Mild Cognitive Impairment Cases Research Articles

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.

Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study.

We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants. Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. Among the11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

Alzheimer's Disease Diagnosis via Intuitionistic Fuzzy Random Vector Functional Link Network

Alzheimer's disease (AD) is a prominent neurodegenerative disorder, which leads to memory loss and cognitive impairment. The progression is irreversible and shows atrophies in cerebral cortex. Multiple studies revealed that the early diagnosis and early treatment can slow the progress of dementia, and hence, further atrophies can be controlled. Brain imaging data, such as magnetic resonance imaging (MRI), have been prominently used for the diagnosis of AD. Multiple approaches have been proposed for the diagnosis of AD. We propose a novel intuitionistic fuzzy random vector functional link network (IFRVFL) for the diagnosis of AD. Unlike standard random vector functional link (RVFL) network, extreme learning machine (ELM), and kernel ridge regression (KRR), which uses a uniform weighting approach for generating the optimal classifier, the proposed IFRVFL uses a fuzzy weighted approach for generating the optimal classifier. A uniform weighting scheme assumes that all the data samples are equally important; however, in real-world scenarios, this assumption may not hold true due to the presence of outliers and noise. Hence, it results in lower generalization. The proposed IFRVFL assigns each sample an intuitionistic fuzzy number (IFN), which is a function of membership and nonmembership score of a sample. The membership score is a function of the sample distance from the centroid of its corresponding class and the nonmembership score is a function of sample distance from the centroid as well as the neighborhood of the given sample. The proposed IFRVFL effectively reduces the influence of outliers. To evaluate the efficiency of the proposed IFRVFL model, we employed it for the diagnosis of AD. Experimental results demonstrate that the proposed IFRVFL model is superior in mild cognitive impairment (MCI) versus AD case. Thus, IFRVFL can be used in the clinical setting for the early diagnosis of AD. Furthermore, to check the robustness of the proposed IFRVFL model, we also evaluated it on benchmark datasets. Experimental results and the statistical tests reveal that the proposed IFRVFL is better in comparison to baseline models. The source code of the proposed IFRVFL formulation is available at https://github.com/mtanveer1/.

EARLY DETECTION OF ALZHEIMER’S DISEASE AND RELATED DEMENTIAS FROM VOICE RECORDINGS: THE FRAMINGHAM HEART STUDY

Abstract With the aging global population and the increasing prevalence of dementia, there is a growing focus on identifying mild cognitive impairment (MCI), a pre-dementia state, to enable timely interventions that could potentially slow down neurodegeneration. Producing speech is a cognitively complex task that engages various cognitive domains, while the ease of audio data collection underscores the potential cost-effectiveness and noninvasiveness that voice may offer. This study aims to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification, to identify a set of acoustic features that exhibit strong MCI detection capability. The study included 100 MCI cases and 100 healthy controls (HC) matched for age, sex, and education from the Framingham Heart Study. Participants’ speech responses during cognitive tests were recorded, and the recorded audio was processed to identify segments of each participant’s voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using the OpenSMILE and Praat softwares. Subsequently, we constructed a random forest model using the features that exhibited significant differences between the MCI and HC groups. We identified an optimal subset of 29 features that resulted in an AUC of 0.87, with a 90% confidence interval ranging from 0.82 to 0.93. This study showcases the potential of human voice as a valuable resource for improving early detection of ADRD and motivates future opportunities to use passive voice collection tools, such as hearing aids, to measure brain health.

ApoER2-Dab1 disruption as the origin of pTau-associated neurodegeneration in sporadic Alzheimer’s disease

In sporadic Alzheimer’s disease (sAD) specific regions, layers and neurons accumulate hyperphosphorylated Tau (pTau) and degenerate early while others remain unaffected even in advanced disease. ApoER2-Dab1 signaling suppresses Tau phosphorylation as part of a four-arm pathway that regulates lipoprotein internalization and the integrity of actin, microtubules, and synapses; however, the role of this pathway in sAD pathogenesis is not fully understood. We previously showed that multiple ApoER2-Dab1 pathway components including ApoE, Reelin, ApoER2, Dab1, pP85αTyr607, pLIMK1Thr508, pTauSer202/Thr205 and pPSD95Thr19 accumulate together within entorhinal-hippocampal terminal zones in sAD, and proposed a unifying hypothesis wherein disruption of this pathway underlies multiple aspects of sAD pathogenesis. However, it is not yet known whether ApoER2-Dab1 disruption can help explain the origin(s) and early progression of pTau pathology in sAD. In the present study, we applied in situ hybridization and immunohistochemistry (IHC) to characterize ApoER2 expression and accumulation of ApoER2-Dab1 pathway components in five regions known to develop early pTau pathology in 64 rapidly autopsied cases spanning the clinicopathological spectrum of sAD. We found that (1) these selectively vulnerable neuron populations strongly express ApoER2; and (2) multiple ApoER2-Dab1 components representing all four arms of this pathway accumulate in abnormal neurons and neuritic plaques in mild cognitive impairment (MCI) and sAD cases and correlate with histological progression and cognitive deficits. Multiplex-IHC revealed that Dab1, pP85αTyr607, pLIMK1Thr508, pTauSer202/Thr205 and pPSD95Thr19 accumulate together within many of the same ApoER2-expressing neurons and in the immediate vicinity of ApoE/ApoJ-enriched extracellular plaques. Collective findings reveal that pTau is only one of many ApoER2-Dab1 pathway components that accumulate in multiple neuroanatomical sites in the earliest stages of sAD and provide support for the concept that ApoER2-Dab1 disruption drives pTau-associated neurodegeneration in human sAD.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Microglia from individuals with mild cognitive impairment (MCI) and AD show differences in transcriptional burst kinetics

AbstractBackgroundMicroglia are the innate immune cells in the central nervous system that responds to brain injury and pathogens. The loss of homeostatic microglia is associated with neurodegenerative diseases. Multiple studies of AD mouse models have identified AD‐associated microglia subtypes, e.g., disease associated microglia (DAM). However, studies of human microglia have not reported similar microglia subtypes. Therefore, how human microglia change in the presence of amyloid‐ protein deposition and phosphorylated Tau remains an open question.MethodWe have developed an approach that estimates transcriptional kinetics from scRNA‐seq data. This approach models how “bursty” and variable a gene expression is. We hypothesize that transcriptional bursts of key genes are different within AD cases compared to MCI individuals. We used the ROSMAP scRNAseq microglia dataset of 8 AD cases and 4 MCI cases and applied our approach to estimate the transcriptional kinetics for each subject. We then compared the difference in transcriptional kinetics of genes that are estimated both in AD and MCI.ResultFirst, we found that 96% of microglia cells in AD and MCI shows a similar co‐occurrence pattern of expressed genes and, therefore, likely reflect the same cell type. Within the assumed homogeneous microglia cells, we identified two genes, C1QC and ACTB, showing significant differences in their transcriptional kinetics between AD and MCI cases. Both C1QC and ACTB show larger burst sizes in MCI than in AD cases (p‐value for C1QC:0.0038; p‐value for ACTB: 0.00918). Consequently, C1QC and ACTB show a higher expression level variability among microglia cells in MCI cases than AD.ConclusionOur result suggests C1QC and ACTB may be associated with AD risk via changes in variability of gene expression rather than total gene expression levels. The increased variability in transcription of C1QC and ACTB in MCI individuals relative to AD cases might indicate more mobile and active physiological states of microglia. These results may point to functional changes in these genes with AD progression and are prime targets for further experimental validation.

Estimating the prevalence of mild cognitive impairment from clinical notes in U.S. Veterans

AbstractBackgroundIndividuals with mild cognitive impairment (MCI), a noticeable decline in cognitive abilities, are at increased risk of developing Alzheimer’s disease (AD) or other dementia. Risk factors for dementia, such as obesity, stroke, depression are highly prevalent among U.S. veterans We report on the trends in the prevalence of MCI using clinical notes over the past 5 years in the Veteran Affairs, the largest integrated healthcare system in the U.S.MethodPrevalent cases of MCI were identified from VA clinical notes for all Veterans aged 50 years and older in VA care from fiscal years (FYs) 2015‐19. VA clinical notes are maintained in searchable text integration utility files (TIU) available in the VA Corporate Data Warehouse (CDW). Pharmacy clinical notes were excluded. “Mild cognitive impairment” keyword was searched in clinical notes to identify patients with MCI. We validated the keyword “mild cognitive impairment” in 199 clinical notes. The positive predictive value for this validation was approximately 91%. For each FY, age and sex adjusted rates directly standardized to the 2010 U.S. Census general population were estimated.ResultA total of 308,930 prevalent cases of MCI were identified from VA clinical notes from FYs 2015‐19. Standardized prevalence rates for MCI increased steeply, with prevalence of 0.9% in FY 2015, increasing to 1.31% in FY 2019. Standardized age adjusted MCI prevalence rates also increased sharply for male (0.77% in FY 2015 to 1.10% in FY 2019) and female veterans (1.01% in FY 2015 to 1.50% in FY 2019) with rates higher for females than males for each FY.ConclusionWe observed recent trends of increasing prevalence rates for MCI among veterans identified from VA clinical notes. Considering the increased risk of dementia among individuals with MCI, our study finding of increasing prevalence of MCI among veterans highlights the critical need for effective interventions and treatment to target veterans with MCI.

Drug utilization among patients with mild cognitive impairment and Alzheimer’s disease in diverse populations: A large nationwide electronic health record cohort study

AbstractBackgroundCholinesterase inhibitors (ChE‐Is), such as donepezil, rivastigmine, and galantamine, and N‐methyl‐d‐aspartate receptor antagonists, such as memantine, are prescribed to decrease symptoms associated with cognitive impairment. Few studies have examined the initiation and utilization of these medications among diverse populations. This study’s objective was to characterize drug utilization among patients with mild cognitive impairment (MCI) and/or Alzheimer’s disease (AD) in the United States and stratify by gender, age and race/ethnicity.MethodThis retrospective cohort study was conducted using the Optum® Market Clarity database, which contains electronic health records and insurance claims. Cases of MCI and AD were identified utilizing the International Classification of Diseases, Ninth and Tenth Revisions diagnosis codes. Drug prescriptions were determined to occur before, on, or after the date of first diagnosis. All cases were required to have ≥12 months of enrollment in the database prior to first diagnosis. The study period was from January 2017 through September 2021. Descriptive statistics were calculated stratified by demographic groups.ResultA large number of cases were identified (197,346 MCI and 144,321 AD cases). Approximately 6% of individuals with MCI had prescriptions for ChE‐Is prior to diagnosis and 14% after diagnosis. For memantine, the proportions with prescriptions before and after MCI diagnosis were 2% and 5%, respectively. Among individuals with AD, prescriptions for ChE‐Is occurred in 22% prior to diagnosis and 32% after diagnosis. Memantine prescriptions increased from 10% prior to AD diagnosis to 18% after. The results did not differ by gender. The highest proportions with ChE‐Is and memantine prescriptions were older age groups &lt;85 years. When examining race/ethnic groups, the proportion of MCI and AD cases with these prescriptions was generally lowest among African Americans.ConclusionAs expected, prescriptions for ChE‐Is and memantine increased after diagnosis. The proportion of MCI and/or AD cases with prescriptions were similar across genders. The proportion with prescriptions was generally lowest among African Americans. Future research into the discrepancy in the occurrence of prescriptions by race/ethnicity as well as the timing of certain prescriptions will be valuable in further understanding the patient population and their access to appropriate medications.

Cognitive lapses reported by daily digital diary, and not by recall‐based questionnaires, predict future MCI in a demographically diverse cohort of community‐dwelling older adults

AbstractBackgroundAlthough cognitive lapses are a daily occurrence for many older adults, widely used self‐reports of cognitive function ask respondents to recollect lapses occurring over months or years. We used daily diaries to capture memory and other cognitive difficulties close in time to their occurrence and determined whether daily reports predicted concurrent and/or new cases of mild cognitive impairment (MCI) in the next year (i.e., at annual follow‐up).MethodIn the Einstein Aging Study, daily digital diary indicators of cognitive lapses were assessed each evening over a two‐week period (82% of diaries completed). Conventional recall‐based indicators were the Cognitive Change Index total score and a single item capturing worsening memory (with/without worry), both assessed at each annual visit. Using time‐dependent lagged models with generalized estimating equations (GEE) to correct for within person correlation, we evaluated the association of current cognitive lapses with the probability of being MCI at the current or next annual visit among those diagnosed with MCI after baseline (n = 31 new cases).ResultIn 307 community‐dwelling participants (Mage = 77.05, SD = 4.95; 67% female, 47% White) who were free of MCI at baseline, higher numbers of daily problems significantly predicted the likelihood of being diagnosed with MCI at the next annual visit (average number of visits = 2.72, range 1‐6 years). For every additional daily cognitive lapse, there was a 5% greater odds of being classified at the next visit (OR = 1.05, 95% CI: 1.01‐1.09). By contrast, conventional indicators did not significantly predict transition to MCI at the next visit (ps = .48 and .62, respectively). However, the single item regarding worsening memory was related to concurrent MCI diagnostic status (p&lt;.01). Sensitivity analyses examining the first 3 days or first 7 days to determine the number of daily reports required to identify new MCI cases indicated that both 3 and 7 days significantly predicted future MCI (OR = 1.34, 95% CI: 1.12‐1.61; OR = 1.07, 95% CI: 1.00‐1.14, respectively).ConclusionA brief daily diary assessment of cognitive lapses predicted new cases of MCI. The combination of daily and conventional methods may represent complementary approaches that offer the optimal approach for capturing self‐perceived cognitive deficits associated with current and future MCI.

CSF synapse marker changes in predementia Alzheimer´s Disease: AMPA receptor modulators NPTX2 and NPTXR mediate cognition within A/T/N stages

AbstractBackgroundSynapse pathology has been linked to excitotoxicity and glutamate receptor dysregulation (such as AMPAR) in Alzheimer´s Disease (AD) progression. Of the core cerebrospinal fluid (CSF) AD biomarkers for amyloid, tau pathology and neurodegeneration (A/T/N), T/N status is proposed to reflect levels of neuronal plasticity, but fluid biomarkers for precision interventions in subgroups with increased excitotoxicity are lacking. Herein, we assess group differences of 19 CSF synapse markers including the AMPAR‐modulating NPTX, in both cognitively normal (CN) and Mild Cognitive Impairment (MCI) with either A+/T‐/N‐ and A+/T or N+ (A+/T/N+) as compared to CN A‐/T‐/N‐ and assess relationships with verbal memory recall and hippocampal volume.MethodsA/T/N was determined using the CSF Aβ42/40 ratio, p‐tau181 and total‐tau in predementia AD cases (n = 194) and CN participants with normal AD biomarkers (n = 145) in the Dementia Disease Initiation cohort. CSF synapse marker concentrations were determined using mass‐spectrometry (table 1) and ELISA for GAP‐43. Adjusted for age and sex, we compared marker concentrations between CN cases (A+/T‐/N‐ n = 20; A+/T/N+ n = 40) and MCI cases (A+/T‐/N‐ n = 25;A+/T/N+ n = 109) with CN A‐/T‐/N‐ (n = 145) as the reference group. Associations to verbal memory recall and brain imaging were performed in the total sample and respective A/T/N groups adjusted for covariates. Results p&lt;.01 were considered significant.ResultsTop markers based on between‐group results are reported (Table 1 &amp; figure 1‐2 for all markers). Compared to CN A‐/T‐/N‐, 14‐3‐3 ζ/δ concentrations were higher in all A+/T/N+ groups (p&lt;.001), but unaltered in A+/T‐/N‐ groups. In A+/T‐/N‐, NPTX2 concentrations were lower in MCI (p&lt;.001) but not in CN. In A+/T/N+, NPTX2 was higher in CN (p&lt;.001) but not in MCI. 14‐3‐3 ζ/δ was associated with worse memory in the total sample (p&lt;.001) but not in the A+ groups. Lower NPTX2 was associated with worse memory in the total sample (p&lt;.001) and in both A+ groups (p&lt;.01; p&lt;001). Neither 14‐3‐3 ζ/δ nor NPTX2 were significantly associated with hippocampal volume (figure 2).ConclusionOverall, increased 14‐3‐3 ζ/δ was most closely associated with worse memory recall. Lower NPTX2 was linked to poorer cognitive status within A+ groups, suggesting links to AMPAR dysregulation and excitotoxicity.

Six‐month risk of non‐traumatic intracranial hemorrhage in cognitively impaired patients following Covid‐19 infection

AbstractBackgroundStudies suggest dementia patients have higher rates of non‐traumatic intracranial hemorrhage (nt‐ICH) compared to cognitively‐unimpaired peers. Recent studies also suggest increased risk of nt‐ICH, following infection with Covid‐19 in the general population. However, the risk of nt‐ICH following Covid‐19 infection in cognitively‐impaired individuals is currently unknown.MethodUsing US electronic health records ICD codes, we matched Alzheimer’s disease (AD), dementia, and mild cognitive impairment (MCI) cases with a history of Covid‐19 to similarly cognitively impaired controls without Covid‐19 history and examined incidence of nt‐ICH over six‐months following infection. Cases and controls were propensity score matched on age, sex, race, ethnicity, time since cognitive‐diagnosis, anticoagulant use, antiplatelet use, and 37 comorbidities. Analyses focused on the first year of the pandemic in 2020, prior to availability of at‐home tests and vaccines, to reduce misclassification bias. Individuals with prior history of nt‐ICH, traumatic ICH, or stroke were excluded to focus on incident nt‐ICH. Cox proportional hazard models compared risk between those with and without Covid‐19.ResultOur sample included 2,287 AD cases with Covid‐19 matched to 6,861 AD controls; 5,726 dementia cases matched to 17,178 dementia controls; and 807 MCI cases matched to 2,419 MCI controls. The incidence of nt‐ICH following Covid‐19, among AD cases, was 12.53/1,000 person‐years (95% CI: 7.66‐19.36) vs. 4.16/1,000 person‐years (95% CI: 2.51‐6.50) in AD patients without Covid‐19; HR = 3.1 (95% CI: 1.7, 5.8), p = 0.0004. Similarly in dementia patients, Covid‐19 increased the risk by 3.0 times (95% CI: 1.8, 4.7), p&lt;0.0001. The incidence in dementia Covid‐19 cases was 8.48/1,000 person‐years (95% CI: 5.91, 11.80) vs. 2.97/1,000 person‐years (95% CI: 2.07, 4.13) in dementia patients without a history of Covid‐19 over the study period. The incidence in MCI patients with a history of Covid‐19 was 5.03/1,000 person‐years (95% CI: 1.37, 12.87), vs. 1.56/1,000 person‐years (95% CI: 0.32, 4.57) in MCI controls, but risk was not significantly different; HR = 3.4 (95% CI: 0.8, 15.0), p = 0.11.ConclusionAlthough nt‐ICH was rare among cognitively impaired patients following Covid‐19 infection in 2020, the risk over 6‐months in AD and dementia cases was approximately 3 times higher than controls without a history of Covid‐19.

Incidence of Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) in the United States Medicare population

AbstractBackgroundAlzheimer’s disease (AD) is a chronic, neurodegenerative condition. Mild cognitive impairment (MCI) is characterized by cognitive decline, without dementia. We aimed to estimate the incidence of AD and MCI among Medicare beneficiaries.MethodPatients were identified from the US Center for Medicare and Medicaid Services (CMS) database using International Classification of Diseases (ICD) codes for AD (331.0, G30.9, G30.8, or G30.1,) and MCI (331.83 or G31.84). Both AD and MCI were identified using the search rule of 2 ICD codes ≥30 days apart ≤365 days from both Medicare fee‐for‐service (FFS: 2008‐2018) and Medicare Advantage (MA: 2015‐2018). US regions were derived using zip codes and categorized by National Center for Chronic Disease Prevention/Health Promotion regions. Yearly incidence was defined as the number of new cases of AD or MCI in each year divided by the population at risk in the year. Confidence intervals were not reported due to small intervals.ResultThe incidence of AD and MCI per 10,000 person‐years was 82.9 and 4.9, 80.3 and 7.4, 74.5 and 9.3, 69.8 and 10.9, 65.3 and 13.4, 62.8 and 15.8, 60.7 and 17.4, 65.5 and 20.7, 61.4 and 22.4, 56.0 and 22.9, and 52.2 and 23.2 spanning from 2008 through 2018, respectively. In 2018, the incidence of AD and MCI per 10,000 person‐years by demographic subgroups was: 43 and 21 for men and 59 and 25 for women; 16 and 10 among those aged 65‐69 years, 30 and 18 for ages 70‐74 years, 58 and 30 for ages 75‐79, 102 and 44 for ages 80‐84, 176 and 57 for age ≥85 years old; 56 and 26 for Whites, 38 and 13 for Blacks, and 43 and 15 for Asians. The incidence of AD and MCI per 10,000 person‐years in 2018 was highest in the US Northeast, especially in New Hampshire (77 and 39), Vermont (74 and 47), and New Jersey (73 and 33).ConclusionAmong Medicare beneficiaries from 2008 to 2018, AD incidence generally decreased while MCI increased. Women, older age, White race, and Northeast region were associated with higher incidence of AD and MCI in 2018.

Occurrence of sleep disorders, anxiety, and depression among patients with mild cognitive impairment and Alzheimer’s disease in diverse populations: A large nationwide electronic health record cohort study

AbstractBackgroundPatients affected with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) often experience anxiety, depression, and sleep disturbances. Some of the published studies reported a higher incidence of these disorders in minority populations. The objectives of this study were to characterize the occurrence of sleep disorders, anxiety, and depression among patients with MCI and/or AD, stratified by gender, age and race/ethnicity in the United States clinical practice setting.MethodA retrospective cohort study was conducted using electronic health records and insurance claims from the Optum® Market Clarity database containing data from more than 63 million people in the US insured through Medicare, Medicaid, and other insurance providers. The study included patients diagnosed, utilizing ICD diagnosis codes, with MCI and/or AD between January 2017 and September 2021. Prevalence of depression, anxiety, and sleep disorders, as well as associated medication prescriptions, were identified in the 12 months before and after MCI and/or AD diagnoses. The occurrence of these diagnoses and prescriptions was evaluated stratified by gender, age, and race/ethnicity.ResultA total of 197,346 patients were diagnosed with MCI (56.9% female, mean age of 70.0 [SD 14.8]) and 144,321 with AD (63.9% female, mean age of 79.3 [SD 8.2]) at the baseline. Overall, a large proportion of MCI and AD cases had depression (pre‐MCI diagnosis: 40.3%; post‐MCI diagnosis: 44.8%; pre‐AD diagnosis: 32.2%; post‐AD diagnosis: 38.3%), anxiety (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 40.2%; pre‐AD diagnosis: 26.4%; post‐AD diagnosis: 31.5%), and sleep disorders (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 39.3%; pre‐AD diagnosis: 23.2%; post‐AD diagnosis: 25.4%). Occurrences of depression and anxiety were highest among females. Among individuals with MCI and/or AD, the gap between the proportion experiencing depression and the proportion prescribed an antidepressant was greatest for African American males and females. This was not observed for anxiety and anti‐anxiety medications.ConclusionA large proportion of individuals with MCI and/or AD also had depression, anxiety, and sleep issues. Further understanding of the populations with these comorbidities and their associated prescriptions by different ages, sex, and races/ethnicities is important when supporting patients with MCI and AD.

Identification of leukocyte surface biomarkers associated with late‐onset Alzheimer’s disease

AbstractBackgroundAltered innate immunity has been long associated with late‐onset Alzheimer’s disease (AD) and its associated blood‐based biomarkers are important for AD diagnosis and prognosis.MethodWe collected 38 participants from the AIBL study, including 22 cognitive normal (CN) individuals with negative amyloid burden (Centiloid [CL] &lt; 15), 5 CN individuals with positive amyloid burden (CL &gt; 15), and 11 mild cognitive impairment (MCI) and AD cases. A total of 17 leukocyte surface antigens were examined by flow cytometry immunophenotyping and compared between healthy controls (CN Ab ‐ve), pre‐clinical patients (CN Ab +ve), and clinical cases (MCI &amp; AD), including CD36, MerTK, Clec7a, RAGE, Tyro3, CR1 (CD35), CX3CR1, CCR2, Axl, LILRB2, LILRB3, LILRB4, PILRA, and P2×7.ResultWe identified leukocyte surface markers differentially expressed between HC, pre‐clinical patients, and clinical cases. Mean fluorescence intensities of CD85d and CD85k were significantly reduced in pre‐clinical patients and cases compared with HC, implicating CD85d and CD85k downregulation in AD pathogenesis. Significant upregulation of RAGE, Tyro3, CCR2, CD85a, and PILRA was also noted in cases compared with HC. Leukocyte surface expressions of RAGE, CCR2, PILRA, and CD85k were significantly associated with the Preclinical Alzheimer’s Cognitive Composite (PACC), which is one of the most accurate estimates of cognition in AD diagnosis.ConclusionOur preliminary investigations into these leukocyte markers demonstrated their dysregulations in pre‐clinical stage of AD, implicating early deficits in innate immunity, as supported by genomic studies of AD. This project will continue collecting more AIBL participants up to 200 to evaluate leukocyte surface markers more comprehensively, which would benefit AD screening and diagnosis.

A 10‐minute Digitally Administered and Scored Neuropsychological Protocol Assessing Executive Control and Episodic Memory

AbstractBackgroundDigital assessment technology provides rapid and reliable assessment of neurocognitive abilities associated with Alzheimer’s Disease and Related Dementia (ADRD) and Mild Cognitive Impairment (MCI). Dysexecutive and amnestic impairment typify many ADRD and MCI disorders. Here, we evaluate a 10‐minute digitally administered protocol assessing these neurocognitive constructs.Method229 community dwelling participants were assessed (age = 68.32+9.85; education = 15.01+2.58, MMSE = 26.84+2.78; 144 females). Executive abilities were assessed with three trials of 5‐digits backward digit span test (BDST) and the ‘animal’ fluency test. Episodic memory was assessed with a 6‐word Philadelphia (repeatable) Verbal Learning Test (PrVLT) consisting of 2 immediate free recall (IFR) trials, delayed free recall (DFR), and delayed recognition. Traditional pencil and paper testing was not used. Data were obtained with an iPad processed using automatic speech recognition.ResultA K‐means cluster analysis (k = 3) classified participants with possible dementia (pDem = 31), MCI (n = 96), and cognitively normal (CN, n = 100; Table 1). Groups differed from each other on all three P(r)VLT parameters (p = 0.001). CN participants outperformed other groups on executive measures (p = 0.001). All between‐group analyses yielded large effect sizes (η2 = 0.277–0.642). Logistic regression analysis using P(r)VLT‐DFR, P(r)VLT‐recognition, BDST, and ‘animal’ fluency parameters classify MCI and pDem participants into their respective groups compared to CN participants (p = 0.003). For the pDem group, within‐group t‐tests found rapid forgetting (PrVLT‐DFT&lt;PrVLT‐IFR trial 2, p = 0.013; PrVLT‐recognition&lt;PrVLT‐DFR (p = 0.013). Additional, within‐group pDem group comparisons found no differences between P(r)VLT‐recognition and either executive tests. Within group comparisons for the MCI group found P(r)VLT delayed recognition improved compared to P(r)VLT delayed free recall (p&lt;0.001), and worse performance on both executive measures compared to P(r)VLT‐ recognition (p = .001). All of these comparisons are consistent with a mild dysexecutive syndrome.ConclusionThe protocol described above is efficient, reliable, removes any inter‐rater reliability issues, and is able to operationally‐define dysexecutive and amnestic cognitive impairment ‐ key neurocognitive constructs related to ADRD and MCI. This 10‐minute digitally administered and automatically scored neuropsychological protocol assessing executive abilities and episodic memory could easily be deployed in clinical trials and in primary‐ and specialty‐care settings to screen for emergent cases of MCI and ADRD by efficient assessment of episodic memory and executive abilities.

A 10‐minute Digitally Administered and Scored Neuropsychological Protocol Assessing Executive Control and Episodic Memory

AbstractBackgroundDigital assessment technology provides rapid and reliable assessment of neurocognitive abilities associated with Alzheimer’s Disease and Related Dementia (ADRD) and Mild Cognitive Impairment (MCI). Dysexecutive and amnestic impairment typify many ADRD and MCI disorders. Here, we evaluate a 10‐minute digitally administered protocol assessing these neurocognitive constructs.Method229 community dwelling participants were assessed (age = 68.32+9.85; education = 15.01+2.58, MMSE = 26.84+2.78; 144 females). Executive abilities were assessed with three trials of 5‐digits backward digit span test (BDST) and the ‘animal’ fluency test. Episodic memory was assessed with a 6‐word Philadelphia (repeatable) Verbal Learning Test (PrVLT) consisting of 2 immediate free recall (IFR) trials, delayed free recall (DFR), and delayed recognition. Traditional pencil and paper testing was not used. Data were obtained with an iPad processed using automatic speech recognition.ResultA K‐means cluster analysis (k = 3) classified participants with possible dementia (pDem = 31), MCI (n = 96), and cognitively normal (CN, n = 100; Table 1). Groups differed from each other on all three P(r)VLT parameters (p = 0.001). CN participants outperformed other groups on executive measures (p = 0.001). All between‐group analyses yielded large effect sizes (η2 = 0.277–0.642). Logistic regression analysis using P(r)VLT‐DFR, P(r)VLT‐recognition, BDST, and ‘animal’ fluency parameters classify MCI and pDem participants into their respective groups compared to CN participants (p = 0.003). For the pDem group, within‐group t‐tests found rapid forgetting (PrVLT‐DFT&lt;PrVLT‐IFR trial 2, p = 0.013; PrVLT‐recognition&lt;PrVLT‐DFR (p = 0.013). Additional, within‐group pDem group comparisons found no differences between P(r)VLT‐recognition and either executive tests. Within group comparisons for the MCI group found P(r)VLT delayed recognition improved compared to P(r)VLT delayed free recall (p&lt;0.001), and worse performance on both executive measures compared to P(r)VLT‐ recognition (p = .001). All of these comparisons are consistent with a mild dysexecutive syndrome.ConclusionThe protocol described above is efficient, reliable, removes any inter‐rater reliability issues, and is able to operationally‐define dysexecutive and amnestic cognitive impairment ‐ key neurocognitive constructs related to ADRD and MCI. This 10‐minute digitally administered and automatically scored neuropsychological protocol assessing executive abilities and episodic memory could easily be deployed in clinical trials and in primary‐ and specialty‐care settings to screen for emergent cases of MCI and ADRD by efficient assessment of episodic memory and executive abilities.

Diagnosis patterns among diverse populations with mild cognitive impairment and Alzheimer’s disease: A large nationwide electronic health record cohort study

AbstractBackgroundWith recent advances and emerging research on diagnostic biomarkers and evolving diagnostic criteria, practitioners managing patients with cognitive complaints operate within a complex, changing environment. Although some evidence is available on the importance of prompt diagnosis, little information exists on the diagnostic process used in real‐world settings among diverse populations. This study’s objective was to characterize diagnosis patterns among adults with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and/or dementia in the United States, and stratify by gender, age and race/ethnicity.MethodThis retrospective observational cohort study utilized the Optum® Market Clarity database from January 1, 2017–September 30, 2021. Eligible individuals had ≥1 diagnosis of MCI, AD, and/or dementia in their electronic health records or ≥2 diagnosis codes separated by ≥30 days in claims data, had ≥12 months of continuous enrollment before the cohort entry date, and had ≥1 day of continuous healthcare plan enrollment after the index date. Demographic information and the occurrence of diagnostic tests were also obtained from electronic medical records and insurance claims. Descriptive statistics were calculated and stratified by demographic factors.ResultA total of 179,419 newly diagnosed MCI cases, 81,267 AD cases, and 338,739 dementia cases were identified. The mean age at MCI, AD, or dementia diagnosis was similar overall for males and females. Among Caucasians, the mean age of MCI diagnosis was 70.5±14.8 for females and 69.8±14.5 for males. Among African Americans, the mean age at MCI diagnosis was 66.3±15.4 for females and 63.6±16.1 for males. PET scans and CSF biomarker screening occurred less often than MRI and CT (MCI: PET 0.6%, CSF 1.6%, MRI 16.6%, CT 17.5%; AD: PET 0.5%; CSF 0.9%; MRI 9.0%; CT 18.4%; dementia: PET 0.2%; CSF 1.6%; MRI 9.5%; CT 25.9%).ConclusionThe mean age at MCI, AD, and dementia diagnosis varied by demographic groups. The diagnosis of MCI, AD, and dementia remains largely clinical, with low use of brain imaging or CSF biomarkers. This knowledge of the patient populations and diagnostic experiences in a real‐world setting allows for greater understanding and growth to improve diagnosis across diverse groups and lead to faster support.

Genetic and Environmental Influences of Plasma Biomarkers and Their Relation to APOE status and Mild Cognitive Impairment

AbstractBackgroundBefore onset of Alzheimer’s dementia, aggregation of amyloid‐beta (Aβ) is thought to drive accumulation of Tau, followed by neurodegeneration (indexed here by neurofilament light chain [NFL]). Why or how Aβ increases Tau accumulation remains unclear. Using genetically informative twins aged 61.4 to 73.3 years, we sought to: (1) identify genetic and environmental influences on plasma Aβ (Aβ40, Aβ42, Aβ42/40), total Tau (t‐Tau), and NFL; (2) determine if their genetic and environmental influences are correlated with one another; (3) determine if Aβ40 and Aβ42 causally impact Tau levels; (4) measure associations between biomarkers and APOE‐ε4 status; and (5) measure associations between these biomarkers and mild cognitive impairment (MCI).MethodData were from 1,035 male twins in the Vietnam Era Twin Study of Aging. Average age was 67.1 years (SD = 2.6). Twin models were used to estimate heritability, genetic and environmental correlations, model the direction of causation between biomarkers, and associations with APOE‐ε4 status. Mixed effect linear regressions were applied to regress MCI on biomarkers.ResultHeritability for Aβ40, Aβ42, t‐Tau and NFL ranged from 43% to 51%, whereas Aβ42/40 heritability was not significantly different from zero. Phenotypic correlations between t‐Tau and Aβ40 (rP = 0.01) and Aβ42 (rP = 0.14) were small. Genetic correlations between t‐Tau and Aβ40 (rG = ‐0.05) and Aβ42 (rG = 0.05) were also very small. In contrast, genetic correlations between NFL and Aβ40 and Aβ42 were higher (rG ranging = 0.31‐0.37). We found no evidence to suggest that Aβ40 or Aβ42 causally impacts t‐Tau. APOE‐ε4 accounted for a significant, but very small amount of variance in Aβ40 and Aβ42. Finally, none of the biomarkers was significantly associated with MCI, which affected 15.1% of the sample.ConclusionBoth Aβ40 or Aβ42 appear heritable, but have very little phenotypic and genetic association with t‐Tau. The associations between the amyloid‐beta biomarkers and t‐Tau are mostly explained by non‐shared environmental factors, and the direction of causation is unclear. The small associations between APOE‐ε4 and the biomarkers, including their lack of association with MCI, may stem from the low sensitivity of plasma biomarkers, the relatively young age or the low proportion of MCI cases in this community‐dwelling sample.

6 Depressive Symptoms are Associated With Decline Over Time in Verbal Fluency Performance in Female but not Male Community-Dwelling Older Adults

Objective:Late-life depression is prevalent among older adults and the presence of depressive symptoms has been shown to be associated cross-sectionally with worse verbal fluency performance. There is limited and mixed evidence as to whether depressive symptoms impact change in verbal fluency performance over time, and whether gender impacts this relationship.Participants and Methods:Participants were community-dwelling older adults who were dementia-free at baseline (N = 522; M age = 75.96, SD + 6.46 years). Baseline depressive symptoms were measured using the Geriatric Depression Scale. Category fluency and letter fluency performance, using the Controlled Oral Word Association Test (COWAT), were examined annually. Linear mixed effects models stratified by gender examined whether associations between baseline depressive symptoms and changes in fluency performance over five years were different in female (n = 289) as compared to male (n = 233) participants. Sensitivity analyses excluding participants with prevalent or incident mild cognitive impairment (MCI) (n = 141), excluding participants with incident dementia (n = 28), and excluding participants with prevalent or incident MCI or incident dementia (n = 169) were run. All analyses were adjusted for age, years of education, estimated premorbid functioning, and health comorbidities.Results:Depression was minimal across participants (m = 4.72, SD + 3.96). A subset of participants (n = 44) reported “possible depression,” namely levels suggestive of subclinical depression, according to clinical cutoffs. The “possible depression” group included 31 females (10.73% of females) and 13 males (5.58% of males), and the “no depression” group included 258 females (89.27% of females) and 220 males (94.42% of males). Baseline levels of depressive symptoms suggestive of subclinical depression were associated with worse decline in category fluency performance during longitudinal follow-up in females (estimate = -0.16, p = .002) but not males (estimate = -0.03, p = .658). Results remained the same when excluding prevalent and incident MCI cases (estimate = -0.19, p = .005), excluding incident dementia cases (estimate = -0.12, p = .017), and excluding prevalent and incident MCI and incident dementia cases (estimate = -0.20, p = .004). Letter fluency performance did not decline over time and was not influenced by levels of depressive symptoms in females (estimate = -0.03, p = .502) or males (estimate = 0.05, p = .452).Conclusions:Baseline presence of depressive symptoms suggestive of subclinical depression was associated with worse decline in category fluency performance during longitudinal follow-up in female but not male participants. Letter fluency performance did not decline and was not impacted by levels of depressive symptoms. Results remained significant when accounting for covariates and potential confounders. The present study elucidated the combined influence of gender and depressive symptoms on change in fluency performance in older adults and can aid in identifying individuals who may be at a greater risk of cognitive decline.