Jack, Clifford R.

Abstract Introduction The aim of this study was to examine white matter hyperintensities (WMH) and fractional anisotropy (FA) in empirically derived incident mild cognitive impairment (MCI) subtypes. Methods We evaluated 188 participants with incident MCI in the Mayo Clinic Study of Aging (MCSA) identified as having one of four cluster‐derived subtypes: subtle cognitive impairment, amnestic, dysnomic, and dysexecutive. We used linear regression models to evaluate whole brain and regional WMH volumes. We examined fractional anisotropy (FA) on a subset of 63 participants with diffusion tensor imaging. Results Amnestic and dysexecutive subtypes had higher WMH volumes in differing patterns than cognitively unimpaired; the dysexecutive subtype had higher WMH than subtle cognitive impairment. There was widespread WM degeneration in long association and commissural fibers in the amnestic, dysnomic, and dysexecutive subtypes, and corpus callosum FA accounted for significant variability in global cognition. Discussion White matter changes likely contribute to cognitive symptoms in incident MCI.

AbstractBackgroundBlood phosphorylated tau (p‐tau) has proven to be the primary candidate as an accessible and scalable biomarker for Alzheimer’s disease (AD). Both p‐tau181 and p‐tau217 are highly accurate to detect AD within dementia cases. Our recent work in cerebrospinal fluid, however, indicates p‐tau231 to be associated to incipient AD pathology. In light of this, we developed a Single molecule array (Simoa) to detect plasma p‐tau231 and assessed its performance in multiple domains, specifically, its ability to detect preclinical AD.MethodIn all cohorts, plasma p‐tau231 was quantified using an in‐house Simoa method developed at the Clinical Neurochemistry Laboratory, Gothenburg University, Sweden. Data presented in this study are samples acquired from Translational Biomarkers of Aging and Dementia (TRIAD, n=503), King’s College London (n=55), Mayo Clinic Study of Aging (n=145), KARVIAH (n=140), Baltimore Longitudinal Study of Aging (n=430), University College London (n=70), Paris University (n=213), PREVENT‐AD (n=257), University of California (n=309) and ALFA+ (n=385).ResultIn the TRIAD cohort, plasma p‐tau231 demonstrated high accuracy in determining AD from young individuals (AUC=0.95), Aβ‐ elderly (AUC=0.92), Aβ‐ MCI (AUC=0.88) and other neurodegenerative disorders (AUC=0.92). In an autopsy study, plasma p‐tau231 could detect neuropathologically confirmed AD amongst dementia cases (AUC=0.99). In comparing healthy elderly Aβ‐ and Aβ+ individuals, p‐tau231 predicted preclinical Aβ pathology (AUC=0.83). Furthermore, inflection points of the weighted regression curves demonstrate that increases in plasma p‐tau231 occur before plasma p‐tau181 as a function of Aβ deposition load. Plasma p‐tau231 data from additional cohorts will focus on cross‐sectional and longitudinal preclinical datasets. In addition, further data will show the performance of p‐tau231 in symptomatic and presymptomatic PSEN1/APP mutation carriers, memory clinic cohorts, longitudinal neuropathology confirmed and acute neurological injury.ConclusionPlasma p‐tau231 is a new biomarker specific for AD pathology and at higher concentrations relative to p‐tau181 and p‐tau217. Our initial results indicate that plasma p‐tau231 is equivalent to p‐tau181 in diagnostic utility but has greater potential as a biomarker to monitor emerging Aβ pathology. This presentation will give an overview of novel cross‐sectional and longitudinal plasma p‐tau231 data, in more than 3000 individuals, that encompasses the AD continuum, familial AD and other neurological conditions.

AbstractBackgroundDementia disproportionately impacts people of color in the U.S.. Whether these disparities are due to differences in vascular risk factors, cognitive reserve, or AD pathophysiology remains unclear.MethodBlack and White participants from four U.S. communities were recruited into the Atherosclerosis Risk in Communities (ARIC) study at ages 45‐64. Vascular risk factors were evaluated in midlife and at multiple visits over 30+ years, as were markers of cognitive reserve, with expert classification of dementia. In participants with brain MRI, brain volumes were evaluated in association with dementia. In nondemented participants in the ARIC‐PET ancillary study, florbetapir PET scans were used to quantify brain cortical amyloid; differences in levels by race, vascular risk factors, and white matter hyperintensities (WMH) were evaluated.ResultIn 15744 ARIC participants, dementia was more common in Black vs White participants. Although midlife vascular risk factors were related to dementia, the impact of these risk factors on dementia risk and cortical volumes didn’t differ by race. Lower education was nonsignificantly more strongly associated with dementia in Blacks vs Whites, and an APOE e4 allele was more strongly associated with dementia in Whites. In ARIC‐PET (N=322), vascular risk factors from midlife were associated with PET amyloid, without similar associations for late‐life risk factors; these associations did not significantly differ by race. WMH volume was more strongly associated (cross‐sectionally) with PET amyloid in Blacks vs Whites, but differences were not statistically significantly different. Finally, Blacks had an over two‐fold increased odds of elevated florbetapir PET vs Whites, independent of vascular risk factors, demographics, WMH volume, cognitive status, and APOE genotype.ConclusionConsideration of racial disparities in dementia should consider vascular risk, cognitive reserve, and social determinants of health, across the life course. Vascular risk factors do not appear to differentially impact dementia risk or brain amyloid in Blacks vs Whites, but the greater prevalence of these risk factors in Blacks must be considered. Although the A/T/N framework may clarify underlying biology contributing to Alzheimer’s disease, inclusion of these additional factors is needed to understand and reduce racial disparities in dementia rates.

AbstractBackgroundBlood‐based biomarkers of amyloid pathology and neurodegeneration are entering clinical use. It is critical to understand what factors affect these plasma marker levels for accurate clinical interpretation. Herein we examined factors associated with plasma Ab42, Ab40, neurofilament light (NfL), total tau (T‐tau), and the ratio of Ab42 to Ab40 (Ab42/40).MethodThe plasma markers were measured for 996 participants (859 cognitively unimpaired [CU], 133 mild cognitive impairment [MCI], 4 dementia) from the Mayo Clinic Study of Aging (MCSA), aged 51 to 95 years. The Quanterix Simoa® HD‐1 analyzer was employed to measure the plasma markers using the Simoa® Neurology 3‐Plex A (N3PA) (T‐tau, Ab42, Ab40) (catalog #101995) and the Simoa® NF‐light (catalog #103180). All other data was collected during in‐person MCSA visits or abstracted from the medical record. Statistical significance was determined using an alpha level of 0.05.ResultAmong CU, all of the plasma markers showed correlation with age. T‐tau (Spearman’s rho = 0.113, p<0.001) showed the weakest correlation and NfL (rho = 0.695, p<0.001) had the strongest. Women had higher levels of T‐tau (Median women: 2.782 vs. 2.390, p<0.001) and lower Ab42/40 (0.033 vs. 0.035, p=0.024) compared to men. In linear regression models predicting z‐scored plasma markers and adjusted for age and sex, several statistically significant predictors were found. For example, higher Charlson comorbidity index and chronic kidney disease were associated with higher levels of all markers; slower gait speed was associated with higher levels for all but Ab42/40 where no relationship was seen. A higher BMI was associated with higher levels of Ab42, Ab40, and T‐tau whereas NfL tended to be lower with increased BMI. Among participants with MCI or dementia, the relationship between gait speed or chronic kidney disease with NfL and T‐tau strengthened considerably.ConclusionMultiple variables affect plasma markers of amyloid pathology and neurodegeneration among CU in the general population. Some relationships differed among MCI and dementia participants. Incorporating this information is critical for accurate interpretation of the marker levels and for the development of reference ranges.

AbstractBackgroundThe overarching goal of the Longitudinal Early‐onset Alzheimer Disease study (LEADS) is to optimally characterize early‐onset AD (EOAD) and establish an EOAD clinical trials network. Here we report the baseline demographic and imaging biomarker comparisons of the LEADS cohort to late‐onset AD (LOAD) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI3).Method123 amyloid‐positive EOAD, 47 amyloid‐negative EOnonAD, 60 cognitively normal young controls were compared to 130 amyloid‐positive LOAD, 110 amyloid‐negative LOnonAD and 286 amyloid‐negative cognitively normal older controls. To account for the effect of cognitive aging between EO and LO populations, each cognitive measure was Z‐transformed. Cortical and hippocampal atrophy were quantified using W‐scores adjusted for age, sex and total intracranial volume. Z‐scores and W‐scores were compared using t‐test or Wilcoxson rank test as appropriate. All p‐values were corrected for multiple comparisons using the false discovery rate correction.ResultEOAD showed greater pathology burden and greater cortical atrophy (AD signature) relative to LOAD. EOAD also showed greater cognitive impairment across all cognitive tests. EOAD showed greater functional impairment, more depression but less neuropsychiatric behaviors overall compared to LOAD (Table 1 and Figure 1, all ps<0.05). Repeating the analyses stratified by cognitive stage (MCI/dementia) or CDR global rating (0.5/1) did not result in any major differences.EOnonAD differed from LOnonAD by also showing greater impairment on all cognitive and functional measures There were no significant differences in amyloid and tau burden, or atrophy W‐scores between these groups. EOnonAD were more depressed and showed more functional impairment compared to LOnonAD (Table 2 and Figure 2, all ps<0.05). Repeating the analyses split by cognitive stage (MCI/dementia) or CDR global rating (0.5/1) did not result in any major differences.ConclusionConsistent with our preexisting hypotheses, EOAD and EOnonAD perform much worse relative to their LO counterparts. EOAD also show greater pathological burden as expected. The reported analyses were done in chronological rather than disease time (time since disease onset). Benchmarking individuals along the disease spectrum might prove to be a better strategy especially when conducting analyses on rate of disease progression.

AbstractBackgroundThe Neurodegenerative Research Group (NRG) at Mayo clinic has followed patients with degenerative speech and/or language disorders for over a decade. In this presentation I will present data from this cohort to inform a broader discussion about the importance of apraxia of speech (AOS) and its subtypes, heterogeneity within the agrammatic PPA variant, and perils of mixed phenotypes.MethodWe reviewed the NRG database to identify patients who initially presented with isolated (PPAOS), AOS in conjunction with agrammatic aphasia (AOS‐PAA), isolated agrammatic aphasia (PAA), and patients who had PAA or AOS overlapping with another degenerative diagnosis. Data from the following clinical instruments were abstracted: WAB AQ (aphasia severity), UPDRS III (parkinsonism), WAB Praxis (ideomotor apraxia), and the MoCA (cognitive ability). We assessed the effect of baseline diagnosis and AOS subtype on these outcomes over time using Bayesian linear mixed effects models. We qualitatively reviewed longitudinal diagnostic trajectories. Preliminary analyses were done on available autopsy data.ResultPAA was associated with greater aphasia at baseline and more rapid decline; PPAOS was associated with normal language at baseline and slow decline; and PAA‐AOS with intermediate baseline aphasia and decline. However, for those with AOS longitudinal decline differed based on AOS subtype: prosodic PPAOS was associated with the slowest decline and phonetic PAA‐AOS with the fastest. Some patients met criteria for non‐PPA disorders during follow up. However, compared to patients who met criteria for these disorders at enrollment, the speech/language‐first patients remained qualitatively distinct, and neither resembled the cases that were mixed at onset. Preliminary autopsy data suggests that AOS is predictive of tau and that the specific tauopathy may be related to the initial syndrome and AOS subtype.ConclusionThe differences between PAA, PAA‐AOS and PPAOS suggest that these disorders should not be lumped. In fact, the longitudinal trajectories are sufficiently different that doing so would add noise to observational and interventional studies. Instead, more detailed phenotyping, including AOS subtype, can aid prognostication and may improve prediction of the underlying pathology. Finally, caution should be exercised when considering PPAOS and PPA patients for other FTLD diagnostic categories.

AbstractBackgroundThere have been no standard definitions of successful cognitive aging (SA). Our goal was to apply different SA definitions to the 80+ population in the Mayo Clinic Study of Aging (MCSA) on the basis of (i) cross‐sectional vs longitudinal cognition (“Exceptionality” vs “Maintenance”) and (ii) memory vs global cognitive scores and explore the participant characteristics for each SA definition on the basis of baseline imaging markers and mid/late‐life health.MethodWe included 2157 participants from the MCSA, all ≥80 years at baseline and classified them into two SA definitions using memory z‐scores or global z‐scores: “Exceptionality” when high cognitive performance was observed (z‐scores ≥ 1 SD), “Maintenance” when cognitive function was preserved over time (Slope ≥ ‐0.1/year using mixed‐effect models). Our imaging markers were: PET‐based amyloid positivity (A+), MRI‐based neurodegeneration positivity (N+), WMH, FDG‐PET SUVR markers of resilience (anterior cingulate, ACC) and AD‐related areas (posterior cingulate). We also examined APOE4 status and other health characteristics. Group comparison was performed within each definition (e.g. exceptional vs non‐exceptional memory) using ANCOVA and binary logistic regressions with adjustments for age, sex, and education.ResultA lower number of participants met the “Exceptionality” criteria (10%: memory and 4%: global). A greater number met the “Maintenance” criteria (53%: memory and 32%: global). Less than 7% showed both “Exceptional” cognition and “Maintenance” over time. Participants across SA definitions were in general younger, more often women, with a higher education/occupation and less often APOE4 carriers. All SA groups showed higher ACC metabolism and lower N+ compared to the corresponding non‐SA group. More than 50% of the participants in the “Exceptional” groups were A+ (57%: memory, 50%: global). A+ frequency was significantly lower in the “Maintainers” (<42%). Better mid/late‐life health indicators were observed in the SA groups, including lower cardio‐metabolic conditions, higher mid‐life cognitive activities, lower depressive symptomatology, lower hypersomnolence and better self‐perception of health.ConclusionDifferent SA definitions identified mainly non‐overlapping 80+ groups that showed several common features including higher education/occupation, percent of women, better lifestyles, lower N+ frequency, and higher ACC metabolism. Lower A+ frequency was a key distinct feature of “Maintenance”.

Abstract The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW‐ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public‐private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI‐1, ADNI‐GO, ADNI‐2, and ADNI‐3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW‐ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW‐ADNI researchers who shared updates from ADNI‐3 and their vision for ADNI‐4.

AbstractBackgroundThe Mayo Clinic Study of Aging and Alzheimer’s Disease Research Center used GE scanners exclusively for MRI from 2005‐2017, employing consistent imaging parameters throughout. In 2017 the MRI protocol was updated and migrated to Siemens Prisma scanners; 113 participants were scanned with each configuration. The GE protocol included a 2D FLAIR. The Siemens protocol includes 3D FLAIR and T2‐weighted images. Both vendor protocols included a 3D T1. Experience with multi‐center trials suggests such changes may introduce systematic variations.MethodsGE scanning was carried out on 3T scanners with 12 channel phased array head coils. Siemens Prisma scanning was carried out with 64 channel head/neck coils. Imaging sequences and parameters are given in Table 1. We used the MCALT template and priors to perform standard six‐class segmentation on T1‐weighted (T1w) images. SPM12 unified segmentation supports the simultaneous use of multiple input contrasts; segmentation of multi‐contrast inputs was done using eight tissue classes including deep grey and white matter hyperintensity (WMH) priors. For a given MRI study (either GE or Siemens) all images were registered and resampled to the study’s T1w image. Two‐channel (T1+FLAIR) segmentation was performed on GE scans. Two and three‐channel segmentations were performed on Siemens scans. Comparisons included total intracranial volume, total tissue volume and total grey matter volumes, as well as hippocampal volumes and grey matter volume in a temporal lobe meta‐ROI.ResultsFour participants were excluded for poor image quality: one poor GE T1w quality, one poor Siemens T1, T2 and FLAIR quality, and two poor Siemens T2 quality. Comparisons were made among 109 participants aged 34–97 (Table 2). By visual assessment, adding T2‐weighted images always improved intracranial volume correctness. Using the combination of single‐channel GE and three‐channel Siemens generally improved cross‐vendor agreement on grey matter volumes with the exceptions of hippocampal and intracranial volume estimates.ConclusionRequiring multiple input channels increases the likelihood that the result of a study being excluded. Combining T1w, FLAIR and T2w images allows the delineation of WMH and deep grey matter probability outputs but may introduce differences in GM volumes.