Diagnosis Of Mild Cognitive Impairment Research Articles

The effect of herpes zoster vaccination at different stages of the disease course of dementia: Two quasi-randomized studies.

The varicella zoster virus, a neurotropic herpesvirus, has been hypothesized to play a role in the pathophysiology of dementia, such as through neuroinflammatory processes or intracerebral vasculopathy. Using unique natural experiments, our group has previously found that live-attenuated herpes zoster (HZ) vaccination reduced the incidence of new diagnoses of dementia in both Wales and Australia. To inform further research and ultimately clinical care, it is crucial to understand at which stage of the disease course of dementia the HZ vaccine has its effect. Representing the two opposing ends of the dementia disease course as it can be ascertained from electronic health record data, the aims of this study were twofold: to determine the effect of HZ vaccination on i) new diagnoses of mild cognitive impairment (MCI) among individuals without any record of cognitive impairment, and ii) deaths due to dementia among individuals living with dementia. Our approach took advantage of the fact that at the time of the start date (September 1 2013) of the HZ vaccination program in Wales, individuals who had their eightieth birthday just after this date were eligible for HZ vaccination for one year whereas those who had their eightieth birthday just before were ineligible and remained ineligible for life. This eligibility rule created comparison groups just on either side of the September 2 1933 date-of-birth eligibility threshold who differed in their age by merely a week but had a large difference in their probability of receiving HZ vaccination. The key strength of our study is that these comparison groups should be similar in their health characteristics and behaviors except for a minute difference in age. We used regression discontinuity analysis to estimate the difference in our outcomes between individuals born just on either side of the date-of-birth eligibility threshold for HZ vaccination. Our dataset consisted of detailed country-wide electronic health record data from primary care in Wales, linked to hospital records and death certificates. We restricted our dataset to individuals born between September 1 1925 and September 1 1942. Among our study cohort of 282,557 without any record of cognitive impairment at baseline, HZ vaccination eligibility and receipt reduced the incidence of a new MCI diagnosis by 1.5 (95% CI: 0.5 - 2.9, p=0.006) and 3.1 (95% CI: 1.0 - 6.2, p=0.007) percentage points over nine years, respectively. Similarly, among our study cohort of 14,350 individuals who were living with dementia at baseline, being eligible for and receiving HZ vaccination reduced deaths due to dementia by 8.5 (95% CI: 0.6 - 18.5, p=0.036) and 29.5 (95% CI: 0.6 - 62.9, p=0.046) percentage points over nine years, respectively. Except for dementia, HZ vaccination did not have an effect on any of the ten most common causes of morbidity and mortality among adults aged 70 years and older in Wales in either of our two study cohorts. The protective effects of HZ vaccination for both MCI and deaths due to dementia were larger among women than men. Our findings suggest that the live-attenuated HZ vaccine has benefits for the dementia disease process at both ends of the disease course of dementia.

Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.

Improved Accuracy of the Addenbrooke's Cognitive Examination-Revised in the Diagnosis of Mild Cognitive Impairment, Mild Dementia Due to Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Mokken Scale Analysis.

The Addenbrooke's Cognitive Examination-Revised (ACE-R) is an accessible cognitive tool that supports the early detection of mild cognitive impairment (MCI), Alzheimer's disease (AD), and behavioral variant frontotemporal dementia (bvFTD). To investigate the diagnostic efficacy of the ACE-R in MCI, AD, and bvFTD through the identification of novel coefficients for differentiation between these diseases. We assessed 387 individuals: 102 mild AD, 37 mild bvFTD, 87 with amnestic MCI patients, and 161 cognitively unimpaired controls. The Mokken scaling technique facilitated the extraction out of the 26 ACE-R items that exhibited a common latent trait, thereby generating the Mokken scales for the AD group and the MCI group. Subsequently, we performed logistic regression, integrating each Mokken scales with sociodemographic factors, to differentiate between AD and bvFTD, as well as between AD or MCI and control groups. Ultimately, the Receiver Operating Characteristic curve analysis was employed to assess the efficacy of the coefficient's discrimination. The AD-specific Mokken scale (AD-MokACE-R) versus bvFTD exhibited an Area Under the Curve (AUC) of 0.922 (88% sensitivity and specificity). The AD-MokACE-R versus controls achieved an AUC of 0.968 (93% sensitivity, 94% specificity). The MCI-specific scale (MCI-MokACE-R) versus controls demonstrated an AUC of 0.859 (78% sensitivity, 79% specificity). The ACE-R's capacity is enhanced through statistical methods and demographic integration, allowing for accurate differentiation between AD and bvFTD, as well as between MCI and controls. This new method not only reinforces its clinical value in early diagnosis but also surpasses traditional approaches noted in prior studies.

Transition of mild cognitive impairment to Alzheimer's disease: Medications as modifiable risk factors.

Mild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer's disease (AD). Understanding the transition probabilities across the disease continuum of AD, ranging from MCI to AD to Mortality is crucial for the economic modeling of AD and effective planning of future interventions and healthcare resource allocation decisions. This study uses the Multi-state Markov model to quantify the transition probabilities along the disease progression and specifically investigates medications as modifiable risk factors of AD associated with accelerated or decelerated transition times from MCI to AD, MCI to mortality, and AD to mortality. Individuals with MCI were identified from the National Alzheimer's Coordinating Center between September 2005 and May 2021. A three-state Markov model was postulated to model the disease progression among three states: MCI, AD, and mortality with adjustment for demographics, genetic characteristics, comorbidities and medications. Transition probabilities, the total length of stay in each state, and the hazard ratios of the use of medications for diabetes, hypertension, and hypercholesterolemia (the known modifiable risk factors of AD) were evaluated for these transitions. 3,324 individuals with MCI were identified. The probability of developing AD after one year since the initial diagnosis of MCI is 14.9%. After approximately 6 years from the initial diagnosis of MCI, the probability of transitioning to AD increases to nearly 41.7% before experiencing a subsequent decline. The expected total lengths of stay were 5.38 (95% CI: 0.002-6.03) years at MCI state and 7.61 (95%CI: 0.002-8.88) years at AD state. Patients with active use of lipid-lowering agents were associated with significantly lower hazards of transitioning from MCI to AD (HR: 0.83, 95%CI:0.71-0.96), MCI to mortality (HR: 0.51, 95%CI:0.34-0.77), and AD to mortality (HR: 0.81, 95%CI:0.66-0.99). Results suggest that lipid-lowering agents may confer a protective effect, delaying the onset of AD. Additionally, lipid-lowering agents indicate a favorable association with a longer survival time.

Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach.

Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown. Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes. We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8). Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

Comparison of the Diagnostic Accuracy of Five Cognitive Screening Tests for Diagnosing Mild Cognitive Impairment in Patients Consulting for Memory Loss.

Objectives: We aimed to evaluate and compare the diagnostic capacity of five cognitive screening tests for the diagnosis of mild cognitive impairment (MCI) in patients consulting by memory loss. Methods: A cross-sectional study involving 140 participants with a mean age of 74.42 ± 7.60 years, 87 (62.14%) women. Patients were classified as MCI or cognitively unimpaired according to a comprehensive neuropsychological battery. The diagnostic properties of the following screening tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE-III) and Mini-Addenbrooke (M-ACE), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). Results: The area under the curve (AUC) was 0.861 for the ACE-III, 0.867 for M-ACE, 0.791 for MoCA, 0.795 for MMSE, 0.731 for RUDAS, and 0.672 for MIS. For the memory components, the AUC was 0.869 for ACE-III, 0.717 for MMSE, 0.755 for MoCA, and 0.720 for RUDAS. Cronbach's alpha was 0.827 for ACE-III, 0.505 for MMSE, 0.896 for MoCA, and 0.721 for RUDAS. Correlations with Free and Cued Selective Reminding Test were moderate with M-ACE, ACE-III, and MoCA, and moderate for the other tests. The M-ACE showed the best balance between diagnostic capacity and time of administration. Conclusions: ACE-III and its brief version M-ACE showed better diagnostic properties for the diagnosis of MCI than the other screening tests. MoCA and MMSE showed adequate properties, while the diagnostic capacity of MIS and RUDAS was limited.

Sparse Graph Representation Learning Based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis.

Resting-state functional magnetic resonance imaging (rs-fMRI) has gained attention as a reliable technique for investigating the intrinsic function patterns of the brain. It facilitates the extraction of functional connectivity networks (FCNs) that capture synchronized activity patterns among regions of interest (ROIs). Analyzing FCNs enables the identification of distinctive connectivity patterns associated with mild cognitive impairment (MCI). For MCI diagnosis, various sparse representation techniques have been introduced, including statistical- and deep learning-based methods. However, these methods face limitations due to their reliance on supervised learning schemes, which restrict the exploration necessary for probing novel solutions. To overcome such limitation, prior work has incorporated reinforcement learning (RL) to dynamically select ROIs, but effective exploration remains challenging due to the vast search space during training. To tackle this issue, in this study, we propose an advanced RL-based framework that utilizes a divide-and-conquer approach to decompose the FCN construction task into smaller sub-problems in a subject-specific manner, enabling efficient exploration under each sub-problem condition. Additionally, we leverage the learned value function to determine the sparsity level of FCNs, considering individual characteristics of FCNs. We validate the effectiveness of our proposed framework by demonstrating its superior performance in MCI diagnosis on publicly available cohort datasets.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Managing medications among individuals with mild cognitive impairment and dementia: Patient-caregiver perspectives.

With changing cognitive abilities, individuals with mild cognitive impairment (MCI) and dementia face challenges in successfully managing multidrug regimens. We sought to understand how individuals with MCI or dementia and their family caregivers manage multidrug regimens and better understand patient-to-caregiver transitions in medication management responsibilities. We conducted qualitative interviews among patient-caregiver dyads. Eligibility included: patients with a diagnosis of MCI, mild or moderate dementia, managing ≥3 chronic conditions, ≥5 prescription medications, who also had a family caregiver ≥18 years old. Semi-structured interview guides, informed by the Medication Self-Management model, ascertained roles and responsibilities for medication management and patient-to-caregiver transitions in medication responsibilities. We interviewed 32 patient-caregiver dyads. Older adults and caregivers favored older adult autonomy in medication management, and individuals with MCI and mild dementia largely managed their medications independently using multiple strategies (e.g., establishing daily routines, using pillboxes). Among individuals with moderate dementia, caregivers assumed all medication-related responsibilities except when living separately. In those scenarios, caregivers set up organizers and made reminder calls, but did not observe family members taking medications. Patient-to-caregiver transitions in medication responsibilities frequently occurred after caregivers observed older adults making errors with medications. As caregivers sought to assume greater responsibilities with family members' medicines, they faced multiple barriers. Most barriers were dyadic; they affected both the older adult and the caregiver and/or the relationship. Some barriers were specific to caregivers; these included caregivers' competing responsibilities or inaccurate perceptions of dementia, while other barriers were related to the healthcare system. To ease medication management transitions, balance must be sought between preservation of older adult autonomy and early family caregiver involvement. Clinicians should work to initiate conversations with family caregivers and individuals living with MCI or dementia about transitioning medication responsibilities as memory loss progresses, simplify regimens, and deprescribe, as appropriate.

Psychological well-being trajectories preceding incident mild cognitive impairment and dementia

BackgroundPoorer psychological well-being has been related to an increased dementia risk, but changes in psychological well-being along the dementia course are unclear. We explored psychological well-being trajectories before and after...

Examining the Stability and Predictive Utility of Across- and Within-Domain Intra-Individual Variability in Mild Cognitive Impairment.

Dispersion is a form of intra-individual variability across neuropsychological tests that has been shown to predict cognitive decline. However, few studies have investigated the stability and predictive utility of both across- and within-domain dispersion. The current study aims to fill these gaps in the literature by examining multiple indices of dispersion in a longitudinal clinical sample of individuals diagnosed with mild cognitive impairment (MCI) at baseline. Two hundred thirty-eight MCI patients from a cognitive disorders clinic underwent testing at baseline and after approximately 1.5 years. Linear regression was used to examine whether baseline across- and within-domain dispersion predicted cognitive decline in individuals whose diagnostic classification progressed to dementia (i.e., MCI-Decline) and those who retained an MCI diagnosis at follow-up (i.e., MCI-Stable). Cognitive decline was operationalized dichotomously using group status and continuously using standardized regression-based (SRB) z-scores. Dispersion variables at baseline and follow-up were positively correlated in both groups, with the exception of within-domain executive functioning and language dispersion in the MCI-Decline group. None of the dispersion variables predicted diagnostic conversion to MCI. Using SRB z-scores, greater across-domain dispersion predicted greater overall cognitive decline at follow-up, but this was not the case for within-domain variables with the exception of visuospatial skills. Results suggest that across- and within-domain dispersion are relatively stable across time, and that across-domain dispersion is predictive of subtle cognitive decline in patients with MCI. However, these results also highlight that findings may differ based on the tests included in dispersion calculations.

Pain management inequities by demographic and geriatric-related variables in older adult inpatients.

Pain is ubiquitous, yet understudied. The objective of this study was to analyze inequities in pain assessment and management for hospitalized older adults focusing on demographic and geriatric-related variables. This was a retrospective cohort study from January 2013 through September 2021 of all adults 65 years or older on the general medicine service at UCSF Medical Center. Primary exposures included (1) demographic variables including race/ethnicity and limited English proficiency (LEP) status and (2) geriatric-related variables including age, dementia or mild cognitive impairment diagnosis, hearing or visual impairment, end-of-life care, and geriatrics consult involvement. Primary outcomes included (1) adjusted odds of numeric pain assessment versus other assessments and (2) adjusted opioids administered, measured by morphine milligram equivalents (MME). A total of 15,809 patients were included across 27,857 hospitalizations with 1,378,215 pain assessments, with a mean age of 77.8 years old. Patients were 47.4% White, 26.3% with LEP, 49.6% male, and 50.4% female. Asian (OR 0.75, 95% CI 0.70-0.80), Latinx (OR 0.90, 95% CI 0.83-0.99), and Native Hawaiian or Pacific Islander (OR 0.77, 95% CI 0.64-0.93) patients had lower odds of a numeric assessment, compared with White patients. Patients with LEP (OR 0.70, 95% CI 0.66-0.74) had lower odds of a numeric assessment, compared with English-speaking patients. Patients with dementia, hearing impairment, patients 75+, and at end-of-life were all less likely to receive a numeric assessment. Compared with White patients (86 MME, 95% CI 77-96), Asian patients (55 MME, 95% CI 46-65) received fewer opioids. Patients with LEP, dementia, hearing impairment and those 75+ years old also received significantly fewer opioids. Older, hospitalized, general medicine patients from minoritized groups and with geriatric-related conditions are uniquely vulnerable to inequitable pain assessment and management. These findings raise concerns for pain underassessment and undertreatment.

Identification of retinal oligomeric, citrullinated, and other tau isoforms in early and advanced AD and relations to disease status

This study investigates various pathological tau isoforms in the retina of individuals with early and advanced Alzheimer’s disease (AD), exploring their connection with disease status. Retinal cross-sections from predefined superior-temporal and inferior-temporal subregions and corresponding brains from neuropathologically confirmed AD patients with a clinical diagnosis of either mild cognitive impairment (MCI) or dementia (n = 45) were compared with retinas from age- and sex-matched individuals with normal cognition (n = 30) and non-AD dementia (n = 4). Retinal tau isoforms, including tau tangles, paired helical filament of tau (PHF-tau), oligomeric-tau (Oligo-tau), hyperphosphorylated-tau (p-tau), and citrullinated-tau (Cit-tau), were stereologically analyzed by immunohistochemistry and Nanostring GeoMx digital spatial profiling, and correlated with clinical and neuropathological outcomes. Our data indicated significant increases in various AD-related pretangle tau isoforms, especially p-tau (AT8, 2.9-fold, pS396-tau, 2.6-fold), Cit-tau at arginine residue 209 (CitR209-tau; 4.1-fold), and Oligo-tau (T22+, 9.2-fold), as well as pretangle and mature tau tangle forms like MC-1-positive (1.8-fold) and PHF-tau (2.3-fold), in AD compared to control retinas. MCI retinas also exhibited substantial increases in Oligo-tau (5.2-fold), CitR209-tau (3.5-fold), and pS396-tau (2.2-fold). Nanostring GeoMx analysis confirmed elevated retinal p-tau at epitopes: Ser214 (2.3-fold), Ser396 (2.6-fold), Ser404 (2.4-fold), and Thr231 (1.8-fold), particularly in MCI patients. Strong associations were found between retinal tau isoforms versus brain pathology and cognitive status: a) retinal Oligo-tau vs. Braak stage, neurofibrillary tangles (NFTs), and CDR cognitive scores (ρ = 0.63–0.71), b) retinal PHF-tau vs. neuropil threads (NTs) and ABC scores (ρ = 0.69–0.71), and c) retinal pS396-tau vs. NTs, NFTs, and ABC scores (ρ = 0.67–0.74). Notably, retinal Oligo-tau strongly correlated with retinal Aβ42 and arterial Aβ40 forms (r = 0.76–0.86). Overall, this study identifies and quantifies diverse retinal tau isoforms in MCI and AD patients, underscoring their link to brain pathology and cognition. These findings advocate for further exploration of retinal tauopathy biomarkers to facilitate AD detection and monitoring via noninvasive retinal imaging.

Neurovascular coupling, functional connectivity, and cerebrovascular endothelial extracellular vesicles as biomarkers of mild cognitive impairment.

Mild cognitive impairment (MCI) is a prodromal stage of dementia. Understanding the mechanistic changes from healthy aging to MCI is critical for comprehending disease progression and enabling preventative intervention. Patients with MCI and age-matched controls (CN) were administered cognitive tasks during functional near-infrared spectroscopy (fNIRS) recording, and changes in plasma levels of extracellular vesicles (EVs) were assessed using small-particle flow cytometry. Neurovascular coupling (NVC) and functional connectivity (FC) were decreased in MCI compared to CN, prominently in the left-dorsolateral prefrontal cortex (LDLPFC). We observed an increased ratio of cerebrovascular endothelial EVs (CEEVs) to total endothelial EVs in patients with MCI compared to CN, correlating with structural MRI small vessel ischemic damage in MCI. LDLPFC NVC, CEEV ratio, and LDLPFC FC had the highest feature importance in the random Forest group classification. NVC, CEEVs, and FC predict MCI diagnosis, indicating their potential as markers for MCI cerebrovascular pathology. Neurovascular coupling (NVC) is impaired in mild cognitive impairment (MCI). Functional connectivity (FC) compensation mechanism is lost in MCI. Cerebrovascular endothelial extracellular vesicles (CEEVs) are increased in MCI. CEEV load strongly associates with cerebral small vessel ischemic lesions in MCI. NVC, CEEVs, and FC predict MCI diagnosis over demographic and comorbidity factors.

Meaningful benefit of disease-modifying treatment: Evaluating changes in health-related resource use.

Are reductions in the rate of decline from the new disease-modifying treatments (DMTs) in early Alzheimer's disease (AD) meaningful? We examined whether such reductions may be reflected in changes in health-related resource use. Patients with Clinical Dementia Rating (CDR) = 0.5 or 1 with a clinical diagnosis of mild cognitive impairment or AD, reflecting clinical trial populations. Health-related resource use was reported using the Resource Use Inventory (RUI) including direct medical care, non-medical care, unpaid informal care, and time use. Faster decline in CDR-Sum of Boxes (CDR-SB) from baseline was independently associated with higher likelihood and hours of informal care received, and lower likelihood of employment/volunteer work, but not with direct medical care. Reductions in the rate of decline in CDR-SB seen from DMTs significantly affect patients' work capacity and need for informal care, indicators of economic impact meaningful to patients, families, and health systems. These measures are not readily captured in administrative data sets. Following a cohort of participants with MCI or mild dementia due to AD that mimics participants targeted for AD trials, this study showed slower decline in CDR-SB have significant effects on patients' work capacity and need for informal care, but not on their direct medical care utilization such as hospitalizations, ED use, and doctors' visits.Capturing potential benefits in health-related resource use may require direct measures of informal care and work/volunteer effort which are meaningful outcomes to patients, families and health systems.Caution is needed in our effort to assess benefits of recently developed disease modifying treatment in AD using electronic health records and administrative data from which utilization of direct medical care are routinely collected as these data sources may not capture the most apparent changes in resource utilization during early disease stages.

Dementia severity at incident diagnosis in a population representative sample of older Americans.

We provide the first analysis of distribution of dementia severity at incident diagnosis for a population representative sample of older Americans. Using data from the Aging, Demographics, and Memory Study (ADAMS), the Health Retirement Study (HRS), and traditional Medicare claims, we estimated the Clinical Dementia Rating Scale for ADAMS respondents and applied parameter estimates to predict dementia severity for HRS respondents with claims-based incident dementia diagnosis. Seventy percent of older adults received a dementia diagnosis of mild cognitive impairment or mild dementia (early stages). Fewer individuals were diagnosed at early stages in years 2000 to 2008 (65%) compared to years 2009 to 2016 (76%). About 72% of non-Hispanic white persons were diagnosed at early stages, compared to 63% non-Hispanic black and 59% Hispanic persons. More males than females were diagnosed at early stages (75%vs 67%). These data linkages allow population surveillance of early and equitable dementia detection in the older US population to assess clinical and policy levers to improve detection. For the US population 70 and older, 30% were diagnosed with dementia at a moderate or severe stage.Fewer were diagnosed at early stages in years 2000 to 2008 compared to 2009 to 2016 (65%vs 76%).A total of 72% of white persons were diagnosed at early stages, compared to 63% black and 59% Hispanic persons.More males than females were diagnosed at early stages (75%vs 67%).High wealth and education level were associated with diagnosis at early stages disease.

Rural-Urban mild cognitive impairment comparison in West Michigan through EHR.

Mild cognitive impairment (MCI) is a significant public health concern and a potential precursor to Alzheimer's disease (AD). This study leverages electronic health record (EHR) data to explore rural-urban differences in MCI incidence, risk factors, and healthcare navigation in West Michigan. Analysis was conducted on 1,528,464 patients from Corewell Health West, using face-to-face encounters between 1/1/2015 and 7/31/2022. MCI cases were identified using International Classification of Diseases (ICD) codes, focusing on patients aged 45+ without prior MCI, dementia, or AD diagnoses. Incidence rates, cumulative incidences, primary care physicians (PCPs), and neuropsychology referral outcomes were examined across rural and urban areas. Risk factors were evaluated through univariate and multivariate Cox regression analyses. The geographic distribution of patient counts, hospital locations, and neurology department referrals were examined. Among 423,592 patients, a higher MCI incidence rate was observed in urban settings compared to rural settings (3.83 vs. 3.22 per 1,000 person-years). However, sensitivity analysis revealed higher incidence rates in rural areas when including patients who progressed directly to dementia. Urban patients demonstrated higher rates of referrals to and completion of neurological services. While the risk factors for MCI were largely similar across urban and rural populations, urban-specific factors for incident MCI are hearing loss, inflammatory bowel disease, obstructive sleep apnea, insomnia, being African American, and being underweight. Common risk factors include diabetes, intracranial injury, cerebrovascular disease, coronary artery disease, stroke, Parkinson's disease, epilepsy, chronic obstructive pulmonary disease, depression, and increased age. Lower risk was associated with being female, having a higher body mass index, and having a higher diastolic blood pressure. This study highlights rural-urban differences in MCI incidence and access to care, suggesting potential underdiagnosis in rural areas likely due to reduced access to specialists. Future research should explore socioeconomic, environmental, and lifestyle determinants of MCI to refine prevention and management strategies across geographic settings. Leveraged EHRs to explore rural-urban differences in MCI in West Michigan.Revealed a significant underdiagnosis of MCI, especially in rural areas.Observed lower rates of neurological referrals and completions for rural patients.Identified risk factors specific to rural and urban populations.

A Novel Peptidome Technology for the Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease by Selected Reaction Monitoring

Background: With the aging of populations worldwide, Alzheimer’s disease (AD) has become a concern due to its high prevalence and the continued lack of established treatments. Early diagnosis is required as a preventive intervention to modify the disease’s progression. In our previous study, we performed peptidomic analysis of serum samples obtained from AD patients and age-matched healthy subjects to seek peptide biomarker candidates for AD by using BLOTCHIP-MS analysis, and identified four peptides as AD biomarker candidates. Objective: The objective was to validate the serum biomarker peptides to distinguish mild cognitive impairment (MCI) and AD in comparison to cognitively healthy controls using a new peptidome technology, the Dementia Risk Test. Methods: We enrolled 195 subjects with normal cognitive function (NC; n = 70), MCI (n = 55), and AD (n = 70), The concentrations of cognitive impairment marker peptides (Fibrinogen α chain (FAC), Fibrinogen β chain (FBC), Plasma protease C1 inhibitor (PPC1I), α2-HS-glycoprotein (AHSG)) were quantified by using a selected reaction monitoring assay based on liquid chromatography-MS/MS. Results: The present study confirmed that three peptides, FAC, FBC, and PPC1I, were significantly upregulated during the onset of AD. This three-peptide set was both highly sensitive in determining AD (sensitivity: 85.7%, specificity: 95.7%, AUC: 0.900) and useful in distinguishing MCI (sensitivity: 61.8%, specificity: 98.6%, AUC: 0.824) from NC. Conclusions: In this validation study, we confirmed the high diagnostic potential of the three peptides identified in our previous study as candidate serum biomarkers for AD. The Dementia Risk Test may be a powerful tool for detecting AD-related pathological changes.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis.

Mild cognitive impairment (MCI) heightens Alzheimer's disease (AD) risk, with diabetes mellitus (DM) potentially exacerbating this vulnerability. This study identifies the optimal intervention period and neurobiological targets in MCI to AD progression using the Alzheimer's Disease Neuroimaging Initiative dataset. Analysis of 980 MCI patients, categorized by DM status, used propensity score matching and inverse probability treatment weighting to assess rate of conversion from MCI to AD, neuroimaging, and cognitive changes. DM significantly correlates with cognitive decline and an increased risk of progressing to AD, especially within the first year of MCI follow-up. It adversely affects specific brain structures, notably accelerating nucleus accumbens atrophy, decreasing gray matter volume and sulcal depth. Findings suggest the first year after MCI diagnosis as the critical window for intervention. DM accelerates MCI-to-AD progression, targeting specific brain areas, underscoring the need for early therapeutic intervention. Diabetes mellitus (DM) accelerates mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) progression within the first year after MCI diagnosis. DM leads to sharper cognitive decline within 12 months of follow-up. There is notable nucleus accumbens atrophy observed in MCI patients with DM. DM causes significant reductions in gray matter volume and sulcal depth. There are stronger correlations between cognitive decline and brain changes due to DM.