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WASHINGTON, DC—A combination of biomarkers may significantly help select cognitively normal individuals to include in clinical trials of interventions for Alzheimer’s disease and assess disease progression in response to treatment, according to Marilyn S. Albert, PhD, Director of the Division of Cognitive Neuroscience in the Department of Neurology at Johns Hopkins University School of Medicine and Director of the Johns Hopkins Alzheimer’s Disease Research Center in Baltimore. Dr. Albert is part of an international research effort to identify biomarkers associated with the changes that occur in the brains of normal individuals who later develop Alzheimer’s disease. “By definition, we’re looking for measures—or biomarkers—that might reflect the underlying disease process when the clinical symptoms are minimal,” she said at the Alzheimer’s Association International Conference 2015.
The most recent data from the Biomarkers for Older Controls at Risk for Dementia (BIOCARD) study show that combinations of biomarkers obtained at least six years prior to symptom onset—including measures of cognition, brain structure, and brain proteins that appear to reflect the accumulated Alzheimer’s disease pathology in people who are cognitively normal—can identify which individuals are most likely to progress to mild cognitive impairment (MCI) due to Alzheimer’s disease. “This [finding] suggests that it is possible to use combinations of these measures to predict who is at high risk so that novel treatments could be initiated once they are developed,” said Dr. Albert.
The BIOCARD Study
Dr. Albert and her colleagues are currently running an extension of the original BIOCARD study, which was begun in 1995 by the National Institute of Mental Health (NIMH) with 349 subjects who were cognitively normal. Approximately 75% of participants enrolled at that time had a family history of dementia, but no clinical signs of Alzheimer’s disease. “Alzheimer’s pathology develops when individuals are cognitively normal and then changes and spreads through the brain throughout the course of the disease,” Dr. Albert explained, “so we know that there must be some individuals who are cognitively normal with this pathology.” The main objective of the study is to understand the preclinical phase of Alzheimer’s disease so that it might be possible to intervene when individuals are cognitively normal.
Dr. Albert outlined the considerable challenges to determining what types of biomarkers might be predictive. First, a large group of cognitively normal individuals needs to be followed over a long period of time to measure changes that could reflect early disease. Next, skilled clinicians are needed to evaluate outcomes and ensure that they are related to Alzheimer’s disease, and finally, the study needs to include enough outcomes to enable a useful statistical analysis, she said.
The initial NIMH study was halted in 2005. Johns Hopkins researchers were funded in 2009 to continue the study. They re-enrolled the majority of the original participants (approximately 30 patients had died). Half of the participants were female, and the mean education level was high (17 years). The mean age at the initial time of enrollment was 57. Some participants have been followed for as long as 20 years, and the minimum follow-up is about 10 years, Dr. Albert said.
Data for the original study were collected from each participant in the form of biannual spinal taps to obtain samples from CSF, and structural MRI studies, along with annual cognitive testing and clinical evaluations. Approximately 60 of the participants have developed MCI or dementia due to Alzheimer’s disease.
Combining Measures Improved Accuracy
The investigators performed Cox regression analyses on all measures and calculated hazard ratios (HR) to identify measures associated with the time to onset of clinical symptoms. They found significant associations between baseline levels of amyloid beta and p-tau in CSF and time to onset of MCI. Right hippocampal volume, right entorhinal cortex thickness, and test scores of episodic and incidental memory at baseline also were significantly associated with time to onset of MCI, Dr. Albert reported.
Baseline measures of amyloid beta (HR = 0.72) were found to be lower in people who progressed more rapidly to clinical symptoms, and baseline p-tau (HR = 1.39) levels were higher. In addition, lower right entorhinal cortex (EC) thickness (HR = 0.74) and lower right hippocampal volume (HR = 0.71) were both associated with decreased time to onset of Alzheimer’s disease, said Dr. Albert.
Although nine cognitive tests appear to predict Alzheimer’s disease, the Paired Associates Immediate Recall (PA) test and the Digit Symbol Substitution (DSS) test had the most significant hazard ratios (0.53 and 0.41, respectively) at least six years prior to the onset of clinical symptoms, Dr. Albert observed.
Identifying these hazard ratios is important to understanding the general risk of Alzheimer’s disease progression, based on these measures. Despite evidence of global trends that were useful, however, no single measure demonstrated enough accuracy to predict MCI in specific individuals. “What we really want to know is who, on an individual basis, is likely to be at risk,” Dr. Albert said, because “those are the people that we want to treat and track for the impact of medication.”The researchers therefore searched for combinations of domains that could be used on a large scale in the general population and that would be highly sensitive and specific for the development of MCI. The investigators wanted to identify the best measures from each of the domains using time-dependent receiver operating characteristic. They conducted this analysis from several perspectives, evaluating issues such as invasiveness and cost. “The best combination is the one that uses the least number of measures and seems to be the most predictive,” she explained, adding that the optimal biomarker would need to have both a sensitivity and specificity of at least 0.80.
Although no individual measure was able to achieve this threshold, the combination of multiple domains significantly enhanced both the sensitivity and specificity. The combination of the best measures from each domain—a genetic variable (APOE-4), cognitive variables (DSS and PA Immediate Recall), a CSF variable (p-tau), and MRI variables (right EC thickness and right hippocampal volume)—had a sensitivity of 0.80 and a specificity of 0.75 and an area under the curve of 0.85 in relation to the baseline, with respect to the progression of symptoms five years after enrollment in the study, Dr. Albert reported. “This is obviously approaching [the optimum], and we are quite pleased with this result,” she said.
These results would need to be replicated to validate this or other combinations for use in individual cases, Dr. Albert said. Researchers are developing a consortium consisting of five centers around the world that will be combining their data together to make diagnosis and treatment of Alzheimer’s disease in its earliest phases possible. Future goals are to identify less-invasive testing tools and to improve the accuracy of prediction.
In 2014, the BIOCARD study received new funding to continue follow-up and collections of CSF, MRI, and amyloid imaging from patients in the database. “I think it’s clear that biomarkers are critical for early identification,” Dr. Albert said. “We are hopeful [that] this approach will be useful to selecting patients for future clinical trials.”
—Linda Peckel
WASHINGTON, DC—A combination of biomarkers may significantly help select cognitively normal individuals to include in clinical trials of interventions for Alzheimer’s disease and assess disease progression in response to treatment, according to Marilyn S. Albert, PhD, Director of the Division of Cognitive Neuroscience in the Department of Neurology at Johns Hopkins University School of Medicine and Director of the Johns Hopkins Alzheimer’s Disease Research Center in Baltimore. Dr. Albert is part of an international research effort to identify biomarkers associated with the changes that occur in the brains of normal individuals who later develop Alzheimer’s disease. “By definition, we’re looking for measures—or biomarkers—that might reflect the underlying disease process when the clinical symptoms are minimal,” she said at the Alzheimer’s Association International Conference 2015.
The most recent data from the Biomarkers for Older Controls at Risk for Dementia (BIOCARD) study show that combinations of biomarkers obtained at least six years prior to symptom onset—including measures of cognition, brain structure, and brain proteins that appear to reflect the accumulated Alzheimer’s disease pathology in people who are cognitively normal—can identify which individuals are most likely to progress to mild cognitive impairment (MCI) due to Alzheimer’s disease. “This [finding] suggests that it is possible to use combinations of these measures to predict who is at high risk so that novel treatments could be initiated once they are developed,” said Dr. Albert.
The BIOCARD Study
Dr. Albert and her colleagues are currently running an extension of the original BIOCARD study, which was begun in 1995 by the National Institute of Mental Health (NIMH) with 349 subjects who were cognitively normal. Approximately 75% of participants enrolled at that time had a family history of dementia, but no clinical signs of Alzheimer’s disease. “Alzheimer’s pathology develops when individuals are cognitively normal and then changes and spreads through the brain throughout the course of the disease,” Dr. Albert explained, “so we know that there must be some individuals who are cognitively normal with this pathology.” The main objective of the study is to understand the preclinical phase of Alzheimer’s disease so that it might be possible to intervene when individuals are cognitively normal.
Dr. Albert outlined the considerable challenges to determining what types of biomarkers might be predictive. First, a large group of cognitively normal individuals needs to be followed over a long period of time to measure changes that could reflect early disease. Next, skilled clinicians are needed to evaluate outcomes and ensure that they are related to Alzheimer’s disease, and finally, the study needs to include enough outcomes to enable a useful statistical analysis, she said.
The initial NIMH study was halted in 2005. Johns Hopkins researchers were funded in 2009 to continue the study. They re-enrolled the majority of the original participants (approximately 30 patients had died). Half of the participants were female, and the mean education level was high (17 years). The mean age at the initial time of enrollment was 57. Some participants have been followed for as long as 20 years, and the minimum follow-up is about 10 years, Dr. Albert said.
Data for the original study were collected from each participant in the form of biannual spinal taps to obtain samples from CSF, and structural MRI studies, along with annual cognitive testing and clinical evaluations. Approximately 60 of the participants have developed MCI or dementia due to Alzheimer’s disease.
Combining Measures Improved Accuracy
The investigators performed Cox regression analyses on all measures and calculated hazard ratios (HR) to identify measures associated with the time to onset of clinical symptoms. They found significant associations between baseline levels of amyloid beta and p-tau in CSF and time to onset of MCI. Right hippocampal volume, right entorhinal cortex thickness, and test scores of episodic and incidental memory at baseline also were significantly associated with time to onset of MCI, Dr. Albert reported.
Baseline measures of amyloid beta (HR = 0.72) were found to be lower in people who progressed more rapidly to clinical symptoms, and baseline p-tau (HR = 1.39) levels were higher. In addition, lower right entorhinal cortex (EC) thickness (HR = 0.74) and lower right hippocampal volume (HR = 0.71) were both associated with decreased time to onset of Alzheimer’s disease, said Dr. Albert.
Although nine cognitive tests appear to predict Alzheimer’s disease, the Paired Associates Immediate Recall (PA) test and the Digit Symbol Substitution (DSS) test had the most significant hazard ratios (0.53 and 0.41, respectively) at least six years prior to the onset of clinical symptoms, Dr. Albert observed.
Identifying these hazard ratios is important to understanding the general risk of Alzheimer’s disease progression, based on these measures. Despite evidence of global trends that were useful, however, no single measure demonstrated enough accuracy to predict MCI in specific individuals. “What we really want to know is who, on an individual basis, is likely to be at risk,” Dr. Albert said, because “those are the people that we want to treat and track for the impact of medication.”The researchers therefore searched for combinations of domains that could be used on a large scale in the general population and that would be highly sensitive and specific for the development of MCI. The investigators wanted to identify the best measures from each of the domains using time-dependent receiver operating characteristic. They conducted this analysis from several perspectives, evaluating issues such as invasiveness and cost. “The best combination is the one that uses the least number of measures and seems to be the most predictive,” she explained, adding that the optimal biomarker would need to have both a sensitivity and specificity of at least 0.80.
Although no individual measure was able to achieve this threshold, the combination of multiple domains significantly enhanced both the sensitivity and specificity. The combination of the best measures from each domain—a genetic variable (APOE-4), cognitive variables (DSS and PA Immediate Recall), a CSF variable (p-tau), and MRI variables (right EC thickness and right hippocampal volume)—had a sensitivity of 0.80 and a specificity of 0.75 and an area under the curve of 0.85 in relation to the baseline, with respect to the progression of symptoms five years after enrollment in the study, Dr. Albert reported. “This is obviously approaching [the optimum], and we are quite pleased with this result,” she said.
These results would need to be replicated to validate this or other combinations for use in individual cases, Dr. Albert said. Researchers are developing a consortium consisting of five centers around the world that will be combining their data together to make diagnosis and treatment of Alzheimer’s disease in its earliest phases possible. Future goals are to identify less-invasive testing tools and to improve the accuracy of prediction.
In 2014, the BIOCARD study received new funding to continue follow-up and collections of CSF, MRI, and amyloid imaging from patients in the database. “I think it’s clear that biomarkers are critical for early identification,” Dr. Albert said. “We are hopeful [that] this approach will be useful to selecting patients for future clinical trials.”
—Linda Peckel
WASHINGTON, DC—A combination of biomarkers may significantly help select cognitively normal individuals to include in clinical trials of interventions for Alzheimer’s disease and assess disease progression in response to treatment, according to Marilyn S. Albert, PhD, Director of the Division of Cognitive Neuroscience in the Department of Neurology at Johns Hopkins University School of Medicine and Director of the Johns Hopkins Alzheimer’s Disease Research Center in Baltimore. Dr. Albert is part of an international research effort to identify biomarkers associated with the changes that occur in the brains of normal individuals who later develop Alzheimer’s disease. “By definition, we’re looking for measures—or biomarkers—that might reflect the underlying disease process when the clinical symptoms are minimal,” she said at the Alzheimer’s Association International Conference 2015.
The most recent data from the Biomarkers for Older Controls at Risk for Dementia (BIOCARD) study show that combinations of biomarkers obtained at least six years prior to symptom onset—including measures of cognition, brain structure, and brain proteins that appear to reflect the accumulated Alzheimer’s disease pathology in people who are cognitively normal—can identify which individuals are most likely to progress to mild cognitive impairment (MCI) due to Alzheimer’s disease. “This [finding] suggests that it is possible to use combinations of these measures to predict who is at high risk so that novel treatments could be initiated once they are developed,” said Dr. Albert.
The BIOCARD Study
Dr. Albert and her colleagues are currently running an extension of the original BIOCARD study, which was begun in 1995 by the National Institute of Mental Health (NIMH) with 349 subjects who were cognitively normal. Approximately 75% of participants enrolled at that time had a family history of dementia, but no clinical signs of Alzheimer’s disease. “Alzheimer’s pathology develops when individuals are cognitively normal and then changes and spreads through the brain throughout the course of the disease,” Dr. Albert explained, “so we know that there must be some individuals who are cognitively normal with this pathology.” The main objective of the study is to understand the preclinical phase of Alzheimer’s disease so that it might be possible to intervene when individuals are cognitively normal.
Dr. Albert outlined the considerable challenges to determining what types of biomarkers might be predictive. First, a large group of cognitively normal individuals needs to be followed over a long period of time to measure changes that could reflect early disease. Next, skilled clinicians are needed to evaluate outcomes and ensure that they are related to Alzheimer’s disease, and finally, the study needs to include enough outcomes to enable a useful statistical analysis, she said.
The initial NIMH study was halted in 2005. Johns Hopkins researchers were funded in 2009 to continue the study. They re-enrolled the majority of the original participants (approximately 30 patients had died). Half of the participants were female, and the mean education level was high (17 years). The mean age at the initial time of enrollment was 57. Some participants have been followed for as long as 20 years, and the minimum follow-up is about 10 years, Dr. Albert said.
Data for the original study were collected from each participant in the form of biannual spinal taps to obtain samples from CSF, and structural MRI studies, along with annual cognitive testing and clinical evaluations. Approximately 60 of the participants have developed MCI or dementia due to Alzheimer’s disease.
Combining Measures Improved Accuracy
The investigators performed Cox regression analyses on all measures and calculated hazard ratios (HR) to identify measures associated with the time to onset of clinical symptoms. They found significant associations between baseline levels of amyloid beta and p-tau in CSF and time to onset of MCI. Right hippocampal volume, right entorhinal cortex thickness, and test scores of episodic and incidental memory at baseline also were significantly associated with time to onset of MCI, Dr. Albert reported.
Baseline measures of amyloid beta (HR = 0.72) were found to be lower in people who progressed more rapidly to clinical symptoms, and baseline p-tau (HR = 1.39) levels were higher. In addition, lower right entorhinal cortex (EC) thickness (HR = 0.74) and lower right hippocampal volume (HR = 0.71) were both associated with decreased time to onset of Alzheimer’s disease, said Dr. Albert.
Although nine cognitive tests appear to predict Alzheimer’s disease, the Paired Associates Immediate Recall (PA) test and the Digit Symbol Substitution (DSS) test had the most significant hazard ratios (0.53 and 0.41, respectively) at least six years prior to the onset of clinical symptoms, Dr. Albert observed.
Identifying these hazard ratios is important to understanding the general risk of Alzheimer’s disease progression, based on these measures. Despite evidence of global trends that were useful, however, no single measure demonstrated enough accuracy to predict MCI in specific individuals. “What we really want to know is who, on an individual basis, is likely to be at risk,” Dr. Albert said, because “those are the people that we want to treat and track for the impact of medication.”The researchers therefore searched for combinations of domains that could be used on a large scale in the general population and that would be highly sensitive and specific for the development of MCI. The investigators wanted to identify the best measures from each of the domains using time-dependent receiver operating characteristic. They conducted this analysis from several perspectives, evaluating issues such as invasiveness and cost. “The best combination is the one that uses the least number of measures and seems to be the most predictive,” she explained, adding that the optimal biomarker would need to have both a sensitivity and specificity of at least 0.80.
Although no individual measure was able to achieve this threshold, the combination of multiple domains significantly enhanced both the sensitivity and specificity. The combination of the best measures from each domain—a genetic variable (APOE-4), cognitive variables (DSS and PA Immediate Recall), a CSF variable (p-tau), and MRI variables (right EC thickness and right hippocampal volume)—had a sensitivity of 0.80 and a specificity of 0.75 and an area under the curve of 0.85 in relation to the baseline, with respect to the progression of symptoms five years after enrollment in the study, Dr. Albert reported. “This is obviously approaching [the optimum], and we are quite pleased with this result,” she said.
These results would need to be replicated to validate this or other combinations for use in individual cases, Dr. Albert said. Researchers are developing a consortium consisting of five centers around the world that will be combining their data together to make diagnosis and treatment of Alzheimer’s disease in its earliest phases possible. Future goals are to identify less-invasive testing tools and to improve the accuracy of prediction.
In 2014, the BIOCARD study received new funding to continue follow-up and collections of CSF, MRI, and amyloid imaging from patients in the database. “I think it’s clear that biomarkers are critical for early identification,” Dr. Albert said. “We are hopeful [that] this approach will be useful to selecting patients for future clinical trials.”
—Linda Peckel