Marta Cuervo Zapatel-rekin lankidetzan egindako argitalpenak (53)

2022

  1. A weight-loss model based on baseline microbiota and genetic scores for selection of dietary treatments in overweight and obese population

    Clinical Nutrition, Vol. 41, Núm. 8, pp. 1712-1723

  2. Differentially methylated regions (DMRs) in PON3 gene between responders and non-responders to a weight loss dietary intervention: a new tool for precision management of obesity

    Epigenetics, Vol. 17, Núm. 1, pp. 81-92

2021

  1. Diet- and sex-related changes of gut microbiota composition and functional profiles after 4 months of weight loss intervention

    European Journal of Nutrition, Vol. 60, Núm. 6, pp. 3279-3301

2020

  1. Interplay of an obesity-based genetic risk score with dietary and endocrine factors on insulin resistance

    Nutrients, Vol. 12, Núm. 1

  2. Modeling of an integrative prototype based on genetic, phenotypic, and environmental information for personalized prescription of energy-restricted diets in overweight/obese subjects

    American Journal of Clinical Nutrition, Vol. 111, Núm. 2, pp. 459-470

2019

  1. Associations between olfactory pathway gene methylation marks, obesity features and dietary intakes

    Genes and Nutrition, Vol. 14, Núm. 1

  2. Changes in anxiety and depression traits induced by energy restriction: Predictive value of the baseline status

    Nutrients, Vol. 11, Núm. 6

  3. Definition of nutritionally qualitative categorizing (Proto)nutritypes and a pilot quantitative nutrimeter for mirroring nutritional well-being based on a quality of life health related questionnaire

    Nutricion Hospitalaria, Vol. 36, Núm. 4

  4. Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition

    Therapeutic Advances in Endocrinology and Metabolism, Vol. 10

  5. Lifestyle and clinical factors affecting the quality of life related to health

    Journal of Negative and No Positive Results: JONNPR, Vol. 4, Núm. 2, pp. 159-171

  6. Models integrating genetic and lifestyle interactions on two adiposity phenotypes for personalized prescription of energy-restricted diets with different macronutrient distribution

    Frontiers in Genetics, Vol. 10, Núm. JUL

2018

  1. Association of the Gly482Ser PPARGC1A gene variant with different cholesterol outcomes in response to two energy-restricted diets in subjects with excessive weight

    Nutrition, Vol. 47, pp. 83-89

  2. DNA methylation patterns at sweet taste transducing genes are associated with BMI and carbohydrate intake in an adult population

    Appetite, Vol. 120, pp. 230-239

  3. Differential lipid metabolism outcomes associated with ADRB2 gene polymorphisms in response to two dietary interventions in overweight/obese subjects

    Nutrition, Metabolism and Cardiovascular Diseases, Vol. 28, Núm. 2, pp. 165-172

  4. Dopamine gene methylation patterns are associated with obesity markers and carbohydrate intake

    Brain and Behavior, Vol. 8, Núm. 8

  5. Interaction between an ADCY3 genetic variant and two weight-lowering diets affecting body fatness and body composition outcomes depending on macronutrient distribution: A randomized trial

    Nutrients, Vol. 10, Núm. 6

  6. Macronutrient-specific effect of the MTNR1B genotype on lipid levels in response to 2 year weight-loss diets

    Journal of Lipid Research, Vol. 59, Núm. 1, pp. 155-161

  7. Phenotype and genotype predictors of BMI variability among European adults

    Nutrition and Diabetes, Vol. 8, Núm. 1

  8. Prediction of blood lipid phenotypes using obesity-related genetic polymorphisms and lifestyle data in subjects with excessive body weight

    International Journal of Genomics, Vol. 2018

2017

  1. Effect of the interaction between diet composition and the PPM1K genetic variant on insulin resistance and β cell function markers during weight loss: Results from the Nutrient Gene Interactions in Human Obesity: Implications for dietary guidelines (NUGENOB) randomized trial

    American Journal of Clinical Nutrition, Vol. 106, Núm. 3, pp. 902-908