Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Diabetes Metab J. 2022 Mar;46(2):319-326. doi: 10.4093/dmj.2021.0014. Epub 2021 Sep 16.
Screening for diabetic peripheral neuropathy (DPN) is important to prevent severe foot complication, but the detection rate of DPN is unsatisfactory. We investigated whether SUDOSCAN combined with Michigan Neuropathy Screening Instrument (MNSI) could be an effective tool for screening for DPN in people with type 2 diabetes mellitus (T2DM) in clinical practice.
We analysed the data for 144 people with T2DM without other cause of neuropathy. The presence of DPN was confirmed according to the Toronto Consensus criteria. Electrochemical skin conductance (ESC) of the feet was assessed using SUDOSCAN. We compared the discrimination power of following methods, MNSI only vs. SUDOSCAN only vs. MNSI plus SUDOSCAN vs. MNSI plus 10-g monofilament test.
Confirmed DPN was detected in 27.8% of the participants. The optimal cut-off value of feet ESC to distinguish DPN was 56 μS. We made the DPN screening scores using the corresponding odds ratios for MNSI-Questionnaire, MNSI-Physical Examination, SUDOSCAN, and 10-g monofilament test. For distinguishing the presence of DPN, the MNSI plus SUDOSCAN model showed higher areas under the receiver operating characteristic curve (AUC) than MNSI only model (0.717 vs. 0.638, P=0.011), and SUDOSCAN only model or MNSI plus 10-g monofilament test showed comparable AUC with MNSI only model.
The screening model for DPN that includes both MNSI and SUDOSCAN can detect DPN with acceptable discrimination power and it may be useful in Korean patients with T2DM.
筛查糖尿病周围神经病变（DPN）对于预防严重足部并发症很重要，但 DPN 的检出率并不理想。我们研究了 SUDOSCAN 联合密歇根神经病变筛查工具（MNSI）是否可作为 2 型糖尿病（T2DM）患者临床实践中筛查 DPN 的有效工具。
我们分析了 144 例无其他神经病变病因的 T2DM 患者的数据。根据多伦多共识标准确定 DPN 的存在。使用 SUDOSCAN 评估足部的电化学皮肤电导（ESC）。我们比较了以下方法的鉴别能力，仅 MNSI 与仅 SUDOSCAN 与 MNSI 加 SUDOSCAN 与 MNSI 加 10g 单丝测试。
27.8%的参与者确诊为 DPN。区分 DPN 的足部 ESC 最佳截断值为 56μS。我们使用 MNSI-问卷、MNSI-体格检查、SUDOSCAN 和 10g 单丝测试的相应比值制作 DPN 筛查评分。对于区分 DPN 的存在，MNSI 加 SUDOSCAN 模型的受试者工作特征曲线下面积（AUC）高于仅 MNSI 模型（0.717 比 0.638，P=0.011），而仅 SUDOSCAN 模型或 MNSI 加 10g 单丝测试与仅 MNSI 模型的 AUC 相当。
包含 MNSI 和 SUDOSCAN 的 DPN 筛查模型可检测 DPN，具有可接受的鉴别能力，可能对韩国 T2DM 患者有用。
