Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT₂₃₁) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

中文翻译：

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预测2型糖尿病患者心血管疾病风险的责任阈值模型：韩国成年人的多队列研究

糖尿病心血管疾病（DCVD）的个性化风险预测是​​2型糖尿病（T2D）精准医学的核心。我们首先使用线性混合模型在从四个基于人群的韩国队列中获得的2378名T2D患者中，确定了由15种，47种和231种与DCVD相关的单核苷酸多态性（tSNPs）组成的三个标记物组。使用对表型差异具有中等影响的遗传变异，我们观察到风险分层准确性提高了，超过了传统风险因素（AUC，0.63至0.97）。截止点为0.21，由231个SNP组成的离散遗传责任阈值模型（GLT ₂₃₁）正确地将2378名T2D患者中的87.7％归为DCVD的高风险或低风险。对于同一组SNP标记，GLT和多基因风险评分（PRS）模型显示出相似的预测性能，并且我们观察到GLT和PRS模型之间的一致性，因为基于大量SNP标记的模型显示出大大改善的可预测性。在计算机基因表达分析中，提供了有关此研究中鉴定的基因的功能作用的其他信息。特别是HDAC4，CDKN2B，CELSR2和MRAS似乎是DCVD功能基因网络中的主要枢纽。基于责任阈值模型的拟议风险预测方法可通过进一步的外部验证来帮助识别东亚人群中具有较高CVD风险的T2D患者。