Author Identifier (ORCID)
Xiuhua Guo: https://orcid.org/0000-0001-6657-6940
Abstract
Real-world evidence for wearable noninvasive glucose monitoring (NIGM) remains limited. To evaluate the functional equivalence of a wearable NIGM device and explore its utility for T2DM and prediabetes screening. In this multicenter study, 12-h daytime glucose profiles obtained by a flexible reverse iontophoresis-based electrochemical sensor were compared with capillary glucose using functional equivalence. Subgroup analyses were conducted. Screening models of T2DM and prediabetes were developed using elastic net and Logistic regression. A total of 135 participants (mean age 35.3 years; 60.0% female) were included, and no serious device-related adverse events were reported. Compared to the capillary measurements, functional equivalence was confirmed (T = −6.537 < threshold = −2.081) in the general population but not in older adults or T2DM patients. The T2DM noninvasive screening model demonstrated discrimination and reclassification performance comparable to those of the capillary-based model (AUC: 0.906 vs. 0.850, NRI: 0.044, IDI: −0.078, p > 0.05). Functional principal component scores facilitated the identification of prediabetes (AUC = 0.760). The device demonstrated acceptable accuracy and functional equivalence with reference methods. Its capability to detect T2DM and early glycemic anomalies supports its feasibility as a wearable, interpretative adjunct tool for large-scale screening in free-living populations.
Keywords
Machine learning, noninvasive glucose monitoring, population screening, type 2 diabetes mellitus, wearable device
Document Type
Journal Article
Date of Publication
4-1-2026
Volume
16
Issue
4
PubMed ID
42041435
Publication Title
Biosensors
Publisher
MDPI
School
School of Medical and Health Sciences
Funders
Capital’s Funds for Health Improvement and Research (CFH 2024-1G-4261) / Program of the Natural Science Foundation of China (82373683)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Xie, W., Wang, J., Liu, H., Chen, S., Wang, P., Han, Y., Chen, X., Fang, Z., Zhao, Z., Zhang, G., & Guo, X. (2026). A flexible wearable glucose sensor for noninvasive diabetes screening: Functional equivalence and model interpretability. Biosensors, 16(4), 214. https://doi.org/10.3390/bios16040214