Evidence map›Paper›PMID 40329189›Full record

ArticleBMC pediatrics2025

Genetic etiology and clinical features of non-syndromic pediatric obesity in the Chinese population: a large cohort study.

Huang Hui, Yang Yu, Liang Yiwei, Yang Li, Xie Liling, Zhang Dongguang

Abstract read
In one paragraph

Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Huang HuiJiangxi Medical College, Nanchang University, Nanchang, China.
Yang YuJiangxi Provincial Key Laboratory of Child Development and Genetics, Jiangxi Provincial Children's Hospital, Nanchang, China. yangyu_202501@163.com.
Liang YiweiDepartment of Child Health, Jiangxi Provincial Children's Hospital, Nanchang, China.
Yang LiDepartment of Endocrinology, Genetics and Metabolism, Jiangxi Provincial Children's Hospital Clinical Medical Research Center of Genetic Metabolic Diseases in Children, Nanchang, China.
Xie LilingDepartment of Endocrinology, Genetics and Metabolism, Jiangxi Provincial Children's Hospital Clinical Medical Research Center of Genetic Metabolic Diseases in Children, Nanchang, China.
Zhang DongguangDepartment of Endocrinology, Genetics and Metabolism, Jiangxi Provincial Children's Hospital Clinical Medical Research Center of Genetic Metabolic Diseases in Children, Nanchang, China.

Funding

Jiangxi Province Key Laboratory of Child Development 2024SSY06191Jiangxi Provincial Clinical Medical Research Center for Pediatric Genetic Metabolic Diseases 20192BCD42004Major Program of Jiangxi Provincial Health Commission 2023ZD007Nanchang Medical College NYTD202211
6 · The paper itself

Abstract

backgroundThis study aimed to investigate the genetic etiology and clinical features of non-syndromic pediatric obesity in a large Chinese cohort, providing insights into the genetic profile and its correlation with clinical phenotypes.

methodsWe enrolled 391 children, aged 7-14 years, diagnosed with non-syndromic pediatric obesity at Jiangxi Provincial Children's Hospital from January 2020 to June 2022. Whole-exome sequencing was employed to identify potential genetic causes, focusing on 79 candidate genes associated with obesity. Multivariate logistic regression analysis was performed on the clinical data of the non-syndromic obesity gene-positive group and the gene-negative group.

resultsAmong the 391 patients, 32 (8.2%) carried 18 non-syndromic obesity genes, with UCP3 and MC4R being the most common. Seven cases (1.8%) were rated as likely pathogenic by the American College of Medical Genetics and Genomics (ACMG). Clinical phenotype and genetic correlation analysis revealed that urinary microalbumin, fT4, GGT, uric acid, serum phosphorus, paternal weight, family history, impaired glucose tolerance (IGT), non-HDL cholesterol (non-HDL-C), and metabolic syndrome (MetS) showed significant statistical differences (P < 0.05). Serum phosphorus is an independent risk factor associated with genetic predispositions to obesity in children and adolescents (P < 0.05).

conclusionOur findings highlight the genetic heterogeneity of non-syndromic pediatric obesity and identify UCP3 and MC4R as potential hotspot genes in the Chinese population. The study underscores the potential of genetic testing for early diagnosis and personalized management of pediatric obesity.

Indexed as

East Asian PeoplePediatric ObesityAdolescentChildChinaCohort StudiesExome SequencingFemaleHumansMalePhenotypeReceptor, Melanocortin, Type 4Uncoupling Protein 3MC4R protein, humanReceptor, Melanocortin, Type 4UCP3 protein, humanUncoupling Protein 3MC4RNon-syndromic obesityPediatric obesityUCP3Whole-exome sequencing

Identifiers

PMID40329189
PMCPMC12057247

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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.