Denna webbsida är endast avsedd för läkare och sjukvårdspersonal med förskrivningsrätt.

Nytt test förutspår demens upp till 9 år före klinisk diagnos

Ett nytt test, utvecklat av en forskargrupp vid Wolfson Institute of Population Health vid Queen Mary University of London, kan förutsäga demens upp till nio år före en klinisk diagnos. Med >80 % noggrannhet är denna metod ett mer tillförlitligt test för att förutsäga demens än vanliga metoder för demensdiagnostik, såsom minnestester eller mätningar av hjärnans krympning.

A new test, developed bya WIPH-led research team, is able to predict dementia up to nine years before a clinical diagnosis. With >80% accuracy, this method is a more reliable test for predicting dementia than commonly used methods of dementia diagnosis, such as memory tests or measurements of brain shrinkage.

Led by Professor Charles Marshall, the team developed the predictive test by analysing functional MRI (fMRI) scans to detect changes in the brain’s ‘default mode network’ (DMN). The DMN connects regions of the brain to perform specific cognitive functions, and is the first neural network to be affected by Alzheimer’s disease. The researchers used fMR scans from over 1,100 volunteers from the UK Biobank, a large-scale biomedical database and research resource containing genetic and health information from half a million UK participants, to estimate the effective connectivity between ten regions of the brain that constitute the default mode network.

The researchers assigned each patient a probability of dementia value, based on the extent to which their effective connectivity pattern conformed to a pattern that indicates dementia or a control-like pattern. They compared these predictions with the recorded UK Biobank medical data for each patient.

The findings showed that the model accurately predicted onset of dementia up to nine years before an official diagnosis was made, and with greater than 80% accuracy. In the cases where the volunteers had gone on to develop dementia, it was also found that the model could predict, within a two-year margin of error, how long it would take for that diagnosis to be made.

The researchers also examined whether changes to the DMN might be caused by known risk factors for dementia. Their analysis showed that genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the DMN, supporting the idea that these changes are specific to Alzheimer’s disease. They also found that social isolation was likely to increase risk of dementia through its effect on connectivity in the DMN.

Professor Charles Marshall, who led the research team from the WIPH Centre for Preventive Neurology said: ‘Predicting who is going to get dementia in the future will be vital for developing treatments that can prevent the irreversible loss of brain cells that causes the symptoms of dementia. Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease, many people live for decades with these proteins in their brain without developing symptoms of dementia. We hope that the measure of brain function that we have developed will allow us to be much more precise about whether someone is actually going to develop dementia, and how soon, so that we can identify whether they might benefit from future treatments.’

Samuel Ereira, lead author from the WIPH Centre for Preventive Neurology, said: ‘Using these analysis techniques with large datasets we can identify those at high dementia risk, and also learn which environmental risk factors pushed these people into a high-risk zone. Enormous potential exists to apply these methods to different brain networks and populations, to help us better understand the interplays between environment, neurobiology and illness, both in dementia and possibly other neurodegenerative diseases. fMRI is a non-invasive medical imaging tool, and it takes about 6 minutes to collect the necessary data on an MRI scanner, so it could be integrated into existing diagnostic pathways, particularly where MRI is already used.’

Hojjat Azadbakht, CEO of AINOSTICS (an AI company collaborating with world-leading research teams to develop brain imaging approaches for the early diagnosis of neurological disorders) said: ‘The approach developed has the potential to fill an enormous clinical gap by providing a non-invasive biomarker for dementia. In the study published by the team at QMUL, they were able to identify individuals who would later develop Alzheimer’s disease up to nine years before they received a clinical diagnosis. It is during this pre-symptomatic stage that emerging disease-modifying treatments are likely to offer the most benefit for patients.’

Charles R. Marshall, Sam Ereira, Sheena Wates, Adeel Razi. Early detection of dementia with default-mode network effective connectivity. Published in Nature Mental Health

*UKB data access was funded by a grant from the Tom and Sheila Springer Charity.