1Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.
2Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.
3Department of Clinical Chemistry, Clínica Universidad de Navarra (CUN), Pamplona, Spain.
4Department of Radiology, Clínica Universidad de Navarra (CUN), Pamplona, Spain.
5Liver Unit, Clinica Universidad de Navarra (CUN), Pamplona, Spain.
6Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain.
7Biomedical Research Centre Network in Physiopathology of Obesity and Nutrition (CIBERobn), Carlos III Health Institute, Madrid, Spain.
8Department of Medical Sciences, University of Turin, Turin, Italy.
9Balearic Islands Institute for Health Research (IDISBA), University of Balearic Islands, Palma, Spain.
10Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain - email@example.com.
Background: Non-alcoholic fatty liver disease (NAFLD) development is linked to insulin resistance and influenced by environmental factors, but it also underlined a genetic predisposition. The aim of this research was to build a predictive model based on genetic and hepatic health information, deeming insulin resistance markers in order to personalize dietary treatment in overweight/obese subjects with NAFLD.
Methods: A 6-month nutritional intervention was conducted in 86 overweight/obese volunteers with NAFLD randomly assigned to 2 energy-restricted diets: the American Heart Association (AHA) diet and the Fatty Liver in Obesity (FLiO) diet. Individuals were genotyped using a predesigned panel of 95 genetic variants. A Genetic Risk Score (GRS) for each diet was computed using statistically relevant SNPs for the change on Fatty Liver Index (FLI) after 6-months of nutritional intervention. Body composition, liver injury and insulin resistance markers, as well as physical activity and dietary intake were also assessed.
Results: Under energy restriction, both the AHA and FLiO diets induced similar significant improvements on body composition, insulin resistance markers, hepatic health and dietary and lifestyle outcomes. The calculated score included in the linear mixed regression model was able to predict the change of FLI adjusted by diet, age and sex. This model allowed to personalize the most suitable diet for 72% of the volunteers. Similar models were also able to predict the changes on Triglycerides and Glucose (TyG) Index and retinol-binding protein 4 (RBP4) levels depending on diet.
Conclusions: Models integrating genetic screening and insulin resistance markers can be useful for the personalization of NAFLD weight loss treatments.