Personalized or Precision Medicine in the Dietary Approach to Obesity

The main objective of this project is to apply a precision medicine approach to try to explain the intra-individual variability of the response to different weight loss approaches: a balanced hypocaloric diet in macronutrients (MedDiet), a very low carbohydrate diet (KetoDiet) and an intermittent fasting (IF) approach, and try to establish in a personalized manner with the individual variability in genetics, metabolites, intestinal microbiome, and environmental factors the best dietary strategy for weight loss. As secondary objectives the investigators pretend to O1: To analyze whether individual variability in genetics, epigenetics, intestinal microbiome, and environmental factors determine the changes in insulin resistance, blood pressure, lipid levels and NASH markers after three different dietary interventions. O2: To analyze whether individual variability in genetics, epigenetics, intestinal microbiome, and environmental factors determine the changes in the body composition and the different ratio of free-fat/ fat mass loss after three different dietary interventions. O3: To determine the most effective intervention to increase the loss of fat mass, preserve the free-fat mass and trigger a better metabolic profile. O4: To follow-up changes in gut microbiota and DNA methylation after each of the cross-over dietary interventions. O5: To evaluate the transcriptional response of adipose tissue and elucidate its predictive value for the body-composition changes in patients subjected to the different dietary interventions. O6: To evaluate the influence of D-ß-hydroxybutyrate as well as other short-chain acyl-CoA precursor metabolites in human adipocytes lipolysis by in vitro experimentation and elucidate the influence of metabolite-sensitive histone modifications in the shaping of adipose transcriptional program and lipolysis sensitivity. O7: To develop a machine learning algorithm based on genetics, epigenetics, intestinal microbiome, and environmental factors for the prediction of the best dietary approach for weight loss in a personalized manner. To try to respond to these objectives, the investigators will apply two models: a randomized cross-over study testing three different dietary weight-loss interventions: MedDiet, KetoDiet, and IF with wash-out periods before each intervention.

The main objective of this project is to apply a precision medicine approach to try to explain the intra-individual variability of the response to different weight loss approaches: a balanced hypocaloric diet in macronutrients (MedDiet), a very low carbohydrate diet (KetoDiet) and an intermittent fasting (IF) approach, and try to establish in a personalized manner with the individual variability in genetics, metabolites, intestinal microbiome, and environmental factors the best dietary strategy for weight loss. As secondary objectives the investigators pretend to O1: To analyze whether individual variability in genetics, epigenetics, intestinal microbiome, and environmental factors determine the changes in insulin resistance, blood pressure, lipid levels and NASH markers after three different dietary interventions. O2: To analyze whether individual variability in genetics, epigenetics, intestinal microbiome, and environmental factors determine the changes in the body composition and the different ratio of free-fat/ fat mass loss after three different dietary interventions. O3: To determine the most effective intervention to increase the loss of fat mass, preserve the free-fat mass and trigger a better metabolic profile. O4: To follow-up changes in gut microbiota and DNA methylation after each of the cross-over dietary interventions. O5: To evaluate the transcriptional response of adipose tissue and elucidate its predictive value for the body-composition changes in patients subjected to the different dietary interventions. O6: To evaluate the influence of D-ß-hydroxybutyrate as well as other short-chain acyl-CoA precursor metabolites in human adipocytes lipolysis by in vitro experimentation and elucidate the influence of metabolite-sensitive histone modifications in the shaping of adipose transcriptional program and lipolysis sensitivity. O7: To develop a machine learning algorithm based on genetics, epigenetics, intestinal microbiome, and environmental factors for the prediction of the best dietary approach for weight loss in a personalized manner. To try to respond to these objectives, the investigators will apply two models: a randomized cross-over study testing three different dietary weight-loss interventions: MedDiet, KetoDiet, and IF with wash-out periods before each intervention in patients with obesity; and a second cellular approach with adipose tissue from the patients as well as with commercial cells.