Deep Learning, Genomic Data May Help Predict Alzheimer’s Disease

Using deep learning and genomic data, researchers from Beaumont Health have discovered a simple blood test that may help predict Alzheimer’s disease in patients.

In a study published in PLOS ONE, the team described using deep learning processes to analyze extracted genomic DNA from whole blood samples. The analysis uncovered 152 significant genetic differences in patients with Alzheimer’s compared to healthy patients.

The new deep learning method has the potential to diagnose patients much earlier in the disease process, before symptoms develop and the brain is irreversibly damaged. Experts believe that the brain changes in Alzheimer’s disease precede the onset of symptoms by years.

Globally, more than 47 million individuals have Alzheimer’s, with women making up more than 60 percent of patients. As the population continues to age, it’s expected that 75 million people will be affected by Alzheimer’s by 2030, with a subsequent rise to 131 million by 2050.

"The holy grail is to identify patients in the pre-clinical stage so effective early interventions, including new medications, can be studied and ultimately used," said Ray Bahado-Singh, chairman of the Beaumont Department of Obstetrics and Gynecologist and an expert in women's health. "That's why we are excited about the results of this research."

Most patients with Alzheimer’s aren’t diagnosed until later stages of the disease, when the brain has already suffered irreversible damage. There is currently no cure for Alzheimer’s, and treatment is limited to drugs that attempt to treat symptoms and have little impact on the disease’s progression.

"Drugs used in the late stage of the disease do not seem make much difference, so there is a tremendous amount of interest in diagnosis in the early stages of the disease," said Khaled Imam, Beaumont Health's Director of Geriatric Medicine.

"Any delay in symptom onset is likely to be very beneficial.  Also, a spinal tap or MRI can identify the start of the disease. But that is invasive and/or expensive. And you cannot do a spinal tap on everyone over age 65. So, blood is thought to be a desirable way of approaching this. And it would be relatively cheap and minimally invasive as compared to an MRI or spinal tap."

In the analysis, researchers compared blood samples from 24 Alzheimer’s patients and 24 cognitively healthy patients. The team analyzed white blood cells in the blood samples and compared biomarkers to see if they had been generally affected in patients with Alzheimer’s disease.

Part of the Alzheimer’s disease process is brain inflammation thought to trigger the production of white blood cells, or leukocytes, which then become genetically altered while fighting the disease. Researchers looked for telling genetic markers, or methylation marks, an important chemical modification of genes leading to altered gene function that indicate the disease process has started.

"It's almost as if the leukocytes have become a newspaper to tell us, 'This is what's going on in the brain,'" Bahado-Singh said.

The team used six different artificial intelligence and deep learning platforms to look at about 800,000 changes in the genome of the leukocytes.

Researchers noted that the results could potentially advance precision medicine for Alzheimer’s disease, and provide evidence that epigenetic factors may play a critical role in Alzheimer’s development.

Going forward, the group will aim to organize a much larger study to replicate the study’s initial findings over the next year or so.

"What the results said to us is there are significant changes in accessible blood cells that we can use possibly to detect Alzheimer's," Bahado-Singh said.

"We found that the genetic analysis accurately predicted the absence or presence of Alzheimer's, allowing us to read what is going on in the brain through the blood.  The results also gave us a readout of the abnormalities that are causing Alzheimer's disease. This has future promise for developing targeted treatment to interrupt the disease process."