Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study

Abstract

Obesity is the result of interactions between genes and environmental factors. Since monogenic etiology is only known in some obesity-related genes, a genetic risk score (GRS) could be useful to determine the genetic predisposition to obesity. Therefore, the aim of our study was to build a GRS able to predict genetic predisposition to overweight and obesity in European adolescents. A total of 1069 adolescents (51.3% female), aged 11–19 years participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study were genotyped. The sample was divided in non-overweight (non-OW) and overweight/obesity (OW/OB). From 611 single nucleotide polymorphisms (SNP) available, a first screening of 104 SNPs univariately associated with obesity (p < 0.20) was established selecting 21 significant SNPs (p < 0.05) in the multivariate model. Unweighted GRS (uGRS) was calculated by summing the number of risk alleles and weighted GRS (wGRS) by multiplying the risk alleles to each estimated coefficient. The area under curve (AUC) was calculated in uGRS (0.723) and wGRS (0.734) using tenfold internal cross-validation. Both uGRS and wGRS were significantly associated with body mass index (BMI) (p < .001). Both GRSs could potentially be considered as useful genetic tools to evaluate individual’s predisposition to overweight/obesity in European adolescents.

Plasma Protein Levels Analysis in Multiple Sclerosis Sardinian Families Identified C9 and CYP24A1 as Candidate Biomarkers

“…Since all 119 MS-risk SNPs could not be included in a single model, as parameters would outnumber observations, we first refined the search for MS-risk SNPs potentially associated with protein levels following the approach in [82,83]. First, each SNP was included as a covariate in a univariate LMM formulated as: (3) where all the variables were already defined in Equation (1) except for SNP ij , which denotes the number of effect alleles (minor allele), with β 1 the respective additive linear effect on protein levels.…”

Mediterranean Diet and Genetic Determinants of Obesity and Metabolic Syndrome in European Children and Adolescents

“…However, the use of a specific PRS based on children’s BMI could even increase the percentage of variance explained. Hence, the development of PRSs and genetic risk scores (GRS) (wide-genome significant) in early age populations would help to understand the expression mechanisms of obesity predisposition during childhood [ 76 ].…”

“…Although PRSs could contribute in a greater way to explain genetic variance to be at risk of a certain disease, a combination of genome-wide associated SNPs in a GRS form could contribute, a priori, to adding as much precision in the predictive ability to assess the risk of obesity and MetS as non-genome wide significant approaches (PRS), for clinical utility [ 85 ]. In any case, both methodologies have been shown to be strong and useful genetic tools to predict obesity and MetS genetic susceptibility in children and adolescents [ 73 , 76 , 83 ].…”

“…To assess the MD adherence, a nine single component MD score was used [ 56 , 92 ], collected from 24 h recall questionnaires. Obesity-specific GRSs were developed in order to measure the genetic predisposition to adiposity and MetS [ 76 ]. The interaction effect resulted in being significantly associated to the main outcomes in both sex groups ( p < 0.05).…”

Artificial intelligence predicting the risk of obesity in children

“…В основе работ по прогнозированию риска развития ожирения у детей лежат генетические исследования [25][26][27]. Они более точны (95% и выше), но трудоемки, продолжительны и основаны на дорогостоящих лабораторных анализах (в некоторых случаях требуется до 15 исследований).…”