Neurodegenerative Diseases Progression Markers (MARKERS-NDD) (MARKERS-NDD)

September 11, 2024 updated by: Casa di Cura San Raffaele Cassino

Neurodegenerative Diseases Progression Markers (MARKERS-NDD): a Real-world Data Longitudinal Prospective Study

MARKERS-NDD is a prospective, observational, longitudinal study, which aims to collect data from patients affected by neurodegenerative diseases (NDD) followed longitudinally for routine examinations performed as part of normal clinical practice. Data collected from clinical evaluations, movement analysis, brain imaging, neuropsychological and electroencephalographic assessments, blood chemistry tests will be analysed to carry out statistical investigations and predictive analyses, also using artificial intelligence systems, which allow the identification of new early markers of diagnosis and prognosis of neurodegenerative diseases.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Considering the current estimates and the global social and economic burden of neurodegenerative diseases, changes in the manner and timing of a diagnosis of these diseases are urgently needed as well as in the timeliness with which effective therapeutic interventions are carried out. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the population of patients affected by neurodegenerative diseases present enormous challenges to the development of early diagnostic tools and systems capable of predicting the course of the disease.

Despite intensive research in the field of pharmacology, surgery and rehabilitation, neurodegenerative diseases remain chronic progressive diseases without a therapy that can change the course.

MARKERS-NDD is a prospective, observational, longitudinal study, which aims to collect data from patients affected by neurodegenerative diseases (NDD) followed longitudinally for routine examinations performed as part of normal clinical practice. Data collected from clinical evaluations, movement analysis, brain imaging, neuropsychological and electroencephalographic assessments, blood chemistry tests will be analysed to carry out statistical investigations and predictive analyses, also using artificial intelligence systems, which allow the identification of new early markers of diagnosis and prognosis of neurodegenerative diseases.

Quantitative movement analysis, with the aid of standard motion capture systems (gait analysis) and with wearable inertial sensors, is a valid tool both for supporting clinical diagnostics and to assess the response to pharmacological treatment, and to monitor the progression of NDDs. From this perspective, the kinematic analysis of gait and graphic gesture can reveal early alterations of motor features to support the differential diagnosis and stratification of the patient and allow us to follow the progression of the disease over time.

Although motor symptoms represent key aspects for differential diagnosis between parkinsonian syndrome and dementia, alterations of cognitive functions, and in particular, of executive functions can be also present in the early stages of the disease in patients with synucleinopathies such as PD and Lewy Body Dementia.

In Parkinson's disease (PD), these cognitive disorders can evolve over time towards a mild cognitive decline (Mild cognitive impairment, PD-MCI) up to dementia (Parkinson Disease Dementia, PDD).

The identification of neuropsychological markers that can predict the progress of these diseases and the risk of conversion in patients with PD is a crucial aspect for the treatment and management of patients in the different phases of the disease.

In the era of artificial intelligence (AI), with the introduction of AI-driven computer vision, the human movement can be tracked and analysed in real time by the support of a simple camera of mobile devices, such as tablets and smartphones, potentially eliminating the need for additional sensors or specialized equipment. Therefore, an approach such as telemedicine using AI could offer new possibilities and challenges for remote diagnosis, telemonitoring and telerehabilitation in neurological disorders such as neurodegenerative diseases. Movement analysis based on AI-driven computer vision could reduce the number of patient movements, especially in the advanced stages of the disease, often characterized by severe disability, alleviate the burden of caregivers who are sometimes elderly or engaged in work activities, and allow consultations to be carried out in remote areas that are not easily reachable.

The comparison and validation of these systems with the gold standard of movement analysis represented by gait analysis with motion-captures and the already validated analysis systems with inertial sensors, constitutes a key piece in the development of clinical tools that support remote diagnosis and telemonitoring of therapies pharmacological and rehabilitation treatments.

Similarly, the application of artificial intelligence systems for the kinematic analysis of the graphic gesture has allowed the development of various pattern recognition systems for the automatic recognition of handwriting in different application fields.

Dysgraphic features were related to abnormalities of motor and cognitive functions revealed by clinical and neuropsychological tests, as well as to neurophysiological correlates of handwriting-related cortical activity such as electroencephalogram (EEG).

Handwriting analysis system which uses AI systems and involves the execution of validated neuropsychological tests, with the aid of commercial graphics tablets, carried out as part of normal clinical practice - such as a simple outpatient visit or during of a structural neuropsychological examination as a component of the now common multidisciplinary approach that characterizes the management of patients with NDD - could be particularly useful in PD-MCI to follow the progression of both motor and cognitive symptoms. As a cost-effective and non-invasive measure of motor and cognitive performance, graphical gesture analysis systems could be applied repeatedly over time without serial or meta-learning effects.

A further key aspect in the early diagnosis of NDDs is represented by voice analysis: the production of the human voice occurs through complex and synergistic movements of systems and subsystems (vocal cords, larynx, glottis, oral cavity and more), which can be influenced by the health conditions of the speaker. In particular, among neurodegenerative diseases, PD and Parkinsonism involve dramatic, objective and measurable changes in vocal production, which may include (among others) increased noise levels (due to incomplete closure of the vocal folds) and loss of voice (dysarthrophonia, dysarthria and hypophonia). Although the evaluation of speech disorder can indeed be performed via laryngoscope and video-stroboscopic instruments, these are very expensive tests, requiring a lot of time and qualified personnel. Voice-based artificial intelligence systems that make use of commercial systems (mobile devices equipped with microphones with sound robustness) could allow the analysis of the voice with artificial intelligence algorithms, to diagnose the disease in its early stages.

A long-term analysis of common laboratory blood chemistry parameters and analysis of different biological samples, such as fecal samples for microbiota analysis, performed as routine checks by patients with chronic diseases such as NDDs could allow the identification of associations and early markers useful in the diagnosis and monitoring of disease progression.

At the same time, the introduction of increasingly powerful and accurate brain imaging systems, supported by machine learning systems and artificial intelligence techniques, to obtain an increasingly accurate etiological diagnosis.

Relevant is that each neurodegenerative disease favours a specific brain network which in turn is associated with a specific loss of tissue in particular brain regions. Therefore, the possibility of identifying new neuroimaging markers through machine learning systems to guide the differential diagnosis and accurately evaluate the course of the disease.

In conclusion, the approach adopted by the MARKERS-NDD study is fundamental to increase the potential for success of an ambitious strategy that aims to develop markers of progression of neurodegenerative diseases that accelerate the search for disease-modifying therapy.

Study Type

Observational

Enrollment (Estimated)

600

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Italy
    • Frosinone
      • Cassino, Frosinone, Italy, 03043

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Patients with Neurodegerative Diseases

Description

Inclusion Criteria:

  • Patients with diagnosis of Parkinson's Disease, Parkinsonism and Movement Disorders

    • Patients with diagnosis of Parkinson's Disease

      • Diagnosis of Parkinson's Disease according to the United Kingdom (UK) Parkinson's Disease Society Brain Bank

    • Diagnosis of Movement Disorder not related to Parkinson's Disease

      • Diagnosis of Multiple System Atrophy (MSA) in accordance with Second Consensus Statement on Diagnosis of Multiple System Atrophy;
      • Diagnosis of Progressive Supranuclear Palsy according to Movement Disorder Society for Diagnosis of Progressive Supranuclear Palsy;
      • Diagnosis of Essential Tremor
      • Willing to participate in the study, understand the procedures and sign the informed consent.

  • Patients affected by cognitive impairment (CI) and dementia

    • Diagnosis of probable:

      • Lewy Body Dementia
      • Alzheimer's Disease
      • Mild Cognitive Decline
      • Subjective memory complaints
      • Willing to participate in the study, understand the procedures and sign the informed consent.

Exclusion Criteria:

  • There are no restrictions for participation in the study based on age, severity of illness or presence of cognitive impairment, as long as the person is able to complete the research assessments.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients with diagnosis of Parkinson's Disease, Parkinsonism and Movement Disorders
Other: Observation
In MARKERS-NDD up to 600 participants will be enrolled and followed longitudinally once identified, over the course of 1-10 years
Patients affected by cognitive impairment (CI) and dementia
Other: Observation
In MARKERS-NDD up to 600 participants will be enrolled and followed longitudinally once identified, over the course of 1-10 years

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To monitor and predict the course of neurodegenerative diseases through the study of clinical markers and the creation of AI-models
Time Frame: Baseline to 120 months
To identify markers of neurodegenerative disease progression for use in clinical monitoring
Baseline to 120 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To predict the risk to develop neurodegenerative diseases through the study of clinical markers and the creation of AI-models
Time Frame: Baseline to 120 months
To identify markers of neurodegenerative disease for use in early clinical diagnosis
Baseline to 120 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Casa di Cura San Raffaele Cassino

Collaborators

University of Roma La Sapienza
Politecnico di Milano
IRCCS San Raffaele Roma
University of Urbino "Carlo Bo"
San Raffaele Telematic University
University of Cassino and Southern Lazio

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 9, 2024

Primary Completion (Estimated)

September 9, 2034

Study Completion (Estimated)

September 9, 2034

Study Registration Dates

First Submitted

August 9, 2024

First Submitted That Met QC Criteria

September 11, 2024

First Posted (Estimated)

September 19, 2024

Study Record Updates

Last Update Posted (Estimated)

September 19, 2024

Last Update Submitted That Met QC Criteria

September 11, 2024

Last Verified

September 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 001-2024

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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