Knopman, David S.

Background: The most frequent dementias are a mix of Alzheimer’s, vascular and other pathologies which evolve over many years. Our objective is to determine how many years in advance of dementia cognitive scores are affected. Methods: 13,749 dementia-free ARIC participants had a cognitive exam at Visit 2 (1990-1992, mean age 57), 10,813 at Visit 4 (1996-1998), 5,605 at Visit 5 (2011-2013) and 3,516 at Visit 6 (2016-2017). Within 5-year intervals after each visit, we compared the combined mean of three cognitive tests (Delayed Word Recall, Digit Symbol Substitution, and Word Fluency) z-scores at baseline in two groups - participants who were diagnosed with dementia within each interval vs. those who survived to the interval without dementia. Baseline visit z-scores were adjusted for age, gender, race-center and education level in separate linear regression models for each visit. We plotted the adjusted z-score mean [95% confidence interval] by time interval following each visit. Results: Dementia incidence during follow-up yielded 2,981 , 2,397, 961 and 160 cases after Visits 2, 4, 5, and 6 respectively. The three-test combined scores were already significantly lower at 10-15 years before dementia compared to those who did not experience dementia by that period (Figure). As expected at intervals closer to dementia onset the differences between dementia and non-dementia groups increased, up to their widest point of approximately 1 SD at only 0-5 years prior to onset. Delayed Word and Digit Symbol Substitution tests has similar findings while Word Fluency began to decrease only within 5 years prior to dementia onset. Conclusion: We found that the differences in combined cognitive function z-score appeared to be associated with dementia diagnosis as early as 15 years prior to diagnosis. As we got closer to the diagnosis date, the difference in scores of people with and without dementia widened substantially. Periodic testing might allow us to intervene earlier in persons likely to develop dementia.

Introduction: Hypertension in midlife, but not late life, is a robust predictor of subsequent dementia. However, the fraction of dementia attributable to hypertension might vary depending on the age of the population considered, and the age through which dementia onset occurs. Here, we investigated associations of hypertension assessed at various ages with incident dementia by age 80 or 90, then estimated population attributable fractions (PAF) for dementia from hypertension categories at each of these age groups. Hypothesis: Given hypertension prevalence increases with age but its relative risk for dementia tends to decrease, we had no prior expectation of the age at which the PAF’s would be highest. We hypothesized, however, that PAFs for incident dementia would be higher when hypertension is measured in midlife age groups versus late-life age groups. Methods: We leveraged 32 years of follow-up data from the Atherosclerosis Risk in Communities study (ARIC), a prospective, community-based cohort that enrolled participants aged 45-64 years from four US communities (Washington County, MD; Forsyth County, NC; Jackson, MS; and selected suburbs of Minneapolis, MI) at ARIC visit 1 (1987-1989). Participants were followed through 2018-2019. We used Cox proportional hazards models to estimate hazard ratios, then quantified PAFs, of dementia by age 80 and 90 from hypertension assessed at ages of 45-54 (ARIC visit 1), 55-64 (ARIC visits 1 and 4), 65-74 (ARIC visits 4 and 5), and 75-84 (ARIC visit 5). Results: Hypertension was assessed among 7,572 participants aged 45-54; 12,033 aged 55-64; and 6,561 aged 65-74. By age 80 the number identified as having incident dementia was 711, 910, and 372, respectively. Relative to normotension, hypertension in each age category was associated with hazard ratios (HRs) greater than 1.0 for dementia from hypertension by age 80. The PAF for dementia by age 80 from hypertension measured at age 45-54 was 15.3% (95% CI = 6.9, 22.3%), 19.1% (95% CI = 9.9, 26.9%) at ages 55-64, and 19.9% (95% CI = -4.4, 38.5%) at ages 65-74. By age 90, the number of incident dementia cases were 958; 2,302; and 1,327, respectively; and, among the 2,086 participants aged 75-84, 400 incident dementia cases. HRs were slightly smaller than those by age 80, while hypertension assessed in the 75-84 age group was not significant. The PAFs for dementia by age 90 from hypertension at age 45-54 was 13.8% (95% CI = 6.6, 20.0%), 12.9% (95% CI = 7.0, 18.2%) at ages 55-64, 10.9% (95% CI = 0.02, 21.0%) at ages 65-74, and non-significant at ages 75-84. Conclusions: Public health interventions targeting hypertension in midlife through early late-life might reduce a sizeable proportion of dementia cases.

AbstractFrom a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer’s disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer’s disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer’s disease from the normative cohort at the patient level, as well as between Alzheimer’s disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer’s disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer’s disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer’s disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer’s disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer’s disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer’s disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

AbstractINTRODUCTIONThe fraction of dementia attributable to hypertension might vary depending on the age of the population considered and the age through which dementia occurs.METHODSIn the Atherosclerosis Risk in Communities study, we quantified population attributable fractions (PAF) of dementia by age 80 and 90 from hypertension assessed at ages of 45–54 (n = 7572), 55–64 (n = 12,033), 65–74 (n = 6561), and 75–84 (n = 2086).RESULTSThe PAF for dementia by age 80 from all non‐normal blood pressure at ages 45–54 was 15.3% (95% confidence interval [CI] = 6.9%–22.3%), 19.1% (95% CI = 9.9%–26.9%) at ages 55–64, and 19.9% (95% CI = −4.4%–38.5%) at ages 65–74. The strongest PAFs were from stage 2 hypertension (11.9%–21.3%). For dementia by age 90, PAFs from non‐normal blood pressure up through age 75 were smaller (10.9%–13.8%), and non‐significant by age 75–84.DISCUSSIONInterventions targeting hypertension even in early late life might reduce a sizeable proportion of dementia.Highlights We estimated prospective population attributable risks of dementia for hypertension. 15%–20% of dementia cases by age 80 are from non‐normal blood pressure (BP). Associations between hypertension and dementia persisted through age 75. Midlife to early late‐life BP control might reduce a large proportion of dementia.

Background: The National Institute on Aging-Alzheimer’s Association Research Framework proposes defining Alzheimer’s disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear. Objective: To determine whether cerebrospinal fluid (CSF)-neurodegenerative biomarkers (N) map differentially to Alzheimer’s disease pathology measured by Aβ 42 (an indicator of amyloidosis, [A]), p-tau (an indicator of tau deposition, [T]), and MRI vascular pathology indicators (measured by white-matter integrity, infarcts, and microbleeds [V]). Methods: Participants were from Mayo Clinic Study of Aging (MCSA) with CSF measures of NfL, Ng, t-tau, Aβ 42 , and p-tau and available MRI brain imaging. Linear models assessed associations between CSF neurodegeneration (N) markers, amyloid markers (A), tau (T), and vascular pathology (V). Results: Participants ( n = 408) had a mean age of 69.2±10.7; male, 217 (53.2%); cognitively unimpaired, 359 (88%). All three neurodegeneration biomarkers correlated with age ( p < 0.001 for NfL and t-tau, p = 0.018 for Ng). Men had higher CSF-NfL levels; women had higher Ng ( p < 0.001). NfL and t-tau levels correlated with infarcts ( p = 0.009, p = 0.034 respectively); no biomarkers correlated with white-matter integrity. N biomarkers correlated with p-tau levels (T, p < 0.001). Higher Aβ 42 levels associated with higher N-biomarker levels but only among cognitively unimpaired (A, p < 0.001). Conclusion: The influence of vascular pathology in the general population on CSF (N) biomarkers is modest, with greater influence of infarcts than white-matter disruption. Neurodegeneration markers more closely correlated with tau than amyloid markers.

Abstract Staging the severity of Alzheimer’s disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer’s clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1–2, 3–4, 5–6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1–42 and Aβ1–40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78–0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72–0.85 versus C = 0.73–0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer’s disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Abstract Dysexecutive Alzheimer’s disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors (“eigenbrains”) accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. “left-dominant,” “right-dominant,” “bi-parietal-dominant,” and “heteromodal-diffuse.” Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain–behavior relationships relevant to clinical practice and disease physiology.

Background: Phosphorylated cytoplasmic tau inclusions correlate with and precede cognitive deficits in Alzheimer’s disease (AD). However, pathological tau accumulation and relationships to synaptic changes remain unclear. Objective: To address this, we examined postmortem brain from 50 individuals with the full spectrum of AD (clinically and neuropathologically). Total tau, pTau231, and AMPA GluR1 were compared across two brain regions (entorhinal and middle frontal cortices), as well as clinically stratified groups (control, amnestic mild cognitive impairment, AD dementia), NIA-AA Alzheimer’s Disease Neuropathologic Change designations (Not, Low, Intermediate, High), and Braak tangle stages (1–6). Significant co-existing pathology was excluded to isolate changes attributed to pathologic AD. Methods: Synaptosomal fractionation and staining were performed to measure changes in total Tau, pTau231, and AMPA GluR1. Total Tau and pTau231 were quantified in synaptosomal fractions using Quanterix Simoa HD-X. Results: Increasing pTau231 in frontal postsynaptic fractions correlated positively with increasing clinical and neuropathological AD severity. Frontal cortex is representative of early AD, as it does not become involved by tau tangles until late in AD. Entorhinal total tau was significantly higher in the amnestic mild cognitive impairment group when compared to AD, but only after accounting for AD associated synaptic changes. Alterations in AMPA GluR1 observed in the entorhinal cortex, but not middle frontal cortex, suggest that pTau231 mislocalization and aggregation in postsynaptic structures may impair glutamatergic signaling by promoting AMPA receptor dephosphorylation and internalization. Conclusion: Results highlight the potential effectiveness of early pharmacological interventions targeting pTau231 accumulation at the postsynaptic density.