ALBERT

Description: Highlights • Information of elemental and metalloprotean homeostasis regarding diabetes and hypothyroidism diseases is scare. • The powerful QQQ-ICP tehcnique was coupled with HPLC to determine a large number of elements. • Classification of serum samples from both groups of study was assessed by means of principal component analysis. • Transition elements (associated to protean fractions) probed to have the most influence in the classification model. Diabetes and hypothyroidism are both metabolic diseases with great incidence worldwide. Metalloproteins and metals play key roles in normal glucose metabolism and thyroid hormone synthesis, which are altered in their respective pathologies. The aim of this work was to establish the corresponding multielemental and metalloprotean profiles in a control group (n = 20) compared with a diabetic (n = 20) or hypothyroidism group (n = 20), by exploring a multivariate principal components model. Classification to discriminate these groups was possible based in the quantification of 23 elements (Mg, Al, K, Ca, V, Cr, Zn, Fe, Se, Rb, Pb, Cu, Mn, Co, Ni, U, Sr, Mo, Sb, Ba, Tl, Cd, Ag), and alternatively on the metalloprotein profiles obtained by SEC-ICPMS. Determinations were assessed by means of QQQ-ICP and SEC-ICPMS for total and metalloprotean content, respectively. Samples were classified using Principal Component Analysis chemometric tool. Results showed that there were statistical differences in transitional elements concentrations, such as Zn, Cu, Co, Mn, V, and Cr. For the metal associated protein study, the expression of the fractions of the same transitional elements also were statistically different when compared between control vs diabetic patients, and control vs hypothyroid patients. Se levels showed no differences in both studies among groups. This screening study demonstrates that mass spectrometry methods and data analysis with chemometrics tools may be valuable in order to find possible biomarkers in serum samples of diabetic and hypothyroid patients. Future proteomics analysis are necessary to complete these findings.