Introduction

Chronic liver disease is a major global health concerns, contributing to over 1.4 million annual deaths worldwide¹. Its primary fatal outcomes, hepatic complications^2,3, arise from liver cirrhosis, hepatocellular carcinoma and hepatic decompensation. The early, reversible stages of chronic liver disease are frequently asymptomatic and overlooked, leading to delayed diagnoses and irreversible complications that treatment options are limited.^4,5. Therefore, reliable tools to identify whether a person will develop hepatic complications in the future have become a public health priority, enabling early diagnosis and timely intervention in high-risk populations.

Aims & Methods

This study aimed to employ machine learning to develop proteomics-based models for the early identification of hepatic complications. In this study, 37,178 adults without hepatic complications at baseline were analyzed from a prospective cohort from United Kingdom, with plasma levels of 2,736 proteins measured using Olink technology. Cox regression was used to identify significant proteins associated with incident hepatic complications. Models based on importance-top-ranking proteins were constructed using LightGBM learners of Automated Machine Learning (AutoML) and internal validated through 5-fold cross validation in the derivation set (n = 37,178). Independent validation was then conducted in a geographically distinct UKB cohort (n = 15,767).

Results

Over a median follow-up of 13.8 years, 338 participants developed hepatic complications. Cox regression identified 593 proteins significantly associated with incident hepatic complications. Among them, GDF15, PROC, GGT1, ANXA10, IL18, MFAP4, COL4A1, CDCP1 and IL6 were ranked highest in protein importance ordering. The protein model showed a considerable predictive accuracy for hepatic complications under different time scenarios in the UKB validation set: all-year (area under the curve [AUC] = 0.89), within 5 years (AUC = 0.88), within 10 years (AUC = 0.91), over 10 years (AUC = 0.81), and achieved superior predictive performance compared to models with demographic predictors, laboratory indicators, polygenic risk score (PRS) and traditional risk score models (Protein 0.89 vs. Demographics 0.83, Laboratory 0.85, PRS 0.49; Fibrosis-4 index [FIB-4] 0.76, Aspartate aminotransferase to platelet ratio index [APRI] 0.78; all P < 0.05). Combined with clinical data and PRS, the predictor performance is further enhanced: all-year (AUC = 0.91), within 5 years (AUC = 0.91), within 10 years (AUC = 0.90), over 10 years (AUC = 0.83). Individuals in the high-risk group stratified by the protein model were 16.27 times more likely to develop hepatic complications.

Conclusion

Our study constructed a novel protein model for hepatic complications risk stratification and could be used to predict individuals who will develop hepatic complications up to 16 years in advance in the general population.

References

1. Tham EKJ, Tan DJH, Danpanichkul P, et al. The Global Burden of Cirrhosis and Other Chronic Liver Diseases in 2021. Liver Int 2025;45:e70001.
2. Åberg F, Asteljoki J, Männistö V, et al. Combined use of the CLivD score and FIB-4 for prediction of liver-related outcomes in the population. Hepatol Baltim Md 2024;80:163–172.
3. Innes H, Morling JR, Buch S, et al. Performance of routine risk scores for predicting cirrhosis-related morbidity in the community. J Hepatol 2022;77:365–376.
4. Karlsen TH, Sheron N, Zelber-Sagi S, et al. The EASL–Lancet Liver Commission: protecting the next generation of Europeans against liver disease complications and premature mortality. The Lancet 2022;399:61–116.
5. Serra-Burriel M, Juanola A, Serra-Burriel F, et al. Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study. Lancet Lond Engl 2023;402:988–996.

Introduction

Acute pancreatitis (AP) is an acute inflammatory autodigestive disorder triggered by dysfunction of pancreatic exocrine activity and represents the most common gastrointestinal emergency in clinical practice. Global data indicate an annual incidence rate of 33.74 per 100,000 population and a mortality rate of 1.16 per 100,000. Despite ongoing optimization of therapeutic strategies, current clinical management primarily relies on supportive care and surgery. Hypertriglyceridemia (HTG) is the second leading etiological factor for AP, accounting for 10.36% of cases. Compared to other causes, HTG-associated acute pancreatitis (HTG-AP) exhibits higher recurrence rates and more severe complications (e.g., pancreatic necrosis and renal insufficiency). With the rising prevalence of HTG, the disease burden of HTG-AP is increasing, yet its specific pathogenic mechanisms remain unclear.

Aims & Methods

This study integrated whole-genome sequencing (WGS) and next-generation sequencing (NGS) to screen for shared mutations in the exonic regions of 5 HTG-AP family members. Candidate genes were identified based on MutationTaster classification and CADD scores. A mouse model carrying the PDLIM2 gene rs145349678 mutation was constructed using CRISPR/Cas9 technology, and an HTG-AP animal model was established. Pancreatic and hepatic histopathology (HE staining) and serological markers (TG, lipase, amylase, IL-6) were analyzed to evaluate the impact of the mutation on pancreatitis severity and hepatic lipid deposition.

Results

A clinical HTG-AP family was identified, with all 5 members exhibiting hypertriglyceridemia, including 2 cases of recurrent pancreatitis. WGS revealed 278 mutation sites across 10 chromosomes (involving 23 genes) in the family, and NGS validation narrowed these to 7 candidate loci. The PDLIM2 rs145349678 mutation (NM_001368120.1:exon9:c.C1055T:p.A352V), located within the LIM domain (which mediates protein localization and signaling transduction), was selected as the target due to cross-species conservation. Experimental results demonstrated that HTG-AP mice with the PDLIM2 mutation exhibited significantly aggravated pancreatic pathological damage (edema, hemorrhage, and inflammatory cell infiltration; *P* < 0.05), elevated serological markers (TG: 2.3 ± 0.4 vs. 1.5 ± 0.3 mmol/L; lipase: 520 ± 68 vs. 320 ± 45 U/L; IL-6: 48.2 ± 6.1 vs. 28.5 ± 4.3 pg/mL; *P* < 0.01 for all), and a 2.3-fold increase in hepatic lipid deposition (*P* < 0.001) compared to controls.

Conclusion

The PDLIM2 rs145349678 mutation exacerbates inflammatory responses and hepatic lipid accumulation in HTG-AP by modulating lipid metabolism. This finding provides new insights into the pathogenesis of HTG-AP and potential therapeutic targets.