Metallomic profiling and linkage map analysis of early Parkinson's disease: a new insight to aluminum marker for the possible diagnosis

Abstract

Background: Parkinson's disease (PD) is the most common neurodegenerative disorder. The diagnosis of PD is challenging and currently none of the biochemical tests have proven to help in diagnosis. Serum metallomic analysis may suggest the possibility of diagnosis of PD.

Methodology/results: The metallomic analysis was targeted on 31 elements obtained from 42 healthy controls and 45 drug naive PD patients using ICP-AES and ICP-MS to determine the concentration variations of elements between PD and normal. The targeted metallomic analysis showed the significant variations in 19 elements of patients compared to healthy control (p<0.04). The partial least squares discriminant analysis (PLS-DA) showed aluminium, copper, iron, manganese and zinc are the key elements, contributes the separation of PD patients from control samples. The correlation coefficient analysis and element-element ratio confirm the imbalance of inter-elements relationship in PD patients' serum. Furthermore, elements linkage map analysis showed aluminium is a key element involved in triggering of phosphorus, which subsequently lead to imbalance of homeostatic in PD serum. The execution of neural network using elements concentrations provides 95% accuracy in detection of disease.

Conclusions/significance: These results suggest that there is a disturbance in the elements homeostasis and inter-elements relationship in PD patients' serum. The analysis of serum elements helps in linking the underlying cellular processes such as oxidative stress, neuronal dysfunction and apoptosis, which are the dominating factors in PD. Also, these results increase the prospect of detection of early PD from serum through neural network algorithm.

Figure 1. PLS-DA analysis of normal and PD serum elements.

PLS-DA score's plot displays a significant separation between control subjects (n = 42) and unmedicated PD patients (n = 45) using complete digital maps. The observations coded according to class membership: triangle is normal and rhombus is PD. Each data point on a plot represents one individual.

Figure 2. Biomarker detection: Elements which are dominating the separation of disease from normal.

The loading coefficient map showing that aluminum, copper, iron, manganese, and zinc were predominantly responsible for the classification of groups. The blackened triangle represents elements; grey circle represents normal (N) and Parkinson's disease (P).

Figure 3. Element linkage map representing the interactions of elements.

The interactions of 21 elements were configured. (A) Represents the basic interaction occurs in mammals. (B) Represents the interactions in PD. The single head arrow indicates the increase of an element X, decrease the element Y (negative correlation). The double headed arrow indicates, increase of an element X will increase the element Y or vice versa (positive correlation). The blackened arrow indicates similar significant patterns in (A) and (B); gray arrows indicate significant variations in interaction in PD and blackened dotted arrows indicate insignificant interaction. The grey box indicates insignificant variation in concentration between normal and disease (ANOVA), and their interactions analysis was not carried out.

Figure 4. Neural network predication.

Neural network classification of 23 individuals (x-axis) with known clinical information. Values (y-axis) are predicted over the trained network and are 0.1 to 1; values (x) ≤0.54 reflect a neural-network classification of “normal,” and values (x) ≥0.55 reflect a neural-network classification of PD. Individuals denoted by a triangle  =  normal, circle  =  PD and square  =  misdiagnosed individual.