Related Experiment Video
Updated: May 5, 2026
Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations
Published on: March 29, 2017
Volatomics for Diagnosis and Risk Stratification of MASLD: A Proof-Of-Concept Study
R Sinha1, S L Gillespie1, P Brinkman2
1Hepatology Laboratory and Centre of Liver and Digestive Diseases, Royal Infirmary of Edinburgh, The University of Edinburgh, Edinburgh, UK.
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.
More Related Videos
08:14MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
07:15Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
