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, raising the prospect of early intervention when effective treatments become available.
In a study, investigators used six AI methodologies, including Deep Learning, to assess blood leukocyte epigenomic biomarkers. They found more than 150 genetic differences among study participants with Alzheimer’s disease in comparison with participants who did not have Alzheimer’s disease.
All of the AI platforms were effective in predicting Alzheimer’s disease. Deep Learning’s assessment of intragenic cytosine-phosphate-guanines (CpGs) had sensitivity and specificity rates of 97%.
“It’s almost as if the leukocytes have become a newspaper to tell us, ‘This is what is going on in the brain,’ “ lead author Ray Bahado-Singh, MD, chair of the department of obstetrics and gynecology, Oakland University, Auburn Hills, Mich., said in a news release.
The researchers noted that the findings, if replicated in future studies, may help in providing Alzheimer’s disease diagnoses “much earlier” in the disease process. “The holy grail is to identify patients in the preclinical stage so effective early interventions, including new medications, can be studied and ultimately used,” Dr. Bahado-Singh said.
“This certainly isn’t the final step in Alzheimer’s research, but I think this represents a significant change in direction,” he told attendees at a press briefing.
The findings were published online March 31 in PLOS ONE.
Silver tsunami
The investigators noted that Alzheimer’s disease is often diagnosed when the disease is in its later stages, after irreversible brain damage has occurred. “There is currently no cure for the disease, and the treatment is limited to drugs that attempt to treat symptoms and have little effect on the disease’s progression,” they noted.
Coinvestigator Khaled Imam, MD, director of geriatric medicine for Beaumont Health in Michigan, pointed out that although MRI and lumbar puncture can identify Alzheimer’s disease early on, the processes are expensive and/or invasive.
“Having biomarkers in the blood ... and being able to identify [Alzheimer’s disease] years before symptoms start, hopefully we’d be able to intervene early on in the process of the disease,” Dr. Imam said.
It is estimated that the number of Americans aged 85 and older will triple by 2050. This impending “silver tsunami,” which will come with a commensurate increase in Alzheimer’s disease cases, makes it even more important to be able to diagnose the disease early on, he noted.
The study included 24 individuals with late-onset Alzheimer’s disease (70.8% women; mean age, 83 years); 24 were deemed to be “cognitively healthy” (66.7% women; mean age, 80 years). About 500 ng of genomic DNA was extracted from whole-blood samples from each participant.
The researchers used the Infinium MethylationEPIC BeadChip array, and the samples were then examined for markers of methylation that would “indicate the disease process has started,” they noted.
In addition to Deep Learning, the five other AI platforms were the Support Vector Machine, Generalized Linear Model, Prediction Analysis for Microarrays, Random Forest, and Linear Discriminant Analysis.
These platforms were used to assess leukocyte genome changes. To predict Alzheimer’s disease, the researchers also used Ingenuity Pathway Analysis.
Significant “chemical changes”
Results showed that the Alzheimer’s disease group had 152 significantly differentially methylated CpGs in 171 genes in comparison with the non-Alzheimer’s disease group (false discovery rate P value < .05).
As a whole, using intragenic and intergenic/extragenic CpGs, the AI platforms were effective in predicting who had Alzheimer’s disease (area under the curve [AUC], ≥ 0.93). Using intragenic markers, the AUC for Deep Learning was 0.99.
“We looked at close to a million different sites, and we saw some chemical changes that we know are associated with alteration or change in gene function,” Dr. Bahado-Singh said.
Altered genes that were found in the Alzheimer’s disease group included CR1L, CTSV, S1PR1, and LTB4R – all of which “have been previously linked with Alzheimer’s disease and dementia,” the researchers noted. They also found the methylated genes CTSV and PRMT5, both of which have been previously associated with cardiovascular disease.
“A significant strength of our study is the novelty, i.e. the use of blood leukocytes to accurately detect Alzheimer’s disease and also for interrogating the pathogenesis of Alzheimer’s disease,” the investigators wrote.
Dr. Bahado-Singh said that the test let them identify changes in cells in the blood, “giving us a comprehensive account not only of the fact that the brain is being affected by Alzheimer’s disease but it’s telling us what kinds of processes are going on in the brain.
“Normally you don’t have access to the brain. This gives us a simple blood test to get an ongoing reading of the course of events in the brain – and potentially tell us very early on before the onset of symptoms,” he added.
Cautiously optimistic
During the question-and-answer session following his presentation at the briefing, Dr. Bahado-Singh reiterated that they are at a very early stage in the research and were not able to make clinical recommendations at this point. However, he added, “There was evidence that DNA methylation change could likely precede the onset of abnormalities in the cells that give rise to the disease.”
Coinvestigator Stewart Graham, PhD, director of Alzheimer’s research at Beaumont Health, added that although the initial study findings led to some excitement for the team, “we have to be very conservative with what we say.”
He noted that the findings need to be replicated in a more diverse population. Still, “we’re excited at the moment and looking forward to seeing what the future results hold,” Dr. Graham said.
Dr. Bahado-Singh said that if larger studies confirm the findings and the test is viable, it would make sense to use it as a screen for individuals older than 65. He noted that because of the aging of the population, “this subset of individuals will constitute a larger and larger fraction of the population globally.”
Still early days
Commenting on the findings, Heather Snyder, PhD, vice president of medical and scientific relations at the Alzheimer’s Association, noted that the investigators used an “interesting” diagnostic process.
“It was a unique approach to looking at and trying to understand what might be some of the biological underpinnings and using these tools and technologies to determine if they’re able to differentiate individuals with Alzheimer’s disease” from those without Alzheimer’s disease, said Dr. Snyder, who was not involved with the research.
“Ultimately, we want to know who is at greater risk, who may have some of the changing biology at the earliest time point so that we can intervene to stop the progression of the disease,” she said.
She pointed out that a number of types of biomarker tests are currently under investigation, many of which are measuring different outcomes. “And that’s what we want to see going forward. We want to have as many tools in our toolbox that allow us to accurately diagnose at that earliest time point,” Dr. Snyder said.
“At this point, [the current study] is still pretty early, so it needs to be replicated and then expanded to larger groups to really understand what they may be seeing,” she added.
Dr. Bahado-Singh, Dr. Imam, Dr. Graham, and Dr. Snyder have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
, raising the prospect of early intervention when effective treatments become available.
In a study, investigators used six AI methodologies, including Deep Learning, to assess blood leukocyte epigenomic biomarkers. They found more than 150 genetic differences among study participants with Alzheimer’s disease in comparison with participants who did not have Alzheimer’s disease.
All of the AI platforms were effective in predicting Alzheimer’s disease. Deep Learning’s assessment of intragenic cytosine-phosphate-guanines (CpGs) had sensitivity and specificity rates of 97%.
“It’s almost as if the leukocytes have become a newspaper to tell us, ‘This is what is going on in the brain,’ “ lead author Ray Bahado-Singh, MD, chair of the department of obstetrics and gynecology, Oakland University, Auburn Hills, Mich., said in a news release.
The researchers noted that the findings, if replicated in future studies, may help in providing Alzheimer’s disease diagnoses “much earlier” in the disease process. “The holy grail is to identify patients in the preclinical stage so effective early interventions, including new medications, can be studied and ultimately used,” Dr. Bahado-Singh said.
“This certainly isn’t the final step in Alzheimer’s research, but I think this represents a significant change in direction,” he told attendees at a press briefing.
The findings were published online March 31 in PLOS ONE.
Silver tsunami
The investigators noted that Alzheimer’s disease is often diagnosed when the disease is in its later stages, after irreversible brain damage has occurred. “There is currently no cure for the disease, and the treatment is limited to drugs that attempt to treat symptoms and have little effect on the disease’s progression,” they noted.
Coinvestigator Khaled Imam, MD, director of geriatric medicine for Beaumont Health in Michigan, pointed out that although MRI and lumbar puncture can identify Alzheimer’s disease early on, the processes are expensive and/or invasive.
“Having biomarkers in the blood ... and being able to identify [Alzheimer’s disease] years before symptoms start, hopefully we’d be able to intervene early on in the process of the disease,” Dr. Imam said.
It is estimated that the number of Americans aged 85 and older will triple by 2050. This impending “silver tsunami,” which will come with a commensurate increase in Alzheimer’s disease cases, makes it even more important to be able to diagnose the disease early on, he noted.
The study included 24 individuals with late-onset Alzheimer’s disease (70.8% women; mean age, 83 years); 24 were deemed to be “cognitively healthy” (66.7% women; mean age, 80 years). About 500 ng of genomic DNA was extracted from whole-blood samples from each participant.
The researchers used the Infinium MethylationEPIC BeadChip array, and the samples were then examined for markers of methylation that would “indicate the disease process has started,” they noted.
In addition to Deep Learning, the five other AI platforms were the Support Vector Machine, Generalized Linear Model, Prediction Analysis for Microarrays, Random Forest, and Linear Discriminant Analysis.
These platforms were used to assess leukocyte genome changes. To predict Alzheimer’s disease, the researchers also used Ingenuity Pathway Analysis.
Significant “chemical changes”
Results showed that the Alzheimer’s disease group had 152 significantly differentially methylated CpGs in 171 genes in comparison with the non-Alzheimer’s disease group (false discovery rate P value < .05).
As a whole, using intragenic and intergenic/extragenic CpGs, the AI platforms were effective in predicting who had Alzheimer’s disease (area under the curve [AUC], ≥ 0.93). Using intragenic markers, the AUC for Deep Learning was 0.99.
“We looked at close to a million different sites, and we saw some chemical changes that we know are associated with alteration or change in gene function,” Dr. Bahado-Singh said.
Altered genes that were found in the Alzheimer’s disease group included CR1L, CTSV, S1PR1, and LTB4R – all of which “have been previously linked with Alzheimer’s disease and dementia,” the researchers noted. They also found the methylated genes CTSV and PRMT5, both of which have been previously associated with cardiovascular disease.
“A significant strength of our study is the novelty, i.e. the use of blood leukocytes to accurately detect Alzheimer’s disease and also for interrogating the pathogenesis of Alzheimer’s disease,” the investigators wrote.
Dr. Bahado-Singh said that the test let them identify changes in cells in the blood, “giving us a comprehensive account not only of the fact that the brain is being affected by Alzheimer’s disease but it’s telling us what kinds of processes are going on in the brain.
“Normally you don’t have access to the brain. This gives us a simple blood test to get an ongoing reading of the course of events in the brain – and potentially tell us very early on before the onset of symptoms,” he added.
Cautiously optimistic
During the question-and-answer session following his presentation at the briefing, Dr. Bahado-Singh reiterated that they are at a very early stage in the research and were not able to make clinical recommendations at this point. However, he added, “There was evidence that DNA methylation change could likely precede the onset of abnormalities in the cells that give rise to the disease.”
Coinvestigator Stewart Graham, PhD, director of Alzheimer’s research at Beaumont Health, added that although the initial study findings led to some excitement for the team, “we have to be very conservative with what we say.”
He noted that the findings need to be replicated in a more diverse population. Still, “we’re excited at the moment and looking forward to seeing what the future results hold,” Dr. Graham said.
Dr. Bahado-Singh said that if larger studies confirm the findings and the test is viable, it would make sense to use it as a screen for individuals older than 65. He noted that because of the aging of the population, “this subset of individuals will constitute a larger and larger fraction of the population globally.”
Still early days
Commenting on the findings, Heather Snyder, PhD, vice president of medical and scientific relations at the Alzheimer’s Association, noted that the investigators used an “interesting” diagnostic process.
“It was a unique approach to looking at and trying to understand what might be some of the biological underpinnings and using these tools and technologies to determine if they’re able to differentiate individuals with Alzheimer’s disease” from those without Alzheimer’s disease, said Dr. Snyder, who was not involved with the research.
“Ultimately, we want to know who is at greater risk, who may have some of the changing biology at the earliest time point so that we can intervene to stop the progression of the disease,” she said.
She pointed out that a number of types of biomarker tests are currently under investigation, many of which are measuring different outcomes. “And that’s what we want to see going forward. We want to have as many tools in our toolbox that allow us to accurately diagnose at that earliest time point,” Dr. Snyder said.
“At this point, [the current study] is still pretty early, so it needs to be replicated and then expanded to larger groups to really understand what they may be seeing,” she added.
Dr. Bahado-Singh, Dr. Imam, Dr. Graham, and Dr. Snyder have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
, raising the prospect of early intervention when effective treatments become available.
In a study, investigators used six AI methodologies, including Deep Learning, to assess blood leukocyte epigenomic biomarkers. They found more than 150 genetic differences among study participants with Alzheimer’s disease in comparison with participants who did not have Alzheimer’s disease.
All of the AI platforms were effective in predicting Alzheimer’s disease. Deep Learning’s assessment of intragenic cytosine-phosphate-guanines (CpGs) had sensitivity and specificity rates of 97%.
“It’s almost as if the leukocytes have become a newspaper to tell us, ‘This is what is going on in the brain,’ “ lead author Ray Bahado-Singh, MD, chair of the department of obstetrics and gynecology, Oakland University, Auburn Hills, Mich., said in a news release.
The researchers noted that the findings, if replicated in future studies, may help in providing Alzheimer’s disease diagnoses “much earlier” in the disease process. “The holy grail is to identify patients in the preclinical stage so effective early interventions, including new medications, can be studied and ultimately used,” Dr. Bahado-Singh said.
“This certainly isn’t the final step in Alzheimer’s research, but I think this represents a significant change in direction,” he told attendees at a press briefing.
The findings were published online March 31 in PLOS ONE.
Silver tsunami
The investigators noted that Alzheimer’s disease is often diagnosed when the disease is in its later stages, after irreversible brain damage has occurred. “There is currently no cure for the disease, and the treatment is limited to drugs that attempt to treat symptoms and have little effect on the disease’s progression,” they noted.
Coinvestigator Khaled Imam, MD, director of geriatric medicine for Beaumont Health in Michigan, pointed out that although MRI and lumbar puncture can identify Alzheimer’s disease early on, the processes are expensive and/or invasive.
“Having biomarkers in the blood ... and being able to identify [Alzheimer’s disease] years before symptoms start, hopefully we’d be able to intervene early on in the process of the disease,” Dr. Imam said.
It is estimated that the number of Americans aged 85 and older will triple by 2050. This impending “silver tsunami,” which will come with a commensurate increase in Alzheimer’s disease cases, makes it even more important to be able to diagnose the disease early on, he noted.
The study included 24 individuals with late-onset Alzheimer’s disease (70.8% women; mean age, 83 years); 24 were deemed to be “cognitively healthy” (66.7% women; mean age, 80 years). About 500 ng of genomic DNA was extracted from whole-blood samples from each participant.
The researchers used the Infinium MethylationEPIC BeadChip array, and the samples were then examined for markers of methylation that would “indicate the disease process has started,” they noted.
In addition to Deep Learning, the five other AI platforms were the Support Vector Machine, Generalized Linear Model, Prediction Analysis for Microarrays, Random Forest, and Linear Discriminant Analysis.
These platforms were used to assess leukocyte genome changes. To predict Alzheimer’s disease, the researchers also used Ingenuity Pathway Analysis.
Significant “chemical changes”
Results showed that the Alzheimer’s disease group had 152 significantly differentially methylated CpGs in 171 genes in comparison with the non-Alzheimer’s disease group (false discovery rate P value < .05).
As a whole, using intragenic and intergenic/extragenic CpGs, the AI platforms were effective in predicting who had Alzheimer’s disease (area under the curve [AUC], ≥ 0.93). Using intragenic markers, the AUC for Deep Learning was 0.99.
“We looked at close to a million different sites, and we saw some chemical changes that we know are associated with alteration or change in gene function,” Dr. Bahado-Singh said.
Altered genes that were found in the Alzheimer’s disease group included CR1L, CTSV, S1PR1, and LTB4R – all of which “have been previously linked with Alzheimer’s disease and dementia,” the researchers noted. They also found the methylated genes CTSV and PRMT5, both of which have been previously associated with cardiovascular disease.
“A significant strength of our study is the novelty, i.e. the use of blood leukocytes to accurately detect Alzheimer’s disease and also for interrogating the pathogenesis of Alzheimer’s disease,” the investigators wrote.
Dr. Bahado-Singh said that the test let them identify changes in cells in the blood, “giving us a comprehensive account not only of the fact that the brain is being affected by Alzheimer’s disease but it’s telling us what kinds of processes are going on in the brain.
“Normally you don’t have access to the brain. This gives us a simple blood test to get an ongoing reading of the course of events in the brain – and potentially tell us very early on before the onset of symptoms,” he added.
Cautiously optimistic
During the question-and-answer session following his presentation at the briefing, Dr. Bahado-Singh reiterated that they are at a very early stage in the research and were not able to make clinical recommendations at this point. However, he added, “There was evidence that DNA methylation change could likely precede the onset of abnormalities in the cells that give rise to the disease.”
Coinvestigator Stewart Graham, PhD, director of Alzheimer’s research at Beaumont Health, added that although the initial study findings led to some excitement for the team, “we have to be very conservative with what we say.”
He noted that the findings need to be replicated in a more diverse population. Still, “we’re excited at the moment and looking forward to seeing what the future results hold,” Dr. Graham said.
Dr. Bahado-Singh said that if larger studies confirm the findings and the test is viable, it would make sense to use it as a screen for individuals older than 65. He noted that because of the aging of the population, “this subset of individuals will constitute a larger and larger fraction of the population globally.”
Still early days
Commenting on the findings, Heather Snyder, PhD, vice president of medical and scientific relations at the Alzheimer’s Association, noted that the investigators used an “interesting” diagnostic process.
“It was a unique approach to looking at and trying to understand what might be some of the biological underpinnings and using these tools and technologies to determine if they’re able to differentiate individuals with Alzheimer’s disease” from those without Alzheimer’s disease, said Dr. Snyder, who was not involved with the research.
“Ultimately, we want to know who is at greater risk, who may have some of the changing biology at the earliest time point so that we can intervene to stop the progression of the disease,” she said.
She pointed out that a number of types of biomarker tests are currently under investigation, many of which are measuring different outcomes. “And that’s what we want to see going forward. We want to have as many tools in our toolbox that allow us to accurately diagnose at that earliest time point,” Dr. Snyder said.
“At this point, [the current study] is still pretty early, so it needs to be replicated and then expanded to larger groups to really understand what they may be seeing,” she added.
Dr. Bahado-Singh, Dr. Imam, Dr. Graham, and Dr. Snyder have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
FROM PLOS ONE