Los Angeles -- A specific pattern of brain metabolism visualized
with PET imaging can predict which patients are most likely to benefit from Alzheimer's
disease therapy. In a retrospective study of patients who received Alzheimer's treatments,
those with the identified pattern experienced stabilized cognitive performance,
while patients with other patterns had significant cognitive decline. This
study was presented at the Society of Nuclear Medicine and Molecular Imaging
(SNMMI) 2026 Annual Meeting, where it received top recognition as the Abstract
of the Year.
Each year, SNMMI chooses an abstract that best exemplifies the most promising
advances in the field of nuclear medicine and molecular imaging. This year, the
SNMMI Henry N. Wagner, Jr., Abstract of the Year was chosen from nearly 1,500
abstracts submitted to the meeting and voted on by reviewers and the society leadership.
The hallmark of Alzheimer's disease is amyloid plaques that accumulate in the
brain. Two anti-amyloid therapies that target these plaques were recently
approved by the U.S. Food and Drug Administration (FDA). While these treatments
have been effective on the whole, there remains
substantial variability in individual success rates.
"Numerous large-scale prior studies have shown that many
people who meet the requirements for the clinical diagnosis of Alzheimer's
disease as the patients for whom anti-amyloid therapies are currently being
prescribed do are actually found to have other diagnoses underlying their
cognitive impairment after autopsy or long-term follow-up," said Amanda Rose Nguyen,
DO, MS, a clinical fellow in nuclear medicine at the David Geffen School of
Medicine at the University of California, Los Angeles. "This could account for
the variability in the success rates of these therapies."
Knowing the 18F-FDG PET scans can diagnose Alzheimer's
disease with high accuracy even in its earliest stages, researchers assessed
the relationship of brain metabolic data from PET scans to prescription
practices and clinical outcomes in patients receiving anti-amyloid therapy.
The study examined a consecutive series of 124
patients whose cases were reviewed by a university committee for
consideration of receiving amyloid immunotherapy. Brain 18F-FDG PET data,
treatment decisions, and clinical outcomes were analyzed for all patients who
underwent treatment for at least one year with respect to cognitive assessment
scores before and after treatment. Brain metabolism patterns on PET were
categorized as being consistent or not consistent with Alzheimer's disease, and
the corresponding subsequent changes in cognitive assessment scores were
calculated.
The brain metabolism patterns were representative of
Alzheimer's disease, Lewy body disease, Limbic-predominant Age-related TDP-43
Encephalopathy-type pathology, or frontotemporal lobar degeneration. Those with
Alzheimer's disease metabolism patterns experienced an increase in their
cognitive performance scores, while all other subjects suffered significant
cognitive decline as measured by their cognitive assessments.
"This work demonstrates that 18F-FDG PET is an important
tool in the diagnosis of dementia," said Nguyen. "Armed with powerful brain
metabolic data, physicians can provide more personalized care, prescribing
anti-amyloid therapy to individuals who are most likely to benefit from it, and
conversely sparing others from ineffective treatments, potential harmful
adverse effects, and unnecessary expense."
Nguyen anticipates larger, higher-powered analyses from an expanded sample by
year-end, which will better define the predictive value and potential role of brain
metabolism patterns. In the meantime, she recommends that physicians gather
comprehensive neuroimaging data on an individual patient basis to help guide
treatment decisions.