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Comment on “Development and Validation of Machine Learning‐Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease”

Year: 2025

Type: article

Abstract: Recently, Lushan Xiao and colleagues [1] developed a novel predictive model, the in silico scores for nonalcoholic fatty liver disease (NAFLD) (ISNLD), aimed at facilitating the early identification of individuals at high risk for NAFLD and predicting their progression to advanced liver disease and metabolic complications.The authors reported that ISNLD demonstrated robust discriminatory capacity for high-risk NAFLD populations, with an area under the curve (AUC) exceeding 0.80, and significantly outperformed the widely used noninvasive index fibrosis-4 (FIB-4).Furthermore, in high-risk individuals, ISNLD exhibited strong prognostic value for the subsequent development of severe liver diseases, including cirrhosis and hepatocellular carcinoma.These findings suggest that ISNLD represents a valuable tool for the early detection, individualized management, and targeted intervention of NAFLD, with considerable implications for both clinical practice and public health.NAFLD is the most prevalent chronic liver disorder worldwide and can progress to cirrhosis or hepatocellular carcinoma, representing a significant threat to global health.[2] Early diagnosis and timely intervention are essential to delay disease onset and progression, thereby improving population health outcomes.However, liver biopsy, which is the current diagnostic gold standard, has inherent limitations, including sampling variability, invasiveness, and high cost, which limit its feasibility for large-scale clinical application.[3] Consequently, there is an urgent need for efficient, noninvasive, and easily implementable diagnostic tools capable of accurately assessing prognostic risk.[4] In this context, Lushan Xiao and colleagues [1] developed the ISNLD, a model incorporating readily available clinical parameters and genetic single-nucleotide polymorphisms (SNPs), thus circumventing the invasive nature of histological assessment.We

Source: Advanced Science

DOI: 10.1002/advs.202515549

Authors: Hongye Peng, Shaojie Duan

Institutions: Third Xiangya Hospital, Taizhou Central Hospital

Language: English

Topic: Liver Disease Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $6,730