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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
450
A balanced hypocaloric diet in macronutrients (MedDiet)
A very low carbohydrate diet (KetoDiet).
An intermittent fasting (IF) approach
Hospital Universitario Virgen de la Victoria
Málaga, Spain
Changes in body weight after each intervention
Weight in kg
Time frame: From baseline to 1 month
Changes in body composition and in the ratio of free-fat / fat mass loss after the three different dietary interventions.
Ratio in %
Time frame: From baseline to 1 month
Changes in the degree of insulin resistance.
Measured by the HOMA-IR ratio
Time frame: From baseline to 1 month
Changes in the systolic blood pressure
Blood pressure measured in millimeters of mercury
Time frame: From baseline to 1 month
Changes in the diastolic blood pressure
Blood pressure measured in millimeters of mercury
Time frame: From baseline to 1 month
Changes in lipid profile (triglycerides)
Measured in mg/dl
Time frame: From baseline to 1 month
Changes in lipid profile (cholesterol)
Measured in mg/dl
Time frame: From baseline to 1 month
Changes in the degree of ketosis
Measured in mmol/l
Time frame: From baseline to 1 month
Changes in gut microbiota
Change from baseline in 16S rRNA amplicons after 1 month
Time frame: From baseline to 1 month
DNA methylation.
Measured by a Methylation Array of the whole genome interrogating 850000 CpGs.
Time frame: From baseline to 1 month
Changes in the punctuation in neurocognitive test - Trailmaking Test (A - B)
Trailmaking Test (A - B) allows evaluating visual search speed, working memory, motor skills, visual-spatial sequencing, sustained attention, divided attention and mental flexibility (time: reduction in seconds)
Time frame: From baseline to 1 month
Changes in the punctuation in neurocognitive test - Stroop
Stroop measures selective attention and inhibitory control. (increasing scores)
Time frame: From baseline to 1 month
Changes in the punctuation in neurocognitive test - WAISspan
Letters and numbers from the WAISspan for working memory, concentration, auditory sequencing and executive attention. (time: reduction in seconds)
Time frame: From baseline to 1 month
Changes in the punctuation in neurocognitive test - UPPS-P
* Trailmaking Test (A - B) allows evaluating visual search speed, working memory, motor skills, visual-spatial sequencing, sustained attention, divided attention and mental flexibility (time: reduction in seconds) * Stroop: Measures selective attention and inhibitory control. (increasing scores) * Letters and numbers from the WAISspan for working memory, concentration, auditory sequencing and executive attention. (time: reduction in seconds) * UPPS-P: Impulse BehaviorScale (Cyders et al. 2007; validated in Spanish by Candido et al, 2012). Self- administered scale that evaluates impulsivity. Scale of items using a 4-point likert scale (min 59 /max 136 points).
Time frame: From baseline to 1 month
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