遗传风险评分在预测2型糖尿病中的进展

曹慧颖; 冯磊; 唐灵通; 刘艳枚; 毕千叶; 骆贝贝; 师瑞; 章艳碧

doi:10.16766/j.cnki.issn.1674-4152.003128

遗传风险评分在预测2型糖尿病中的进展

doi: 10.16766/j.cnki.issn.1674-4152.003128

1.
昆明医科大学第六附属医院医学检验科，云南玉溪 653100
2.
广州医科大学附属第六医院清远市人民医院检验医学部，广州清远 511518
3.
玉溪市中心血站检验科，云南玉溪 653100

基金项目:

国家自然科学基金项目 82160402

云南省医学领军人才培养专项经费资助项目 L-2019022

云南省科技厅—昆明医科大学应用基础研究联合专项资金项目 202001AY070001-096

云南省教育厅科学研究基金项目 2019J1309

详细信息

通讯作者:
冯磊, E-mail: fngj2004@163.com

中图分类号: R587.1
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- 被引次数: 0
出版历程
- 收稿日期: 2023-02-15
- 网络出版日期: 2023-09-13

Progress of genetic risk scores in predicting type 2 diabetes

1.
Department of Clinical Laboratory, the Sixth Affiliated Hospital of Kunming Medical University, Yuxi, Yunnan 653100, China

摘要

摘要: 据报道，全球20~79岁的糖尿病患者约5.37亿，约有670万成年人死于糖尿病或糖尿病并发症。而我国已成为世界上糖尿病患者人数最多的国家。2型糖尿病(type 2 diabetes mellitus，T2DM)是环境和遗传因素共同作用的结果，近年来T2DM发病率持续上升，预防和控制T2DM的发生发展变得十分重要。为解决这一现状，T2DM研究团队进行了T2DM风险预测模型的建立，目前纳入T2DM预测模型中的机制大多是脂代谢、胰岛功能、糖代谢、饮食习惯等，基本为传统危险因素，导致国内外T2DM预测模型存在“同质化现象”，现存的模型在不同人群中的预测效果不理想，迫切需要新思路新方法加入。随着人类基因组计划的完成，易感基因与传统危险因子联合应用建立T2DM预测模型的研究越来越多，因此本文拟将易感基因与传统危险因子联合应用，建立纳入新机制的T2DM预测模型，将“先天性”遗传因素和“后天性”生理生化指标整合起来，旨在精准预测T2DM，同时遗传因素采用遗传风险评分(gene risk score，GRS)表示，探索GRS如何应用到预测模型中也是本文的目的之一。本文通过检索国内外文献进行了2型糖尿病遗传风险评分计算、遗传风险评分与传统临床危险因素联合应用预测2型糖尿病以及遗传风险评分如何应用这几个方面的综述，为今后2型糖尿病预测模型的完善提供思路和依据。
- 2型糖尿病 /
- 遗传风险评分 /
- 基因-环境相互作用
Abstract: It is reported that there are about 537 million diabetes patients aged 20-79 years around the world, and about 6.7 million adults die of diabetes or diabetes complications. China has become the country with the largest number of diabetes patients in the world. Type 2 diabetes mellitus (T2DM) is the result of environmental and genetic factors. In recent years, the incidence rate of T2DM continues to rise, so it is very important to prevent and control the occurrence and development of T2DM. To address this situation, the T2DM research team has established a T2DM risk prediction model. Currently, the mechanisms included in the T2DM prediction model are mostly lipid metabolism, pancreatic function, glucose metabolism, dietary habits, etc., which are basically traditional risk factors. This has led to the phenomenon of "homogenization" in domestic and foreign T2DM prediction models. The prediction effect of existing models in different populations is not ideal, and there is an urgent need for new ideas and methods to be added. With the completion of the Human Genome Project, more and more studies have been carried out on the establishment of T2DM prediction models based on the joint application of susceptibility genes and traditional risk factors. Therefore, this paper plans to jointly apply susceptibility genes and traditional risk factors to establish T2DM prediction models incorporating new mechanisms, integrate "congenital" genetic factors and "acquired" physiological and biochemical indicators, and aim to accurately predict T2DM. At the same time, genetic factors are represented by the genetic risk score (GRS), and exploring how GRS can be applied to prediction models is also one of the purposes of this article. This paper reviewed the calculation of genetic risk score for type 2 diabetes mellitus, the combination of genetic risk score and traditional clinical risk factors in predicting type 2 diabetes mellitus, and how to apply genetic risk score through searching domestic and foreign literature, providing ideas and basis for the improvement of prediction model for type 2 diabetes mellitus in the future.
- Type 2 diabetes mellitus /
- Genetic risk score /
- Gene-environment interactions

HTML全文

表 1 具有遗传标记的T2DM相关研究

Table 1. T2DM related studies with genetic marker

作者	地区	SNP数量	OR值
SHIN J H等^[13]	韩国	6	1.14
YUAN F等^[14]	中国	7	0.75~1.39
PLENGVIDHYA N等^[15]	泰国	8	1.65~2.02
KIM D S等^[16]	韩国	9	1.11~1.29
MIRANDA-LORA A L等^[17]	墨西哥	10	1.22
GOTO A等^[18]	日本	11	1.73
SABIHA B等^[19]	巴基斯坦	16	0.68~1.42
LOH M等^[20]	南亚	21	1.04~1.40
WANG B等^[21]	中国	21	1.23
PAN W等^[22]	欧洲	23	1.02~1.13
CHENG C F等^[23]	中国	35	1.32~3.72
XUE A L等^[24]	欧洲	39	0.93~1.10
RAGHAVAN S等^[25]	美国	67	1.05
ZHUANG P等^[26]	英国	424	1.55

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