Jie Wang, Xiongfeng Pan, Jia Wei, Xiongwei Li, Haixiang Zhou, Ning'an Xu, Rutong Kang, Yan Zhong, Jiayou Luo
{"title":"Association of adipocytokine pathway gene polymorphisms with NAFLD in obese children.","authors":"Jie Wang, Xiongfeng Pan, Jia Wei, Xiongwei Li, Haixiang Zhou, Ning'an Xu, Rutong Kang, Yan Zhong, Jiayou Luo","doi":"10.11817/j.issn.1672-7347.2024.230098","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Non-alcoholic fatty liver disease (NAFLD) has significant genetic susceptibility. Adipocytokines play a crucial role in NAFLD development by participating in insulin resistance and hepatic steatosis. However, the association between adipocytokine pathway genes and NAFLD remains unclear. This study aims to explore the association of gene polymorphisms in the adipocytokine pathway and their interactions with NAFLD in obese children.</p><p><strong>Methods: </strong>A case-control study was conducted, dividing obese children into NAFLD and control groups. Peripheral venous blood (2 mL) was collected from each participant for DNA extraction. A total of 14 single nucleotide polymorphisms (SNP) in the adipocytokine pathway were genotyped using multiplex PCR and high-throughput sequencing. Univariate and multivariate Logistic regression analyses were used to assess the association between SNP and NAFLD in obese children. Dominant models were used to analyze additive and multiplicative interactions via crossover analysis and Logistic regression. Generalized multifactor dimensionality reduction (GMDR) was used to detect gene-gene interactions among the 14 SNPs and their association with NAFLD in obese children.</p><p><strong>Results: </strong>A total of 1 022 children were included, with 511 in the NAFLD group and 511 in the control group. After adjusting for age, gender, and BMI, multivariate Logistic regression showed that <i>PPARG</i> rs1801282 was associated with NAFLD in the obese children in 3 genetic models: heterozygote model (CG vs CC, <i>OR</i>=0.58, 95% <i>CI</i> 0.36 to 0.95, <i>P</i>=0.029), dominant model (GG+CG vs CC, <i>OR</i>=0.62, 95% <i>CI</i> 0.38 to 1.00, <i>P</i>=0.049), and overdominant model (CC+GG vs CG, <i>OR</i>=1.72, 95% <i>CI</i> 1.06 to 2.80, <i>P</i>=0.028). <i>PRKAG2</i> rs12703159 was associated with NAFLD in 4 genetic models: heterozygous model (CT vs CC, <i>OR</i>=1.51, 95% <i>CI</i> 1.10 to 2.07, <i>P</i>=0.011), dominant model (CT+TT vs CC, <i>OR</i>=1.50, 95% <i>CI</i> 1.10 to 2.03, <i>P</i>=0.010), overdominant model (CC+TT vs CT, <i>OR</i>=0.67, 95% <i>CI</i> 0.49 to 0.92, <i>P</i>=0.012), and additive model (CC vs CT vs TT, <i>OR</i>=1.40, 95% <i>CI</i> 1.07 to 1.83, <i>P</i>=0.015). No significant multiplicative or additive interaction between <i>PPARG</i> rs1801282 and <i>PRKAG2</i> rs12703159 was found in association with NAFLD. GMDR analysis, adjusted for age, gender, and BMI, revealed no statistically significant interactions among the 14 SNPs (all <i>P</i>>0.05).</p><p><strong>Conclusions: </strong>Mutations in <i>PPARG</i> rs1801282 and <i>PRKAG2</i> rs12703159 are associated with NAFLD in obese children. However, no gene-gene interactions among the SNP are found to be associated with NAFLD in obese children.</p>","PeriodicalId":39801,"journal":{"name":"中南大学学报(医学版)","volume":"49 5","pages":"775-783"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341219/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中南大学学报(医学版)","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.11817/j.issn.1672-7347.2024.230098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Non-alcoholic fatty liver disease (NAFLD) has significant genetic susceptibility. Adipocytokines play a crucial role in NAFLD development by participating in insulin resistance and hepatic steatosis. However, the association between adipocytokine pathway genes and NAFLD remains unclear. This study aims to explore the association of gene polymorphisms in the adipocytokine pathway and their interactions with NAFLD in obese children.
Methods: A case-control study was conducted, dividing obese children into NAFLD and control groups. Peripheral venous blood (2 mL) was collected from each participant for DNA extraction. A total of 14 single nucleotide polymorphisms (SNP) in the adipocytokine pathway were genotyped using multiplex PCR and high-throughput sequencing. Univariate and multivariate Logistic regression analyses were used to assess the association between SNP and NAFLD in obese children. Dominant models were used to analyze additive and multiplicative interactions via crossover analysis and Logistic regression. Generalized multifactor dimensionality reduction (GMDR) was used to detect gene-gene interactions among the 14 SNPs and their association with NAFLD in obese children.
Results: A total of 1 022 children were included, with 511 in the NAFLD group and 511 in the control group. After adjusting for age, gender, and BMI, multivariate Logistic regression showed that PPARG rs1801282 was associated with NAFLD in the obese children in 3 genetic models: heterozygote model (CG vs CC, OR=0.58, 95% CI 0.36 to 0.95, P=0.029), dominant model (GG+CG vs CC, OR=0.62, 95% CI 0.38 to 1.00, P=0.049), and overdominant model (CC+GG vs CG, OR=1.72, 95% CI 1.06 to 2.80, P=0.028). PRKAG2 rs12703159 was associated with NAFLD in 4 genetic models: heterozygous model (CT vs CC, OR=1.51, 95% CI 1.10 to 2.07, P=0.011), dominant model (CT+TT vs CC, OR=1.50, 95% CI 1.10 to 2.03, P=0.010), overdominant model (CC+TT vs CT, OR=0.67, 95% CI 0.49 to 0.92, P=0.012), and additive model (CC vs CT vs TT, OR=1.40, 95% CI 1.07 to 1.83, P=0.015). No significant multiplicative or additive interaction between PPARG rs1801282 and PRKAG2 rs12703159 was found in association with NAFLD. GMDR analysis, adjusted for age, gender, and BMI, revealed no statistically significant interactions among the 14 SNPs (all P>0.05).
Conclusions: Mutations in PPARG rs1801282 and PRKAG2 rs12703159 are associated with NAFLD in obese children. However, no gene-gene interactions among the SNP are found to be associated with NAFLD in obese children.
目标:非酒精性脂肪肝(NAFLD)具有显著的遗传易感性。脂肪细胞因子通过参与胰岛素抵抗和肝脏脂肪变性,在非酒精性脂肪肝的发病过程中发挥着至关重要的作用。然而,脂肪细胞因子通路基因与非酒精性脂肪肝之间的关联仍不清楚。本研究旨在探讨肥胖儿童脂肪细胞因子通路基因多态性及其与非酒精性脂肪肝的相互作用:方法:将肥胖儿童分为非酒精性脂肪肝组和对照组,进行病例对照研究。每位受试者采集外周静脉血(2 mL)进行 DNA 提取。利用多重 PCR 和高通量测序技术对脂肪细胞因子通路中的 14 个单核苷酸多态性(SNP)进行了基因分型。采用单变量和多变量 Logistic 回归分析评估 SNP 与肥胖儿童非酒精性脂肪肝之间的关系。通过交叉分析和 Logistic 回归,使用显性模型分析加性和乘性相互作用。利用广义多因素降维(GMDR)检测14个SNP之间的基因-基因相互作用及其与肥胖儿童非酒精性脂肪肝的关系:共纳入了 1 022 名儿童,其中非酒精性脂肪肝组和对照组各占 511 人。在调整年龄、性别和体重指数后,多变量 Logistic 回归显示 PPARG rs1801282 在 3 个遗传模型中与肥胖儿童的非酒精性脂肪肝相关:杂合子模型(CG vs CC,OR=0.58,95% CI 0.36~0.95,P=0.029)、显性模型(GG+CG vs CC,OR=0.62,95% CI 0.38~1.00,P=0.049)和超显性模型(CC+GG vs CG,OR=1.72,95% CI 1.06~2.80,P=0.028)。PRKAG2 rs12703159 在 4 个遗传模型中与非酒精性脂肪肝相关:杂合子模型(CT vs CC,OR=1.51,95% CI 1.10 至 2.07,P=0.011)、显性模型(CT+TT vs CC,OR=1.50,95% CI 1.10至2.03,P=0.010)、过优势模型(CC+TT vs CT,OR=0.67,95% CI 0.49至0.92,P=0.012)和加法模型(CC vs CT vs TT,OR=1.40,95% CI 1.07至1.83,P=0.015)。PPARG rs1801282 和 PRKAG2 rs12703159 与非酒精性脂肪肝之间没有发现明显的乘法或加法交互作用。根据年龄、性别和体重指数调整后的 GMDR 分析显示,14 个 SNP 之间没有统计学意义上的交互作用(所有 P>0.05):结论:PPARG rs1801282 和 PRKAG2 rs12703159 基因突变与肥胖儿童的非酒精性脂肪肝有关。结论:PPARG rs1801282 和 PRKAG2 rs12703159 基因突变与肥胖儿童的非酒精性脂肪肝有关,但未发现 SNP 与肥胖儿童的非酒精性脂肪肝有基因间的相互作用。
期刊介绍:
Journal of Central South University (Medical Sciences), founded in 1958, is a comprehensive academic journal of medicine and health sponsored by the Ministry of Education and Central South University. The journal has been included in many important databases and authoritative abstract journals at home and abroad, such as the American Medline, Pubmed and its Index Medicus (IM), the Netherlands Medical Abstracts (EM), the American Chemical Abstracts (CA), the WHO Western Pacific Region Medical Index (WPRIM), and the Chinese Science Citation Database (Core Database) (CSCD); it is a statistical source journal of Chinese scientific and technological papers, a Chinese core journal, and a "double-effect" journal of the Chinese Journal Matrix; it is the "2nd, 3rd, and 4th China University Excellent Science and Technology Journal", "2008 China Excellent Science and Technology Journal", "RCCSE China Authoritative Academic Journal (A+)" and Hunan Province's "Top Ten Science and Technology Journals". The purpose of the journal is to reflect the new achievements, new technologies, and new experiences in medical research, medical treatment, and teaching, report new medical trends at home and abroad, promote academic exchanges, improve academic standards, and promote scientific and technological progress.