Liver fibrosis index-based nomograms for identifying esophageal varices in patients with chronic hepatitis B related cirrhosis

Xu, Shihao; Wu, Fang; Guo, Le‐Hang; Zhang, Weibing; Xu, Huixiong

doi:10.3748/wjg.v26.i45.7204

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Liver fibrosis is a kind of common liver disease, which is mainly caused by the hyperplasia and deposition of extracellular matrix (ECM) resulting from hepatic stellate cell (HSC) activation, as well as an imbalance between ECM synthesis and degradation ( 6 , 7 ). HSC activation and hyperplasia are the central links in the occurrence and development of liver fibrosis ( 8 ), and suppressing HSC activation is the key to the prevention and treatment of liver fibrosis.…”

Section: Introductionmentioning

confidence: 99%

Double-Negative T Cells Regulate Hepatic Stellate Cell Activation to Promote Liver Fibrosis Progression via NLRP3

et al. 2022

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AimWe mainly explored the role and mechanism of double-negative T cells (DNTs) in liver fibrosis.MethodsDNTs were co-cultured with mouse hepatic stellate cells (HSCs). Later, cell viability was detected by Cell Counting Kit-8 (CCK-8) assay; α-SMA expression was measured through fluorescence staining; TNF-α, IL-6, and MMP-9 levels were measured by ELISA; and the expression of Bcl-2, TGF-β1, NLRP3, ASC, and TNFR1 proteins in HSCs was detected by Western blotting (WB) assay. At the same time, HSC-NLRP3−/− and HSC-TNFR1−/− are used to explore the mechanism. In mouse experiments, mice were intraperitoneally injected with DNTs; afterward, the hepatic tissue fibrosis degree was detected by Masson staining, α-SMA expression was measured through immunohistochemistry (IHC) assay, and histopathological changes were detected by sirius-red staining and H&E staining.ResultsThe results suggested that DNTs promoted HSC activation and NLRP3 activation. The effect of DNTs on activating HSC-NLRP3−/− was suppressed, and the difference was significant as compared with HSCs. HSC-TNFR1−/− activation was also inhibited. To explore the mechanism of DNT-secreted TNF-α in TNFR1-NLRP3 activation, we transfected DNTs with TNF-α siRNA; as a result, DNTs with TNF-α silencing did not significantly affect HSC activation. DNTs promoted hepatic tissue fibrosis progression and HSC activation; after treatment with NLRP3 inhibitor, the effect of DNTs on promoting fibrosis was suppressed.ConclusionWe discovered that DNTs played an important role in liver fibrosis and that DNTs promoted HSC activation via the TNF-α–TNFR1-NLRP3 signal axis, thus further promoting liver fibrosis progression.

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Section: Introductionmentioning

confidence: 99%

Double-Negative T Cells Regulate Hepatic Stellate Cell Activation to Promote Liver Fibrosis Progression via NLRP3

et al. 2022

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“…Currently, there are many studies on EGV in cirrhosis. The main predictors include PLT count, INR, gallbladder wall thickness, spleen diameter and liver fibrosis index, AAR, FIB-4, APRI and AARPRI [5,8–11,15,16]. These indicators showed good predictive efficiency.…”

Section: Discussionmentioning

confidence: 99%

Nomogram model for predicting esophsagogastric varices in hepatocellular carcinoma with cirrhosis

Zhang

Deng

Guo

et al. 2022

European Journal of Gastroenterology &Amp; Hepatology

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Background/aims The prognosis for hepatocellular carcinoma (HCC) with cirrhosis is poor. The risk of death also increases in patients with esophagogastric varices (EGV). Based on routine clinical features and related noninvasive parameters, a nomogram prediction model was developed in this study to facilitate the early identification of EGV in HCC patients. Methods A retrospective cohort analysis of patients with HCC in the Renmin Hospital of Wuhan University from 2020 to 2021 was performed. Clinical and noninvasive parameters closely related to EGV risk were screened by univariate and multivariate logistic regression analysis and integrated into a nomogram. The nomogram was validated internally and externally by calibration, receiver operating characteristic curve and decision curve analysis (DCA).Results A total of 165 patients with HCC-related cirrhosis were recruited. In the raining cohort, multivariate logistic regression analysis identified platelet (PLT) [odds ratio (OR), 0.950; 95% confidence interval (CI), 0.925-0.977; P < 0.001], D-dimer (OR. 3.341; 95% CI, 1.751-6.376, P < 0.001), spleen diameter (SD) (OR, 2.585; 95% CI, 1.547-4.318; P < 0.001) as independent indicators for EGV. The nomogram for predicting EGV risk was well calibrated with a favorable discriminative ability and an area under curve of 0.961. In addition, the nomogram showed better net benefits in the DCA. The results were validated in the validation cohort. Conclusions The proposed nomogram model based on three indicators (PLT, D-dimer and SD) showed an excellent predictive effect, leading to the avoidance of unnecessary esophagogastroduodenoscopy.

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“…Histopathological examination is the "gold standard" for diagnosing and evaluating cirrhosis [7]; nonetheless, this type of examination requires a collection of tissue (liver biopsy), which is an invasive procedure. On the other hand, other non-invasive methods such as imaging methods, elastography techniques, and serum biomarkers based on laboratory tests have shown moderate diagnostic accuracy in assessing portal hypertension of HBV-related cirrhosis [8][9][10][11][12]. Furthermore, studies have shown the potential diagnostic value of metabolic biomarkers in differentiating advanced liver fibrosis or hepatocellular carcinoma from chronic hepatitis B virus (CHB) or HBVrelated cirrhosis [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Serum metabolomic profiling of patients with liver cirrhosis at different stages

XiaoJuan Li,

Xinxin Xu,

Yu Li

et al. 2024

Cell Mol Biol (Noisy-le-grand)

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An accurate and non-invasive diagnosis of the clinical stage is critical for effectively managing liver cirrhosis. This study aimed to identify serum metabolite biomarkers and clinical features that may reliably predict highrisk cirrhosis. This cross-sectional study recruited 94 cirrhotic patients (70 for identification cohort, 24 for validation cohort) from Minhang Hospital Affiliated with Fudan University between 2018 and 2021, who were analyzed by targeted quantitative metabolomics technique. Baseline clinical characteristics were collected, and different stage cirrhosis classification was performed according to the presence or absence of decompensated events. Potential metabolite biomarkers were screened, and a model for predicting the decompensation stage was created. Finally, the incidence of decompensated outcomes was analyzed. A total of 560 metabolites were detected in the identification cohort. Indole-3-propionic acid (IPA) was the most significantly decreased metabolic biomarker in the decompensated group (P<0.01, |log2FC| >2), having the strongest correlation with hyaluronic acid (r=-0.50, P<0.01). It also performed well for differentiating decompensated cirrhosis with an area under the curve (AUC) of 0.79(0.75 at internal validation). Another diagnostic model consisting of indole-3-propionic acid, hemoglobin, and albumin showed better predictive performance with an AUC of 0.97 (0.91 at internal validation). Also, 31 (44.29%) patients developed decompensated events at a median followup of 22.76±15.24 months. The cumulative incidence of decompensated events based on IPA subgroups (IPA <39.67ng/ml and ≥39.67ng/ml) showed a significant difference (P<0.01). "Indole-3-propionic acid" and a diagnostic model of hemoglobin and albumin can non-invasively identify cirrhotic populations at risk for decompensation, aiding in future management of liver cirrhosis.

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Liver fibrosis index-based nomograms for identifying esophageal varices in patients with chronic hepatitis B related cirrhosis

Cited by 5 publications

References 43 publications

Double-Negative T Cells Regulate Hepatic Stellate Cell Activation to Promote Liver Fibrosis Progression via NLRP3

Double-Negative T Cells Regulate Hepatic Stellate Cell Activation to Promote Liver Fibrosis Progression via NLRP3

Nomogram model for predicting esophsagogastric varices in hepatocellular carcinoma with cirrhosis

Serum metabolomic profiling of patients with liver cirrhosis at different stages

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