Independent and interactive effect of trace elements imbalance and TNF-α promoter variants as risk modifiers in pediatric asthma

IF 1 Q4 GENETICS & HEREDITY

Gene Reports Pub Date : 2025-02-26 DOI:10.1016/j.genrep.2025.102179

Shivani Singh , Anumesh K. Pathak , Manish Raj Kulshrestha , Aditi Singh , Vandana Tiwari , Shetanshu Srivastava

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引用次数: 0

Abstract

Objective

To investigate the association between trace element (TEs) levels (zinc, copper, iron, selenium, and magnesium) and TNF-α-308 G/A polymorphism and childhood asthma risk, severity, inflammatory markers (hs-CRP, TNF-α), and cell counts (neutrophils and eosinophils) in an Indian cohort.

Methods

This prospective case-control study included 433 children (230 patients with asthma and 203 controls). Serum TEs levels were quantified using inductively coupled plasma mass spectrometry, TNF-α-308 G/A polymorphism was analyzed via restriction fragment length polymorphism, and serum TNF-α levels were measured using ELISA. Logistic regression models (adjusted for age, sex, and family history) and correlation analyses were conducted.

Results

Asthmatic children had significantly higher copper levels (1650.40 vs. 1274.30 μg/L;p = 0.002) and lower zinc (464.16 vs. 632.43 μg/L) and iron (36.00 vs. 60.00 μg/L; p < 0.0001) levels. The TNF-α-308 GG genotype significantly increased the asthma risk by 3.2-fold, and the GA genotype conferred a 2.5-fold increased risk. Low zinc levels combined with variant genotypes (GA + GG) increased the risk by 1.9-fold, while low iron levels with these genotypes amplified the risk by 3.2-fold. High Cu levels with variant genotypes increased the risk by 2.5-fold. Zinc was negatively correlated with TNF-α (r = −0.23), hs-CRP (r = −0.33), and neutrophil counts (r = −0.64), whereas copper was positively correlated with TNF-α (r = 0.36; p < 0.0001) and neutrophil counts (r = 0.68). Elevated hs-CRP and TNF-α levels are associated with reduced lung function (FEV1) and increased asthma severity.

Conclusion

The interplay between TEs imbalances, TNF-α polymorphisms, and inflammatory responses significantly influences the risk and severity of childhood asthma.

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微量元素失衡和TNF-α启动子变异作为儿童哮喘危险调节剂的独立和相互作用

目的探讨印度人群中微量元素（TEs）水平（锌、铜、铁、硒和镁）和TNF-α-308 G/A多态性与儿童哮喘风险、严重程度、炎症标志物（hs-CRP、TNF-α）和细胞计数（中性粒细胞和嗜酸性粒细胞）之间的关系。方法本前瞻性病例对照研究纳入433例儿童（哮喘患者230例，对照组203例）。采用电感耦合血浆质谱法测定血清TEs水平，采用限制性内切片段长度多态性分析TNF-α-308 G/A多态性，ELISA法测定血清TNF-α水平。进行了Logistic回归模型（调整了年龄、性别和家族史）和相关性分析。结果哮喘患儿血清铜（1650.40 vs. 1274.30 μg/L, p = 0.002）、锌（464.16 vs. 632.43 μg/L）、铁（36.00 vs. 60.00 μg/L）含量显著升高；p & lt;0.0001)水平。TNF-α-308 GG基因型使哮喘风险增加3.2倍，GA基因型使哮喘风险增加2.5倍。低锌水平结合变异基因型（GA + GG）使风险增加了1.9倍，而低铁水平结合这些基因型使风险增加了3.2倍。不同基因型的高铜水平增加了2.5倍的风险。锌与TNF-α (r = - 0.23)、hs-CRP （r = - 0.33）、中性粒细胞计数呈负相关（r = - 0.64），而铜与TNF-α呈正相关(r = 0.36；p & lt;0.0001)和中性粒细胞计数（r = 0.68）。hs-CRP和TNF-α水平升高与肺功能（FEV1）降低和哮喘严重程度增加相关。结论TEs失衡、TNF-α多态性和炎症反应之间的相互作用显著影响儿童哮喘的风险和严重程度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Gene Reports Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

3.30

自引率

7.70%

发文量

246

审稿时长

49 days

期刊介绍： Gene Reports publishes papers that focus on the regulation, expression, function and evolution of genes in all biological contexts, including all prokaryotic and eukaryotic organisms, as well as viruses. Gene Reports strives to be a very diverse journal and topics in all fields will be considered for publication. Although not limited to the following, some general topics include: DNA Organization, Replication & Evolution -Focus on genomic DNA (chromosomal organization, comparative genomics, DNA replication, DNA repair, mobile DNA, mitochondrial DNA, chloroplast DNA). Expression & Function - Focus on functional RNAs (microRNAs, tRNAs, rRNAs, mRNA splicing, alternative polyadenylation) Regulation - Focus on processes that mediate gene-read out (epigenetics, chromatin, histone code, transcription, translation, protein degradation). Cell Signaling - Focus on mechanisms that control information flow into the nucleus to control gene expression (kinase and phosphatase pathways controlled by extra-cellular ligands, Wnt, Notch, TGFbeta/BMPs, FGFs, IGFs etc.) Profiling of gene expression and genetic variation - Focus on high throughput approaches (e.g., DeepSeq, ChIP-Seq, Affymetrix microarrays, proteomics) that define gene regulatory circuitry, molecular pathways and protein/protein networks. Genetics - Focus on development in model organisms (e.g., mouse, frog, fruit fly, worm), human genetic variation, population genetics, as well as agricultural and veterinary genetics. Molecular Pathology & Regenerative Medicine - Focus on the deregulation of molecular processes in human diseases and mechanisms supporting regeneration of tissues through pluripotent or multipotent stem cells.