National Alzheimer's Coordinating Research Articles

Robust integration of secondary outcomes information into primary outcome analysis in the presence of missing data.

In clinical and observational studies, secondary outcomes are frequently collected alongside the primary outcome for each subject, yet their potential to improve the analysis efficiency remains underutilized. Moreover, missing data, commonly encountered in practice, can introduce bias to estimates if not appropriately addressed. This article presents an innovative approach that enhances the empirical likelihood-based information borrowing method by integrating missing-data techniques, ensuring robust data integration. We introduce a plug-in inverse probability weighting estimator to handle missingness in the primary analysis, demonstrating its equivalence to the standard joint estimator under mild conditions. To address potential bias from missing secondary outcomes, we propose a uniform mapping strategy, imputing incomplete secondary outcomes into a unified space. Extensive simulations highlight the effectiveness of our method, showing consistent, efficient, and robust estimators under various scenarios involving missing data and/or misspecified secondary models. Finally, we apply our proposal to the Uniform Data Set from the National Alzheimer's Coordinating Center, exemplifying its practical application.

A Transformer Approach for Cognitive Impairment Classification and Prediction.

Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. We analyzed 3 target feature-sets from the National Alzheimer's Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)-dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants' future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

APOE4 homozygozity represents a distinct genetic form of Alzheimer's disease.

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer's disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer's Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

Sex differences in histopathological markers of cerebral amyloid angiopathy and related hemorrhage.

Men with cerebral amyloid angiopathy (CAA) may have an earlier onset of intracerebral hemorrhage, and a more hemorrhagic disease course compared to women. In this cohort study we investigated sex differences in histopathological markers associated with amyloid-β burden and hemorrhage in cognitively impaired individuals and patients with CAA, using neuropathological data from two autopsy databases. First, we investigated presence of parenchymal (Thal score) and vascular amyloid-β (CAA severity score) in cognitively impaired individuals from the National Alzheimer's Coordinating Center (NACC) neuropathology database. Next, we examined sex differences in hemorrhagic ex vivo MRI-markers and local cortical iron burden, and the interaction of sex on factors associated with cortical iron burden (CAA percentage area and vessel remodelling) in patients with pathologically confirmed clinical CAA from the Massachusetts General Hospital (MGH) CAA neuropathology database. In 6120 individuals from the NACC database (45% women, mean age 80y), the presence of parenchymal amyloid-β (OR [95%CI]=0.68 [0.53-0.88]) but not vascular amyloid-β was less in men compared to women. In 19 patients with definite CAA from the MGH CAA database (35% women, mean age 75y), a lower microbleed count (p<0.001), but a higher proportion of cortical superficial siderosis and a higher local cortical iron burden was found in men (p<0.001) compared to women. CAA percentage area was comparable in men and women (p=0.732). Exploratory analyses demonstrated a possible stronger negative relation between cortical CAA percentage area and cortical iron density in men compared to women (p=0.03). Previously observed sex differences in hemorrhage onset and progression in CAA patients are likely not due to differences in global CAA severity between men and women. Other factors, such as vascular remodelling, may contribute, but future studies are necessary to replicate our findings in larger datasets and to further investigate the underlying mechanisms behind these complex sex differences.

Multi-Template Meta-Information Regularized Network for Alzheimer's Disease Diagnosis Using Structural MRI.

Structural magnetic resonance imaging (sMRI) has been widely applied in computer-aided Alzheimer's disease (AD) diagnosis, owing to its capabilities in providing detailed brain morphometric patterns and anatomical features in vivo. Although previous works have validated the effectiveness of incorporating metadata (e.g., age, gender, and educational years) for sMRI-based AD diagnosis, existing methods solely paid attention to metadata-associated correlation to AD (e.g., gender bias in AD prevalence) or confounding effects (e.g., the issue of normal aging and metadata-related heterogeneity). Hence, it is difficult to fully excavate the influence of metadata on AD diagnosis. To address these issues, we constructed a novel Multi-template Meta-information Regularized Network (MMRN) for AD diagnosis. Specifically, considering diagnostic variation resulting from different spatial transformations onto different brain templates, we first regarded different transformations as data augmentation for self-supervised learning after template selection. Since the confounding effects may arise from excessive attention to meta-information owing to its correlation with AD, we then designed the modules of weakly supervised meta-information learning and mutual information minimization to learn and disentangle meta-information from learned class-related representations, which accounts for meta-information regularization for disease diagnosis. We have evaluated our proposed MMRN on two public multi-center cohorts, including the Alzheimer's Disease Neuroimaging Initiative (ADNI) with 1,950 subjects and the National Alzheimer's Coordinating Center (NACC) with 1,163 subjects. The experimental results have shown that our proposed method outperformed the state-of-the-art approaches in both tasks of AD diagnosis, mild cognitive impairment (MCI) conversion prediction, and normal control (NC) vs. MCI vs. AD classification.

Association of Heart Failure With Cognitive Decline and Development of Mild Cognitive Impairment and Dementia

Background Incidence of cognitive impairment and its consequences have not been fully examined in heart failure (HF). Objective The aim of this study was to examine associations of HF with cognitive decline, frequencies and risks of, and time-to-develop mild cognitive impairment (MCI) or dementia during 15-year follow-up. Methods For this retrospective cohort study, data were retrieved from the National Alzheimer's Coordinating Center. Cognitive decline was assessed using the Uniform Data Set neuropsychological battery. Development of MCI and dementia was assessed using clinically diagnosed cognitive status. Results Compared with participants without HF (n = 12 904), participants with HF (n = 256) had more decline in attention, executive function, and memory while controlling for covariates including apolipoprotein E4. Participants with HF developed MCI or dementia more frequently (44.9% vs 34.4%), developed dementia faster from normal cognition, and had a lower risk of dementia from MCI after controlling for covariates (hazard ratio, 0.71) than participants without HF. Conclusions Heart failure was associated with accelerated cognitive decline.

Neuropsychiatric symptoms and white matter hyperintensities in older adults without dementia.

We aimed to examine associations between neuropsychiatric symptoms (NPS) and white matter hyperintensities (WMH) status in older adults without dementia under the hypothesis that WMH increased the odds of having NPS. Longitudinal analysis of data acquired from the National Alzheimer's Coordinating Center Uniform Data Set. Data were derived from 46 National Institute on Aging - funded Alzheimer's Disease Research Centers. NACC participants aged ≥50 years with available data on WMH severity with a diagnosis of mild cognitive impairment (MCI) or who were cognitively unimpaired (CU) were studied. Among 4617 CU participants, 376 had moderate and 54 extensive WMH. Among 3170 participants with MCI, 471 had moderate and 88 had extensive WMH. Using Cardiovascular Health Study (CHS) scores, WMH were coded as no to mild (CHS score: 0-4), moderate (score: 5-6) or extensive (score: 7-8). NPS were quantified on the Neuropsychiatric Inventory Questionnaire. Binary logistic regression models estimated the odds of reporting each of 12 NPS by WMH status separately for individuals with MCI or who were CU. Compared to CU individuals with no to mild WMH, the odds of having elation [9.87, (2.63-37.10)], disinhibition [4.42, (1.28-15.32)], agitation [3.51, (1.29-9.54)] or anxiety [2.74, (1.28-5.88)] were higher for the extensive WMH group, whereas the odds of having disinhibition were higher for the moderate WMH group [1.94, (1.05-3.61)]. In the MCI group, he odds of NPS did not vary by WMH status. Extensive WMH were associated with higher odds of NPS in CU older adults but not in those with MCI.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.

Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships. 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features. The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05). The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. Cluster analysis used neuropsychological data from participants without dementia (mean age=71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n=26,255) and the "normal cognition" subsample (n=16,005). Survival analyses examined MCI or dementia progression. Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN<tCN<aMCI<mMCI-Mild<mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All<Low-WM=Low-Memory<aMCI<naMCI). Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Extending the phenotypic spectrum assessed by the CDR plus NACC FTLD in genetic frontotemporal dementia.

We aimed to expand the range of the frontotemporal dementia (FTD) phenotypes assessed by the Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains (CDR plus NACC FTLD). Neuropsychiatric and motor domains were added to the standard CDR plus NACC FTLD generating a new CDR plus NACC FTLD-NM scale. This was assessed in 522 mutation carriers and 310 mutation-negative controls from the Genetic Frontotemporal dementia Initiative (GENFI). The new scale led to higher global severity scores than the CDR plus NACC FTLD: 1.4% of participants were now considered prodromal rather than asymptomatic, while 1.3% were now considered symptomatic rather than asymptomatic or prodromal. No participants with a clinical diagnosis of an FTD spectrum disorder were classified as asymptomatic using the new scales. Adding new domains to the CDR plus NACC FTLD leads to a scale that encompasses the wider phenotypic spectrum of FTD with further work needed to validate its use more widely. The new Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains neuropsychiatric and motor (CDR plus NACC FTLD-NM) rating scale was significantly positively correlated with the original CDR plus NACC FTLD and negatively correlated with the FTD Rating Scale (FRS).No participants with a clinical diagnosis in the frontotemporal dementia spectrum were classified as asymptomatic with the new CDR plus NACC FTLD-NM rating scale.Individuals had higher global severity scores with the addition of the neuropsychiatric and motor domains.A receiver operating characteristic analysis of symptomatic diagnosis showed nominally higher areas under the curve for the new scales.

Difference of Cerebrospinal Fluid Biomarkers and Neuropsychiatric Symptoms Profiles among Normal Cognition, Mild Cognitive Impairment, and Dementia Patient.

The delineation of biomarkers and neuropsychiatric symptoms across normal cognition, mild cognitive impairment (MCI), and dementia stages holds significant promise for early diagnosis and intervention strategies. This research investigates the association of neuropsychiatric symptoms, evaluated via the Neuropsychiatric Inventory (NPI), with cerebrospinal fluid (CSF) biomarkers (Amyloid-β42, P-tau, T-tau) across a spectrum of cognitive states to enhance diagnostic accuracy and treatment approaches. Drawing from the National Alzheimer's Coordinating Center's Uniform Data Set Version 3, comprising 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. To assess neuropsychiatric symptoms, we employed the NPI to understand the behavioral and psychological symptoms associated with each cognitive category. For the analysis of CSF biomarkers, we measured levels of Amyloid-β42, P-tau, and T-tau using the enzyme-linked immunosorbent assay (ELISA) and Luminex multiplex xMAP assay protocols. These biomarkers are critical in understanding the pathophysiological underpinnings of Alzheimer's disease and its progression, with specific patterns indicative of disease stage and severity. This study cohort consists of 1896 participants, which is composed of 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. Dementia is characterized by significantly higher NPI scores, which are largely reflective of mood-related symptoms (p < 0.001). In terms of biomarkers, normal cognition shows median Amyloid-β at 656.0 pg/mL, MCI at 300.6 pg/mL, and dementia at 298.8 pg/mL (p < 0.001). Median P-tau levels are 36.00 pg/mL in normal cognition, 49.12 pg/mL in MCI, and 58.29 pg/mL in dementia (p < 0.001). Median T-tau levels are 241.0 pg/mL in normal cognition, 140.6 pg/mL in MCI, and 298.3 pg/mL in dementia (p < 0.001). Furthermore, the T-tau/Aβ-42 ratio increases progressively from 0.058 in the normal cognition group to 0.144 in the MCI group, and to 0.209 in the dementia group (p < 0.001). Similarly, the P-tau/Aβ-42 ratio also escalates from 0.305 in individuals with normal cognition to 0.560 in MCI, and to 0.941 in dementia (p < 0.001). The notable disparities in NPI and CSF biomarkers among normal, MCI and Alzheimer's patients underscore their diagnostic potential. Their combined assessment could greatly improve early detection and precise diagnosis of MCI and dementia, facilitating more effective and timely treatment strategies.

Characterization of African-American Super-Agers in the National Alzheimer's Coordinating Center cohort.

"Super-agers" are adults aged ≥80 with cognitive performance similar to persons two to three decades younger. Characteristics such as larger hippocampal volume, APOE-ε4 allele absence, higher educational attainment, female sex, and lifelong cognitive stimulation are associated with cognitive performance compatible with super-aging. These findings are based on predominantly white research samples. Limited data are available on African-American super-agers. To fill this gap, we explored potential factors associated with super-aging in older African-American adults. Data from African-American participants aged ≥80 in the National Alzheimer's Coordinating Center (NACC) dataset were analyzed. Using global Clinical Dementia Rating (CDR) scores, participants were first categorized as impaired (score ≥0.5) or non-impaired/normal cognition (NC) (score = 0). From the NC group, super-agers were identified using NACC-data-driven cutoffs. Participants were considered super-agers if their memory performance was similar to persons aged 50-60 with NC, and their performance on other domains was within one standard deviation of the mean for persons aged ≥80. We examined group characteristics (NC, super-ager, impaired) using chi-square and ANOVA with pairwise comparisons. Multinomial logistic regression, adjusted for sex and education, evaluated correlates of super-ager group assignment. Data for 1285 African-American participants aged ≥80 were analyzed. We identified 24.7% (n = 316) NC, 4.8% (n = 61) super-agers, and 70.6% (n = 905) impaired. Super-agers were mostly female and more educated, had similar vascular comorbidities as the other groups, and had less sleep disorders, depression, and alcohol use. After adjusting for sex and education, super-ager group assignment was associated with less sleep disorders, less depression, and moderate alcohol use. Participants with controlled vascular risk, mental health, alcohol use, and sleep disorders tended to be in the super-ager group. These factors may be important focus areas in clinical practice to support cognitive resilience with aging in older African-American adults.

Dissecting Alzheimer's Disease Risk in Asian American Elders: A Classification and Regression Tree Approach.

Alzheimer's disease (AD) poses a growing public health challenge, particularly with an aging population. While extensive research has explored the relationships between AD, socio-demographic factors, and cardiovascular risk factors, a notable gap exists in understanding these connections within the Asian American elderly population. This study aims to address this gap by employing the Classification and Regression Tree (CART) approach to investigate the intricate interplay of socio-demographic variables, cardiovascular risk factors, sleep patterns, prior antidepressant use, and AD among Asian American elders. Data from the 2017 Uniform Data Set, provided by the National Alzheimer's Coordinating Center, were analyzed, focusing on a sample of Asian American elders (n = 4,343). The analysis utilized the Classification and Regression Tree (CART) approach. CART analysis identified critical factors, including levels of independence, specific age thresholds (73.5 and 84.5 years), apnea, antidepressant use, and body mass index, as significantly associated with AD risk. These findings have far-reaching implications for future research, particularly in examining the roles of gender, cultural nuances, socio-demographic factors, and cardiovascular risk elements in AD within the Asian American elderly population. Such insights can inform tailored interventions, improved healthcare access, and culturally sensitive policies to address the complex challenges posed by AD in this community.

Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition. We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency. We subsequently tested large-scale brain network FC as a potential mediator of the beneficial relationship between social engagement and cognitive performance. 112 older adults (70.7±7.3 years, range 54.6-89.7; 84 women) completed the Lubben Social Network Scale 6 (LSNS-6), National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3 (UDS-3) cognitive battery, and resting state fMRI. We completed seed-based correlational analysis in the default mode and salience networks. Significant associations between social engagement scores and cognitive performance, as well as between social engagement and FC of brain networks, informed the construction of mediation models. Social engagement was significantly associated with executive function and global cognition, with greater social engagement associated with better cognitive performance. Social engagement was significantly associated with salience network FC, with greater social engagement associated with higher connectivity. Salience network FC partially mediated associations between social engagement and both executive function and global cognition. Our results suggest that the salience network is a key mediator of the beneficial relationship between social engagement and cognition in older adults.

The Montreal Cognitive Assessment: Norms and Reliable Change Indices for Standard and MoCA-22 Administrations.

The Montreal Cognitive Assessment (MoCA) is among the most frequently administered cognitive screening tests, yet demographically diverse normative data are needed for repeated administrations. Data were obtained from 18,410 participants using the National Alzheimer's Coordinating Center Uniform Data Set. We developed regression-based norms using Tobit regression to account for ceiling effects, explored test-retest reliability of total scores and by domain stratified by age and diagnosis with Cronbach's alpha, and reported the cumulative change frequencies for individuals with serial MoCA administrations to gage expected change. Strong ceiling effects and negative skew were observed at the total score, domain, and item levels for the cognitively normal group, and performances became more normally distributed as the degree of cognitive impairment increased. In regression models, years of education was associated with higher MoCA scores, whereas older age, male sex, Black and American Indian or Alaska Native race, and Hispanic ethnicity were associated with lower predicted scores. Temporal stability was adequate and good at the total score level for the cognitively normal and cognitive disorders groups, respectively, but fell short of reliability standards at the domain level. MoCA total scores are adequately reproducible among those with cognitive diagnoses, but domain scores are unstable. Robust regression-based norms should be used to adjust for demographic performance differences, and the limited reliability, along with the ceiling effects and negative skew, should be considered when interpreting MoCA scores.

Robust reference group normative data for neuropsychological tests accounting for primary language use in Asian American older adults.

The present study aimed to develop neuropsychological norms for older Asian Americans with English as a primary or secondary language, using data from the National Alzheimer's Coordinating Center (NACC). A normative sample of Asian American participants was derived from the NACC database using robust criteria: participants were cognitively unimpaired at baseline (i.e., no MCI or dementia) and remained cognitively unimpaired at 1-year follow-up. Clinical and demographic characteristics were compared between Primary and Secondary English speakers using analyses of variance for continuous measures and chi-square tests for categorical variables. Linear regression models compared neuropsychological performance between the groups, adjusting for demographics (age, sex, and education). Regression models were developed for clinical application to compute demographically adjusted z-scores. Secondary English speakers were younger than Primary English speakers (p < .001). There were significant differences between the groups on measures of mental status (Mini-Mental State Examination, p = .002), attention (Trail Making Test A, Digit Span Forward Total Score, p <.001), language (Boston Naming Test, Animal Fluency, Vegetable Fluency, p < .001), and executive function (Trail Making Test B, p = .02). Separate normative data are needed for Primary vs. Secondary English speakers from Asian American backgrounds. We provide normative data on older Asian Americans to enable clinicians to account for English use in the interpretation of neuropsychological assessment scores.

Investigating the impact of hypertension with and without diabetes on Alzheimer's disease risk: A clinico-pathological study.

Hypertension and diabetes are common cardiovascular risk factors that increase Alzheimer's disease (AD) risk. However, it is unclear whether AD risk differs in hypertensive individuals with and without diabetes. Cognitively normal individuals (N=11,074) from the National Alzheimer's Coordinating Center (NACC) were categorized as having (1) hypertension with diabetes (HTN+/DM+), (2) hypertension without diabetes (HTN+/DM-), or (3) neither (HTN-/DM-). AD risk in HTN+/DM+ and HTN+/DM- was compared to HTN-/DM-. This risk was then investigated in those with AD neuropathology (ADNP), cerebral amyloid angiopathy (CAA), cerebrovascular neuropathology (CVNP), arteriolosclerosis, and atherosclerosis. Finally, AD risk in HTN-/DM+ was compared to HTN-/DM-. Seven percent (N=830) of individuals developed AD. HTN+/DM+ (hazard ratio [HR]=1.31 [1.19-1.44]) and HTN+/DM- (HR=1.24 [1.17-1.32]) increased AD risk compared to HTN-/DM-. AD risk was greater in HTN+/DM+ with ADNP (HR=2.10 [1.16-3.79]) and CAA (HR=1.52 [1.09-2.12]), and in HTN+/DM- with CVNP (HR=1.54 [1.17-2.03]). HTN-/DM+ also increased AD risk (HR=1.88 [1.30-2.72]) compared to HTN-/DM-. HTN+/DM+ and HTN+/DM- increased AD risk compared to HTN-/DM-, but pathological differences between groups suggest targeted therapies may be warranted based on cardiovascular risk profiles. AD risk was studied in hypertensive (HTN+) individuals with/without diabetes (DM+/-). HTN+/DM+ and HTN+/DM- both had an increased risk of AD compared to HTN-/DM-. Post mortem analysis identified neuropathological differences between HTN+/DM+ and HTN+/DM-. In HTN+/DM+, AD risk was greater in those with AD neuropathology and CAA. In HTN+/DM-, AD risk was greater in those with cerebrovascular neuropathology.

VentRa: distinguishing frontotemporal dementia from psychiatric disorders.

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

Depression, Vascular Burden, and Dementia Prevalence in Late Middle-Aged and Older Black Adults.

Late-life depression and white matter hyperintensities (WMH) have been linked to increased dementia risk. However, there is a dearth of literature examining these relationships in Black adults. We investigated whether depression or WMH volume are associated with a higher likelihood of dementia diagnosis in a sample of late middle-aged to older Black adults, and whether dementia prevalence is highest in individuals with both depression and higher WMH volume. Secondary data analysis involved 443 Black participants aged 55+ with brain imaging within 1 year of baseline visit in the National Alzheimer's Coordinating Center Uniform Data Set. Chi-square analyses and logistic regression models controlling for demographic variables examined whether active depression in the past 2 years, WMH volume, or their combination were associated with higher odds of all-cause dementia. Depression and higher WMH volume were associated with a higher prevalence of dementia. These associations remained after controlling for demographic factors, as well as vascular disease burden. Dementia risk was highest in the depression/high WMH volume group compared to the depression-only group, high WMH volume-only group, and the no depression/low WMH volume group. Post hoc analyses comparing the Black sample to a demographically matched non-Hispanic White sample showed associations of depression and the combination of depression and higher WMH burden with dementia were greater in Black compared to non-Hispanic White individuals. Results suggest late-life depression and WMH have independent and joint relationships with dementia and that Black individuals may be particularly at risk due to these factors.

Impact of incidental synucleinopathy in mild cognitive impairment due to Alzheimer disease.

Recent evidence suggests that the presence of α-synuclein Lewy bodies (LBs) correlates with accelerated disease progression in patients with Alzheimer disease (AD) but it is unclear whether this effect is also exerted in the mild cognitive impairment (MCI) phase of AD. We sought to determine whether incidental LB pathology in patients with MCI due to AD is associated with a faster rate of cognitive decline compared to MCI controls without LB pathology. We identified patients within the National Alzheimer's Coordinating Center (NACC) database with MCI due to AD and stratified the cohort by the presence or absence of synucleinopathy. We utilized a repeated measures longitudinal analysis of Mini-Mental State Examination (MMSE) scores to determine whether the decline in performance occurred at a greater rate in the synucleinopathy patients. A total of 206 participants were studied; 80 had coincident synucleinopathy. The rate of decline in MMSE scores between the groups did not differ. This may suggest that a synergistic effect of LB and AD neuropathology is only appreciable in the later stages of disease progression. Further investigation into the effect of mixed LB and AD pathology in the early stages of cognitive impairment is warranted to highlight opportunities for targeted early intervention in patients.