Association between 10 Single Nucleotide Polymorphisms (SNPs) Genetic Risk Score and Obesity in Mexican, Spanish, and Argentinian schoolchildren

ALAMINOS TORRES, A; NAVAZO B; CALDERON G; LOPEZ EJEDA M; CESANI MF; MARRODÁN MD

Alicante

Congreso; XVII World Congress of Kinantropometry. ISAK-UA; 2022

ISAK

Introduction: Obesity has been associated with an increased risk of cardiovascular disease, diabetes, or cancer. Common obesity is complex, and it is related to both environmental and genetic factors. In this study we aim to analyze nutritional status assessed from ten SNPs in Mexican, Spanish, and Argentinean children population. Method: A total of 921 schoolchildren from Mexico, Spain and Argentina were analyzed. The mean age was 10.17 ± 2.33 years. Weight, height and four subcutaneous adipose folds (triceps, biceps, subscapular and suprailiac) were measured. From these variables, body mass index(BMI), waist-to-height ratio(WHtR) and %body fat(%BF) were calculated and classified according to Cole et al.(2000)(1), Hsieh and Muto(2005)(2) and Lohman(1987)(3), respectively. The genetic risk score (GRS) was calculated using ten SNPs (rs6548238, rs7566605, rs10938397, rs1801260, rs944990, rs7138803, rs12429545, rs1558902, rs17817449, rs9939609). To calculate the Genetic Risk Score, each SNP was categorized according to the following score: the heterozygotes for the risk allele of each polymorphism add two points, heterozygotes one point and homozygotes without risk allele zero points. The total genetic risk is obtained from the sum of the score of all SNPs. The averages of each anthropometric variable werecompared according to the GRS. Results: Significant differences (p≤0.05) were found in the GRS between countries (Mexico:4.50 ±2.47; Spain: 6.53±2.74; Argentina: 4.96±2.47). 32.3% presented abdominal adiposity, 30.2% had high to very high %BF and 21,9% overweight and 9,1% obesity according to BMI. The means of the anthropometric parameters were compared according to GRS quartiles (firstly specific cut-off points were established for each population). Comparing Q1-Q2: (BMI and WHtR: NS); (%BF: p=0.012); Q1-Q3: (BMI: p= 0.011); (WHtR: NS); (%BF: p≤ 0.001); Q1-Q4: (BMI: p = 0.003); (WHtR: p=0.056); (%BF: p≤ 0.001). Conclusions: The average polygenic risk wassignificantly different according to the population origin. A higher GRS is associated withan increase in excess weight and adiposity assessed by the anthropometric indicators used. The GRS elaborated from these ten SNPs could be useful in the prediction of obesity measured by anthropometry, in particular the body composition.