Screening For Alzheimer's Disease Research Articles

The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics.

Diagnosis and prediction of Alzheimer's disease (AD) are increasingly pressing in the early stage of the disease because the biomarker-targeted therapies may be most effective. Diagnosis of AD largely depends on the clinical symptoms of AD. Currently, cerebrospinal fluid biomarkers and neuroimaging techniques are considered for clinical detection and diagnosis. However, these clinical diagnosis results could provide indications of the middle and/or late stages of AD rather than the early stage, and another limitation is the complexity attached to limited access, cost, and perceived invasiveness. Therefore, the prediction of AD still poses immense challenges, and the development of novel biomarkers is needed for early diagnosis and urgent intervention before the onset of obvious phenotypes of AD. Blood-based biomarkers may enable earlier diagnose and aid detection and prognosis for AD because various substances in the blood are vulnerable to AD pathophysiology. The application of a systematic biological paradigm based on high-throughput techniques has demonstrated accurate alterations of molecular levels during AD onset processes, such as protein levels and metabolite levels, which may facilitate the identification of AD at an early stage. Notably, proteomics and metabolomics have been used to identify candidate biomarkers in blood for AD diagnosis. This review summarizes data on potential blood-based biomarkers identified by proteomics and metabolomics that are closest to clinical implementation and discusses the current challenges and the future work of blood-based candidates to achieve the aim of early screening for AD. We also provide an overview of early diagnosis, drug target discovery and even promising therapeutic approaches for AD.

Hippocampal volumes in UK Biobank are associated with APOE only in older adults.

The hippocampus atrophies with age and is implicated in neurodegenerative disorders including Alzheimer's disease (AD). We examined the interplay between age and apolipoprotein E (APOE) genotype on total hippocampal volume. Using neuroimaging data from 37,463 UK Biobank participants, we applied linear regression to quantify the association of age and APOE with hippocampal volume and identified the age when volumes of ε2/ε3, ε3/ε4, and ε4/ε4 carriers significantly deviated from ε3/ε3 using generalized additive modeling. Total hippocampal volume declined with age, with significant differences by APOE genotype emerging after age 60. ε3/ε4 and ε4/ε4 carriers displayed reduced volumes from ages 69 and 61, respectively, while ε2/ε3 showed delayed decline starting at the age of 76. The association of APOE and hippocampal volume is age-dependent, with differences in volumes of ε4/ε4 carriers detected as early as age 61. This work underscores the importance of APOE genotype in determining when to begin screening for AD. Apolipoprotein E (APOE) genotype shows an age-dependent association with total hippocampal volume.No association between APOE and total hippocampal volume was detected before age 60.Accelerated decline was observed in ε4/ε4 carriers at age 61 and ε3/ε4 at age 69.Delayed decline was evident in ε2/ε3 carriers starting at age 76.

Hippocampal volumes in UK Biobank are associated with APOE only in older adults.

The hippocampus atrophies with age and is implicated in neurodegenerative disorders including Alzheimer's disease (AD). We examined the interplay between age and APOE genotype on total hippocampal volume. Utilizing neuroimaging data from 37,463 UK Biobank participants, we applied linear regression to quantify the association of age and APOE with hippocampal volume and identified the age when volumes of ε2/ε3, ε3/ε4, and ε4/ε4 carriers significantly deviated from ε3/ε3 using generalized additive modeling. Total hippocampal volume declined with age, with significant differences by APOE genotype emerging after age 60. ε3/ε4 and ε4/ε4 carriers displayed reduced volumes from ages 69 and 61, respectively, while ε2/ε3 showed delayed decline starting at age 76. The association of APOE and hippocampal volume is age-dependent, with differences in volumes of ε4/ε4 carriers detected as early as age 61. This work underscores the importance of APOE genotype in determining when to begin screening for AD.

Association of genetically predicted 486 blood metabolites on the risk of Alzheimer's disease: a Mendelian randomization study.

Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention. We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings. After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data. By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.

Monitoring Alzheimer’s disease via ultraweak photon emission

In an innovative experiment, we detected ultraweak photon emission (UPE) from the hippocampus of male rat brains and found significant correlations between Alzheimer's disease (AD), memory decline, oxidative stress, and UPE intensity. These findings may open up novel methods for screening, detecting, diagnosing, and classifying neurodegenerative diseases, particularly AD. The study suggests that UPE from the brain's neural tissue can serve as a valuable indicator. It also proposes the development of a minimally invasive brain-computer interface (BCI) photonic chip for monitoring and diagnosing AD, offering high spatiotemporal resolution of brain activity. The study used a rodent model of sporadic AD, demonstrating that STZ-induced sAD resulted in increased hippocampal UPE, which was associated with oxidative stress. Treatment with donepezil reduced UPE and improved oxidative stress. These findings support the potential utility of UPE as a screening and diagnostic tool for AD and other neurodegenerative diseases.

2023 China Alzheimer’s disease: facts and figures

In China, the aging population and the increasing incidence of Alzheimer's disease (AD) and other dementias have emerged as major medical concerns. Extensive research and new technologies on AD have provided possibilities for early intervention, including emerging biomarkers for detection and new drugs with disease-modifying therapy. This report includes a large amount of facts and figures to describe the understanding of patients and their families about the disease, discuss the problems they encounter in the current medical environment, and outline potential difficulties in clinical diagnosis and treatment status. Furthermore, people’s concerns, the overall effect on caregivers, and the development of AD treatment are presented in this report. Overall, to increase public awareness of the disease, this report suggests early detection, screening, and intervention for AD while calling on all sectors to work together to help AD patients.

Research trends and hotspots of glial fibrillary acidic protein within the area of Alzheimer's disease: a bibliometric analysis.

Our aim was to analyze the trends and hotspots on glial fibrillary acidic protein (GFAP) within the area of Alzheimer's disease (AD) by using a bibliometric method, which is currently missing. All articles and reviews on GFAP within the area of AD from inception to December 31, 2022, were searched from the Web of Science Core Collection. Full records were derived, imported into Microsoft Excel, and analyzed by BIBLIOMETRC, VOSviewer, and CiteSpace. In total, 2,269 publications, including 2,166 articles, were ultimately included. The number of publications from 81 countries/regions and 527 academic journals increased annually. The top three prolific countries and institutions were the USA, China, and England, the University of Gothenburg (Sweden), Universidade Federal Rio Grande do Sul (Brasilia), and UCL Queen Square Institute of Neurology (England). Henrik Zetterberg from the University of Gothenburg, Kaj Blennow from the University of Gothenburg, and Alexei Verkhratsky from the University of Manchester were the top three prolific and cited authors; Journal of Alzheimer's Disease, Brain Research, and Neuroscience contributed the most publications. The top key areas of research included "molecular, biology, and genetics" and "molecular, biology, and immunology," and the top published and linked meaningful keywords included oxidative stress, inflammation/neuroinflammation, microglia, hippocampus, amyloid, cognitive impairment, tau, and dysfunction. Based on the bibliometric analysis, the number of publications on GFAP within the area of AD has been rapidly increasing, especially in the past several years. Oxidative stress and inflammation are research hotspots, and GFAP in body fluids, especially blood, could be used for large-scale screening for AD.

Attitudes, Motivations, and Barriers to Pre-Symptomatic Alzheimer's Disease Screening: Development and Validation of the 'Perceptions regarding pRE-symptomatic Alzheimer's Disease Screening' (PRE-ADS) Questionnaire.

Pre-symptomatic screening methods for detecting a higher risk of Alzheimer's disease (AD) are gaining popularity; thus, more people are seeking these tests. However, to date, not much is known about the attitudes toward pre-symptomatic AD screening. The goal of this study is to examine the psychometric properties of a tool for assessing the attitudes, barriers, and motivations to pre-symptomatic AD screening. This is a cross-sectional study performed on 208 Greek participants (189 students and 19 caregivers) provided with an online questionnaire. Psychometric properties were assessed through the examination of its construct validity (principal component analysis) and internal consistency. Exploratory factor analysis revealed the presence of four factors. The first factor is labeled as "Perceived harms of testing" (10 items), the second "Acceptance of testing" (5 items), the third "Perceived benefits of testing" (6 items), and the fourth factor "Need for knowledge" (4 items). The reliability (internal consistency) of each factor separately was acceptable to good (0.70-0.87) while the internal consistency of the overall questionnaire (25 items) was good (Cronbach's α=0.82). PRE-ADS is a valid questionnaire that might help in the research of peoples' attitudes related to the pros and cons of pre-symptomatic screening for AD, and the development of effective counseling programs and prevention strategies. However, future research is required in the target population.

Brief Test of Olfactory Dysfunction Based on Diagnostic Features of Specific Odors in Early-Stage Alzheimer Disease.

BACKGROUND Olfactory impairment is an early symptom of Alzheimer disease (AD). However, it is rarely assessed in clinical practice. This study aimed to assess the identification and discrimination of specific odors in patients with early-stage AD using the Sniffin' Sticks test and determine the items that would be most valuable in the diagnosis of early-stage AD in order to create a brief test of olfactory dysfunction. MATERIAL AND METHODS Three groups of participants were enrolled, including 30 patients with mild cognitive impairment due to AD (MCI-AD group), 30 with mild dementia due to AD (MD-AD group), and 30 older participants with normal cognition (NC group). All participants underwent cognitive (Clinical Dementia Rating, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and verbal fluency tests) and olfactory (Burghart Sniffin' Sticks odor identification and odor discrimination tests) assessments. RESULTS The MD-AD group scored significantly lower than the MCI-AD group and the MCI-AD group scored significantly lower than the NC group in both the odor identification (P<0.001) and discrimination (P<0.05) tasks. The shortened versions of the odor identification and discrimination tasks showed good diagnostic properties in differentiating patients with AD from the NC participants (receiver operating characteristic [ROC] area under the curve [AUC]=0.912 and 0.954, respectively) and differentiating patients with MCI-AD from the NC participants (ROC AUC=0.871 and 0.959, respectively). CONCLUSIONS The brief versions of olfactory tests, containing selected items that were found to differ the most between cognitively normal participants and early-stage AD patients, have good diagnostic qualities and can aid clinicians in screening for early-stage AD.

Screening for preclinical Alzheimer's disease: Deriving optimal policies using a partially observable Markov model.

Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.

Is the Alzheimer's Disease Assessment Scale-Cognitive Subscale Useful in Screening for Mild Cognitive Impairment and Alzheimer's Disease? A Systematic Review.

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are screened to distinguish whether the cognitive decline in older adults is attributed to pathological causes rather than normal aging. The purpose of this review was to analyze the diagnostic performance of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) in screening for MCI and AD. Electronic searches were performed on MEDLINE, EMBASE, CINAHL, and PsycArticles databases using the following keywords: dementia and ADAS-Cog. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to check the risk of bias in the diagnostic studies. We reviewed 14 studies, including 3,875 patients who met the selection criteria. In 2,624 MCI patients from nine studies, the pooled sensitivity of ADAS-Cog was 0.80 (95% confidence interval [CI], 0.68-0.88), the pooled specificity was 0.84 (95% CI, 0.75-0.90), and the area under the curve of summary receiver-operating characteristic curves (SROC AUC) was 0.89 (SE = 0.03). In 2,517 AD patients from 10 studies, the pooled sensitivity and pooled specificity were 0.91 (95% CI, 0.86-0.95) and 0.93 (95% CI, 0.88-0.95), respectively, and the sROC AUC was 0.97 (SE = 0.01). Although sub-analyzed according to age and years of education, there was no significant difference in the predictive validity of the ADAS-Cog. The ADAS-Cog has high predictive validity as a screening tool in both MCI and AD and has better diagnostic performance in patients with AD. When early screening for AD is desired, ADAS-Cog is a first-stage screening tool that can be initially employed.

Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals.

The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management. In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests. The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC. The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring–mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

A remote speech-based AI system to screen for early Alzheimer's disease via smartphones.

Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD). The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ-) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity. Eighty-six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC]=0.85, ±0.07) but not Aβ (AUC=0.62±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC=0.78 ±0.14; CU: AUC=0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results. Speech-based testing offers simple and accessible screening for early-stage AD.

Cognitive testing for early detection of Alzheimer's disease in people with Down syndrome: A systematic review and meta‐analysis

AbstractBackgroundImproved health care for people with Down syndrome (DS) has resulted in an increase in their life expectancy therefore increasing comorbidities associated with age related problems in this population, the most frequent being Alzheimer's disease (AD). The development of brain lesions appears years before AD. Through this progressive neuronal loss, patients can firstly begin to have a perception of cognitive difficulties, i.e., a subjective cognitive decline (SCD). This preclinical phase ends with the onset of mild cognitive impairment (MCI) which is characterized by a set of cognitive symptoms. Knowing that people with DS have already pre‐existing cognitive deficits, screening at the preclinical and clinical stages of AD therefore cannot be done through traditional cognitive tests. The aim of this systematic review is to synthesis cognitive assessment for the diagnosis of AD, MCI or SCD in people with DS.MethodA meta‐analysis is in development. Articles were collected from Pubmed and Psychinfo databases through keywords related to three categories: 1) DS, 2) AD and 3) assessment. The inclusion criteria are studies 1) with participants over 18 years old with DS, 2) which assess cognitive abilities in the context of screening for AD or its early stages, 3) presenting the effect sizes or data allowing their calculation.ResultEighteen articles met the inclusion criteria. Fifty cognitive tests for the diagnosis of AD, MCI or SCD were found. From these tests, fourteen evaluated cognition through an informant questionnaire and thirty‐six directly evaluated the patient’s cognitive abilities.ConclusionTheoretical and practical implications of these results are discussed.

Language Decline Characterizes Amnestic Mild Cognitive Impairment Independent of Cognitive Decline.

Purpose This research investigated the nature of cognitive decline in prodromal Alzheimer's disease (AD), particularly in mild cognitive impairment, amnestic type (aMCI). We assessed language in aMCI as compared with healthy aging (HA) and healthy young (HY) with new psycholinguistic assessment of complex sentences, and we tested the degree to which deficits on this language measure relate to performance in other general cognitive domains such as memory. Method Sixty-one individuals with aMCI were compared with 24 HA and 10 HY adults on a psycholinguistic measure of complex sentence production (relative clauses). In addition, HA, HY, and a subset of the aMCI participants (n = 22) were also tested on a multidomain cognitive screen, the Addenbrooke's Cognitive Examination-Revised (ACE-R), and on a verbal working memory Brown-Peterson (BP) test. General and generalized linear mixed models were used to test psycholinguistic results and to test whether ACE-R and BP performance predicted performance on the psycholinguistic test similarly in the aMCI and HA groups. Results On the psycholinguistic measure, sentence imitation was significantly deficited in aMCI in comparison with that in HA and HY. Experimental factorial designs revealed that individuals with aMCI had particular difficulty repeating sentences that especially challenged syntax-semantics integration. As expected, the aMCI group also performed significantly below the HY and HA groups on the ACE-R. Neither the ACE-R Memory subtest nor the BP total scores predicted performance on the psycholinguistic task for either the aMCI or the HA group. However, the ACE-R total score significantly predicted psycholinguistic task performance, with increased ACE-R performance predicting increased psycholinguistic task performance only for the HA group, not for the aMCI group. Conclusions Results suggest a selective deterioration in language in aMCI, specifically a weakening of syntax-semantics integration in complex sentence processing, and a general independence of this language deficit and memory decline. Results cohere with previous assessments of the nature of difficulty in complex sentence formation in aMCI. We argue that clinical screening for prodromal AD can be strengthened by supplementary testing of language, as well as memory, and extended evaluation of strength of their relation.

Erythrocyte sphingolipid species as biomarkers of Alzheimer's disease

Diagnosing Alzheimer's disease (AD) in the early stage is challenging. Informative biomarkers can be of great value for population-based screening. Metabolomics studies have been used to find potential biomarkers, but commonly used tissue sources can be difficult to obtain. The objective of this study was to determine the potential utility of erythrocyte metabolite profiles in screening for AD. Unlike some commonly-used sources such as cerebrospinal fluid and brain tissue, erythrocytes are plentiful and easily accessed. Moreover, erythrocytes are metabolically active, a feature that distinguishes this sample source from other bodily fluids like plasma and urine. In this preliminary pilot study, the erythrocyte metabolomes of 10 histopathologically confirmed AD patients and 10 patients without AD (control (CTRL)) were compared. Whole blood was collected post-mortem and erythrocytes were analyzed using ultra-performance liquid chromatography tandem mass spectrometry. Over 750 metabolites were identified in AD and CTRL erythrocytes. Seven were increased in AD while 24 were decreased (P<0.05). The majority of the metabolites increased in AD were associated with amino acid metabolism and all of the decreased metabolites were associated with lipid metabolism. Prominent among the potential biomarkers were 10 sphingolipid or sphingolipid-related species that were consistently decreased in AD patients. Sphingolipids have been previously implicated in AD and other neurological conditions. Furthermore, previous studies have shown that erythrocyte sphingolipid concentrations vary widely in normal, healthy adults. Together, these observations suggest that certain erythrocyte lipid phenotypes could be markers of risk for development of AD.

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification.

Background:Memory dysfunction is characteristic of aging and often attributed to Alzheimer’s disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management.Objective:Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening and assessing cognitive impairment.Methods:We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions.Results:ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both % responses and % correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648–0.680) and selected general health questions (0.713–0.769).Conclusion:Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

RISK STRATIFICATION AND COGNITIVE SCREENING: PREPARING FOR ALZHEIMER’S DISEASE-MODIFYING MEDICATIONS

When a disease-modifying therapy for Alzheimer's disease (AD) becomes available, many researchers and industry leaders believe that the U.S. health care system will lack the capacity to provide patients with access to treatment within a reasonable timeframe. Access to care begins with effective identification of those patients appropriate for disease-modifying treatment. Risk stratification capacity can be expanded by employing validated cognitive self-assessments in the home setting, enabling patients to bring screening data to their health care professionals. Utilizing cognitive self-assessment screens as a first step toward effective risk stratification has potential to identify older adults who would benefit from a disease-modifying treatment. However, few self-assessments have been validated that are sensitive to mild cognitive impairment (MCI) or early AD. We conducted a national online survey of geriatric health care professionals on the potential use of self-assessment cognitive screening for MCI and AD to address these future health care needs. Next, we developed a rapid self-assessment screen (myMemCheck) designed for older adults who have concerns about their cognitive function. The psychometric properties of this instrument were investigated in two separate Maryland, USA nursing home and assisted living samples (Study 1, N = 63; Study 2, N = 200). The instrument evidenced adequate reliability and strong construct validity across both studies. Receiver operating characteristic analysis yielded an optimal cut score for identifying older adults with MCI or early AD. We discuss implications of using a reliable and valid self-assessment for determining whether a comprehensive test or evaluation for MCI or AD is indicated. Wider detection of MCI and AD by accurate at-home cognitive self-assessments could reduce the screening burden on primary care physicians.

Establishment of induced pluripotent stem cell line (ZZUi010-A) from an Alzheimer's disease patient carrying an APP gene mutation

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders. Previous studies have identified mutations in several genes, such as amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2), in patients with early-onset (<65years) familial AD. Recently, a patient with an APP gene mutation was identified; the dermal fibroblasts of the patient were obtained and a line of induced pluripotent stem cells (iPSCs) was successfully generated using the Sendai-virus (SeV) delivery system. The iPSC line will be useful for further study of the pathomechanism and drug screening for AD.