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1 型糖尿病临床诊断模型分类糖尿病的组织学验证。

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

机构信息

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.

Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.

出版信息

Diabet Med. 2020 Dec;37(12):2160-2168. doi: 10.1111/dme.14361. Epub 2020 Jul 23.

Abstract

AIMS

Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes.

METHODS

We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n = 111) or non-type 1 (n = 42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC).

RESULTS

Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median < 17 pmol/l (limit of detection) vs. 1037 pmol/l in non-type 1 diabetes; P < 0.0001].

CONCLUSIONS

Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

摘要

目的

由于 1 型和 2 型糖尿病的临床特征重叠,糖尿病的误诊很常见。最近已经开发出结合临床和生物标志物信息的综合诊断模型,可以辅助分类,但尚未使用胰腺病理学进行验证。我们评估了一个临床诊断模型与组织学定义的 1 型糖尿病的对比。

方法

我们使用组织病理学将来自胰腺器官捐赠者网络(nPOD)生物库的病例分类为 1 型(n=111)或非 1 型(n=42)糖尿病。1 型糖尿病的定义是沿胰岛丢失含胰岛素的胰岛,同时伴有多个胰岛素缺乏的胰岛。我们评估了先前描述的基于临床特征(诊断时的年龄、BMI)和生物标志物数据[自身抗体、1 型糖尿病遗传风险评分(T1D-GRS)]的 1 型糖尿病诊断模型的判别性能,以及通过接收者操作特征(ROC)曲线下面积(AUC-ROC)识别 1 型糖尿病的单一特征。

结果

诊断模型与组织学定义的 1 型糖尿病吻合良好。结合临床特征、胰岛自身抗体和 T1D-GRS 的模型对 1 型糖尿病具有很强的鉴别能力,优于单独使用临床特征(AUC-ROC 0.97 与 0.95;P=0.03)。1 型糖尿病的组织学分类与血清 C 肽一致[中位数 <17 pmol/l(检测下限)与非 1 型糖尿病的 1037 pmol/l;P<0.0001]。

结论

本研究提供了有力的组织学证据,表明一种结合临床特征和生物标志物的临床诊断模型可以改善糖尿病的分类。我们的研究还提供了保证,即基于 C 肽的 1 型糖尿病定义是一种合适的替代终点,可以在不可能进行组织学定义的大型临床研究中使用。本研究的部分内容以摘要形式在美国佛罗里达州胰腺器官捐赠者网络会议、英国利物浦的英国糖尿病协会专业会议上进行了展示,时间分别为 2019 年 2 月 19-22 日和 2019 年 3 月 6-8 日。

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本文引用的文献

1
2. Classification and Diagnosis of Diabetes: .2. 糖尿病的分类和诊断: 。
Diabetes Care. 2020 Jan;43(Suppl 1):S14-S31. doi: 10.2337/dc20-S002.

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