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Wearable Sweat Biosensors for Inflammatory Biomarkers (CRP, IL-6 and TNF-α): A Systematic Review of Analytical and
Raghad S Alsharidah1, Razique Anwer2, Maysoon Anwar Abdelhamid1
1College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13317-4233, Saudi Arabia.
Abstract:
Background: Wearable sweat biosensors have emerged as a promising non-invasive approach for monitoring inflammatory biomarkers, offering an alternative to repeated blood sampling. This systematic review evaluated the current evidence on the detection of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in sweat for infection and inflammatory disease monitoring. Methods: Following PRISMA 2020 guidelines (PROSPERO: CRD420261371221), PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched through April 2026. Human studies measuring CRP, IL-6, or TNF-α in sweat using wearable or biosensor-based platforms were included. Risk of bias and certainty of evidence were assessed using a modified Newcastle-Ottawa Scale/JBI checklist and GRADE. Results: Thirteen studies (2020-2025, 5-80 participants) were included. CRP showed the most consistent sweat-serum agreement (r = 0.844) and good diagnostic performance in one inflammatory bowel disease cohort (AUC = 0.845). TNF-α demonstrated the highest diagnostic accuracy in a single longitudinal study (AUC = 0.962), whereas IL-6 showed moderate sweat-serum correlations (R2 = 0.60-0.72) but limited diagnostic discrimination. IL-6 and TNF-α were more consistently detected in passively collected eccrine sweat, although IL-6 was also successfully measured following pilocarpine iontophoresis in one study. Most studies had moderate methodological quality. Conclusions: Sweat-based measurement of CRP, IL-6, and TNF-α is analytically feasible for non-invasive inflammatory biomarker monitoring. However, current evidence remains limited by small, heterogeneous studies, underscoring the need for standardized protocols and larger prospective clinical validation.