Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China; Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China.
Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China.
Neuroimage Clin. 2022;33:102870. doi: 10.1016/j.nicl.2021.102870. Epub 2021 Nov 26.
To determine whether there is a correlation between obesity-related variants and regional brain volumes.
Based on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants (discovery cohort N = 19,997, validation cohort N = 9,423) in UK Biobank. The statistically significant brain regions in association analysis were classified into 6 major neural networks (dopamine (DA) motive system, central autonomic network (CAN), cognitive emotion regulation, visual object recognition network, auditory object recognition network, and sensorimotor system). We summarized the association between obesity-related variants (metabolically healthy obesity variants, metabolically unhealthy obesity variants, and unclassified obesity-related variants) and neural networks.
From association analysis, we determined that 17 obesity-related SNPs were associated with 51 regional brain volumes. Several single SNPs (e.g., rs13107325-T (SLC39A8), rs1876829-C (CRHR1), and rs1538170-T (CENPW)) were associated with multiple regional brain volumes. In addition, several single brain regions (e.g., the white matter, the grey matter in the putamen, subcallosal cortex, and insular cortex) were associated with multiple obesity-related variants. The metabolically healthy obesity variants were mainly associated with the regional brain volumes in the DA motive system, sensorimotor system and cognitive emotion regulation neural networks, while metabolically unhealthy obesity variants were mainly associated with regional brain volumes in the CAN and total tissue volumes. In addition, unclassified obesity-related variants were mainly associated with auditory object recognition network and total tissue volumes. The results of MeSH (medical subject headings) enrichment analysis showed that obesity genes associated with brain structure pointed to the functional relatedness with 5-Hydroxytryptamine receptor 4 (5-HT4), growth differentiation factor 5 (GDF5), and high mobility group protein AT-hook 2 (HMGA2 protein).
In summary, we found that obesity-related variants were associated with different brain volume measures. On the basis of the multiple SNPs, we found that metabolically healthy and unhealthy obesity-related SNPs were associated with different brain neural networks. Based on our enrichment analysis, modifications of the 5-HT4 pathway might be a promising therapeutic strategy for obesity.
确定肥胖相关变体与脑区体积之间是否存在相关性。
基于混合线性模型（MLM），我们分析了 GWAS 目录中 1498 个肥胖相关 SNP 与来自英国生物银行 29420 名参与者（发现队列 N=19997，验证队列 N=9423）的 164 个区域脑容量之间的关联。关联分析中具有统计学意义的脑区被分为 6 个主要的神经网络（多巴胺（DA）动机系统、中枢自主神经网络（CAN）、认知情绪调节、视觉物体识别网络、听觉物体识别网络和感觉运动系统）。我们总结了肥胖相关变体（代谢健康肥胖变体、代谢不健康肥胖变体和未分类肥胖相关变体）与神经网络之间的关联。
通过关联分析，我们确定了 17 个肥胖相关 SNP 与 51 个区域脑容量相关。一些单核苷酸多态性（如 rs13107325-T（SLC39A8）、rs1876829-C（CRHR1）和 rs1538170-T（CENPW））与多个区域脑容量相关。此外，一些单一大脑区域（如白质、壳核灰质、胼胝下皮层和岛叶皮层）与多个肥胖相关变体相关。代谢健康肥胖变体主要与 DA 动机系统、感觉运动系统和认知情绪调节神经网络中的区域脑容量相关，而代谢不健康肥胖变体主要与 CAN 和总组织容量相关。此外，未分类肥胖相关变体主要与听觉物体识别网络和总组织容量相关。MeSH（医学主题词）富集分析的结果表明，与脑结构相关的肥胖基因与 5-羟色胺受体 4（5-HT4）、生长分化因子 5（GDF5）和高迁移率族蛋白 AT 钩 2（HMGA2 蛋白）等功能相关。
总之，我们发现肥胖相关变体与不同的脑容量测量值相关。基于多个 SNP，我们发现代谢健康和不健康肥胖相关 SNP 与不同的脑神经网络相关。基于我们的富集分析，5-HT4 途径的修饰可能是肥胖治疗的一种有前途的策略。
