Hearing intervention versus health education control to reduce cognitive decline in older adults with hearing loss in the USA (ACHIEVE): a multicentre, randomised controlled trial.

BACKGROUND: Hearing loss is associated with increased cognitive decline and incident dementia in older adults. We aimed to investigate whether a hearing intervention could reduce cognitive decline in cognitively healthy older adults with hearing loss. METHODS: The ACHIEVE study is a multicentre, parallel-group, unmasked, randomised controlled trial of adults aged 70-84 years with untreated hearing loss and without substantial cognitive impairment that took place at four community study sites across the USA. Participants were recruited from two study populations at each site: (1) older adults participating in a long-standing observational study of cardiovascular health (Atherosclerosis Risk in Communities [ARIC] study), and (2) healthy de novo community volunteers. Participants were randomly assigned (1:1) to a hearing intervention (audiological counselling and provision of hearing aids) or a control intervention of health education (individual sessions with a health educator covering topics on chronic disease prevention) and followed up every 6 months. The primary endpoint was 3-year change in a global cognition standardised factor score from a comprehensive neurocognitive battery. Analysis was by intention to treat. This trial was registered at ClinicalTrials.gov, NCT03243422. FINDINGS: From Nov 9, 2017, to Oct 25, 2019, we screened 3004 participants for eligibility and randomly assigned 977 (32·5%; 238 [24%] from ARIC and 739 [76%] de novo). We randomly assigned 490 (50%) to the hearing intervention and 487 (50%) to the health education control. The cohort had a mean age of 76·8 years (SD 4·0), 523 (54%) were female, 454 (46%) were male, and most were White (n=858 [88%]). Participants from ARIC were older, had more risk factors for cognitive decline, and had lower baseline cognitive scores than those in the de novo cohort. In the primary analysis combining the ARIC and de novo cohorts, 3-year cognitive change (in SD units) was not significantly different between the hearing intervention and health education control groups (-0·200 [95% CI -0·256 to -0·144] in the hearing intervention group and -0·202 [-0·258 to -0·145] in the control group; difference 0·002 [-0·077 to 0·081]; p=0·96). However, a prespecified sensitivity analysis showed a significant difference in the effect of the hearing intervention on 3-year cognitive change between the ARIC and de novo cohorts (pinteraction=0·010). Other prespecified sensitivity analyses that varied analytical parameters used in the total cohort did not change the observed results. No significant adverse events attributed to the study were reported with either the hearing intervention or health education control. INTERPRETATION: The hearing intervention did not reduce 3-year cognitive decline in the primary analysis of the total cohort. However, a prespecified sensitivity analysis showed that the effect differed between the two study populations that comprised the cohort. These findings suggest that a hearing intervention might reduce cognitive change over 3 years in populations of older adults at increased risk for cognitive decline but not in populations at decreased risk for cognitive decline. FUNDING: US National Institutes of Health.

Patterns of cognitive domain abnormalities enhance discrimination of dementia risk prediction: The ARIC study.

INTRODUCTION: The contribution of neuropsychological assessments to risk assessment for incident dementia is underappreciated. METHODS: We analyzed neuropsychological testing results in dementia-free participants in the Atherosclerosis Risk in Communities (ARIC) study. We examined associations of index domain-specific neuropsychological test performance with incident dementia using cumulative incidence curves and Cox proportional hazards models. RESULTS: Among 5296 initially dementia-free participants (mean [standard deviation] age of 75.8 [5.1] years; 60.1% women, 22.2% Black) over a median follow-up of 7.9 years, the covariate-adjusted hazard ratio varied substantially depending on the pattern of domain-specific performance and age, in an orderly manner from single domain language abnormalities (lowest risk) to single domain executive or memory abnormalities, to multidomain abnormalities including memory (highest risk). DISCUSSION: By identifying normatively defined cognitive abnormalities by domains based on neuropsychological test performance, there is a conceptually orderly and age-sensitive spectrum of risk for incident dementia that provides valuable information about the likelihood of progression. HIGHLIGHTS: Domain-specific cognitive profiles carry enhanced prognostic value compared to mild cognitive impairment. Single-domain non-amnestic cognitive abnormalities have the most favorable prognosis. Multidomain amnestic abnormalities have the greatest risk for incident dementia. Patterns of domain-specific risks are similar by sex and race.

Test-retest reliability of 3D velocity-selective arterial spin labeling for detecting normal variations of cerebral blood flow.

Velocity-selective inversion (VSI) based velocity-selective arterial spin labeling (VSASL) has been developed to measure cerebral blood flow (CBF) with low susceptibility to the prolonged arterial transit time and high sensitivity to brain perfusion signal. The purpose of this magnetic resonance imaging study is to evaluate the test-retest reliability of a VSI-prepared 3D VSASL protocol with whole-brain coverage to detect baseline CBF variations among cognitively normal participants in different brain regions. Coefficients of variation (CoV) of both absolute and relative CBF across scans or sessions, subjects, and gray matter regions were calculated, and corresponding intraclass correlation coefficients (ICC) were computed. The higher between-subject CoV of absolute CBF (13.4 ± 2.0%) over within-subject CoV (within-session: 3.8 ± 1.1%; between-session: 4.9 ± 0.9%) yielded moderate to excellent ICC (within-session: 0.88±0.08; between-session: 0.77±0.14) to detect normal variations of individual CBF. The higher between-region CoV of relative CBF (11.4 ± 3.0%) over within-region CoV (within-session: 2.3 ± 0.9%; between-session: 3.3 ± 1.0%) yielded excellent ICC (within-session: 0.92±0.06; between-session: 0.85±0.12) to detect normal variations of regional CBF. Age, blood pressure, end-tidal CO2, and hematocrit partially explained the variability of CBF across subjects. Together these results show excellent test-retest reliability of VSASL to detect both between-subject and between-region variations supporting its clinical utility.

Accelerated longitudinal changes and ordering of Alzheimer disease biomarkers across the adult lifespan.

The temporal evolutions and relative orderings of Alzheimer disease biomarkers, including CSF amyloid-ß42 (Aß42), Aß40, total tau (Tau) and phosphorylated tau181 (pTau181), standardized uptake value ratio (SUVR) from the molecular imaging of cerebral fibrillar amyloid-ß with PET using the 11C-Pittsburgh Compound-B (PiB), MRI-based hippocampal volume and cortical thickness and cognition have been hypothesized but not yet fully tested with longitudinal data for all major biomarker modalities among cognitively normal individuals across the adult lifespan starting from 18 years. By leveraging a large harmonized database from 8 biomarker studies with longitudinal data from 2609 participants in cognition, 873 in MRI biomarkers, 519 in PET PiB imaging and 475 in CSF biomarkers for a median follow-up of 5-6 years, we estimated the longitudinal trajectories of all major Alzheimer disease biomarkers as functions of baseline age that spanned from 18 to 103 years, located the baseline age window at which the longitudinal rates of change accelerated and further examined possible modifying effects of apolipoprotein E (APOE) genotype. We observed that participants 18-45 years at baseline exhibited learning effects on cognition and unexpected directions of change on CSF and PiB biomarkers. The earliest acceleration of longitudinal change occurred for CSF Aß42 and Aß42/Aß40 ratio (with an increase) and for Tau, and pTau181 (with a decrease) at the next baseline age interval of 45-50 years, followed by an accelerated increase for PiB SUVR at the baseline age of 50-55 years and an accelerated decrease for hippocampal volume at the baseline age of 55-60 years and finally by an accelerated decline for cortical thickness and cognition at the baseline age of 65-70 years. Another acceleration in the rate of change occurred at the baseline age of 65-70 years for Aß42/Aß40 ratio, Tau, pTau181, PiB SUVR and hippocampal volume. Accelerated declines in hippocampal volume and cognition continued after 70 years. For participants 18-45 years at baseline, significant increases in Aß42 and Aß42/Aß40 ratio and decreases in PiB SUVR occurred in APOE ɛ4 non-carriers but not carriers. After age 45 years, APOE ɛ4 carriers had greater magnitudes than non-carriers in the rates of change for all CSF biomarkers, PiB SUVR and cognition. Our results characterize the temporal evolutions and relative orderings of Alzheimer disease biomarkers across the adult lifespan and the modification effect of APOE ɛ4. These findings may better inform the design of prevention trials on Alzheimer disease.

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

The association of motoric cognitive risk with incident dementia and neuroimaging characteristics: The Atherosclerosis Risk in Communities Study.

INTRODUCTION: Motoric cognitive risk (MCR), a clinical syndrome characterized by slow gait speed and subjective cognitive complaints, has been associated with dementia risk. The neuropathological features underlying MCR remain poorly understood. METHODS: The Atherosclerosis Risk in Communities (ARIC) community-based cohort study classified participants using standardized criteria as MCR+/- and mild cognitive impairment (MCI)+/- at study baseline (2011-2013). We examined the 5-year dementia risk and baseline brain structural/molecular abnormalities associated with MCR+ and MCI+ status. RESULTS: Of 5023 nondemented participants included, 204 were MCR+ and 1030 were MCI+. Both MCR+ and MCI+ participants demonstrated increased dementia risk. The pattern of structural brain abnormalities associated with MCR+ differed from that of MCI+. Whereas MCI+ was associated with comparatively smaller volumes in brain regions vulnerable to Alzheimer's disease pathology, MCR+ status was associated with smaller volumes in frontoparietal regions and greater white matter abnormalities. DISCUSSION: MCR may represent a predementia syndrome characterized by prominent white matter abnormalities and frontoparietal atrophy.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

Genetic modifiers of risk and age at onset in GBA associated Parkinson's disease and Lewy body dementia.

Parkinson's disease is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of Parkinson's disease, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as Parkinson's disease and Lewy body dementia. These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known Parkinson's disease risk variants. Using multiple, large case-control datasets, totalling 217 165 individuals (22 757 Parkinson's disease cases, 13 431 Parkinson's disease proxy cases, 622 Lewy body dementia cases and 180 355 controls), we identified 1691 Parkinson's disease cases, 81 Lewy body dementia cases, 711 proxy cases and 7624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent Parkinson's disease-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall Parkinson's disease genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S induced pluripotent cell-derived neurons were shown to have decreased cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated Parkinson's disease risk and age at onset, although the total contribution of common genetics variants is not large. We further demonstrate that common variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have implications for selection of GBA carriers for therapeutic interventions.

MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols.

The concept of vascular contributions to cognitive impairment and dementia (VCID) derives from more than two decades of research indicating that (1) most older individuals with cognitive impairment have post mortem evidence of multiple contributing pathologies and (2) along with the preeminent role of Alzheimer's disease (AD) pathology, cerebrovascular disease accounts for a substantial proportion of this contribution. Contributing cerebrovascular processes include both overt strokes caused by etiologies such as large vessel occlusion, cardioembolism, and embolic infarcts of unknown source, and frequently asymptomatic brain injuries caused by diseases of the small cerebral vessels. Cerebral small vessel diseases such as arteriolosclerosis and cerebral amyloid angiopathy, when present at moderate or greater pathologic severity, are independently associated with worse cognitive performance and greater likelihood of dementia, particularly in combination with AD and other neurodegenerative pathologies. Based on this evidence, the US National Alzheimer's Project Act explicitly authorized accelerated research in vascular and mixed dementia along with frontotemporal and Lewy body dementia and AD itself. Biomarker development has been consistently identified as a key step toward translating scientific advances in VCID into effective prevention and treatment strategies. Validated biomarkers can serve a range of purposes in trials of candidate interventions, including (1) identifying individuals at increased VCID risk, (2) diagnosing the presence of cerebral small vessel disease or specific small vessel pathologies, (3) stratifying study participants according to their prognosis for VCID progression or treatment response, (4) demonstrating an intervention's target engagement or pharmacodynamic mechanism of action, and (5) monitoring disease progression during treatment. Effective biomarkers allow academic and industry investigators to advance promising interventions at early stages of development and discard interventions with low success likelihood. The MarkVCID consortium was formed in 2016 with the goal of developing and validating fluid- and imaging-based biomarkers for the cerebral small vessel diseases associated with VCID. MarkVCID consists of seven project sites and a central coordinating center, working with the National Institute of Neurologic Diseases and Stroke and National Institute on Aging under cooperative agreements. Through an internal selection process, MarkVCID has identified a panel of 11 candidate biomarker "kits" (consisting of the biomarker measure and the clinical and cognitive data used to validate it) and established a range of harmonized procedures and protocols for participant enrollment, clinical and cognitive evaluation, collection and handling of fluid samples, acquisition of neuroimaging studies, and biomarker validation. The overarching goal of these protocols is to generate rigorous validating data that could be used by investigators throughout the research community in selecting and applying biomarkers to multi-site VCID trials. Key features of MarkVCID participant enrollment, clinical/cognitive testing, and fluid biomarker procedures are summarized here, with full details in the following text, tables, and supplemental material, and a description of the MarkVCID imaging biomarker procedures in a companion paper, "MarkVCID Cerebral small vessel consortium: II. Neuroimaging protocols." The procedures described here address a range of challenges in MarkVCID's design, notably: (1) acquiring all data under informed consent and enrollment procedures that allow unlimited sharing and open-ended analyses without compromising participant privacy rights; (2) acquiring the data in a sufficiently wide range of study participants to allow assessment of candidate biomarkers across the various patient groups who might ultimately be targeted in VCID clinical trials; (3) defining a common dataset of clinical and cognitive elements that contains all the key outcome markers and covariates for VCID studies and is realistically obtainable during a practical study visit; (4) instituting best fluid-handling practices for minimizing avoidable sources of variability; and (5) establishing rigorous procedures for testing the reliability of candidate fluid-based biomarkers across replicates, assay runs, sites, and time intervals (collectively defined as the biomarker's instrumental validity). Participant Enrollment Project sites enroll diverse study cohorts using site-specific inclusion and exclusion criteria so as to provide generalizable validation data across a range of cognitive statuses, risk factor profiles, small vessel disease severities, and racial/ethnic characteristics representative of the diverse patient groups that might be enrolled in a future VCID trial. MarkVCID project sites include both prospectively enrolling centers and centers providing extant data and samples from preexisting community- and population-based studies. With approval of local institutional review boards, all sites incorporate MarkVCID consensus language into their study documents and informed consent agreements. The consensus language asks prospectively enrolled participants to consent to unrestricted access to their data and samples for research analysis within and outside MarkVCID. The data are transferred and stored as a de-identified dataset as defined by the Health Insurance Portability and Accountability Act Privacy Rule. Similar human subject protection and informed consent language serve as the basis for MarkVCID Research Agreements that act as contracts and data/biospecimen sharing agreements across the consortium. Clinical and Cognitive Data Clinical and cognitive data are collected across prospectively enrolling project sites using common MarkVCID instruments. The clinical data elements are modified from study protocols already in use such as the Alzheimer's Disease Center program Uniform Data Set Version 3 (UDS3), with additional focus on VCID-related items such as prior stroke and cardiovascular disease, vascular risk factors, focal neurologic findings, and blood testing for vascular risk markers and kidney function including hemoglobin A1c, cholesterol subtypes, triglycerides, and creatinine. Cognitive assessments and rating instruments include the Clinical Dementia Rating Scale, Geriatric Depression Scale, and most of the UDS3 neuropsychological battery. The cognitive testing requires ≈60 to 90 minutes. Study staff at the prospectively recruiting sites undergo formalized training in all measures and review of their first three UDS3 administrations by the coordinating center. Collection and Handling of Fluid Samples Fluid sample types collected for MarkVCID biomarker kits are serum, ethylenediaminetetraacetic acid-plasma, platelet-poor plasma, and cerebrospinal fluid (CSF) with additional collection of packed cells to allow future DNA extraction and analyses. MarkVCID fluid guidelines to minimize variability include fasting morning fluid collections, rapid processing, standardized handling and storage, and avoidance of CSF contact with polystyrene. Instrumental Validation for Fluid-Based Biomarkers Instrumental validation of MarkVCID fluid-based biomarkers is operationally defined as determination of intra-plate and inter-plate repeatability, inter-site reproducibility, and test-retest repeatability. MarkVCID study participants both with and without advanced small vessel disease are selected for these determinations to assess instrumental validity across the full biomarker assay range. Intra- and inter-plate repeatability is determined by repeat assays of single split fluid samples performed at individual sites. Inter-site reproducibility is determined by assays of split samples distributed to multiple sites. Test-retest repeatability is determined by assay of three samples acquired from the same individual, collected at least 5 days apart over a 30-day period and assayed on a single plate. The MarkVCID protocols are designed to allow direct translation of the biomarker validation results to multicenter trials. They also provide a template for outside groups to perform analyses using identical methods and therefore allow direct comparison of results across studies and centers. All MarkVCID protocols are available to the biomedical community and intended to be shared. In addition to the instrumental validation procedures described here, each of the MarkVCID kits will undergo biological validation to determine whether the candidate biomarker measures important aspects of VCID such as cognitive function. Analytic methods and results of these validation studies for the 11 MarkVCID biomarker kits will be published separately. The results of this rigorous validation process will ultimately determine each kit's potential usefulness for multicenter interventional trials aimed at preventing or treating small vessel disease related VCID.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). METHODS: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. RESULTS: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aß) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Association of peripheral inflammatory markers with connectivity in large-scale functional brain networks of non-demented older adults.

BACKGROUND: Systemic inflammation has emerged as a risk factor for cognitive decline and Alzheimer's disease, but inflammation's effect on distributed brain networks is unclear. We examined the relationship between peripheral inflammatory markers and subsequent functional connectivity within five large-scale cognitive networks and evaluated the modifying role of cortical amyloid and APOE ε4 status. METHODS: Blood levels of soluble tumor necrosis factor-alpha receptor-1 and interleukin 6 were assessed in 176 participants (at baseline mean age: 65 (SD 9) years; 63% women; 85% cognitively normal, 15% mild cognitive impairment (MCI)) and were combined to derive an Inflammatory Index. Approximately six years later, participants underwent resting-state functional magnetic resonance imaging to quantify functional connectivity; a subset of 137 participants also underwent 11C Pittsburgh compound-B (PiB) PET imaging to assess cortical amyloid burden. RESULTS: Using linear regression models adjusted for demographic characteristics and cardiovascular risk factors, a higher Inflammatory Index was associated with lower connectivity within the Default Mode (ß = -0.013; 95% CI: -0.023, -0.003) and the Dorsal Attention Networks (ß = -0.017; 95% CI: -0.028, -0.006). The strength of these associations did not vary by amyloid status (positive/negative). However, there was a significant interaction between Inflammatory Index and APOE ε4 status, whereby ε4-positive participants with a higher Inflammatory Index demonstrated lower connectivity. Inflammatory Index was unrelated to connectivity within other large-scale cognitive networks (Control, Limbic, and Salience/Ventral Attention networks). CONCLUSION: Peripheral pro-inflammatory signaling in older adults without dementia, especially among APOE ε4-positive individuals, is associated with altered connectivity within two large-scale cognitive networks.

Depressive symptoms in relation to clinical symptom onset of mild cognitive impairment.

ABSTRACTObjective:There is increasing evidence of an association between depressive symptoms and mild cognitive impairment (MCI) in cross-sectional studies, but the longitudinal association between depressive symptoms and risk of MCI onset is less clear. The authors investigated whether baseline symptom severity of depression was predictive of time to onset of symptoms of MCI. METHOD: These analyses included 300 participants from the BIOCARD study, a cohort of individuals who were cognitively normal at baseline (mean age = 57.4 years) and followed for up to 20 years (mean follow-up = 2.5 years). Depression symptom severity was measured using the Hamilton Depression Scale (HAM-D). The authors assessed the association between dichotomous and continuous HAM-D and time to onset of MCI within 7 years versus after 7 years from baseline (reflecting the mean time from baseline to onset of clinical symptoms in the cohort) using Cox regression models adjusted for gender, age, and education. RESULTS: At baseline, subjects had a mean HAM-D score of 2.2 (SD = 2.8). Higher baseline HAM-D scores were associated with an increased risk of progression from normal cognition to clinical symptom onset ≤ 7 years from baseline (p = 0.043), but not with progression > 7 years from baseline (p = 0.194). These findings remained significant after adjustment for baseline cognition. CONCLUSIONS: These results suggest that low levels of depressive symptoms may be predictive of clinical symptom onset within approximately 7 years among cognitively normal individuals and may be useful in identifying persons at risk for MCI due to Alzheimer's disease.

Neuropathologic, genetic, and longitudinal cognitive profiles in primary age-related tauopathy (PART) and Alzheimer's disease.

INTRODUCTION: Primary age-related tauopathy (PART) is a recently described entity that can cause cognitive impairment in the absence of Alzheimer's disease (AD). Here, we compared neuropathological features, tau haplotypes, apolipoprotein E (APOE) genotypes, and cognitive profiles in age-matched subjects with PART and AD pathology. METHODS: Brain autopsies (n = 183) were conducted on participants 85 years and older from the Baltimore Longitudinal Study of Aging and Johns Hopkins Alzheimer's Disease Research Center. Participants, normal at enrollment, were followed with periodic cognitive evaluations until death. RESULTS: Compared with AD, PART subjects showed significantly slower rates of decline on measures of memory, language, and visuospatial performance. They also showed lower APOE ε4 allele frequency (4.1% vs. 17.6%, P = .0046). DISCUSSION: Our observations suggest that PART is separate from AD and its distinction will be important for the clinical management of patients with cognitive impairment and for public health care planning.

Midlife vascular risk factors and midlife cognitive status in relation to prevalence of mild cognitive impairment and dementia in later life: The Atherosclerosis Risk in Communities Study.

INTRODUCTION: The interplay between midlife vascular risk factors and midlife cognitive function with later life mild cognitive impairment (MCI) and dementia (DEM) is not well understood. METHODS: In the Atherosclerosis Risk in Communities Study, cardiovascular risk factors and cognition were assessed in midlife, ages 45-64 years. In 2011-2013, 20-25 years later, all consenting Atherosclerosis Risk in Communities participants underwent a cognitive and neurological evaluation and were given adjudicated diagnoses of cognitively normal, MCI, or DEM. RESULTS: In 5995 participants with complete covariate data, midlife diabetes, hypertension, obesity, and hypercholesterolemia were associated with late-life MCI and DEM. Low midlife cognition function was also associated with greater likelihood of late-life MCI or DEM. Both midlife vascular risk factors and midlife cognitive function remained associated with later life MCI or DEM when both were in the model. DISCUSSION: Later life MCI and DEM were independently associated with midlife vascular risk factors and midlife cognition.

Neuroimaging Correlates of Cerebral Microbleeds: The ARIC Study (Atherosclerosis Risk in Communities).

BACKGROUND AND PURPOSE: Cerebral microbleed (CMB) location (deep versus strictly lobar) may elucidate underlying pathology with deep CMBs being more associated with hypertensive vascular disease and lobar CMBs being more associated with cerebral amyloid angiopathy. The objective of this study was to determine whether neuroimaging signs of vascular disease and Alzheimer pathology are associated with different types of CMBs. METHODS: Among 1677 nondemented ARIC (Atherosclerosis Risk in Communities) participants (mean age=76±5 years; 40% men; 26% black) with 3-Tesla MRI scans at the fifth examination (2011-2013), we fit multinomial logistic regression models to quantify relationships of brain volumes (Alzheimer disease signature regions, total gray matter, frontal gray matter, and white matter hyperintensity volumes), infarct frequencies (lacunar, nonlacunar, and total), and apolipoprotein E (number of ε4 alleles) with CMB location (none, deep/mixed, or strictly lobar CMBs). Models were weighted for the sample selection scheme and adjusted for age, sex, education, hypertension, ever smoking status, diabetes mellitus, race site membership, and estimated intracranial volume (brain volume models only). RESULTS: Deep/mixed and strictly lobar CMBs had prevalences of 8% and 16%, respectively. Larger white matter hyperintensity burden, greater total infarct frequency, smaller frontal volumes (in women only), and smaller total gray matter volume were associated with greater risk of both deep and lobar CMBs relative to no CMBs. Greater white matter hyperintensity volume was also associated with greater risk of deep relative to lobar CMBs. Higher lacunar and nonlacunar infarct frequencies were associated with higher risk of deep CMBs, whereas smaller Alzheimer disease signature region volume and apolipoprotein E ε4 homozygosity were associated with greater risk of lobar CMBs. CONCLUSIONS: CMBs are a common vascular pathology in the elderly. Markers of hypertensive small-vessel disease may contribute to deep CMBs while cerebral amyloid angiopathy may drive development of lobar CMBs.

Next-generation sequencing reveals substantial genetic contribution to dementia with Lewy bodies.

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia after Alzheimer's disease. Although an increasing number of genetic factors have been connected to this debilitating condition, the proportion of cases that can be attributed to distinct genetic defects is unknown. To provide a comprehensive analysis of the frequency and spectrum of pathogenic missense mutations and coding risk variants in nine genes previously implicated in DLB, we performed exome sequencing in 111 pathologically confirmed DLB patients. All patients were Caucasian individuals from North America. Allele frequencies of identified missense mutations were compared to 222 control exomes. Remarkably, ~25% of cases were found to carry a pathogenic mutation or risk variant in APP, GBA or PSEN1, highlighting that genetic defects play a central role in the pathogenesis of this common neurodegenerative disorder. In total, 13% of our cohort carried a pathogenic mutation in GBA, 10% of cases carried a risk variant or mutation in PSEN1, and 2% were found to carry an APP mutation. The APOE ε4 risk allele was significantly overrepresented in DLB patients (p-value <0.001). Our results conclusively show that mutations in GBA, PSEN1, and APP are common in DLB and consideration should be given to offer genetic testing to patients diagnosed with Lewy body dementia.

GBA Variants are associated with a distinct pattern of cognitive deficits in Parkinson's disease.

BACKGROUND: Loss-of-function mutations in the GBA gene are associated with more severe cognitive impairment in PD, but the nature of these deficits is not well understood and whether common GBA polymorphisms influence cognitive performance in PD is not yet known. METHODS: We screened the GBA coding region for mutations and the E326K polymorphism in 1,369 PD patients enrolled at eight sites from the PD Cognitive Genetics Consortium. Participants underwent assessments of learning and memory (Hopkins Verbal Learning Test-Revised), working memory/executive function (Letter-Number Sequencing Test and Trail Making Test A and B), language processing (semantic and phonemic verbal fluency), visuospatial abilities (Benton Judgment of Line Orientation), and global cognitive function (MoCA). We used linear regression to test for association between genotype and cognitive performance with adjustment for important covariates and accounted for multiple testing using Bonferroni's corrections. RESULTS: Mutation carriers (n = 60; 4.4%) and E326K carriers (n = 65; 4.7%) had a higher prevalence of dementia (mutations, odds ratio = 5.1; P = 9.7 × 10(-6) ; E326K, odds ratio = 6.4; P = 5.7 × 10(-7) ) and lower performance on Letter-Number Sequencing (mutations, corrected P[Pc ] = 9.0 × 10(-4) ; E326K, Pc = 0.036), Trail Making B-A (mutations, Pc = 0.018; E326K, Pc = 0.018), and Benton Judgment of Line Orientation (mutations, Pc = 0.0045; E326K, Pc = 0.0013). CONCLUSIONS: Both GBA mutations and E326K are associated with a distinct cognitive profile characterized by greater impairment in working memory/executive function and visuospatial abilities in PD patients. The discovery that E326K negatively impacts cognitive performance approximately doubles the proportion of PD patients we now recognize are at risk for more severe GBA-related cognitive deficits.

Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: atherosclerosis risk in communities-neurocognitive study.

BACKGROUND AND PURPOSE: The relationships between cerebrovascular lesions visible on imaging and cognition are complex. We explored the possibility that the cerebral cortical volume mediated these relationships. METHODS: Total of 1906 nondemented participants (59% women; 25% African-American; mean age, 76.6 years) in the Atherosclerosis Risk in Communities (ARIC) study underwent cognitive assessments, risk factor assessments, and quantitative MRI for white matter hyperintensities (WMH) and infarcts. The Freesurfer imaging analysis pipeline was used to determine regional cerebral volumes. We examined the associations of cognitive domain outcomes with cerebral volumes (hippocampus and separate groups of posterior and frontal cortical regions of interest) and cerebrovascular imaging features (presence of large or small cortical/subcortical infarcts and WMH volume). We performed mediation pathway analyses to assess the hypothesis that hippocampal and cortical volumes mediated the associations between cerebrovascular imaging features and cognition. RESULTS: In unmediated analyses, WMH and infarcts were both associated with worse psychomotor speed/executive function. In mediation analyses, WMH and infarct associations on psychomotor speed/executive function were significantly attenuated, but not abolished, by the inclusion of the posterior cortical regions of interest volume in the models, and the infarcts on psychomotor speed/executive function association were attenuated, but not abolished, by inclusion of the frontal cortical regions of interest volume. CONCLUSIONS: Both WMH and infarcts were associated with cortical volume, and both lesions were also associated with cognitive performance, implying shared pathophysiological mechanisms. Although cross-sectional, our findings suggest that WMH and infarcts could be proxies for clinically covert processes that directly damage cortical regions. Microinfarcts are 1 candidate for such a clinically covert process.

Hearing impairment and cognitive decline: a pilot study conducted within the atherosclerosis risk in communities neurocognitive study.

Hearing impairment (HI) is prevalent, is modifiable, and has been associated with cognitive decline. We tested the hypothesis that audiometric HI measured in 2013 is associated with poorer cognitive function in 253 men and women from Washington County, Maryland (mean age = 76.9 years) in a pilot study carried out within the Atherosclerosis Risk in Communities Neurocognitive Study. Three cognitive tests were administered in 1990-1992, 1996-1998, and 2013, and a full neuropsychological battery was administered in 2013. Multivariable-adjusted differences in standardized cognitive scores (cross-sectional analysis) and trajectories of 20-year change (longitudinal analysis) were modeled using linear regression and generalized estimating equations, respectively. Hearing thresholds for pure tone frequencies of 0.5-4 kHz were averaged to obtain a pure tone average in the better-hearing ear. Hearing was categorized as follows: ≤25 dB, no HI; 26-40 dB, mild HI; and >40 dB, moderate/severe HI. Comparing participants with moderate/severe HI to participants with no HI, 20-year rates of decline in memory and global function differed by -0.47 standard deviations (P = 0.02) and -0.29 standard deviations (P = 0.02), respectively. Estimated declines were greatest in participants who did not wear a hearing aid. These findings add to the limited literature on cognitive impairments associated with HI, and they support future research on whether HI treatment may reduce risk of cognitive decline.