Obesity genetics and cardiometabolic health: Potential for risk prediction

Abstract

The increasing burden of obesity worldwide and its effect on cardiovascular disease (CVD) risk is an opportunity for evaluation of preventive approaches. Both obesity and CVD have a genetic background and polymorphisms within genes which enhance expression of variant proteins that influence CVD in obesity. Genome‐based prediction may therefore be a feasible strategy, but the identification of genetically driven risk factors for CVD manifesting as clinically recognized phenotypes is a major challenge. Clusters of such risk factors include hyperglycaemia, hypertension, ectopic liver fat, and inflammation. All involve multiple genetic pathways having complex interactions with variable environmental influences. The factors that make significant contributions to CVD risk include altered carbohydrate homeostasis, ectopic deposition of fat in muscle and liver, and inflammation, with contributions from the gut microbiome. A futuristic model depends on harnessing the predictive power of plausible genetic variants, phenotype reversibility, and effective therapeutic choices based on genotype–phenotype interactions. Inverting disease phenotypes into ideal cardiovascular health metrics could improve genetic and epigenetic assessment, and form the basis of a future model for risk detection and early intervention.

Associations between Genotype–Diet Interactions and Weight Loss—A Systematic Review

“…2 Interaction term on weight loss in kg for genotype and fat intake in energy %. 3 Interaction term on weight loss in kg for genotype and Mediterranean diet. 4 Interaction term on weight loss in kg for genotype and fat intake according to the intake of the population (below or above the median).…”

“…No significant interactions between the amylase alpha 1A-amylase alpha 2A/B (AMY1-AMY2) SNP rs11185098 [40]; CYR2R1 SNP rs10741657 [46]; 7-dehydrocholesterol reductase (DHCR7) SNP rs12785878 [46]; GC vitamin D-binding protein (GC) SNP rs2282679 [46]; gastric inhibitory polypeptide receptor (GIPR) SNP rs2287019 [43]; lactase (LCT) SNP rs4988235 [39]; neuropeptide Y (NPY) SNP rs16147 [51]; PPM1K SNP rs1440581 [49]; and TCF7L2 SNPS rs7903146, rs12255372 (LD r 2 > 0.8) [42] and the protein intake on weight loss was found (Table 4). Whites n. s. 3,5 IRS1 rs2943641…”

“…According to the World Health Organization (WHO), over one-third of the world's population is overweight and 13% are described as obese [2]. Obesity adversely affects almost all physiological functions of the body and increases the risk of developing multiple diseases such as type 2 diabetes, cardiovascular diseases, and certain cancers [3,4]. Overweight and obesity are mainly caused by a long-term positive energy balance as a result of the modern lifestyle which is characterized by low physical activity and high consumption of energy-dense food [5].…”

“…2 years p = 0.84 2,5 ; p = 0.59 2,6 591 Whites 6 months p < 0.05 3,5 ; p < 0.05 3,6 Additive model: carriers of the TT (minor allele T) genotype greater weight loss with the highest-carbohydrate diet than carriers of the TC or CC genotype.…”

Genetic Polymorphisms, Adherence to Mediterranean Diet and Microbiota-Associated Urolithin Metabotypes: A Complex Cocktail to Predict the Obesity in Childhood-Adolescence

“…Candidate genes and polymorphisms were identified after browsing the Single Nucleotide Polymorphism Database (dbSNP) and examining the published literature regarding each known gene and variant (favourable and unfavourable) associations 29,30,31 . On the same day of the anthropometric evaluation, saliva samples were obtained by gently rubbing the inside part of the cheek with a sterile swab, free of human RNA and DNA (Deltalab, Barcelona, Spain).…”