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1型糖尿病遗传风险评分有助于区分青年成人的1型和2型糖尿病。

A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults.

作者信息

Oram Richard A, Patel Kashyap, Hill Anita, Shields Beverley, McDonald Timothy J, Jones Angus, Hattersley Andrew T, Weedon Michael N

机构信息

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K. Clinical Islet Transplant Program, University of Alberta, Edmonton, Alberta, Canada National Institute for Health Research Exeter Clinical Research Facility, Exeter, U.K.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K. National Institute for Health Research Exeter Clinical Research Facility, Exeter, U.K.

出版信息

Diabetes Care. 2016 Mar;39(3):337-44. doi: 10.2337/dc15-1111. Epub 2015 Nov 17.

Abstract

OBJECTIVE

With rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes.

RESEARCH DESIGN AND METHODS

We developed genetic risk scores (GRSs) from published T1D- and T2D-associated variants. We first tested whether the scores could distinguish clinically defined T1D and T2D from the Wellcome Trust Case Control Consortium (WTCCC) (n = 3,887). We then assessed whether the T1D GRS correctly classified young adults (diagnosed at 20-40 years of age, the age-group with the most diagnostic difficulty in clinical practice; n = 223) who progressed to severe insulin deficiency <3 years from diagnosis.

RESULTS

In the WTCCC, the T1D GRS, based on 30 T1D-associated risk variants, was highly discriminative of T1D and T2D (area under the curve [AUC] 0.88 [95% CI 0.87-0.89]; P < 0.0001), and the T2D GRS added little discrimination (AUC 0.89). A T1D GRS >0.280 (>50th centile in those with T1D) is indicative of T1D (50% sensitivity, 95% specificity). A low T1D GRS (<0.234, <5th centile T1D) is indicative of T2D (53% sensitivity, 95% specificity). Most discriminative ability was obtained from just nine single nucleotide polymorphisms (AUC 0.87). In young adults with diabetes, T1D GRS alone predicted progression to insulin deficiency (AUC 0.87 [95% CI 0.82-0.92]; P < 0.0001). T1D GRS, autoantibody status, and clinical features were independent and additive predictors of severe insulin deficiency (combined AUC 0.96 [95% CI 0.94-0.99]; P < 0.0001).

CONCLUSIONS

A T1D GRS can accurately identify young adults with diabetes who will require insulin treatment. This will be an important addition to correctly classifying individuals with diabetes when clinical features and autoimmune markers are equivocal.

摘要

目的

随着肥胖率上升,在年轻成年人中区分1型糖尿病(T1D)和2型糖尿病(T2D)变得越来越困难。最近在确定常见基因变异对T1D和T2D的贡献方面取得了重大进展。我们旨在确定由常见基因变异产生的分数是否可用于区分T1D和T2D,以及预测患有糖尿病的年轻成年人中的严重胰岛素缺乏。

研究设计与方法

我们根据已发表的与T1D和T2D相关的变异开发了遗传风险评分(GRS)。我们首先测试这些分数是否能将临床定义的T1D和T2D与威康信托病例对照研究联盟(WTCCC)(n = 3887)中的病例区分开来。然后,我们评估T1D GRS是否能正确分类那些在诊断后<3年进展为严重胰岛素缺乏的年轻成年人(20 - 40岁诊断,这是临床实践中诊断最困难的年龄组;n = 223)。

结果

在WTCCC中,基于30个与T1D相关的风险变异的T1D GRS对T1D和T2D具有高度鉴别力(曲线下面积[AUC]为0.88[95%CI 0.87 - 0.89];P < 0.0001),而T2D GRS的鉴别力提升不大(AUC为0.89)。T1D GRS > 0.280(在T1D患者中>第50百分位数)提示T1D(敏感性50%,特异性95%)。低T1D GRS(< 0.234,< T1D患者中的第5百分位数)提示T2D(敏感性53%,特异性95%)。仅九个单核苷酸多态性就获得了大部分鉴别能力(AUC为0.87)。在患有糖尿病的年轻成年人中,单独的T1D GRS可预测进展为胰岛素缺乏(AUC为0.87[95%CI 0.82 - 0.92];P < 0.0001)。T1D GRS、自身抗体状态和临床特征是严重胰岛素缺乏的独立且相加的预测因素(联合AUC为0.96[95%CI 0.94 - 0.99];P < 0.0001)。

结论

T1D GRS可以准确识别需要胰岛素治疗的糖尿病年轻成年人。当临床特征和自身免疫标志物不明确时,这将是正确分类糖尿病个体的一项重要补充。

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本文引用的文献

1
Standards of medical care in diabetes-2015 abridged for primary care providers.
Clin Diabetes. 2015 Apr;33(2):97-111. doi: 10.2337/diaclin.33.2.97.
3
Role of Type 1 Diabetes-Associated SNPs on Risk of Autoantibody Positivity in the TEDDY Study.
Diabetes. 2015 May;64(5):1818-29. doi: 10.2337/db14-1497. Epub 2014 Nov 24.
4
The SEARCH for Diabetes in Youth study: rationale, findings, and future directions.
Diabetes Care. 2014 Dec;37(12):3336-44. doi: 10.2337/dc14-0574.
5
minimac2: faster genotype imputation.
Bioinformatics. 2015 Mar 1;31(5):782-4. doi: 10.1093/bioinformatics/btu704. Epub 2014 Oct 22.
6
Fine mapping of type 2 diabetes susceptibility loci.
Curr Diab Rep. 2014;14(11):549. doi: 10.1007/s11892-014-0549-2.
7
Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes.
Diabetologia. 2014 Dec;57(12):2521-9. doi: 10.1007/s00125-014-3362-1. Epub 2014 Sep 4.
8
Improving prediction of type 1 diabetes by testing non-HLA genetic variants in addition to HLA markers.
Pediatr Diabetes. 2014 Aug;15(5):355-62. doi: 10.1111/pedi.12092. Epub 2013 Nov 8.
9
Imputing amino acid polymorphisms in human leukocyte antigens.
PLoS One. 2013 Jun 6;8(6):e64683. doi: 10.1371/journal.pone.0064683. Print 2013.
10
The clinical utility of C-peptide measurement in the care of patients with diabetes.
Diabet Med. 2013 Jul;30(7):803-17. doi: 10.1111/dme.12159.

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