Appraisal of Metal Imbalances in the Blood of Thyroid Cancer Patients in Comparison with Healthy Subjects.
Kalsoom BibiMunir Hussain ShahPublished in: Biological trace element research (2021)
Cancer incidence and mortality rates have been increasing rapidly worldwide. A growing body of evidence revealed that exposure to trace metals is the most important aetiology for development of the cancer. Therefore, present study was intended to evaluate the imbalances in the concentrations of selected metals (Na, K, Ca, Mg, Sr, Li, Fe, Zn, Cu, Co, Mn, Ag, Cd, Cr, Ni and Pb) in the blood of newly diagnosed thyroid cancer patients in comparison with counterpart healthy subjects/controls. Concentrations of the metals were quantified by flame atomic absorption spectrometry by employing nitric acid/perchloric acid-based wet digestion method. Average concentrations of Pb (774.6 μg/dL), Cr (757.9 μg/dL), Cd (472.5 μg/dL) and Ni (360.5 μg/dL) were found to be significantly higher in the blood of cancer patients than controls. Correlation study and multivariate analysis showed strong mutual relationships among Fe-Cd-Ca-Mg-Pb, Co-Sr-Zn, Li-Ag-Na-K and Cu-Ni in the blood of thyroid cancer patients while Na-K-Fe-Co-Pb, Zn-Sr-Cr, Ca-Mg and Li-Ag-Cu-Ni exhibited strong mutual associations in the blood of healthy donors. Significant variations in the trace metal levels were observed with the age, gender, habitat, food habits and smoking habits of both donor groups. Metal levels also exhibited considerable disparities with the stages and types of thyroid cancer. Multivariate analysis of the metal data revealed significantly divergent apportionment of the metals in the blood of cancer patients compared with the healthy group.
Keyphrases
- heavy metals
- health risk assessment
- metal organic framework
- health risk
- aqueous solution
- human health
- risk assessment
- newly diagnosed
- papillary thyroid
- squamous cell carcinoma
- risk factors
- young adults
- high resolution
- coronary artery disease
- climate change
- mental health
- electronic health record
- visible light
- data analysis
- single cell
- smoking cessation
- machine learning
- cardiovascular events
- clinical evaluation
- deep learning