Maalmi Haifa, Nguyen Phong B H, Strom Alexander, Bönhof Gidon J, Rathmann Wolfgang, Ziegler Dan, Menden Michael P, Roden Michael, Herder Christian
Institute for Clinical Diabetology, German Diabetes Center (Deutsches Diabetes-Zentrum/DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Diabetes Metab Res Rev. 2024 Nov;40(8):e70009. doi: 10.1002/dmrr.70009.
Diabetic sensorimotor polyneuropathy (DSPN) is often asymptomatic and remains undiagnosed. The ability of clinical and anthropometric variables to identify individuals likely to have DSPN might be limited. Here, we aimed to integrate protein biomarkers for reliably predicting present DSPN.
Using the proximity extension assay, we measured 135 neurological and protein biomarkers of inflammation in blood samples of 423 individuals with recent-onset diabetes from the German Diabetes Study (GDS). DSPN was diagnosed based on the Toronto Consensus Criteria. We constructed (i) a protein-based prediction model using LASSO logistic regression, (ii) an optimised traditional risk model with age, sex, waist circumference, height and diabetes type and (iii) a model combining both. All models were bootstrapped to assess the robustness, and optimism-corrected AUCs (95% CI) were reported.
DSPN was present in 16% of the study population. LASSO logistic regression selected the neurofilament light chain (NFL) and fibroblast growth factor-19 (FGF-19) as the most predictive protein biomarkers for detecting DSPN in individuals with recent-onset diabetes. The protein-based model achieved an AUC of 0.66 (0.59, 0.73), while the traditional risk model had an AUC of 0.66 (0.61, 0.74). However, combined features boosted the model performance to an AUC of 0.72 (0.67, 0.79).
We developed a prediction model for DSPN in recent-onset diabetes based on two protein biomarkers and five standard anthropometric, demographic and clinical variables. The model has a fair discrimination performance and might be used to inform the referral of patients for further testing.
糖尿病感觉运动性多发性神经病变（DSPN）通常无症状，仍未被诊断出来。临床和人体测量学变量识别可能患有DSPN个体的能力可能有限。在此，我们旨在整合蛋白质生物标志物以可靠地预测当前的DSPN。
我们使用邻位延伸分析，测量了来自德国糖尿病研究（GDS）的423例近期发病糖尿病患者血样中的135种神经学和炎症蛋白生物标志物。根据多伦多共识标准诊断DSPN。我们构建了（i）使用LASSO逻辑回归的基于蛋白质的预测模型，（ii）包含年龄、性别、腰围、身高和糖尿病类型的优化传统风险模型，以及（iii）结合两者的模型。对所有模型进行自助抽样以评估稳健性，并报告经乐观校正的AUC（95%CI）。
16%的研究人群存在DSPN。LASSO逻辑回归选择神经丝轻链（NFL）和成纤维细胞生长因子-19（FGF-19）作为检测近期发病糖尿病个体中DSPN最具预测性的蛋白质生物标志物。基于蛋白质的模型AUC为0.66（0.59，0.73），而传统风险模型的AUC为0.66（0.61，0.74）。然而，联合特征将模型性能提高到AUC为0.72（0.67，0.79）。
我们基于两种蛋白质生物标志物以及五个标准人体测量学、人口统计学和临床变量，开发了一种用于近期发病糖尿病中DSPN的预测模型。该模型具有较好的鉴别性能，可用于指导患者转诊进行进一步检测。
