Selenium speciation analysis in the cerebrospinal fluid of patients with Parkinson's disease.

BACKGROUND: The aim of the study was to investigate if speciation analysis by liquid chromatography coupled to mass spectrometry could be used to detect organic and inorganic binding forms of selenium in the cerebrospinal fluid (CSF) of patients with Parkinson's disease (PD) and age-matched control subjects (AMC). METHODS: PD patients and control subjects were enrolled from three different neurological departments. CSF samples were collected according to standardized biomarker protocols and subjected to inductively coupled plasma mass spectrometry (ICP-MS) for total selenium determination and ion exchange chromatography (IEC) hyphenated to ICP-MS for selenium speciation analysis. RESULTS: 75 PD patients and 68 age-matched controls were enrolled for speciation analysis. 8 different species could be detected, but only selenoprotein P (SELENOP), human serum albumin-bound Se (Se-HSA), selenomethionine (Se-Met) and an unidentified Se-compound (U2) presented with more than 50% values above the limit of quantification, without showing significant differences between both groups (p > 0.05). The Se-HSA / Se-Met ratio yielded a significant difference between PD and AMC (p = 0.045). The inorganic species Se-IV and Se-VI were only detectable in a minor part of PD and AMC samples. A highly significant correlation between total selenium levels and SELENOP (PD p < 0.0001; AMC p < 0.0001) and Se-HSA (PD p < 0.0001; AMC p < 0.0001) could be demonstrated, respectively. CONCLUSIONS: Speciation analysis yielded new insight into selenium homeostasis in PD but cannot be used to establish a diagnostic biomarker. The small number of detectable values for Se-IV and Se-VI suggests an inferior role of these potentially neurotoxic binding forms in PD pathology in contrast to other neurodegenerative disorders.

Proteomic analysis of tear fluid reveals disease-specific patterns in patients with Parkinson's disease - A pilot study.

BACKGROUND: The diagnosis of Parkinson's disease (PD) is still challenging and biomarkers could contribute to an improved diagnostic accuracy. Tear fluid (TF) is an easily accessible body fluid reflecting pathophysiological changes in systemic and ocular diseases and is already used as a biomarker source for several ophthalmological disorders. Here, we analyzed the TF of patients with PD and controls (CTR) to describe disease-related changes in TF and identify putative biomarkers for the diagnosis of PD. METHODS: Unstimulated TF samples of a pilot cohort with 36 PD patients and 18 CTR were collected via Schirmer tear test strips and then analyzed via a Bottom-up liquid chromatography electrospray ionization tandem mass spectrometry (BULCMS) workflow, followed by functional analysis encompassing protein-protein interaction as well as cellular component and pathway analysis. RESULTS: BULCMS analysis lead to the identification of 571 tear proteins (false discovery rate, FDR < 1%), whereby 31 proteins were exclusively detected in the PD group and 7 only in the CTR group. Whereas 21 proteins were significantly increased in the PD versus CTR groups, 19 proteins were significantly decreased. Core networks of proteins involved in immune response, lipid metabolism and oxidative stress were distinctly altered in PD patients. CONCLUSIONS: To our best knowledge, this is the first description of TF proteome in PD patients. Tear protein level alterations suggest the contribution of different disease-related mechanisms in ocular pathology in PD and propose candidate proteins to be validated as potential biomarkers in larger cohorts.

Elemental fingerprint as a cerebrospinal fluid biomarker for the diagnosis of Parkinson's disease.

The diagnosis of Parkinson's disease (PD) still lacks objective diagnostic markers independent of clinical criteria. Cerebrospinal fluid (CSF) samples from 36 PD and 42 age-matched control patients were subjected to inductively coupled plasma-sector field mass spectrometry and a total of 28 different elements were quantified. Different machine learning algorithms were applied to the dataset to identify a discriminating set of elements yielding a novel biomarker signature. Using 19 stably detected elements, the extreme gradient tree boosting model showed the best performance in the discrimination of PD and control patients with high specificity and sensitivity (78.6% and 83.3%, respectively), re-classifying the training data to 100%. The 10 times 10-fold cross-validation yielded a good area under the receiver operating characteristic curve of 0.83. Arsenic, magnesium, and selenium all showed significantly higher mean CSF levels in the PD group compared to the control group (p = 0.01, p = 0.04, and p = 0.03). Reducing the number of elements to a discriminating minimum, we identified an elemental cluster (Se, Fe, As, Ni, Mg, Sr), which most importantly contributed to the sample discrimination. Selenium was identified as the element with the highest impact within this cluster directly followed by iron. After prospective validation, this elemental fingerprint in the CSF could have the potential to be used as independent biomarker for the diagnosis of PD. Next to their value as a biomarker, these data also argue for a prominent role of these highly discriminating six elements in the pathogenesis of PD.