Weiner, Mike

AbstractIntroductionLumbar puncture (LP) to collect and examine cerebrospinal fluid (CSF) is an important option for the evaluation of Alzheimer's disease (AD) biomarkers but it is not routinely performed due to its invasiveness and link to adverse effects (AE).MethodsWe include all participants who received at least one LP in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Study. For comparison between groups, two‐sample t‐tests for continuous, and Pearson's chi‐square test for categorical variables were performed.ResultsTwo hundred twenty‐seven LP‐related AEs were reported by 172 participants after 1702 LPs (13.3%). The mean age of participants who reported at least one AE was 69.79 (standard deviation (SD) 6.3) versus none 72.44 (7.17) years (p < 0.001) with female predominance (115/172 = 67.4% vs 435/913 = 48%), and had greater entorhinal cortical thickness and hippocampal volume (3.903 (0.782) vs 3.684 (0.775) mm, p = 0.002; 7.38 (1.06) vs 7.05 (1.15) mm3, p < 0.001), respectively.DiscussionWe found that younger age, female sex, and greater thickness of the entorhinal cortex were associated with a higher rate of LP‐related AE reports.

AbstractBackgroundRecent advances in the development of novel Alzheimer’s disease (AD) measures of amyloid, tau, and neurodegeneration in cerebrospinal fluid (CSF) and blood have enabled a better understanding of the links between amyloid‐beta (Aβ), tau species and neurodegeneration in the brain, CSF, and blood. The discoveries of novel tau species in CSF and blood, including specific phospho‐tau (p‐tau) and truncated species containing the microtubule binding region of tau (MTBR‐tau) which aggregates in tau tangles, have greatly expanded our understanding of tau biology and tau target development. The longitudinal Aβ, tau, and neurofilament light chain (NfL) changes previously measured in CSF are now being measured accurately in blood, enabling interpretation and understanding of AD progression in much larger and more robust populations.MethodWe analyzed longitudinal CSF and/or blood plasma mass spectrometry measures of Aβ42/Aβ40, multiple p‐tau species (including p‐tau217, 231, 181, 205, etc.), total tau, MTBR‐tau (e.g., MTBR‐tau243, MTBR‐tau299, and MTBR‐tau354), and NfL in sporadic AD and dominantly inherited AD cohorts. Mass spectrometry results were compared to amyloid and tau aggregation measures by Positron Emission Tomography (PET) scans and clinical and cognitive measures in local and international clinical cohorts including ADNI, AIBL, BioFINDER, Mayo Clinic, the Knight ADRC, and the DIAN.ResultThe longitudinal results indicate that a pathophysiological cascade of events begin with altered CSF and blood plasma Aβ42/Aβ40 ratio, followed by increases in amyloid plaques as measured by amyloid PET, associated with increased phosphorylation of specific CSF tau species (e.g., p‐tau217, p‐tau181, etc.), before increases in p‐tau205, NfL, total tau concentrations, hypometabolism, and atrophy. Finally, some species of MTBR‐tau increase before or with tau aggregation by tau PET and onset of clinical symptoms and correlate with longitudinal tau aggregation and clinical progression.ConclusionThese findings indicate that CSF and blood plasma Aβ, p‐tau, MTBR‐tau and NfL measures are highly precise biomarkers of brain amyloidosis, tauopathy, and neurodegeneration and can accurately identify stages of preclinical and clinical AD.

AbstractBackgroundAn analysis of the ethnoracial and educational composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, and the relationship between ethnoracial/education and screening, enrollment, dropout, and biomarkers is needed to assess the generalizability of ADNI data to diverse populations, and to inform efforts to increase diversity.MethodData from all 4 ADNI phases were used to determine ethnoracial and educational breakdown and differences of the following ADNI participation metrics: screening, screen fails, enrollment, as well as drop‐out within and between ADNI phases. Multivariable logistic regression was used to analyze the association between ethnoracial and educational group and either amyloid positivity or ApoE e4 status, adjusting for age, gender, and diagnostic group.ResultAcross the 4 ADNI phases (screened: n=3,739; enrolled: n=2,286), 11% of screened and enrolled participants identified as Latinx, non‐Latinx Black, or non‐Latinx‐Asian and 16% of the screened and 15% of the enrolled participants reported having =<12 years of education. In contrast, the 2019 US Census American Community Survey for adults 60+ reports 25% non‐White, and 44 % =< 12 years of education. There were no observed differences between participants from historically underrepresented ethnoracial groups and NLW participants in screen fail and dropout rates. We found that individuals with =<12 years of education failed screening at a significantly higher rate (p=.01) and rolled‐over significantly less to subsequent ADNI phases (p<.001) compared to individuals with an education >12 years. Identifying as non‐Latinx Asian was associated with lower odds of being amyloid positive (OR=0.36; p=.014) and an ApoE4 carrier (OR=0.36; p=.008) compared to NLW. Identifying as Latinx was associated with being less likely to be amyloid positive than NLW participants (OR=0.54; p=.031).ConclusionCompared to the US Census, ADNI underrepresents enrollment of Latinx, non‐Latinx Black, and non‐Latinx Asian participants and those with low educational attainment. Hence, ADNI reflects the on‐going challenges with recruiting and enrolling underrepresented populations into most AD research. Our findings highlight the need for ADNI to increase enrollment and engagement of underrepresented ethnoracial and educational populations. ADNI is actively undertaking steps to implement improved recruitment approaches aimed at increasing ethnoracial and socioeconomical representation in multi‐center cohort studies.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.