Faculty of Public Health - Andalas University - OCS, 13th IEA SEA Meeting and ICPH - SDev

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Iron Profile and Parkinson’s disease risk: evidence from Discriminant Analysis
CHANDRA BHUSHAN TRIPATHI

Last modified: 2018-08-27

Abstract


Background: Parkinson’s disease is a second most common among the neurodegenerative disorders, affecting 2% of the population over the age of 65 years. It is a multifactorial disease with the involvement of age, genetic & environmental factors. Neurochemical studies have implicated metals in pathogenesis of PD, including Copper & Iron.

Objectives: The main objective of the study was to develop a linear discriminant function model, based on independent biochemical marker variables (Transferrin, Ferritin, Transferrin saturation & UIBC), that could best separate the Parkinson’s disease cases and otherwise healthy controls.

Materials and methods: In the present study, identification of biomarker pool in case-control study involving 79 PD cases and 80 healthy controls were performed to examine association among multiple biomarkers. The Discriminant Analysis was applied to develop a linear model of independent biochemical marker variables by which PD cases and controls could be correctly classified.

Results: The results of independent t-test analysis showed that PD cases presented significantly higher level of transferrin, TIBC, UIBC and urea than controls. Discriminant analysis was performed to determine the factors that best discriminates between the categories of an outcome variables (Disease status = PD & Control) and total of five biochemical independent variables (UIBC, transferrin, serum iron, transferrin saturation, and copper) were taken into consideration. In present study, UIBC (µmol/L) has emerged out to be highest discriminating powerful independent variable among considered independent variables. The Discriminant model developed, was also cross-validated, and observed that the developed discriminant function, was more sensitive (98.73%) than specific (83.75%) as by this function, only one PD case (0.013 %) was wrongly classified as control whereas only 13 controls (0.16%), out of 80, were wrongly classified as PD cases.

Conclusion: This is retrospective study and discriminant model, with the independent variables, was developed on the basis of already diagnosed patients by experienced Neurologists. In Future, a prospective study may conducted to validate this model which may help in effectively diagnosing the PD cases and controls.

 

Keywords: Parkinson’s disease, Iron Profile, Discriminant analysis, Sensitivity, Specificity