Exploring Diagnostic Markers and Therapeutic Targets in Parkinson’s Disease: A Comprehensive 1H-NMR Metabolomic Analysis – Systematic Review

Introduction

1.

Parkinson’s disease (PD) affects approximately 6 million people globally (Dorsey et al. 2018). Many individuals with Parkinsonism or tremor are often unrecognized or misdiagnosed, particularly in the early stages of the disease. Currently, the progression of PD is assessed based on clinical features, but validated biomarkers that are both sensitive and specific for detecting and tracking PD progression are lacking. This review focuses on the current understanding of diagnostic biomarkers and those associated with PD progression (Hall et al. 2015; Andersen et al. 2017).

There is an ongoing challenge in developing improved methods for diagnosing PD and measuring its progression. Despite significant efforts to identify biochemical indicators, reliable biomarkers with high specificity and sensitivity have remained elusive, even within cerebrospinal fluid, which is adjacent to neurodegeneration sites in the brain (Surguchov 2022). This issue extends to substances critical to PD pathophysiology, such as α-synuclein and dopamine metabolites, which do not consistently differentiate PD cases from controls (Gelpi et al. 2014).

A key challenge in biomarker discovery is determining the breadth of the search for PD indicators. Historically, PD pathology was believed to be confined to dopaminergic neurons in the substantia nigra, making a focus on reduced dopamine neurotransmission seem logical for defining and monitoring the disease. However, the manifestations of underlying PD proteinopathy, particularly α-synuclein aggregates, occur throughout the body. Research indicates that the bacterial population in the large intestine is altered in PD (Shen et al. 2021). Additionally, PD’s subcellular effects extend to mitochondria and the proteasome-lysosome system. Consequently, shifting the search to PD biomarkers to encompass the entire biochemical environment of the body appears promising (Derkinderen et al. 2014; Gelpi et al. 2014; Carvalho et al. 2015; Hall et al. 2015).

Metabolomics has emerged as one of the most effective technologies in recent biomarker research, capable of analyzing hundreds of low-molecular weight (<1.5 kDa) compounds in biological samples and characterizing a wide range of human metabolic environments. Independent analytical methods for identifying biomarkers do not rely on functional relationships between biochemicals, such as established metabolic pathways. However, subsequent analyses can evaluate whether biomarker results align with known pathways and can investigate common physicochemical properties, including indicators of oxidative stress (Caudle et al. 2010; Vilchez et al. 2014).

More than 1 million people in the United States and approximately 6 million people worldwide are affected by PD (Dorsey et al. 2018; Yang et al. 2020a,b). The disease is characterized by resting tremor, bradykinesia, cogwheel rigidity, and, in later stages, postural instability. Diagnosis is primarily based on clinical presentation. Recent studies indicate that even movement disorder specialists can misdiagnose the initial symptoms of PD in at least 10% of cases, while the misdiagnosis rate in primary care settings may reach as high as 50%. Patients misidentified as having PD in primary care often have conditions such as drug-induced tremor, essential tremor, or psychogenic tremor. In cases where patients exhibit parkinsonian symptoms without tremors, distinguishing between atypical Parkinsonism—such as multiple system atrophy or progressive supranuclear palsy—and PD can be challenging. Additionally, patients developing parkinsonian symptoms while taking dopamine-blocking agents may experience drug-induced Parkinsonism rather than PD (Lawton et al. 2008; Evans et al. 2009; Guo et al. 2015).

Biomarkers that assist clinicians in differentiating PD from essential tremor, drug-induced Parkinsonism from psychogenic tremor, and atypical Parkinsonism from PD and drug-induced Parkinsonism will be invaluable in clinical practice. These biomarkers can help prevent misdiagnoses, reduce unnecessary diagnostic procedures, and improve patient prognosis. While some biomarkers serve as diagnostic tools, others that track disease progression are vital for evaluating the effects of interventions on the course of PD, demonstrating findings such as “neuroprotective” or “disease-modifying” impacts. Currently, the gold standard for assessing PD progression is clinical scales, including the Unified Parkinson’s Disease Rating Scale (Hiller et al. 2009; Guo et al. 2015). Certain biomarkers may also aid in selecting the most effective therapy for patients by predicting potential responses to various medications, thereby allowing for tailored disease-modifying or symptomatic treatments. The concept of “personalized medicine” for PD patients—using pharmacogenetic, biochemical, and clinical methods—is becoming increasingly plausible, especially given its application in cancer chemotherapy studies and in determining optimal initial dosing for warfarin therapy (Hiller et al. 2009). Modern analytical techniques are known for their high sensitivity, precision, accuracy, and reproducibility (Lawton et al. 2008; Drexler et al. 2011). Among the most widely used methods are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) (Peironcely et al. 2011; Gelpi et al. 2014). MS works by measuring the mass- to-charge ratio of ions, which enables the identification of specific substances (Kozioł et al. 2023). When combined with chromatographic techniques, MS has led to the development of two powerful methods for analyzing chemical compounds in body fluids: gas chromatography coupled with mass spectrometry (GC-MS) and liquid chromatography coupled with mass spectrometry (LC-MS). Although these techniques are highly effective for metabolite analysis, they can present challenges in data interpretation. For example, the simultaneous elution of compounds with similar molecular masses and polarities can make them difficult to distinguish. Additionally, the “matrix effect” can interfere with metabolite detection, where the composition of the sample matrix alters the results, leading to potential inaccuracies in measurement and reduced reproducibility (Drexler et al. 2011; Kozioł et al. 2023). Despite these challenges, MS remains a highly specific and sensitive method for selectively detecting metabolites and has found widespread use in fields such as drug detection (Lawton et al. 2008).

NMR is considered one of the best techniques for studying urine metabolites and is particularly effective for identifying chemical compounds excreted by the kidneys (Peironcely et al. 2011; Gelpi et al. 2014). NMR can detect a wide range of metabolites, but MS is generally regarded as the more effective method for identifying larger numbers of metabolites—sometimes up to several thousand. Additionally, NMR does not alter the chemical structure of compounds during analysis, unlike GC-MS and LC-MS, which can cause modifications to the substances being studied (Caudle et al. 2010; Kozioł et al. 2023).

Each of these methods has its limitations and potential sources of error. Therefore, combining both NMR and MS is often recommended to provide a more comprehensive analysis of metabolomics in body fluids (Derkinderen et al. 2014; Gelpi et al. 2014; Hall et al. 2015; Andersen et al. 2017). Metabolomics techniques are increasingly being applied across various fields of medicine, particularly in oncology and in the study of metabolic diseases and their underlying pathomechanisms.

Materials and Methods

2.

Study design

2.1.

The review was designed based on the PRISMA declaration (Page et al. 2021). The databases Scopus, Web of Science, PubMed, and Embase were systematically reviewed to identify relevant articles. Given the limited number of articles retrieved during the preliminary search, broader search parameters were implemented using the query “Parkinson AND NMR AND metabolites”. Following the synthesis of search results, non-English publications, duplicate entries, and non-original research articles were excluded. Subsequently, the titles, abstracts, and full texts of the remaining articles were thoroughly examined. A detailed breakdown of the data across each stage of the review process is presented in Figure 1. The review of manuscripts, evaluation of inclusion and exclusion criteria, and assessment of risk of bias were conducted independently by two authors. Discrepancies or borderline cases were resolved by a third author with extensive experience in the field. The risk of bias was evaluated using the ROBINS-I tool (Sterne et al. 2016), with final decisions in cases of conflict determined independently by the author with the longest tenure in scientific research.

Fig 1.

Results

3.

The data from 11 selected manuscripts were included in the final synthesis, and key metabolites were identified from them. The reproducibility of these findings is evaluated in Table 1. The full synthesis is included in Table S1 in Supplementary Materials. Most components of the bias risk assessment were rated as low risk. Three ratings indicated significant risk, primarily due to insufficient information to confirm a low bias risk in specific areas. Despite this, the conclusions of these articles aligned with those rated more favorably, and other components did not raise significant concerns. Therefore, no articles were excluded due to bias risk. Detailed assessments are provided in Table S2 in Supplementary Materials.

Following a review of the results, the data were synthesized to identify key metabolites associated with the progression of PD, as summarized in Table 1. Based on the analyzed data, five metabolites were selected. These metabolites, along with their corresponding pathways and an evaluation of reproducibility, are detailed in Table 2. The summary provided in Tables 1 and 2 does not include the data from Meoni et al. (2022), which did not identify a characteristic differentiating metabolite. Instead, the study suggests that the sequence of metabolites serves as the distinguishing factor.

Discussion

4.

This study focused on ¹H-NMR spectroscopy, emphasizing its key distinctions from MS. Unlike MS, NMR spectroscopy— commonly proton NMR—does not destroy the sample, enabling subsequent analyses using other methods. Additionally, each NMR measurement includes a standard reference substance, enhancing reliability. Consequently, NMR spectroscopy is often preferred for its lower costs associated with quantitative testing and more frequent use in certain applications compared with MS. Furthermore, body fluid samples generally require minimal preparation for NMR analysis, although lipid testing necessitates an extraction step (Kozioł et al. 2023). The analysis of the metabolites identified in this study provides crucial insights into the pathophysiology of PD. Table 2 highlights key metabolites and their associated pathways, emphasizing their roles in disease progression and the underlying biochemical disruptions in PD. Glutamate is a key metabolite implicated in the pathophysiology of PD, primarily through its role in glial dysfunction, which leads to glutamate-induced excitotoxicity—a major contributor to neurodegeneration. This aligns with evidence showing that altered glutamate receptor activity disrupts neuronal excitability, neurotransmitter release, and long-term synaptic plasticity. These findings highlight the pivotal role of glutamate in PD’s neurochemical mechanisms and suggest that targeting glutamate signaling could reduce excitotoxic damage, presenting a promising therapeutic avenue (Zhang et al. 2019; Iovino et al. 2020).

Taurine was identified as another significant metabolite, closely linked to oxidative stress. The depletion of taurine due to elevated reactive oxygen species generation correlates with its role in regulating enzymes of cysteine synthesis, a process disrupted in PD. The association between oxidative stress and neurodegeneration in PD is well-documented, suggesting that therapeutic strategies targeting taurine levels could ameliorate oxidative damage. However, the moderate reproducibility of taurine findings highlights the need for further validation in larger cohorts (Dias et al. 2013).

Myo-inositol, particularly its derivative myo-inositol-1,4,5- triphosphate (IP3), is implicated in the dysregulation of autophagy via the mTOR signaling pathway. Autophagy deficits in PD result in the accumulation of misfolded proteins and dysfunctional organelles, exacerbating neuronal damage. This positions myo-inositol as a potential biomarker and therapeutic target (Motoi et al. 2014).

The role of glutamine as a precursor for glutamate further illustrates the interconnected nature of these metabolites. Disruptions in glutamine metabolism may exacerbate glutamate excitotoxicity, reinforcing the need for metabolic homeostasis in PD management (Zhang et al. 2019; Iovino et al. 2020). Creatine, another identified metabolite, highlights the energy deficit characteristic of PD. As a key player in neuronal energy metabolism, creatine’s involvement suggests that energy-targeting therapies, such as supplementation, may mitigate disease progression (Chaturvedi and Beal 2013). However, its low reproducibility (3/10 studies) necessitates further exploration.

The reproducibility of these findings varied, with glutamate detected in ≥5/10 studies, taurine and myo-inositol in 4–5/10 studies, and creatine in 3/10 studies. These variations underscore the complexity of the PD metabolomic landscape and the importance of integrating metabolomics with other omics approaches to achieve a more comprehensive understanding of PD pathogenesis.

In conclusion, the identified metabolites provide valuable insights into the molecular mechanisms underlying PD. Glutamate emerges as a central contributor to neurodegeneration, while taurine, myo-inositol, glutamine, and creatine elucidate additional pathological mechanisms, including oxidative stress, autophagy dysfunction, and energy deficits. Future research should aim to validate these findings and explore targeted therapeutic interventions, potentially offering novel approaches to slow the progression of PD.