So we’ve been very interested in this concept of resilience, which of course is the ability of an organism to still function normally in the presence of pathology. And since we know so many people as they age will develop changes in their brain with Alzheimer’s pathology, with vascular pathology, but not everybody will develop dementia, even if they share common risk factors. We became very interested in sort of studying healthy brain aging and what potentially sets people up to either succeed or fail with their brain aging...
So we’ve been very interested in this concept of resilience, which of course is the ability of an organism to still function normally in the presence of pathology. And since we know so many people as they age will develop changes in their brain with Alzheimer’s pathology, with vascular pathology, but not everybody will develop dementia, even if they share common risk factors. We became very interested in sort of studying healthy brain aging and what potentially sets people up to either succeed or fail with their brain aging. So we’ve developed a number of measures that allow us to sort of quantify brain health. We developed a resilience factor that let us look at these different components of resilience, including cognitive reserve, physical activity, cognitive activity, diet, mindfulness, social engagement. We developed a vulnerability index to let us look at risk factors that contribute to the higher incidence of Alzheimer’s and related disorders across different populations. And we created some models of testing so we could look at people’s cognitive performance in the cross-sectional moment. But then we said, well, we have all these different scores. Could we leverage this to really index brain health? And so what we did was we used machine learning techniques and we created something we call the brain health index. So this combines our resilience, our vulnerability, and our performance into a single score, which allows us to look at the complex interplay between risk factors, protective factors, and cognitive performance. Our brain health index has very strong correlations with cognitive and functional performance, as well as with biomarker measures. And so then we took this to leverage us to try to study different populations. So because we could put everybody sort of on the same scale, we can look at differences between younger adults and older adults, between men and women, between blacks and whites, from people who live in urban areas to versus people who live in rural areas. So that’s very advantageous. And what we found is when we look at these brain health indexes, not surprisingly, you know, people who are healthy controls have better brain health than people who have mild cognitive impairment, who have better brain health than people who have Alzheimer’s or related disorders. But within each group, people who have higher brain health indices have better performance. And we’re actually able to separate brain health indexes between people, even within healthy controls, of those who have subjective complaints versus those who do not. So we’re really able to tease this out in a really great way. Because we can tease this all out, that gives us the possibility now to study mechanisms. So we’ve been following people in our longitudinal project now for about four years where we have data. And so what we found was that the people who were healthy controls, who stayed healthy controls, their brain health index actually stayed very high. People who were impaired, who stayed impaired and progressed, their brain health index would slowly decline. But what was really interesting is healthy controls who eventually converted to MCI, we could actually see an inflection point in their brain health index that occurred about a year before their conversion. And even potentially really interesting is that those people who were MCI, who reverted back to controls, we could see an inflection point in their brain health index before their reversion back. So it gives us a really good way of sort of understanding what may be happening to this. So knowing that we have a unitary measure, that we could then look for biomarkers of this unitary measure. So we initially kind of started this by looking at the proteome. And so we have a new LISA system in our lab that allows us to look at, you know, over 130 markers, soon to be even more markers, all at the same time. And so we studied the proteome and we found 19 candidate proteins that can help explain brain health. And many of these have been linked in basic science models to Alzheimer’s disease and related disorders. We then also looked at the epigenomics, and we were able to identify a number of CPG methylated sites that map to seven specific genes. And then we looked at sort of the multi-omic approach. What we can see is our 19 proteomic hits and our 11 epigenetic hits actually have convergence in about a quarter of those. And they correspond to genes that are important for cytoskeletal integrity, for transcriptional regulation, for neurotransmitter receptors, and neuroinflammatory processes. So by doing this, we’re able to really start to begin to think about how we can quantify brain health that may help us predict who’s going to convert to MCI, who’s going to progress, and who may even revert. And using these multi-omic approaches, we now identified several mechanistically integrated pathways as potential modifiable targets for brain health. That allows us then to begin to think about druggable targets. And we’re really excited about that. So this is what I was going to be covering in the topic of the conference.
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