Reston, VA (February 24, 2026)--A novel quantitative PET- and MRI-based imaging approach can objectively identify a recently recognized type of dementia--limbic-predominant age-related TDP-43 encephalopathy, or LATE--that is often mistaken as Alzheimer's disease. This research, published online in The Journal of Nuclear Medicine, may enable earlier differentiation of LATE and Alzheimer's disease, or their co-existence in the clinic, guiding targeted diagnostic work-up and personalized care for dementia patients.
LATE is a distinct neurodegenerative disorder in older adults that manifests as memory-related cognitive decline closely resembling Alzheimer's disease. It has attracted significant attention from clinicians and investigators because of its high prevalence among elderly people. Whereas Alzheimer's disease is closely associated with the accumulation of amyloid and tau proteins, LATE is characterized by TDP-43 inclusions in the limbic system.
"The distinction in the causes of these types of dementia is critical, especially in the era of anti-amyloid therapies," said Satoshi Minoshima, MD, PhD, FSNMMI, professor of Radiology and Imaging Sciences at the University of Utah in Salt Lake City. "Because LATE has a different underlying pathology and a seemingly different prognosis, it cannot be diagnosed or treated in the same way as Alzheimer's disease."
Currently, LATE can only be definitively distinguished from Alzheimer's disease through postmortem neuropathology. Because no clinically approved biomarker is available to identify LATE--although efforts are underway to develop such biomarkers-- physicians rely on a diagnostic framework that focuses on PET, MRI, and clinical assessment.
"This has resulted in a significant gap in the diagnosis and management of LATE," stated Minoshima. "Our study aimed to introduce a quantitative diagnostic framework using commonly available imaging tests to identify LATE, which can be applied in clinics with limited access to advanced biomarkers."
In the study, researchers created stereotactic surface projections (3D-SSP) PET templates from autopsy-confirmed datasets for LATE neuropathologic change and Alzheimer's disease neuropathologic change. Next, z score maps were generated from the autopsy-confirmed PET templates.
Using the autopsy-derived z score maps, z score product indices were generated for 944 18F-FDG PET cases that were referred from cognitive disorder clinics in a tertiary care center. Patients were grouped into probable LATE, probable LATE and Alzheimer's disease, and probable Alzheimer's disease groups. Clinical and quantitative MRI volumetry data were then compared across the groups.
Of the 944 consecutive clinical cases, 13 percent were characterized as probable LATE (2.4 percent pure LATE and 10.6 percent LATE and Alzheimer's disease) and 23.7 percent were characterized as probable Alzheimer's disease without LATE. MRI volumetry revealed that the medial temporal lobe was most affected in pure LATE cases, whereas the orbitofrontal gyrus and lateral temporal lobe were most vulnerable in mixed LATE and Alzheimer's disease cases. LATE and Alzheimer's disease related changes did not appear to occur independently; rather, the same hemisphere tended to be involved in both LATE and Alzheimer's disease within the same subject, possibly indicating pathogenic synergy between these two conditions.
"The imaging patterns identified on PET and MRI in this study provide clinicians with a practical tool to detect potential LATE pathology in patients with cognitive impairment and to inform clinical management and future investigations of LATE," noted Minoshima. "These efforts will ultimately advance precision diagnostics and treatment stratification in molecular imaging and nuclear medicine."
The authors of " Copathologies of Limbic-Predominant Age-Related TDP-43 Encephalopathy and Alzheimer Disease: [18F]FDG PET Statistical Mapping and Quantitative MRI Volumetry " include Pei Ing Ngam, Department of Diagnostic Imaging, National University Hospital Singapore, Singapore , and Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; Yoshimi Anzai and Satoshi Minoshima, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; Christine J. Cliatt Brown and Nicholas Alonzo Frost, Department of Neurology, University of Utah, Salt Lake City, Utah; Michelle Keown Sorweid, Division of Geriatrics, University of Utah, Salt Lake City, Utah; and Tanyaluck Thientunyakit, Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand; for the Alzheimer s Disease Neuroimaging Initiative.
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