Three cognitive trajectories identified in preclinical Alzheimer’s disease

Cognitive decline in Alzheimer's disease differs substantially from one person to the next and is not well predicted by existing medical tests. Among people with preclinical Alzheimer's disease who began one of two related studies with no symptoms, researchers from the Keck School of Medicine of USC found three distinct patterns: stable, slow cognitive decline and fast cognitive decline. About 70% of participants remained stable over the study period of approximately six years. The research, funded in part by the National Institutes of Health, was just published in Alzheimer's & Dementia: The Journal of the Alzheimer's Association.

"Most studies look at the average across participants, which can make it seem like everyone is slowly getting worse at the same rate," said the paper's corresponding author, Michael Donohue, PhD, professor of neurology and associate director of biostatistics at the USC Epstein Family Alzheimer's Therapeutic Research Institute at the Keck School of Medicine. "But we found that this approach masks major differences between people, suggesting that Alzheimer's disease is more variable than often depicted."

While prior research suggested that people with Alzheimer's disease decline at different rates, the present study is one of the first to tie those patterns to biomarker data. The researchers tested whether certain biomarkers, including specific blood tests and brain scans, could predict who was likely to remain stable and who was likely to worsen. Their models classified participants with about 70% accuracy.

Though it needs further refinement, this kind of prediction tool could someday give patients a more accurate prognosis when they are diagnosed with Alzheimer's disease. Better predictive models could also support more effective clinical trials of potential treatments. The researchers say current trials may be oversimplifying the disease by assuming everyone follows the same path.

"These results suggest we may need to rethink how we design clinical trials in preclinical Alzheimer's disease," said Runpeng (Tony) Li, PhD, a postdoctoral scholar at the Keck School of Medicine and the study's first author. "Many people with Alzheimer's remain stable over the course of a study, which can make it hard to tell if a treatment is working. Identifying those who are more likely to decline could make trials more efficient and more informative."

Patterns of cognitive decline

For the study, researchers analyzed data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study, a clinical trial of the monoclonal antibody solanezumab. They also included data from the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration Extension (LEARN), a companion study of people without the elevated levels of amyloid in the brain which are an early sign of Alzheimer's disease.

Before and during the trial, participants completed a series of cognitive tests that measured their memory, attention and thinking. Scores on these tests were used to track their rate of cognitive decline. The researchers also collected brain scans and blood tests, including phosphorylated tau (P-tau217), a marker of the protein tau, one of the hallmarks of Alzheimer's disease.

The data analysis revealed three distinct trajectories of cognitive decline: stable (no change or improvement); slow decline (gradual drop in test scores); and fast decline (rapid and more pronounced drop in test scores). Participants who showed a gradual or a fast decline had higher P-tau217 levels when the study began, as well as higher levels of tau on brain scans than those who remained stable. They also had a smaller hippocampus, an area of the brain linked to memory and one of the first affected by Alzheimer's disease. Using biomarker data, the researchers could correctly predict whether participants were likely to stay stable or worsen about 70% of the time.

"P-tau217 was one of the strongest signs of which participants would decline, but we still cannot predict exactly how an individual person's disease will progress," Donohue said.

Rethinking Alzheimer's trials

An important next step is to refine the model to more accurately predict which patients will decline quickly. Adding more blood tests, brain scans or other biomarkers is one possible way to do that.

The findings also point to a major challenge in Alzheimer's prevention research. In the disease's early stages, many participants may remain stable even without treatment, making it harder to detect whether a drug is working. The researchers say future trials should focus less on average results and more on different patterns of decline. 

Next, Donohue and his colleagues plan to look at the "misfits" in the model-participants who were predicted to remain stable but worsened-or those predicted to decline who remained symptom-free.

"What is different about certain patients that makes them more resilient-and can these insights be leveraged to slow down Alzheimer's disease in others?" Donohue said.