Diabetes Mellitus Impacts on the Performance of Hepatocellular Carcinoma Risk Scores in Chronic Hepatitis B Patients

Chronic Viral Hepatitis B and C Outweigh MASLD in the Associated Risk of Cirrhosis and HCC

BACKGROUND & AIMS

The impact of metabolic dysfunction-associated steatotic liver disease (MASLD) on the development of cirrhosis and hepatocellular carcinoma (HCC) by chronic hepatitis B (CHB) or C infection and antiviral treatment statuses is not well-known.

METHODS

A total of 336,866 adults aged ≥30 years were prospectively enrolled in a health screening program between 1997-2013. MASLD was identified by abdominal ultrasonography and cardiometabolic profiles. Data linkage was performed using 3 nationwide databases-National Health Insurance, Cancer Registry, and Death Certification System-to obtain information on antiviral treatment, vital status, and newly diagnosed cirrhosis and HCC. Follow-up was conducted until December 31, 2019.

RESULTS

In the total population, 122,669 (36.4%) had MASLD. Over a mean follow-up of 15 years, 5562 new cases of cirrhosis and 2273 new cases of HCC were diagnosed. Although MASLD significantly increased the cumulative risks of cirrhosis or HCC (P < .0001), the associated risk was more pronounced when comparing CHB or C infection with the presence of MASLD. Stratifying the participants based on their MASLD and CHB or C statuses, hazard ratios (HRadj) with 95% confidence intervals for HCC were 8.81 (7.83-9.92) for non-steatotic liver disease (SLD) with CHB or C, 1.52 (1.32-1.74) for MASLD without CHB or C, and 8.86 (7.76-10.12) for MASLD with CHB or C, compared with non-SLD without CHB or C (all P < .0001). Among CHB or C patients who received antivirals during follow-up, MASLD was associated with increased risks of cirrhosis and HCC, with HRadj of 1.23 (1.01-1.49) and 1.32 (1.05-1.65), respectively.

CONCLUSIONS

These findings underscore the need to prioritize treatment of chronic viral hepatitis before addressing MASLD.

A Liver Stiffness-Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma

BACKGROUND & AIMS

The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness-based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs).

METHODS

MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384). Model performance was assessed according to Harrell's C-index and time-dependent receiver operating characteristic (ROC) curve.

RESULTS

We developed the SMART-HCC score, a liver stiffness-based ML HCC risk score, with liver stiffness measurement ranked as the most important among 9 clinical features. The Harrell's C-index of the SMART-HCC score in HK and Europe validation cohorts were 0.89 (95% confidence interval, 0.85-0.92) and 0.91 (95% confidence interval, 0.87-0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years was ≥0.89 in both validation cohorts. The performance of SMART-HCC score was significantly better than existing HCC risk scores including aMAP score, Toronto HCC risk index, and 7 hepatitis B-related risk scores. Using dual cutoffs of 0.043 and 0.080, the annual HCC incidence was 0.09%-0.11% for low-risk group and 2.54%-4.64% for high-risk group in the HK and Europe validation cohorts.

CONCLUSIONS

The SMART-HCC score is a useful machine learning-based tool for clinicians to stratify HCC risk in patients with CLDs.

Therapeutic strategy of biological macromolecules based natural bioactive compounds of diabetes mellitus and future perspectives: A systematic review

High blood glucose levels are a hallmark of the metabolic syndrome known as diabetes mellitus. More than 600 million people will have diabetes by 2045 as the global prevalence of the disease continues to rise. Contemporary antidiabetic drugs reduce hyperglycemia and its consequences. However, these drugs come with undesirable side effects, so it's encouraging that research into plant extracts and bioactive substances with antidiabetic characteristics is on the rise. Natural remedies are preferable to conventional anti-diabetic drugs since they are safer for the body, more affordable and have fewer potential adverse effects. Biological macromolecules such as liposomes, niosomes, polymeric nanoparticles, solid lipid nanoparticles, nanoemulsions and metallic nanoparticles are explored in this review. Current drug restrictions have been addressed, and the effectiveness of plant-based antidiabetic therapies has enhanced the merits of these methods. Plant extracts' loading capacity and the carriers' stability are the primary obstacles in developing plant-based nanocarriers. Hydrophilic, hydrophobic, and amphiphilic drugs are covered, and a brief overview of the amphipathic features of liposomes, phospholipids, and lipid nanocarriers is provided. Metallic nanoparticles' benefits and attendant risks are highlighted to emphasize their efficiency in treating hyperglycemia. Researchers interested in the potential of nanoparticles loaded with plant extracts as antidiabetic therapeutics may find the current helpful review.