Validation of obesity susceptibility loci identified by genome‐wide association studies in early childhood in South Brazilian children

Zandoná, Marília Remuzzi; Sangalli, Caroline Nicola; Campagnolo, Paula Dal Bó; Vítolo, Márcia Regina; Almeida, Silvana; Mattevi, Vanessa Suñé

doi:10.1111/ijpo.12113

Cited by 24 publications

(30 citation statements)

References 30 publications

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“…To date, GWA analyses have identified more than thirty susceptibility loci robustly associated with obesity measured by body mass index (BMI). Those loci are in or near genes, including GNPDA2, SH2B1, TMEM18, MTCH2, CDKAL1, FAIM2, and MC4R [5][6][7][8]. It has been reported that the genotype-phenotype association varies in diverse groups of patients, and results need to be verified in a specific population [9].…”

Section: Introductionmentioning

confidence: 99%

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

et al. 2020

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Background: Several obesity susceptibility loci in genes, including GNPDA2, SH2B1, TMEM18, MTCH2, CDKAL1, FAIM2, and MC4R, have been identified by genome-wide association studies. The purpose of this study was to investigate whether these loci are associated with the concurrence of obesity and type 2 diabetes in Chinese Han patients. Methods: Using the SNaPshot technique, we genotyped seven single nucleotide polymorphisms (SNPs) in 439 Chinese patients living in Northeast China who presented at The Second Hospital of Jilin University. We analyzed the associations between these seven alleles and clinical characteristics. Results: Risk alleles near TMEM18 (rs6548238) were associated with increased waist circumference, waist/hip ratio, body mass index (BMI), fasting plasma glucose, hemoglobin A1c, diastolic blood pressure, triglycerides, total cholesterol, and low-density lipoprotein-cholesterol; risk alleles of CDKAL1 (rs7754840) were associated with increased waist circumference and waist/hip ratio; and FAIM2 (rs7138803) risk alleles were linked to increased BMI, diastolic blood pressure, and triglycerides (all P < 0.05). After adjusting for sex and age, loci near TMEM18 (rs6548238) and FAIM2 (rs7138803), but not SH2B1 (rs7498665), near GNPDA2 (rs10938397), MTCH2 (rs10838738) and near MC4R (rs12970134), were associated with increased risk for type 2 diabetes in obese individuals. Conclusion: We found that loci near TMEM18 (rs6548238), CDKAL1 (rs7754840), and FAIM2 (rs7138803) may be associated with obesity-related indicators, and loci near TMEM18 (rs6548238) and FAIM2 (rs7138803) may increase susceptibility of concurrent type 2 diabetes associated with obesity.

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Section: Introductionmentioning

confidence: 99%

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

et al. 2020

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“…Ideally, these studies would be designed with repeated use of validated tools throughout early childhood to assess eating behaviour or energy intake and conducted in populations with higher prevalence of overweight. As previous studies have shown that these BMI variants could be relevant also to other non‐Caucasian populations, it would be interesting to conduct similar analyses in non‐Caucasian populations.…”

Section: Discussionmentioning

confidence: 96%

Association between genetic obesity susceptibility and mother‐reported eating behaviour in children up to 5 years

et al. 2019

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Summary Background Many genetic polymorphisms identified by genome‐wide association studies for adult body mass index (BMI) have been suggested to regulate food intake. Objective The objective was to study the associations between a genetic obesity risk score, appetitive traits, and growth of children up to age 5 years, with a longitudinal design. Methods In 1142 children from the Etude des Déterminants pre et post natals de la santé de l'ENfant (EDEN) birth cohort, a combined obesity risk‐allele score (BMI genetic risk score [GRS]) was related to appetitive traits (energy intake up to 12 mo, a single item on appetite from 4 mo to 3 y, a validated appetite score at 5 y) using Poisson regressions with robust standard errors. The potential mediation of appetitive traits on the association between BMI‐GRS and growth was assessed by the Sobel test. Results Children with a high BMI‐GRS were more likely to have high energy intake at 1 year and high appetite at 2 and 5 years. High energy intake in infancy and high appetite from 1 year were related to higher subsequent BMI. High 2‐year appetite seemed to partially mediate the associations between BMI‐GRS and BMI from 2 to 5 years (all P ≤ 0.05). Conclusions Genetic susceptibility to childhood obesity seems to be partially explained by appetitive traits in infancy, followed by an early childhood rise in BMI.

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“…SEC16B proteins, which are expressed ubiquitously in various tissues, may have a role in regulating the secretion of appetite‐related peptides, such as orexin, neuropeptide Y, and proopiomelanocortin . The function of Sec16 has not been clearly proven; however, Sec16 has been hypothesized to be a possible factor involved in the regulation of food intake, especially increased energy, carbohydrate, and lipid intake, and thus, alterations in the levels or function of Sec16 could be related to the development of obesity. Furthermore, some GWASs identified that rs591120 in SEC16B is associated with the obesity phenotype, which is consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

Weighting approaches for a genetic risk score and an oxidative stress score for predicting the incidence of obesity

Park

Yoo

Jee

et al. 2019

Diabetes Metabolism Res

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Background:We aimed to predict the incidence of obesity in a Korean population using a genetic risk score (GRS) constructed with obesity-related single nucleotide polymorphisms (SNPs) along with an oxidative stress score (OSS). Methods:A total of 9460 Korean subjects and 356 974 SNPs were included. The GRS was constructed using three significant obesity-related SNP loci, and the OSS was calculated with three reliable oxidative stress biomarkers. Results:The GRS showed a more significant association with increased obesity (OR = 2.879) than did individual SNPs after adjusting for age and sex. Three oxidative stress biomarkers, including malondialdehyde, oxidized low-density lipoprotein, and 8-epiprostaglandin F 2α , showed significantly high levels in the obese group. The OSS, which was the sum of each oxidative stress biomarker score, showed a markedly high association with the incidence of obesity, with an OR of 3.213. Based on the results of the regression tests and a receiver-operating characteristic (ROC) curve analysis, we found that HOMA-IR, high-sensitivity C-reactive protein (hs-CRP), the GRS, and the OSS were the most relevant factors for the increased risk of obesity and were significantly associated with the incidence of obesity. The area under the ROC curve was improved when the GRS was added to the model (from 74.2% to 75.1%). Conclusions:We first identified that subjects with an obesity GRS and a high OSS might have a higher risk of obesity. Our findings and weighting approaches were effective in predicting the incidence of obesity; furthermore, the GRS is a relevant factor that significantly predicts the risk of obesity. K E Y W O R D Sgenetic risk score, Korean population, obesity, oxidative stress score, prediction

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Validation of obesity susceptibility loci identified by genome‐wide association studies in early childhood in South Brazilian children

Abstract: Our results indicate that genetic variants in/near these genes contribute to obesity susceptibility in childhood and highlight the age at which they begin to affect obesity-related phenotypes.

Cited by 24 publications

References 30 publications

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

Association between genetic obesity susceptibility and mother‐reported eating behaviour in children up to 5 years

Weighting approaches for a genetic risk score and an oxidative stress score for predicting the incidence of obesity

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