Predicting diabetes risk in diverse populations: what next?
Predicting diabetes risk in diverse populations: what next?
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DOI：
10.1016/s2213-8587(21)00287-4
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Rich SS
中科院分区：
文献类型：
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作者：
Mercader JM;Ng MCY;Manning AK;Rich SS
快门股随着年龄、种族或民族以及疾病的自然病史而变化，包括发病前危险因素的特征和外观。将不受时间变化影响的遗传风险与临床风险因素结合起来，可能会改善结果，并揭示精确医学在糖尿病治疗中的前景。针对糖尿病亚型的全基因组关联研究(GWAS)已经确定了数百个风险修正等位基因。这些关联可以被聚集成每个个体的风险等位基因的加权和。这提供了一种称为多基因风险评分(Prs)的单一遗传评分，它可以估计罹患疾病(如糖尿病)的可能性。纵向研究表明，PRSS可以预测复杂疾病的发生。在FINRISK以人群为基础的研究中，2 2型糖尿病的PRS的每一个标准差与疾病发生的危险比1/74相关。此外，前2.5%的受试者一生中罹患2型糖尿病的风险增加2.3倍。2无论年龄大小，在临床危险因素变得明显之前，2型糖尿病的PRS都能改善预测，使早期预测成为可能，并提供早期生活方式干预的机会。2在1型糖尿病患者中，PRS有助于优先筛选胰岛自身抗体筛查个体。3例有多个胰岛自身抗体的患者有资格接受加强监测以预防糖尿病酮症酸中毒，并优先进入免疫干预试验。3此外，PRS可以高精度地区分1型糖尿病和2型糖尿病，并识别需要胰岛素以实现最佳血糖控制的2型糖尿病患者。4PRS还可以更好地揭示糖尿病的异质性病程。2型糖尿病及其相关性状的GWAS已被聚集到生理相关的途径中，以获得特定过程的PRSS。这些PRS有助于以精确医学的方式区分与不同临床结果相关的2型糖尿病亚型。5大多数针对糖尿病和其他常见疾病的GWASS是在具有北欧血统的个人身上进行的；然而，糖尿病是一种全球性疾病。事实上，糖尿病对非欧洲血统的人的影响不成比例。在美国，非裔美国人和拉丁裔社区的糖尿病患病率最高，这些患者群体更有可能出现糖尿病并发症。在应用PRSS进行风险预测时，这些健康差异可能会加剧，因为从具有欧洲血统的人群中得出的PR模型显示，可移植到其他祖先的能力很差。6由于危险等位基因频率和危险等位基因之间的连锁不平衡模式的群体差异，以及祖先特有的危险等位基因的存在，无法外推PRS模型。目前糖尿病的PRS显示，在具有非欧洲血统的人群中预测能力很差。Diamante研究联盟对2型糖尿病的全基因组关联研究进行了荟萃分析，其中包括1 463 694名不同祖先的个人(49%非欧洲人)，并测试了跨祖先和祖先特有的PR的表现。7在所有人群中，跨系PR比特定于祖先的PR表现得更好，这解释了在具有欧洲白人血统的人群中，2型糖尿病风险高达6%。对其他祖先解释的风险较低，在具有非洲血统的人群中解释的最低方差仅为2%左右。在1型糖尿病患者中，使用…
Shutterstock varies with age, race or ethnicity, and natural history of disease, including the characteristics and appearance of risk factors before disease onset. Incorporation of genetic risks, not affected by temporal changes, with clinical risk factors might improve outcomes and reveal the promise of precision medicine in diabetes. Genome-wide association studies (GWASs) for diabetes subtypes have identified hundreds of risk-modifying alleles. These associations can be aggregated into a weighted sum of the number of risk alleles in each individual. This provides a single genetic score called a polygenic risk score (PRS), which estimates the probability of developing disease (eg, diabetes). Longitudinal studies have shown that PRSs can predict onset of complex diseases. In the FINRISK population-based study, 2 each standard deviation of the PRS for type 2 diabetes was associated with a hazard ratio of 1· 74 for occurrence of disease. Furthermore, individuals at the top 2· 5% of the PRS distribution had a 2· 3 times increased lifetime risk of developing type 2 diabetes. 2 The PRS for type 2 diabetes improves prediction, regardless of age and before clinical risk factors become apparent, enabling early prediction and offering the opportunity of early lifestyle interventions. 2 In type 1 diabetes, the PRS facilitated the prioritisation of individuals for islet autoantibody screening. 3 Patients with multiple islet autoantibodies are eligible for increased monitoring for prevention of diabetic ketoacidosis and prioritised for entry into immune intervention trials. 3 In addition, the PRS can distinguish between both type 1 diabetes and type 2 diabetes with high accuracy and identify individuals with type 2 diabetes who will require insulin for optimal glucose control. 4 The PRS can also be valuable to better inform the heterogenous course of diabetes. GWASs of type 2 diabetes and related traits have been clustered into physiologically relevant pathways to obtain processspecific PRSs. These PRSs aid in distinguishing subtypes of type 2 diabetes that are associated with distinct clinical outcomes in a precision medicine manner. 5 Most GWASs for diabetes and other common diseases have been done in individuals with northern European ancestry; however, diabetes is a global disease. In fact, diabetes disproportionally affects individuals with non-European ancestry. In the USA, the prevalence of diabetes is highest in African American and Latinx communities, and these patient populations are more likely to develop diabetic complications. These health disparities could be exacerbated in the application of PRSs for risk prediction because PRS models derived from populations with European ancestry showed poor transferability to other ancestries. 6 The inability to extrapolate PRS models is due to population differences in risk allele frequencies and linkage disequilibrium patterns between risk alleles, as well as the presence of ancestry-specific risk alleles. Current PRSs for diabetes show poor prediction in populations with non-European ancestries. The DIAMANTE study consortium performed a meta-analysis on genome-wide association studies for type 2 diabetes, which included 1 463 694 individuals of diverse ancestries (49% non-Europeans), and tested the performance of transancestry and ancestry-specific PRS. 7 The transancestry PRS performed better than did the ancestry-specific PRS in all populations, explaining the type 2 diabetes risk of up to 6% in populations with whiteEuropean ancestry. The risks explained were lower for other ancestries, and the lowest variance explained was only around 2% in populations with African ancestry. In type 1 diabetes, use of a …