Making Sense of the Nonalcoholic Fatty Liver Disease Clinical Practice Guidelines: What Clinicians Need to Know

MASLD Risk Score (MRS) and Random Forest model (RF Model): Novel tools for screening and severity assessment of MASLD

BACKGROUND AND AIMS

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) spans from simple steatosis to progressive forms like non-alcoholic steatohepatitis (NASH), fibrosis, and cirrhosis, making early diagnosis and grading crucial. This study aimed to develop and validate predictive models for diagnosing and assessing MASLD severity using routinely available biomarkers from both public and clinical datasets.

APPROACH & RESULTS

We developed a novel MASLD Risk Score (MRS) using data from the CDC NHANES (2000-2020) and validated it in a clinically profiled Indian cohort. Unlike existing indices, the predictors were derived through data-driven feature selection from large dataset, ensuring statistical robustness. It integrates novel (Uric Acid, HOMA-IR) and established (liver enzymes, triglycerides, waist circumference, BMI) biomarkers to improve metabolic profiling and predictive accuracy. The MRS also uniquely enables grading of MASLD severity, addressing a key limitation of previous models. The MRS achieved AUROCs of 0.91 (public) and 0.85 (clinical) with accuracies of 94 % and 82 %, respectively. A Random Forest (RF) model built on the same features provided AUROCs of 0.87 (public) and 0.94 (clinical), with accuracies of 83 % and 82 %. MRS parameters were optimized using a diverse population, improving generalizability across demographics. Both models showed strong correlation with ultrasonography results and outperformed existing indices.

CONCLUSIONS

The MRS offers a novel, interpretable, and cost-effective solution for MASLD screening. Its development from a large, demographically diverse population and incorporation of varied biomarkers supports generalizability. While results are promising, external validation in multi-center clinical settings is needed to confirm broad utility.

A narrative review of lifestyle management guidelines for metabolic dysfunction-associated steatotic liver disease

BACKGROUND AND AIMS

Metabolic dysfunction-associated steatotic liver disease management guidelines have been published worldwide; we aimed to summarize, categorize, and compare their lifestyle intervention recommendations.

APPROACH AND RESULTS

We searched metabolic dysfunction-associated steatotic liver disease/NAFLD management guidelines published between January 1, 2013, and June 31, 2024, through databases including PubMed/MEDLINE, Cochrane, and CINAHL. In total, 35 qualifying guidelines were included in the final analysis. Guideline recommendations were categorized into 5 domains (ie, weight reduction goals, physical activity, nutrition, alcohol, and tobacco smoking) and were ranked based on how frequently they appeared. A recommendation was defined as widely adopted if recommended in ≥24 (≥66.6%) of the guidelines. These included increasing physical activity; reducing body weight by 7%-10% to improve steatohepatitis and/or fibrosis; restricting caloric intake; undertaking 150-300 or 75-150 minutes/week of moderate or vigorous-intensity physical activity, respectively; and decreasing consumption of commercially produced fructose. The least mentioned topics, in ≤9 of the guidelines, evaluated environmental determinants of health, mental health, referring patients for psychological or cognitive behavioral therapy, using digital health interventions, and assessing patients' social determinants of health.

CONCLUSIONS

Most guidelines recommend weight reduction through physical activity and improving nutrition, as these have proven positive effects on health outcomes when sustained. However, gaps regarding mental health and the social and environmental determinants of metabolic dysfunction-associated steatotic liver disease were found. To optimize behavioral modifications and treatment, we recommend carrying out studies that will provide further evidence on social support, environmental factors, and mental health, as well as further exploring digital health interventions.