors (LSM ≥ 6.0 kPa, indicating any degree of fibrosis), and predictive performance was compared with that of traditional indices. Internal validation was performed using bootstrap resampling with 5000 iterations (bias-corrected accelerated method). A sensitivity analysis further adjusted for age, sex, diabetes, and use of glucose-lowering and lipid-lowering medications.(exploratory, not bootstrapped). IL-6 levels were positively correlated with CAP ( R  = 0.390, P  = 0.007), triglycerides, and uric acid levels, and negatively correlated with high density lipoprotein (HDL) levels. hs-CRP showed strong correlations with LSM ( R  = 0.639, P  < 0.001), body mass index (BMI), and liver enzymes. Waist circumference (odds ratio [OR] = 1.21, P  = 0.004) and cholesterol levels (OR = 4.80, P  = 0.011) independently predicted early fibrosis in the parsimonious model. After adjusting for age, sex, diabetes, and medication use, waist circumference (OR = 1.226, 95% CI: 1.055–1.425, P  = 0.008) and cholesterol (OR = 4.677, 95% CI: 1.249–17.509, P  = 0.022) remained significant, with AUC 0.897, sensitivity 73.7%, specificity 86.2%, and accuracy 81.3%. Bootstrap internal validation confirmed model stability, with bootstrap-corrected ORs of 1.230 (95% CI: 1.070–1.499) for waist circumference and 5.366 (95% CI: 1.376–32.187) for cholesterol, with AUC 0.866, sensitivity 73.7%, specificity 89.7%, and accuracy 83.3%. The combined model yielded an area under the curve (AUC) of 0.862 (95% CI: 0.744–0.980), which compared favorably with APRI (0.722), FIB-4 (0.537), and AST/ALT ratio (0.340). Central obesity and lipid dysfunction are key determinants of MAFLD-related fibrosis. A simple model using waist circumference and cholesterol provides robust, internally validated risk stratification for early fibrosis, independent of common metabolic confounders. External validation in larger cohorts is required before clinical implementation.

Lipid Profiles in Liver Steatosis and Liver Fibrosis in Patients with Type 2 Diabetes and Coronary Artery Disease

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes (T2DM) have been associated with dyslipidemia. This study aimed to investigate the association between lipid values and markers of hepatic steatosis and fibrosis in patients with T2DM. Methods: This is a post hoc analysis of the AleCardio randomized controlled trial in patients with T2DM and recent acute coronary syndrome. Risk of liver steatosis was estimated with the Hepatic Steatosis Index (HSI), risk of liver fibrosis with the NAFLD Fibrosis Score (NFS). Results: Of 7226 participants, 526 were untreated with lipid-lowering medication and formed the analysis cohort. Mean age was 61 ± 10 years, and body mass index (BMI) was 28.5 ± 5.8 kg/m2. A total of 75% of individuals had an HSI score > 36, associated with high risk of liver steatosis, elevated NFS scores > 0.67, associated with high risk of liver fibrosis, were present in 15%. Patients with elevated HSI scores had higher BMI, HOMA IR, triglycerides, remnant cholesterol, and lower high-density lipoprotein compared to those with lower scores. Patients with elevated NFS scores had greater BMI and HOMA IR, and had lower triglycerides, remnant cholesterol, and apolipoprotein B in linear regression analyses. Conclusion: Patients with T2DM, cardiovascular disease, and blood-based indices suggesting liver steatosis or fibrosis differed significantly with respect to the lipid profile. An elevated HSI score, suggesting steatosis, was associated with dyslipidemia, an elevated NFS score, suggesting fibrosis, was not. A possible explanation for the latter findings is reduced very-low-density lipoprotein (VLDL-C) synthesis or secretion accompanying advanced hepatocellular dysfunction.