So, in this work, we wanted to validate and study whether the measurement of brain-aging which has been ongoing in the field over the last years. We want to validate it with biomarkers of Alzheimer’s disease and neurodegeneration.
For this, we implemented and trained an algorithm using structural MRI data from UKBiobank cohort to predict chronological age, which will be a proxy of biological brain age...
So, in this work, we wanted to validate and study whether the measurement of brain-aging which has been ongoing in the field over the last years. We want to validate it with biomarkers of Alzheimer’s disease and neurodegeneration.
For this, we implemented and trained an algorithm using structural MRI data from UKBiobank cohort to predict chronological age, which will be a proxy of biological brain age. Once we have this model trained, we predict brain age for different independent cohorts, which in this case are our in-house cohort ALFA+, ADNI, OASIS, and EPAD, which are three publicly available ones. We did this in order to introduce independent cohorts and really validate it by studying the robustness of this measurement whether the same results remain for the different cohorts.
So, once we had this predicted brain age, we actually wanted to, as I mentioned, to validate it using biomarkers of Alzheimer’s disease as it would feel over the last few years this measurement of brain age, which is computed from structural MRI, has been validated in different diseases such as normal controls versus Alzheimer’s disease, multiple sclerosis, and so on, but it was really lacking a validation with this type of markers.
So, in our results, what we did was to study the association that we got from linear regression models, and we obtained that individuals which were amyloid positive, which was captured by CSF amyloid-beta or amyloid-PET, had a larger brain age in comparison to the people with amyloid negative.
We also computed the AT stages when we had CSF amyloid and CSF pTau. And we obtained that individuals which were further in the Alzheimer’s continuum had also higher brain age in comparison to the ones in the previous stages.
And regarding risk factors, we saw that individuals who are APOE4 carriers had also a brain age delta higher than individuals which were APOE3 carriers, for example, and APOE3 had a higher brain-age than the ones who were APOE2 carriers. Although these differences were not significant, but we could see this trend. This was maintained for cognitively impaired and mild cognitively impaired individuals. So, we studied them independently, and it was one thing.
In addition, we also wanted to study whether this measurement of brain-age is associated with specific markers of neurodegeneration. And for this, we used plasma and CSF neurofilament light, and we saw that a higher brain age was associated with higher plasma NfL in CU and MCI individuals.
In addition, we also found that cerebrovascular disease measured by white matter hyperintensities was also positively associated with brain-age. So, we saw this and, in addition, we also wanted to study further whether there were sex differences regarding this association. And what we found was that females were the ones that were driving the association between brain age and plasma neurofilament light, and also in CSF neurofilament light.
So, we could say from this study that we validated this measurement of brain age with biomarkers of Alzheimer’s disease and neurodegeneration, and there are sex differences in the trajectories of brain aging, which are going to be further studied.
Currently, I’m continuing working on the brain age part because now we have developed this brain age measurement, which is a global biomarker, but I think it is of interest and the way to go because aging trajectories are heterogeneous, so it’s not a single trajectory of brain-age.
Now, we are working in developing different models to try to disentangle these different trajectories of brain aging and studying the association with different biomarkers, genetic risk factors, and so on, to try to understand until what point we can see on an individual basis how is this brain evolving and whether a person, for example, has a brain-aging that is older than expected, that it’s because of cardiovascular risk factors or because of Alzheimer’s disease, and then being able to relate these trajectories in the brain to see the changes in the neuroimaging data.
