It has been estimated that alcohol causes around 40% of premature liver deaths in Europe each year, although this number is probably underestimated. Alcohol-related liver disease (ALD) is the most common cause of liver cirrhosis and liver death in Europe with a peak age of deaths occurring among individuals aged 40 to 50. Despite these findings, ALD is little studied with only 5% of all clinical trials in the field of liver disease recorded on ClinicalTrials.gov and only 5% of all publications in the same research area. Liver cancer is the second most common cause of cancer-related death (15-20% survival at 5 years) and the second most common cause of alcohol-related cancers worldwide. Like other complex diseases, ALD-HCC results from the interaction between environmental determinants and genetic variations but knowledge of gene-environment interactions is currently lacking in this area. The GENIAL project will address these needs through a comprehensive evaluation of gene-environment interactions concerning ALD-HCC.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
PREVENTION
Masking
NONE
Enrollment
1,000
the impact of risk factors and their interaction on the incidence of disease through a score that predicts HCC and select patients for whom screening is convenient.
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico - Istituto di Ricovero e Cura a Carattere Scientifico di natura pubblica
Milan, Milano, Italy
RECRUITINGImpact of genetic risk factors
The research aims to conduct the Measurement of the Frequency/Incidence (expressed as the percentage of the study population) of new genetic variants, identified using DNA Sequencing and subsequent bioinformatics analysis, that are associated with HCC in patients with ALD and related NAFLD. This measurement will be followed by the assessment of the CORRELATION between the newly identified genetic variants and the Prevalence (expressed as the percentage of the general population) of the clinical phenotype ALD-HCC.
Time frame: up to 60 months
Impact of genetic risk factor
The primary goal is the assessment of the Predictive Capacity (measured via Area Under the Receiver Operating Characteristic Curve (AUC) and P-value) of a newly created Polygenic-Clinical Risk Score (PRS-C) for the development of HCC. Subsequently, the Predictive Accuracy (measured in percentage of correct predictions, Sensitivity, and Specificity) of an Artificial Intelligence (AI) Algorithm will be evaluated. This Algorithm will be trained by integrating the aforementioned PRS (derived from the combination of genetic and non-genetic information) and other clinical and demographic variables, aiming to obtain predictive information on the individual risk of developing the disease.
Time frame: up to 60 months
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