Nutritional patterns as machine learning predictors of liver health in a population of elderly subjects

Non-alcoholic hepatic steatosis affects 25% of adults worldwide and its prevalence increases with age. There is currently no definitive treatment for NAFLD but international guidelines recommend a lifestyle-based approach, including a healthy diet. The aim of this study was to investigate the interactions between eating habits and the risk of steatosis and/or hepatic fibrosis, using a machine learning approach, in a non-institutionalized elderly population.

We recruited 1929 subjects, mean age 74 years, from the population-based Salus in Apulia Study. Dietary habits and the risk of steatosis and hepatic fibrosis were evaluated with a validated food frequency questionnaire, the Fatty Liver Index (FLI) and the FIB-4 score, respectively. Two dietary patterns associated with the risk of steatosis and hepatic fibrosis have been identified. They are both similar to a "western" diet, defined by a greater consumption of refined foods, with a rich content of sugars and saturated fats, and alcoholic and non-alcoholic calorie drinks.

This study further supports the concept of diet as a factor that significantly influences the development of the most widespread liver diseases. However, longitudinal studies are needed to better understand the causal effect of the consumption of particular foods on fat accumulation in the liver.