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Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

ABSTRACT: This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is maintained or is inferior to with or without the presence of type 2 diabetes. Overall, 874 patients with biopsy-proven NAFLD were enrolled. After propensity-score matching by age, sex, and the prevalence of dyslipidemia, 311 patients were enrolled in each group of with or without diabetes. To evaluate the effect of diabetes, we compared the diagnostic accuracy of the fibrosis-4 (FIB-4) index, the NAFLD fibrosis score (NFS), the aspartate aminotransferase to platelet ratio index (APRI), and type IV collagen 7S (COL4-7S) in patients with NAFLD with and without diabetes. The areas under the receiver operating characteristic curve (AUROC) for identifying advanced fibrosis in patients without diabetes were 0.879 for the FIB-4 index, 0.851 for the NFS, 0.862 for the APRI, and 0.883 for COL4-7S. The AUROCs in patients with diabetes were 0.790 for the FIB-4 index, 0.784 for the NFS, 0.771 for the APRI, and 0.872 for COL4-7S. The AUROC of COL4-7S was significantly larger than that of the other NITs in patients with NAFLD with diabetes than in those without diabetes. The optimal high and low cutoff points of COL4-7S were 5.9 ng/mL and 4.8 ng/mL, respectively. At the low cutoff point, the accuracy of COL4-7S was better than that of the other NITs, especially in patients with diabetes. Conclusion: COL4-7S measurement might be the best NIT for identifying advanced fibrosis in NAFLD, especially in NAFLD with diabetes.

SUBMITTER: Ishiba H

PROVIDER: S-EPMC8034577 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Rationale & objectivePathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for COL4A3/A4/A5 variants.Study design setting & participantsRare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria.Tests compared & outcomesAll rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort.ResultsIn silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results.LimitationsOverestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories.ConclusionsOur results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines.

Project description:BackgroundThere are emerging studies suggesting that non-alcoholic fatty liver disease (NAFLD) is a heterogeneous disease with multiple etiologies and molecular phenotypes. Fibrosis is the key process in NAFLD progression. In this study, we aimed to explore molecular phenotypes of NAFLD with a particular focus on the fibrosis phenotype and also aimed to explore the changes of macrophage subsets in the fibrosis subset of NAFLD.MethodsTo assess the transcriptomic alterations of key factors in NAFLD and fibrosis progression, we included 14 different transcriptomic datasets of liver tissues. In addition, two single-cell RNA sequencing (scRNA-seq) datasets were included to construct transcriptomic signatures that could represent specific cells. To explore the molecular subsets of fibrosis in NAFLD based on the transcriptomic features, we used a high-quality RNA-sequencing (RNA-seq) dataset of liver tissues from patients with NAFLD. Non-negative matrix factorization (NMF) was used to analyze the molecular subsets of NAFLD based on the gene set variation analysis (GSVA) enrichment scores of key molecule features in liver tissues.ResultsThe key transcriptomic signatures on NAFLD including non-alcoholic steatohepatitis (NASH) signature, fibrosis signature, non-alcoholic fatty liver (NAFL) signature, liver aging signature and TGF-β signature were constructed by liver transcriptome datasets. We analyzed two liver scRNA-seq datasets and constructed cell type-specific transcriptomic signatures based on the genes that were highly expressed in each cell subset. We analyzed the molecular subsets of NAFLD by NMF and categorized four main subsets of NAFLD. Cluster 4 subset is mainly characterized by liver fibrosis. Patients with Cluster 4 subset have more advanced liver fibrosis than patients with other subsets, or may have a high risk of liver fibrosis progression. Furthermore, we identified two key monocyte-macrophage subsets which were both significantly correlated with the progression of liver fibrosis in NAFLD patients.ConclusionOur study revealed the molecular subtypes of NAFLD by integrating key information from transcriptomic expression profiling and liver microenvironment, and identified a novel and distinct fibrosis subset of NAFLD. The fibrosis subset is significantly correlated with the profibrotic macrophages and M2 macrophage subset. These two liver macrophage subsets may be important players in the progression of liver fibrosis of NAFLD patients.

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Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

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