Exploration of a Novel Prognostic Prediction Model for Liver Cirrhosis

Overview

The liver, a key organ for metabolism and synthesis, is involved in protein, fat, and carbohydrate metabolism, as well as energy production. Hepatic injury and functional decline can lead to metabolic abnormalities in these three major nutrients, as well as in vitamins and trace elements. Malnutrition, one of the most common complications in cirrhosis patients, has a broader impact than traditional complications like hepatic encephalopathy, esophageal variceal bleeding, refractory ascites, and spontaneous bacterial peritonitis. It is closely related to patient prognosis. Therefore, malnutrition should be considered as important as ascites and hepatic encephalopathy in diagnosis and treatment, and nutritional metabolism should be incorporated into prognostic prediction models or scoring systems for cirrhosis patients. Currently, the nutrition assessment of cirrhosis patients mostly uses relatively subjective methods such as scales and scores. There is no specific gold-standard diagnostic criterion for malnutrition in cirrhosis patients. Also, existing prognostic models for cirrhosis patients do not adequately consider the impact of nutritional factors on disease prognosis. Metabolomics technology can detect changes in the types and levels of nutritional metabolites in cirrhosis patients and analyze the differences in nutritional metabolites under various nutritional statuses and their relationship with the prognosis of cirrhosis patients. This helps objectively reveal the predictive value of nutrition metabolism for the prognosis of cirrhosis patients. However, metabolomics has been rarely used in nutrition assessment studies of cirrhosis patients and merits further research. This study will employ a prospective cohort study design to analyze the baseline nutritional status of patients with liver cirrhosis, investigate the impact of nutritional factors on long-term prognosis, and develop a prognostic prediction model for liver cirrhosis that incorporates nutritional parameters.

Patients with cirrhosis attending the Second Affiliated Hospital of Chongqing Medical University were included in this prospective study. Eligible subjects were determined based on the inclusion and exclusion criteria. Clinical data were collected, including gender, age, blood routine, liver and kidney function, coagulation function, comorbidities, complications, and disease progression. Various indicators were calculated, including body mass index (BMI), Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Model for End-Stage Liver Disease (MELD) score, MELD-Na score, and MELD 3.0 score. Nutritional risk screening and malnutrition assessment were also performed. Resting energy expenditure was measured by professionals, and blood samples were collected for non-targeted nutritional metabolite profiling. Patients were followed up at 3 months, 6 months, 1 year, and 2 years to assess survival status, hepatic adverse events, and complication occurrence. Statistical analysis was conducted to identify nutritional scores, indicators, or differential metabolite levels closely associated with patient prognosis. Variables for model construction and scoring were determined, assigned scores, and a novel prognostic prediction model for cirrhosis patients was developed and optimized. This study aims to integrate nutritional assessment into the prognostic evaluation system for cirrhosis patients, establishing a more objective, accurate, and comprehensive model. It provides evidence for nutritional intervention and improving patient prognosis and lays a foundation for further research. Additionally, metabolomics analysis of differential metabolites in cirrhosis patients with varying nutritional statuses and prognoses offers insights into the molecular mechanisms of nutritional metabolism's impact on prognosis.