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Explores non-invasive metabolic syndrome assessment in digital health, suggesting new diagnostic opportunities.
Metabolic syndrome (MetS) is a multifactorial condition associated with an increased risk of type 2 diabetes, cardiovascular disease, and other comorbidities. Current diagnosis relies on episodic clinical assessments and invasive laboratory tests, limiting early detection and continuous monitoring in real-world settings. Digital health technologies, particularly wearable biosensors, may help address these limitations through continuous and user-centered monitoring. The MiWear project explores miniaturized mid-infrared (Mid-IR) spectroscopy combined with artificial intelligence (AI) to support non-invasive biomarker monitoring in interstitial fluid (ISF). By focusing on Mid-IR molecular signatures relevant to MetS, the approach could enable earlier risk stratification and longitudinal metabolic assessment outside traditional clinical environments. We discuss sex- and gender-related considerations, analytical and clinical validation needs, regulatory pathways, and implementation scenarios across primary care, population health, and telemedicine. This translational roadmap highlights opportunities and challenges for integrating Mid-IR wearable biosensing in preventive, patient-centered metabolic healthcare.
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Androutsos et al. (2026) studied this question.
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