A research team from the University of Coimbra (UC) has demonstrated the impact that certain chronic diseases associated with cognitive decline – such as Alzheimer’s disease, type 2 diabetes and schizophrenia – can have on brain aging.
Using artificial intelligence techniques and several databases at a local and global level, it was possible to differentiate biological age from chronological age, which represents a new way of measuring the impact of these chronic diseases that – directly or indirectly – affect the brain. In cases of Alzheimer’s disease, ageing can be up to 9 years older than the patient’s actual age.
The study – which was recently published in the journal Brain Communications and has as its first author Maria Fátima Dias, a researcher at the Center for Biomedical Imaging and Translational Research (CIBIT) of the Institute of Nuclear Sciences Applied to Health at UC and the Center for Informatics and Systems at the University of Coimbra (CISUC), under the supervision of professors and researchers Miguel Castelo-Branco (Director of CIBIT and professor at the Faculty of Medicine at UC) and Paulo de Carvalho (Director of the Clinical Informatics Laboratory at CIUSC and professor at the Faculty of Science and Technology at UC) – is based on the new concept of brain age gap estimation, the difference between a person’s chronological age and their estimated brain age (determined through artificial intelligence models that analyzed magnetic resonance images of the brain), to show the impact of certain diseases on brain aging.
“The estimated brain age is the ‘biological age’ of the brain, predicted by models that analyse brain images. Its comparison with the ‘chronological age’ (a person’s real age, measured in years) allows us to indicate whether the brain has aged more or less quickly than expected. A positive value of the brain age gap indicates accelerated brain ageing, while a negative value indicates a younger brain from a biological point of view, with delayed ageing,” explains Miguel Castelo-Branco, senior author of the article.
In the study, using several artificial intelligence models, maps were obtained that allowed the interpretation of which regions of the brain contributed most to the calculation of biological age. And metrics were established that allowed us to conclude the average impact of each of the diseases studied (all three are associated with or are a risk factor for cognitive decline) on brain ageing. “In the case of schizophrenia, brain ageing takes around 2 years, in type 2 diabetes it takes 5 years, and in Alzheimer’s disease it takes up to 9 years”, describes the researcher and Director of CIBIT.
These findings may open new avenues for diagnosing cognitive decline associated with these diseases. “In practice, it will be possible to use this measurement as a useful biomarker in the early diagnosis of neurodegenerative diseases,” concludes Miguel Castelo-Branco.
This study involved researchers from the UC School of Medicine, the Centre for Biomedical Imaging and Translational Research, the Institute of Nuclear Sciences Applied to Health, the UC Centre for Informatics and Systems, and the Associated Laboratory for Intelligent Systems.
The published article is available at
https://academic.oup.com/braincomms/article/7/2/fcaf109/8069058.
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