Developing Cognitive Markers of Alzheimer's Disease for Primary Care: Implications for Behavioral and Global Prevention

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

Dementia remains an underdiagnosed syndrome, and there is a need to improve the early detection of cognitive decline. This narrative review examines the role of neuropsychological assessment in the characterization of cognitive changes associated with dementia syndrome at different states. The first section describes the early indicators of cognitive decline and the major barriers to their identification. Further, the optimal cognitive screening conditions and the most widely accepted tests are described. The second section analyzes the main differences in cognitive performance between Alzheimer's disease and other subtypes of dementia. Finally, the current challenges of neuropsychological assessment in aging/dementia and future approaches are discussed. Essentially, we find that current research is beginning to uncover early cognitive changes that precede dementia, while continuing to improve and refine the differential diagnosis of neurodegenerative disorders that cause dementia. However, neuropsychology faces several barriers, including the cultural diversity of the populations, a limited implementation in public health systems, and the adaptation to technological advances. Nowadays, neuropsychological assessment plays a fundamental role in characterizing cognitive decline in the different stages of dementia, but more efforts are needed to develop harmonized procedures that facilitate its use in different clinical contexts and research protocols.

Virtual Reality and Serious Videogame-Based Instruments for Assessing Spatial Navigation in Alzheimer's Disease: A Systematic Review of Psychometric Properties

Over the past decade, research using virtual reality and serious game-based instruments for assessing spatial navigation and spatial memory in at-risk and AD populations has risen. We systematically reviewed the literature since 2012 to identify and evaluate the methodological quality and risk of bias in the analyses of the psychometric properties of VRSG-based instruments. The search was conducted primarily in July-December 2022 and updated in November 2023 in eight major databases. The quality of instrument development and study design were analyzed in all studies. Measurement properties were defined and analyzed according to COSMIN guidelines. A total of 1078 unique records were screened, and following selection criteria, thirty-seven studies were analyzed. From these studies, 30 instruments were identified. Construct and criterion validity were the most reported measurement properties, while structural validity and internal consistency evidence were the least reported. Nineteen studies were deemed very good in construct validity, whereas 11 studies reporting diagnostic accuracy were deemed very good in quality. Limitations regarding theoretical framework and research design requirements were found in most of the studies. VRSG-based instruments are valuable additions to the current diagnostic toolkit for AD. Further research is required to establish the psychometric performance and clinical utility of VRSG-based instruments, particularly the instrument development, content validity, and diagnostic accuracy for preclinical AD screening scenarios. This review provides a straightforward synthesis of the state of the art of VRSG-based instruments and suggests future directions for research.

Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions

 This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score

Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i.e., I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables' ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.

Systematic Review on the Potential Effect of Berry Intake in the Cognitive Functions of Healthy People

The increase in life expectancy poses health challenges, such as increasing the impairment of cognitive functions. Berries show a neuroprotective effect thanks to flavonoids, able to reduce neuroinflammatory and to increase neuronal connections. The aim of this systematic review is to explore the impact of berries supplementation on cognitive function in healthy adults and the elderly. Twelve studies were included for a total of 399 participants, aged 18–81 years (mean age: 41.8 ± 4.7 years). Six studies involved young adults (23.9 ± 3.7 years), and four studies involved the elderly (60.6 ± 6.4 years). Most studies investigated effects of a single berry product, but one used a mixture of 4 berries. Non-significant differences were detected across cognition domains and methodologies, but significant and positive effects were found for all cognitive domains (attention and concentration, executive functioning, memory, motor skills and construction, and processing speed), and in most cases they were present in more than one study and detected using different methodologies. Although some limitations should be taken into account to explain these results, the positive findings across studies and methodologies elicit further studies on this topic, to endorse the consumption of berries in healthy populations to prevent cognitive decline.

Toward Pre-Diagnostic Detection of Dementia in Primary Care

Background: Preventing dementia warrants the pragmatic engagement of primary care. Objective: This study predicted conversion to dementia 12 months before diagnosis with indicators that primary care can utilize within the practical constraints of routine practice. Methods: The study analyzed data from the Alzheimer’s Disease Neuroimaging Initiative (Total sample = 645, converting participants = 54). It predicted the conversion from biological (plasma neurofilament light chain), cognitive (Trails Making Test– B), and functional (Functional Activities Questionnaire) measures, in addition to demographic variables (age and education). Results: A Gradient Booster Trees classifier effectively predicted the conversion, based on a Synthetic Minority Oversampling Technique (n = 1,290, F1 Score = 92, AUC = 94, Recall = 87, Precision = 97, Accuracy = 92). Subsequent analysis indicated that the MCI False Positive group (i.e., non-converting participants with cognitive impairment flagged by the model for prospective conversion) scored significantly lower on multiple cognitive tests (Montreal Cognitive Assessment, p < 0.002; ADAS-13, p < 0.0004; Rey Auditory Verbal Learning Test, p < 0.002/0.003) than the MCI True Negative group (i.e., correctly classified non-converting participants with cognitive impairment). These groups also differed in CSF tau levels (p < 0.04), while consistent effect size differences emerged in the all-pairwise comparisons of hippocampal volume and CSF Aβ1 - 42. Conclusion: The model effectively predicted 12-month conversion to dementia and further identified non-converting participants with MCI, in the False Positive group, at relatively higher neurocognitive risk. Future studies may seek to extend these results to earlier prodromal phases. Detection of dementia before diagnosis may be feasible and practical in primary care settings, pending replication of these findings in diverse clinical samples.

Risk Assessment During Longitudinal Progression of Cognition in Older Adults: A Community-based Bayesian Networks Model

BACKGROUND

Cognitive dysfunction, particularly in Alzheimer's disease (AD), seriously affects the health and quality of life of older adults. Early detection can prevent and slow cognitive decline.

OBJECTIVE

This study aimed at evaluating the role of socio-demographic variables, lifestyle, and physical characteristics in cognitive decline during AD progression and analyzing the probable causes and predicting stages of the disease.

METHODS

By analyzing data of 301 subjects comprising normal elderly and patients with mild cognitive impairment (MCI) or AD from six communities in Taiyuan, China, we identified the influencing factors during AD progression by a Logistic Regression model (LR) and then assessed the associations between variables and cognition using a Bayesian Networks (BNs) model.

RESULTS

The LR revealed that age, sex, family status, education, income, character, depression, hypertension, disease history, physical exercise, reading, drinking, and job status were significantly associated with cognitive decline. The BNs model revealed that hypertension, education, job status, and depression affected cognitive status directly, while character, exercise, sex, reading, income, and family status had intermediate effects. Furthermore, we predicted probable cognitive stages of AD and analyzed probable causes of these stages using a model of causal and diagnostic reasoning.

CONCLUSION

The BNs model lays the foundation for causal analysis and causal inference of cognitive dysfunction, and the prediction model of cognition in older adults may help the development of strategies to control modifiable risk factors for early intervention in AD.

Modifiable Barriers for Recruitment and Retention of Older Adults Participants from Underrepresented Minorities in Alzheimer's Disease Research

Clinical Alzheimer's disease (AD) trials currently face a critical shortfall of thousands of eligible participants, which inflates the duration and cost of the clinical study as well as threatens the scientific merit of promising clinical interventions. This recruitment crisis is further compounded by the fact that underrepresented and marginalized populations-particularly those identifying as a racial or ethnic minority, those with low socioeconomic status, or living in rural areas-have been historically underrepresented in ongoing AD clinical trials despite overwhelming evidence that such populations are at increased risk for developing dementia. As a result of various recruitment barriers, current AD clinical studies frequently reflect a decreasingly representative segment of the US population, which threatens the overall generalizability of these findings. The current narrative review provides an updated examination and critique of common recruitment barriers and potential solutions, as well as a discussion of theoretical approaches that may address barriers disproportionately experienced by underrepresented communities. AD clinical researchers are encouraged to take purposive action aimed at increasing diversity of enrolled AD clinical trial cohorts by actively identifying and quantifying barriers to research participation-especially recruitment barriers and health disparities that disproportionately prevent underrepresented and marginalized populations from participating in research. Furthermore, researchers are encouraged to closely track which individuals who express interest in AD research ultimately enroll in research studies to examine whether AD research participation is appropriately representative of the intended population for whom these new and novel AD interventions are being designed.

A comprehensive systematic review of CSF proteins and peptides that define Alzheimer's disease

BACKGROUND

During the last two decades, over 100 proteomics studies have identified a variety of potential biomarkers in CSF of Alzheimer's (AD) patients. Although several reviews have proposed specific biomarkers, to date, the statistical relevance of these proteins has not been investigated and no peptidomic analyses have been generated on the basis of specific up- or down- regulation. Herein, we perform an analysis of all unbiased explorative proteomics studies of CSF biomarkers in AD to critically evaluate whether proteins and peptides identified in each study are consistent in distribution; direction change; and significance, which would strengthen their potential use in studies of AD pathology and progression.

METHODS

We generated a database containing all CSF proteins whose levels are known to be significantly altered in human AD from 47 independent, validated, proteomics studies. Using this database, which contains 2022 AD and 2562 control human samples, we examined whether each protein is consistently present on the basis of reliable statistical studies; and if so, whether it is over- or under-represented in AD. Additionally, we performed a direct analysis of available mass spectrometric data of these proteins to generate an AD CSF peptide database with 3221 peptides for further analysis.

RESULTS

Of the 162 proteins that were identified in 2 or more studies, we investigated their enrichment or depletion in AD CSF. This allowed us to identify 23 proteins which were increased and 50 proteins which were decreased in AD, some of which have never been revealed as consistent AD biomarkers (i.e. SPRC or MUC18). Regarding the analysis of the tryptic peptide database, we identified 87 peptides corresponding to 13 proteins as the most highly consistently altered peptides in AD. Analysis of tryptic peptide fingerprinting revealed specific peptides encoded by CH3L1, VGF, SCG2, PCSK1N, FBLN3 and APOC2 with the highest probability of detection in AD.

CONCLUSIONS

Our study reveals a panel of 27 proteins and 21 peptides highly altered in AD with consistent statistical significance; this panel constitutes a potent tool for the classification and diagnosis of AD.

Risk classification for conversion from mild cognitive impairment to Alzheimer's disease in primary care

There is a pressing need to identify individuals at high risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on available repeated cognitive measures in primary care. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we applied a joint latent class mixed model (JLCM) to derive a 3-class solution: low risk (72.65%), medium risk (20.41%) and high risk (6.94%). In the low-risk group, individuals with lower daily activity and ApoEε4 carriers were at greater risk of conversion from MCI to AD. In the medium-risk group, being female, single, and an ApoEε4 carrier increased risk of conversion to AD. In the high-risk group, individuals with lower education level and single individuals were at greater risk of conversion to AD. Individual dynamic prediction for conversion from MCI to AD after 10 years was derived. Accurate identification of conversion from MCI to AD contributes to earlier close monitoring, appropriate management, and targeted interventions. Thereby, it can reduce avoidable hospitalizations for the high-risk MCI population. Moreover, it can avoid expensive follow-up tests that may provoke unnecessary anxiety for low-risk individuals and their families.

Machine Learning Enhances the Efficiency of Cognitive Screenings for Primary Care

BACKGROUND

Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol against one of the most extensively used, clinician-administered instruments-the Montreal Cognitive Assessment (MoCA).

METHOD

Two hundred six participants (mean age = 67.44, standard deviation [SD] = 11.63) completed the MoCA and the computerized test. Three machine learning algorithms (ie, Support Vector Machine, Random Forest, and Gradient Boosting Trees) were trained to classify participants according to the clinical cutoff score of the MoCA (ie, < 26) from participant performance on 25 features of the computerized test. Analysis employed Synthetic Minority Oversampling TEchnic to correct the sample for class imbalance.

RESULTS

Gradient Boosting Trees achieved the highest performance (accuracy = 0.81, specificity = 0.88, sensitivity = 0.74, F1 score = 0.79, and area under the curve = 0.81). A subsequent K-means clustering of the prediction features yielded 3 categories that corresponded to the unimpaired (mean = 26.98, SD = 2.35), mildly impaired (mean = 23.58, SD = 3.19), and moderately impaired (mean = 17.24, SD = 4.23) ranges of MoCA score ( F = 222.36, P < .00). In addition, compared to the MoCA, the computerized test correlated more strongly with age in unimpaired participants (ie, MoCA ≥26, n = 165), suggesting greater sensitivity to age-related changes in cognitive functioning.

CONCLUSION

Future studies should examine ways to improve the sensitivity of the computerized test by expanding the cognitive domains it measures without compromising its efficiency.

Identifying memory impairment and early dementia in primary care

INTRODUCTION

This study examined the operating characteristics of two-stage case finding to identify memory impairment and very mild dementia.

METHODS

Primary care patients underwent two-stage testing and a subsequent diagnostic assessment to assess outcomes. Patients who screen positive for subjective cognitive decline on the Informant Questionnaire on Cognitive Decline in the Elderly undergo memory testing with the Free and Cued Selective Reminding Test with Immediate Recall. Outcomes were determined without access to these data. A split-half design with discovery and confirmatory samples was used.

RESULTS

One hundred seventeen of 563 (21%) patients had dementia and 68 (12%) had memory impairment but not dementia. Operating characteristics were similar in the discovery and confirmatory samples. In the pooled sample, combined, patients with memory impairment or dementia were identified with good sensitivity (72%) and high specificity (90%). Differences in ethnicity, educational level, or age (≤75, >75) did not affect classification accuracy.

DISCUSSION

Two-stage screening facilitates the efficient identification of older adults with memory impairment or dementia.