Acanthosis Nigricans as a Clinical Risk Marker for Metabolic Dysfunction-Associated Steatotic Liver Disease

Abstract

Background: Despite the association of insulin resistance (IR) in the pathophysiology of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), few studies have evaluated the utility of acanthosis nigricans (AN) as a clinical predictor for this condition. Thus, we aimed to determine the role of AN as a risk factor and clinical predictor of MASLD. Methods: This cross-sectional study conducted a comprehensive clinical history and physical examination. The presence of acanthosis nigricans was assessed in the neck, elbows, axillae, and knuckles. Liver biochemical parameters were measured, hepatic fat was analyzed using the controlled attenuation parameter (CAP), while hepatic stiffness was evaluated in kilopascals (kPA) with the FibrosScan 530 Compact and Smart Exam software. Multiple analyses were performed to determine the relationship between acanthosis nigricans and MASLD. Results: We recruited 251 adult participants. The mean BMI was 29.9 ± 7.3 kg/m2, including 40.2% of participants with obesity and 63.7% with AN. Transient elastography evaluation resulted in 65.7% and 6% participants with hepatic steatosis (S3, 48.6%) and fibrosis, respectively. A non-adjusted (OR 2.63, 95% CI 1.57-4.52) and adjusted model (OR 1.97, 95% CI 0.95-4.12) were determined for AN as a risk factor for liver steatosis. Furthermore, the presence of AN in knuckles resulted in an association to predict liver steatosis (OR 2.09, 95% CI 1.01-4.35), while a multivariate analysis indicated that AN predicts a higher steatosis grade (S2 OR = 6.58, CI 95% 1.18-36.53; S3 OR = 2.36, CI 95% 1.04-5.3). Conclusions: Acanthosis nigricans demonstrated to predict a higher steatosis grade in adults with overweight and obesity. Our study supports the clinical applicability of AN as a screening tool for MASLD to identify high-risk subjects in resource-limited settings. Additional studies are needed to define alternative diagnostic tools for the early identification of metabolic risk factors in populations with specific clinical or demographic characteristics.