The ALERT Score: A Clinical Tool to Enrich for Trial‐Eligible Liver Fat Content in MASH and MASLD Clinical Trials
Year: 2025
Type: article
Abstract: ABSTRACT Efficient enrolment remains a major hurdle for MASH/MASLD trials, many of which require liver fat content (LFC) ≥ 8%. We aimed to develop and validate a predictive score based on routine clinical data, optimised for patients with type 2 diabetes (T2D) to prescreen individuals likely to have elevated LFC and thereby reduce screen failures and facilitate clinical trial enrolment. Data were pooled from multiple clinical studies ( N = 1075, 53% male, age: 54 years) spanning a broad BMI and including individuals with suspected or confirmed MASLD ( N = 694) and those with T2D ( N = 478). LFC was quantified by MRI proton‐density fat fraction (MRI‐PDFF) or proton magnetic resonance spectroscopy ( 1 H‐MRS). The study cohort was dichotomised at < 8% vs. ≥ 8% LFC, a criterion for clinical trial eligibility. Candidate models were trained and tested using multivariable logistic regression; comparators included the fatty liver index (FLI), hepatic steatosis index (HSI), and, where available, Vibration‐Controlled Transient Elastography with Controlled Attenuation Parameter (VCTE‐CAP). The ALERT score, based on ALT, triglycerides and T2D status, showed high accuracy for detecting LFC ≥ 8% (AUC 0.86, 95% CI 0.81–0.91), outperforming FLI (AUC 0.80, CI 0.74–0.86), HSI (AUC 0.76, CI 0.70–0.83), and VCTE‐CAP (AUC 0.66, CI 0.58–0.74). Using a threshold of 0.60, ALERT achieved 91% specificity and 85% PPV. The overall screen failure rate was 5%, indicating strong enrichment for LFC ≥ 8%. The ALERT score, a simple three‐variable score, enriches for LFC ≥ 8% with high specificity and PPV, offering a practical tool to streamline MASH/MASLD Phase 2–3 trial enrolment. The tool's performance in T2D and in a diverse real‐world population supports its broad generalisability to current trial settings.
DOI: 10.1002/lci2.70029
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416780040