Literature DB >> 31472367

Interaction between genes involved in energy intake regulation and diet in obesity.

Louise Crovesy1, Eliane L Rosado2.   

Abstract

Obesity is a multifactorial, complex, and public health problem worldwide. Interaction between genes and environment as associated with diet may predispose an individual to obesity. In this sense, nutrigenetics appears to be a strategy that can improve understanding of the gene-diet interaction. The aim of this literature review was to summarize data from studies of genes involved in the regulation of energy intake (melanocortin 4 receptor [MC4R], fat mass and obesity-associated [FTO], ghrelin [GHRL], leptin [LEP], and cholecystokinin [CCK]) and diet interaction in obesity. The presence of polymorphisms in MC4R, FTO, leptin, and the respective receptor appear to be associated with higher energy and total lipid consumption. Polymorphisms in FTO, leptin, and leptin receptor are also related to increased intake of saturated fatty acids. Individuals with the MC4R, FTO, and ghrelin polymorphisms, who submitted themselves for weight loss intervention, appeared to achieve weight loss similar to individuals without polymorphisms in these genes. Additionally, protein seems to interact with these genes, which increases or decreases appetite, or to drive or lessen body weight recovery. Additionally, polymorphisms in these genes were found to be associated with inappropriate eating behaviors, such as increased consumption of sweets and snacks, consumption of large food portions, desire to eat, and eating associated with emotional issues. Preliminary data has supported the gene-diet interaction in determining weight loss and gain in individuals with polymorphisms in the genes involved in energy intake. Despite the advent of nutrigenetics in obesity, it is still too early to define the dietary management for weight loss based on the presence or absence of obesity polymorphisms.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CCK; FTO; Gene–diet interaction; Ghrelin; Leptin; MC4R; Nutrigenetic

Mesh:

Substances:

Year:  2019        PMID: 31472367     DOI: 10.1016/j.nut.2019.06.027

Source DB:  PubMed          Journal:  Nutrition        ISSN: 0899-9007            Impact factor:   4.008


  13 in total

Review 1.  Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation.

Authors:  Rodrigo San-Cristobal; Juan de Toro-Martín; Marie-Claude Vohl
Journal:  Curr Nutr Rep       Date:  2022-08-11

2.  Structural mechanism of calcium-mediated hormone recognition and Gβ interaction by the human melanocortin-1 receptor.

Authors:  Shanshan Ma; Yan Chen; Antao Dai; Wanchao Yin; Jia Guo; Dehua Yang; Fulai Zhou; Yi Jiang; Ming-Wei Wang; H Eric Xu
Journal:  Cell Res       Date:  2021-08-27       Impact factor: 46.297

Review 3.  Personalized Nutrition in the Management of Female Infertility: New Insights on Chronic Low-Grade Inflammation.

Authors:  Gemma Fabozzi; Giulia Verdone; Mariachiara Allori; Danilo Cimadomo; Carla Tatone; Liborio Stuppia; Marica Franzago; Nicolò Ubaldi; Alberto Vaiarelli; Filippo Maria Ubaldi; Laura Rienzi; Gianluca Gennarelli
Journal:  Nutrients       Date:  2022-05-03       Impact factor: 6.706

Review 4.  Personalized Nutrition for Management of Micronutrient Deficiency-Literature Review in Non-bariatric Populations and Possible Utility in Bariatric Cohort.

Authors:  Shannon Galyean; Dhanashree Sawant; Andrew C Shin
Journal:  Obes Surg       Date:  2020-06-20       Impact factor: 4.129

5.  LMX1B rs10733682 Polymorphism Interacts with Macronutrients, Dietary Patterns on the Risk of Obesity in Han Chinese Girls.

Authors:  Qi Zhu; Kun Xue; Hong Wei Guo; Yu Huan Yang
Journal:  Nutrients       Date:  2020-04-26       Impact factor: 5.717

6.  Sperm DNA Methylation at Metabolism-Related Genes in Vegan Subjects.

Authors:  Marica Franzago; Iva Sabovic; Sara Franchi; Maria De Santo; Andrea Di Nisio; Alice Luddi; Paola Piomboni; Ester Vitacolonna; Liborio Stuppia; Carlo Foresta
Journal:  Front Endocrinol (Lausanne)       Date:  2021-03-09       Impact factor: 5.555

7.  Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing.

Authors:  Maged N Kamel Boulos; Keumseok Koh
Journal:  Int J Health Geogr       Date:  2021-03-03       Impact factor: 3.918

8.  Ethnicity Differences in the Association of UCP1-3826A/G, UCP2-866G/A and Ala55Val, and UCP3-55C/T Polymorphisms with Type 2 Diabetes Mellitus Susceptibility: An Updated Meta-Analysis.

Authors:  Rong Huang; Tingting Cai; Yunting Zhou; Yuming Wang; Huiying Wang; Ziyang Shen; Wenqing Xia; Xiaomei Liu; Bo Ding; Yong Luo; Rengna Yan; Huiqin Li; Jindan Wu; Jianhua Ma
Journal:  Biomed Res Int       Date:  2021-10-19       Impact factor: 3.411

9.  MC4R Gene Polymorphisms Interact With the Urbanized Living Environment on Obesity: Results From the Yi Migrant Study.

Authors:  Ye Wang; Li Pan; Shaoping Wan; Wuli Yihuo; Fang Yang; Huijing He; Zheng Li; Zhengping Yong; Guangliang Shan
Journal:  Front Genet       Date:  2022-04-14       Impact factor: 4.772

10.  Associations of MC4R, LEP, and LEPR Polymorphisms with Obesity-Related Parameters in Childhood and Adulthood.

Authors:  Asta Raskiliene; Alina Smalinskiene; Vilma Kriaucioniene; Vaiva Lesauskaite; Janina Petkeviciene
Journal:  Genes (Basel)       Date:  2021-06-21       Impact factor: 4.096

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.