A six-metabolite panel as potential blood-based biomarkers for Parkinson’s disease
Original author: Stephan Klatt, et al. (2021) (DOI: 10.1038/s41531-021-00239-x)
Summary
Content writer – Clinical
October 18, 2022
Introduction
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease (AD) and affects around five million people worldwide. People with PD typically exhibit symptoms when 50% or more dopaminergic neurons of the substantia nigra are demolished. Therefore, the early introduction of disease-modifying treatments would result in maximum effectiveness. Ideally, this would be before major neuronal death and, hence, long before clinical symptoms become apparent. Currently, there aren't any disease-modifying treatments for PD, thus early and precise diagnosis of PD might help researchers find such treatments. Research into the understanding of the early pathophysiological event in PD would be supported by pre-symptomatic detection. Presently, there is no trustworthy biomarker for pre-symptomatic idiopathic PD (iPD) detection. This emphasizes the significance of the development of a new diagnostic marker to enable early diagnosis and evaluation of new potential treatments.
Methodology
In this study, 38 iPD-relevant metabolites were extracted from the blood serum of 231 individuals, and the concentration of these metabolites was quantified by using a targeted triple quadrupole liquid chromatography-mass spectrometry (QQQ LC/MS) method, to uncover changes solely based on the disease. This cohort is presently one of the largest metabolomic studies including iPD patients, drug-naive iPD, healthy controls, and patients with Alzheimer’s disease as a disease-specific control group. All the experiments were conducted under The University of Melbourne human ethics committee approval ID1136882. All participants provided written informed consent before their inclusion in the study. They examined the impact of L-DOPA on the tested metabolites along with the impact of age and sex before and after confounder adjustment. Additionally, they examined changes in the ratios and interactions of all targeted metabolites and used this to recognize potential biomarkers.
Results
The study found six metabolites (3-hydroxykynurenine, aspartate, beta-alanine, homoserine, ornithine, and tyrosine) that were significantly different between iPD patients and control participants. There were no substantial interactions between either age and control/iPD or between gender and control/iPD associated with metabolites. They also investigated all possible ratios and interactions. Comparing the mean ratio/interaction levels between the control and iPD groups, they identified 11 ratios and 23 significantly different interactions (p < 0.00009) between the two groups.
In addition, they performed a multivariate analysis including both individual analytes and ratios/ interactions to examine if a panel of markers collectively could provide better discrimination between control and disease groups. Using a combination of feature selection (LASSO) and model selection via Akaike information criterion (AIC) reduction, seven markers including Cys, 2-aminobutyric acid, Tyr, L-KYN, a ratio of Arg/3-AA, a ratio of Asp/L-KYN, and product of β-ala*Orn were selected in a linear model to separate control from iPD participants. These seven metabolites resulted in an AUC value of 0.905 with an accuracy of 86.2%. Using the same method, a set of six markers were selected in a linear model to separate control from AD participants (Asp, Cys, Tryp, Homoserine/N-Acetyl-phenylalanine, Pro/3-HK, and Gln*Typtamine). A multivariate model to predict AD from controls had an area under the curve (AUC) of 0.884, with an accuracy of 79.3%.
Impact of research
The study measured the concentration of relevant metabolites extracted from the blood serum of 231 individuals to uncover changes solely based on the disease. This study also identified potential biomarkers to distinguish between control and disease (PD and AD). Presently, various tissue- and fluid-based biomarkers are being explored to enhance PD diagnosis, monitor disease progression, assess treatment responses, or categorize PD subtypes. Therefore, a panel of biomarkers could aid to increase the accuracy of iPD diagnosis and treatment outcomes and they have a great potential for utilization in clinical practice.
Conclusion
iPD and its progression appear to cause global metabolic alterations in peripheral body fluids as well as the brain. This study adds to the evidence that amino acids and metabolites of the kynurenine pathway are changed in patients with iPD. The present study suggested that the panel of metabolites might be used as a potential prognostic or diagnostic assay for clinical trial prescreening, or for assisting in diagnosing pathological diseases in clinics.
