Polygenic risk score of metabolic dysfunction-associated steatotic liver disease amplifies the health impact on severe liver disease and metabolism-related outcomes.

BACKGROUND: Although the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes. METHODS: Participants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan-Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes. RESULTS: Sixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54-3.90) for severe liver disease (SLD), and 2.81 (2.60-3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes. CONCLUSIONS: High PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management.

Association between sarcopenia-related markers and cholelithiasis: A prospective and Mendelian randomization study.

Cholelithiasis is a common digestive disease that drives a myriad of adverse complications. The correlation between sarcopenia and various digestive disorders has been extensively researched, whereas its association with cholelithiasis remains unreported. We aimed to investigate the association through prospective and Mendelian randomization (MR) analyses and establish a quantitative score reflecting the impact of sarcopenia-related markers on cholelithiasis. The prospective study involved 448 627 participants from the UK Biobank. Cox proportional hazard models were employed to investigate the correlation between sarcopenia-related markers and cholelithiasis. To quantitatively assess cholelithiasis risk, the SARCHO score was derived from a multivariable Cox model. Bidirectional two-sample MR analysis was conducted to validate the causal association. A total of 16 738 individuals developed cholelithiasis during a median follow-up of 12 years. Hazard ratios (HRs) of cholelithiasis decreased stepwise over skeletal muscle index tertiles (highest tertile: reference; middle tertile: 1.23, p < .001; lowest tertile: 1.33, p < .001). The tertiles of grip strength showed a similar pattern. Individuals with slow walking pace had a higher risk of cholelithiasis compared to those with normal walking pace (HR 1.23; p < .001). Our SARCHO score better quantifies the risk of cholelithiasis. MR analysis showed a causal relationship between muscle mass and cholelithiasis (OR 0.81; p < .001). No causal effect of cholelithiasis on lean mass was observed. Prospective and MR analyses have consistently demonstrated an increased risk of cholelithiasis in individuals with decreased muscle mass. Additionally, SARCHO score further quantified the cholelithiasis occurrence risk. These findings provide compelling evidence for muscle strengthening in preventing cholelithiasis.

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions.