Study: Cognitive abilities may best biomarkers for Alzheimer's detection
Compared with changes in biomarkers, changes in cognitive abilities appear to be stronger predictors of whether an individual with mild cognitive impairment (MCI) will develop Alzheimer’s disease, based on research published in the September issue of Archives of General Psychiatry.
Biomarkers such as changes in brain volume or in cerebrospinal fluid levels of some proteins have helped scientists learn about how Alzheimer’s disease develops and whether treatments for it are effective, according to background information in the article. Behavioral markers such as cognitive changes, genetic risk factors and demographic variables also seem to be associated with the condition.
However, the authors wrote, “despite formidable evidence for the predictive validity of individual biomarkers and behavioral markers, they have rarely been examined in combined models.”
Jesus J. Gomar, PhD, from the Benito Menni Complex Assistencial en Salut Mental in Barcelona, Spain, and colleagues sought to determine how well different classes of biomarkers and cognitive markers could predict whether patients with MCI would develop Alzheimer’s disease. They also wanted to assess whether any of these factors was associated with a disproportionate magnitude of decline. The longitudinal study used information from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.
The study included 116 participants with MCI that converted to Alzheimer’s disease in two years, 204 participants with MCI that did not convert to Alzheimer’s disease and 197 cognitively healthy participants as controls.
The researchers used a variety of neuropsychological tests to assess participants’ cognition and ability to function. They obtained cerebrospinal fluid samples when the study began and at annual visits for two years.
At the beginning of the study, participants gave a blood sample which was examined for the presence of genes associated with Alzheimer’s disease. Gomar and colleagues also obtained information about participants’ brain volume and cortical thickness from MRI results included in the ADNI.
Analysis of the variables showed that two measures of delayed memory, as well as the cortical thickness of the left middle temporal lobe in the brain, were associated with a higher chance of converting from MCI to Alzheimer’s disease at two years. A change in participants’ scores on a measure of functional activities appeared to show a larger rate of decline than did changes in biomarkers.
In particular, a decline in scores on the Functional Assessment Questionnaire and the Trail Making Test, part B, appeared to predict whether an individual with MCI would develop Alzheimer’s disease within one year.
“Cognitive markers at baseline were more robust predictors of conversion than most biomarkers,” wrote the authors. “Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.”
The researchers added that in clinical practice and in clinical trials, the optimal way to accurately predict conversion to Alzheimer’s disease is to use all available data.
Biomarkers such as changes in brain volume or in cerebrospinal fluid levels of some proteins have helped scientists learn about how Alzheimer’s disease develops and whether treatments for it are effective, according to background information in the article. Behavioral markers such as cognitive changes, genetic risk factors and demographic variables also seem to be associated with the condition.
However, the authors wrote, “despite formidable evidence for the predictive validity of individual biomarkers and behavioral markers, they have rarely been examined in combined models.”
Jesus J. Gomar, PhD, from the Benito Menni Complex Assistencial en Salut Mental in Barcelona, Spain, and colleagues sought to determine how well different classes of biomarkers and cognitive markers could predict whether patients with MCI would develop Alzheimer’s disease. They also wanted to assess whether any of these factors was associated with a disproportionate magnitude of decline. The longitudinal study used information from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.
The study included 116 participants with MCI that converted to Alzheimer’s disease in two years, 204 participants with MCI that did not convert to Alzheimer’s disease and 197 cognitively healthy participants as controls.
The researchers used a variety of neuropsychological tests to assess participants’ cognition and ability to function. They obtained cerebrospinal fluid samples when the study began and at annual visits for two years.
At the beginning of the study, participants gave a blood sample which was examined for the presence of genes associated with Alzheimer’s disease. Gomar and colleagues also obtained information about participants’ brain volume and cortical thickness from MRI results included in the ADNI.
Analysis of the variables showed that two measures of delayed memory, as well as the cortical thickness of the left middle temporal lobe in the brain, were associated with a higher chance of converting from MCI to Alzheimer’s disease at two years. A change in participants’ scores on a measure of functional activities appeared to show a larger rate of decline than did changes in biomarkers.
In particular, a decline in scores on the Functional Assessment Questionnaire and the Trail Making Test, part B, appeared to predict whether an individual with MCI would develop Alzheimer’s disease within one year.
“Cognitive markers at baseline were more robust predictors of conversion than most biomarkers,” wrote the authors. “Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.”
The researchers added that in clinical practice and in clinical trials, the optimal way to accurately predict conversion to Alzheimer’s disease is to use all available data.