Wang Suijian, Liu Sihua, Zhang Hongqiang, Sun Lijie, Tan Huiling, Shi Yu, Pan Lanxin, Geng Mengya, Chen Minghui, Gao Beibei, Wang Kui, Zhang Haoqiang, Yue Tong, Weng Jianping, Zheng Xueying
Department of Endocrinology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
Metabolism. 2026 Apr;177:156509. doi: 10.1016/j.metabol.2026.156509. Epub 2026 Jan 20.
Obesity is a systemic disorder with heterogeneous fat distribution and complex metabolic complications. Conventional genome-wide association studies (GWAS) typically analyze individual obesity-related traits separately, limiting the identification of shared genetic architecture and key regulatory mechanisms, particularly those involving non-coding variants.
We integrated GWAS data for five obesity traits (body mass index, waist circumference, visceral fat, liver fat, and body fat percentage) using genomic structural equation modeling (GSEM) to construct a multivariate phenotype (mvObesity). Functional genomic integration combined adipose chromatin accessibility, enhancer promoter interactions, and expression quantitative trait loci (eQTL) data with transcriptome-wide and proteome-wide (TWAS and PWAS) analyses, fine-mapping, and colocalization. Trait-relevant cell types were identified using single-cell and single-cell polygenic association of GWAS (scPagwas) analyses.
Multi-omics integration in adipose tissue identified 799 independent SNPs across 548 loci, including 45 previously unreported signals. Fine-mapping and TWAS defined 150 high-confidence candidate genes enriched for neuronal signaling, synaptic organization, and lipid metabolism pathways. MAGMA-based enrichment further revealed significant overrepresentation in brain regions such as the cerebellum, hippocampus, and hypothalamus, indicating central regulatory involvement. Single-cell analyses highlighted adipocytes, preadipocytes, and smooth muscle cells as major genetically influenced types, while cross-tissue TWAS and scRNA-seq supported coordinated neuro-metabolic transcriptional regulation. Multi-omic prioritization identified key genes such as MED13L, GBE1, CADM2, PIK3R3, ERBB4, and PTK2B and demonstrated significant genome-wide and local genetic overlap between mvObesity and cardiometabolic traits.
This multivariate, multi-omics framework delineates a cross-tissue neuro-adipose regulatory axis underlying obesity, providing mechanistic insight and a genetically informed candidate framework for future precision metabolic intervention research.
肥胖是一种具有异质性脂肪分布和复杂代谢并发症的系统性疾病。传统的全基因组关联研究（GWAS）通常分别分析与肥胖相关的各个性状，这限制了对共享遗传结构和关键调控机制的识别，特别是那些涉及非编码变异的机制。
我们使用基因组结构方程模型（GSEM）整合了五个肥胖性状（体重指数、腰围、内脏脂肪、肝脏脂肪和体脂百分比）的GWAS数据，以构建一个多变量表型（mvObesity）。功能基因组整合将脂肪染色质可及性、增强子-启动子相互作用以及表达定量性状位点（eQTL）数据与全转录组和全蛋白质组（TWAS和PWAS）分析、精细定位和共定位相结合。使用单细胞和GWAS的单细胞多基因关联（scPagwas）分析来识别与性状相关的细胞类型。
脂肪组织中的多组学整合在548个位点鉴定出799个独立的单核苷酸多态性（SNP），其中包括45个先前未报道的信号。精细定位和TWAS确定了150个高可信度候选基因，这些基因在神经元信号传导、突触组织和脂质代谢途径中富集。基于MAGMA的富集进一步揭示了在小脑、海马体和下丘脑等脑区的显著过度代表性，表明中枢调节的参与。单细胞分析突出了脂肪细胞、前脂肪细胞和平滑肌细胞是主要受遗传影响的类型，而跨组织TWAS和单细胞RNA测序（scRNA-seq）支持协调的神经代谢转录调控。多组学优先排序确定了MED13L、GBE1、CADM2、PIK3R3、ERBB4和PTK2B等关键基因，并证明了mvObesity与心血管代谢性状之间在全基因组和局部遗传上的显著重叠。
这个多变量、多组学框架描绘了肥胖背后的跨组织神经-脂肪调节轴，为未来的精准代谢干预研究提供了机制性见解和基于遗传信息的候选框架。
