Andersen Jonas Dahl, Stoltenberg Carsten Wridt, Jensen Morten Hasselstrøm, Vestergaard Peter, Hejlesen Ole, Hangaard Stine
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Steno Diabetes Center North Denmark, Aalborg, Denmark.
J Diabetes Sci Technol. 2024 Aug 2:19322968241267779. doi: 10.1177/19322968241267779.
Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities.
Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots.
Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality.
The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain.
心血管疾病（CVD）和糖尿病肾病（DKD）等合并症是1型糖尿病（T1D）的主要负担。预测有发生合并症高风险的人群将有助于早期干预。本研究旨在开发纳入社会经济地位（SES）的模型，以预测成年T1D患者的CVD、DKD和死亡率，从而改善合并症的早期识别。
使用丹麦全国登记数据。建立逻辑回归模型以预测T1D诊断后五年内CVD、DKD的发生及死亡率。特征包括年龄、性别、个人收入和教育程度。通过五折交叉验证，利用受试者工作特征曲线下面积（AUROC）和精确召回率曲线下面积（PR-AUC）评估模型性能。从特征重要性图评估SES的重要性。
在纳入的6572名年龄≥21岁的成年T1D患者中，379人（6%）发生CVD，668人（10%）发生DKD，921人（14%）在五年随访期内死亡。CVD的AUROC（±标准差）为0.79（±0.03），DKD为0.61（±0.03），死亡率为0.87（±0.01）。PR-AUC分别为0.18（±0.01）、0.15（±0.03）和0.49（±0.02）。基于特征重要性图，SES是DKD模型中最重要的特征，但对CVD和死亡率模型的影响最小。
所开发的模型在预测CVD和死亡率方面表现良好，表明它们有助于早期识别T1D患者的这些结局。SES在糖尿病个体预测中的重要性仍不确定。
