Oh Jeongmin, Cha Junho, Choi Sungkyoung
Department of Applied Mathematics, College of Science and Convergence Technology, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea.
Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea.
Int J Mol Sci. 2025 Mar 13;26(6):2597. doi: 10.3390/ijms26062597.
Type 2 diabetes (T2D) is a prevalent chronic disease in the Korean population, influenced by lifestyle, dietary habits, and genetics. This study aimed to identify the effects of food intake and genetic factors on T2D progression in Korean adults using a multi-state illness-death model. We analyzed three transition models: normal glucose tolerance (NGT) to prediabetes (PD), NGT to T2D, and PD to T2D. We first identified dietary patterns significantly associated with each transition, using multivariate Cox proportional hazards models. Then, we assessed the impact of single-nucleotide polymorphisms (SNPs) on each transition, incorporating these dietary patterns as covariates. Our analysis revealed significant associations between the identified dietary patterns and the risk of PD and T2D incidence among individuals with NGT. We also identified novel genetic variants associated with disease progression: two SNPs ( in Glucokinase [] and in Calcium/Calmodulin-Dependent Protein Kinase II Beta []) in the NGT to PD model, and eight SNPs in the NGT to T2D model, including variants in the Zinc Finger Protein 106 (), PTOV1 Extended AT-Hook Containing Adaptor Protein (), Proprotein Convertase Subtilisin/Kexin Type 2 (), Forkhead Box D2 (), Solute Carrier Family 38 Member 7 (), and Neuronal Growth Regulator 1 () genes. Functional annotation analysis using ANNOVAR revealed that () and () exhibited high Combined Annotation-Dependent Depletion (CADD) and Deleterious Annotation of Genetic Variants using Neural Networks (DANN) scores, suggesting potential pathogenicity and providing a functional basis for their association with T2D progression. Integrating dietary and genetic factors with a multi-state model, this comprehensive approach offers valuable insights into T2D development and highlights potential targets for prevention and personalized interventions.
2型糖尿病(T2D)是韩国人群中一种普遍存在的慢性疾病,受生活方式、饮食习惯和基因影响。本研究旨在使用多状态疾病-死亡模型,确定食物摄入和遗传因素对韩国成年人T2D进展的影响。我们分析了三种转变模型:正常糖耐量(NGT)到糖尿病前期(PD)、NGT到T2D以及PD到T2D。我们首先使用多变量Cox比例风险模型,确定与每种转变显著相关的饮食模式。然后,我们将这些饮食模式作为协变量,评估单核苷酸多态性(SNP)对每种转变的影响。我们的分析揭示了所确定的饮食模式与NGT个体中PD风险和T2D发病率之间的显著关联。我们还确定了与疾病进展相关的新遗传变异:NGT到PD模型中有两个SNP(葡萄糖激酶[]中的和钙/钙调蛋白依赖性蛋白激酶IIβ[]中的),NGT到T2D模型中有八个SNP,包括锌指蛋白106()、含扩展AT钩的PTOV1衔接蛋白()、枯草杆菌蛋白酶/凯欣样丝氨酸蛋白酶2型()、叉头框D2()、溶质载体家族38成员7()和神经生长调节因子1()基因中的变异。使用ANNOVAR进行的功能注释分析表明,()和()表现出高综合注释依赖损耗(CADD)和使用神经网络的遗传变异有害注释(DANN)分数,表明潜在的致病性,并为它们与T2D进展的关联提供了功能基础。通过多状态模型整合饮食和遗传因素,这种综合方法为T2D的发展提供了有价值的见解,并突出了预防和个性化干预的潜在靶点。