Large-scale identification of undiagnosed hepatic steatosis using natural language processing

The common p.Ile291Val variant of ERLIN1 enhances TM6SF2 function and is associated with protection against MASLD

BACKGROUND

The ERLIN1 p.Ile291Val single-nucleotide polymorphism (rs2862954) is associated with protection from steatotic liver disease (SLD), but effects of this variant on metabolic phenotypes remain uncertain.

METHODS

Metabolic phenotypes and outcomes associated with ERLIN1 p.Ile291Val were analyzed by using a genome-first approach in the UK Biobank (UKB), Penn Medicine BioBank (PMBB), and All of Us cohort.

FINDINGS

ERLIN1 p.Ile291Val carriers exhibited significantly lower serum levels of alanine aminotransferase and aspartate aminotransferase as well as higher levels of triglycerides, low-density lipoprotein cholesterol, Apolipoprotein B, high-density lipoprotein cholesterol, and Apolipoprotein A1 in UKB, and these values were affected by ERLIN1 p.Ile291Val in an allele-dose-dependent manner. Homozygous ERLIN1 p.Ile291Val carriers had a significantly reduced risk of developing metabolic dysfunction-associated SLD (MASLD, adjusted odds ratio [aOR] = 0.92, 95% confidence interval [CI], 0.88-0.96). The protective effect of this variant was enhanced in patients with alcoholic liver disease. Our results were replicated in PMBB and the All of Us cohort. Strikingly, the protective effects of ERLIN1 p.Ile291Val were not apparent in individuals carrying the TM6SF2 p.Glu167Lys variant associated with increased risk of SLD. We analyzed the effects of predicted loss-of-function ERLIN1 variants and found that they had opposite effects, namely reduced plasma lipids, suggesting that ERLIN1 p.Ile291Val may be a gain-of-function variant.

CONCLUSION

Our study contributes to a better understanding of ERLIN1 by investigating a coding variant that has emerged as a potential gain-of-function mutation with protective effects against MASLD development.

Identification of hepatic steatosis among persons with and without HIV using natural language processing

BACKGROUND

Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis systematically within large clinical repositories of imaging reports. We validated the performance of an NLP algorithm for the identification of SLD in clinical imaging reports and applied this tool to a large population of people with and without HIV.

METHODS

Patients were included in the analysis if they enrolled in the Veterans Aging Cohort Study between 2001 and 2017, had an imaging report inclusive of the liver, and had ≥2 years of observation before the imaging study. SLD was considered present when reports contained the terms "fatty," "steatosis," "steatotic," or "steatohepatitis." The performance of the SLD NLP algorithm was compared to a clinical review of 800 reports. We then applied the NLP algorithm to the first eligible imaging study and compared patient characteristics by SLD and HIV status.

RESULTS

NLP achieved 100% sensitivity and 88.5% positive predictive value for the identification of SLD. When applied to 26,706 eligible Veterans Aging Cohort Study patient imaging reports, SLD was identified in 72.2% and did not significantly differ by HIV status. SLD was associated with a higher prevalence of metabolic comorbidities, alcohol use disorder, and hepatitis B and C, but not HIV infection.

CONCLUSIONS

While limited to those undergoing radiologic study, the NLP algorithm accurately identified SLD in people with and without HIV and offers a valuable tool to evaluate the determinants and consequences of hepatic steatosis.

Artificial Intelligence in Liver Diseases: Recent Advances

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.