Biomarker characterization of clinical subtypes of Parkinson Disease

Correlation of inflammatory markers with depression and sleep disorders accompanying the prodromal stage of Parkinson's disease

Parkinson's disease (PD) is a common neurodegenerative disorder with increasing incidence and disability rates globally, placing a heavy burden on patients and their families. In the prodromal phase of PD, nonmotor symptoms, particularly depression and sleep disorders, are frequent, with profound effects on disease progression and patient quality of life. Emerging research highlights the critical role of inflammatory markers-including interleukins and tumor necrosis factor-in the pathogenesis of prodromal PD. These inflammatory mediators participate in neurodegenerative processes and may induce or exacerbate depressive symptoms and sleep disorders by disrupting the function of the hypothalamic-pituitary-adrenal axis and affecting neurotransmitter, including serotonin, metabolism. Understanding their correlations with nonmotor symptoms in prodromal PD remains incomplete, limiting our ability to develop targeted interventions. This comprehensive review aims to investigate the specific correlations between inflammatory markers and nonmotor symptoms-particularly depression and sleep disorders-in prodromal PD. The findings could have important practical applications, potentially leading to the development of new diagnostic tools and therapeutic strategies for managing PD. By identifying and understanding these correlations, healthcare providers may better predict disease progression and implement more effective treatments for nonmotor symptoms in PD.

Evolving Landscape of Parkinson's Disease Research: Challenges and Perspectives

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It occurs due to a gradual deficit of dopamine-producing brain cells, particularly in the substantia nigra. The precise etiology of PD is not fully understood, but it likely involves a combination of genetic and environmental factors. The therapies available at present alleviate symptoms but do not stop the disease's advancement. Research endeavors are currently directed at inventing disease-controlling therapies that aim at the inherent mechanisms of PD. PD biomarker breakthroughs hold enormous potential: earlier diagnosis, better monitoring, and targeted treatment based on individual response could significantly improve patient outcomes and ease the burden of this disease. PD research is an active and evolving field, focusing on understanding disease mechanisms, identifying biomarkers, developing new treatments, and improving care. In this report, we explore data from the CAS Content Collection to outline the research progress in PD. We analyze the publication landscape to offer perspective into the latest expertise advancements. Key emerging concepts are reviewed and strategies to fight disease evaluated. Pharmacological targets, genetic risk factors, as well as comorbid diseases are explored, and clinical usage of products against PD with their production pipelines and trials for drug repurposing are examined. This review aims to offer a comprehensive overview of the advancing landscape of the current understanding about PD, to define challenges, and to assess growth prospects to stimulate efforts in battling the disease.

Parkinson's disease subtypes: Approaches and clinical implications

Parkinson's disease (PD) is a complex neurodegenerative disorder with significant heterogeneity in disease presentation and progression. Subtype identification remains a top priority in the field of PD clinical research. Several PD subtypes have been identified. Hypothesis-driven subtypes refer to pre-defined subtypes based on specific criteria. Under hypothesis-driven subtypes, motor subtypes are the most common empirical subtype in both research and clinical settings. The concept of the non-motor symptoms (NMS) subtypes is relatively new and less well studied. Mild cognitive impairment (MCI) is one of the more prevalent NMS subtypes of PD. Data-driven subtyping is a hypothesis-free approach, that defines disease phenotypes by comprehensively evaluating multidimensional data. In this review, we summarize the main features for the different PD subtypes: from hypothesis-driven subtypes to data-driven subtypes. NMS and data-driven subtypes are still not yet well understood particularly with regard to biomarker and progression characterization. Future PD subtyping based on specific biological makers will enable us to better reflect the underlying pathophysiological underpinnings and enhance our search for specific therapeutic targets. The goal is to develop a simple algorithm to subtype PD patients at an early stage of PD that will enable good prognostication of their disease course, targeted therapies to be delivered, and proactive prevention of complications. Understanding PD subtypes and heterogeneity will also guide future clinical trial design and aid clinicians to better manage PD patients that will enable targeted disease surveillance and personalized treatment. The graphical abstract can be seen below.

Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

Bidirectional relationship between olfaction and Parkinson's disease

Hyposmia (decreased smell function) is a common early symptom of Parkinson's disease (PD). The shared genetic architecture between hyposmia and PD is unknown. We leveraged genome-wide association study (GWAS) results for self-assessment of 'ability to smell' and PD diagnosis to determine shared genetic architecture between the two traits. Linkage disequilibrium score (LDSC) regression found that the sense of smell negatively correlated at a genome-wide level with PD. Local Analysis of [co]Variant Association (LAVA) found negative correlations in four genetic loci near GBA1, ANAPC4, SNCA, and MAPT, indicating shared genetic liability only within a subset of prominent PD risk genes. Using Mendelian randomization, we found evidence for a strong causal relationship between PD and liability towards poorer sense of smell, but weaker evidence for the reverse direction. This work highlights the heritability of olfactory function and its relationship with PD heritability and provides further insight into the association between PD and hyposmia.

Machine Learning for Early Detection of Cognitive Decline in Parkinson's Disease Using Multimodal Biomarker and Clinical Data

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson's Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods-AutoScore, Random Forest, K-Nearest Neighbors and Neural Network-were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

Synaptic Vesicle Glycoprotein 2C: a role in Parkinson's disease

Synaptic Vesicle Glycoprotein 2C (SV2C), characterized by its selective expression in discrete brain regions such as the midbrain, has recently emerged as a promising player in Parkinson's Disease (PD) - a debilitating neurodegenerative disorder affecting millions worldwide. This review aims to consolidate our current understanding of SV2C's function, its involvement in PD pathogenesis, and to evaluate its potential as a therapeutic target. Integrating previous findings of SV2C, from genetics to molecular studies, and drawing on insights from the largest East Asian genome-wide association study that highlights SV2C as a novel risk factor for PD, we explore the potential pathways through which SV2C may influence the disease. Our discussion extends to the implications of SV2C's role in synaptic vesicle trafficking, neurotransmitter release, and α-synuclein homeostasis, thereby laying the groundwork for future investigations that could pave the way for novel therapeutic strategies in combating PD.

Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectives

The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.

Methods

We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.

Results

Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.

Conclusions

The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.

How Lifetime Evolution of Parkinson's Disease Could Shape Clinical Trial Design: A Shared Patient-Clinician Viewpoint

Parkinson's disease (PD) has a long, heterogeneous, pre-diagnostic phase, during which pathology insidiously accumulates. Increasing evidence suggests that environmental and lifestyle factors in early life contribute to disease risk and progression. Thanks to the extensive study of this pre-diagnostic phase, the first prevention trials of PD are being designed. However, the highly heterogenous evolution of the disease across the life course is not yet sufficiently taken into account. This could hamper clinical trial success in the advent of biological disease definitions. In an interdisciplinary patient-clinician study group, we discussed how an approach that incorporates the lifetime evolution of PD may benefit the design of disease-modifying trials by impacting population, target and outcome selection. We argue that the timepoint of exposure to risk and protective factors plays a critical role in PD subtypes, influencing population selection. In addition, recent developments in differential disease mechanisms, aided by biological disease definitions, could impact optimal treatment targets. Finally, multimodal biomarker panels using this lifetime approach will likely be most sensitive as progression markers for more personalized trials. We believe that the lifetime evolution of PD should be considered in the design of clinical trials, and that such initiatives could benefit from more patient-clinician partnerships.

The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography

BACKGROUND

Predicting Parkinson's disease (PD) can provide patients with targeted therapies. However, disease severity can be roughly evaluated in clinical practice based on the patient's symptoms and signs.

OBJECTIVE

The current study attempted to explore the factors linked with PD severity and construct a predictive model.

METHOD

The PD patients and healthy controls were recruited from our study center while recording their basic demographic information. The serum inflammatory markers levels, such as Cystatin C (Cys C), C-reactive protein (CRP), RANTES (regulated on activation, normal T cell expressed and secreted), Interleukin-10 (IL-10), and Interleukin-6 (IL-6) were determined for all the participants. PD patients were categorized into early and mid-advanced groups based on the Hoehn and Yahr (H-Y) scale and evaluated using PD-related scales. LASSO logistic regression analysis (Model C) helped select variables based on clinical scale evaluations, serum inflammatory factor levels, and transcranial sonography measurements. The optimal harmonious model coefficient λ was determined via 10-fold cross-validation. Moreover, Model C was compared with multivariate (Model A) and stepwise (Model B) logistic regression. The area under the curve (AUC) of a receiver operator characteristic (ROC), brier score, calibration curve, and decision curve analysis (DCA) helped determine the discrimination and calibration of the predictive model, followed by configuring a forest plot and column chart.

RESULTS

The study included 113 healthy individuals and 102 PD patients, with 26 early and 76 mid-advanced patients. Univariate analysis of variance screened out statistically significant differences among inflammatory markers Cys C and RANTES. The average Cys C level in the mid-advanced stage was significantly higher than in the early stage (p < 0.001) but not for RANTES (p = 0.740). The LASSO logistic regression model (λ.1se = 0.061) associated with UPDRS-I, UPDRS-II, UPDRS-III, HAMA, PDQ-39, and Cys C as the included independent variables revealed that the Model C discrimination and calibration (AUC = 0.968, Brier = 0.049) were superior to Model A (AUC = 0.926, Brier = 0.079) and Model B (AUC = 0.929, Brier = 0.071) models.

CONCLUSION

The study results show multiple factors are linked with PD assessment. Moreover, the inflammatory marker Cys C and transcranial sonography measurement could objectively predict PD symptom severity, helping doctors monitor PD evolution in patients while targeting interventions.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Disease Progression of Data-Driven Subtypes of Parkinson's Disease: 5-Year Longitudinal Study from the Early Parkinson's Disease Longitudinal Singapore (PALS) Cohort

Background

The detailed trajectory of data-driven subtypes in Parkinson's disease (PD) within Asian cohorts remains undisclosed.

Objective

To evaluate the motor, non-motor symptom (NMS) progression among the data-driven PD clusters.

Methods

In this 5-year longitudinal study, NMS scale (NMSS), Hospital Anxiety Depression Scale (HADS), and Epworth sleepiness scale (ESS) were carried out annually to monitor NMS progression. H& Y staging scale, MDS-UPDRS part III motor score, and postural instability gait difficulty (PIGD) score were assessed annually to evaluate disease severity and motor progression. Five cognitive standardized scores were used to assess detailed cognitive progression. Linear mixed model was performed to assess the annual progression rates of the longitudinal outcomes.

Results

Two hundred and six early PD patients, consisting of 43 patients in cluster A, 98 patients in cluster B and 65 subjects in cluster C. Cluster A (severe subtype) had significantly faster progression slope in NMSS Domain 3 (mood/apathy) score (p = 0.01), NMSS Domain 4 (perceptual problems) score (p = 0.02), NMSS Domain 7 (urinary) score (p = 0.03), and ESS Total Score (p = 0.04) than the other two clusters. Cluster A also progressed significantly in PIGD score (p = 0.04). For cognitive outcomes, cluster A deteriorated significantly in visuospatial domain (p = 0.002), while cluster C (mild subtype) deteriorated significantly in executive domain (p = 0.04).

Conclusions

The severe cluster had significantly faster progression, particularly in mood and perceptual NMS domains, visuospatial cognitive performances, and postural instability gait scores. Our findings will be helpful for clinicians to stratify and pre-emptively manage PD patients by developing intervention strategies to counter the progression of these domains.

Association between the risk and severity of Parkinson's disease and plasma homocysteine, vitamin B12 and folate levels: a systematic review and meta-analysis

Background

Parkinson's disease (PD) is recognized as the second most prevalent progressive neurodegenerative disease among the elderly. However, the relationship between PD and plasma homocysteine (Hcy), vitamin B12, and folate has yielded inconsistent results in previous studies. Hence, in order to address this ambiguity, we conducted a meta-analysis to summarize the existing evidence.

Methods

Suitable studies published prior to May 2023 were identified by searching PubMed, EMBASE, Medline, Ovid, and Web of Science. The methodological quality of eligible studies was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Meta-analysis and publication bias were then performed using R version 4.3.1.

Results

The results of our meta-analysis, consisting of case-control and cross-sectional studies, showed that PD patients had lower folate and vitamin B12 levels (SMD [95%CI]: -0.30[-0.39, -0.22], p < 0.001 for Vitamin B12; SMD [95%CI]: -0.20 [-0.28, -0.13], p < 0.001 for folate), but a significant higher Hcy level (SMD [95%CI]: 0.86 [0.59, 1.14], p < 0.001) than healthy people. Meanwhile, PD was significantly related to hyperhomocysteinemia (SMD [95%]: 2.02 [1.26, 2.78], p < 0.001) rather than plasma Hcy below 15 μmol/L (SMD [95%]: -0.31 [-0.62, 0.00], p = 0.05). Subgroup analysis revealed associations between the Hcy level of PD patients and region (p = 0.03), age (p = 0.03), levodopa therapy (p = 0.03), Hoehn and Yahr stage (p < 0.001), and cognitive impairment (p < 0.001). However, gender (p = 0.38) and sample size (p = 0.49) were not associated.

Conclusion

Hcy, vitamin B12, and folic acid potentially predict the onset and development of PD. Additionally, multiple factors were linked to Hcy levels in PD patients. Further studies are needed to comprehend their roles in PD.

Correlation of SV2C rs1423099 single nucleotide polymorphism with sporadic Parkinson's disease in Han population in Southern China

BACKGROUND

The synaptic vesicle glycoprotein 2 (SV2) has been implicated in synaptic function throughout the brain. Accumulating evidence investigated that SV2C contributed to dopamine release and the disrupted expression of SV2C was considered to be a unique feature of PD that may facilitate dopaminergic neuron dysfunction.

OBJECTIVE

This study aimed to examine the relationship between the SV2C rs1423099 single nucleotide polymorphism and sporadic Parkinson's disease (PD) in the Chinese Han population.

MATERIALS AND METHODS

This study enrolled 351 patients with sporadic PD and 240 normal controls in Chinese Han population. Peripheral blood DNA was extracted by DNA extraction kits and the rs1423099 genotype was analyzed by Agena MassARRAY DNA mass spectrometry. The differences in genotype and allele distribution frequencies between PD patients and control groups were compared using chi-squared tests or Fisher's exact tests.

RESULTS

No statistical difference was revealed in age and sex distribution between the cases and control groups, and the distribution of genotype and allele frequencies was consistent with the Hardy-Weinberg equilibrium test. In SV2C rs1423099 dominant model, the frequency of the CC/CT genotype was significantly higher in the PD group compared to the control group (OR = 4.065,95% CI: 2.801-10.870, p = 0.002). Nevertheless, in the recessive model, CC or CT/TT genotypes have no statistical difference in the two groups (p = 0.09). Additionally, in allelic analysis, the C allele was investigated to increase the risk of PD (OR = 1.346, 95% CI: 1.036-1.745, p = 0.026); Furthermore, subgroup analysis suggested that those carrying the C allele in the male subgroup were at a higher risk to afflicted with PD (OR = 1.637, 95% CI: 1.147-2.336, p = 0.006).

CONCLUSION

SV2C rs1423099 single nucleotide polymorphism was associated with sporadic Parkinson's disease in the Chinese Han population, particularly in males.

Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests

We have recently published the notion of the "vitals" of Parkinson's, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This "dashboard," termed the Chaudhuri's vitals of Parkinson's, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson's. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson's syndrome to describe Parkinson's disease, as the term "disease" is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson's, which is now considered by many as a syndrome.

Plasma Soluble ST2 Levels Are Higher in Neurodegenerative Disorders and Associated with Poorer Cognition

BACKGROUND

Suppressor of tumorgenicity 2 (ST2) is highly expressed in brain tissue and is a receptor for interleukin 33 (IL-33). ST2 exists in two forms, a transmembrane receptor (ST2L) and a soluble decoy receptor (sST2). IL-33 binds to ST2L, triggering downstream signaling pathways involved in amyloid plaque clearance. Conversely, sST2 binds competitively to IL-33, attenuating its neuroprotective effects. High sST2 levels have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), suggesting that the IL-33/ST2 signaling pathway may be implicated in neurodegenerative diseases.

OBJECTIVE

To investigate plasma sST2 levels in controls and patients with MCI, AD, frontotemporal dementia (FTD), and Parkinson's disease (PD).

METHODS

Plasma sST2 levels were measured using ELISA in 397 subjects (91 HC, 46 MCI, 38 AD, 28 FTD, and 194 PD). Cerebrospinal fluid (CSF) levels of sST2 were measured in 22 subjects. Relationship between sST2 and clinical outcomes were analyzed.

RESULTS

Plasma sST2 levels were increased across all disease groups compared to controls, with highest levels seen in FTD followed by AD and PD. Dementia patients with higher sST2 had lower cross-sectional cognitive scores in Frontal Assessment Battery and Digit Span Backward. At baseline, PD-MCI patients had higher sST2, associated with worse attention. In the longitudinal PD cohort, higher sST2 significantly associated with decline in global cognition and visuospatial domains. Plasma sST2 levels correlated with CSF sST2 levels.

CONCLUSION

Plasma sST2 is raised across neurodegenerative diseases and is associated with poorer cognition. Higher baseline sST2 is a potential biomarker of disease severity in neurodegeneration.