Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages

Feng, Guohua; Zhang, Xiaoxun; Zhang, Liangjun; Liu, Wen‐Yue; Geng, Shi; Yuan, Hai-Yang; Sha, Jun‐Cheng; Wang, Xiao Dong; Sun, Dan‐Qin; Targher, Giovanni; Byrne, Christopher D.; Zheng, Tian‐Lei; Ye, Feng; Zheng, Ming‐Hua; Chai, Jin

doi:10.1111/liv.15565

Liver International

2023

DOI: 10.1111/liv.15565

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Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages

Guohua Feng

Xiaoxun Zhang

Liangjun Zhang

et al.

Abstract: Background & Aims: There is an unmet clinical need for non-invasive tests to diagnose non-alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2). Methods: We collected urine samples from 100 patients with biopsy-confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohor… Show more

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Diagnostic accuracy of exhaled nitric oxide for the non-invasive identification of patients with fibrotic metabolic dysfunction-associated steatohepatitis

Zhang,

Huang,

Chen

et al. 2024

Annals of Medicine

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Background Fibrotic metabolic dysfunction-associated steatohepatitis (MASH) is a condition at risk of progressing to advanced liver disease. We examined whether an innovative exhaled nitric oxide (eNO) breath test (BT) can accurately diagnose fibrotic MASH without requiring blood tests. Methods One hundred and forty-seven patients with MASH were recruited, and all tests were undertaken within 1 week of recruitment. With fibrotic MASH (NAS ≥ 4 and fibrosis stage ≥ 2) as the main outcome indicator, the diagnostic efficacy of eNO in identifying fibrotic MASH was compared to other validated models for advanced fibrosis requiring venesection, namely FAST, Agile 3 + , and FIB-4 scores. Results The mean age was 40.36 ± 12.28 years, 73.5% were men. Mean body mass index was 28.83 ± 4.31 kg/m 2 . The proportion of fibrotic MASH was 29.25%. The area under the receiver operating curve for eNO in diagnosing fibrotic MASH was 0.737 [95% CI 0.650–0.823], which was comparable to FAST (0.751 [0.656–0.846]), Agile 3 + (0.764 [0.670–0.858]), and FIB-4 (0.721 [0.620-0.821]) (all DeLong test p > 0.05). A cut-off of eNO <8.5 ppb gave a sensitivity of 86.0% and a negative predictive value of 88.5% for ruling-out fibrotic MASH. A cut-off of eNO >13.5 ppb provided a specificity of 91.3% and a positive predictive value of 65.4% for ruling-in fibrotic MASH. Sensitivity analyses demonstrated that the diagnostic efficacy of eNO was similar across characteristics such as age. Moreover, adding vibration-controlled transient elastography-LSM (liver stiffness measurement) reduced the uncertainty interval from 46.9% to 39.5%. Conclusions The eNO-BT is a promising simple test for non-invasively identifying fibrotic MASH, and its performance is further improved by adding LSM measurement.

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Diagnostic accuracy of exhaled nitric oxide for the non-invasive identification of patients with fibrotic metabolic dysfunction-associated steatohepatitis

Zhang,

Huang,

Chen

et al. 2024

Annals of Medicine

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Causal relationship between key genes and metabolic dysfunction‐associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis

Feng,

He,

Gao

et al. 2024

Diabetes Obesity Metabolism

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AimNon‐invasive diagnostics for metabolic dysfunction–associated fatty liver disease (MAFLD) remain challenging. We aimed to identify novel key genes as non‐invasive biomarkers for MAFLD, elucidate causal relationships between biomarkers and MAFLD and determine the role of immune cells as potential mediators.Materials and MethodsUtilizing published transcriptome data of patients with biopsy‐proven MAFLD, we applied linear models for microarray data, least absolute shrinkage and selector operation (LASSO) regressions and receiver operating characteristic (ROC) curve analyses to identify and validate biomarkers for MAFLD. Using the expression quantitative trait loci database and a cohort of 778 614 Europeans, we used Mendelian randomization to analyse the causal relationships between key biomarkers and MAFLD. Additionally, mediation analysis was performed to examine the involvement of 731 immunophenotypes in these relationships.ResultsWe identified 31 differentially expressed genes, and LASSO regression showed three hub genes, IGFBP2, PEG10, and P4HA1, with area under the receiver operating characteristic (AUROC) curve of 0.807, 0.772 and 0.791, respectively, for identifying MAFLD. The model of these three genes had an AUROC of 0.959 and 0.800 in the development and validation data sets, respectively. This model was also validated using serum‐based enzyme‐linked immunosorbent assay data from MAFLD patients and control subjects (AUROC: 0.819, 95% confidence interval: 0.736–0.902). PEG10 was associated with an increased MAFLD risk (odds ratio = 1.106, p = 0.032) via inverse variance–weighted analysis, and about 30% of this risk was mediated by the percentage of CD11c + CD62L– monocytes.ConclusionsThe MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L– monocytes.

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Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Ghosh,

Zhao,

Alim

et al. 2024

Gut

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Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

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Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages

Cited by 5 publications

References 59 publications

Diagnostic accuracy of exhaled nitric oxide for the non-invasive identification of patients with fibrotic metabolic dysfunction-associated steatohepatitis

Diagnostic accuracy of exhaled nitric oxide for the non-invasive identification of patients with fibrotic metabolic dysfunction-associated steatohepatitis

Causal relationship between key genes and metabolic dysfunction‐associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis

Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

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