Background: Steatosis is now the most common liver disease in the world, present in approximately 25% of the global population. The aim of this study was to study the association between food intake and liver disease and evaluate the differences in blood parameters in age classes and steatosic condition.
Methods: The present study included 1483 participants assessed in the fourth recall of the MICOL study. Patients were subdivided by age (</>65 years) and administered a validated food frequency questionnaire (FFQ) with 28 food groups.
Results: The prevalence of steatosis was 55.92% in the adult group and 55.88% in the elderly group. Overall, the results indicated many statistically significant blood parameters and dietary habits. Analysis of food choices with a machine learning algorithm revealed that in the adult group, olive oil, grains, processed meat, and sweets were associated with steatosis, while the elderly group preferred red meat, dairy, seafood, and fruiting vegetables. Furthermore, the latter ate less as compared with the adult group.
Conclusions: Many differences were found between the two age groups, both in blood parameters and food intake. The random forest also revealed different foods predicted steatosis in the two groups. Future analysis will be useful to understand the molecular basis of these differences and how different food intake causes steatosis in people of different ages.
Keywords: food intake; machine learning; steatosis.