Integrating Microsampling in Human Biomonitoring: Methodologies, Regulatory Frameworks, and Case Study Insights
Karthikeyan Rajamani1, Senthamil Selvi Poongavanam2
1Laboratory for Biomonitoring, Department of General Medicine, Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
Abstract:
Microsampling matrices support human biomonitoring using non-invasive and minimally invasive biological samples, such as saliva, urine, hair, nails, dried blood spots (DBS), sweat, breath, buccal swabs, stool, and tears. This review compiles analytical techniques, case studies, strategies for point-of-care (POC) integration, and applications in population surveillance for assessing exposure and effect biomarkers . We elaborate on validated Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) workflows, immunoassay platforms, electrochemical sensors, and molecular diagnostics, along with storage stability, detection limits (pg/mL to ng/mL range), and matrix-specific quality control needs. Organization for Economic Co-operation and Development (OECD) harmonization efforts recognize microsampling data as comparable to conventional sampling for occupational biomonitoring levels (OBLs), and human biomonitoring guidance values (HBM-GVs). Representative case studies illustrate practical applications: NHANES salivary cotinine, HBM4EU urine exposome profiling, segmental hair analysis, DBS diabetes screening, and FIT stool screening were included. POC integration includes wearable sweat sensors, smartphone-linked saliva Lateral Flow Immunoassays (LFIA), DBS cartridges, and smart contact lenses for real-time monitoring. Decision matrices, and workflow frameworks support selection for pediatric screening, occupational surveillance, remote field studies, and low-resource settings. Microsampling overcomes limitations of conventional biomonitoring by reducing invasiveness, logistics, and cost, while improving participant acceptability, scalability for global health surveillance.
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