Metabolic dysfunction-associated steatotic liver disease: A silent pandemic

Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024

Artificial intelligence (AI) methods enable humans to analyse large amounts of data, which would otherwise not be feasibly quantifiable. This is especially true for unstructured visual and textual data, which can contain invaluable insights into disease. The hepatology research landscape is complex and has generated large amounts of data to be mined. Many open questions can potentially be addressed with existing data through AI methods. However, the field of AI is sometimes obscured by hype cycles and imprecise terminologies. This can conceal the fact that numerous hepatology research groups already use AI methods in their scientific studies. In this review article, we aim to assess the contemporaneous use of AI methods in hepatology in Europe. To achieve this, we systematically surveyed all scientific contributions presented at the EASL Congress 2024. Out of 1,857 accepted abstracts (1,712 posters and 145 oral presentations), 6 presentations (∼4%) and 69 posters (∼4%) utilised AI methods. Of these, 55 posters were included in this review, while the others were excluded due to missing posters or incomplete methodologies. Finally, we summarise current academic trends in the use of AI methods and outline future directions, providing guidance for scientific stakeholders in the field of hepatology.

Blood cell counts and nonalcoholic fatty liver disease: Evidence from Mendelian randomization analysis

BACKGROUND

Previous research has highlighted correlations between blood cell counts and chronic liver disease. Nonetheless, the causal relationships remain unknown.

AIM

To evaluate the causal effect of blood cell traits on liver enzymes and nonalcoholic fatty liver disease (NAFLD) risk.

METHODS

Independent genetic variants strongly associated with blood cell traits were extracted from a genome-wide association study (GWAS) conducted by the Blood Cell Consortium. Summary-level data for liver enzymes were obtained from the United Kingdom Biobank. NAFLD data were obtained from a GWAS meta-analysis (8434 cases and 770180 controls, discovery dataset) and the Fingen GWAS (2275 cases and 372727 controls, replication dataset). This analysis was conducted using the inverse-variance weighted method, followed by various sensitivity analyses.

RESULTS

One SD increase in the genetically predicted haemoglobin concentration (HGB) was associated with a β of 0.0078 (95%CI: 0.0059-0.0096), 0.0108 (95%CI: 0.0080-0.0136), 0.0361 (95%CI: 0.0156-0.0567), and 0.0083 (95%CI: 00046-0.0121) for alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transferase, respectively. Genetically predicted haematocrit was associated with ALP (β = 0.0078, 95%CI: 0.0052-0.0104) and ALT (β = 0.0057, 95%CI: 0.0039-0.0075). Genetically determined HGB and the reticulocyte fraction of red blood cells increased the risk of NAFLD [odds ratio (OR) = 1.199, 95%CI: 1.087-1.322] and (OR = 1.157, 95%CI: 1.071-1.250). The results of the sensitivity analyses remained significant.

CONCLUSION

Novel causal blood cell traits related to liver enzymes and NAFLD development were revealed through Mendelian randomization analysis, which may facilitate the diagnosis and prevention of NAFLD.

Beyond Auto-Brewery: Why Dysbiosis and the Legalome Matter to Forensic and Legal Psychology

International studies have linked the consumption of ultra-processed foods with a variety of non-communicable diseases. Included in this growing body of research is evidence linking ultra-processed foods to mental disorders, aggression, and antisocial behavior. Although the idea that dietary patterns and various nutrients or additives can influence brain and behavior has a long history in criminology, in the absence of plausible mechanisms and convincing intervention trials, the topic was mostly excluded from mainstream discourse. The emergence of research across nutritional neuroscience and nutritional psychology/psychiatry, combined with mechanistic bench science, and human intervention trials, has provided support to epidemiological findings, and legitimacy to the concept of nutritional criminology. Among the emergent research, microbiome sciences have illuminated mechanistic pathways linking various socioeconomic and environmental factors, including the consumption of ultra-processed foods, with aggression and antisocial behavior. Here in this review, we examine this burgeoning research, including that related to ultra-processed food addiction, and explore its relevance across the criminal justice spectrum—from prevention to intervention—and in courtroom considerations of diminished capacity. We use auto-brewery syndrome as an example of intersecting diet and gut microbiome science that has been used to refute mens rea in criminal charges. The legalome—microbiome and omics science applied in forensic and legal psychology—appears set to emerge as an important consideration in matters of criminology, law, and justice.