Alzheimer’s disease is the most common form of dementia, and it is currently one of the leading cause of death worldwide. Beyond its mortality impact, Alzheimer’s disease drivers dependency among older adults placing a disease emotional and economic burden on individuals, families, and healthcare systems. As our population ages, the early detection and prevention of Alzheimer’s disease have become more critical than ever. New technologies from advanced brain imaging to machine learning models offers possibilities to assess an individual’s risk of developing Alzheimer´s disease.
On the trail of Alzheimer’s disease
PhD candidate Petra Parviainen is aiming to advance the early detection of Alzheimer’s disease at Turku PET Centre. By combining PET imaging results with registry data, it can be possible to assess whether brain amyloid accumulation, that may be an early sign of developing Alzheimer's disease, predicts the development of memory disorders already during the mild cognitive impairment phase or even before any symptoms appear. In addition, by utilizing artificial intelligence (AI), the study subjects will be classified automatically into diagnostic categories and these diagnoses given by AI will be compared to the diagnosis already given to the patient by the doctor, neurologist or geriatrician.
How emerging technologies help us detect and prevent the disease
Positron emission tomography (PET) scanning is an imaging technique that can detect chemical changes in the brain, such as amyloid accumulation, that indicate the development of Alzheimer’s disease. This is possible even before the first visible symptoms appear. Early detection of the disease allows treatment to be started earlier, which can mitigate the symptoms and hopefully delay disease onset, thus improve quality of life.
Neuropsychological tests are an important tool that assess cognitive functions, such as memory, learning and thinking. In the early stages of Alzheimer’s disease, there may be subtle changes in these functions that are not always easily noticeable. However, neuropsychological tests can help to identify these subtle changes, which might be the first signs of mild cognitive impairment that may predict the development of Alzheimer’s disease.
Registry data provides valuable background information about a patient's health. Such information can include, for example, previous illnesses and possible used medications that may have an impact on developing or preventing the risk of Alzheimer’s disease. When combing registry data with other assessment methods, the information can lead to more accurate predictions and later more personalized and helpful treatments.
In recent years, AI has emerged as an important tool in assessing the risk of Alzheimer’s disease. AI can analyze huge amounts of data, such as PET scan results, and identify patterns that humans may not notice. AI can be used to create more accurate predictions and develop new diagnostic tools that support doctors’ diagnosis making.
By combining the above-mentioned methods, more accurate and earlier assessments of Alzheimer’s disease risk can be obtained. The results may provide an important basis for further research to develop more accurate clinical tools for use in healthcare and the pharmaceutical industry. Treatment is not yet started in the asymptomatic stage, but it is being studied. Early diagnosis would allow treatment to be started earlier, which can improve the patient's quality of life. In drug development, the efficacy and safety of new drugs can be more accurately assessed, as patients can be selected more accurately, and treatment responses can be closely monitored.
Alzheimer’s disease risk assessment is no longer a dream – it is a reality!
Petra Parviainen
The writer is a Doctoral Researcher at the Doctoral Programme in Clinical Research, conducting research at the Turku PET Centre, studying the risk factors for Alzheimer's disease and assesses how large a role they play in the development of the disease.
