Buzzetti R, Prudente S, Copetti M, Dauriz M, Zampetti S, Garofolo M, Penno G, Trischitta V
Department of Experimental Medicine, "Sapienza" University of Rome, Rome, Italy; UOC Diabetology, Polo Pontino, "Sapienza" University of Rome, Rome, Italy.
Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Nutr Metab Cardiovasc Dis. 2017 Feb;27(2):99-114. doi: 10.1016/j.numecd.2016.08.005. Epub 2016 Aug 26.
We are currently facing several attempts aimed at marketing genetic data for predicting multifactorial diseases, among which diabetes mellitus is one of the more prevalent. The present document primarily aims at providing to practicing physicians a summary of available data regarding the role of genetic information in predicting diabetes and its chronic complications.
Firstly, general information about characteristics and performance of risk prediction tools will be presented in order to help clinicians to get acquainted with basic methodological information related to the subject at issue. Then, as far as type 1 diabetes is concerned, available data indicate that genetic information and counseling may be useful only in families with many affected individuals. However, since no disease prevention is possible, the utility of predicting this form of diabetes is at question. In the case of type 2 diabetes, available data really question the utility of adding genetic information on top of well performing, easy available and inexpensive non-genetic markers. Finally, the possibility of using the few available genetic data on diabetic complications for improving our ability to predict them will also be presented and discussed. For cardiovascular complication, the addition of genetic information to models based on clinical features does not translate in a substantial improvement in risk discrimination. For all other diabetic complications genetic information are currently very poor and cannot, therefore, be used for improving risk stratification.
In all, nowadays the use of genetic testing for predicting diabetes and its chronic complications is definitively of little value in clinical practice.
目前我们面临着多项旨在将基因数据用于预测多因素疾病的尝试，其中糖尿病是较为常见的疾病之一。本文档主要旨在为执业医师提供有关基因信息在预测糖尿病及其慢性并发症中作用的现有数据总结。
首先，将介绍风险预测工具的特征和性能的一般信息，以帮助临床医生熟悉与所讨论主题相关的基本方法学信息。然后，就1型糖尿病而言，现有数据表明基因信息和咨询可能仅在有许多受影响个体的家庭中有用。然而，由于无法预防这种疾病，预测这种形式糖尿病的效用受到质疑。对于2型糖尿病，现有数据确实质疑在性能良好、易于获得且价格低廉的非基因标志物之上添加基因信息的效用。最后，还将介绍和讨论使用关于糖尿病并发症的少量现有基因数据来提高我们预测它们能力的可能性。对于心血管并发症，在基于临床特征的模型中添加基因信息并不能显著改善风险辨别能力。对于所有其他糖尿病并发症，目前基因信息非常有限，因此不能用于改善风险分层。
总之，如今在临床实践中使用基因检测来预测糖尿病及其慢性并发症的价值确实不大。
