Biomarkers for Parkinson's [corrected] disease: tools to assess Parkinson's disease onset and progression

Practical use of DAT SPECT imaging in diagnosing dementia with Lewy bodies: a US perspective of current guidelines and future directions

Background

Diagnosing Dementia with Lewy Bodies (DLB) remains a challenge in clinical practice. The use of 123I-ioflupane (DaTscan™) SPECT imaging, which detects reduced dopamine transporter (DAT) uptake-a key biomarker in DLB diagnosis-could improve diagnostic accuracy. However, DAT imaging is underutilized despite its potential, contributing to delays and suboptimal patient management.

Methods

This review evaluates DLB diagnostic practices and challenges faced within the U.S. by synthesizing information from current literature, consensus guidelines, expert opinions, and recent updates on DaTscan FDA filings. It contrasts DAT SPECT with alternative biomarkers, provides recommendations for when DAT SPECT imaging may be indicated and discusses the potential of emerging biomarkers in enhancing diagnostic approaches.

Results

The radiopharmaceutical 123I-ioflupane for SPECT imaging was initially approved in Europe (2000) and later in the US (2011) for Parkinsonism/Essential Tremor. Its application was extended in 2022 to include the diagnosis of DLB. DaTscan's diagnostic efficacy for DLB, with its sensitivity, specificity, and predictive values, confirms its clinical utility. However, US implementation faces challenges such as insurance barriers, costs, access issues, and regional availability disparities.

Conclusion

123I-ioflupane SPECT Imaging is indicated for DLB diagnosis and differential diagnosis of Alzheimer's Disease, particularly in uncertain cases. Addressing diagnostic obstacles and enhancing physician-patient education could improve and expedite DLB diagnosis. Collaborative efforts among neurologists, geriatric psychiatrists, psychologists, and memory clinic staff are key to increasing diagnostic accuracy and care in DLB management.

Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT

Purpose

FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans.

Methods

We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale.

Results

The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model.

Conclusion

A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05816-7.

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.

MPTP Induces Systemic Parkinsonism in Middle-Aged Cynomolgus Monkeys: Clinical Evolution and Outcomes

In this study, we developed a systemic PD model in middle-aged cynomolgus monkeys using individualized low-dose MPTP, to explore effective indicators for the early prediction of clinical outcomes. MPTP was not stopped until the animals showed typical PD motor symptoms on days 10 to 13 after MPTP administration when the Kurlan score reached 10; this abrogated the differences in individual susceptibility to MPTP. The clinical symptoms persisted, peaking on days 3 to 12 after MPTP withdrawal (rapid progress stage), and then the Kurlan score plateaued. A Kurlan score at the end of the rapid progress stage >15 reflected stable or slowly-progressive PD, while a score <15 indicated spontaneous recovery. The entire clinical evolution and outcome of the systemic PD model was characterized in this study, thus providing options for therapeutic and translational research.

Fluorine-18 Radiolabeled PET Tracers for Imaging Monoamine Transporters: Dopamine, Serotonin, and Norepinephrine

This review focuses on the development of fluorine-18 radiolabeled PET tracers for imaging the dopamine transporter (DAT), serotonin transporter (SERT), and norepinephrine transporter (NET). All successful DAT PET tracers reported to date are members of the 3β-phenyl tropane class and are synthesized from cocaine. Currently available carbon-11 SERT PET tracers come from both the diphenylsulfide and 3β-phenyl nortropane class, but so far only the nortropanes have found success with fluorine-18 derivatives. NET imaging has so far employed carbon-11 and fluorine-18 derivatives of reboxetine but due to defluorination of the fluorine-18 derivatives further research is still necessary.

An Alzheimer's Disease-Derived Biomarker Signature Identifies Parkinson's Disease Patients with Dementia

Biomarkers from multiple modalities have been shown to correlate with cognition in Parkinson's disease (PD) and in Alzheimer's disease (AD). However, the relationships of these markers with each other, and the use of multiple markers in concert to predict an outcome of interest, are areas that are much less explored. Our objectives in this study were (1) to evaluate relationships among 17 biomarkers previously reported to associate with cognition in PD or AD and (2) to test performance of a five-biomarker classifier trained to recognize AD in identifying PD with dementia (PDD). To do this, we evaluated a cross-sectional cohort of PD patients (n = 75) across a spectrum of cognitive abilities. All PD participants had 17 baseline biomarkers from clinical, genetic, biochemical, and imaging modalities measured, and correlations among biomarkers were assessed by Spearman's rho and by hierarchical clustering. We found that internal correlation among all 17 candidate biomarkers was modest, showing a maximum pairwise correlation coefficient of 0.51. However, a five-marker subset panel derived from AD (CSF total tau, CSF phosphorylated tau, CSF amyloid beta 42, APOE genotype, and SPARE-AD imaging score) discriminated cognitively normal PD patients vs. PDD patients with 80% accuracy, when employed in a classifier originally trained to recognize AD. Thus, an AD-derived biomarker signature may identify PDD patients with moderately high accuracy, suggesting mechanisms shared with AD in some PDD patients. Based on five measures readily obtained during life, this AD-derived signature may prove useful in identifying PDD patients most likely to respond to AD-based crossover therapies.

Unbiased approaches to biomarker discovery in neurodegenerative diseases

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and frontotemporal dementia have several important features in common. They are progressive, they affect a relatively inaccessible organ, and we have no disease-modifying therapies for them. For these brain-based diseases, current diagnosis and evaluation of disease severity rely almost entirely on clinical examination, which may be only a rough approximation of disease state. Thus, the development of biomarkers-objective, relatively easily measured, and precise indicators of pathogenic processes-could improve patient care and accelerate therapeutic discovery. Yet existing, rigorously tested neurodegenerative disease biomarkers are few, and even fewer biomarkers have translated into clinical use. To find new biomarkers for these diseases, an unbiased, high-throughput screening approach may be needed. In this review, I will describe the potential utility of such an approach to biomarker discovery, using Parkinson's disease as a case example.

Biomarkers in Parkinson's disease (recent update)

Parkinson's disease (PD) is the second most common neurodegenerative disorder mostly affecting the aging population over sixty. Cardinal symptoms including, tremors, muscle rigidity, drooping posture, drooling, walking difficulty, and autonomic symptoms appear when a significant number of nigrostriatal dopaminergic neurons are already destroyed. Hence we need early, sensitive, specific, and economical peripheral and/or central biomarker(s) for the differential diagnosis, prognosis, and treatment of PD. These can be classified as clinical, biochemical, genetic, proteomic, and neuroimaging biomarkers. Novel discoveries of genetic as well as nongenetic biomarkers may be utilized for the personalized treatment of PD during preclinical (premotor) and clinical (motor) stages. Premotor biomarkers including hyper-echogenicity of substantia nigra, olfactory and autonomic dysfunction, depression, hyposmia, deafness, REM sleep disorder, and impulsive behavior may be noticed during preclinical stage. Neuroimaging biomarkers (PET, SPECT, MRI), and neuropsychological deficits can facilitate differential diagnosis. Single-cell profiling of dopaminergic neurons has identified pyridoxal kinase and lysosomal ATPase as biomarker genes for PD prognosis. Promising biomarkers include: fluid biomarkers, neuromelanin antibodies, pathological forms of α-Syn, DJ-1, amyloid β and tau in the CSF, patterns of gene expression, metabolomics, urate, as well as protein profiling in the blood and CSF samples. Reduced brain regional N-acetyl-aspartate is a biomarker for the in vivo assessment of neuronal loss using magnetic resonance spectroscopy and T2 relaxation time with MRI. To confirm PD diagnosis, the PET biomarkers include [(18)F]-DOPA for estimating dopaminergic neurotransmission, [(18)F]dG for mitochondrial bioenergetics, [(18)F]BMS for mitochondrial complex-1, [(11)C](R)-PK11195 for microglial activation, SPECT imaging with (123)Iflupane and βCIT for dopamine transporter, and urinary salsolinol and 8-hydroxy, 2-deoxyguanosine for neuronal loss. This brief review describes the merits and limitations of recently discovered biomarkers and proposes coenzyme Q10, mitochondrial ubiquinone-NADH oxidoreductase, melatonin, α-synculein index, Charnoly body, and metallothioneins as novel biomarkers to confirm PD diagnosis for early and effective treatment of PD.

Cerebrospinal fluid biomarker candidates for parkinsonian disorders

The Parkinsonian disorders are a large group of neurodegenerative diseases including idiopathic Parkinson’s disease (PD) and atypical Parkinsonian disorders (APD), such as multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, and dementia with Lewy bodies. The etiology of these disorders is not known although it is considered to be a combination of genetic and environmental factors. One of the greatest obstacles for developing efficacious disease-modifying treatment strategies is the lack of biomarkers. Reliable biomarkers are needed for early and accurate diagnosis, to measure disease progression, and response to therapy. In this review several of the most promising cerebrospinal biomarker candidates are discussed. Alpha-synuclein seems to be intimately involved in the pathogenesis of synucleinopathies and its levels can be measured in the cerebrospinal fluid and in plasma. In a similar way, tau protein accumulation seems to be involved in the pathogenesis of tauopathies. Urate, a potent antioxidant, seems to be associated to the risk of developing PD and with its progression. Neurofilament light chain levels are increased in APD compared with PD and healthy controls. The new “omics” techniques are potent tools offering new insights in the patho-etiology of these disorders. Some of the difficulties encountered in developing biomarkers are discussed together with future perspectives.

Dopamine transporter imaging is associated with long-term outcomes in Parkinson's disease

Dopamine (DA) transporter (DAT) imaging has been studied as a diagnostic tool for degenerative parkinsonism. Our aim was to measure the prognostic value of imaging for motor and nonmotor outcomes in Parkinson's disease (PD). We prospectively evaluated a Parkinson's cohort after enrollment in a de novo clinical trial with a battery of motor (UPDRS), cognitive (Montreal Cognitive Assessment), and behavioral measures. DAT imaging with [(123)I][β]-CIT and single-photon emission computerized tomography (SPECT) was performed at baseline and after 22 months. In total, 491 (91%) of the 537 subjects had evidence of DA deficiency on their baseline scan, consistent with PD, and were included in the analyses. The cohort was followed for 5.5 (0.8) years, with a mean duration of diagnosis of 6.3 (1.2). Lower striatal binding at baseline was independently associated with higher risk for clinical milestones and measures of disease severity, including motor-related disability, falling and postural instability, cognitive impairment, psychosis, and clinically important depressive symptoms. Subjects in the bottom quartile for striatal binding, compared to the top quartile, had an odds ratio (95% confidence interval) of 3.3 (1.7, 6.7) for cognitive impairment and 12.9 (2.6, 62.4) for psychosis. Change from baseline in imaging after 22 months was also independently associated with motor, cognitive, and behavioral outcomes. DAT imaging with [(123)I][β]-CIT and SPECT, shortly after the diagnosis of PD, was independently associated with clinically important long-term motor and nonmotor outcomes. These results should be treated as hypothesis generating and require confirmation.

Postural sway as a marker of progression in Parkinson's disease: a pilot longitudinal study

Objective measures of postural control that are sensitive to Parkinson's disease (PD) progression would improve patient care and accelerate clinical trials. Although measures of postural sway during quiet stance in untreated PD have been shown to differ from age-matched control subjects, it is not known if sway measures change with disease progression in early PD. In this pilot study, we asked whether accelerometer-based metrics of sway could provide a practical tool for monitoring progression of postural dyscontrol in people with untreated or newly treated PD. We examined 13 subjects with PD and 12 healthy, age-matched control subjects. The PD subjects had been recently diagnosed and had not started any antiparkinsonian medications at the baseline session. All subjects were tested 3-6 months and 12 months after the baseline session. Subjects were asked to stand quietly for two minutes while wearing an inertial sensor on their posterior trunk that measured trunk linear acceleration. Our results suggested that objective sway measures deteriorated over one year despite minimal changes in UPDRS motor scores. Medio-lateral (ML) sway measures were more sensitive than antero-posterior sway measures in detecting progression. The ML JERK was larger in the PD group than the control group across all three testing sessions. The ML sway dispersion and ML sway velocity were also significantly higher in PD compared to control subjects by the 12-month evaluation. It is feasible to measure progression of PD prior to onset of treatment using accelerometer-based measures of quiet standing.

Biomarker candidates of neurodegeneration in Parkinson's disease for the evaluation of disease-modifying therapeutics

Reliable biomarkers that can be used for early diagnosis and tracking disease progression are the cornerstone of the development of disease-modifying treatments for Parkinson's disease (PD). The German Society of Experimental and Clinical Neurotherapeutics (GESENT) has convened a Working Group to review the current status of proposed biomarkers of neurodegeneration according to the following criteria and to develop a consensus statement on biomarker candidates for evaluation of disease-modifying therapeutics in PD. The criteria proposed are that the biomarker should be linked to fundamental features of PD neuropathology and mechanisms underlying neurodegeneration in PD, should be correlated to disease progression assessed by clinical rating scales, should monitor the actual disease status, should be pre-clinically validated, and confirmed by at least two independent studies conducted by qualified investigators with the results published in peer-reviewed journals. To date, available data have not yet revealed one reliable biomarker to detect early neurodegeneration in PD and to detect and monitor effects of drug candidates on the disease process, but some promising biomarker candidates, such as antibodies against neuromelanin, pathological forms of α-synuclein, DJ-1, and patterns of gene expression, metabolomic and protein profiling exist. Almost all of the biomarker candidates were not investigated in relation to effects of treatment, validated in experimental models of PD and confirmed in independent studies.

Blood-based protein biomarkers for diagnosis and classification of neurodegenerative diseases: current progress and clinical potential

Biomarker research is a rapidly advancing field in medicine. Recent advances in genomic, genetic, epigenetic, neuroscientific, proteomic, and metabolomic knowledge and technologies have opened the way to thriving research. In the most general sense, a biomarker refers to any useful characteristic that can be measured and used as an indicator of a normal biologic process, a pathogenic process, or a pharmacologic response to a therapeutic agent. Despite the extensive resources concentrated on this area, there are very few biomarkers currently available that qualify and are satisfactorily validated for mental disorders, and there is still a major lack of biomarkers for typifying neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. This article provides an overview of this field of research and focuses on recent advances in biomarker research in Alzheimer's disease and Parkinson's disease.

Biopsable neural tissues: toward new biomarkers for Parkinson's disease?

Biomarkers for Parkinson's disease (PD) are mainly intended for the early diagnosis of the disease and to monitor its progression, two aspects insufficiently covered by clinical evaluation. In the last 20 years, the search for biomarkers has been supported by technological advances in the fields of molecular genetics and neuroimaging. Nevertheless, no fully validated biomarker is yet available, and there is still a need for biomarkers that will complement those already available. Development of biomarkers for PD has been hampered by the fact that the core pathology lies in the brainstem, hidden from direct study in living patients. In this context, the recent observations that clearly demonstrated the presence of PD pathology in peripheral neural tissues provide new opportunities to develop original histopathological markers of the disease. Some of these peripheral tissues, especially the enteric nervous system, by being assessable using routine biopsies, could represent a window to assess in vivo the neuropathological processes occurring in PD.