Researchers at Lund University in Sweden have developed an algorithm that combines data from simple blood tests and brief memory tests to predict, with great accuracy, patients who will develop Alzheimer’s disease in the future. It is hoped that this algorithm will enable clinicians to recruit patients for drug trials with Alzheimer’s at an early stage, which is when new drugs have a better chance of slowing the course of the disease.
Alzheimer’s disease diagnosis
Alzheimer’s disease is an irreversible, progressive neurological disorder that slowly destroys the patient’s memory and thinking skills, and eventually, the ability to carry out simple tasks. It is estimated that around 50 million people globally are living with Alzheimer’s disease or other dementias, and the condition is currently ranked as the sixth leading cause of death in the US. The condition is believed to be caused by β-amyloid plaques and neurofibrillary tangles of tau proteins that form in the brain, affecting neuronal functioning and connectivity. The cause of most Alzheimer’s cases is still largely unknown, but in 1-2% of cases genetic differences have been observed.
Approximately 20-30% of patients with Alzheimer’s disease are wrongly diagnosed within specialist healthcare organisations, and diagnosis is even more difficult in primary care facilities. Accuracy of diagnostics can be significantly improved by measuring the tau-protein and β-amyloid levels via spinal fluid samples or PET scans. However, these techniques are expensive and are only available at a relatively few specialist memory clinics worldwide. Moreover, early and accurate diagnosis of Alzheimer’s disease is becoming even more pressing, as new drugs that slow down the progression of the disease are currently being developed.
Developing a simple diagnostic tool for Alzheimer’s diagnosis
This study demonstrated that a combination of relatively easily accessible tests can be used for early and reliable diagnosis of Alzheimer’s disease. The researchers examined the data from 340 patients with mild memory impairment in the Swedish Biofinder Study, and confirmed the results using the data from a US study consisting of 543 people.
The team found that a combination of a simple blood test, which measures a variant of the tau protein and the APOE genotype (a risk gene associated with Alzheimer’s development), and three short cognitive tests that only take 10 minutes, was able to predict with over 90% certainty patients that would go on to develop Alzheimer’s dementia within 4 years. The researchers used the patient data to develop a simple prognostic algorithm that is based on blood analysis of phosphorylated tau and the APOE genotype, combined with testing of memory and executive function. The group led by Professor Oskar Hansson went on to develop a prototype online tool to estimate the individual risk of a person with mild memory complains developing Alzheimer’s within four years.
Advantages of their algorithm as a diagnostic tool for Alzheimer’s
This algorithm was significantly more accurate than the clinical predictions made by dementia experts who examined the patients, but did not have access to expensive spinal fluid testing of PET scans. Another clear advantage of the team’s algorithm is that it has been developed for use in clinics without access to advanced diagnostic treatments. Therefore, this algorithm can help overcome the issues with the current expensive diagnostic tools for Alzheimer’s and will help to make diagnosis possible in primary healthcare institutions. The algorithm has currently only been tested on patients who have been examined in memory clinics. However, the team hope that it will also be validated for use in primary healthcare as well as in developing countries with limited resources.
The importance of early Alzheimer’s diagnosis for drug development
The majority of Alzheimer’s drugs that are currently being developed work to slow the progression of the disease. However, for the efficacy and safety of these drugs to be tested before approval, researchers need to be able to identify patients that are in early stages of the disease so that they are able to track the effect of the new drug on disease progression. Therefore, this simple diagnostic tool could help researchers to recruit suitable study participants in a time- and cost-effective manner, which is paramount for the development of new therapeutics. The group leader states that ‘the algorithm will enable us to recruit people with Alzheimer’s at an early stage, which is when new drugs have a better chance of slowing the course of the disease’.
This study developed a new algorithm that can be used as a cheap and effective diagnostic tool for Alzheimer’s disease. When tested using patient data, the study was able to predict with over 90% certainty patients that would go on to develop Alzheimer’s dementia within 4 years. This tool can be used by clinicians to quickly identify patients in early stages of the condition, which is when new drugs have a better chance of slowing the course of the disease, therefore helping to recruit better drug trial participants in a time- and cost- effective manner.
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