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Obesity genetics and cardiometabolic health: Potential for risk prediction
Author(s) -
Sanghera Dharambir K.,
Bejar Cynthia,
Sharma Sonali,
Gupta Rajeev,
Blackett Piers R.
Publication year - 2019
Publication title -
diabetes, obesity and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.445
H-Index - 128
eISSN - 1463-1326
pISSN - 1462-8902
DOI - 10.1111/dom.13641
Subject(s) - disease , obesity , phenotype , epigenetics , bioinformatics , biology , genetics , genome wide association study , quantitative trait locus , medicine , genotype , gene , endocrinology , single nucleotide polymorphism
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.