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

ABSTRACT 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 different prognoses. During the 5‐year follow‐up period, Cluster 2 was identified as a higher‐risk subgroup for progression (42%, p = 0.03), liver‐related decompensation events (17%, p = 0.06), and all‐cause mortality (12.5%). Conclusion eNose can discriminate patients with MASLD from healthy volunteers and, using unbiased clustering analysis, identify patients with a significantly worse prognosis. These results warrant prospective validation in independent MASLD populations. Trial Registration ClinicalTrials.gov identifier: NCT02950610