Blood Test Algorithm Cuts Unnecessary Alzheimer’s PET Scans in Clinical Trial Screening

A new blood-based screening algorithm developed at the University of Southern California has sharply reduced the number of costly, unnecessary PET imaging scans required to identify candidates for a major Alzheimer’s disease clinical trial, according to research from scientists at the Keck School of Medicine of USC published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. The algorithm, deployed during recruitment for the international phase 3 AHEAD 3-45 study, cut the proportion of candidates who underwent a positron-emission tomography scan but ultimately did not qualify for the trial from more than 70 percent to 31 percent. The finding represents one of the clearest demonstrations yet that simple blood tests, combined with thoughtful statistical modeling, can transform the logistics of recruiting participants for studies of a disease that begins silently in the brain decades before memory fades.

The AHEAD 3-45 trial is testing a deceptively simple question: whether treating people with an approved Alzheimer’s drug earlier, before any outward sign of decline, can produce better outcomes than waiting for symptoms to appear. The drug in question, lecanemab, works by clearing sticky aggregates of amyloid-beta protein that accumulate in the brain and are closely associated with the progression of Alzheimer’s disease. In patients who already show cognitive symptoms, the drug can slow the clinical progression of the disease by roughly 30 percent. But it is currently prescribed only for people who have already begun to decline, leaving open the tantalizing possibility that intervening earlier could do far more.

That possibility creates an enormous recruitment challenge. The AHEAD 3-45 trial specifically targets people with early amyloid-beta buildup, a process that can begin as much as twenty or thirty years before the first noticeable problems with memory or thinking. Yet only about 30 percent of cognitively healthy adults over the age of 65 carry amyloid levels high enough to qualify for a trial like AHEAD. Finding those individuals means screening large numbers of older adults who feel perfectly well and have no obvious symptoms, then confirming with certainty that the biological hallmarks of the disease are present before enrolling them in a study that will involve repeated assessments and, ultimately, treatment with an amyloid-clearing antibody.

Before blood plasma screening entered the picture, that confirmation depended almost entirely on PET imaging. Positron-emission tomography is considered the gold standard for visualizing amyloid in the living brain, but it is expensive, requires access to specialized scanners and radiotracers, and adds months to the enrollment timeline. Candidates for the AHEAD 3-45 trial once faced a screening process stretching up to three months from their first visit to enrollment, and after undergoing a PET scan, more than 70 percent would learn they were ineligible. In other words, for every participant enrolled, the trial was paying for and performing multiple scans that revealed nothing more than the absence of the very biology the study was hunting for.

“The screening part of an Alzheimer’s trial is usually one of the costliest parts of the clinical trial for study sites,” said corresponding author Oliver Langford, MS, modeling and simulation director at the USC Epstein Family Alzheimer’s Therapeutic Research Institute at the Keck School of Medicine. “We definitely helped reduce the burden for participating clinical sites and for patients by reducing the number of individuals having to undergo PET scans.” The savings cascade beyond trial budgets. Fewer scans mean fewer clinic visits, less exposure to radiation and needles for volunteers, and a faster path from first contact to enrollment for the people the trial most needs to reach.

The algorithmic tool at the heart of the study incorporated two blood plasma biomarkers that have risen rapidly to prominence in Alzheimer’s research. The first is the amyloid-beta ratio, an early signal of amyloid accumulation that reflects the gradual sequestration of the protein into plaques in the brain. The second is p-tau217, a recently discovered marker of a phosphorylated form of the tau protein that more reliably reflects the actual amyloid burden carried in an individual’s brain. Blood tests measuring these proteins have shown steadily improving accuracy in recent years, and their performance has fueled a broader shift toward making Alzheimer’s detection accessible in ordinary clinical settings rather than confined to specialized imaging centers.

Building the model required data on a remarkable scale. The USC team developed the algorithm using information from 1,080 participants in the AHEAD study and then validated it in an independent dataset drawn from the Wisconsin Registry for Alzheimer’s Prevention, a long-running observational study of dementia risk. Beyond the two plasma biomarkers, the model incorporated age and APOE4 carrier status, two well-established factors that increase the likelihood of amyloid accumulation. Crucially, the algorithm was not frozen at birth. It was refined across three successive versions during active recruitment, which ran from 2020 to 2024, allowing the researchers to fold in lessons from real-world screening performance as the trial progressed.

The staged rollout demonstrated how quickly each refinement translated into efficiency. When first introduced in February 2022, the version of the algorithm built on the amyloid-beta ratio alone reduced the proportion of participants who received a PET scan but proved ineligible from 71 percent to 50 percent. In May 2023, a second version integrated p-tau217, and that figure fell further to 31 percent. The improvement enabled the trial to enroll participants with both intermediate and elevated amyloid levels far more efficiently, sparing more than half of the would-be scanned candidates an unnecessary imaging procedure. For a phase 3 study recruiting across dozens of sites internationally, the cumulative effect on cost, time and volunteer burden is substantial.

Central to the algorithm’s design is a statistical technique known as Mixture of Experts, or MoE, which allows the model to grapple with a biological reality that simpler tools ignore: amyloid does not accumulate uniformly across the population. Rather than forcing each person into a binary positive or negative category, the team built a model that estimates where an individual falls along a continuous spectrum of amyloid buildup. “When we look at amyloid levels at a population level, we see a peak where people are amyloid-negative and another peak for those with elevated amyloid plaque buildup,” Langford explained. “There is a region in the intermediate range that isn’t fully captured by the plasma marker alone. The Mixture of Experts approach helps model that uncertainty more effectively and is better suited to the type of data collected.” By explicitly modeling the murky middle of the distribution, the algorithm can flag borderline cases for confirmatory imaging while confidently routing clear-cut cases either into the trial or out of it.

The implications extend well beyond a single clinical trial. The results add to a growing body of evidence that blood tests can serve as a practical first-pass filter before more intensive and expensive diagnostics, a reordering of the diagnostic pipeline that could reshape both research and routine care. “It’s going to allow more people to access testing that can help determine whether they have Alzheimer’s disease pathology,” Langford said. “If you are able to go to your doctor and get a blood test done, you’ll be able to understand whether you have the disease earlier.” More efficient screening for trials like AHEAD 3-45 may also bring the field a step closer to its ultimate ambition: primary prevention of Alzheimer’s disease. “If we can intervene earlier,” Langford said, “we’ll have a larger effect and hopefully prevent people from having symptoms.” In addition to Langford, the study’s authors include Rema Raman, Paul Aisen, Doris Molina-Henry, Gustavo A. Jimenez-Maggiora, Robert A. Rissman and Michael C. Donohue of USC; Reisa Sperling, Keith Johnson, Colin Birkenbihl, Madison Cuppels and Rachel F. Buckley of Harvard Medical School, Brigham and Women’s Hospital and Massachusetts General Hospital; Pallavi Sachdev and David Li of Eisai Inc.; and Sterling C. Johnson of C2N Diagnostics. The AHEAD Study is conducted through the Alzheimer’s Clinical Trials Consortium, funded by the National Institute on Aging of the National Institutes of Health under award U24AG057437, and is a public-private partnership supported by the NIA, Eisai, the GHR Foundation, the Alzheimer’s Association and other philanthropic organizations.

Subject of Research: Development and validation of a blood-based biomarker screening algorithm to improve recruitment efficiency for a preclinical Alzheimer's disease clinical trial

Article Title: USC develops algorithmic tool to improve screening of patients for Alzheimer's clinical trial

Article References: USC develops algorithmic tool to improve screening of patients for Alzheimer's clinical trial. (n.d.). Original publication

Image Credits: AI Generated

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Keywords: Alzheimer's disease, blood biomarkers, PET imaging, lecanemab, amyloid-beta, p-tau217, clinical trial recruitment, Mixture of Experts, AHEAD 3-45, Keck School of Medicine of USC, plasma screening, early detection