Study Results
Results pending
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Basic Information
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Recruitment Status
UNKNOWN
Total Enrollment
60 participants
Study Classification
OBSERVATIONAL
Study Start Date
2021-12-01
Study Completion Date
2022-12-31
Brief Summary
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Parkinson's disease (PD) is the second most common neurodegenerative disease and its prevalence is expected to double over the next 30 years, making it a leading cause of neurological disability \[GBD 2016 Neurology Collaborators, 2019; Dorsey et al, 2018\]. PD is characterized by motor symptoms, such as muscle stiffness, tremor, slowness of movement (bradykinesia) and postural instability, and non-motor symptoms, such as sphincter disorders, postural hypotension, cognitive disorders, depression, hyposmia, constipation and REM sleep behavioral disturbance. Unfortunately, the mechanisms leading to neuronal dysfunction and death in PD remain poorly known and there are currently no therapies capable of modifying their course \[Bloem et al, 2021\].
In this study we aim at defining a new set of biomarkers based on the combination between PET, blood metabolomics and natural language extracted from the keywords of electronic health records.
In this study we aim at defining a new set of biomarkers based on the combination between PET, blood metabolomics and natural language extracted from the keywords of electronic health records.
Detailed Description
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Much evidence suggests the existence of a preclinical stage of disease that begins many years before PD is diagnosed, when an individual appears normal (there are no symptoms or signs), but is already developing typical neuropathological changes. The prodromal phase follows, in which symptoms and signs are present, but are still insufficient to define the disease. In the clinical phase, cardinal motor symptoms become sufficiently evident to diagnose PD \[Schaeffer et al, 2020; Toulouse et al, 2021\]. For this reason, it is important to have reliable biomarkers that can help in early diagnosis, especially considering that disease-modifying therapies have a greater chance of success if they are started early, before a considerable number of dopaminergic neurons have undergone. death. Furthermore, there is also a need to better define PD subtypes that not only have different clinical presentation and prognosis, but also differ in the underlying pathogenetic mechanisms, requiring personalized therapeutic approaches \[Tolosa et al, 2021\]. Such biomarkers should be sensitive, specific, non-invasive, inexpensive, easily detectable and measurable \[Du et al, 2021\].
In recent years, numerous biomarkers of risk and / or prodromal phase have been identified and combined in search criteria for prodromal PD, with the aim of calculating the probability with which a patient is in the prodromal phase of PD. Apart from specific genetic risk markers, including above all GBA and LRRK2 mutations, REM sleep behavioral disturbance and PET / SPECT abnormalities are currently considered the most important prodromal biomarkers, capable of predicting PD with a high probability. \[Berg et al, 2015; Heinzel et al, 2019\]. However, new biomarkers are needed for a better understanding of the prodromal phase and its potential clinical-pathological subtypes and for a more precise calculation of the probability of MP \[Bloem et al, 2021; Schaeffer et al, 2020\].
Goals: The main objective of the present study is to identify new, more reliable biomarkers of PD and to develop a new, more accurate predictive model of disease.
The design is that of a longitudinal observational study. Participants will be divided into 6 groups (each with at least 20 subjects) based on clinical characteristics: 1) patients with clinically defined PD; 2) patients with clinically probable PD; 3) patients with neurodegenerative parkinsonism, such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), Lewy body disease (DLB) or cortico-basal degeneration (CBD); 4) patients with secondary parkinsonism (vascular, iatrogenic, psychogenic, etc.); 5) patients with "probable" or "possible" prodromal PD; 6) healthy subjects with risk or prodromal factors for PD; 7) healthy subjects of the same age and sex without any risk or prodromal factor for PD.
All participants will undergo a careful medical history, general and neurological physical examination, neuropsychological tests, structural brain imaging (MRI or CT) and PET with F-DOPA (or SPECT with DATSCAN), venous blood sampling for routine blood chemistry ( including blood count, erythrocyte sedimentation rate, urea and electrolytes, thyroid function, vitamin B12 and folic acid), metabolomic analysis, including lipidomics, genetic analysis, mononuclear cell separation, and the search for known biomarkers of PD. The severity and progression of the disease will be assessed through the use of specific scores, including the Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr scale.
Metabolomic analysis, including lipidomics, will be performed through liquid chromatography coupled with tandem mass spectrometry and genetic analysis using next generation sequencing (NGS). Structural imaging and PET with F-DOPA (or SPECT with DATSCAN) of each subject will be subjected to texture analysis, using dedicated software, in search of new, more reliable neuroradiological markers. In the texture analysis, 103 structural parameters will be extracted from the 89 regions of the Hammers brain, for a total of 8779 parameters. For each brain region the parameters will be reduced by Principal Component Analysis (PCA), selecting only the principal components that express 99.5% of the total variance. The ultimate goal will be to quantify, in each individual subject, the risk of developing PD using a convolutional neural network (CNN) with inputs consisting of the various clinical parameters evaluated and the main components selected from metabolomics and PET data.
All participants will be clinically re-examined on a three-monthly basis.
The analysis will be carried out together with the partner "NIM Competence Center for Digital Healthcare GmbH" (NIM), holder of the "GATEKEEPER 1st open call" research grant. The data, appropriately anonymized, will be included in the European Gatekeeper digital medicine platform by the NIM partner.
In recent years, numerous biomarkers of risk and / or prodromal phase have been identified and combined in search criteria for prodromal PD, with the aim of calculating the probability with which a patient is in the prodromal phase of PD. Apart from specific genetic risk markers, including above all GBA and LRRK2 mutations, REM sleep behavioral disturbance and PET / SPECT abnormalities are currently considered the most important prodromal biomarkers, capable of predicting PD with a high probability. \[Berg et al, 2015; Heinzel et al, 2019\]. However, new biomarkers are needed for a better understanding of the prodromal phase and its potential clinical-pathological subtypes and for a more precise calculation of the probability of MP \[Bloem et al, 2021; Schaeffer et al, 2020\].
Goals: The main objective of the present study is to identify new, more reliable biomarkers of PD and to develop a new, more accurate predictive model of disease.
The design is that of a longitudinal observational study. Participants will be divided into 6 groups (each with at least 20 subjects) based on clinical characteristics: 1) patients with clinically defined PD; 2) patients with clinically probable PD; 3) patients with neurodegenerative parkinsonism, such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), Lewy body disease (DLB) or cortico-basal degeneration (CBD); 4) patients with secondary parkinsonism (vascular, iatrogenic, psychogenic, etc.); 5) patients with "probable" or "possible" prodromal PD; 6) healthy subjects with risk or prodromal factors for PD; 7) healthy subjects of the same age and sex without any risk or prodromal factor for PD.
All participants will undergo a careful medical history, general and neurological physical examination, neuropsychological tests, structural brain imaging (MRI or CT) and PET with F-DOPA (or SPECT with DATSCAN), venous blood sampling for routine blood chemistry ( including blood count, erythrocyte sedimentation rate, urea and electrolytes, thyroid function, vitamin B12 and folic acid), metabolomic analysis, including lipidomics, genetic analysis, mononuclear cell separation, and the search for known biomarkers of PD. The severity and progression of the disease will be assessed through the use of specific scores, including the Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr scale.
Metabolomic analysis, including lipidomics, will be performed through liquid chromatography coupled with tandem mass spectrometry and genetic analysis using next generation sequencing (NGS). Structural imaging and PET with F-DOPA (or SPECT with DATSCAN) of each subject will be subjected to texture analysis, using dedicated software, in search of new, more reliable neuroradiological markers. In the texture analysis, 103 structural parameters will be extracted from the 89 regions of the Hammers brain, for a total of 8779 parameters. For each brain region the parameters will be reduced by Principal Component Analysis (PCA), selecting only the principal components that express 99.5% of the total variance. The ultimate goal will be to quantify, in each individual subject, the risk of developing PD using a convolutional neural network (CNN) with inputs consisting of the various clinical parameters evaluated and the main components selected from metabolomics and PET data.
All participants will be clinically re-examined on a three-monthly basis.
The analysis will be carried out together with the partner "NIM Competence Center for Digital Healthcare GmbH" (NIM), holder of the "GATEKEEPER 1st open call" research grant. The data, appropriately anonymized, will be included in the European Gatekeeper digital medicine platform by the NIM partner.
Conditions
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Parkinson Disease
Keywords
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Parkinson Disease
Study Design
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Observational Model Type
COHORT
Study Time Perspective
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Patients with clinically defined or probable PD will need to meet the criteria for the diagnosis of PD (clinically defined or probable) prepared by the Movement Disorder Society (MDS) in 2015 \[Postuma et al, 2015\]. Patients with neurodegenerative parkinsonism will have to meet the published diagnostic criteria for MSA \[Gilman et al, 2008\], PSP \[Höglinger et al, 2017\], DLB \[McKeith et al, 2017\] and CBD \[Armstrong et al, 2013\]. Patients with prodromal PD will have to meet the diagnostic criteria proposed by the MDS for the diagnosis of "probable" prodromal PD if the risk percentage of developing PD\> 80%, or "possible" if the risk percentage of developing PD\> 50 % \[Berg et al, 2015; Heinzel et al, 2019\]. The risk or prodromal factors proposed by the MDS to calculate the probability of prodromal PD include: male sex, exposure to pesticides, exposure to solvents, caffeine intake, smoking habit, familiarity (first degree relative with PD), hyperechogenicity of the substantia nigra on transcranial ultrasound, possible subthreshold parkinsonism (as assessed by UPDRS-III), REM sleep behavioral disorder (RBD), olfactory impairment, constipation, excessive daytime sleepiness, orthostatic hypotension, urinary dysfunction, severe erectile dysfunction, depression / anxiety, activity of the dopamine transporter, RBD confirmed by polysomnography, genetic tests, type II diabetes mellitus, physical activity, serum urate levels, cognitive functions assessed with the Mini Mental State Examination \[Berg et al, 2015; Heinzel et al, 2019\]. Healthy subjects with risk or prodromal factors for PD should have a probability of developing PD \<50% according to the criteria of the MDS \[Berg et al, 2015; Heinzel et al, 2019\]. In addition to the selected patients, a study will also be conducted on patient data in the reference database of the proposing institution.
Exclusion Criteria
\- Exclusion: Patients who do not consent to study participation
Eligible Sex
ALL
Accepts Healthy Volunteers
Yes
Sponsors
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NIM Competence Center for Digital Healthcare GmbH
UNKNOWN
Sponsor Role
collaborator
Neuromed IRCCS
OTHER
Sponsor Role
lead
Responsible Party
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Nicola D'Ascenzo
Director Department Medical Physics and Engineering
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Nicola D'Ascenzo
Role: PRINCIPAL_INVESTIGATOR
Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S
Locations
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Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S.
Pozzilli, , Italy
Site Status
Countries
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Italy
Central Contacts
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Facility Contacts
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Nicola D'Ascenzo
Role: primary
References
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Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, Boxer AL, Dickson DW, Grossman M, Hallett M, Josephs KA, Kertesz A, Lee SE, Miller BL, Reich SG, Riley DE, Tolosa E, Troster AI, Vidailhet M, Weiner WJ. Criteria for the diagnosis of corticobasal degeneration. Neurology. 2013 Jan 29;80(5):496-503. doi: 10.1212/WNL.0b013e31827f0fd1.
Reference Type
BACKGROUND
Berg D, Postuma RB, Adler CH, Bloem BR, Chan P, Dubois B, Gasser T, Goetz CG, Halliday G, Joseph L, Lang AE, Liepelt-Scarfone I, Litvan I, Marek K, Obeso J, Oertel W, Olanow CW, Poewe W, Stern M, Deuschl G. MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2015 Oct;30(12):1600-11. doi: 10.1002/mds.26431.
Reference Type
BACKGROUND
Bloem BR, Okun MS, Klein C. Parkinson's disease. Lancet. 2021 Jun 12;397(10291):2284-2303. doi: 10.1016/S0140-6736(21)00218-X. Epub 2021 Apr 10.
Reference Type
BACKGROUND
Dorsey ER, Sherer T, Okun MS, Bloem BR. The Emerging Evidence of the Parkinson Pandemic. J Parkinsons Dis. 2018;8(s1):S3-S8. doi: 10.3233/JPD-181474.
Reference Type
BACKGROUND
Du T, Wang L, Liu W, Zhu G, Chen Y, Zhang J. Biomarkers and the Role of alpha-Synuclein in Parkinson's Disease. Front Aging Neurosci. 2021 Mar 23;13:645996. doi: 10.3389/fnagi.2021.645996. eCollection 2021.
Reference Type
BACKGROUND
GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019 May;18(5):459-480. doi: 10.1016/S1474-4422(18)30499-X. Epub 2019 Mar 14.
Reference Type
BACKGROUND
Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, Wood NW, Colosimo C, Durr A, Fowler CJ, Kaufmann H, Klockgether T, Lees A, Poewe W, Quinn N, Revesz T, Robertson D, Sandroni P, Seppi K, Vidailhet M. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008 Aug 26;71(9):670-6. doi: 10.1212/01.wnl.0000324625.00404.15.
Reference Type
BACKGROUND
Heinzel S, Berg D, Gasser T, Chen H, Yao C, Postuma RB; MDS Task Force on the Definition of Parkinson's Disease. Update of the MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2019 Oct;34(10):1464-1470. doi: 10.1002/mds.27802. Epub 2019 Aug 14.
Reference Type
BACKGROUND
Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, Mollenhauer B, Muller U, Nilsson C, Whitwell JL, Arzberger T, Englund E, Gelpi E, Giese A, Irwin DJ, Meissner WG, Pantelyat A, Rajput A, van Swieten JC, Troakes C, Antonini A, Bhatia KP, Bordelon Y, Compta Y, Corvol JC, Colosimo C, Dickson DW, Dodel R, Ferguson L, Grossman M, Kassubek J, Krismer F, Levin J, Lorenzl S, Morris HR, Nestor P, Oertel WH, Poewe W, Rabinovici G, Rowe JB, Schellenberg GD, Seppi K, van Eimeren T, Wenning GK, Boxer AL, Golbe LI, Litvan I; Movement Disorder Society-endorsed PSP Study Group. Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord. 2017 Jun;32(6):853-864. doi: 10.1002/mds.26987. Epub 2017 May 3.
Reference Type
BACKGROUND
McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D, Aarsland D, Galvin J, Attems J, Ballard CG, Bayston A, Beach TG, Blanc F, Bohnen N, Bonanni L, Bras J, Brundin P, Burn D, Chen-Plotkin A, Duda JE, El-Agnaf O, Feldman H, Ferman TJ, Ffytche D, Fujishiro H, Galasko D, Goldman JG, Gomperts SN, Graff-Radford NR, Honig LS, Iranzo A, Kantarci K, Kaufer D, Kukull W, Lee VMY, Leverenz JB, Lewis S, Lippa C, Lunde A, Masellis M, Masliah E, McLean P, Mollenhauer B, Montine TJ, Moreno E, Mori E, Murray M, O'Brien JT, Orimo S, Postuma RB, Ramaswamy S, Ross OA, Salmon DP, Singleton A, Taylor A, Thomas A, Tiraboschi P, Toledo JB, Trojanowski JQ, Tsuang D, Walker Z, Yamada M, Kosaka K. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology. 2017 Jul 4;89(1):88-100. doi: 10.1212/WNL.0000000000004058. Epub 2017 Jun 7.
Reference Type
BACKGROUND
Schaeffer E, Postuma RB, Berg D. Prodromal PD: A new nosological entity. Prog Brain Res. 2020;252:331-356. doi: 10.1016/bs.pbr.2020.01.003. Epub 2020 Feb 10.
Reference Type
BACKGROUND
Tolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson's disease. Lancet Neurol. 2021 May;20(5):385-397. doi: 10.1016/S1474-4422(21)00030-2.
Reference Type
BACKGROUND
Other Identifiers
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GATEKEEPER-857223
Identifier Type: -
Identifier Source: org_study_id