Risk factors of hepatocellular carcinoma in type 2 diabetes patients: A two-centre study in a developing country

Inverse association between type 2 diabetes and hepatocellular carcinoma in East Asian populations

Aims

To investigate the potential association between type 2 diabetes (T2D) and hepatocellular carcinoma (HCC) in East Asian populations using Mendelian randomization (MR) analyses.

Methods

Bidirectional Mendelian randomization (MR) studies were conducted using summary statistics from genome-wide association studies (GWAS) related to T2D and HCC. The potential effects of confounders such as chronic hepatitis B, chronic hepatitis C, body mass index, and alcohol intake frequency were corrected using a multivariate MR study. Various MR methods, including the inverse variance weighted (IVW) approach, were used to estimate the associations between T2D and HCC. Sensitivity analysis and assessment of heterogeneity were performed to ensure the robustness of the results.

Results

In the forward MR study, the IVW approach of MR analysis suggested an inverse association between T2D and HCC, with a risk odds ratio of 0.8628 (95% confidence interval [CI], 0.7888-0.9438). Furthermore, even after adjusting for BMI, chronic hepatitis B, and alcohol intake frequency, this study still supports the inverse association between T2D and HCC. Additional MR methods provided further support for this relationship. Sensitivity analysis and assessment of heterogeneity confirmed the robustness of the results. The reverse MR analysis did not show a clear impact of genetic liability to HCC on reduced risk of T2D(OR=0.9788; 95% CI, 0.9061-1.0574).

Conclusion

This study provides evidence of an inverse association between T2D and HCC in East Asian populations using MR analysis. Further studies are warranted to validate these findings.

Corrected and republished from: Metabolic associated liver disease

Alcohol consumption (AC) and metabolic syndrome (MS) represent the first cause of liver disease, hepatocellular carcinoma and liver transplantation. The habit of consuming alcoholic beverages and the presence of MS and non-alcoholic fatty liver disease (NAFLD) often coexist in the same patient. The histoclinical boundaries between alcohol related liver disease (ALD) and NAFLD are often not well defined. The co-presence of AC and MS increases the risk of hepatic and extra-hepatic disease. The terminological evolution from NAFLD to metabolic associated fatty liver disease (MAFLD) is certainly a useful advance. However, it is known that the appearance of liver fibrosis increases oncologic and cardiovascular disease risk, which in the case of cirrhosis can be present even in the absence of steatosis and that the mechanisms of fibrogenesis can act independently of the presence of steatosis/steatohepatitis. For this reason, as already stated recently, a further terminological evolution can be hypothesized. This article was originally published with mistakes in the text. The new corrected citable version appears below.

Cuproptosis- and m6A-Related lncRNAs for Prognosis of Hepatocellular Carcinoma

Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.

Metabolic associated liver disease

In real practice the patient with liver disease is often the carrier of multiple etiological factors such as metabolic syndrome (MS) and alcohol consumption (AC). Their copresence is often underestimated and AC is not adequately studied. Traditionally to diagnose non-alcoholic fatty liver disease (NAFLD), AC must not exceed 30 gr for men and 20 gr for women per day. This limit should still be reduced, especially in relation to the AC and fibrogenesis ratio and also frequent misestimation of AC or unrecognized MS may underestimate multi caused liver injury. AC is a contributing cause of MS and alcoholic and non-alcoholic liver disease have a substantially overlapping histopathological picture. Moreover, AC and MS are cause and contributing cause of extra-hepatic morbidity and mortality. It can be concluded that the possible simplification of terminology at metabolic associated liver disease (MALD) makes clinical activity more usable and immediate, facilitates better communication and cooperation between scientific societies and specialists who apparently deal with different medical sectors, facilitates early identification of related hepatic and extra-hepatic pathology, allows to "see the person in a unitary way," to create more streamlined care pathways, to reduce the hospitalization rate with relative cost-benefit advantage and to create unitary prevention and health promotion policies.

Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model

Background

Hepatocellular carcinoma (HCC) among type-2 diabetes (T2D) patients is an increasing burden to diabetes management. This study aims to develop and select the best machine learning (ML) classification model for predicting HCC in T2D for HCC early detection.

Methods

A case-control study was conducted utilising computerised medical records in two hepatobiliary centres. The predictors were chosen using multiple logistic regression. IBM SPSS Modeler® was used to assess the discriminative performance of support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), chi-square automatic interaction detection (CHAID), and their ensembles.

Results

Subjects (N = 424) were split into 60% training (n = 248) and 40% testing (n = 176) groups. The independent predictors identified were race, viral hepatitis, abdominal pain/discomfort, unintentional weight loss, statins, alcohol consumption, non-alcoholic fatty liver, platelet <150 ×10³/μL, alkaline phosphatase >129 IU/L, and alanine transaminase ≥25 IU/L. The performances of all models differed significantly (Cochran’s Q-test,p = 0.001) but not between the ensembled and SVM model (McNemar test, p = 0.687). SVM model was selected as the best model due to its simplicity, high accuracy (85.28%), and high AUC (0.914). A web-based application was developed using the best model’s algorithm for HCC prediction.

Conclusions

If further validation studies confirm these results, the SVM model’s application potentially augments early HCC detection in T2D patients.

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Hepatocellular carcinoma (HCC) in type-2 diabetes (T2D) is increasing.

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A web-based application using a machine learning model has been developed.

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The model has 85.28% accuracy and high AUC (0.914) in HCC prediction.

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Potential risk stratification tool in T2D clinic to predict HCC.

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HCC prediction in T2D clinic may aid early detection and improve survival.