Introduction

Metabolic dysfunction-associated Steatotic Liver disease (MASLD) is complicated by various cardiometabolic (CMR) manifestations, and the prognosis is severely burdened by acute cardiovascular events (ACE).
Even though several drugs have been tested to target CMR comorbidities, lifestyle modifications remain the cornerstone intervention. Unfortunately, the dietary-behavioral prescription is constantly limited by poor compliance, with the sociodemographic-determinant factors impacting this remaining undetermined.
Moreover, despite emerging evidence supporting behavioral therapy's relevance, the real long-term benefits of offering motivational support to MASLD individuals represent an unexplored scenario in real life.

Aims & Methods

This study aimed to evaluate the efficacy of multidisciplinary management on clinical outcomes in MASLD, investigating compliance with specialist-tailored indications. We consecutively enrolled individuals presenting MASLD diagnostic criteria (n.286). After recruitment, patients were randomized into three cohorts: subjects (n.72) receiving generic hepatologist-provided advice (“H”); subjects (n.71) also receiving the nutritionists-prescribed individualized intervention (“HN”); patients (n.143) treated with an approach additionally involving cognitive/behavioral-based psychological support (“HNP”). H and HN represented the “standard of care” (n.143), whereas “HNP” constituted the “experimental group” (n.143). At baseline, anthropometric, biochemical, clinical, liver stiffness (LSM), controlled attenuation parameter (CAP), lifestyle habits (including sociodemographic determinants), and body composition values were recorded. Along 24 months, on semestral hepatological, nutritional (H and HN), and psychological (HNP) follow-ups, all parameters were reassessed, the occurrence of ACEs was recorded, and compliance was opportunely evaluated. Three time-study points were considered: baseline (T0), intermediate (i.e., after 18 months – T1), and end of the study (T2).

Results

Intermediate evaluations revealed a significantly higher prevalence of individuals achieving a >10% decrease in body weight (HNP:62.09%; HN:44.9%; H:35.8%; chi-square, HNP vs. HN, p:0.01; HNP vs. H, p:0.0002) after 18 months (T1) in the experimental group. At the end of the observation period (T2), in this setting, a further increase in the proportion of individuals presenting and preserving >10% decrease in body weight was reported (HNP: 72.03%; HN: 46.7%; H: 39.2%, chi-square T1, HNP vs. HN, p:0.02; HNP vs. H, p:0.0001; McNemar T1-T2 HNP vs HN, p:0.001, HNP vs H, p:0.001), as wells as a significant improvement in Homeostatic-model-assessment-for-insulin-resistance (p:0.001), HDL (p < 0.0001), LSM (p:0.007), CAP (p:0.002), and fat-mass (p < 0.0001) was observed (HNP, T0 vs T2).
Relevantly, HNP patients presented a significantly lower risk of ACEs [HR: 0.497, IC (0.236-0.751) 95%, p:0.04)]. Logistic regression analysis (adjusted for sex, age, drug administration, and cardiovascular risk factors) revealed that HNP adherence was significantly associated with lower ACE occurrence (aOR: 0.81; C.I. 95%: 0.55-0.97; p:0.02). Consistently, the loss of compliance rate was significantly lower in HNP (HNP:12.08%; HN:34.7%; H:45.8%; HNP vs HN, HNP vs H, p:0.001). Finally, the analysis of sociodemographic factors revealed large family (i.e., > 3 individuals), dynamic work (i.e., < 2 hours/die dedicated to main meals), and city life as social determinants impacting compliance (OR: 2.11, 1.78, 1.12) (all p < 0.05).

Conclusion

In the era of Precision Medicine, the correct management of MASLD patients should contemplate the integration of standard hepatological-nutritional with psychological support.

References

1. Romeo M, Dallio M, Cipullo M, Coppola A, Mazzarella C, Mammone S, Iadanza G, Napolitano C, Vaia P, Ventriglia L, Federico A. Nutritional and Psychological Support as a Multidisciplinary Coordinated Approach in the Management of Chronic Liver Disease: A Scoping Review. Nutr Rev. 2025 Feb 24:nuaf001. doi: 10.1093/nutrit/nuaf001. Epub ahead of print. PMID: 39992295.
2. Righetti R, Cinque F, Volpe MT, Sebastiani G. Integrating behavioral interventions into a holistic approach to metabolic dysfunction-associated steatotic liver disease. Expert Rev Gastroenterol Hepatol. 2024 Jul;18(7):303-313. doi: 10.1080/17474124.2024.2385487. Epub 2024 Aug 5. PMID: 39075881.
3. European Association for the Study of the Liver (EASL) Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024 Sep;81(3):492-542. doi: 10.1016/j.jhep.2024.04.031. Epub 2024 Jun 7. PMID: 38851997.

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

Endoscopic ultrasound (EUS) is the gold standard for evaluating pancreatic lesions, allowing for real-time imaging and biopsy sampling. Nevertheless, the differentiation between solid pancreatic lesions — such as pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs) — as well as cystic lesions is often troublesome. Specifically, identifying intraductal papillary mucinous neoplasms (IPMNs) with varying degrees of dysplasia is crucial for guiding clinical management. However, the diagnostic accuracy of current methods remains suboptimal. This multicenter study aimed to develop and validate convolutional neural networks (CNNs) to characterize solid and cystic pancreatic lesions and stratify IPMNs into high-grade dysplasia/carcinoma (HGD/C) and low-grade dysplasia (LGD).

Aims & Methods

This multicenter study included EUS images from 12 centers in Spain, Brazil, the United Kingdom and the United States of America. Solid lesion differentiation (PDAC vs. PNET) was achieved using a CNN trained on 49034 images from 95 patients (n=69 for PDAC and n=26 for PNET), with diagnoses confirmed via EUS-guided fine-needle biopsy or surgical specimen analysis. A second CNN was trained on 79395 images from 82 patients (n=64 for mucinous cystic lesions and n=18 for non-mucinous cystic lesions). Finally, a third model used the subset of IPMNs (n=51046 frames, 30 patients) to distinguish between HGD/C (n=5 patients) and LGD (n=25 patients). Histological confirmation was used as the gold standard. Performance metrics included sensitivity, specificity, accuracy, and area under the precision-recall curve (AUC-PR). To comprehensively determine the model's performance, we evaluated multiple thresholds for the confidence score and the IoU, between 0.45 and 0.55 with a step of 0.02 ([0.45, 0.47, 0.50, 0.52, 0.55]). The datasets were split into training (70%), validation (20%), and testing (10%) subsets.

Results

For solid lesions, the model differentiated PDAC from PNETs with an accuracy of 91.8%, with sensitivities of 96.2% and 97.8%, respectively. The AUC-PR was 0.982 for PDAC and 0.977 for PNET. Regarding cystic lesions the model detected mucinous cystic lesions with a sensitivity of 97.5%, an overall accuracy of 89.7% and an AUC-PR of 0.977. When the subset of IPMNs was specifically analyzed, the model distinguished IPMNs with HGD/C from those with LGD with a sensitivity of 97.5%, specificity of 94.3%, and an overall accuracy of 89.8%. The AUC was 0.988.

Conclusion

This is the first multicenter, transatlantic AI study aiming to provide a holistic approach to both solid and cystic pancreatic lesions, focusing on those with a significantly higher malignant potential. Our models achieved high accuracy in identifying and differentiating these conditions. Furthermore, to our knowledge this is the first integrative model to include the ability to stratify IPMNs into LGD and HGD/C, which has significant clinical implications, potentially reducing unnecessary surgeries and ensuring timely intervention for high-risk patients. The inclusion of data from diverse regions reduces demographic bias and enhances model generalizability. Real-time implementation of these solutions could significantly improve decision-making in EUS and optimize patient outcomes.