Developing early vulnerable area aggregates for PET detection of beta‐amyloid based on young and middle‐aged adults from the Framingham Heart Study. (20th December 2022) - Thibault, Emma G.; Farrell, Michelle E.; Beiser, Alexa S; Becker, Alex; Satizabal, Claudia L; Jacobs, Heidi I.L.; DeCarli, Charles S.; Hanseeuw, Bernard; Killiany, Ronald J; Sperling, Reisa A.; Seshadri, Sudha; Johnson, Keith A.
Abstract: Background: As Alzheimer's clinical trials shift earlier and earlier in the disease process, current global PET measures of beta‐amyloid (Aβ) positivity may be insufficient for detecting the earliest Aβ deposits. Regional PET measures may better detect the earliest deposits. Previous efforts to identify early‐accumulating regions have inferred which regions may be most vulnerable based on older adults. Our aim was to identify early vulnerable areas by looking earlier in the lifespan. Method: 235 clinically‐normal adults ages 33‐74 from the Framingham Heart Study (FHS) underwent one time‐point of dynamic Pittsburgh Compound B (PIB) PET (Tab.1). PIB was regionally quantified in Desikan regions using distribution volume ratio (DVR, cerebellar reference). Linear and quadratic (Age + Age 2 ) models were run to determine association of age with region of interest (ROI) PIB DVR, and ROIs with significantly increasing DVRs were selected for the generation of candidate early vulnerable area (EVA) aggregates. Multiple aggregate EVA DVRs (EVA2‐EVA11) were generated by sequential addition of regions in rank order of descending Age 2 estimate. EVA positivity was derived from both full‐sample GMM and < 45 sample mean + 2SD thresholds. Finally, EVA aggregates were tested using an independent cohort of older adults from the Harvard Aging Brain Study (HABS) that were imaged under an identical protocol (n=250, ages 50‐92), computing sensitivity and specificity of each aggregate at baseline to predict progression to global PIB positivity 3 years later. Result: Of 35 Desikan regions each evaluated on the left and right, 11 emerged as quadratic age relationships ( p <0.05; Fig.1) and were used to generate EVA aggregates (Fig.2). Independent validation in baseline global PIB‐ individuals from HABS indicated EVA9 and EVA10 best predicted future accumulation (Fig.3). GMM‐based thresholds provided excellent specificity (SP=1.0) but weak sensitivity (SE=0.41) to predict progression to global PIB positivity 3 years later, while the more liberal mean+2SD thresholds improved sensitivity but decreased specificity (SE=0.88, SP=0.88). Conclusion: Utilizing lifespan data from FHS, we identified a set of early vulnerable regions that are predictive of future accumulation in an independent sample of older adults. These findings are potentially useful in identifying the earliest deposits of Aβ for use in clinical trial design.