Oxidative gaseous air pollutant exposure interacts with PNPLA3-I148M genotype to influence liver fat fraction and multi-omics profiles in young adults

IF 7.3 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Environmental Pollution Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI:10.1016/j.envpol.2025.125692
William B. Patterson , Nathan D. Young , Elizabeth A. Holzhausen , Frederick Lurmann , Donghai Liang , Douglas I. Walker , Dean P. Jones , Jiawen Liao , Zhanghua Chen , David V. Conti , Lida Chatzi , Jesse A. Goodrich , Tanya L. Alderete
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Abstract

PNPLA3-I148M genotype is the strongest predictive single-nucleotide polymorphism for liver fat. We examine whether PNPLA3-I148M modifies associations between oxidative gaseous air pollutant exposure (Oxwt) with i) liver fat and ii) multi-omics profiles of miRNAs and metabolites linked to liver fat. Participants were 69 young adults (17–22 years) from the Meta-AIR cohort. Prior-month residential Oxwt exposure (redox-weighted oxidative capacity of nitrogen dioxide and ozone) was spatially interpolated from monitoring stations via inverse-distance-squared weighting. Liver fat fraction was assessed by MRI. Serum miRNAs and metabolites were assayed via NanoString nCounter and LC-HRMS, respectively. Multi-omics factor analysis (MOFA) was used to identify latent factors with shared variance across omics layers. Multivariable linear regression models adjusted for age, sex, body mass index, and genotype with liver fat or MOFA factors as an outcome and examined PNPLA3 (rs738409; CC/CG vs. GG) as a multiplicative interaction term. Overall, a standard deviation difference in Oxwt exposure was associated with 8.9% relative increase in liver fat (p = 0.04) and this relationship differed by PNPLA3 genotype (p-value for interaction term: pintx<0.001), whereby relative increases in liver fat for GG and CC/CG participants were 71.8% and 2.4%, respectively. There was no main effect of Oxwt on MOFA Factor 1 expression (p = 0.85), but there was an interaction with PNPLA3 genotype (pintx = 0.01), whereby marginal slopes were 0.211 and −0.017 for GG and CC/CG participants, respectively. MOFA Factor 1 in turn was associated with liver fat (p = 0.006). MOFA Factor 1 miRNAs targeted genes in Fatty Acid Biosynthesis and Metabolism and Lysine Degradation pathways. MOFA Factor 9 was also associated with liver fat and was comprised of branched-chain keto acid and amino acid metabolites. The effects of Oxwt exposure on liver fat is exacerbated in young adults with two PNPLA3 risk alleles, potentially through differential effects on miRNA and/or metabolite profiles.

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氧化性气态空气污染物暴露与PNPLA3-I148M基因型相互作用影响年轻人肝脏脂肪分数和多组学特征
PNPLA3-I148M基因型是预测肝脏脂肪的最强单核苷酸多态性。我们研究了PNPLA3-I148M是否改变了氧化性气态空气污染物暴露(Oxwt)与i)肝脏脂肪和ii)与肝脏脂肪相关的mirna和代谢物的多组学特征之间的关联。参与者是来自Meta-AIR队列的69名年轻人(17-22岁)。通过反距离平方加权,在空间上插值监测站的前一个月居民Oxwt暴露(二氧化氮和臭氧的氧化还原加权氧化能力)。用MRI评估肝脏脂肪分数。分别通过NanoString nCounter和LC-HRMS检测血清mirna和代谢物。采用多组学因子分析(MOFA)识别具有组学层间共同变异的潜在因子。多变量线性回归模型校正了年龄、性别、体重指数和以肝脂肪或MOFA因素为结果的基因型,并检查了PNPLA3 (rs738409;CC/CG vs. GG)作为乘法交互项。总体而言,Oxwt暴露的标准差差异与肝脏脂肪相对增加8.9%相关(p=0.04),这种关系因PNPLA3基因型而异(相互作用项的p值:pinx<;0.001),因此GG和CC/CG参与者的肝脏脂肪相对增加分别为71.8%和2.4%。Oxwt对MOFA因子1的表达没有主要影响(p=0.85),但与PNPLA3基因型存在交互作用(pinx =0.01), GG和CC/CG参与者的边际斜率分别为0.211和-0.017。MOFA因子1与肝脏脂肪相关(p=0.006)。脂肪酸生物合成代谢和赖氨酸降解途径中的MOFA因子1 miRNAs靶向基因。MOFA因子9也与肝脏脂肪有关,由支链酮酸和氨基酸代谢物组成。Oxwt暴露对肝脏脂肪的影响在具有两个PNPLA3风险等位基因的年轻人中加剧,可能是通过对miRNA和/或代谢物谱的不同影响。
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来源期刊
Environmental Pollution
Environmental Pollution 环境科学-环境科学
CiteScore
16.00
自引率
6.70%
发文量
2082
审稿时长
2.9 months
期刊介绍: Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.
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