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Annals of Indian Academy of Neurology
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Evidence-based discriminant analysis: A new insight into iron profile for the diagnosis of parkinson's disease


1 Department of Biostatistics, Institute of Human Behavior and Allied Sciences, Delhi, India
2 Department of Neurochemistry, Institute of Human Behavior and Allied Sciences, Delhi, India
3 Department of Neurology, Institute of Human Behavior and Allied Sciences, Delhi, India

Correspondence Address:
Rachna Agarwal,
Department of Neurochemistry Institute of Human Behavior and Allied Sciences, New Delhi
India
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aian.AIAN_419_20

Introduction: Parkinson's disease is the second most common neurodegenerative disorder. Neurochemical studies have implicated metals in pathogenesis of PD. Objectives: To examine the association of serum iron, transferrin, ferritin, transferrin saturation and UIBC in PD patients and to derive the Discrimination Function with scores of these variables to correctly classify PD cases and healthy controls. Methods: In the present study, identification of biomarker pool in case-control study involving 79 PD cases and 80 healthy controls were performed. Results: The results of independent t-test analysis showed that PD cases presented significantly higher (P < 0.01) level of transferrin, total iron binding capacity (TIBC), unsaturated iron binding capacity (UIBC) and urea than controls. As only one-third of transferrin is saturated with iron, so the transferrin present in serum has the extra binding capacity (67%), this is called UIBC. Discriminant analysis was performed to determine the factors that best discriminate between the categories of an outcome variables (Disease status = PD and Control) and total of five biochemical independent variables (UIBC, transferrin, iron, transferrin saturation, and copper) were taken into consideration. UIBC has emerged out to be highest discriminating, powerful and independent variable among considered independent variables, which indicates iron deficiency. After development of Discriminant Function (Z) and calculation of discriminant function cut points, a cross-validation analysis of PD cases and controls were conducted. The sensitivity of the developed model was 98.73% and specificity 83.75%. Receiver operating characteristics (ROC) was plotted, and the findings of ROC curve corroborated with the results obtained from discriminant function analysis. Conclusion: Prospective validation of Discriminant model in large cohort is warranted in future studies.


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