Contrary to previous understanding, amyloid buildup isn’t the first sign of the disease.
A team of scientists operating out of the Montreal Neurological Institute and Hospital have used Big Data and an Open Science model to better understand the progression of late-onset Alzheimer’s disease (LOAD).
Patient data came from a partnership of over 30 institutions across Canada and the US, highlighting the importance of scientific collaboration.
The researchers analyzed over 7,700 brain images from 1,171 Alzheimer’s patients in various stages of disease progression, and they employed a number of techniques, including MRI and PET scans, blood and cerebrospinal fluid, and the patient’s levels of cognition.
According to the researchers, it’s so important to understand LOAD because it’s the most common cause of human dementia. Previous research into the mechanisms behind LOAD has been “limited in scope and did not provide a complete picture of this complex disease,” according to the press release.
Now, publishing their research in Nature Communications, the researchers find that the first physiological sign of Alzheimer’s disease is a decrease in blood flow in the brain — contrary to the previous understanding that an increase in amyloid protein was the first detectable sign. Further, the study finds that changes in cognition begin earlier in the disease progression than previously believed.
"The lack of an integrative understanding of LOAD pathology, its multifactorial mechanisms, is a crucial obstacle for the development of effective, disease-modifying therapeutic agents," first author Yasser Iturria Medina, a post-doctoral fellow at the MNI, said in the release.
The researchers say that compiling and analyzing the data took thousands of computing hours to complete — the trajectory of each biological factor was recorded using data from every patient over a 30-year period, and the process was repeated 500 times to ensure robust and stable results.
Although this study is one of the most thorough studies conducted on Alzheimer’s progression, the researchers say they want to take the research a step further. Instead of simply recording each mechanism of the disease, they want to determine the cause of each mechanism, which is key to working towards better treatments.
"This is a computational, mathematical challenge that goes beyond anything we've done so far," said study lead Dr. Alan Evans, a professor of neurology, neurosurgery and biomedical engineering at the Neuro.
"Our goal is to go to a high-level, causal modeling of the interactions amongst all of the factors of disease, but you need huge computational power to do that. It's our job to be ready with the software, the algorithms, and the data while we wait for the hardware to appear."
Medina adds that it’s critical for more integrative, data-driven studies to delve into all of the possible biological factors involved, as well as the direct interactions among these factors.
"Without that, we cannot dream of effective treatments,” he said. “We would continue to work in the dark."
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