Citations

AbstractBackgroundThe NIA Health Disparities Research (NIA‐HD) Framework organizes factors in four domains (i.e., environmental, sociocultural, behavioral, and biological), which work together over the lifetime to influence health and health disparities.MethodIn previous work, we employed a 2‐stage approach using factor and principal component (PC) analyses to identify patterns of factors in the domains of the NIA‐HD Framework. We found three PCs that identified related groupings from more than one NIA‐HD domain. PC1: retirement status, residence type, multivitamin use, heart disease, Charlson comorbidity index, walking/balance difficulties, and Beck Depression Inventory (BDI)‐II questions – mainly from the somatic dimension; PC2: physical activity/exercise, social and group activities, education, occupation, cardiometabolic risk factors, and self‐rated health; PC3: national and state area deprivation index ranking and BDI‐II questions – mainly in the cognitive dimension. Age and sex‐adjusted linear regression models were used to assess the cross‐sectional association of each PC with AD‐plasma biomarkers measured using the Quanterix Simoa platform in the Mayo Clinic Study of Aging (MCSA) participants. AD‐plasma biomarkers included Aβ 42/40, phosphorylated‐tau 181 (p‐tau181), plasma glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). All analyses were considered statistically significant at a p <0.05.ResultThere were 1300 cognitively unimpaired participants, 50 years old or older, with available plasma biomarkers (mean age (SD) 68.6 (9.5) years; 49.1% were female) at the current study baseline. PC1 and PC3 were associated with higher NfL and p‐tau 181 values (p<0.05). PC1 and PC2 were associated with higher GFAP values (p<0.05). No PCs were associated with the Aβ 42/40 ratio. Findings were mostly similar after additionally adjusting for global cognitive performance and chronic kidney disease.ConclusionWe found that patterns in a wide range of environmental, sociocultural, behavioral, and biological factors were associated with AD plasma biomarkers. Longitudinal studies are warranted to validate findings and support multi‐faceted approaches to address health disparities and achieve disease delay and prevention.

AbstractBackgroundThe rapid development of blood‐based biomarkers (BBMs) has improved Alzheimer’s disease (AD) diagnostics with some tests now potentially suitable for clinical use. This aligns with the clinical availability of anti‐Aβ immunotherapies for early symptomatic AD, where BBMs can help address the increasing need for more rapid and early diagnosis. Clinical practice guidelines (CPG) can help guide healthcare professionals in incorporating BBMs into their clinical practice. The Alzheimer’s Association is collaborating with methodologists and a panel of 11 clinical and subject‐matter experts to develop a CPG for specialists, informed by a systematic review, on the use of BBMs for AD.MethodThe panel drafted several clinical questions prioritizing two questions on the use of BBMs in cognitively impaired or cognitively unimpaired individuals as the populations of immediate interest. Plasma p‐tau and Aβ biomarkers tests are index tests, and amyloid PET imaging, neuropathology, and CSF are reference standards. Experts in the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) approach are conducting a systematic review of the diagnostic accuracy of BBMs index tests by searching three databases (Pubmed, Embase, Cochrane Library) and will include assessments of the quality of collected evidence to inform the panel’s decision‐making process. Patients/caregivers and key external organizations will be engaged throughout.ResultThis session will cover preliminary evidence for answering the two clinical questions. At the time of abstract submission, the search strategy resulted in 880 titles/abstracts for the study selection process. Data abstraction, analysis, and evidence development are pending. Recommendations for answering clinical questions will be developed after July 2024, and all recommendations will be finalized and published by early 2025.ConclusionThe integration of BBMs into clinical care offers the potential to improve earlier and more accurate AD diagnosis for a broader patient population and expand access to new AD therapies. The development of evidence‐based CPGs is a critical step forward in optimizing and standardizing the clinical use of BBMs. As the field of AD diagnostics and treatment continues to evolve, this CPG on the use of BBMs for AD will expand and be regularly updated to include new target audiences/settings, patient populations, and associated evidence.

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) occurs before age 65 and has more diverse disease presentations than late‐onset AD. To improve our understanding of phenotypic heterogeneity among EOAD individuals, we analyzed cognitive scores using data‐driven statistical analysis.MethodBaseline cognitive data from 286 sporadic EOAD individuals from the Longitudinal EOAD study (LEADS) were transformed to z‐scores using data from 95 cognitively normal (CN) individuals. Cognitive composites were generated for domains of memory, language, speed/attention, visuospatial, and executive function. Residuals from linear regression models on Z‐scores adjusted for age, sex, and education were obtained. Cluster analysis using the Ward method on the cognitive domain residuals was performed and scree plot using the pseudo T‐squared determined the optimal number of clusters for the EOAD sample. We also compared gray matter density (GMD) of each EOAD cluster to CN participants using voxel‐wise multiple linear regressions.ResultsThree clusters of cognitive performance were identified from the EOAD sample. Disease duration was not significantly different across clusters. Using a z‐score of ‐1.5 SD as the impairment threshold, all clusters were impaired across most domains (Table 1). Cluster‐3 was more impaired than cluster‐2 in all domains (Table 2; all p<.0001), and in all domains except episodic memory compared to cluster‐1 (all p<.01). Cluster‐1 (n = 71; 85.9% amnestic) was most impaired in executive function, visuospatial, and speed/attention. Cluster‐2 (n = 133; 88.7% amnestic) was most impaired in episodic memory. Cluster‐3 (n = 82; 69.5% amnestic) was most impaired in executive function, visuospatial, and speed/attention (Table 1). 3D‐comparisons showed all EOAD clusters had reduced GMD compared to CN. Cluster‐1 and cluster‐3 both showed widespread atrophy, with cluster‐3 being more severe. Cluster‐2 showed the most atrophy in the temporal and parietal lobes (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among sporadic EOAD individuals. Cluster‐3 appeared to reflect widespread impairment, and cluster‐2 represented an amnestic‐only presentation. Despite comparable disease duration, some EOAD patients progress faster, while some are more resilient. 3D‐comparisons showed neurodegenerative changes affecting brain regions responsible for respective impaired cognitive functions in each cluster (e.g., cluster‐2 is primarily amnestic‐impaired and has temporoparietal atrophy). Future work should explore amyloid‐PET and tau‐PET burden.

AbstractBackgroundAlzheimer’s disease (AD) is neuropathologically heterogeneous and can be objectively classified along a spectrum of corticolimbic tangle distribution as hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. The olfactory bulb is an early area of tau accumulation with a direct connection to the amygdala. Although tau pathology has been identified in the olfactory bulb, its association with AD subtypes remains unclear. To investigate these differences, tau markers were characterized within the olfactory bulb among AD subtypes.MethodThe cohort included 39 AD cases (HpSp AD: n = 15, typical AD: n = 12, limbic AD: n = 12). Immunofluorescence staining was conducted on 5 µm thick formalin‐fixed paraffin‐embedded olfactory bulb sections using phosphorylated tau (AT8 & PHF‐1) and C‐terminal tau (2E9) antibodies. Slides were digitally scanned on the PhenoImager™ HT slide scanner and analyzed using QuPath Digital Analysis software, quantifying tau burden as a percentage of positive staining.ResultIn whole olfactory bulbs, AT8 was observed as intrasomal tangle‐like immunoreactivity with some extrasomal neuritic staining. AT8 burden did not differ among subtypes (HpSp AD median = 0.28% [interquartile range 0.014‐1.7%], typical AD = 0.28% [0.027‐2.24%], limbic AD = 0.39% [interquartile range 0.39‐0.74%]). PHF‐1 was observed as intrasomal tangle‐like immunoreactivity with abundant extrasomal neuritic pathology. PHF‐1 was observed to be highest in limbic AD (HpSp AD = 3.5% [0.63‐11%)], typical AD = 3.2% [0.38‐13%], and limbic AD = 5.3% [1.97‐10%]) and differed from typical AD (p<0.05). 2E9 was observed as intrasomal tangle‐like immunoreactivity with minimal extrasomal staining and not found to differ among subtypes (HpSp AD = 0.18% [0.036‐2.2%], typical AD = 0.15% [0.021‐2.4%], and limbic AD = 0.29% [0.0030‐8.2%]).ConclusionAbundant tau accumulation was observed among all AD subtypes with some suggestion of higher tau burden in limbic predominant AD. Given the direct connection between olfactory bulb and amygdala, it is interesting to speculate of an environmental contribution to limbic predominant AD through olfactory bulb damage. Future studies will expand the cohort to include AD cases with Lewy body disease co‐pathology and primary tauopathies with a focus on the anterior olfactory nucleus as the primary site of tau pathology.

AbstractBackgroundRecent transcriptome analysis has demonstrated increased expression of Vascular Endothelial Growth Factor receptor‐1 (VEGFR‐1/FLT1) and decreased expression of VEGFR‐2/KDR in AD brain. Increased expression of VEGFR‐1 and its ligand VEGFB were associated with a more rapid rate of cognitive decline, providing evidence of a potential link between aberrant VEGFR‐1 expression in AD pathogenesis. In this study, we explored the potential role of aberrant VEGFR‐1 expression in neurons on AD pathology.MethodTo confirm VEGFR‐1 expression in AD brains, we performed immunostaining in AD brain sections (AD n = 5 (Braak stage V‐VI, and normal controls n = 5 Braak 0‐II) in AD brains. To determine a potential detrimental role of neuronal VEGFR‐1 expression on AD associated pathologies, we exposed SH‐SY5Y human neuroblastoma cells and mouse primary neurons to either hypoxia conditions (1%O2) or 5uM Aß 1‐42 oligomers for 72hrs.ResultWe found increased VEGFR‐1 immunoreactivity in dystrophic neurites in the vicinity of Thio‐S positive “cored” amyloid plaques, whereas VEGFR‐2 antibody staining showed minimal neuronal staining in AD samples. And treatment with hypoxia or Aß oligomers both in SH‐SY5Y cells and mouse primary neurons showed increased VEGFR‐1 expression and cleaved caspase 3 activation, leading to neuronal cell death. Significantly, siRNA mediated knockout of VEGFR‐1 in the neurons prevented both hypoxia and Aß oligomer induced cell death. We also show that treatment with either hypoxia or Aß oligomers reduced mRNA expression of cell survival genes including Hippo pathway YAP1 and VEGFR‐2, and siRNA mediated VEGFR‐1 knockdown normalized both YAP1 and VEGFR‐2 levels. Significantly, over‐expression of the VEGFR‐1 by itself reduced mRNA expression of YAP1 and VEGFR‐2 and readily induced neuronal cell death, implicating the VEGFR‐1/VEGFR‐2/YAP1 signaling axis in hypoxia and Aß oligomer induced neuronal cell death, which is a novel finding.ConclusionThese results demonstrate that VEGFR‐1 may play an important transcriptional regulatory role in neuronal cell survival responses and suggest that modulating VEGFR‐1 expression and signaling could have beneficial effects in AD pathogenesis.

AbstractBackgroundAnti‐amyloid monoclonal antibody therapies for AD have documented plaque removal on amyloid PET scans. We evaluate the findings for amyloid PET, tau PET, FDG PET and volumetric MRI over the course the open label extension (OLE) for the DIAN‐TU gantenerumab treatment, compared to the last imaging obtained prior to the OLE (to evaluate for potential rebound effects) and in comparison to longitudinal imaging in the DIAN Observational study.MethodThis double‐blind, phase 2/3 trial (2012‐2019), followed by open‐label extension (OLE), investigated varying gantenerumab doses up to 1500 mg SQ q2 weeks [NCT NCT01760005]. We report long‐term amyloid‐plaque removal by PET and findings for metabolic FDG PET and tau PET, and additionally changes of atrophy on volumetric MRI. Imaging measures were secondary outcome analyses. Comparison is made between the treatment cohort and controls (DB placebo participants who didn’t enter OLE, plus external controls from the DIAN OBS). Estimated years to onset was included as a covariate. The gap period of ∼1 year is specifically analyzed to estimate any “rebound” effect on amyloid or tau accumulation or neurodegeneration. Volumetric MRI measures include hippocampal volumes, lateral ventricular volumes, and white matter hyperintensities.ResultIn participants who were asymptomatic at baseline and treated with gantenerumab (n = 53), amyloid‐plaque removal measured by PIB‐PET in Centiloids was dose‐dependent. Few participants had reached completely negative levels (below 24). No statistical significance was found for FDG PET, tau PET, or volumetric MRI, including hippocampal volumes. Impact of the gap period of ∼1 year (gap in treatment) on the rate of amyloid accumulation compared to the rate of accumulation predicted in observational studies of this cohort is under evaluation, as are the other imaging measures.ConclusionWhile no overall effect was observed in the total group for non‐amyloid measures, results suggest dose dependent clearance of amyloid plaque in asymptomatic DIAD mutation carriers following long‐term, high‐dose gantenerumab. The potential “rebound effect” of elevated amyloid accumulation for participants for whom there was a significant gap in therapy between the trial and the OLE is under evaluation and will be presented, as will the variables for FDG PET, tau PET, and volumetric MRI.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

AbstractBackgroundImplementation of Alzheimer disease blood‐based biomarkers (AD BBBs) in the clinical laboratory requires careful evaluation of test performance and selection of interpretative cut‐points to prevent patient misclassification that may lead to further evaluation, misdiagnosis, and/or potentially unnecessary treatment.MethodParticipants with mild cognitive impairment or mild dementia (n = 427) and with an EDTA‐plasma sample available within 6 months of amyloid‐PET imaging were selected through the Mayo Clinic Study of Aging and the Mayo Clinic Alzheimer’s Disease Research Center in Rochester, Minnesota. Amyloid‐PET was performed with 11C‐Pittsburg Compound B; abnormal amyloid (A+) was defined as Centiloid ≥25 (SUVR≥1.52) (n = 274 A+, 153 A−). EDTA‐plasma samples were measured using the ALZpath Simoa pTau217 assay and the Fujirebio Lumipulse pTau217, Aβ42/Aβ40 and pTau217/Aβ42 ratio assays. The clinical performance of a 1‐cutpoint as well as a 2‐cutpoint model that incorporates an intermediate range were assessed. For the 2‐cutpoint model, various combinations of sensitivity and specificity were examined to identify thresholds that maximize overall test performance.ResultWhile both pTau217 assays displayed high ROC AUC (>0.90), neither assay achieved ≥90% in both sensitivity and specificity using a 1‐cutpoint model. Using a 2‐cutpoint model for the Lumipulse pTau217 assay, the combination of 92% sensitivity and 96% specificity to define lower and upper cutpoints provided the optimal balance of false positives and false negatives, while keeping 20% or fewer results categorized as intermediate. For the ALZpath assay, 92% sensitivity and 96% specificity could only be achieved with 39% of the patients in this cohort falling into the intermediate range. Using this approach, the Lumipulse pTau217 assay would reduce more costly CSF or PET testing by 80%.ConclusionCutpoints for the identification of amyloid pathology were defined in a cohort of individuals with MCI and mild dementia. Information obtained on the performance of these defined cut‐points was used to select the most appropriate assay and cut‐points to implement in clinical practice.

Despite their similar nomenclature, Neurofibromatosis type 1 (NF1) and “Neurofibromatosis type 2” are discrete and clinically distinguishable entities. The name of “neurofibromatosis type 2” has been changed to NF2-related schwannomatosis, to reflect the fact that neurofibromas do not occur in this syndrome and therefore the name “Neurofibromatosis” is factually incorrect. Furthermore, multiple schwannomas, a hallmark feature of NF2, can also occur in patients with mutations in genes including SMARCB1 and LZTR1, all exhibiting overlapping clinical features. Current understanding suggests that schwannomatosis (SWN) encompasses a range of clinical presentations consisting of clearly defined, separate subtypes which share a common phenotype of schwannomas. Recognizing these newly emerging subtypes, the International Consensus Group on Neurofibromatosis Diagnostic Criteria (I-NF-DC) proposed a revised nomenclature for NF2 and related disorders in 2022. This review article focuses on this critical update in diagnostic terminology, highlighting the key gene-related SWN subtypes relevant to neuroradiologists. By emphasizing molecular testing alongside clinical features, the revised system facilitates a more precise diagnosis, potentially paving the way for personalized treatment strategies. Additionally, the flexible structure accommodates future discoveries of genes associated with SWN.

ABSTRACTThe development of new first‐line treatments for patients with follicular lymphoma (FL) is becoming increasingly challenging due to already excellent survival outcomes. The present study investigated the outcomes of patients with FL who underwent contemporary first‐line therapies but would not have been eligible for inclusion in recent trials and explored how commonly used in/exclusion criteria impacted their survival outcomes. This study included adult patients diagnosed with FL in the period 2000–2018 registered in the Danish Lymphoma Registry. Through searches on ClinicalTrials.gov, four recent 1st line phase 3 randomized controlled trials with R‐Bendamustine, R‐CVP, and/or R‐CHOP as control or experimental arms were included. Inclusion and exclusion criteria for each trial were retrieved and categorized. Patients were then divided into trial‐eligible and ineligible groups according to blood test results correlated to organ function and ECOG performance score (PS). Survival outcomes were significantly worse among trial‐ineligible patients, with adjusted differences between trial‐eligible and ineligible patients of 12%–20% in five‐year overall survival (OS) overall. Inclusion criteria based on PS and renal function were the main drivers of OS differences. More inclusive trials will lead to faster recruitment and secure focus on developing medicines for the group of patients with the worst outcomes.