How CSF Tests May Help Recruitment for Alzheimer’s Drug Clinical Trials
A recent study shows these tests may make it easier and more cost-effective to identify patients for studies of new PET treatments.
Cerebral spinal fluid (CSF) laboratory tests detecting biomarkers associated with Alzheimer’s may make it easier and more cost-effective to identify patients for studies of new PET treatments for the disease, according to a study published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association on Feb. 28. Here is a briefing of the study and its potential implications for future Alzheimer’s disease drug treatment trials.
The Diagnostic ChallengeDiagnosis of Alzheimer’s disease is based on detecting the presence of amyloid beta (Aβ) plaques and tau neurofibrillary tangles (NFT) in the brain. While Aβ is believed to trigger pathological changes associated with Alzheimer’s, Aβ therapies have proven largely ineffective. Moreover, there is growing evidence to suggest that NFT aggregation is a better indicator of neurological decline caused by Alzheimer’s. All of this has led to an increase in clinical trials testing new tau-targeting treatments for Alzheimer’s, i.e., those that use longitudinal positron emission tomography (PET) imaging with tau-sensitive radiotracers to evaluate both target engagement and efficacy of anti-tau drugs. To perform these trials, it is necessary to recruit individuals who are at risk for greater longitudinal tau accumulation. This in turn creates a need to identify cost-effective biomarkers that can predict longitudinal accumulation of aggregated tau as measured by tau-PET. To date, Aβ PET scan is the only established method found to be capable of reliably predicting longitudinal tau accumulation. But baseline Aβ PET scan detection is difficult and relatively expensive. Identifying biomarkers that are more accessible and affordable would thus go a long way in facilitating tau-targeting PET Alzheimer’s treatment trials. One group of candidates are core cerebrospinal fluid (CSF) biomarkers, which in addition to being more affordable and accessible than Aβ-PET, allow for simultaneous assessment of both Aβ and tau pathology using a single procedure.
Comparing the Predictive Capabilities of CSF Biomarker vs Aβ PETUntil now, there has been no study assessing whether CSF biomarkers reliably indicate the presence of neurofibrillary tau tangles found in PET scans in patients diagnosed with Alzheimer’s. With this in mind, a group of Swiss researchers led by senior author Michael Schöll, PhD, of the University Gothenburg compared the predictive capabilities of CSF Alzheimer's disease biomarkers and plasma phosphorylated tau-181 (p-tau181) associated with longitudinal tau accumulation, as measured by tau PET, to the predictive performance of baseline Aβ PET scans. To perform the comparison, the researchers identified participants from earlier Alzheimer's Disease Neuroimaging Initiative studies with serial tau PET scans (either florbetapir or florbetaben), baseline Aβ PET scans, and baseline fluid biomarker (n = 163), or plasma p-tau181 (n = 74) tests. Patients were cognitively unimpaired (CU) or had mild cognitive impairment (MCI) or dementia (CI).
The Study ResultsEmploying a voxel-wise analysis, the Swiss team reached the following findings:
- Baseline levels of all CSF biomarkers showed moderate to strong correlations with longitudinal F-18 flortaucipir standardized uptake value ratio (SUVR) changes among CU and MCI patients;
- Plasma p-tau181 was more closely correlated with F-18 flortaucipir SUVR change in patients with dementia, and predicted tau accumulation with similar but slightly higher values than Aβ PET;
- CSF and p-tau181 levels were significantly associated with F-18 flortaucipir SUVR change in patients who were positive for Aβ plaque, but negative for tau accumulation;
- Higher plasma p-tau-181 levels were associated with faster F-18 flortaucipir SUVR change rates among Aβ-positive yet tau-negative participants (which suggests that elevations of these markers may precede early tau aggregation).