This study aimed at substantiating the associations of the apolipoproein M gene (APOM) with type 2 diabetes (T2D) as well as with metabolic traits in Hong Kong Chinese. In addition, APOM gene function was further characterized to elucidate its activity in cholesterol metabolism. Seventeen APOM SNPs documented in the NCBI database were genotyped. Five SNPs were confirmed in our study cohort of 1234 T2D and 606 control participants. Three of the five SNPs rs707921(C+1871A), rs707922(G+1837T) and rs805264(G+203A) were in linkage disequilibrium (LD). We chose rs707922 to tag this LD region for down stream association analyses and characterized the function of this SNP at molecular level. No association between APOM and T2D susceptibility was detected in our Hong Kong Chinese cohort. Interestingly, the C allele of rs805297 was significantly associated with T2D duration of longer than 10 years (OR = 1.245, p = 0.015). The rs707922 TT genotype was significantly associated with elevated plasma total- and LDL- cholesterol levels (p = 0.006 and p = 0.009, respectively) in T2D patients. Molecular analyses of rs707922 lead to the discoveries of a novel transcript APOM5 as well as the cryptic nature of exon 5 of the gene. Ectopic expression of APOM5 transcript confirmed rs707922 allele-dependent activity of the transcript in modifying cholesterol homeostasis in vitro. In conclusion, the results here did not support APOM as a T2D susceptibility gene in Hong Kong Chinese. However, in T2D patients, a subset of APOM SNPs was associated with disease duration and metabolic traits. Further molecular analysis proved the functional activity of rs707922 in APOM expression and in regulation of cellular cholesterol content.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044746 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017324 | PLOS |
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College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.
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