Ayers Alessandra T, Ho Cindy N, Billings Liana K, Misra Shivani, Klonoff David C
Diabetes Technology Society, Burlingame, CA, USA.
Department of Medicine, Endeavor Health, Skokie, IL, USA.
J Diabetes Sci Technol. 2025 Mar 28:19322968251329055. doi: 10.1177/19322968251329055.
A tool is needed to distinguish type 1 diabetes (T1D) and type 2 diabetes (T2D) in adults with new-onset diabetes because correct classification is needed for correct diagnoses and treatments. Current classification methods are usually applied to biomarkers using binary or quantitative classification with a cut point and may not be adequately nuanced. Combinations of clinical features are not necessarily specific for classifying and may not always indicate a single diagnosis. A probabilistic decision tree classification tool with multiple branches per decision node is needed for adults with new-onset diabetes to avoid misdiagnosis of actual T1D as T2D, misdiagnosis of actual T2D or monogenic diabetes as T1D, and misclassified patients in future population health studies which will lead to incorrect conclusions and suboptimal patient outcomes.
需要一种工具来区分成年新发糖尿病患者的1型糖尿病（T1D）和2型糖尿病（T2D），因为正确的分类对于正确的诊断和治疗是必要的。当前的分类方法通常使用具有切点的二元或定量分类应用于生物标志物，可能不够细致入微。临床特征的组合不一定对分类具有特异性，并且可能并不总是表明单一诊断。成年新发糖尿病患者需要一种每个决策节点有多个分支的概率决策树分类工具，以避免将实际的T1D误诊为T2D，将实际的T2D或单基因糖尿病误诊为T1D，以及在未来的人群健康研究中对患者进行错误分类，这将导致错误的结论和次优的患者结局。
