Ab'/> Metalloprotein and multielemental content profiling in serum samples from diabetic and hypothyroid persons based on PCA analysis
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Metalloprotein and multielemental content profiling in serum samples from diabetic and hypothyroid persons based on PCA analysis

机译:基于PCA分析的糖尿病患者和甲状腺功能体中血清样品中的金属蛋白和多元素含量分析

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AbstractDiabetes 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.
机译:<![cdata [ 抽象 糖尿病和甲状腺功能减退症都是全世界发病率的代谢疾病。金属蛋白和金属在正常葡萄糖代谢和甲状腺激素合成中发挥关键作用,其在各自的病理中被改变。该作品的目的是通过探索多变量主成分模型,在对照组(n = 20)中建立对照组(n = 20)中的相应多元素和金属普罗特曲线(n = 20)。分类以区分这些群体是可以基于23元素的量化(Mg,Al,K,Ca,V,Cr,Zn,Fe,Se,Rb,Pb,Cu,Mn,Co,Ni,U,Sr,Mo ,Sb,Ba,T1,Cd,Ag),并且或者在通过SEC-ICPM获得的金属蛋白谱上。通过QQQ-ICP和SEC-ICPMS分别进行测定,分别用于总和金属普罗基含量。使用主成分分析化学工具进行分类样品。结果表明,过渡元件浓度统计学差异,如Zn,Cu,Co,Mn,V和Cr。对于金属相关的蛋白质研究,在对照与糖尿病患者的对照与糖尿病患者之间的比较时,相同的过渡元件的级分也具有统计学不同的。 SE水平在群体之间的研究中没有差异。该筛查研究表明,具有化学计量工具的质谱方法和数据分析可能是有价值的,以便在糖尿病和甲状腺功能亢进患者的血清样本中找到可能的生物标志物。未来的蛋白质组学分析是完成这些发现的必要条件。

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