Advances in markers of prodromal Parkinson disease

Prodromal Parkinson's Disease: A Snapshot of the Landscape

Early observations of specific nonmotor and subtle motor symptoms preceding clinical diagnosis of Parkinson's disease (PD) have paved the way for prodromal PD research, significantly propelling our understanding of early, subclinical stages of neurodegeneration. Prodromal PD has emerged as a complex concept with some researchers suggesting that the period before PD onset is divided into the "at-risk," "preclinical," and "prodromal" phases. Advances in genetic, imaging, laboratory, and digital technologies have enabled the identification of pathophysiological patterns and the potential development of diagnostic, progressive, and therapeutic biomarkers, which could lead to early PD detection and intervention.

Midbrain and pallidal iron changes identify patients with REM sleep behaviour disorder and Parkinson's disease

Idiopathic REM sleep behaviour disorder (iRBD) is considered a prodromal form of Parkinson's Disease (PD), potentially exhibiting similar patterns of neurodegeneration, such as brain iron changes. We investigated midbrain and pallidal iron using quantitative susceptibility mapping (QSM) in 16 iRBD patients, 30 PD patients, and 38 age-matched healthy controls (HCs) with 3T MRI. QSM revealed elevated substantia nigra pars compacta (SNc) mean susceptibility in both iRBD and PD patient groups compared to HCs, though iRBD and PD QSM measures did not differ. There were no SN pars reticulata group differences. Mean susceptibility was reduced for PD relative to iRBD and HCs in the globus pallidus externa (GPe). Furthermore, mean susceptibility was reduced for PD relative to iRBD in the GP interna (GPi). GPe/GPi mean susceptibility decreased with PD subgroup motor severity. Consistent with this, QSM in left GPi and MDS-UPDRS-III scores correlated negatively in PD patients, as well as in iRBD and PD patients combined. PD patients also evidenced higher mean susceptibility in the right ventral tegmental area (VTA) compared to iRBD and HCs, consistent with later VTA degeneration. RBD symptomatology did not correlate with QSM values. Combining SNc, GPe, GPi, and VTA QSM values, we distinguished iRBD-HCs, PD-HCs, and iRBD-PD patients at single-subject levels (0.84, 0.86, and 0.81 accuracies), using ROC curve analyses with repeated k-folds cross-validation. Using 3T MRI, QSM values in SNc, GPe, GPi, and VTA demonstrate promise as investigational measures and diagnostic/progression biomarkers of prodromal and early PD.

Identifying Individuals in the Prodromal Phase of Parkinson's Disease: A Prospective Cohort Study

OBJECTIVE

We prospectively evaluated how well combinations of signs and symptoms can identify individuals in the prodromal phase of Parkinson's disease (PD).

METHODS

The study comprised 6,108 men who underwent repeated assessments of key prodromal features and were prospectively followed for the development of PD. Two composite measures of prodromal PD were evaluated: (i) the co-occurrence of constipation, probable rapid eye movement (REM) sleep behavior disorder (pRBD), and hyposmia, and (ii) the probability of prodromal PD based on the Movement Disorders Society (MDS) research criteria. We also examined the progression and heterogeneity of the prodromal PD phase.

RESULTS

One hundred three individuals were newly diagnosed with PD over an average follow-up of 3.4 years. Men with constipation, pRBD, and hyposmia had a 23-fold higher risk of receiving a PD diagnosis in the subsequent 3 years, compared with men without these features (risk ratio [RR] = 23.35, 95% confidence interval [CI] = 10.62-51.33). The risk of PD was 21-fold higher in men with a probability of prodromal PD ≥ 0.8 compared with those with a probability < 0.2 (RR = 21.96, 95% CI = 11.17-43.17). Both the co-occurrence of the 3 non-motor features and an MDS-based probability ≥ 0.8 had comparable predictive values, and both were stronger predictors of PD than any of the features individually. We identified 2 prodromal PD subtypes where RBD and visual color impairment were key discriminators.

INTERPRETATION

Our study demonstrates that combinations of key signs and symptoms strongly predict future clinically manifest PD. These measures may be integrated into screening strategies to identify individuals who could be targeted for enrollment into PD prevention trials. ANN NEUROL 2025;97:720-729.

A Narrative Review on Biochemical Markers and Emerging Treatments in Prodromal Synucleinopathies

Alpha-synuclein has been associated with neurodegeneration, especially in Parkinson's disease (PD). This study aimed to review clinical, biochemical, and neuroimaging markers and management of prodromal synucleinopathies. The prodromal state of synucleinopathies can be better understood with PD pathophysiology, and it can be separated into premotor and pre-diagnostic phases. The incidence of PD in patients with prodromal phase symptoms ranges from 0.07 to 14.30, and the most frequently studied pathology is the REM behavioral disorder (RBD). Neuroimaging markers are related to dopamine denervation, brain perfusion changes, gross anatomy changes, and peripheral abnormalities. α-synuclein assays (SAA) in CSF revealed high sensitivity (up to 97%) and high specificity (up to 92%); in the last decade, there was the development of other matrices (blood, skin, and olfactory mucosa) for obtaining quantitative and qualitative α-synuclein. Other biomarkers are neurofilament light chain, DOPA decarboxylase, and multiplexed mass spectrometry assay. Regarding genetic counseling in α-synucleinopathies, it is an important topic in clinical practice to discuss with patients with high-risk individuals and should involve basic principles of autonomy, beneficence, and non-maleficence. Some of the themes that should be reviewed are the involvement of physical activity, diet (including alcohol, coffee, and vitamin supplementation), smoking, sleep, and stress in the pathophysiology of synucleinopathies. The number of trials related to prodromal symptoms is still scarce, and the number of studies evaluating intervention is even lower.

Structural and functional connectomics of the olfactory system in Parkinson's disease: a systematic review

Olfactory dysfunction, affecting 75-90 % of Parkinson's disease (PD) patients, holds significant predictive value for PD development. Advanced imaging techniques, such as diffusion MRI (dMRI) and functional MRI (fMRI), offer insights into structural and functional changes within olfactory pathways. This review summarizes a decade of research on MRI-based connectivity of the olfactory system in PD, focusing on structural and functional alterations in olfactory brain areas and their links to early olfactory processing changes. Fifteen dMRI and eighteen fMRI studies met inclusion criteria and were carefully reviewed. Among the studies investigating diffusion metrics, the most consistent finding was the reduction of fractional anisotropy in the olfactory tract and anterior olfactory structures, though evidence correlating this result to olfactory dysfunction is limited and contrasting. dMRI support the hypothesis that olfactory function may be correlated to structural alterations at the network-level. In contrast, fMRI studies found more consistent evidence of dysconnectivity in both primary and secondary olfactory areas as directly correlated to olfactory processing and dysfunction. Results suggest a potential dissociation between structural alterations in olfactory brain regions and early functional impairment in olfactory processing, likely related to different patient subtypes. Heterogeneity in clinical and technical factors may limit the generalizability of the results, leaving room for further investigations. By providing a comprehensive perspective on the use of dMRI and fMRI to explore the olfactory connectome in PD, our review might facilitate future research towards earlier diagnosis and more targeted therapeutic and neurorehabilitation strategies.

Deciphering the role of neuropeptides as biomarkers for early diagnosis of Parkinson's disease

Parkinson's disease (PD) is a neurological condition and is characterized by both motor and non-motor symptoms. Early diagnosis is essential for effective therapy and management; nevertheless, present diagnostic methods are frequently insufficient and primarily rely on clinical symptoms that appear later in the disease. Neuropeptides, such as alpha-synuclein (α-syn), Substance P (SP), neurotensin (Nts), Neuropeptide Y (NPY), and somatostatin (SST), exhibit significant potential as biomarkers for the early identification of Parkinson's disease (PD). The pathophysiology of Parkinson's disease is closely associated with the dysregulation of these neuropeptides, which are essential in many neurophysiological processes. Advancements in detection technologies, including the Enzyme-Linked Immunosorbent Assay (ELISA), have rendered it possible to precisely and sensitively quantify neuropeptides in a variety of bodily fluids, including blood, saliva, tears, urine, and cerebrospinal fluid (CSF). Studies show that PD patients have different amounts of neuropeptides in their biological fluids. These differences are correlated with the severity of the disease and help to distinguish PD patients apart from individuals with other neurodegenerative conditions. Despite being less investigated, Nts and SST are also involved in neuroprotection and dopaminergic transmission, they too hold significant characteristics as diagnostic biomarkers. This article highlights the possible use of neuropeptides as PD diagnostic biomarkers. Integrating neuropeptide biomarkers into normal diagnostic processes can substantially enhance early diagnosis. This enables early therapeutic interventions and improves outcomes for individuals with PD.

Individualized brain radiomics-based network tracks distinct subtypes and abnormal patterns in prodromal Parkinson's disease

Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.

Electrochemical biosensors in early detection of Parkinson disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the motor system, with symptoms including tremors, rigidity, bradykinesia, and postural instability. Affecting over six million people globally, PD's pathophysiology is marked by the loss of dopaminergic neurons in the substantia nigra. Early diagnosis is crucial for effective management, yet current methods are limited by low sensitivity, high cost, and the need for advanced equipment. Electrochemical biosensors have emerged as promising tools for early PD diagnosis, converting biological reactions into measurable electrical signals for evaluating PD biomarkers. Advances in nanotechnology and material science have led to innovative sensing platforms with enhanced sensitivity and selectivity. Key biomarkers such as alpha-synuclein (α-syn), dopamine (DA), and microRNAs (miRNAs) have been targeted using these biosensors. For instance, gold nanoparticle-modified graphene immunosensors have shown ultra-sensitive detection of α-syn, while graphene-based biosensors have demonstrated high sensitivity for DA detection. Additionally, nanobiosensors for miR-195 and electrochemical aptasensors have shown potential for early PD diagnosis. The integration of nanomaterials like gold nanoparticles, quantum dots, and carbon nanotubes has further advanced the field, enhancing electrochemical activity and sensitivity. These developments offer a reliable, rapid, and cost-effective approach for early PD diagnosis, paving the way for better management and treatment. Continued research is essential for the commercialization and clinical integration of these biosensors, ultimately improving patient outcomes.

Diagnosis of Parkinson's disease by eliciting trait-specific eye movements in multi-visual tasks

BACKGROUND

Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.

METHODS

We recruited 114 PD patients and 125 healthy controls and collected their eye-tracking data in a VR environment. Participants completed a series of specific VR tasks, including gaze stability, pro-saccades, anti-saccades, and smooth pursuit. After the tasks, eye movement features were extracted from the behaviors of fixations, saccades, and smooth pursuit to establish a PD diagnostic model.

RESULTS

The performance of the models was evaluated through cross-validation, revealing a recall of 97.65%, an accuracy of 92.73%, and a receiver operator characteristic area under the curve (ROC-AUC) of 97.08% for the proposed model.

CONCLUSION

We extracted PD-specific eye movement features from the behaviors of fixations, saccades, and smooth pursuit in a VR environment to create a model with high accuracy and recall for PD diagnosis. Our method provides physicians with a new auxiliary tool to improve the prognosis and quality of life of PD patients.

Prodromal Parkinson's disease and subsequent risk of Parkinson's disease and mortality

Association of prodromal Parkinson's disease (PD) with risk of PD and risk of mortality in individuals with PD warrant investigation through large-scale prospective study. We included 501,475 participants without PD at baseline. Eight prodromal features were measured. Incident PD cases were identified via hospital admission, death register, and self-report. Cox regression models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs). Multivariable-adjusted HRs3+vs.0 prodromal PD features and 95%CIs were 3.12 (2.58-3.78) for men and 2.71 (2.11-3.47) for women. Prodromal PD predicted only PD onset occurred during the first 6 years of follow-up (HR3+vs.0 prodromal features = 10.5; 95% CI: 8.60-12.9), but not after 6 years (HR = 1.00; 95%CI: 0.76-1.32). The presence of prodromal PD conferred a higher risk of mortality among participants with PD. Having prodromal PD were associated with higher probability of developing PD in short-term and higher risk of mortality among individuals with PD.

Predicting Parkinson's Disease Using a Deep-Learning Algorithm to Analyze Prodromal Medical and Prescription Data

BACKGROUND AND PURPOSE

Parkinson's disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information.

METHODS

We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods.

RESULTS

During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931-0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922.

CONCLUSIONS

A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

SynNeurGe: The road ahead for a biological definition of Parkinson's disease

While significant progress has been made in treating Parkinson's disease (PD) symptoms, disease-modifying therapies (DMTs) have consistently failed. To address the underlying molecular mechanisms of PD, two biology-based criteria have been proposed: the "Synucleinopathy-Neurodegeneration-Genetics" (SynNeurGe) and "neuronal α-synuclein disease" (NSD) frameworks. Both frameworks emphasize the importance of biological markers over clinical symptoms. They recognize α-synuclein aggregation and genetic mutations (such as SNCA) as key diagnostic elements, with α-synuclein seed amplification assays (SAA) in cerebrospinal fluid (CSF) used to detect early disease stages. Dopaminergic neurodegeneration, measured by DAT imaging, is also central to both frameworks. These shared features aim to improve early diagnosis and precision medicine for PD. However, SynNeurGe provides a broader, more flexible framework that integrates α-synuclein pathology (S), neurodegeneration (N), and genetics (G), linked to clinical features (C). It aims to accommodate the complexity of PD and related Lewy body diseases, facilitating research on targeted DMTs. In contrast, NSD focuses specifically on PD and Lewy body dementia, introducing a staging system (NSD-ISS) based on biological markers and clinical impairment, helping track disease progression from preclinical to symptomatic stages. Despite their differences, both approaches highlight the need for more specific biomarkers and prospective studies to improve early intervention and personalized treatment. Harmonizing SynNeurGe and NSD concepts will be key in creating a universally accepted framework for precise PD diagnosis and therapy development.

Probing the diagnostic values of plasma cf-nDNA and cf-mtDNA for Parkinson's disease and multiple system atrophy

Background

Cell loss and mitochondrial dysfunction are key pathological features of idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). It remains unclear whether disease-specific changes in plasma circulating cell-free nuclear DNA (cf-nDNA) and mitochondrial DNA (cf-mtDNA) occur in patients with PD and MSA. In this study, we investigated whether plasma cf-nDNA, cf-mtDNA levels, as well as cf-mtDNA integrity, are altered in patients with PD and MSA.

Methods

TaqMan probe-based quantitative PCR was employed to measure plasma cf-nDNA levels, cf-mtDNA copy numbers, and cf-mtDNA deletion levels in 171 participants, including 76 normal controls (NC), 62 PD patients, and 33 MSA patients. A generalized linear model was constructed to analyze differences in circulating cell-free DNA (cfDNA) biomarkers across clinical groups, while a logistic regression model was applied to assess the predictive values of these biomarkers for developing PD or MSA. Spearman correlations were used to explore associations between the three cfDNA biomarkers, demographic data, and clinical scales.

Results

No significant differences in plasma cf-nDNA levels, cf-mtDNA copy numbers, or cf-mtDNA deletion levels were observed among the PD, MSA, and NC groups (all P > 0.05). Additionally, these measures were not associated with the risk of developing PD or MSA. In PD patients, cf-nDNA levels were positively correlated with Hamilton Anxiety Rating Scale scores (Rho = 0.382, FDR adjusted P = 0.027). In MSA patients, cf-nDNA levels were positively correlated with International Cooperative Ataxia Rating Scale scores (Rho = 0.588, FDR adjusted P = 0.011) and negatively correlated with Montreal Cognitive Assessment scores (Rho = -0.484, FDR adjusted P = 0.044). Subgroup analysis showed that PD patients with constipation had significantly lower plasma cf-mtDNA copy numbers than those without constipation (P = 0.049). MSA patients with cognitive impairment had significantly higher cf-nDNA levels compared to those without (P = 0.008).

Conclusion

Plasma cf-nDNA level, cf-mtDNA copy number, and cf-mtDNA deletion level have limited roles as diagnostic biomarkers for PD and MSA. However, their correlations with clinical symptoms support the hypothesis that cell loss and mitochondrial dysfunction are involved in PD and MSA development.

A Parkinson's disease-related nuclei segmentation network based on CNN-Transformer interleaved encoder with feature fusion

Automatic segmentation of Parkinson's disease (PD) related deep gray matter (DGM) nuclei based on brain magnetic resonance imaging (MRI) is significant in assisting the diagnosis of PD. However, due to the degenerative-induced changes in appearance, low tissue contrast, and tiny DGM nuclei size in elders' brain MRI images, many existing segmentation models are limited in the application. To address these challenges, this paper proposes a PD-related DGM nuclei segmentation network to provide precise prior knowledge for aiding diagnosis PD. The encoder of network is designed as an alternating encoding structure where the convolutional neural network (CNN) captures spatial and depth texture features, while the Transformer complements global position information between DGM nuclei. Moreover, we propose a cascaded channel-spatial-wise block to fuse features extracted by the CNN and Transformer, thereby achieving more precise DGM nuclei segmentation. The decoder incorporates a symmetrical boundary attention module, leveraging the symmetrical structures of bilateral nuclei regions by constructing signed distance maps for symmetric differences, which optimizes segmentation boundaries. Furthermore, we employ a dynamic adaptive region of interests weighted Dice loss to enhance sensitivity towards smaller structures, thereby improving segmentation accuracy. In qualitative analysis, our method achieved optimal average values for PD-related DGM nuclei (DSC: 0.854, IOU: 0.750, HD95: 1.691 mm, ASD: 0.195 mm). Experiments conducted on multi-center clinical datasets and public datasets demonstrate the good generalizability of the proposed method. Furthermore, a volumetric analysis of segmentation results reveals significant differences between HCs and PDs. Our method holds promise for assisting clinicians in the rapid and accurate diagnosis of PD, offering a practical method for the imaging analysis of neurodegenerative diseases.

Recognition and characterising non-motor profile in early onset Parkinson's disease (EOPD)

Early onset Parkinson's disease (EOPD) has been recently defined as a clinical entity with subjects presenting with Parkinson's disease (PD) between the ages of 21-50 and replaces the term Young Onset PD (YOPD). Presentations in this age group are somewhat different to the typical Late Onset sporadic PD (LOPD) and genetic basis may play an important role. The presentations are however, to be differentiated from other causes of juvenile onset or early onset parkinsonism, which are often driven by rare genetic, brain metal deposition, or metabolic progressive disorders with a levolevodopa unresponsive or poorly responsive phenotype. Specific genetic mutations can also underpin EOPD and include nonmotor symptoms of EOPD, which have not been studied extensively. However, some real-life comparator studies with LOPD suggest a nonmotor profile in EOPD dominated by neuropsychiatric symptoms (anxiety), pain, sexual dysfunction, and a higher risk of impulse control disorders and segregation to the recently described noradrenergic and Park-sleep nonmotor endophenotypes may occur. Awareness of the phenotypic variants and nonmotor expression will pave the way for future precision and personalised medicine.

Definition and diagnosis of Parkinson's disease: guideline "Parkinson's disease" of the German Society of Neurology

BACKGROUND

Accurate definition and operational criteria for diagnosing Parkinson's disease (PD) are crucial for evidence-based, patient-centered care.

OBJECTIVE

To offer evidence-based recommendations for defining and diagnosing PD, incorporating contemporary clinical, imaging, biomarker, and genetic insights.

METHODS

The guideline development began with the steering committee establishing key PICO (patient, intervention, comparison, outcome) questions, which were refined by the coauthors. Systematic literature searches identified relevant studies, reviews, and meta-analyses. Recommendations were drafted, evaluated, optimized, and voted upon by the German Parkinson's Guideline Group.

RESULTS

Parkinson's disease (PD) is now understood to encompass a broader spectrum of etiologies than previously recognized. Advances in molecular pathogenesis, neuroimaging, and early clinical phenotypes suggest that PD is not a uniform disease entity and is often not idiopathic. This necessitates an updated framework for PD definition and diagnosis. The German Society for Neurology now endorses a broader concept of PD, incorporating both idiopathic and hereditary forms, as opposed to the previously narrower concept of "idiopathic Parkinson syndrome." The revised guidelines recommend using the 2015 Movement Disorders Society diagnostic criteria, emphasize the importance of long-term clinical follow-up for improved diagnostic accuracy, and highlight the significance of non-motor symptoms in clinical diagnosis. Specific recommendations are provided for the use of imaging and fluid biomarkers and genetic testing to support the clinical diagnosis.

CONCLUSION

The updated guidelines from the German Society for Neurology enhance diagnostic accuracy for PD, promoting optimized clinical care.

A Framework for the Human-Centered Design of Service Processes Enabled by Medical Devices: A Case Study of Wearable Devices for Parkinson's Disease

The successful introduction of medical devices (MDs) in real-world settings hinges on designing service processes that cater to stakeholders' needs. While human-centered design (HCD) approaches have been widely applied to service process innovation, the literature lacks a methodology that leverages MDs' key features to design service processes that meet stakeholders' needs. This study aims to fill this gap by developing a framework for the HCD of service processes enabled by MDs. The proposed framework mixes and adapts methodological elements from HCD and technology-enabled design approaches and proposes four new tools. The five-phase framework was applied to the design of a new Parkinson's disease diagnosis and treatment process (PD-DTP) enabled by two wearable MDs for the detection of motor symptoms. The case study lasted five months and involved 42 stakeholders in 21 meetings (interviews, focus groups, etc.). Thanks to the case study, the framework was tested, and a new PD-DTP that could benefit all stakeholders involved was identified. This study provides a framework that, in addition to contributing to theory, could assist MDs developers and healthcare managers in designing service processes that cater to stakeholders' needs by leveraging MDs' key features.

AIMP2 accumulation in brain leads to cognitive deficits and blood secretion in Parkinson's disease

BACKGROUND

Propagation of neuronal α-synuclein aggregate pathology to the cortex and hippocampus correlates with cognitive impairment in Parkinson's disease (PD) dementia and dementia with Lewy body disease. Previously, we showed accumulation of the parkin substrate aminoacyl-tRNA synthetase interacting multifunctional protein-2 (AIMP2) in the temporal lobe of postmortem brains of patients with advanced PD. However, the potential pathological role of AIMP2 accumulation in the cognitive dysfunction of patients with PD remains unknown.

METHODS

We performed immunofluorescence imaging to examine cellular distribution and accumulation of AIMP2 in brains of conditional AIMP2 transgenic mice and postmortem PD patients. The pathological role of AIMP2 was investigated in the AIMP2 transgenic mice by assessing Nissl-stained neuron counting in the hippocampal area and Barnes maze to determine cognitive functions. Potential secretion and cellular uptake of AIMP2 was monitored by dot blot analysis and immunofluorescence. The utility of AIMP2 as a new PD biomarker was evaluated by dot blot and ELISA measurement of plasma AIMP2 collected from PD patients and healthy control followed by ROC curve analysis.

RESULTS

We demonstrated that AIMP2 is toxic to the dentate gyrus neurons of the hippocampus and that conditional AIMP2 transgenic mice develop progressive cognitive impairment. Moreover, we found that neuronal AIMP2 expression levels correlated with the brain endothelial expression of AIMP2 in both AIMP2 transgenic mice and in the postmortem brains of patients with PD. AIMP2, when accumulated, was released from the neuronal cell line SH-SY5Y cells. Secreted AIMP2 was taken up by human umbilical vein endothelial cells. Consistent with the fact that AIMP2 can be released into the extracellular space, we showed that AIMP2 transgenic mice have higher levels of plasma AIMP2. Finally, ELISA-based assessment of AIMP2 in plasma samples from patients with PD and controls, and subsequent ROC curve analysis proved that high plasma AIMP2 expression could serve as a reliable molecular biomarker for PD diagnosis.

CONCLUSIONS

The pathological role in the hippocampus and the cell-to-cell transmissibility of AIMP2 provide new therapeutic avenues for PD treatment, and plasma AIMP2 combined with α-synuclein may improve the accuracy of PD diagnosis in the early stages.

Prospective study of bipolar disorder and neurodegenerative diseases

Bipolar disorder (BD) is linked to an increased risk of neurodegenerative diseases such as dementia and Parkinson disease (PD), yet several uncertainties still remain and the extent to which the associations could be explained by BD-related medications (antipsychotics, lithium, and antiepileptics) was unknown. This study included 501,233 UK Biobank participants (mean [standard deviation] age, 56.5 [8.10] years; 54.4% women), free of dementia and PD at baseline. After a median 13.8 year follow-up, 9422 cases of dementia and 3457 PD cases were identified. Participants with BD had a significantly higher risk of dementia (adjusted hazard ratio [HR] 2.52, 95% CI 2.00-3.19) and PD (adjusted HR 2.88, 95% CI 2.03-4.08). Findings suggest that up to two-thirds of the association of neurodegenerative diseases with BD may be mediated by BD-related medications. Further research is needed to confirm these findings and explore the underlying mechanisms.

Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder

BACKGROUND

Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone.

OBJECTIVE

The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario.

METHODS

This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic.

RESULTS

A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening.

CONCLUSION

We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

Promotion of a Mediterranean Diet Alters Constipation Symptoms and Fecal Calprotectin in People with Parkinson's Disease: A Randomized Controlled Trial

Parkinson's disease is associated with gastrointestinal (GI) dysfunction, including constipation symptoms and abnormal intestinal permeability and inflammation. A Mediterranean diet (MediDiet) may aid in disease management. This parallel, randomized, controlled trial in people with Parkinson's (PwP) and constipation symptoms compared a MediDiet against standard of care on change in constipation symptoms, dietary intake, and fecal zonulin and calprotectin concentrations as markers of intestinal permeability and inflammation, respectively. Participants were randomized to either standard of care for constipation (control; n = 17, 65.1 ± 2.2 years) or a MediDiet plus standard of care (n = 19, 68.8 ± 1.4 years) for 8 weeks. Constipation scores decreased with both interventions (p < 0.01), but changes from baseline were not different between groups (MediDiet, -0.5 [-1.0, 0]; control, -0.8 [-1.0, 0.2]; median [25th, 75th]; p = 0.60). The MediDiet group had a higher intake of dietary fiber at week 4 than the control group (13.1 ± 0.7 g/1000 kcal vs. 9.8 ± 0.7 g/1000 kcal; p < 0.001). No differences in fecal zonulin were observed between groups (p = 0.33); however, fecal calprotectin tended to be lower in the MediDiet group at week 8 (45.8 ± 15.1 µg/g vs. 93.9 ± 26.8 µg/g; p = 0.05). The MediDiet and standard interventions reduced constipation symptoms; however, the MediDiet provided additional benefit of increased dietary fiber intake and less intestinal inflammation.

Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank

Importance

Higher physical activity levels have been suggested as a potential modifiable risk factor for lowering the risk of incident Parkinson's disease (PD). This study uses objective measures of physical activity to investigate the role of reverse causation in the observed association.

Objective

To investigate the association between accelerometer-derived daily step count and incident PD, and to assess the impact of reverse causation on this association.

Design

This prospective cohort study involved a follow-up period with a median duration of 7.9 years, with participants who wore wrist-worn accelerometers for up to 7 days.

Setting

The study was conducted within the UK Biobank, a large, population-based cohort.

Participants

The analysis included 94,696 participants aged 43-78 years (56% female) from the UK Biobank who provided valid accelerometer data and did not have prevalent PD.

Exposure

Daily step counts were derived using machine learning models to determine the median daily step count over the monitoring period.

Main Outcomes and Measures

The primary outcome was incident PD, identified through hospital admission and death records. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between daily step count and incident PD, with adjustments for various covariates and evaluation of reverse causation by splitting follow-up periods.

Results

During a median follow-up of 7.9 years (IQR: 7.4-8.4), 407 incident PD cases were identified. An inverse linear association was observed between daily step count and incident PD. Participants in the highest quintile of daily steps (>12,369 steps) had an HR of 0.41 (95% CI 0.31-0.54) compared to the lowest quintile (<6,276 steps; HR 1.00; 95% CI 0.84-1.19). A per 1,000 step increase was associated with an HR of 0.92 (95% CI 0.89-0.94). However, after excluding the first six years of follow-up, the association was not significant (HR 0.96, 95% CI 0.92-1.01).

Conclusions and Relevance

The observed association between higher daily step count and lower incident PD is likely influenced by reverse causation, suggesting changes in physical activity levels occur years before PD diagnosis. While step counts may serve as a predictor for PD, they may not represent a modifiable risk factor. Further research with extended follow-up periods is warranted to better understand this relationship and account for reverse causation.

Heart rate variability-based Model for estimating the severity of orthostatic hypotension in patients with REM sleep behavior disorder

BACKGROUND

Orthostatic hypotension (OH) is a typical autonomic dysfunction in patients with idiopathic REM sleep behavior disorder (iRBD). The Schellong test is a well-known method to evaluate the presence of OH; however, it is burdensome for both patients and the medical staff from the viewpoint of ensuring patient safety. In this study, we developed a machine learning (ML) model to discriminate the presence or absence of OH based on heart rate variability (HRV) in the supine position.

METHODS

We recruited elderly healthy participants (HC) and iRBD patients and measured participants' R-R interval (RRI) during the Schellong test. The HRV features were calculated from the RRIs and were used as inputs for the ML model. We trained an ML model that combines two binary classifiers. The first model classifies HC and iRBD, and the second model discriminates the classified patients with iRBD between OH(-) and OH(+).

RESULTS

The macro average classification performance was accuracy of 81%, recall of 73%, precision of 82%, and F-measure of 68%. In addition, the sensitivity for OH(+)iRBD was 100%.

CONCLUSION

Utilizing this ML model will help to reduce the burden of the Schellong test.

Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI

OBJECTIVE

This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD).

METHODS

Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry.

RESULTS

The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected.

CONCLUSIONS

Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.

Machine Learning Models for Parkinson Disease: Systematic Review

Background

With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems.

Objective

To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression.

Methods

We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms.

Results

Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results.

Conclusions

This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.

Molecular beacon-based detection of circulating microRNA-containing extracellular vesicle as an α-synucleinopathy biomarker

Early and precise diagnosis of α-synucleinopathies is challenging but critical. In this study, we developed a molecular beacon-based assay to evaluate microRNA-containing extracellular vesicles (EVs) in plasma. We recruited 1203 participants including healthy controls (HCs) and patients with isolated REM sleep behavior disorder (iRBD), α-synucleinopathies, or non-α-synucleinopathies from eight centers across China. Plasma miR-44438-containing EV levels were significantly increased in α-synucleinopathies, including those in the prodromal stage (e.g., iRBD), compared to both non-α-synucleinopathy patients and HCs. However, there are no significant differences between Parkinson's disease (PD) and multiple system atrophy. The miR-44438-containing EV levels negatively correlated with age and the Hoehn and Yahr stage of PD patients, suggesting a potential association with disease progression. Furthermore, a longitudinal analysis over 16.3 months demonstrated a significant decline in miR-44438-containing EV levels in patients with PD. These results highlight the potential of plasma miR-44438-containing EV as a biomarker for early detection and progress monitoring of α-synucleinopathies.

Adenosine A2A Receptor Blockade Provides More Effective Benefits at the Onset Rather than after Overt Neurodegeneration in a Rat Model of Parkinson's Disease

Adenosine A2A receptor (A2AR) antagonists are the leading nondopaminergic therapy to manage Parkinson's disease (PD) since they afford both motor benefits and neuroprotection. PD begins with a synaptic dysfunction and damage in the striatum evolving to an overt neuronal damage of dopaminergic neurons in the substantia nigra. We tested if A2AR antagonists are equally effective in controlling these two degenerative processes. We used a slow intracerebroventricular infusion of the toxin MPP+ in male rats for 15 days, which caused an initial loss of synaptic markers in the striatum within 10 days, followed by a neuronal loss in the substantia nigra within 30 days. Interestingly, the initial loss of striatal nerve terminals involved a loss of both dopaminergic and glutamatergic synaptic markers, while GABAergic markers were preserved. The daily administration of the A2AR antagonist SCH58261 (0.1 mg/kg, i.p.) in the first 10 days after MPP+ infusion markedly attenuated both the initial loss of striatal synaptic markers and the subsequent loss of nigra dopaminergic neurons. Strikingly, the administration of SCH58261 (0.1 mg/kg, i.p. for 10 days) starting 20 days after MPP+ infusion was less efficacious to attenuate the loss of nigra dopaminergic neurons. This prominent A2AR-mediated control of synaptotoxicity was directly confirmed by showing that the MPTP-induced dysfunction (MTT assay) and damage (lactate dehydrogenase release assay) of striatal synaptosomes were prevented by 50 nM SCH58261. This suggests that A2AR antagonists may be more effective to counteract the onset rather than the evolution of PD pathology.

Noradrenergic alterations in Parkinson's disease: a combined 11C-yohimbine PET/neuromelanin MRI study

Degeneration of the noradrenergic system is now considered a pathological hallmark of Parkinson's disease, but little is known about its consequences in terms of parkinsonian manifestations. Here, we evaluated two aspects of the noradrenergic system using multimodal in vivo imaging in patients with Parkinson's disease and healthy controls: the pigmented cell bodies of the locus coeruleus with neuromelanin sensitive MRI; and the density of α2-adrenergic receptors (ARs) with PET using 11C-yohimbine. Thirty patients with Parkinson's disease and 30 age- and sex-matched healthy control subjects were included. The characteristics of the patients' symptoms were assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Patients showed reduced neuromelanin signal intensity in the locus coeruleus compared with controls and diminished 11C-yohimbine binding in widespread cortical regions, including the motor cortex, as well as in the insula, thalamus and putamen. Clinically, locus coeruleus neuronal loss was correlated with motor (bradykinesia, motor fluctuations, tremor) and non-motor (fatigue, apathy, constipation) symptoms. A reduction of α2-AR availability in the thalamus was associated with tremor, while a reduction in the putamen, the insula and the superior temporal gyrus was associated with anxiety. These results highlight a multifaceted alteration of the noradrenergic system in Parkinson's disease since locus coeruleus and α2-AR degeneration were found to be partly uncoupled. These findings raise important issues about noradrenergic dysfunction that may encourage the search for new drugs targeting this system, including α2-ARs, for the treatment of Parkinson's disease.

Relationship Between Short-chain Fatty Acids and Parkinson's Disease: A Review from Pathology to Clinic

Parkinson's disease (PD) is a complicated neurodegenerative disease, characterized by the accumulation of α-synuclein (α-syn) in Lewy bodies and neurites, and massive loss of midbrain dopamine neurons. Increasing evidence suggests that gut microbiota and microbial metabolites are involved in the development of PD. Among these, short-chain fatty acids (SCFAs), the most abundant microbial metabolites, have been proven to play a key role in brain-gut communication. In this review, we analyze the role of SCFAs in the pathology of PD from multiple dimensions and summarize the alterations of SCFAs in PD patients as well as their correlation with motor and non-motor symptoms. Future research should focus on further elucidating the role of SCFAs in neuroinflammation, as well as developing novel strategies employing SCFAs and their derivatives to treat PD.

Acupuncture activates IRE1/XBP1 endoplasmic reticulum stress pathway in Parkinson's disease model rats

Acupuncture has demonstrated its efficacy as a treatment for Parkinson's disease (PD). Thus, the objective of this study was to investigate the potential mechanisms underlying acupuncture's effects on PD treatment. Our approach involved several steps. Firstly, we assessed the behavioral changes in PD rats, the modulation of dopamine (DA) and 5-hydroxytryptamine (5-HT) levels in the striatum, as well as the alteration in α-synuclein (α-syn) levels in the midbrain, aiming to evaluate the efficacy of acupuncture in PD treatment. Secondly, we selected endoplasmic reticulum (ER) stress inhibitors and activators to assess the impact of ER stress on PD rats. Lastly, we utilized an IRE1 inhibitor to observe the influence of acupuncture on the IRE1/XBP1 pathway in PD rats. The findings of this study revealed that acupuncture improved the autonomous motor function, balance ability, coordination, and sensory motor integration function in the PD model rats. Additionally, it increased the levels of DA and 5-HT in the striatum while decreasing the levels of α-syn in the midbrain. Acupuncture also activated the expression of ER stress in the midbrain and upregulated the expression of IRE1/XBP1 in the striatum of PD model rats. Based on these results, we concluded that acupuncture may enhance the behavior of PD rats by activating the IRE1/XBP1 ER stress pathway, associated with the reduction of midbrain α-syn expression and the increase in striatal DA and 5-HT levels in unilateral 6-OHDA lesioned rats.

Numerosity estimation of virtual humans as a digital-robotic marker for hallucinations in Parkinson's disease

Hallucinations are frequent non-motor symptoms in Parkinson's disease (PD) associated with dementia and higher mortality. Despite their high clinical relevance, current assessments of hallucinations are based on verbal self-reports and interviews that are limited by important biases. Here, we used virtual reality (VR), robotics, and digital online technology to quantify presence hallucination (vivid sensations that another person is nearby when no one is actually present and can neither be seen nor heard) in laboratory and home-based settings. We establish that elevated numerosity estimation of virtual human agents in VR is a digital marker for experimentally induced presence hallucinations in healthy participants, as confirmed across several control conditions and analyses. We translated the digital marker (numerosity estimation) to an online procedure that 170 PD patients carried out remotely at their homes, revealing that PD patients with disease-related presence hallucinations (but not control PD patients) showed higher numerosity estimation. Numerosity estimation enables quantitative monitoring of hallucinations, is an easy-to-use unobtrusive online method, reaching people far away from medical centers, translating neuroscientific findings using robotics and VR, to patients' homes without specific equipment or trained staff.

Sexual dysfunction in a sample of Egyptian patients with Parkinson's disease

BACKGROUND

Sexual dysfunction (SD) is a common, yet underdiagnosed problem in Parkinson Disease (PD) patients. It can negatively impact their quality of life (QoL) and clinical outcome. we tried to assess SD in a group of Egyptian PD patients.

METHODS

The study is a case-control, cross-sectional study that included 200 participants, consisting of 100 PD patients and 100 matched healthy controls. Social, demographic information, and clinical variables were collected from both groups. Sexual functions were assessed using the Arabic Female Sexual Function Index (ArFSFI), and the Arabic version of International Index of Erectile Function (IIEF).

RESULTS

Women with PD scored worse on FSFI total score compared to controls (p < 0.001). Regarding the FSFI domains, they scored significantly lower in individual domains of desire (p < 0.001), arousal (p < 0.001), lubrication (p = 0.006), orgasm (p < 0.001), satisfaction (p < 0.001), and pain (p = 0.003), compared with controls. Men with PD scored worse on IIEF total scores compared to controls (p < 0.001). They showed significantly worse scores of erectile functions (p < 0.001), orgasmic function (p < 0.001), sexual desire (p < 0.001), intercourse satisfaction (p < 0.001), and overall satisfaction (p < 0.001). Both groups reported significant effect of SD on their QoL. There was a significant correlation between disease severity and SD.

CONCLUSION

SD is common in PD patients. It negatively impacts their QoL and partnership. Healthcare professionals should initiate conversations about SD with the patients and provide appropriate education and treatment options.

Correlation of olfactory function factors with cardiac sympathetic denervation in Parkinson's disease

BACKGROUND

Hyposmia is a common nonmotor symptom of Parkinson's disease (PD) and reportedly associated with dysautonomia in PD. The smell identification test for measuring olfactory function consists of multiple items to discriminate specific scents. In the present study, factor analysis of the smell identification test was performed, and the correlation of extracted factors with cardiac sympathetic denervation (CSD) in patients with PD was investigated.

METHODS

The present study included 183 early PD patients who underwent the Cross-Cultural Smell Identification Test (CC-SIT) and 123I-meta-iodobenzylguanidine (123I-MIBG) myocardial scintigraphy. Factor analysis of 12 items on the CC-SIT was performed using the computed correlation matrix for the binary items, and five smell factors were extracted. Multiple linear regression was performed to determine the correlation of olfactory function with late heart-to-mediastinum (H/M) ratio of 123I-MIBG uptake.

RESULTS

The mean CC-SIT score was 6.1 ± 2.6, and 133 patients (72.7%) had CSD. The CC-SIT score and five smell factors were not associated with dopamine transporter uptake or cognitive functions. However, the CC-SIT score significantly correlated with age (P < 0.001) and late H/M ratio (P < 0.001). Factors 1 and 5 showed an increasing trend with larger H/M ratio, although it was not statistically significant (β = 0.203, P = 0.085 and β = 0.230, P = 0.085, respectively). Factor 5 significantly correlated with the H/M ratio in PD patients with CSD (β = 0.676, P = 0.036).

DISCUSSION

The results showed olfactory dysfunction to be selectively associated with cardiac sympathetic burden in PD. The correlation of certain factors with CSD indicates the possibility of selective hyposmia in PD patients.

Multiomics Analyses With Stool-Type Stratification in Patient Cohorts and Blautia Identification as a Potential Bacterial Modulator in Type 2 Diabetes Mellitus

Heterogeneity in host and gut microbiota hampers microbial precision intervention of type 2 diabetes mellitus (T2DM). Here, we investigated novel features for patient stratification and bacterial modulators for intervention, using cross-sectional patient cohorts and animal experiments. We collected stool, blood, and urine samples from 103 patients with recent-onset T2DM and 25 healthy control subjects (HCs), performed gut microbial composition and metabolite profiling, and combined it with host transcriptome, metabolome, cytokine, and clinical data. Stool type (dry or loose stool), a feature of the stool microenvironment recently explored in microbiome studies, was used for stratification of patients with T2DM as it explained most of the variation in the multiomics data set among all clinical parameters in our covariate analysis. T2DM with dry stool (DM-DS) and loose stool (DM-LS) were clearly differentiated from HC and each other by LightGBM models, optimal among multiple machine learning models. Compared with DM-DS, DM-LS exhibited discordant gut microbial taxonomic and functional profiles, severe host metabolic disorder, and excessive insulin secretion. Further cross-measurement association analysis linked the differential microbial profiles, in particular Blautia abundances, to T2DM phenotypes in our stratified multiomics data set. Notably, oral supplementation of Blautia to T2DM mice induced inhibitory effects on lipid accumulation, weight gain, and blood glucose elevation with simultaneous modulation of gut bacterial composition, revealing the therapeutic potential of Blautia. Our study highlights the clinical implications of stool microenvironment stratification and Blautia supplementation in T2DM, offering promising prospects for microbial precision treatment of metabolic diseases.

ARTICLE HIGHLIGHTS

Animal Approaches to Studying Risk Factors for Parkinson's Disease: A Narrative Review

Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson's disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease's etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment.

Anxiety Change After Dopamine Therapy in Parkinson Disease is Independent of Motor Improvement

BACKGROUND

Several anxiety syndromes have been associated with Parkinson disease (PD), but their interactions with dopamine replacement therapy (DRT) and motor function dynamics are not completely understood. We sought to delineate how DRT impacts anxiety phenomenology in PD and whether these changes are dissociable from improved motoric function.

METHODS

We compared anxiety responses to DRT in two cohorts: 1) a study of 200 PD participants who completed neuropsychiatric assessments before and after taking their dopaminergic medications ("On-Off"); 2) participants in the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort who completed the State-Trait Anxiety Inventory (STAI) at the time of DRT initiation and a subsequent study visit (n = 113, mean 8-month interval).

RESULTS

Among On-Off participants transitioning acutely to the On-state, scores on the Hamilton anxiety rating scale decreased by 37% (t = 14.8, df = 199, p <0.0001). Among PPMI participants, STAI-state scores decreased by 10.4% following DRT initiation (t = 4.5, df = 112, p <0.0001). Item-level anxiety changes exhibited weak and nonsignificant correlations (Spearman ρ: -0.24 to 0.33) with objective MDS-UPDRS motor improvements in both immediate and sustained dopamine replacement contexts.

CONCLUSION

Dopamine repletion effected immediate relief of anxiety in an On-Off state comparison. A similar benefit was observed in the longitudinal PPMI cohort by comparing anxiety before and after DRT initiation, suggesting DRT confers sustained anxiolytic effects in early PD. The weak correlations between improvements to anxiety and motor function on both timescales support the view that these changes are not mediated by improved motor function.

Extracellular Vesicles as Biomarkers for Parkinson's Disease: How Far from Clinical Translation?

Parkinson's disease (PD) is a neurodegenerative disorder affecting about 10 million people worldwide with a prevalence of about 2% in the over-80 population. The disease brings in also a huge annual economic burden, recently estimated by the Michael J Fox Foundation for Parkinson's Research to be USD 52 billion in the United States alone. Currently, no effective cure exists, but available PD medical treatments are based on symptomatic prescriptions that include drugs, surgical approaches and rehabilitation treatment. Due to the complex biology of a PD brain, the design of clinical trials and the personalization of treatment strategies require the identification of accessible and measurable biomarkers to monitor the events induced by treatment and disease progression and to predict patients' responsiveness. In the present review, we strive to briefly summarize current knowledge about PD biomarkers, focusing on the role of extracellular vesicles as active or involuntary carriers of disease-associated proteins, with particular attention to those research works that envision possible clinical applications.

Neurocognitive correlates of semantic memory navigation in Parkinson's disease

Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

Reduced Thickness of the Retina in de novo Parkinson's Disease Shows A Distinct Pattern, Different from Glaucoma

Background

Parkinson's disease (PD) patients experience visual symptoms and retinal degeneration. Studies using optical coherence tomography (OCT) have shown reduced thickness of the retina in PD, also a key characteristic of glaucoma.

Objective

To identify the presence and pattern of retinal changes in de novo, treatment-naive PD patients compared to healthy controls (HC) and early primary open angle glaucoma (POAG) patients.

Methods

Macular OCT data (10×10 mm) were collected from HC, PD, and early POAG patients, at the University Medical Center Groningen. Bayesian informative hypotheses statistical analyses were carried out comparing HC, PD-, and POAG patients, within each retinal cell layer.

Results

In total 100 HC, 121 PD, and 78 POAG patients were included. We showed significant reduced thickness of the inner plexiform layer and retinal pigment epithelium in PD compared to HC. POAG patients presented with a significantly thinner retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, outer plexiform layer, and outer photoreceptor and subretinal virtual space compared to PD. Only the outer segment layer and retinal pigment epithelium were significantly thinner in PD compared to POAG.

Conclusions

De novo PD patients show reduced thickness of the retina compared to HC, especially of the inner plexiform layer, which differs significantly from POAG, showing a more extensive and widespread pattern of reduced thickness across layers. OCT is a useful tool to detect retinal changes in de novo PD, but its specificity versus other neurodegenerative disorders has to be established.

Combining Biomarkers with Genetics In Prodromal/Earliest Phase Parkinson's Disease

 Family studies have linked several rare genetic variants to hereditary forms of Parkinson's disease (PD). In addition to these monogenic forms, many PD cases are associated with genetic risk factors. Asymptomatic individuals carrying pathogenic variants linked to PD are at risk of developing the disease later in life, thereby providing a unique opportunity for the detection of the earliest pathophysiological and later clinical changes and, importantly, also of protective and compensatory features and mechanisms. However, the rarity of monogenic PD-causing variants is a major challenge of this approach. In this review, we discuss recent advances in the search for biomarkers in the prodromal/earliest phase of genetically linked PD.

Infections in the Etiology of Parkinson's Disease and Synucleinopathies: A Renewed Perspective, Mechanistic Insights, and Therapeutic Implications

Increasing evidence suggests a potential role for infectious pathogens in the etiology of synucleinopathies, a group of age-related neurodegenerative disorders including Parkinson's disease (PD), multiple system atrophy and dementia with Lewy bodies. In this review, we discuss the link between infections and synucleinopathies from a historical perspective, present emerging evidence that supports this link, and address current research challenges with a focus on neuroinflammation. Infectious pathogens can elicit a neuroinflammatory response and modulate genetic risk in PD and related synucleinopathies. The mechanisms of how infections might be linked with synucleinopathies as well as the overlap between the immune cellular pathways affected by virulent pathogens and disease-related genetic risk factors are discussed. Here, an important role for α-synuclein in the immune response against infections is emerging. Critical methodological and knowledge gaps are addressed, and we provide new future perspectives on how to address these gaps. Understanding how infections and neuroinflammation influence synucleinopathies will be essential for the development of early diagnostic tools and novel therapies.

Perspectives of People At-Risk on Parkinson's Prevention Research

The movement toward prevention trials in people at-risk for Parkinson's disease (PD) is rapidly becoming a reality. The authors of this article include a genetically at-risk advocate with the LRRK2 G2019 S variant and two patients with rapid eye movement sleep behavior disorder (RBD), one of whom has now been diagnosed with PD. These authors participated as speakers, panelists, and moderators in the "Planning for Prevention of Parkinson's: A Trial Design Forum" hosted by Massachusetts General Hospital in 2021 and 2022. Other authors include a young onset person with Parkinson's (PwP) and retired family physician, an expert in patient engagement in Parkinson's, and early career and veteran movement disorders clinician researchers. Several themes emerged from the at-risk participant voice concerning the importance of early intervention, the legitimacy of their input in decision-making, and the desire for transparent communication and feedback throughout the entire research study process. Challenges and opportunities in the current environment include lack of awareness among primary care physicians and general neurologists about PD risk, legal and psychological implications of risk disclosure, limited return of individual research study results, and undefined engagement and integration of individuals at-risk into the broader Parkinson's community. Incorporating the perspectives of individuals at-risk as well as those living with PD at this early stage of prevention trial development is crucial to success.

Prospective Study of Lung Function with Prodromal, Clinical Parkinson's Disease, and Mortality

Background

The association of lung function with the risk of developing prodromal and clinical-diagnosed Parkinson's disease (PD) and with the risk of mortality among individuals with PD remains unknown.

Objective

To prospectively examine the associations of lung function with the risk of prodromal, clinical-diagnosed PD, and PD-related mortality in participants of the UK Biobank.

Methods

Included were 452,518 participants free of PD at baseline. Baseline lung function, including forced expiratory volume in 1-s (FEV1), forced vital capacity (FVC), peak expiratory flow (PEF), and FEV1/FVC ratio, was assessed. Eight prodromal features were measured using self-reported diagnoses, hospital admission, and primary care data. Incident PD cases were identified using linkages with hospital admission, death register, and self-report. Vital status and date of death were provided by the UK National Health Service (NHS) and the NHS Central Register. We used Cox proportional hazard models to evaluate these associations.

Results

Poor lung function was associated with higher risk of PD in a dose-response relationship: the adjusted hazard ratio comparing the lowest vs. the highest lung function quintile was 1.18 (95% CI, 1.02- 1.37) for FEV1, 1.14 (95% CI, 0.99- 1.29) for FVC, and 1.23 (95% CI, 1.08- 1.41) for PEF (p-trend <0.05 for all). Similar results were obtained for risk of prodromal PD and mortality among individuals with PD.

Conclusions

The current study showed that individuals with poor lung function had a high future risk of prodromal and clinical PD and a higher rate of PD-related mortality.

Altered Outer Retinal Structure, Electrophysiology and Visual Perception in Parkinson's Disease

BACKGROUND

Visual biomarkers of Parkinson's disease (PD) are attractive as the retina is an outpouching of the brain. Although inner retinal neurodegeneration in PD is well-established this has overlap with other neurodegenerative diseases and thus outer retinal (photoreceptor) measures warrant further investigation.

OBJECTIVE

To examine in a cross-sectional study whether clinically implementable measures targeting outer retinal function and structure can differentiate PD from healthy ageing and whether these are sensitive to intraday levodopa (L-DOPA) dosing.

METHODS

Centre-surround perceptual contrast suppression, macular visual field sensitivity, colour discrimination, light-adapted electroretinography and optical coherence tomography (OCT) were tested in PD participants (n = 16) and controls (n = 21). Electroretinography and OCT were conducted before and after midday L-DOPA in PD participants, or repeated after ∼2 hours in controls.

RESULTS

PD participants had decreased center-surround contrast suppression (p < 0.01), reduced macular visual field sensitivity (p < 0.05), color vision impairment (p < 0.01) photoreceptor dysfunction (a-wave, p < 0.01) and photoreceptor neurodegeneration (outer nuclear layer thinning, p < 0.05), relative to controls. Effect size comparison between inner and outer retinal parameters showed that photoreceptor metrics were similarly robust in differentiating the PD group from age-matched controls as inner retinal changes. Electroretinography and OCT were unaffected by L-DOPA treatment or time.

CONCLUSIONS

We show that outer retinal outcomes of photoreceptoral dysfunction (decreased cone function and impaired color vision) and degeneration (i.e., outer nuclear layer thinning) were equivalent to inner retinal metrics at differentiating PD from healthy age-matched adults. These findings suggest outer retinal metrics may serve as useful biomarkers for PD.

Application of OpenArray Technology to Assess Changes in the Expression of Functionally Significant Genes in the Substantia Nigra of Mice in a Model of Parkinson's Disease

Studying the molecular mechanisms of the pathogenesis of Parkinson's disease (PD) is critical to improve PD treatment. We used OpenArray technology to assess gene expression in the substantia nigra (SN) cells of mice in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD and in controls. Among the 11 housekeeping genes tested, Rps27a was taken as the reference gene due to its most stable expression in normal and experimental conditions. From 101 genes encoding functionally significant proteins of nigrostriatal dopaminergic neurons, 57 highly expressed genes were selected to assess their expressions in the PD model and in the controls. The expressions of Th, Ddc, Maoa, Comt, Slc6a3, Slc18a2, Drd2, and Nr4a2 decreased in the experiment compared to the control, indicating decreases in the synthesis, degradation, and transport of dopamine and the impaired autoregulation of dopaminergic neurons. The expressions of Tubb3, Map2, Syn1, Syt1, Rab7, Sod1, Cib1, Gpx1, Psmd4, Ubb, Usp47, and Ctsb genes were also decreased in the MPTP-treated mice, indicating impairments of axonal and vesicular transport and abnormal functioning of the antioxidant and ubiquitin-proteasome systems in the SN. The detected decreases in the expressions of Snca, Nsf, Dnm1l, and Keap1 may serve to reduce pathological protein aggregation, increase dopamine release in the striatum, prevent mitophagy, and restore the redox status of SN cells.

Prodromal Parkinson's disease and the catecholaldehyde hypothesis: Insight from olfactory bulb organotypic cultures

Parkinson's disease (PD) is a progressive, neurodegenerative disorder with an increasing incidence, unknown etiology, and is currently incurable. Advances in understanding the pathological mechanisms at a molecular level have been slow, with little attention focused on the early prodromal phase of the disease. Consequently, the development of early-acting disease-modifying therapies has been hindered. The olfactory bulb (OB), the brain region responsible for initial processing of olfactory information, is particularly affected early in PD at both functional and molecular levels but there is little information on how the cells in this region are affected by disease. Organotypic and primary OB cultures were developed and characterized. These platforms were then used to assess the effects of 3,4-dihydroxyphenylacetylaldehyde (DOPAL), a metabolite of dopamine present in increased levels in post-mortem PD tissue and which is thought to contribute to PD pathogenesis. Our findings showed that DOPAL exposure can recapitulate many aspects of PD pathology. Oxidative stress, depolarization of mitochondrial membranes, and neurodegeneration were all induced by DOPAL addition, as were measured transcriptomic changes consistent with those reported in PD clinical studies. These olfactory models of prodromal disease lend credence to the catecholaldehyde hypothesis of PD and provide insight into the mechanisms by which the OB may be involved in disease progression.

A reversible state of hypometabolism in a human cellular model of sporadic Parkinson's disease

Sporadic Parkinson's Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD.

From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology

With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.