Biomarkers Identified Patients Most Likely to Develop Alzheimer's Disease

Researchers have identified biomarkers that have the potential to identify those at risk of developing Alzheimer's disease.
Researchers have identified biomarkers that have the potential to identify those at risk of developing Alzheimer's disease.

WASHINGTON — New research has identified six biomarkers that could signal the future onset of Alzheimer's disease symptoms in otherwise normal patients. These results may have implications for the design of clinical trials and the monitoring of patients' response to treatment.

“Our study shows that up to 5 years before any Alzheimer's symptoms appear, a small set of factors can tell us, with significant accuracy, which cognitively normal individuals will develop mild cognitive impairment [MCI] due to Alzheimer's,” study investigator Marilyn Albert, PhD, of Johns Hopkins University School of Medicine, Baltimore, said in a statement .

The study was presented at the Alzheimer's Association International Conference here.

For the study, Albert and colleagues collected data on 189 patients from the BIOCARD study who were cognitively normal at baseline. The collected data included serial assessment of cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) findings, and cognitive measures. Time-dependent receiver-operated characteristic (ROC) methods were used to assess combinations of measures that significantly predicted the cognitively normal individuals who were at high risk of progressing to MCI due to Alzheimer's disease five years later.

The researchers evaluated sets of biomarkers via several approaches: from least costly to most costly; from least invasive to most invasive; and the best-fit model. The latter featured two memory and thinking tests (digit symbol substitution and immediate recall of paired associates), two CSF measures (amyloid-beta and ptau), and two MRI scans of the brain (right entorhinal cortex thickness and right hippocampal volume).

According to data, the combined results of these six measures, adjusted for demographics, were particularly useful, with an area under the curve of 0.886, a sensitivity of 0.85, and a specificity of 0.7. Furthermore, researchers observed that the addition of each domain — whether cognitive, CSF, or MRI — significantly improved prognostic accuracy.

“We hope that this information will be useful for designing clinical trials aimed at delaying the onset of symptoms among cognitively normal individuals,” Albert said. “An approach such as ours could be used for determining which people might be most likely to benefit.”

Reference

Albert M, et al. Using Combinations of Variables to Identify Individuals with Preclinical AD. Abstract #1540. Presented at: Alzheimer's Association International Conference 2015; July 18-23, 2015; Washington, DC.

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