Detection of iron and zinc in human skin using non-invasive Raman spectrophotometer - A validation study among children under five years of age living in sub-Saharan Africa

Background: Sub-Saharan Africa (SSA) still experiences a high burden of micronutrient deficiencies. For monitoring of micronutrient status among young children in SSA, non-invasive alternatives to blood-based biomarkers are desirable. Handheld Raman spectrophotometry appears to offer this alternative to quantify intracellular stores of micronutrients. In rural Burkina Faso and Kenya, we validated the Cell-/SO-Check device (ZellCheck(R)) against conventional laboratory-based methods. Methods: For this validation study, we recruited children aged [≥]24 months attending routine clinics within the Health and Demographic Surveillance Systems (HDSS) in Siaya and Nouna. Anthropometric measurements and venous blood samples were taken. Plasma ferritin, soluble transferrin receptor (sTfR) and C-reactive protein (CRP) were measured by ELISA, and plasma zinc by atom absorption. The spectrometer was used to quantify zinc and iron. For continuous outcomes, we generated Bland Altman plots and calculated bias and limits of agreement (LoA). For binary outcomes, we produced Receiver Operator Characteristic (ROC) areas under the curve (AUC), and estimated sensitivity, specificity and predictive values. Results: We analysed data of 48 children from Burkina Faso and 54 children from Kenya (male: 53%; age range: 24-66 months). According to spectrophotometry, the proportions of iron deficiency and zinc deficiency were 16.7% and 25.5%, respectively. The median concentrations were for ferritin 24.0 {micro}g/L (range: 2.0-330.0), for sTfR 5.7 mg/L (2.8-51.0), and for zinc 9.9 {micro}mol/L (5.2-25.0). The corresponding bias for iron levels by spectrophotometry was 42.4 with LoA: -18.7, 103.6. The bias for zinc levels was 7.5 with LoA: -49.3, 64.2. For the classification of deficiency, the ROC-AUC, sensitivity, and specificity for spectrophotometry vs. biomarker-based diagnosis were for iron deficiency 0.62, 68% and 55%, respectively, and for zinc deficiency 0.55, 33% and 91%, respectively. Conclusions: The Cell-/SO-Check device may be used to rank children in population-based studies in SSA according to their zinc status, but not iron status. The method should not replace the standard laboratory measurements for clinical diagnoses of zinc and iron deficiencies.