What are neurodegenerative diseases?
A neurodegenerative disease (ND) is an illness that has progressive and adverse effect on the function or structure of the neurons of the brain. ND is a wide concept ranging from small molecular changes to a complete loss of function of a neural system. In the PredictND context, we define ND as a class of progressive illnesses which affect cognitive functions such as learning and memory ultimately causing dementia. Examples of these illnesses are the Alzheimer's Disease, vascular dementia, fronto-temporal dementia, Lewy-Body dementia, and Parkinson disease. NDs are a major challenge, since dementia alone accounts for costs equivalent to about 1% of the global gross domestic product (GDP)2 - 461 billion euros annually. There were 36 million people living with dementia worldwide in 2010 but the number is expected to increase to 115 million by 2050.
What does the PredictND project aim to achieve?
Neurodegerative diseases are hard to diagnose and many of them are known to progress years or decades before the symptoms appear. When first memory problems are apparent, there is no single test which would immediately give conclusive information to make the correct diagnosis. Instead several tests need to be done to collect hints and clues of the actual cause and to rule out other possibilities. A vast amount of data from patients with memory problems and a known outcome has been collected by healthcare providers over the years. These data can be used for improved diagnostics.
Our first scientific objective is to develop a clinical protocol for enabling earlier and objective differential diagnostics of neurodegenerative diseases based on the principles of data-driven evidence-based medicine.
The ultimate objective in management of neurodegenerative diseases is to identify subjects that could benefit from treatments at a very early phase, preferably at pre-symptomatic phases Although biomarkers of Aβ accumulation such as CSF and Amyloid PET have been shown to become abnormal at a very early stage and several years before clinical symptoms, these biomarkers are not suitable for use in population-based screening programs. There is a clear need for low-cost tests which can be used to stratify populations and to detect persons at high risk. The high risk individuals can thereafter be studied with more accurate clinical differential diagnostic tools/biomarkers for confirming the diagnosis and starting treatments at an early phase. Although low-cost tests will not be adequate for making a final diagnosis, they are suitable to optimise candidate populations, i.e., to enrich populations, for higher-cost biomarker studies.
Our second scientific objective is to develop a low-cost battery of tests for early detection, and a clinical protocol for cost efficient differential diagnostics of neurodegenerative diseases utilising these tests.
NDs are typically diagnosed with a consensus of several experts that have examined the patient and the collected data. The diagnosis will be based on the current guidelines and expertise of the participating specialists. Objective exploitation of data collected from previous patients with similar symptoms is hard. Knowledge of these patients, their tests and outcome should be collected and documented in an intuitive and easy to use form.
Our first technical objective is to develop a decision support software tool to be used in clinical workflows for differential diagnostics of neurodegenerative diseases.
Computerized tools in the diagnostics of neurodegenerative diseases are still very rare. Integrating heterogeneous clinical data in an optimal way for the diagnosis is a challenge. Another challenge is how to integrate clinical information with the information from the everyday life such as results from low-cost tests for an even more holistic view of the patient’s condition.
Our second technical objective is to develop an ICT ecosystem for early and objective diagnostics of neurodegenerative diseases.
What are the projects impacts?
Success in achieving the scientific objectives will result in a novel clinical diagnostic protocol for NDs. This would be a significant advancement in diagnostics compared with the current partially subjective reasoning relying strongly on the expertise of available specialists. Improved and more accurate diagnostics would form also an essential basis for earlier detection of NDs. In addition, the approach would enable objective follow-up of disease progression or treatment efficacy.
The main impact of succeeding in the technical objectives would be a novel ICT ecosystem combined with a low-cost battery of measures. It would enable cost-efficient detection of patients at high-risk and their earlier diagnosis already at the pre-symptomatic phase. If successful, this would be a major step towards a more holistic solution for the challenge of dementia.
 Alzheimer’ Disease International: World Alzheimer Report 2010 – The Global Economic Impact of Dementia