Volatomics for Diagnosis and Risk Stratification of MASLD : A Proof‐Of‐Concept Study

作者：Rohit Sinha, Sarah‐Louise Gillespie, Paul Brinkman, Paul Bassett, K. A. Lockman, Alan Jaap, Jonathan Fallowfield, P C Hayes, John Plevris · 发表于：Alimentary Pharmacology & Therapeutics · 年份：2025 · DOI：10.1111/apt.70176 · 被引用次数：4 · 研究领域：Advanced Chemical Sensor Technologies、Biochemical Analysis and Sensing Techniques、Respiratory and Cough-Related Research

BACKGROUND AND AIMS: Human breath contains numerous volatile organic compounds (VOCs) produced by physiological and metabolic processes or perturbed in pathological states. Electronic nose (eNose) technology has been extensively validated as a non-invasive diagnostic tool for respiratory disease. Using eNose-derived exhaled breath signals, we investigated whether it could discriminate patients with metabolic dysfunction-associated steatotic liver disease (MASLD) from healthy volunteers and identify patients at high risk of disease progression. METHODS: In a prospective single-centre study, exhaled breath VOCs were analysed using an eNose, in a well-characterised cohort comprising patients with Child-Turcotte-Pugh class A MASLD cirrhosis (n = 30), non-cirrhotic MASLD (n = 30) and healthy volunteers (n = 30). An unbiased machine learning clustering technique was applied. Longitudinal clinical data were collected over 5 years for the patient cohort. Logistic regression and univariable analysis were performed to identify risk factors for disease progression, liver-related outcomes, and all-cause mortality. RESULTS: Principal component analysis of breath VOCs discriminated patients with MASLD from healthy volunteers with 100% sensitivity (p < 0.001, cross-validation verification of 96%), independent of age and gender. The eNose breath profile classified patients with MASLD into three distinct subgroups with similar baseline clinical and demographic characteristics but markedly dif...