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Noninvasive On-Skin Biosensors for Monitoring Diabetes Mellitus

Ali Sedighi,Tianyu Kou,Hui Huang,Yi Li

Published 2025 in Nano-Micro Letters

ABSTRACT

A comprehensive and critical evaluation of recent advances in sweat-based biochemical and physiological biomarkers for noninvasive diabetes monitoring. A novel emphasis on multimodal sensor integration—combining biochemical and physiological signals—to enhance accuracy, contextual awareness, and reliability in real-time diabetes management. A forward-looking analysis of AI-driven biosensing systems, standardized protocols, and regulatory and ethical frameworks enabling autonomous, secure, and personalized diabetes care. A comprehensive and critical evaluation of recent advances in sweat-based biochemical and physiological biomarkers for noninvasive diabetes monitoring. A novel emphasis on multimodal sensor integration—combining biochemical and physiological signals—to enhance accuracy, contextual awareness, and reliability in real-time diabetes management. A forward-looking analysis of AI-driven biosensing systems, standardized protocols, and regulatory and ethical frameworks enabling autonomous, secure, and personalized diabetes care. Diabetes mellitus represents a major global health issue, driving the need for noninvasive alternatives to traditional blood glucose monitoring methods. Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers, providing innovative solutions for diabetes diagnosis and monitoring. This review comprehensively discusses the current developments in noninvasive wearable biosensors, emphasizing simultaneous detection of biochemical biomarkers (such as glucose, cortisol, lactate, branched-chain amino acids, and cytokines) and physiological signals (including heart rate, blood pressure, and sweat rate) for accurate, personalized diabetes management. We explore innovations in multimodal sensor design, materials science, biorecognition elements, and integration techniques, highlighting the importance of advanced data analytics, artificial intelligence-driven predictive algorithms, and closed-loop therapeutic systems. Additionally, the review addresses ongoing challenges in biomarker validation, sensor stability, user compliance, data privacy, and regulatory considerations. A holistic, multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.

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