Joseph‐Mathurin, Nelly

AbstractINTRODUCTIONAs the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others.METHODSWe used hierarchical clustering to group 19 cross‐sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation.RESULTSThree primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors.DISCUSSIONThese results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

AbstractBackgroundThough pathogenic variations in PSEN1 leading to Autosomal Dominant Alzheimer's disease (ADAD) are all highly penetrant, there is substantial inter‐individual variability in the rates of cognitive decline and biomarker change observed in ADAD. We hypothesized that this inter‐individual variability may be associated with the location of the pathogenic variant within PSEN1. We tested this hypothesis using biomarker and cognitive data from the Dominantly Inherited Alzheimer Network (DIAN) observational study.MethodPSEN1 pathogenic variant carriers [grouped based on whether their ADAD genotype affected transmembrane (PSEN1 TM; N = 125) or cytoplasmic (PSEN1 CY; N = 62) domains] and non‐carriers [NC; N = 176] enrolled in DIAN underwent clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF). Linear mixed effects models with a restricted cubic spline were used to determine cross‐sectional differences in clinical and biomarker measures between NC, PSEN1 TM, and PSEN1 CY groups, across the disease course (estimated years to symptom onset; EYO).ResultBackground characteristics are reported in Table 1. PSEN1 CY and PSEN1 TM groups had elevated β‐amyloid (PiB‐PET) and phosphorylated‐tau levels (CSF pTauT181) compared to NC, but these biomarker measures were not different between PSEN1 TM and CY groups (Figure 1a‐b). Additionally, the PSEN1 TM group had decreased glucose metabolism compared to NC but was similar to the PSEN1 CY group (Figure 1c). However, we observed that PSEN1 TM carriers had significantly smaller hippocampal volume, greater lateral ventricle volume, and greater cognitive impairment (Figure 1d‐f) across EYO as compared to PSEN1 CY carriers.ConclusionDespite similar β‐amyloid PET and CSF pTauT181 levels across EYO, individuals with pathogenic variants affecting transmembrane domains in PSEN1 may have more rapid progression of disease as measured by cognitive function and neurodegeneration as compared to those with variants affecting cytoplasmic domains. These results have implications both for understanding the heterogeneity in ongoing ADAD clinical trials and for better understanding the pathobiology of ADAD.

AbstractBackgroundAmyloid‐related imaging abnormalities (ARIA), edema (E) or hemorrhagic (H) type, have been reported in trials of anti‐β‐amyloid passive immunotherapies in sporadic and dominantly inherited Alzheimer Disease (DIAD). However, beyond APOE‐ɛ4 the risk factors and clinical implications of ARIA are not well understood, especially in DIAD populations. Here we characterize ARIA, focusing on ARIA‐E in the DIAN‐TU‐001 trial evaluating gantenerumab and solanezumab in DIAD.MethodThe DIAN‐TU‐001 trial enrolled 194 participants, including 144 DIAD mutation‐carriers receiving gantenerumab (n=52), solanezumab (n=52), or a placebo (n=40). Clinical assessments included the Clinical Dementia Rating (CDR). Imaging assessments included PiB‐PET and safety MR, including T2‐FLAIR and T2*‐GRE. Treatment dosage and APOE‐ɛ4 status were also reported.ResultEleven participants developed a total of 14 ARIA‐E episodes (discovered on scheduled safety MR) and three had associated mild symptoms reported in retrospect (e.g. headache, imbalance disorder). Though one ARIA‐E case was observed in the solanezumab arm, the following results focus on the gantenerumab arm to prevent unblinding. Recipients of gantenerumab were more likely to develop ARIA‐E compared to placebo (odds ratio (OR)=9.29, 95% confidence interval (CI)=[1.1,75.9], p‐value<0.05). ARIA‐E participants were PiB‐PET+ and 60% were CDR>0. APOE‐ɛ4 tends to be associated with risk for developing ARIA‐E (OR=5.0, 95%CI=[1.0,30.4], p‐value=0.055). ARIA‐E were not observed at initial 225mg dose and were first observed after a titration step (within 4‐24 weeks, after 1‐6 injections, Table 1). Time‐to‐resolution of ARIA‐E was 10.4±6.2weeks. Seven of ten ARIA‐E patients paused/reduced dose escalation and three discontinued treatment. Overall, developing ARIA‐E did not increase the odds of trial discontinuation (OR=1.57, 95%CI=[0.34,7.33], p‐value=0.57). ARIA‐E occurred primarily in the occipital lobe (71%) with associated incident ARIA‐H (microhemorrhages or superficial siderosis) in 60% of ARIA‐E participants. ARIA‐E size was associated with microhemorrhage count (Spearman’s rho=0.72, p‐value<0.05), rate of change (rho=0.78, p‐value<0.01), and baseline PiB‐PET (rho=0.68, p‐value<0.05). Normalizing by baseline, PiB‐PET decreased similarly in ARIA‐E+ (‐0.21±0.20) and ARIA‐E‐ (‐0.16±0.20) participants.ConclusionIn DIAD, gantenerumab dose over 225mg increased ARIA‐E risk, possibly more for APOE‐ɛ4 carriers. ARIA‐E was reversible, generally asymptomatic, and without increased odds of trial discontinuation. These aspects of drug response give insights for managing ARIA‐E occurrence in future trials.

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Abstract Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir‐based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter‐individual variability of the two tracers were compared using multivariate linear models both cross‐sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

AbstractIntroduction18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer's disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11C‐Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET.MethodsFDG, early frames of PiB images, and relative PiB delivery rate constants (PiB‐R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional cross‐sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history.ResultsMetabolism and perfusion images were spatially correlated. Regional PiB‐R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity.DiscussionHypometabolism estimated by PiB‐R1 may provide a measure of brain perfusion without increasing radiation exposure.