Zhang Wei, Zhang Qinghua, Luo Yan, Ma Leilei, Wang Xuejun, Zheng Qiao, Zhang Xiaotian, Wu Shentao, Li Zhan, Bi Yingfei
Department of Cardiology, First Teaching Hospital of Tianjin university of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China.
First Teaching Hospital of Tianjin university of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China.
Biomol Biomed. 2025 Apr 7. doi: 10.17305/bb.2025.11897.
Observational studies have identified a connection between obesity and microvascular complications in diabetes, yet the genetic contributions to their co-occurrence remain incompletely understood. Our research aims to explore the shared genetic architecture underlying both conditions. We employed linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA) to assess genetic correlations between obesity and diabetic microvascular complications. To validate shared genetic regions, we conducted pleiotropic analysis under the composite null hypothesis (PLACO), functional mapping and annotation (FUMA), and colocalization analysis. Additionally, we applied Multimarker Analysis of GenoMic Annotation (MAGMA), Summary-based Mendelian Randomization (MR), multi-trait colocalization, and enrichment analysis to identify potential functional genes and pathways. Finally, MR and latent causal variable (LCV) analysis were used to clarify causal and pleiotropic relationships across trait pairs. Among 21 trait pairs analyzed, 15 exhibited significant global genetic correlations, and 97 regions showed significant local correlations. PLACO identified 3659-20,489 potentially pleiotropic single nucleotide polymorphisms (SNPs) across 15 trait pairs, with 828 lead SNPs detected via FUMA. Colocalization analysis confirmed 52 shared loci. Gene-based analysis identified seven unique candidate pleiotropic genes, with ribosomal protein S26 (RPS26) emerging as the strongest shared gene. MR and LCV analyses supported a causal relationship between BMI and diabetic kidney disease (DKD). Our findings highlight a shared genetic basis linking obesity with diabetic microvascular complications. These insights offer potential pathways for understanding the mechanisms driving their comorbidity and may inform more integrated therapeutic approaches.
观察性研究已确定肥胖与糖尿病微血管并发症之间存在关联，但对它们共同出现的遗传因素仍未完全了解。我们的研究旨在探索这两种情况背后共同的遗传结构。我们采用连锁不平衡评分回归（LDSC）和[共]变异关联局部分析（LAVA）来评估肥胖与糖尿病微血管并发症之间的遗传相关性。为了验证共享的遗传区域，我们在复合零假设（PLACO）下进行了多效性分析、功能图谱和注释（FUMA）以及共定位分析。此外，我们应用基因组注释多标记分析（MAGMA）、基于汇总数据的孟德尔随机化（MR）、多性状共定位和富集分析来识别潜在的功能基因和途径。最后，使用MR和潜在因果变量（LCV）分析来阐明各性状对之间的因果和多效性关联。在分析的21对性状中，15对表现出显著的全局遗传相关性，97个区域表现出显著的局部相关性。PLACO在15对性状中鉴定出3659 - 20489个潜在的多效性单核苷酸多态性（SNP），通过FUMA检测到828个主效SNP。共定位分析确认了52个共享位点。基于基因的分析鉴定出7个独特的候选多效性基因，其中核糖体蛋白S26（RPS26）是最强的共享基因。MR和LCV分析支持体重指数（BMI）与糖尿病肾病（DKD）之间的因果关系。我们的研究结果突出了肥胖与糖尿病微血管并发症之间共享的遗传基础。这些见解为理解导致它们合并出现的机制提供了潜在途径，并可能为更综合的治疗方法提供依据。
