National Alzheimer's Coordinating Center Research Articles

Use of alpha-adrenergic antagonists for lower urinary tract symptoms is not associated with worsening cognitive function.

Alpha-adrenergic antagonists are widely prescribed for lower urinary tract symptoms (LUTS), however there has been a report that their use is associated with dementia. Our objective was to investigate if new users of alpha-adrenergic antagonists with varying levels of cognitive impairment had an increased risk of cognitive decline compared to non-users. This was a retrospective cohort study, utilizing data from the National Alzheimer's Coordinating Center (NACC) data set. After applying relevant exclusion criteria, 916 people who were newly using alpha-antagonist medications were matched with a propensity score to 916 who were not using these medications. The primary outcome was a clinically relevant cognitive decline measured by the Clinical Dementia Rating (CDR) Dementia Staging Instrument or the mini mental state examination (MMSE). Secondary outcomes included scores from other cognitive assessment tools. The matched cohorts did not differ significantly in baseline characteristics. There were no statistically significant differences in baseline or follow-up cognitive scores between those exposed and nonexposed to alpha-adrenergic antagonists. Clinically significant cognitive decline (as defined by the CDR) occurred in 9.72% of the exposed group and 8.19% of the nonexposed group. There was no observed effect of alpha-adrenergic antagonists on cognitive decline, as measured with the CDR (odds ratio [OR] 1.34, p = 0.14) or the MMSE (OR 0.98, p = 0.92). Stratified analyses by cognitive status and apolipoprotein E genotype interaction assessment also demonstrated no significant associations. Alpha-adrenergic antagonists for LUTS do not appear to increase the risk of cognitive decline, offering reassurance to clinicians and patients.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.

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 magnetic resonance imaging (MRI) markers and local cortical iron burden and the interaction of sex on factors associated with cortical iron burden (CAA percentage area and vessel remodeling) 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 80 years), the presence of parenchymal amyloid-β (odds ratio (OR) (95% confidence interval (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 75 years), 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 remodeling, may contribute, but future studies are necessary to replicate our findings in larger data sets and to further investigate the underlying mechanisms behind these complex sex differences.

Cognitive trajectories preluding the onset of different dementia entities: a descriptive longitudinal study using the NACC database

ObjectiveTo describe the 10-year preclinical cognitive trajectories of older, non-demented individuals towards the onset of the four most prevalent types of dementia, i.e., Alzheimer’s disease(AD), Lewy body(LBD), vascular(VD) and frontotemporal dementia(FTD).MethodsOur analysis focused on data from older (≥ 60years) NACC (National Alzheimer’s Coordinating Center) participants. Four distinct presymptomatic dementia groups (AD-LBD-VD-FTD) and a comparison group of cognitively unimpaired(CU) participants were formed. Comprehensive cognitive assessments involving verbal episodic memory, semantic verbal fluency, confrontation naming, mental processing speed – attention and executive function – cognitive flexibility were conducted at baseline and on an approximately yearly basis. Descriptive analyses (adjusted general linear models) were performed to determine and compare the yearly cognitive scores of each group throughout the follow-up. Exploratory analyses were conducted to estimate the rates of cognitive decline.ResultsThere were 3343 participants who developed AD, 247 LBD, 108 FTD, 155 VD and 3398 composed the CU group. Participants with AD performed worse on episodic memory than those with VD and LBD for about 3 to 4 years prior to dementia onset (the FTD group documented an intermediate course). Presymptomatic verbal fluency and confrontation naming trajectories differentiated quite well between the FTD group and the remaining dementia entities. Participants with incident LBD and VD performed worse than those with AD on executive functions and mental processing speed-attention since about 5 years prior to the onset of dementia, and worse than those with FTD more proximally to the diagnosis of the disorder.ConclusionsHeterogeneous cognitive trajectories characterize the presymptomatic courses of the most prevalent dementia entities.

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 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.

Drivers of Memory Loss Underreport in Mild Cognitive Impairment Due to Alzheimer Versus Vascular Disease.

We examined drivers of self and study partner reports of memory loss in mild cognitive impairment (MCI) from Alzheimer (AD-MCI) and vascular disease (Va-MCI). We performed retrospective cross-sectional analyses of participants with AD-MCI (n=2874) and Va-MCI (n=376) from the National Alzheimer Coordinating Center data set. Statistical analysis utilized 2-sided t test or the Fisher exact test. Compared with AD-MCI, Va-MCI subjects (24.5% vs. 19.7%, P =0.031) and study partners (31.4% vs. 21.6%, P <0.0001) were more likely to deny memory loss. Black/African Americans were disproportionately represented in the group denying memory loss in AD-MCI (20.0% vs. 13.2%, P <0.0001) and Va-MCI (33.7% vs. 18.0%, P =0.0022). Study partners of participants with these features also disproportionately denied memory loss: female (AD-MCI: 60.1% vs. 51.7%, P =0.0002; Va-MCI: 70.3% vs. 52.3%, P =0.0011), Black/African American (AD-MCI: 23.5% vs. 11.98%, P <0.0001; Va-MCI: 48.8% vs. 26.5%, P =0.0002), and <16 years of education (AD-MCI only: 33.9% vs. 16.3%, P =0.0262). In AD-MCI and Va-MCI, participants with anxiety were disproportionately represented in the group endorsing memory loss (AD: 28.2% vs. 17.4%, P <0.0001; Va: 31.5% vs. 16.1%, P =0.0071), with analogous results with depression. The findings would suggest extra vigilance in interview-based MCI detection of persons at-risk for self-based or informant-based misreport.

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.

Incidence of cognitive impairment and its consequences have not been fully examined in heart failure (HF). 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. 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. 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. Heart failure was associated with accelerated cognitive decline.

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.

Mapping of Alzheimer’s disease related data elements and the NIH Common Data Elements

BackgroundAlzheimer’s Disease (AD) is a devastating disease that destroys memory and other cognitive functions. There has been an increasing research effort to prevent and treat AD. In the US, two major data sharing resources for AD research are the National Alzheimer’s Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI); Additionally, the National Institutes of Health (NIH) Common Data Elements (CDE) Repository has been developed to facilitate data sharing and improve the interoperability among data sets in various disease research areas.MethodTo better understand how AD-related data elements in these resources are interoperable with each other, we leverage different representation models to map data elements from different resources: NACC to ADNI, NACC to NIH CDE, and ADNI to NIH CDE. We explore bag-of-words based and word embeddings based models (Word2Vec and BioWordVec) to perform the data element mappings in these resources.ResultsThe data dictionaries downloaded on November 23, 2021 contain 1,195 data elements in NACC, 13,918 in ADNI, and 27,213 in NIH CDE Repository. Data element preprocessing reduced the numbers of NACC and ADNI data elements for mapping to 1,099 and 7,584 respectively. Manual evaluation of the mapping results showed that the bag-of-words based approach achieved the best precision, while the BioWordVec based approach attained the best recall. In total, the three approaches mapped 175 out of 1,099 (15.92%) NACC data elements to ADNI; 107 out of 1,099 (9.74%) NACC data elements to NIH CDE; and 171 out of 7,584 (2.25%) ADNI data elements to NIH CDE.ConclusionsThe bag-of-words based and word embeddings based approaches showed promise in mapping AD-related data elements between different resources. Although the mapping approaches need further improvement, our result indicates that there is a critical need to standardize CDEs across these valuable AD research resources in order to maximize the discoveries regarding AD pathophysiology, diagnosis, and treatment that can be gleaned from them.

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.

Impact of motor dysfunction on neuropsychiatric symptom profile in patients with autopsy-confirmed Alzheimer’s disease

Motor dysfunction, which includes changes in gait, balance, and/or functional mobility, is a lesser-known feature of Alzheimer’s Disease (AD), especially as it relates to the development of neuropsychiatric symptoms (NPS). This study (1) compared rates of NPS between autopsy-confirmed AD patients with and without early-onset motor dysfunction and (2) compared rates of non-AD dementia autopsy pathology (Lewy Body disease, Frontotemporal Lobar degeneration) between these groups. This retrospective longitudinal cohort study utilized National Alzheimer’s Coordinating Center (NACC) data. Participants (N = 856) were required to have moderate-to-severe autopsy-confirmed AD, Clinical Dementia Rating-Global scores of ≤1 at their index visit, and NPS and clinician-rated motor data. Early motor dysfunction was associated with significantly higher NPI-Q total scores (T = 4.48, p < .001) and higher odds of delusions (OR [95%CI]: 1.73 [1.02–2.96]), hallucinations (2.45 [1.35–4.56]), depression (1.51 [1.11–2.06]), irritability (1.50 [1.09–2.08]), apathy (1.70 [1.24–2.36]), anxiety (1.38 [1.01–1.90]), nighttime behaviors (1.98 [1.40–2.81]), and appetite/eating problems (1.56 [1.09–2.25]). Early motor dysfunction was also associated with higher Lewy Body disease pathology (1.41 [1.03–1.93]), but not Frontotemporal Lobar degeneration (1.10 [0.71–1.69]), on autopsy. Our results suggest that motor symptoms in early AD are associated with a higher number and severity of NPS, which may be partially explained by comorbid non-AD neuropathology.

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.

236 Association Between Gait Limitation and Alzheimer’s Disease: Comparison among Elderly in Puerto Rico and Mainland U.S.

OBJECTIVES/GOALS: In 2022, 116,000 Puerto Ricans were estimated to be affected by Alzheimer’s disease (AD). Gait limitation (GL) has been associated with different stages of AD. We study the GL-AD association in two elderly populations: Hispanics residing in PR and Americans in the mainland U.S., and identify the effects, and social determinants of health. METHODS/STUDY POPULATION: We propose a cross-sectional study involving data analysis from elderly (&gt;65 years) Hispanics in PR and Americans in the U.S. Data will come from the National Alzheimer Coordinating Center (NACC) and a cohort of AD in PR. Our analysis will consider gait parameters, cognitive performance assessments, and AD stage (mild, moderate, and severe) as determined by the Alzheimer’s Association. We will examine social determinants of health and their association with GL and cognitive performance by AD. RESULTS/ANTICIPATED RESULTS: Our analysis will evaluate data about GL and AD stages to 1) investigate the association between GL and the distinct stages of AD and 2) evaluate the effects of social determinants of health in the association between GL and AD. We seek to profoundly understand how these factors interact within elderly populations. This exploration encompasses a diverse demographic, including elderly individuals in the U.S. and PR. We seek to identify disparities in social determinants among elderly individuals experiencing GL and AD by comparing PR and US populations. Through this association analysis, our study aspires to offer insight into the connections between GL and AD while considering the effects of social determinants of health in diverse populations. DISCUSSION/SIGNIFICANCE: The prevalence of AD is higher among Hispanic populations (e.g., Puerto Ricans) in the USA when compared to other ethnic groups. Experiencing GL may affect their daily lives, leading to exacerbating AD stages. The findings from this study will contribute to possible interventions to improve their GL and AD progression.

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.

The cognitive effects of stopping and starting antipsychotics on changes in cognitive functioning

Objectives The current study uses longitudinal data from the National Alzheimer’s Coordinating Center (NACC) to assess the effects of antipsychotic medication use on changes in cognitive functioning among adults in the United States. Methods Linear mixed models were conducted that included study visits, days between visits, sex, age, education, and medical history (i.e. diabetes, seizures, traumatic brain injury, stroke, and Parkinson’s disease). The Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to create variables assessing changes in psychotic symptoms, manic symptoms, and agitation/disinhibition. Results The results indicated that starting an antipsychotic medication was associated with a greater decline in semantic fluency (Fruit and Vegetable Naming), processing speed (Digit Symbol and Trail Making Test, Part A), and cognitive flexibility (Trail Making Test, Part B). Conversely, stopping an antipsychotic medication was protective against declines in the same cognitive skills. The effect of antipsychotic medications on cognitive functioning did not appear to differ by reported changes in psychiatric symptoms after adjusting for false discovery. Conclusion The results suggest that prescribers should consider discontinuance of an antipsychotic if the patient is reporting cognitive problems and their symptoms are manageable off the medications. The findings hope to spark future research into the effects of antipsychotic use on biomarkers of progressive dementias (e.g. Alzheimer’s disease).