Evidence map›Paper›PMID 40044886›Full record

ArticlePediatric research2025

Integrative multi-omics analysis reveals molecular signatures of central obesity in children.

Chengzhi Zhao, Xizhou An, Leyuan Xiao, Jingyu Chen, Daochao Huang, Lijing Chen, Shenying Fang, Xiaohua Liang

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Article in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Chengzhi Zhao *Department of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Xizhou An *Department of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Leyuan XiaoDepartment of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Jingyu ChenDepartment of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Daochao HuangDepartment of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Lijing ChenDepartment of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Shenying FangSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China. Fangsy@sustech.edu.cn.ORCID http://orcid.org/0000-0002-0604-793X
Xiaohua LiangDepartment of Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China. xiaohualiang@hospital.cqmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCentral obesity is associated with an increased risk of hypertension and coronary heart disease. However, its molecular mechanisms have not yet been fully understood. This study aims to investigate lipids and proteins related to childhood central obesity, exploring the molecular mechanisms underlying this condition.

methodsA case-control study was conducted, including a total of 169 children (aged 7-16 years, 53.25% male, and 74 children in the central obesity group). Plasma lipidomics were measured in all 169 children, and plasma proteomics was measured in 112 of these children. The transcriptomics and lipidomics of the mice's liver were measured for normal feed and high-fat feed mice.

resultsForty-six key lipids significantly associated with central obesity were identified, predominantly triglycerides (TAG), with a minority being diacylglycerols (DAG). Additionally, six key proteins, namely PLIN1, PLAT, ADH1A, ADH4, LEP, and INHB, were discovered, which may positively influence the central obesity phenotype by modulating levels of lipids such as TAG, DAG, LDL-C, and HDL-C. These proteins exhibited increased expression in the plasma of children with central obesity. Validation using mouse liver samples showed some overlapping differential lipids between mice and children, albeit minimal overlap in differential genes. This discrepancy may stem from inherent differences between transcriptomics and proteomics, species variations, and differing sampling sites.

conclusionsPLIN1, PLAT, ADH1A, ADH4, LEP, and INHB are potential significant biomarkers for childhood central obesity and may influence the phenotype of childhood central obesity by modulating levels of lipids such as TAG, DAG, LDL-C, and HDL-C. IMPACT: PLIN1, PLAT, ADH1A, ADH4, LEP, and INHB are potentially significant biomarkers for childhood central obesity and may influence the phenotype of childhood central obesity by modulating levels of lipids such as TAG, DAG, LDL-C, and HDL-C. Research on integrating lipidomics and proteomics to elucidate the mechanisms of obesity, especially childhood central obesity, remains extremely limited. Our study filled this gap. Our findings highlight potential biomarkers and therapeutic targets that could pave the way for new interventions and treatments to prevent central obesity in children and its harm.

Indexed as

Obesity, AbdominalPediatric ObesityAdolescentAnimalsBiomarkersCase-Control StudiesChildFemaleGene Expression ProfilingHumansLipid MetabolismLipidomicsLipidsLiverMaleMiceBiomarkersLipids

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.