Liver diseases are currently diagnosed through liver biopsy. Its invasiveness, costs, and relatively low diagnostic accuracy require new techniques to be sought. Analysis of volatile organic compounds (VOCs) in human bio-matrices has received a lot of attention. It is known that a musty odour characterises liver impairment, resulting in the elucidation of volatile chemicals in the breath and other body fluids such as urine and stool, which may serve as biomarkers of a disease. Aims: This study aims to review all the studies found in the literature regarding VOCs in liver diseases, and to summarise all the identified compounds that could be used as diagnostic or prognostic biomarkers. The literature search was conducted on ScienceDirect and PubMed, and each eligible publication was qualitatively assessed by two independent evaluators using the SANRA critical appraisal tool. Results: In the search, 58 publications were found, and 28 were kept for inclusion: 23 were about VOCs in the breath, one in the bile, three in urine, and one in faeces. Each publication was graded from zero to ten. A graphical summary of the metabolic pathways showcasing the known liver disease-related VOCs and suggestions on how VOC analysis on liver impairment could be applied in clinical practice are given.

Exhaled breath analysis has become a promising monitoring tool for various ailments by identifying volatile organic compounds (VOCs) as indicative biomarkers excreted in the human body. Throughout the process of sampling, measuring, and data processing, non-biological variations are introduced in the data leading to batch effects. Algorithmic approaches have been developed to cope with within-study batch effects. Batch differences, however, may occur among different studies too, and up-to-date, ways to correct for cross-study batch effects are lacking; ultimately, cross-study comparisons to verify the uniqueness of found VOC profiles for a specific disease may be challenging. This study applies within-study batch-effect-correction approaches to correct for cross-study batch effects; suggestions are made that may help prevent the introduction of cross-study variations. Three batch-effect-correction algorithms were investigated: zero-centering, combat, and the analysis of covariance framework. The breath samples were collected from inflammatory bowel disease ( n = 213 ), chronic liver disease ( n = 189 ), and irritable bowel syndrome ( n = 261 ) patients at different periods, and they were analysed via gas chromatography-mass spectrometry. Multivariate statistics were used to visualise and verify the results. The visualisation of the data before any batch-effect-correction technique was applied showed a clear distinction due to probable batch effects among the datasets of the three cohorts. The visualisation of the three datasets after implementing all three correction techniques showed that the batch effects were still present in the data. Predictions made using partial least squares discriminant analysis and random forest confirmed this observation. The within-study batch-effect-correction approaches fail to correct for cross-study batch effects present in the data. The present study proposes a framework for systematically standardising future breathomics data by using internal standards or quality control samples at regular analysis intervals. Further knowledge regarding the nature of the unsolicited variations among cross-study batches must be obtained to move the field further.

Up to 5% of inflammatory bowel disease patients may at some point develop primary sclerosing cholangitis (PSC). PSC is a rare liver disease that ultimately results in liver damage, cirrhosis and liver failure. It typically remains subclinical until irreversible damage has been inflicted. Hence, it is crucial to screen IBD patients for PSC, but its early detection is challenging, and the disease’s etiology is not well understood. This current study aimed at the early detection of PSC in an IBD population using Volatile Organic Compounds in fecal headspace and exhaled breath. To this aim, fecal material and exhaled breath were collected from 73 patients (n = 16 PSC/IBD; n = 8 PSC; n = 49 IBD), and their volatile profile were analyzed using Gas Chromatography–Mass Spectrometry. Using the most discriminatory features, PSC detection resulted in areas under the ROC curve (AUCs) of 0.83 and 0.84 based on fecal headspace and exhaled breath, respectively. Upon data fusion, the predictive performance increased to AUC 0.92. The observed features in the fecal headspace relate to detrimental microbial dysbiosis and exogenous exposure. Future research should aim for the early detection of PSC in a prospective study design.