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

Area of Science:

• Biomedical engineering
• Hepatology
• Analytical chemistry

Background:

• Human breath contains volatile organic compounds (VOCs) linked to physiological and pathological states.
• Electronic nose (eNose) technology offers a non-invasive method for diagnosing respiratory diseases.
• Metabolic dysfunction-associated steatotic liver disease (MASLD) requires novel diagnostic and prognostic tools.

Purpose of the Study:

• To investigate the utility of eNose technology in discriminating MASLD patients from healthy volunteers.
• To identify MASLD patients at high risk for disease progression using breath volatile organic compound (VOC) profiles.
• To assess the prognostic value of eNose-derived breath signatures in MASLD.

Main Methods:

• A prospective single-centre study analyzed exhaled breath VOCs using eNose in 90 participants (30 MASLD cirrhosis, 30 non-cirrhotic MASLD, 30 healthy).
• Machine learning clustering techniques were applied to eNose data.
• Longitudinal clinical data were collected over 5 years to identify risk factors for progression and mortality.

Main Results:

• eNose breath volatile organic compound (VOC) profiles discriminated MASLD patients from healthy controls with 100% sensitivity.
• Principal component analysis revealed three distinct MASLD subgroups with differing prognoses.
• One subgroup (Cluster 2) showed significantly higher rates of progression (42%), liver-related decompensation (17%), and mortality (12.5%) over 5 years.

Conclusions:

• eNose technology effectively differentiates MASLD patients from healthy individuals.
• Unbiased clustering of breath VOCs can identify MASLD patients with a worse prognosis.
• Further prospective validation in independent MASLD cohorts is warranted.