BBRC study uses artificial intelligence to validate new biomarker of brain ageing
A team led by the Barcelonaβeta Brain Research Center (BBRC), a research centre of the Pasqual Maragall Foundation, has developed a new biomarker of brain ageing based on more than 22,600 magnetic resonance images. This new biomarker has made it possible to demonstrate, for the first time, that the presence of pathological alterations in Alzheimer's disease is associated with accelerated brain ageing, even in cognitively healthy people.
The results of the study, which has the support of the "la Caixa" Foundation, help to better understand the relationship between the process of brain ageing and neurodegenerative diseases, an urgent priority for developing effective strategies in the face of the growing ageing of the population.
Biomarkers are objective measures that provide information about a disease or biological process. In the case of brain ageing, certain morphological characteristics, such as altered thickness or volume in specific regions of the brain, may indicate accelerated ageing. Researchers have used a machine learning model to analyse these parameters from magnetic resonance imaging.
This study is the first to demonstrate the association between biological brain age and the presence of biomarkers and risk factors for Alzheimer's disease (such as the presence of beta-amyloid and tau proteins or the APOE-ε4 genotype) in a total of 2,314 cognitively healthy or cognitively mildly impaired individuals. The study also shows the relationship between brain ageing and markers of neurodegeneration and cerebrovascular pathology. The findings, published in the scientific journal Elife, position this new indicator as a potentially useful tool in the diagnosis of various brain diseases.
Artificial intelligence: a pioneering approach to studying Alzheimer's disease
The difference between chronological age (the time elapsed since birth) and biological brain age (calculated from neuroimaging techniques) provides an estimate of whether the brain has aged faster than expected. This is known as the brain-age delta (brain-age delta), and is an indicator of biological brain ageing. Those with a brain age estimated higher than their chronological age may have an 'older' brain than expected, while an individual with a brain age estimated to be lower than their chronological age would have a 'younger' brain.
"Although age is the main risk factor for Alzheimer's disease and most neurodegenerative diseases, the biological mechanisms that explain this association are still poorly understood," explains Irene Cumplido, pre-doctoral researcher in the BBRC's Neuroimaging Research Group and first author of the paper. "For the study of age, it is necessary to have objective markers of biological brain ageing, beyond chronological age, in the same way that biomarkers are available for Alzheimer's disease", she points out.
In this work, the research team trained a predictive model to calculate brain age in healthy women and men, using more than 22,000 measurements obtained from magnetic resonance imaging. These images were obtained from the UK Biobank, a large-scale biomedical database containing genetic and health information on half a million UK participants.
This is the first time the BBRC has applied machine learning techniques to the study of brain ageing, a methodology that has gained recent popularity thanks to its ability to identify relevant patterns from complex data. "These models learn the association between chronological age and brain morphological features extracted from magnetic resonance imaging, which predicts a brain age for each individual," explains Dr Verónica Vilaplana, associate professor in the Department of Signal Theory and Communications at the Polytechnic University of Catalonia and also an author of the study.
"An increasing amount of research in the last two years is focused on the use of neuroimaging techniques to develop a marker of biological brain ageing," says Dr Juan Domingo Gispert, head of the Neuroimaging Research Group at the BBRC. "Unlike previous studies, the new biomarker we have developed is validated against several biological markers and risk factors associated with ageing, so our study demonstrates the validity of our method as a biomarker of biological brain ageing with relevance for several neurodegenerative diseases".
Largest cohort to predict brain age to date
The study used a total of 22,661 measurements from images in the UK Biobank dataset to predict brain age in more than 2,300 healthy or mildly cognitively impaired people from four independent cohorts: ALFA+, which is supported by the "la Caixa" Foundation (380 people), ADNI (719 people), EPAD (808) and OASIS (407).
"We know that in neurodegenerative disorders such as Alzheimer's disease, accelerated ageing of the brain has been found, but it was necessary to compare these data with specific biological markers of the disease," says Cumplido. To do this, the researchers studied the associations of accelerated brain ageing with various biomarkers and risk factors for Alzheimer's in healthy individuals, such as the presence of beta-amyloid and tau proteins, the APOE-ε4 genotype, the main genetic risk factor for Alzheimer's disease, and other markers of neurodegeneration and cerebrovascular disease. A sex-stratified analysis was also introduced to study differences between men and women with respect to brain age.
The estimate of accelerated brain ageing was associated with abnormal beta-amyloid deposits, more advanced stages of Alzheimer's pathology and the presence of the APOE-e4 genotype; particularly useful results for potential prevention interventions.