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匈牙利人群冠心病遗传易感性的比较:冠心病风险预测模型。

Comparison of Genetic Susceptibility to Coronary Heart Disease in the Hungarian Populations: Risk Prediction Models for Coronary Heart Disease.

机构信息

Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.

Doctoral School of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary.

出版信息

Genes (Basel). 2023 Apr 30;14(5):1033. doi: 10.3390/genes14051033.

Abstract

: It was evaluated whether the integration of genetic risk scores (GRS-unweighted, wGRS-weighted) into conventional risk factor (CRF) models for coronary heart disease or acute myocardial infarction (CHD/AMI) could improve the predictive ability of the models. : Subjects and data collected in a previous survey were used to perform regression and ROC curve analyses as well as to examine the role of genetic components. Thirty SNPs were selected, and genotype and phenotype data were available for 558 participants (general: N = 279 and Roma: N = 279). : The mean GRS (27.27 ± 3.43 vs. 26.68 ± 3.51, = 0.046) and wGRS (3.52 ± 0.68 vs. 3.33 ± 0.62, = 0.001) were significantly higher in the general population. The addition of the wGRS to the CRF model yielded the strongest improvement in discrimination among Roma (from 0.8616 to 0.8674), while the addition of GRS to the CRF model yielded the strongest improvement in discrimination in the general population (from 0.8149 to 0.8160). In addition to that, the Roma individuals were likely to develop CHD/AMI at a younger age than subjects in the general population. : The combination of the CRFs and genetic components improved the model's performance and predicted AMI/CHD better than CRFs alone.

摘要

研究评估了将遗传风险评分(未加权 GRS、加权 GRS)纳入冠心病或急性心肌梗死(CHD/AMI)的传统风险因素(CRF)模型中,是否能够提高模型的预测能力。研究使用了之前调查中收集的受试者和数据,进行回归和 ROC 曲线分析,并检验遗传因素的作用。选择了 30 个 SNP,共有 558 名参与者(一般人群:N=279 人,罗姆人:N=279 人)提供了基因型和表型数据。一般人群的 GRS(27.27±3.43 与 26.68±3.51,=0.046)和 wGRS(3.52±0.68 与 3.33±0.62,=0.001)平均值显著更高。在罗姆人群中,将 wGRS 加入 CRF 模型可显著提高区分度(从 0.8616 提高至 0.8674),而在一般人群中,将 GRS 加入 CRF 模型可显著提高区分度(从 0.8149 提高至 0.8160)。此外,与一般人群相比,罗姆人群发生 CHD/AMI 的年龄更小。将 CRF 和遗传因素相结合,可提高模型性能,比单独使用 CRF 能更好地预测 AMI/CHD。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e6d7/10218435/2ef0b485e1f8/genes-14-01033-g001.jpg

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