Jack, Clifford R.

Background: Cardiovascular health may be used for prevention of cerebral vascular disease; however, data on the association of cardiovascular health across midlife and late-life with late-life cerebral vascular disease are lacking. Our aim was to examine whether midlife or late-life cardiovascular health as well as changes of cardiovascular health within midlife and between midlife and late-life were associated with prevalence of magnetic resonance imaging markers of cerebral vascular disease at late-life. Methods: Prospective cohort study including 1638 participants from the Atherosclerosis Risk in Communities Study who took part in 2 visits at midlife (mean ages, 53 and 59 years), and a late-life visit (mean age, 76 years). A cardiovascular health Life’s Simple 7 score (range, 0–12/0–14, depending on diet availability) including 6 out of 7 items was calculated at each visit, with weight assigned to each item as poor (0), intermediate (1), or ideal (2). Participants underwent 3T brain magnetic resonance imaging scans in late-life visit. Outcomes were white matter hyperintensity volume, microbleeds, and lacunar, subcortical, and cortical infarcts at late-life. Linear and logistic regression models were used to assess the associations of cardiovascular health in midlife and late-life, and improvement of cardiovascular health within midlife, and from midlife to late-life with magnetic resonance imaging markers of cerebral vascular disease, adjusting for potential confounders. Results: A higher cardiovascular health in midlife, improvement of cardiovascular health within midlife, higher cardiovascular health at late-life, and improvement of cardiovascular health from midlife to late-life were associated with a lower prevalence of cerebral vascular disease markers. For example, improvement in cardiovascular health (per point) from midlife to late-life was associated with smaller white matter hyperintensity volume (β, −0.07 [95% CI, −0.10 to −0.04]) and lower odds of microbleeds (odds ratio, 0.93 [0.90–0.97]), lacunar (odds ratio, 0.93 [0.89–0.97]), subcortical (odds ratio, 0.93 [0.89–0.97]), and cortical infarcts (odds ratio, 0.92 [0.87–0.97]). Conclusions: Improving cardiovascular health within midlife and from midlife to late-life may prevent development of cerebral vascular disease.

AbstractBackgroundRecent evidence indicates that abnormal P‐wave parameters (PWPs)—ECG markers of atrial myopathy—are associated with incident dementia, independent of atrial fibrillation (AF) and clinical ischemic stroke. However, the mechanisms remain unclear and may include subclinical vascular brain injury. Hence, we evaluated the association of abnormal PWPs with brain MRI correlates of vascular brain injury in the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC‐NCS).MethodsARIC‐NCS participants who underwent 3T brain MRI scans in 2011–2013 were included. PWPs were measured from standard 12‐lead ECGs. Brain MRI outcomes included cortical infarcts, lacunar infarcts, cerebral microhemorrhages, brain volumes, and white matter disease (WMD) volume. We used weighted multivariable logistic and linear regression to evaluate the associations of abnormal PWPs with brain MRI outcomes.ResultsAmong 1715 participants (mean age, 76.1 years; 61% women; 29% Black), 797 (46%) had ≥1 abnormal PWP. After multivariable adjustment, including adjusting for prevalent AF, abnormal P‐wave terminal force in lead V1 (aPTFV1) and prolonged P‐wave duration (PPWD) were associated with increased odds of both cortical (OR 1.41; 95% CI, 1.14 to 1.74 and OR 1.30; 95% CI, 1.04 to 1.63, respectively) and lacunar infarcts (OR 1.36; 95% CI, 1.15 to 1.63 and OR 1.37; 95% CI, 1.15 to 1.65, respectively). Advanced interatrial block (aIAB) was associated with higher odds of subcortical microhemorrhage (OR 2.04; 95% CI, 1.36 to 3.06). Other than a significant association between aPTFV1 with lower parietal lobe volume, there were no other significant associations with brain or WMD volume.ConclusionIn this exploratory analysis of a US community‐based cohort, ECG surrogates of atrial myopathy are associated with a higher prevalence of brain infarcts and microhemorrhage, suggesting subclinical vascular brain injury as a possible mechanism underlying the association of atrial myopathy with dementia.

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.

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.