Rationale for use of the Clinical Dementia Rating Sum of Boxes as a primary outcome measure for Alzheimer's disease clinical trials

Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid β and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.

Neuropsychological and neuroanatomical underpinnings of the face pareidolia errors on the noise pareidolia test in patients with mild cognitive impairment and dementia due to Lewy bodies

OBJECTIVE

Prior research on the Noise Pareidolia Test (NPT) has demonstrated its clinical utility in detecting patients with mild cognitive impairment and dementia due to Lewy Body Disease (LBD). However, few studies to date have investigated the neuropsychological factors underlying pareidolia errors on the NPT across the clinical spectrum of LBD. Furthermore, to our knowledge, no research has examined the relationship between cortical thickness using MRI data and NPT subscores. As such, this study sought to explore the neuropsychological and neuroanatomical factors influencing performance on the NPT utilizing the National Alzheimer's Coordinating Center Lewy Body Dementia Module.

METHODS

Our sample included participants with normal cognition (NC; n = 56), LBD with mild cognitive impairment (LBD-MCI; n = 97), and LBD with dementia (LBD-Dementia; n = 94). Archival data from NACC were retrospectively analyzed for group differences in neuropsychological test scores and cognitive and psychiatric predictors of NPT scores. Clinicoradiological correlates between NPT subscores and a small subsample of the above LBD participants were also examined.

RESULTS

Analyses revealed significant differences in NPT scores among groups. Regression analysis demonstrated that dementia severity, attention, and visuospatial processing contributed approximately 24% of NPT performance in LBD groups. Clinicoradiological analysis suggests a potential contribution of the right fusiform gyrus, but not the inferior occipital gyrus, to NPT pareidolia error scores.

CONCLUSIONS

Our findings highlight the interplay of attention and visuoperceptual functions in complex pareidolia in LBD. Further investigation is needed to refine the utility of NPT scores in clinical settings, including identifying patients at risk for visual illusions and hallucinations.

Subjective Cognitive Decline in Community-Dwelling Older Adults With Objectively Normal Cognition: Mediation by Depression and Instrumental Activities of Daily Living

OBJECTIVE

Subjective cognitive decline (SCD) refers to self-reported memory loss despite normal cognitive function and is considered a preclinical stage of Alzheimer's disease. This study aimed to examine the mediating effects of depression and Instrumental Activities of Daily Living (IADL) on the association between the scoring of Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Subjective Cognitive Decline Questionnaire (SCD-Q).

METHODS

A sample of 139 community-dwelling older adults aged 65-79 with normal cognitive function completed the SCD-Q, a comprehensive neuropsychological battery, and functional/psychiatric scales. We conducted 1) a correlation analysis between SCD-Q scores and other variables and 2) a path analysis to examine the mediating effects of depression and IADL on the relationship between CDR-SB and SCD-Q.

RESULTS

CDR-SB was found to be indirectly associated with SCD-Q, with depressive symptoms mediating this relationship. However, no direct association was observed between SCD-Q and CDR-SB. Additionally, IADL was not associated with SCD-Q and did not mediate the relationship between CDR-SB and SCD-Q. The model fit was acceptable (minimum discrepancy function by degrees of freedom divided [CMIN/DF]=1.585, root mean square error of approximation [RMSEA]=0.065, comparative fit index [CFI]=0.955, Tucker-Lewis index [TLI]=0.939).

CONCLUSION

Our results suggest that SCD-Q is influenced by depressive symptoms, but not by IADL. The role of depressive symptoms as a mediator between CDR-SB and SCD-Q indicates that psychological factors may contribute to the perception of SCD. Therefore, interventions targeting depression may mitigate the concerns associated with SCD and reduce feelings of worse performance compared to others of the same age group.

Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage

INTRODUCTION

Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages.

METHODS

Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition.

RESULTS

Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures.

DISCUSSION

We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials.

HIGHLIGHTS

Mutation position influences Aβ burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aβ burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Disparate Dementia Risk Factors Are Associated with Cognitive Impairment and Rates of Decline in African Americans

OBJECTIVE

This study was undertaken to evaluate the frequency of modifiable dementia risk factors and their association with cognitive impairment and rate of decline in diverse participants engaged in studies of memory and aging.

METHODS

Modifiable dementia risk factors and their associations with cognitive impairment and cognitive decline were determined in community-dwelling African American (AA; n = 261) and non-Hispanic White (nHW; n = 193) participants who completed ≥2 visits at the Mayo Clinic Alzheimer Disease Research Center in Jacksonville, Florida. Risk factors and their associations with cognitive impairment (global Clinical Dementia Rating [CDR] ≥ 0.5) and rates of decline (CDR Sum of Boxes) in impaired participants were compared in AA and nHW participants, controlling for demographics, APOE ɛ4 status, and Area Deprivation Index.

RESULTS

Hypertension, hypercholesterolemia, obesity, and diabetes were overrepresented in AA participants, but were not associated with cognitive impairment. Depression was associated with increased odds of cognitive impairment in AA (odds ratio [OR] = 4.30, 95% confidence interval [CI] = 2.13-8.67) and nHW participants (OR = 2.79, 95% CI = 1.21-6.44) but uniquely associated with faster decline in AA participants (β = 1.71, 95% CI = 0.69-2.73, p = 0.001). Fewer AA participants reported antidepressant use (9/49, 18%) than nHW counterparts (57/78, 73%, p < 0.001). Vitamin B12 deficiency was also associated with an increased rate of cognitive decline in AA participants (β = 2.65, 95% CI = 0.38-4.91, p = 0.023).

INTERPRETATION

Modifiable dementia risk factors are common in AA and nHW participants, representing important risk mitigation targets. Depression was associated with dementia in AA and nHW participants, and with accelerated declines in cognitive function in AA participants. Optimizing depression screening and treatment may improve cognitive trajectories and outcomes in AA participants. ANN NEUROL 2024;95:518-529.

Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is a neurological disorder with variability in pathology and clinical progression. AD patients may differ in individual-level benefit from amyloid beta removal therapy.

METHODS

Random forest models were applied to the EMERGE trial to create an individual-level treatment response (ITR) score which represents individual-level benefit of high-dose aducanumab relative to the placebo. This ITR score was used to test the existence of heterogeneity in treatment effect (HTE).

RESULTS

We found statistical evidence of HTE in the Clinical Dementia Rating-Sum of Boxes (CDR-SB;P =  0.034). The observed CDR-SB benefit was 0.79 points greater in the group with the top 25% of ITR score compared to the remaining 75% (P = 0.020). Of note, the highest treatment responders had lower hippocampal volume, higher plasma phosphorylated tau 181 and a shorter duration of clinical AD at baseline.

DISCUSSION

This ITR analysis provides a proof of concept for precision medicine in future AD research and drug development.

HIGHLIGHTS

Emerging trials have shown a population-level benefit from amyloid beta (Aβ) removal in slowing cognitive decline in early Alzheimer's disease (AD). This work demonstrates significant heterogeneity of individual-level treatment effect of aducanumab in early AD. The greatest clinical responders to Aβ removal therapy have a pattern of more severe neurodegenerative process.

Improving Early Recognition of Treatment-Responsive Causes of Rapidly Progressive Dementia: The STAM3 P Score

OBJECTIVE

To improve the timely recognition of patients with treatment-responsive causes of rapidly progressive dementia (RPD).

METHODS

A total of 226 adult patients with suspected RPD were enrolled in a prospective observational study and followed for up to 2 years. Diseases associated with RPD were characterized as potentially treatment-responsive or non-responsive, referencing clinical literature. Disease progression was measured using Clinical Dementia Rating® Sum-of-Box scores. Clinical and paraclinical features associated with treatment responsiveness were assessed using multivariable logistic regression. Findings informed the development of a clinical criterion optimized to recognize patients with potentially treatment-responsive causes of RPD early in the diagnostic evaluation.

RESULTS

A total of 155 patients met defined RPD criteria, of whom 86 patients (55.5%) had potentially treatment-responsive causes. The median (range) age-at-symptom onset in patients with RPD was 68.9 years (range 22.0-90.7 years), with a similar number of men and women. Seizures, tumor (disease-associated), magnetic resonance imaging suggestive of autoimmune encephalitis, mania, movement abnormalities, and pleocytosis (≥10 cells/mm3 ) in cerebrospinal fluid at presentation were independently associated with treatment-responsive causes of RPD after controlling for age and sex. Those features at presentation, as well as age-at-symptom onset <50 years (ie, STAM3 P), captured 82 of 86 (95.3%) cases of treatment-responsive RPD. The presence of ≥3 STAM3 P features had a positive predictive value of 100%.

INTERPRETATION

Selected features at presentation reliably identified patients with potentially treatment-responsive causes of RPD. Adaptation of the STAM3 P screening score in clinical practice may minimize diagnostic delays and missed opportunities for treatment in patients with suspected RPD. ANN NEUROL 2024;95:237-248.

The HOPE4MCI study: A randomized double-blind assessment of AGB101 for the treatment of MCI due to AD

INTRODUCTION

In addition to the accumulation of amyloid plaques and neurofibrillary tangles, the presence of excess neural activity is a pathological hallmark of Alzheimer's disease (AD) and a prognostic indicator for progression of AD pathology and clinical/cognitive worsening in mild cognitive impairment due to Alzheimer's disease (MCI due to AD). The HOPE4MCI clinical study tested the efficacy of a therapeutic with demonstrated ability to normalize heightened neural activity in the hippocampus in a randomized controlled trial of 78 weeks duration in patients with MCI due to AD.

METHODS

One hundred and sixty-four participants were randomized to placebo (n = 83) or AGB101 (n = 81), an extended-release formulation of low dose (220 mg) levetiracetam. The primary endpoint was the change in Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB) comparing follow up at 18 months to baseline. The goal of the primary efficacy analysis was to estimate the difference between the AGB101 and placebo arms in the mean change of the primary endpoint.

RESULTS

The mean change in CDR-SB was estimated to be 1.12 (95% confidence interval [CI]: 0.66, 1.69) for the AGB101 arm and 1.22 (95% CI: 0.75, 1.78) for the placebo arm. The estimated difference between arms is -0.10 (95% CI: -0.85, 0.58), which was not statistically significant. In a prespecified analysis, the difference was -0.45 (95% CI: -1.43, 0.53) for ApoE-4 noncarriers and -0.10 (95% CI: -0.92, 0.72) for apolipoprotein E (ApoE)-4 carriers.

DISCUSSION

The possibility that ApoE-4 carriers and noncarriers will respond differently to therapeutic intervention is consistent with recently reported findings from biologics and the present results show further testing of AGB101 in patients with MCI due to AD who are noncarriers of the ApoeE-4 allele is warranted. Conclusions from the HOPE4MCI study are limited primarily due to the small sample size and results can only be regarded as a guide to future research.

Improving Regression Analysis with Imputation in a Longitudinal Study of Alzheimer's Disease

Background

Missing data is prevalent in the Alzheimer's Disease Neuroimaging Initiative (ADNI). It is common to deal with missingness by removing subjects with missing entries prior to statistical analysis; however, this can lead to significant efficiency loss and sometimes bias. It has yet to be demonstrated that the imputation approach to handling this issue can be valuable in some longitudinal regression settings.

Objective

The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer's Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics.

Methods

We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method.

Results

The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation.

Conclusions

Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.

Examining a Preclinical Alzheimer's Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers

Background

The preclinical Alzheimer's cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers.

Objective

We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β.

Methods

We examined 648 participants' neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer's Disease Research Center's Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants.

Results

There was a significant positive relationship between tPACCin - person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin - person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition.

Conclusions

There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer's disease.

The risk of developing dementia in the COVID-19 pandemic; a cohort study

OBJECTIVES

The effects of the COVID-19 pandemic on cognitive decline are not fully understood. Higher social activity and relationships have been associated with decreased risk of dementia. We hypothesised that risk of transition to dementia would increase after the start of the first national lockdown.

METHODS

We obtained data from the Brains for Dementia (BDR) cohort, which has collected roughly annual data on 3726 older adults with and without dementia since 2008. Data continued to be collected during the lockdowns, although by telephone and/or video call instead of in person. Individuals diagnosed with dementia at study entry were excluded from this study as were individuals with only one visit. Cognitive status was classified using the Clinical Dementia Rating (CDR) global score. Poisson regression with cubic splines to account for differences in age was used to compare the incidence of dementia before and after March 1st 2020.

RESULTS

Out of 2242 individuals, 208 individuals developed dementia before and 50 developed dementia after 01/03/20. The incidence rate ratio of developing dementia after 01/03/20 was 0.847 (0.538-1.335) p = 0.570. In our secondary analysis we found that the positive association between mild cognitive impairment (MCI) and dementia incidence decreased after 1/3/20 (interaction effect p = 0.031).

CONCLUSION

The incidence of dementia as defined using the CDR global score did not change significantly after the first lockdown began, but we found evidence that lockdown decreased the positive association between MCI and dementia incidence. This may reflect that individuals were progressing to dementia more rapidly and thus missing the MCI stage or that assessing patients over the phone made diagnosing MCI more challenging.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's

INTRODUCTION

To reduce demands on expert time and improve clinical efficiency, we developed a framework to evaluate whether inexpensive, accessible data could accurately classify Alzheimer's disease (AD) clinical diagnosis and predict the likelihood of progression.

METHODS

We stratified relevant data into three tiers: obtainable at primary care (low-cost), mostly available at specialty visits (medium-cost), and research-only (high-cost). We trained several machine learning models, including a hierarchical model, an ensemble model, and a clustering model, to distinguish between diagnoses of cognitively unimpaired, mild cognitive impairment, and dementia due to AD.

RESULTS

All models showed viable classification, but the hierarchical and ensemble models outperformed the conventional model. Classifier "error" was predictive of progression rates, and cluster membership identified subgroups with high and low risk of progression within 1.5 to 3 years.

DISCUSSION

Accessible, inexpensive clinical data can be used to guide AD diagnosis and are predictive of current and future disease states.

HIGHLIGHTS

Classification performance using cost-effective features was accurate and robustHierarchical classification outperformed conventional multinomial classificationClassification labels indicated significant changes in conversion risk at follow-upA clustering-classification method identified subgroups at high risk of decline.

Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative reference subjects

INTRODUCTION

In clinical practice, differentiating between age-related gray matter (GM) atrophy and neurodegeneration-related atrophy at early disease stages, such as mild cognitive impairment (MCI), remains challenging. We hypothesized that fined-grained adjustment for age effects and using amyloid-negative reference subjects could increase classification accuracy.

METHODS

T1-weighted magnetic resonance imaging (MRI) data of 131 cognitively normal (CN) individuals and 91 patients with MCI from the Alzheimer's disease neuroimaging initiative (ADNI) characterized concerning amyloid status, as well as 19 CN individuals and 19 MCI patients from an independent validation sample were segmented, spatially normalized and analyzed in the framework of voxel-based morphometry (VBM). For each participant, statistical maps of GM atrophy were computed as the deviation from the GM of CN reference groups at the voxel level. CN reference groups composed with different degrees of age-matching, and mixed and strictly amyloid-negative CN reference groups were examined regarding their effect on the accuracy in distinguishing between CN and MCI. Furthermore, the effects of spatial smoothing and atrophy threshold were assessed.

RESULTS

Approaches with a specific reference group for each age significantly outperformed all other age-adjustment strategies with a maximum area under the curve of 1.0 in the ADNI sample and 0.985 in the validation sample. Accounting for age in a regression-based approach improved classification accuracy over that of a single CN reference group in the age range of the patient sample. Using strictly amyloid-negative reference groups improved classification accuracy only when age was not considered.

CONCLUSION

Our results demonstrate that VBM can differentiate between age-related and MCI-associated atrophy with high accuracy. Crucially, age-specific reference groups significantly increased accuracy, more so than regression-based approaches and using amyloid-negative reference groups.

Estimating Transition Probabilities Across the Alzheimer's Disease Continuum Using a Nationally Representative Real-World Database in the United States

INTRODUCTION

Clinical Alzheimer's disease (AD) begins with mild cognitive impairment (MCI) and progresses to mild, moderate, or severe dementia, constituting a disease continuum that eventually leads to death. This study aimed to estimate the probabilities of transitions across those disease states.

METHODS

We developed a mixed-effects multi-state Markov model to estimate the transition probabilities, adjusted for 5 baseline covariates, using the Health and Retirement Study (HRS) database. HRS surveys older adults in the United States bi-annually. Alzheimer states were defined using the modified Telephone Interview of Cognitive Status (TICS-m).

RESULTS

A total of 11,292 AD patients were analyzed. Patients were 70.8 ± 9.0 years old, 54.9% female, and with 12.0 ± 3.3 years of education. Within 1 year from the initial state, the model estimated a higher probability of transition to the next AD state in earlier disease: 12.8% from MCI to mild AD and 5.0% from mild to moderate AD, but < 1% from moderate to severe AD. After 10 years, the probability of transition to the next state was markedly higher for all states, but still higher in earlier disease: 29.8% from MCI to mild AD, 23.5% from mild to moderate AD, and 5.7% from moderate to severe AD. Across all AD states, the probability of transition to death was < 5% after 1 year and > 15% after 10 years. Older age, fewer years of education, unemployment, and nursing home stay were associated with a higher risk of disease progression (p < 0.01).

CONCLUSIONS

This analysis shows that the risk of progression is greater in earlier AD states, increases over time, and is higher in patients who are older, with fewer years of education, unemployed, or in a nursing home at baseline. The estimated transition probabilities can provide guidance for future disease management and clinical trial design optimization, and can be used to refine existing cost-effectiveness frameworks.

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

Pattern and implications of neurological examination findings in autosomal dominant Alzheimer disease

INTRODUCTION

As knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD.

METHODS

Frequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers.

RESULTS

AD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross-sectionally, mutation carriers with AD neurological examination findings showed a more than two-fold faster cognitive decline and had greater parieto-temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time.

DISCUSSION

ADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs.

Metrological properties of neuropsychological tests for measuring cognitive change in individuals with prodromal Alzheimer's disease

OBJECTIVES

In Alzheimer's Disease (AD) research, choosing appropriate method for measuring change in cognitive function over time can be challenging. The aim for this study was to examine the sensitivity of four neuropsychological tests used to measure cognition during the transition from mild cognitive impairment (MCI) to AD, and the impacts of associated covariates.

METHODS

We enrolled 223 patients with MCI who progressed to AD and had completed multiple follow-up assessments in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We constructed nonlinear mixed model for multivariate longitudinal data assuming that multiple neuropsychological tests would exhibit nonlinear transformation of a common factor in the latent cognitive process underlying the progression from MCI to AD.

RESULTS

The Clinical Dementia Rating-Sum of the Boxes (CDR-SB) and Alzheimer's Disease Assessment Scale (11 items; ADAS-11) were more sensitive to cognitive changes in individuals with higher cognitive function, the Functional Activities Questionnaire (FAQ) was more sensitive to cognitive changes in individuals with middle cognitive function, and the Mini-Mental State Examination (MMSE) was more sensitive to cognitive changes in individuals with lower cognitive function. Gender (p = 0.0139) and educational level (p = 0.0094) had varying effects on different tests, such that men performed better on the FAQ and CDR-SB, and individuals with higher educational level tended to perform better on the FAQ and MMSE.

CONCLUSIONS

When choosing appropriate neuropsychological tests in cognitive measurements, the cognitive functional level of the patient as well as the impacts of covariates should be considered.

Different rates of cognitive decline in autosomal dominant and late-onset Alzheimer disease

As prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and "sporadic" late-onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy-confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy-confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.

Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages

Background

The clinical dementia rating (CDR) scale is commonly used to diagnose dementia due to Alzheimer's disease (AD). The sum of boxes of the CDR (CDR-SB) has recently been emphasized and applied to interventional trials for tracing the progression of cognitive impairment (CI) in the early stages of AD. We aimed to study the influence of baseline CDR-SB on disease progression to dementia or reversion to normal cognition (NC).

Materials and methods

The baseline CDR < 1 cohort registered from September 2015 to August 2020 with longitudinal follow-up in the History-based Artificial Intelligence Clinical Dementia Diagnostic System (HAICDDS) database was retrospectively analyzed for the rates of conversion to CDR ≥ 1. A Cox regression model was applied to study the influence of CDR-SB levels on progression, adjusting for age, education, sex, neuropsychological tests, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors.

Results

A total of 1,827 participants were analyzed, including 1,258 (68.9%) non-converters, and 569 (31.1%) converters with mean follow-up of 2.1 (range 0.4-5.5) and 1.8 (range 0.3-5.0) years, respectively. Conversion rates increased with increasing CDR-SB scores. Compared to a CDR-SB score of 0, the hazard ratios (HR) for conversion to dementia were 1.51, 1.91, 2.58, 2.13, 3.46, 3.85, 3.19, 5.12, and 5.22 for CDR-SB scores of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and ≥4.5, respectively (all p < 0.05 except for CDR-SB score = 0.5). In addition, older age, lower education, lower cognitive performance, and a history of diabetes also increased conversion rates. Furthermore, reversions to NC were 12.5, 5.6, 0.9, and 0% for CDR-SB scores of 0.5, 1.0-2.0, 2.5-3.5 and ≥4.0, respectively (p < 0.001).

Conclusion

CDR-SB in predementia or very mild dementia (VMD) stages highly predicts progression to dementia or reversion to NC. Therefore, CDR-SB could be a good candidate for tracing the effectiveness of pharmacological and non-pharmacological interventions in populations without dementia.

Clinically Relevant Changes for Cognitive Outcomes in Preclinical and Prodromal Cognitive Stages: Implications for Clinical Alzheimer Trials

BACKGROUND AND OBJECTIVES

Identifying a clinically meaningful change in cognitive test score is essential when using cognition as an outcome in clinical trials. This is especially relevant because clinical trials increasingly feature novel composites of cognitive tests. Our primary objective was to establish minimal clinically important differences (MCIDs) for commonly used cognitive tests, using anchor-based and distribution-based methods, and our secondary objective was to investigate a composite cognitive measure that best predicts a minimal change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB).

METHODS

From the Swedish BioFINDER cohort study, we consecutively included cognitively unimpaired (CU) individuals with and without subjective or mild cognitive impairment (MCI). We calculated MCIDs associated with a change of ≥0.5 or ≥1.0 on CDR-SB for Mini-Mental State Examination (MMSE), ADAS-Cog delayed recall 10-word list, Stroop, Letter S Fluency, Animal Fluency, Symbol Digit Modalities Test (SDMT) and Trailmaking Test (TMT) A and B, and triangulated MCIDs for clinical use for CU, MCI, and amyloid-positive CU participants. For investigating cognitive measures that best predict a change in CDR-SB of ≥0.5 or ≥1.0 point, we conducted receiver operating characteristic analyses.

RESULTS

Our study included 451 cognitively unimpaired individuals, 90 with subjective cognitive decline and 361 without symptoms of cognitive decline (pooled mean follow-up time 32.4 months, SD 26.8, range 12-96 months), and 292 people with MCI (pooled mean follow-up time 19.2 months, SD 19.0, range 12-72 months). We identified potential triangulated MCIDs (cognitively unimpaired; MCI) on a range of cognitive test outcomes: MMSE -1.5, -1.7; ADAS delayed recall 1.4, 1.1; Stroop 5.5, 9.3; Animal Fluency: -2.8, -2.9; Letter S Fluency -2.9, -1.8; SDMT: -3.5, -3.8; TMT A 11.7, 13.0; and TMT B 24.4, 20.1. For amyloid-positive CU, we found the best predicting composite cognitive measure included gender and changes in ADAS delayed recall, MMSE, SDMT, and TMT B. This produced an AUC of 0.87 (95% CI 0.79-0.94, sensitivity 75%, specificity 88%).

DISCUSSION

Our MCIDs may be applied in clinical practice or clinical trials for identifying whether a clinically relevant change has occurred. The composite measure can be useful as a clinically relevant cognitive test outcome in preclinical AD trials.

Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling

BACKGROUND

Longitudinal item response theory (IRT) models previously suggested that the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor examination has two salient domains, tremor and nontremor, that progress in time and in response to treatment differently.

OBJECTIVE

Apply longitudinal IRT modeling, separating tremor and nontremor domains, to reanalyze outcomes in the previously published clinical trial (Study of Urate Elevation in Parkinson's Disease, Phase 3) that showed no overall treatment effects.

METHODS

We applied unidimensional and multidimensional longitudinal IRT models to MDS-UPDRS motor examination items in 298 participants with Parkinson's disease from the Study of Urate Elevation in Parkinson's Disease, Phase 3 (placebo vs. inosine) study. We separated 10 tremor items from 23 nontremor items and used Bayesian inference to estimate progression rates and sensitivity to treatment in overall motor severity and tremor and nontremor domains.

RESULTS

The progression rate was faster in the tremor domain than the nontremor domain before levodopa treatment. Inosine treatment had no effect on either domain relative to placebo. Levodopa treatment was associated with greater slowing of progression in the tremor domain than the nontremor domain regardless of inosine exposure. Linear patterns of progression were observed. Despite different domain-specific progression patterns, tremor and nontremor severities at baseline and over time were significantly correlated.

CONCLUSIONS

Longitudinal IRT analysis is a novel statistical method addressing limitations of traditional linear regression approaches. It is particularly useful because it can simultaneously monitor changes in different, but related, domains over time and in response to treatment interventions. We suggest that in neurological diseases with distinct impairment domains, clinical or anatomical, this application may identify patterns of change unappreciated by standard statistical methods. © 2022 International Parkinson and Movement Disorder Society.

Are Care-Recipient Outcomes Attributable to Improved Caregiver Well-Being? A Cluster-Randomized Controlled Trial of Benefit-Finding Intervention

OBJECTIVES

The benefit-finding therapeutic (BFT) intervention, training cognitive reappraisal, and alternative thinking to construct positive aspects of caregiving have been found to reduce caregiver depression. This study examines BFT effects on care-recipient outcomes via reduced caregiver depression.

DESIGN

Cluster-randomized double-blind controlled trial.

SETTING

Social centers and clinics.

PARTICIPANTS

A total of 129 caregivers. Inclusion criteria were 1) primary caregiver aged 18+, 2) without cognitive impairment, 3) providing ≥14 care hours weekly to a relative with mild-to-moderate Alzheimer's disease, and 4) scoring ≥3 on the Hamilton Depression Rating Scale. Exclusion criterion was care-recipient having Parkinsonism or other forms of dementia.

INTERVENTIONS

BFT was evaluated against two forms of psychoeducation-standard and simplified (lectures only) psychoeducation.

MEASUREMENTS

Care-recipient outcomes included neuropsychiatric symptoms (NPS), functional impairment, and global dementia severity (Clinical Dementia Rating sum-of-box), measured at baseline, postintervention, and 4- and 10-month follow up.

RESULTS

Mixed-effects regressions showed a significant effect on NPS when compared with simplified psychoeducation only, with BFT participants reporting fewer NPS (especially mood symptoms) at 4-month follow-up (d = -0.52). Furthermore, longitudinal path analysis (using changes in caregiver depression scores at postintervention to predict changes in care-recipient NPS at follow-up) found that this effect was mediated by improved caregiver depression. No other intervention or mediation effects were found or were consistent across analyses.

CONCLUSIONS

Less depressed caregivers may be able to provide better care and more positive interactions, leading to reduced NPS in care-recipients. However, this benefit of BFT was limited to the comparison with simplified psychoeducation only.

Plasma p-tau181 shows stronger network association to Alzheimer's disease dementia than neurofilament light and total tau

INTRODUCTION

We examined the ability of plasma hyperphosphorylated tau (p-tau)181 to detect cognitive impairment due to Alzheimer's disease (AD) independently and in combination with plasma total tau (t-tau) and neurofilament light (NfL).

METHODS

Plasma samples were analyzed using the Simoa platform for 235 participants with normal cognition (NC), 181 with mild cognitive impairment due to AD (MCI), and 153 with AD dementia. Statistical approaches included multinomial regression and Gaussian graphical models (GGMs) to assess a network of plasma biomarkers, neuropsychological tests, and demographic variables.

RESULTS

Plasma p-tau181 discriminated AD dementia from NC, but not MCI, and correlated with dementia severity and worse neuropsychological test performance. Plasma NfL similarly discriminated diagnostic groups. Unlike plasma NfL or t-tau, p-tau181 had a direct association with cognitive diagnosis in a bootstrapped GGM.

DISCUSSION

These results support plasma p-tau181 for the detection of AD dementia and the use of blood-based biomarkers for optimal disease detection.

Mild to moderate decrease in eGFR and cognitive decline in older adults

BACKGROUND

Whether mild to moderately low estimated glomerular filtration rate (eGFR) is associated with cognitive decline in older adults is not clear. We evaluated changes in cognition in relation to baseline eGFR in older adults participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHODS

This is a longitudinal secondary analysis of an established observational cohort. We used data from the ADNI, an National Institutes of Health-funded, multicenter longitudinal observational study that includes participants with and without cognitive impairment who were administered a comprehensive battery of neuropsychological tests every 6 months. We related the Chronic Kidney Disease Epidemiology Collaboration eGFR with previously validated cognition composite scores for memory (ADNI-Mem) and executive function (ADNI-EF) in multivariable linear regression analysis adjusted for age, sex, race and level of education.

RESULTS

A total of 1127 ADNI participants (mean age 74 ± 7 years, 57% men, 97% Caucasian, mean follow-up 6 ± 2.6 years) were included in the analysis. The mean baseline eGFR was 76 ± 19 mL/min/1.73 m2, with 6% with eGFR <45, 22% with eGFR 45-<60, 51% with eGFR 60-90 and 21% with eGFR >90 mL/min/1.73 m2 at baseline. Both ADNI-Mem and ADNI-EF scores declined over time. In the multivariable linear regression model, older age (β = -0.117, P = 0.01), female sex (β = 0.312, P < 0.001) and lower education (β = 0.079, P < 0.001) were associated with a decline in ADNI-Mem scores, whereas baseline eGFR (each 10 mL/min/1.73 m2 change) was not {β = -0.03 [confidence interval (CI) -0.06-0.001], P = 0.11}. Similarly, older age (β = -0.278, P < 0.001) and lower education (β = 0.099, P < 0.001) were associated with a decline in ADNI-EF scores, whereas baseline eGFR was not [β = 0.004 (95% CI -0.04-0.04), P = 0.84].

CONCLUSIONS

In this cohort from the ADNI study, there was no association between baseline eGFR and cognitive decline in older adults with mild to moderately low eGFR.

Development and implementation of an electronic Clinical Dementia Rating and Financial Capacity Instrument-Short Form

Introduction

To address the need for remote assessments of cognitive decline and dementia, we developed and administered electronic versions of the Clinical Dementia Rating (CDR®) and the Financial Capacity Instrument-Short Form (FCI-SF) (F-CAP®), called the eCDR and eFCI, respectively.

Methods

The CDR and FCI-SF were adapted for remote, unsupervised, online use based on item response analysis of the standard instruments. Participants completed the eCDR and eFCI first in clinic, and then at home within 2 weeks.

Results

Of the 243 enrolled participants, 179 (73%) cognitively unimpaired (CU), 50 (21%) with mild cognitive impairment (MCI) or dementia, and 14 (6%) with an unknown diagnosis, 84% and 85% of them successfully completed the eCDR and eFCI, respectively, at home.

Discussion

These results show initial feasibility in developing and administering online instruments to remotely assess and monitor cognitive decline along the CU to MCI/very mild dementia continuum. Validation is an important next step.

Alzheimer's Disease Assessments Optimized for Diagnostic Accuracy and Administration Time

OBJECTIVE

Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer's disease diagnosis.

METHODS

We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items.

RESULTS

We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments.

DISCUSSION

Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.

Development of novel clinical examination scales for the measurement of disease severity in Creutzfeldt-Jakob disease

OBJECTIVE

To use a robust statistical methodology to develop and validate clinical rating scales quantifying longitudinal motor and cognitive dysfunction in sporadic Creutzfeldt-Jakob disease (sCJD) at the bedside.

METHODS

Rasch analysis was used to iteratively construct interval scales measuring composite cognitive and motor dysfunction from pooled bedside neurocognitive examinations collected as part of the prospective National Prion Monitoring Cohort study, October 2008-December 2016.A longitudinal clinical examination dataset constructed from 528 patients with sCJD, comprising 1030 Motor Scale and 757 Cognitive Scale scores over 130 patient-years of study, was used to demonstrate scale utility.

RESULTS

The Rasch-derived Motor Scale consists of 8 items, including assessments reliant on pyramidal, extrapyramidal and cerebellar systems. The Cognitive Scale comprises 6 items, and includes measures of executive function, language, visual perception and memory. Both scales are unidimensional, perform independently of age or gender and have excellent inter-rater reliability. They can be completed in minutes at the bedside, as part of a normal neurocognitive examination. A composite Examination Scale can be derived by averaging both scores. Several scale uses, in measuring longitudinal change, prognosis and phenotypic heterogeneity are illustrated.

CONCLUSIONS

These two novel sCJD Motor and Cognitive Scales and the composite Examination Scale should prove useful to objectively measure phenotypic and clinical change in future clinical trials and for patient stratification. This statistical approach can help to overcome obstacles to assessing clinical change in rapidly progressive, multisystem conditions with limited longitudinal follow-up.

Total cost of care increases significantly from early to mild Alzheimer's disease: 5-year ALSOVA follow-up

INTRODUCTION

We studied the costs of formal and informal care in relation to Alzheimer's disease (AD) progression.

METHODS

231 persons with AD with a family caregiver were followed up for 5 years. The Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) was used to measure AD progression. Health and social care unit costs were used for formal care costs. An opportunity cost method for lost leisure time was applied to analyse the cost of informal care.

RESULTS

Total cost of care in early stage AD (CDR-SB ≤ 4) was 16,448€ (95% CI 13,722-19,716) annually. In mild (CDR-SB 4.5-9), moderate (CDR-SB 9.5-15.5) and severe (CDR-SB ≥ 16) AD, the total costs were 2.3, 3.4 and 4.4 times higher, respectively. A one-unit increase in CDR-SB increased the total, formal and informal costs by 15, 11 and 18%, respectively.

CONCLUSIONS

Compared to early AD, the costs of total, formal and informal care are remarkably higher already in mild AD. This finding emphasises early diagnosis, interventions and family support for persons with AD and their caregivers.

Association of peripheral blood DNA methylation level with Alzheimer's disease progression

Background

Identifying biomarkers associated with Alzheimer’s disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort.

Results

The rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACC_digit and mPACC_trailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants (P < 5.79 × 10⁻⁸ [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACC_digit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACC_trailsB, mPACC_digit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10⁻²⁴), which was associated with both the slope of CDR-SB and the MCI conversion status.

Conclusion

Candidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01179-2.

Developing the ATX(N) classification for use across the Alzheimer disease continuum

Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical-biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-β (Aβ) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood-brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.

Practical Strategies for Extreme Missing Data Imputation in Dementia Diagnosis

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ~50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

ADAMANT: a placebo-controlled randomized phase 2 study of AADvac1, an active immunotherapy against pathological tau in Alzheimer's disease

Alzheimer's disease (AD) pathology is partly characterized by accumulation of aberrant forms of tau protein. Here we report the results of ADAMANT, a 24-month double-blinded, parallel-arm, randomized phase 2 multicenter placebo-controlled trial of AADvac1, an active peptide vaccine designed to target pathological tau in AD (EudraCT 2015-000630-30). Eleven doses of AADvac1 were administered to patients with mild AD dementia at 40 μg per dose over the course of the trial. The primary objective was to evaluate the safety and tolerability of long-term AADvac1 treatment. The secondary objectives were to evaluate immunogenicity and efficacy of AADvac1 treatment in slowing cognitive and functional decline. A total of 196 patients were randomized 3:2 between AADvac1 and placebo. AADvac1 was safe and well tolerated (AADvac1 n = 117, placebo n = 79; serious adverse events observed in 17.1% of AADvac1-treated individuals and 24.1% of placebo-treated individuals; adverse events observed in 84.6% of AADvac1-treated individuals and 81.0% of placebo-treated individuals). The vaccine induced high levels of IgG antibodies. No significant effects were found in cognitive and functional tests on the whole study sample (Clinical Dementia Rating-Sum of the Boxes scale adjusted mean point difference -0.360 (95% CI -1.306, 0.589)), custom cognitive battery adjusted mean z-score difference of 0.0008 (95% CI -0.169, 0.172). We also present results from exploratory and post hoc analyses looking at relevant biomarkers and clinical outcomes in specific subgroups. Our results show that AADvac1 is safe and immunogenic, but larger stratified studies are needed to better evaluate its potential clinical efficacy and impact on disease biomarkers.

Adding cognition to AT(N) models improves prediction of cognitive and functional decline

INTRODUCTION

This study sought to determine whether adding cognition to a model with Alzheimer's disease biomarkers based on the amyloid, tau, and neurodegeneration/neuronal injury-AT(N)-biomarker framework predicts rates of cognitive and functional decline in older adults without dementia.

METHODS

The study included 465 participants who completed amyloid positron emission tomography, cerebrospinal fluid phosphorylated tau, structural magnetic resonance imaging, and serial neuropsychological testing. Using the AT(N) framework and a newly validated cognitive metric as the independent variables, we used linear mixed effects models to examine a 4-year rate of change in cognitive and functional measures.

RESULTS

The inclusion of baseline cognitive status improved model fit in predicting rate of decline in outcomes above and beyond biomarker variables. Specifically, those with worse cognitive functioning at baseline had faster rates of memory and functional decline over a 4-year period, even when accounting for AT(N).

DISCUSSION

Including a newly validated measure of baseline cognition may improve clinical prognosis in non-demented older adults beyond the use of AT(N) biomarkers alone.

Flortaucipir (tau) PET in LGI1 antibody encephalitis

The contributors to persistent cognitive impairment and hippocampal atrophy in leucine-rich glioma-inactivated 1 antibody encephalitis (LGI1) patients are unknown. We evaluated whether tau neuropathology measured with [18 F]flortaucipir PET neuroimaging associated with persistent cognitive impairment and hippocampal atrophy in four recovering LGI1 patients (3 men; median age, 67 [37-88] years). Imaging findings in cases were compared with those observed in age- and gender-similar cognitively normal individuals (n = 124) and individuals with early-symptomatic Alzheimer disease (n = 11). Elevated [18 F]flortaucipir retention was observed in the two LGI1 patients with hippocampal atrophy and persistent cognitive impairment, including one with autopsy-confirmed Alzheimer disease. Tau neuropathology may associate with cognitive complaints and hippocampal atrophy in recovering LGI1 patients.

A high cerebrospinal fluid soluble TREM2 level is associated with slow clinical progression of Alzheimer's disease

INTRODUCTION

The progression rate of Alzheimer's disease (AD) varies and might be affected by the triggering receptor expressed on myeloid cells (TREM2) activity. We explored if cerebrospinal fluid (CSF) soluble TREM2 (sTREM2), a proxy of microglial activity, is associated with clinical progression rate.

METHODS

Patients with clinical AD (N = 231) were followed for up to 3 years after diagnosis. Cognitively healthy controls (N = 42) were followed for 5 years. CSF sTREM2 was analyzed by enzyme-linked immunosorbent assay. Group-based trajectory modeling revealed distinct clinical progression groups.

RESULTS

Higher CSF sTREM2 was associated with slow clinical progression. The slow- and medium-progressing groups had higher CSF sTREM2 than the cognitively healthy, who had a similar level to patients with rapid clinical progression.

DISCUSSION

CSF sTREM2 levels were associated with clinical progression in AD, regardless of core biomarkers. This could be useful in assessing disease development in relation to patient care and clinical trial recruitment.

Development and Preliminary Validation of Standardized Regression-Based Change Scores as Measures of Transitional Cognitive Decline

OBJECTIVE

An increasing focus in Alzheimer's disease and aging research is to identify transitional cognitive decline. One means of indexing change over time in serial cognitive evaluations is to calculate standardized regression-based (SRB) change indices. This paper includes the development and preliminary validation of SRB indices for the Uniform Data Set 3.0 Neuropsychological Battery, as well as base rate data to aid in their interpretation.

METHOD

The sample included 1,341 cognitively intact older adults with serial assessments over 0.5-2 years in the National Alzheimer's Coordinating Center Database. SRB change scores were calculated in half of the sample and then validated in the other half of the sample. Base rates of SRB decline were evaluated at z-score cut-points, corresponding to two-tailed p-values of .20 (z = -1.282), .10 (z = -1.645), and .05 (z = -1.96). We examined convergent associations of SRB indices for each cognitive measure with each other as well as concurrent associations of SRB indices with clinical dementia rating sum of box scores (CDR-SB).

RESULTS

SRB equations were able to significantly predict the selected cognitive variables. The base rate of at least one significant SRB decline across the entire battery ranged from 26.70% to 58.10%. SRB indices for cognitive measures demonstrated theoretically expected significant positive associations with each other. Additionally, CDR-SB impairment was associated with an increasing number of significantly declined test scores.

CONCLUSIONS

This paper provides preliminary validation of SRB indices in a large sample, and we present a user-friendly tool for calculating SRB values.

Regional Cerebral Associations Between Psychometric Tests and Imaging Biomarkers in Alzheimer's Disease

Recently, imaging biomarkers have gained importance for the characterization of patients with Alzheimer's disease; however, the relationship between regional biomarker expression and cognitive function remains unclear. In our study, we investigated associations between scores on CERAD neuropsychological assessment battery (CERAD-NAB) subtests with regional glucose metabolism, cortical thickness and amyloid deposition in patients with early Alzheimer's disease (AD) using [18F]-fluorodeoxyglucose (FDG), structural MRI, and 11C-Pittsburgh Compound B (PiB) positron emission tomography (PET), respectively. A total of 76 patients (mean age 68.4 ± 8.5 years, 57.9% male) with early AD (median global clinical dementia rating (CDR) score = 0.5, range: 0.5-2.0) were studied. Associations were investigated by correlation and multiple regression analyses. Scores on cognitive subtests were most closely predicted by regional glucose metabolism with explained variance up to a corrected R² of 0.518, followed by cortical thickness and amyloid deposition. Prediction of cognitive subtest performance was increased up to a corrected R² of 0.622 for Word List-Delayed Recall, when biomarker information from multiple regions and multiple modalities were included. For verbal, visuoconstructive and mnestic domains the closest associations with FDG-PET imaging were found in the left lateral temporal lobe, right parietal lobe, and posterior cingulate cortex, respectively. Decreased cortical thickness in parietal regions was most predictive of impaired subtest performance. Remarkably, cerebral amyloid deposition significantly predicted cognitive function in about half of the subtests but with smaller extent of variance explained (corrected R² ≤ 0.220). We conclude that brain metabolism and atrophy affect cognitive performance in a regionally distinct way. Significant predictions of cognitive function by PiB-PET in half of CERAD-NAB subtests suggest functional relevance even in symptomatic patients with AD, challenging the concept of plateauing cortical amyloid deposition early in the disease course. Our results underscore the complex spatial relationship between different imaging biomarkers.

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.

Discrete effect of each mild behavioural impairment category on dementia conversion or cognitive decline in patients with mild cognitive impairment

BACKGROUND

Neuropsychiatric symptoms (NPS) have been recognized as risk factors for conversion to dementia in patients with mild cognitive impairment (MCI). Early detection of NPS may allow for possible interventions in such patients. The present study used mild behavioural impairment to explore the role of NPS in a wide range of patients, from those who are cognitively intact to those with dementia.

METHODS

A total of 234 patients with mild cognitive impairment were followed up for up to 3 years in a Japanese cohort study. Longitudinal data from patients who developed dementia during the study and those who did not were statistically analyzed.

RESULTS

Cox regression analysis revealed that only abnormal perception and thought was significant in terms of dementia conversion. Moreover, mixed-effects models indicated that baseline mild behavioural impairment symptoms did not affect cognitive trajectories such as changes in Mini-Mental State Examination or Alzheimer's Disease Assessment Scale-cognitive subscale scores.

CONCLUSION

We conclude that only abnormal perception and thought content were risk factors for dementia and that NPS may not lead to deterioration of cognitive function in patients with mild cognitive impairment.

High-Density Lipoprotein Subclasses and Mild Cognitive Impairment: Study of Outcome and aPolipoproteins in Dementia (STOP-Dementia)1

BACKGROUND

High-density lipoprotein (HDL) containing apolipoprotein A-I is associated with the pathogenesis of Alzheimer's disease (AD). HDL particle size is modified in the presence of pathological conditions, while the significance of the HDL particle size remains controversial.

OBJECTIVE

The aim of this study was to investigate the HDL lipoprotein subclasses in mild cognitive impairment (MCI) and AD.

METHODS

This cross-sectional study included 20 AD patients, 17 MCI patients, and 17 age-matched controls without cognitive impairment, selected from the database of the Study of Outcome and aPolipoproteins in Dementia (STOP-Dementia) registry. The diagnoses of AD and MCI were performed by expert neurologists according to the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition criteria. Serum HDL subclasses were measured by electrophoretic separation of lipoproteins using the Lipoprint System. The neutrophil-lymphocyte ratio (NLR), a marker of inflammation, was calculated by dividing the neutrophil count by the lymphocyte count.

RESULTS

Small-sized HDL particle levels in the MCI group were significantly higher than in the control group, although there was no difference in serum HDL-cholesterol levels between MCI and control groups. NLR in the MCI group was higher than in the control group, but this difference was non-significant (p = 0.09). There was no difference in HDL subclasses or NLR between the AD and control groups.

CONCLUSION

These findings suggest that HDL subclasses might be associated with the development of MCI.

Rapidly Progressive Alzheimer's Disease in Two Distinct Autopsy Cohorts

BACKGROUND

A rapidly progressive phenotype of Alzheimer's disease (AD) has been described in some prion disease cohorts. Limited information regarding rapidly progressive AD (rpAD) is available from longitudinal national cohorts.

OBJECTIVE

To compare the clinical characteristics of rpAD in two different national cohorts.

METHODS

A retrospective analysis was performed on AD subjects with available neuropathology in the National Alzheimer's Coordinating Center (NACC) database and among neuropathologically characterized AD cases from the National Prion Disease Pathology Surveillance Center (NPDPSC) that were evaluated for suspected prion disease. In the NACC cohort, rpAD was delineated by the lower 10th percentile of follow up duration from pre-dementia to death duration among subjects meeting pathological diagnosis of AD.

RESULTS

rpAD from the NPDPSC had a shorter mean symptom duration than the NACC identified rpAD cases (11.6 months versus 62.4 months) and were also younger at the time of their death (60.0 versus 81.8 years). NACC identified rpAD subjects, beginning from a predementia stage, had slower rate of MMSE change per year than NPDPSC cases (2.5 versus 6.0 points).

CONCLUSIONS

rpAD constitute an important subset of AD subjects in whom a rapid course of symptomatic clinical decline is noted, as confirmed in both national cohorts. rpAD was best characterized by survival time (≤3 years), as there were clear differences between the rpAD cohorts in terms of symptom duration, age at death, and MMSE change per year, likely due to the strong selection biases. rpAD could shed light on the biology of rate of progression in AD.

Variability in the validity and reliability of outcome measures identified in a systematic review to assess treatment efficacy of cognitive enhancers for Alzheimer's Dementia

INTRODUCTION

Selection of optimal outcome measures is a critical step in a systematic review; inclusion of uncommon or non-validated outcome measures can impact the uptake of systematic review findings. Our goals were to identify the validity and reliability of outcome measures used in primary studies to assess cognition, function, behaviour and global status; and, to use these data to select outcomes for a systematic review (SR) on treatment efficacy of cognitive enhancers for Alzheimer's Dementia (AD).

METHODS

Articles fulfilling the eligibility criteria of the SR were included in a charting exercise to catalogue outcome measures reported. Outcome measures were then assessed for validity and reliability. Two independent reviewers abstracted data on outcome measures and validity and reliability reported for cognition, function, behaviour and global status.

RESULTS

129 studies were included in the charting exercise; 57 outcome measures were identified for cognition, 21 for function, 13 for behaviour and 10 for global status. A total of 35 (61%) cognition measures, 10 (48%) functional measures, 8 (61%) behavioural measures and four (40%) of global status measures were only used once in the literature. Validity and reliability information was found for 51% of cognition measures, 90% of function and global status measures and 100% of behavioural measures.

CONCLUSIONS

While a large number of outcome measures were used in primary studies, many of these were used only once. Reporting of validity and reliability varied in AD studies of cognitive enhancers. Core outcome sets should be used when available; when they are not available researchers need to balance frequency of reported outcome measures, their respective validity and reliability, and preferences of knowledge users.

SYSTEMATIC REVIEW REGISTRATION

CRD#42012001948.

Evaluating the effects of the novel GLP-1 analogue liraglutide in Alzheimer's disease: study protocol for a randomised controlled trial (ELAD study)

BACKGROUND

Liraglutide is a glucagon-like peptide-1 (GLP-1) analogue currently approved for type 2 diabetes and obesity. Preclinical evidence in transgenic models of Alzheimer's disease suggests that liraglutide exerts neuroprotective effects by reducing amyloid oligomers, normalising synaptic plasticity and cerebral glucose uptake, and increasing the proliferation of neuronal progenitor cells. The primary objective of the study is to evaluate the change in cerebral glucose metabolic rate after 12 months of treatment with liraglutide in participants with Alzheimer's disease compared to those who are receiving placebo.

METHODS/DESIGN

ELAD is a 12-month, multi-centre, randomised, double-blind, placebo-controlled, phase IIb trial of liraglutide in participants with mild Alzheimer's dementia. A total of 206 participants will be randomised to receive either liraglutide or placebo as a daily injection for a year. The primary outcome will be the change in cerebral glucose metabolic rate in the cortical regions (hippocampus, medial temporal lobe, and posterior cingulate) from baseline to follow-up in the treatment group compared with the placebo group. The key secondary outcomes are the change from baseline to 12 months in z scores for clinical and cognitive measures (Alzheimer's Disease Assessment Scale-Cognitive Subscale and Executive domain scores of the Neuropsychological Test Battery, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Cooperative Study-Activities of Daily Living) and the incidence and severity of treatment-emergent adverse events or clinically important changes in safety assessments. Other secondary outcomes are 12-month change in magnetic resonance imaging volume, diffusion tensor imaging parameters, reduction in microglial activation in a subgroup of participants, reduction in tau formation and change in amyloid levels in a subgroup of participants measured by tau and amyloid imaging, and changes in composite scores using support machine vector analysis in the treatment group compared with the placebo group.

DISCUSSION

Alzheimer's disease is a leading cause of morbidity worldwide. As available treatments are only symptomatic, the search for disease-modifying therapies is a priority. If the ELAD trial is successful, liraglutide and GLP-1 analogues will represent an important class of compounds to be further evaluated in clinical trials for Alzheimer's treatment.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01843075 . Registration 30 April 2013.

Progression of Alzheimer's Disease: A Longitudinal Study in Norwegian Memory Clinics

BACKGROUND

The course of Alzheimer's disease (AD) varies considerably between individuals. There is limited evidence on factors important for disease progression.

OBJECTIVE

The primary aim was to study the progression of AD, as measured by the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB). Secondary aims were to investigate whether baseline characteristics are important for differences in progression, and to examine the correlation between progression assessed using three different instruments: CDR-SB (0-18), the cognitive test Mini-Mental State Examination (MMSE, 0-30), and the functional measure Instrumental Activities of Daily Living (IADL, 0-1).

METHODS

The Progression of AD and Resource use (PADR) study is a longitudinal observational study in three Norwegian memory clinics.

RESULTS

In total, 282 AD patients (mean age 73.3 years, 54% female) were followed for mean 24 (16-37) months. The mean annual increase in CDR-SB was 1.6 (SD 1.8), the mean decrease in MMSE score 1.9 (SD 2.6), and the mean decrease in IADL score 0.13 (SD 0.14). Of the 282 patients, 132 (46.8%) progressed slowly, with less than 1 point yearly increase in CDR-SB. Cognitive test results at baseline predicted progression rate, and together with age, ApoE, history of hypertension, and drug use could explain 17% of the variance in progression rate. The strongest correlation of change was found between CDR-SB and IADL scores, the weakest between MMSE and IADL scores.

CONCLUSION

Progression rate varied considerably among AD patients; about half of the patients progressed slowly. Cognitive test results at baseline were predictors of progression rate.

Application of a multidimensional computerized adaptive test for a Clinical Dementia Rating Scale through computer-aided techniques

BACKGROUND

With the increasingly rapid growth of the elderly population, individuals aged 65 years and above now compose 14% of Taiwanese citizens, thereby making Taiwanese society an aged society. A leading factor that affects the elderly population is dementia. A method of precisely and efficiently examining patients with dementia through multidimensional computer adaptive testing (MCAT) to accurately determine the patients' stage of dementia needs to be developed. This study aimed to develop online MCAT that family members can use on their own computers, tablets, or smartphones to predict the extent of dementia for patients responding to the Clinical Dementia Rating (CDR) instrument.

METHODS

The CDR was applied to 366 outpatients in a hospital in Taiwan. MCAT was employed with parameters for items across eight dimensions, and responses were simulated to compare the efficiency and precision between MCAT and non-adaptive testing (NAT). The number of items saved and the estimated person measures was compared between the results of MCAT and NAT, respectively.

RESULTS

MCAT yielded substantially more precise measurements and was considerably more efficient than NAT. MCAT achieved 20.19% (= [53 - 42.3]/53) saving in item length when the measurement differences were less than 5%. Pearson correlation coefficients were highly consistent among the eight domains. The cut-off points for the overall measures were - 1.4, - 0.4, 0.4, and 1.4 logits, which was equivalent to 20% for each portion in percentile scores. Substantially fewer items were answered through MCAT than through NAT without compromising the precision of MCAT.

CONCLUSIONS

Developing a website that family members can use on their own computers, tablets, and smartphones to help them perform online screening and prediction of dementia in older adults is useful and manageable.

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

With rapid aging of world population, Alzheimer’s disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer’s disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer’s disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer’s Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer’s disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.