Stanford researchers develop ARC score to predict Alzheimer’s risk

ARC score quantifies brain connectivity patterns

A machine-learning model uses the ARC score to place a person's brain function on a spectrum between a superager and an Alzheimer’s patient.

Study compares super-agers with Alzheimer’s patients

Researchers compared resting-state brain scans from patients with Alzheimer’s disease to those of super-agers. Newsy-today.com reported the study included 180 super-agers and 110 patients with Alzheimer’s.

Taketonews.com defined super-agers as individuals over 75 with memory performance matching 45-year-olds, a group comprising about ten percent of the elderly population. These participants also maintained executive function that was normal for their age.

Large-scale testing validates predictive accuracy

The model achieved an 80 percent accuracy rate in distinguishing super-agers from Alzheimer’s patients during validation on an independent cohort of 143 individuals. The AI was trained on 290 subjects before this testing phase.

When applied to 1,315 healthy adults, the ARC score proved more effective at predicting future memory issues than previous diagnostic methods. Yahoo.com reported that follow-up data for these participants spanned five to twelve years.

People with lower ARC scores showed faster decline on memory and everyday-functioning assessments over time. Taketonews.com reported that in a broader sample of healthy adults aged 55 to 70, the score identified connectivity patterns leaning toward Alzheimer’s. These individuals experienced accelerated memory decline, increased amyloid buildup, and a higher risk of developing Alzheimer’s disease.

Connectivity patterns reveal insights into neurodegeneration

Comparisons between the two groups uncovered distinct functional connectivity patterns across prefrontal, temporal, insular and parietal regions. Researchers suggest these proteins may be downstream by-products rather than primary triggers of neurodegeneration. Individuals with brain activity patterns more similar to those with Alzheimer’s showed higher levels of amyloid buildup and experienced faster cognitive decline.

Researchers caution against current diagnostic use

Researchers stress that the ARC system is not currently intended to diagnose Alzheimer’s disease. Investigators caution that an individual’s ARC score is not a fixed trajectory. Brain activity changes over time and the proportion of super-ager-like patterns declines with advancing age. The study’s authors say the ARC score is currently a snapshot tool, not a definitive medical verdict. They intend to investigate whether targeted cognitive training can shift vulnerable connectivity patterns toward a super-ager profile.

Potential for future prevention trials

The resting-state imaging approach also identifies older adults who show exceptional resistance to cognitive decline. Lead author Kanhao Zhao stated that while larger and more diverse studies are needed, the ARC score could help recruit high-risk individuals for Alzheimer’s prevention trials before memory decline begins.

ARC score outperforms previous diagnostic methods

The ARC score performed better than older methods for predicting who would develop memory issues, according to newsbytesapp.com.