The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases.

The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer’s disease.

In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 “significant” genetic differences among Alzheimer’s and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer’s symptoms develop and a patient’s brain is irreversibly damaged.

“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 Dr. Ray Bahado-Singh, a Beaumont Health geneticist who led the research.

The need to identify the early signs of Alzheimer’s disease grows as the global population ages. For example, the annual World Alzheimer Report estimates 75 million will be stricken by 2030. Researchers are working to prevent some of those predicted cases by leveraging new deep learning tools to accelerate the diagnoses of a disease that often goes undetected until it is too late to stop the damage.

The Beaumont researchers said they used deep learning and other machine learning platforms along with “genome-wide” DNA analysis of leukocytes, a type of blood cell manufactured in bone marrow and associated with the body’s immune system.

“We used and compared conventional machine learning and deep learning classification algorithms which typically begin with an established set of data … and a certain understanding of how that data is classified” as either Alzheimer’s or healthy patients, said co-investigator Buket Aydas, analytics manager at Blue Cross Blue Shield of Michigan.

“These algorithms are intended to find patterns in data that can be applied to an analytics process,” Aydas added in an email.

The researchers compared the performance of their deep learning framework with five other machine learning algorithms, including a prediction analysis tool. The six platforms scanned about 800,000 changes in the leukocytes genome.

The deep learning algorithm performed best.

“We also found out the important genetic features that contribute most to the [deep learning] prediction and were able to predict the absence or presence of Alzheimer’s by the help of these important genetic features,” Aydas said.

The genetic analysis ultimately predicted either the absence or presence of the disease, “allowing us to read what is going on in the brain through the blood,” Dr. Bahado-Singh said.

One problem encountered by the investigators was “overfitting,” which occurs when data sets fit a machine learning too precisely. Counterintuitively, the snug fit often produces unreliable results.

To avoid overfitting in the deep learning framework, the researchers said they employed standard parameters to tune models and overcome the overfitting problem.

The researchers said the next step is an expanded study over the next year designed to replicate the initial findings of the Alzheimer’s analysis. Advances in this branch of precision medicine could lead to development of targeted treatments to “interrupt the disease process,” according to Dr. Bahado-Singh.

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Deep learning methods analyzed genomic data from whole blood samples and found differences in patients with 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.”

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