Brief Cognitive Tests Used in Primary Care Cannot Accurately Differentiate Mild Cognitive Impairment from Subjective Cognitive Decline

Constipation is associated with emotional and cognitive impairment in patients with Parkinson's disease: A clinical and brain functional study

OBJECTIVE

Constipation frequently occurs in patients with Parkinson's disease (PD) and may be related to cognitive and emotional disorders. The aim of this study is to investigate the clinical and brain functional characteristics of patients with PD presenting with constipation.

METHODS

The motor and non-motor symptoms of patients with PD were evaluated, and a resting-state functional magnetic resonance imaging (RS-fMRI) study was conducted based on propensity score matching. Alterations in brain function were analyzed using regional homogeneity (ReHo) and functional connectivity (FC).

RESULTS

Compared with patients without constipation (PD-NC group), patients with constipation (PD-C group) had more serious motor and non-motor symptoms (including cognitive and emotional disorders along with visual hallucinations). Further, emotional and cognitive disorders were correlated with the occurrence of constipation in patients with PD. Compared with the PD-NC group, the PD-C group showed a reduced ReHo of the right insula and bilateral orbitofrontal cortex (OFC), increased ReHo of the left postcentral gyrus, and enhanced FC between the right OFC and the left middle temporal gyrus (MTG) and middle occipital gyrus (MOG). Additionally, the activity of the OFC and insula was significantly correlated with the constipation, mood, and cognitive levels of patients with PD.

CONCLUSIONS

Constipation in patients with PD is closely related to emotional and cognitive impairments, abnormal activity and FC of brain regions such as the right insula and bilateral OFC may play an important role in this.

Not So Transient?: A Narrative Review on Cognitive Impairment After Transient Ischemic Attack

Transient ischemic attack (TIA) is traditionally viewed as a self-resolving episode of neurological change without persistent impairments and without evidence of acute brain injury on neuroimaging. However, emerging evidence suggests that TIA may be associated with lingering cognitive dysfunction. Cognitive impairment is a prevalent and disabling sequela of ischemic stroke, but the clinical relevance of this phenomenon after TIA is less commonly recognized. We performed a literature search of observational studies of cognitive function after TIA. There is a consistent body of literature suggesting that rates of cognitive impairment following TIA are higher than healthy controls, but the studies included here are limited by heterogeneity in design and analysis methods. We go on to summarize recent literature on proposed pathophysiological mechanisms underlying the development of cognitive impairment following TIA and finally suggest future directions for further research in this field.

The discriminant validity of Rey Complex Figure Test (RCFT) in subjective cognitive decline, mild cognitive impairment (multiple domain) and Alzheimer's disease dementia (ADD; mild stage) in Greek older adults

Aim: The goal of this study was to determine the discriminant potential of the Rey Complex Figure Test (RCFT) in older adults with Mild Cognitive Impairment multiple domain (mdMCI) and Alzheimer's Disease Dementia (ADD; mild subtype) as compared to older adults with Subjective Cognitive Decline (SCD).Materials and methods: We administered RCFT in 608 older adults, dividing them in three groups (217 individuals with SCD; 304 mdMCI population; 106 people with mild ADD, aged 50-90 years; M = 66.9, SD = 8.4) and a mean education of 10.20 (SD 4.3) years.Results: RCFT subtests have excellent discriminant validity, mainly between people with SCD and those with mild ADD. However, its discriminant validity in detecting older adults with SCD among mdMCI population is still questionable.Discussion: The use of RFCT in discriminating older adults with SCD from those with mild ADD both in research as well as in clinical practice is highly recommended.

The Rapid Access Memory Program for Addressing Concerns of Incipient Dementia in Academic Primary Care Settings

BACKGROUND

Expedient diagnosis of incipient dementia is often hindered by time constraints in primary care visits, shortage of dementia specialists, and extended waitlists for comprehensive neuropsychological evaluations.

METHODS

We developed the Rapid Access Memory Program (RAMP) to improve access of neuropsychological services for older adults presenting to our institutional primary care clinics with concerns of cognitive decline. RAMP provides abbreviated neurocognitive assessment, same-day patient feedback, expedited reporting to referring providers, and is financially self-supported. Here, we describe development of RAMP and clinical outcomes from the first 3 years.

RESULTS

Of 160 patients seen, dementia was diagnosed in 30% and Mild Cognitive Impairment in 50%; Alzheimer's disease was the most common suspected etiology. New psychiatric diagnosis was made in about one-third (n = 54). Most frequent recommendations involved medication adjustments (initiating cholinesterase inhibitors, deprescribing anticholinergics), safety (driving, decision-making), and specialist referrals. Additionally, 27 (17%) subsequently enrolled in local research.

CONCLUSIONS

Results support feasibility and utility of RAMP for connecting older adults in primary care with neuropsychological services.

Subjective Cognitive Impairment Can Be Detected from the Decline of Complex Cognition: Findings from the Examination of Remedes 4 Alzheimer's (R4Alz) Structural Validity

R4Alz is utilized for the early detection of minor neurocognitive disorders. It was designed to assess three main dimensions of cognitive-control abilities: working-memory capacity, attentional control, and executive functioning.

OBJECTIVES

To reveal the cognitive-control dimensions that can differentiate between adults and older adults with healthy cognition, people with subjective cognitive impairment, and people diagnosed with mild cognitive impairment by examining the factorial structure of the R4Alz tool.

METHODS

The study comprised 404 participants: (a) healthy adults (n = 192), (b) healthy older adults (n = 29), (c) people with SCI (n = 74), and (d) people diagnosed with MCI (n = 109). The R4Alz battery was administered to all participants, including tests that assess short-term memory storage, information processing, information updating in working memory, and selective, sustained and divided attention), task/rule-switching, inhibitory control, and cognitive flexibility.

RESULTS

A two-factorial structural model was confirmed for R4Alz, with the first factor representing "fluid intelligence (FI)" and the second factor reflecting "executive functions (EF)". Both FI and EFs discriminate among all groups.

CONCLUSIONS

The R4Alz battery presents sound construct validity, evaluating abilities in FI and EF. Both abilities can differentiate very early cognitive impairment (SCI) from healthy cognitive aging and MCI.

Minimal clinically important difference in Alzheimer's disease: Rapid review

INTRODUCTION

We conducted a rapid systematic review of minimal clinically important differences (MCIDs) for Alzheimer's disease (AD) trial endpoints.

METHODS

Two reviewers searched EMBASE, MEDLINE, and PubMed from inception to June 4, 2023.

RESULTS

Ten articles were retrieved. For mild cognitive impairment (MCI), a change of +2 to +3 points on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), +1 points on the Clinical Dementia Rating scale sum of boxes (CDR-SB), -5 points on the integrated Alzheimer's Disease Rating Scale (iADRS), or -1 to -2 points on the Mini-Mental State Examination (MMSE) was considered meaningful. For patients with mild AD, a change of +3 on the ADAS-Cog, +2 points on CDR-SB, -9 points on the iADRS, or -2 points on the MMSE was considered meaningful. For patients with moderate to severe AD, a change of +2 points on the CDR-SB or a change of -1.4 to -3 points on the MMSE was considered meaningful.

CONCLUSION

This review identified previously published MCIDs for AD trial endpoints. Input from patients and caregivers will be needed to derive more meaningful endpoints and thresholds.

HIGHLIGHTS

This systematic rapid review identified thresholds for minimal clinically important differences (MCIDs) for recently used Alzheimer's disease (AD) trial endpoints: Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Clinical Dementia Rating scale sum of boxes (CDR-SB), integrated Alzheimer's Disease Rating Scale (iADRS), Mini-Mental State Examination (MMSE). MCIDs were higher for more severe stages of AD. Average treatment effects in recent trials of anti-amyloid disease modifying monoclonal antibodies are lower than previously published MCIDs. In future trials of disease modifying treatments for AD, the proportion of participants in each treatment group that experienced a clinically meaningful decline could be reported. More work is needed to incorporate the values and preferences of patients and care partners in deriving MCIDs.

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Objective

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.

Method

Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.

Results

Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.

Conclusion

Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms

BACKGROUND

Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

METHODS

A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.

RESULTS

In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.

CONCLUSIONS

The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms

Background

Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

Methods

A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: 1) clinical data only, including demographics, cognitive tests and APOE e4 status, 2) clinical data plus hippocampal volume, 3) clinical data plus all regional MRI gray matter volumes (N=68) extracted using FreeSurfer software, 4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. Models were developed on 80% of subjects (N=267) and tested on the remaining 20% (N=65). Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.

Results

In the test set, 21 patients (32.3%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC)=0.87 and four-year cognitive decline was R2=0.17. The performance was significantly improved for both outcomes when adding hippocampal volume (AUC=0.91, R2=0.26, p-values <0.05) or FreeSurfer brain regions (AUC=0.90, R2=0.27, p-values <0.05). Conversely, the DL model did not show any significant difference from the clinical data model (AUC=0.86, R2=0.13). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included.

Conclusions

The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases

Cost-effective strategies for identifying amyloid-β (Aβ) positivity in patients with cognitive impairment are urgently needed with recent approvals of anti-Aβ immunotherapies for Alzheimer's disease (AD). Blood biomarkers can accurately detect AD pathology, but it is unclear whether their incorporation into a full diagnostic workflow can reduce the number of confirmatory cerebrospinal fluid (CSF) or positron emission tomography (PET) tests needed while accurately classifying patients. We evaluated a two-step workflow for determining Aβ-PET status in patients with mild cognitive impairment (MCI) from two independent memory clinic-based cohorts (n = 348). A blood-based model including plasma tau protein 217 (p-tau217), age and APOE ε4 status was developed in BioFINDER-1 (area under the curve (AUC) = 89.3%) and validated in BioFINDER-2 (AUC = 94.3%). In step 1, the blood-based model was used to stratify the patients into low, intermediate or high risk of Aβ-PET positivity. In step 2, we assumed referral only of intermediate-risk patients to CSF Aβ42/Aβ40 testing, whereas step 1 alone determined Aβ-status for low- and high-risk groups. Depending on whether lenient, moderate or stringent thresholds were used in step 1, the two-step workflow overall accuracy for detecting Aβ-PET status was 88.2%, 90.5% and 92.0%, respectively, while reducing the number of necessary CSF tests by 85.9%, 72.7% and 61.2%, respectively. In secondary analyses, an adapted version of the BioFINDER-1 model led to successful validation of the two-step workflow with a different plasma p-tau217 immunoassay in patients with cognitive impairment from the TRIAD cohort (n = 84). In conclusion, using a plasma p-tau217-based model for risk stratification of patients with MCI can substantially reduce the need for confirmatory testing while accurately classifying patients, offering a cost-effective strategy to detect AD in memory clinic settings.

Clinical effects of Lewy body pathology in cognitively impaired individuals

There is poor knowledge about the clinical effects of Lewy body (LB) pathology in patients with cognitive impairment, especially when coexisting with Alzheimer's disease (AD) pathology (amyloid-β and tau). Using a seed amplification assay, we analyzed cerebrospinal fluid for misfolded LB-associated α-synuclein in 883 memory clinic patients with mild cognitive impairment or dementia from the BioFINDER study. Twenty-three percent had LB pathology, of which only 21% fulfilled clinical criteria of Parkinson's disease or dementia with Lewy bodies at baseline. Among these LB-positive patients, 48% had AD pathology. Fifty-four percent had AD pathology in the whole sample (17% of mild cognitive impairment and 24% of patients with dementia were also LB-positive). When examining independent cross-sectional effects, LB pathology but not amyloid-β or tau, was associated with hallucinations and worse attention/executive, visuospatial and motor function. LB pathology was also associated with faster longitudinal decline in all examined cognitive functions, independent of amyloid-β, tau, cognitive stage and a baseline diagnosis of dementia with Lewy bodies/Parkinson's disease. LB status provides a better precision-medicine approach to predict clinical trajectories independent of AD biomarkers and a clinical diagnosis, which could have implications for the clinical management of cognitive impairment and the design of AD and LB drug trials.

Cognitive effects of Lewy body pathology in clinically unimpaired individuals

α-Synuclein aggregates constitute the pathology of Lewy body (LB) disease. Little is known about the effects of LB pathology in preclinical (presymptomatic) individuals, either as isolated pathology or coexisting with Alzheimer's disease (AD) pathology (β-amyloid (Aβ) and tau). We examined the effects of LB pathology using a cerebrospinal fluid α-synuclein-seed amplification assay in 1,182 cognitively and neurologically unimpaired participants from the BioFINDER study: 8% were LB positive, 26% Aβ positive (13% of those were LB positive) and 16% tau positive. LB positivity occurred more often in the presence of Aβ positivity but not tau positivity. LB pathology had independently negative effects on cross-sectional and longitudinal global cognition and memory and on longitudinal attention/executive function. Tau had cognitive effects of a similar magnitude, but these were less pronounced for Aβ. Participants with both LB and AD (Aβ and tau) pathology exhibited faster cognitive decline than those with only LB or AD pathology. LB, but not AD, pathology was associated with reduced sense of smell. Only LB-positive participants progressed to clinical LB disease over 10 years. These results are important for individualized prognosis, recruitment and choice of outcome measures in preclinical LB disease trials, but also for the design of early AD trials because >10% of individuals with preclinical AD have coexisting LB pathology.

Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer's disease in independently dwelling adults

INTRODUCTION

Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD.

METHOD

Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD.

RESULTS

In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value.

DISCUSSION

AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

Utility of Diffusion Modeling of Cogstate Brief Battery Test Performance in Detecting Mild Cognitive Impairment

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.

An accurate fully automated panel of plasma biomarkers for Alzheimer's disease

INTRODUCTION

There is a great need for fully automated plasma assays that can measure amyloid beta (Aβ) pathology and predict future Alzheimer's disease (AD) dementia.

METHODS

Two cohorts (n = 920) were examined: Panel A+ (n = 32 cognitively unimpaired [CU], n = 106 mild cognitive impairment [MCI], and n = 89 AD) and BioFINDER-1 (n = 461 CU, n = 232 MCI). Plasma Aβ42/Aβ40, phosphorylated tau (p-tau)181, two p-tau217 variants, ApoE4 protein, neurofilament light, and GFAP were measured using Elecsys prototype immunoassays.

RESULTS

The best biomarker for discriminating Aβ-positive versus Aβ-negative participants was Aβ42/Aβ40 (are under the curve [AUC] 0.83-0.87). Combining Aβ42/Aβ40, p-tau181, and ApoE4 improved the AUCs significantly (0.90 to 0.93; P< 0.01). Adding additional biomarkers had marginal effects (ΔAUC ≤0.01). In BioFINDER, p-tau181, p-tau217, and ApoE4 predicted AD dementia within 6 years in CU (AUC 0.88) and p-tau181, p-tau217, and Aβ42/Aβ40 in MCI (AUC 0.87).

DISCUSSION

The high accuracies for Aβ pathology and future AD dementia using fully automated instruments are promising for implementing plasma biomarkers in clinical trials and clinical routine.

R4Alz-Revised: A Tool Able to Strongly Discriminate 'Subjective Cognitive Decline' from Healthy Cognition and 'Minor Neurocognitive Disorder'

BACKGROUND

The diagnosis of the minor neurocognitive diseases in the clinical course of dementia before the clinical symptoms' appearance is the holy grail of neuropsychological research. The R4Alz battery is a novel and valid tool that was designed to assess cognitive control in people with minor cognitive disorders. The aim of the current study is the R4Alz battery's extension (namely R4Alz-R), enhanced by the design and administration of extra episodic memory tasks, as well as extra cognitive control tasks, towards improving the overall R4Alz discriminant validity.

METHODS

The study comprised 80 people: (a) 20 Healthy adults (HC), (b) 29 people with Subjective Cognitive Decline (SCD), and (c) 31 people with Mild Cognitive Impairment (MCI). The groups differed in age and educational level.

RESULTS

Updating, inhibition, attention switching, and cognitive flexibility tasks discriminated SCD from HC (p ≤ 0.003). Updating, switching, cognitive flexibility, and episodic memory tasks discriminated SCD from MCI (p ≤ 0.001). All the R4Alz-R's tasks discriminated HC from MCI (p ≤ 0.001). The R4Alz-R was free of age and educational level effects. The battery discriminated perfectly SCD from HC and HC from MCI (100% sensitivity-95% specificity and 100% sensitivity-90% specificity, respectively), whilst it discriminated excellently SCD from MCI (90.3% sensitivity-82.8% specificity).

CONCLUSION

SCD seems to be stage a of neurodegeneration since it can be objectively evaluated via the R4Alz-R battery, which seems to be a useful tool for early diagnosis.

Clock Drawing Test: Types of Errors and Accuracy in Early Cognitive Screening

BACKGROUND

Clock Drawing Test (CDT) is a commonly used screening tool for cognitive disorders, known for its ease of administration and scoring. Despite frequent use by clinicians, CDT is criticized for its poor predictive value in mild cases of impairment.

OBJECTIVE

To evaluate CDT as a screening tool for early stage of cognitive impairment in biomarker-verified Alzheimer's disease (AD) and depressive disorder (DD).

METHODS

We analyzed CDT of 172 patients with verified AD, 70 patients with DD, in whom neurodegenerative disorder was excluded using cerebrospinal fluid biomarkers, and 58 healthy older adults. CDT was scored using the semi-quantitative (Shulman) and itemized criteria (adapted from Mendez).

RESULTS

Logistic regression showed that for both DD and AD patients with high Mini-Mental State Examination (MMSE) scores (27 and above) the significant predicting variable is uneven number spacing. As MMSE deteriorates (24-26 points), an additional error of setting clock hands is predictive of the disease. In the low MMSE condition, CDT showed an acceptable discrimination for AD (AUC itemized 0.740, Shulman 0.741) and DD (AUC itemized 0.827, Shulman 0.739) using both scoring methods. In the high MMSE condition, discrimination rates were acceptable using itemized scoring but poor using Shulman scoring for both AD (AUC itemized 0.707, Shulman 0.677) and DD (AUC itemized 0.755, Shulman 0.667) groups.

CONCLUSION

Ideally, modern diagnostic process should take place before the cognitive performance drops beneath the healthy range. This makes CDT of little use when screening patients with very mild cognitive deficits.

Plasma amyloid-β42/40 and apolipoprotein E for amyloid PET pre-screening in secondary prevention trials of Alzheimer's disease

The extent to which newly developed blood-based biomarkers could reduce screening costs in secondary prevention trials of Alzheimer's disease is mostly unexplored. We collected plasma amyloid-β42/40, apolipoprotein E ε4 status and amyloid PET at baseline in 181 cognitively unimpaired participants [the age of 72.9 (5.3) years; 61.9% female; education of 11.9 (3.4) years] from the Swedish BioFINDER-1 study. We tested whether a model predicting amyloid PET status from plasma amyloid-β42/40, apolipoprotein E status and age (combined) reduced cost of recruiting amyloid PET + cognitively unimpaired participants into a theoretical trial. We found that the percentage of cognitively unimpaired participants with an amyloid PET + scan rose from 29% in an unscreened population to 64% [(49, 79); P < 0.0001] when using the biomarker model to screen for high risk for amyloid PET + status. In simulations, plasma screening also resulted in a 54% reduction of the total number of amyloid PET scans required and reduced total recruitment costs by 43% [(31, 56), P < 0.001] compared to no pre-screening when assuming a 16× PET-to-plasma cost ratio. Total savings remained significant when the PET-to-plasma cost ratio was assumed to be 8× or 4×. This suggests that a simple plasma biomarker model could lower recruitment costs in Alzheimer's trials requiring amyloid PET positivity for inclusion.

Clinical performance and robustness evaluation of plasma amyloid-β42/40 prescreening

INTRODUCTION

Further evidence is needed to support the use of plasma amyloid β (Aβ) biomarkers as Alzheimer's disease prescreening tools. This study evaluated the clinical performance and robustness of plasma Aβ₄₂ /Aβ₄₀ for amyloid positivity prescreening.

METHODS

Data were collected from 333 BioFINDER and 121 Alzheimer's Disease Neuroimaging Initiative study participants. Risk and predictive values versus percentile of plasma Aβ₄₂ /Aβ₄₀ evaluated the actionability of plasma Aβ₄₂ /Aβ₄₀ , and simulations modeled the impact of potential uncertainties and biases. Amyloid PET was the brain amyloidosis reference standard.

RESULTS

Elecsys plasma Aβ₄₂ /Aβ₄₀ could potentially rule out amyloid pathology in populations with low-to-moderate amyloid positivity prevalence. However, simulations showed small measurement or pre-analytical errors in Aβ₄₂ and/or Aβ₄₀ cause misclassifications, impacting sensitivity or specificity. The minor fold change between amyloid PET positive and negative cases explains the biomarkers low robustness.

DISCUSSION

Implementing plasma Aβ₄₂ /Aβ₄₀ for routine clinical use may pose significant challenges, with misclassification risks.

HIGHLIGHTS

Plasma Aβ₄₂ /Aβ₄₀ ruled out amyloid PET positivity in a setting of low amyloid-positive prevalence. Including (pre-) analytical errors or measurement biases caused misclassifications. Plasma Aβ₄₂ /Aβ₄₀ had a low inherent dynamic range, independent of analytical method. Other blood biomarkers may be easier to implement as robust prescreening tools.

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.

Psychometric Properties and Measurement Invariance of the Chinese Version of the Brief Assessment of Impaired Cognition Questionnaire in Community-Dwelling Older Adults

This study aimed to verify the Chinese version of the Brief Assessment of Impaired Cognition Questionnaire (C-BASIC-Q), and provide a new tool for the future large-scale epidemiological investigation of cognitive function in China. From March to May 2021, a cross-sectional study of 2,144 Chinese community-dwelling older adults (men = 1,075, mean age = 72.01 years, SD = 6.96 years, ranging from 60–99 years) was conducted in Jinan. Exploratory and confirmatory factor analyses were performed to evaluate the factor structure of the C-BASIC-Q. Convergent validity was evaluated by correlations with the Mini-Mental State Examination (MMSE). Internal consistency and test-retest reliability were evaluated using Cronbach's alpha and retest correlations in a sub-sample (n = 129). Linear regression was used to analyze the impact of demographic factors on the MMSE and C-BASIC-Q scores. Measurement invariance was evaluated using a multi-group confirmatory factor analysis. The mean C-BASIC-Q score was 15.94 (SD = 3.43). Factor analysis suggested a three-factor structure of C-BASIC-Q (self-report, orientation, and informant report). The C-BASIC-Q score was significantly positively associated with the MMSE score, showing good convergent validity. Cronbach's alpha of the C-BASIC-Q was 0.862, and the test-retest correlation coefficient was significant (r = 0.952, p < 0.001), indicating good internal consistency and test-retest reliability. Measurement invariance analysis showed that C-BASIC-Q had configural, metric, and scalar invariance across sex, age, residence, education level and marital status. C-BASIC-Q was less affected by age, residence, education, and marital status than the MMSE. In summary, the C-BASIC-Q had good reliability, validity, and measurement invariance, and is a valid tool for evaluating cognitive functioning in Chinese community-dwelling older adults.

Staging Disease Severity Using the Alzheimer's Disease Composite Score (ADCOMS): A Retrospective Data Analysis

INTRODUCTION

The Alzheimer's disease (AD) composite score (ADCOMS) has been shown to be a more sensitive measure of cognitive change in early AD (i.e., mild cognitive impairment [MCI] and mild AD) than commonly used measures. This study derived ADCOMS value ranges associated with different disease severity stages across the predementia and AD continuum.

METHODS

Data from patients enrolled in the Alzheimer's Disease Neuroimaging Initiative were assessed at baseline and month 24. Data were randomly split into derivation and validation samples. Receiver-operating characteristic (ROC) curves of ADCOMS values were generated in the derivation sample to assess the sensitivity and specificity of ADCOMS cutoff values compared with existing disease severity cutoff scores using the Clinical Dementia Rating (CDR) global, CDR Sum of Boxes, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and Mini-Mental State Examination. Optimal ADCOMS cutoff values for each disease stage were compared between the derivation and the validation samples using a χ2 test. The diagnostic accuracy of the derived ADCOMS cutoff values was then assessed. The analyses were repeated for the subset with positive amyloid β confirmation (Aβ +).

RESULTS

The following ADCOMS value ranges for the total population and Aβ + population were identified: < 0.29 indicative of normal cognition, 0.29 to < 0.45 indicative of MCI, 0.45-0.77 indicative of mild AD, and > 0.77 indicative of at least moderate AD. The reliability of these ADCOMS value ranges was supported by diagnostic accuracy tests and tests indicating no significant difference in the ROC curves between the derivation and validation samples.

CONCLUSION

ADCOMS values ranges can be used to assess the severity of cognitive decline. The derived severity threshold score ranges for ADCOMS will enable its use as an endpoint in clinical trials assessing disease progression and clinical outcomes of disease-modifying therapies in persons with MCI or early AD, including patients with Aβ + confirmation.

Progression from Subjective Cognitive Decline to Mild Cognitive Impairment or Dementia: The Role of Baseline Cognitive Performance

Background: Older adults with subjective cognitive decline (SCD) are at an increased risk of progression to mild cognitive impairment (MCI) or dementia. However, few have examined the specific cognitive tests that are associated with progression. Objective: This study examined performance on 18 neuropsychological tests among participants with SCD who later progressed to MCI or dementia. Methods: We included 131 participants from the Czech Brain Aging Study that had SCD at baseline. They completed a comprehensive neuropsychological battery including cognitive tests from the Uniform Data Set 2.0 enriched by the verbal memory test Rey Auditory Verbal Learning Test (RAVLT) and Rey-Osterrieth Complex Figure Test (ROCFT). Results: Fifty-five participants progressed: 53% to non-amnestic MCI (naMCI), 44% to amnestic MCI (aMCI), and 4% to dementia. Scoring one SD below the mean at baseline on the RAVLT 1 and RAVLT 1–5 was associated with 133% (RAVLT 1; HR: 2.33 [1.50, 3.62]) and 122% (RAVLT 1–5; HR: 2.22 [1.55, 3.16]) greater risk of progression to MCI or dementia over 3.84 years on average. Worse performance on the RAVLT 5, RAVLT 1–5, RAVLT 30, and ROCFT–Recall was associated with progression to aMCI whereas worse performance on the RAVLT 1, TMT B, and Boston Naming Test was associated with progression to naMCI. Conclusion: At baseline, lower verbal memory performance was most strongly associated with progression to aMCI whereas lower executive or language performance was most strongly associated with progression to naMCI.

Cognitive Assessment Tools Recommended in Geriatric Oncology Guidelines: A Rapid Review

Cognitive assessment is a cornerstone of geriatric care. Cognitive impairment has the potential to significantly impact multiple phases of a person's cancer care experience. Accurately identifying this vulnerability is a challenge for many cancer care clinicians, thus the use of validated cognitive assessment tools are recommended. As international cancer guidelines for older adults recommend Geriatric Assessment (GA) which includes an evaluation of cognition, clinicians need to be familiar with the overall interpretation of the commonly used cognitive assessment tools. This rapid review investigated the cognitive assessment tools that were most frequently recommended by Geriatric Oncology guidelines: Blessed Orientation-Memory-Concentration test (BOMC), Clock Drawing Test (CDT), Mini-Cog, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Short Portable Mental Status Questionnaire (SPMSQ). A detailed appraisal of the strengths and limitations of each tool was conducted, with a focus on practical aspects of implementing cognitive assessment tools into real-world clinical settings. Finally, recommendations on choosing an assessment tool and the additional considerations beyond screening are discussed.

Discriminant Validity of the WAIS-R Digit Symbol Substitution Test in Subjective Cognitive Decline, Mild Cognitive Impairment (Amnestic Subtype) and Alzheimer's Disease Dementia (ADD) in Greece

Objective: The aim of the current study was to estimate the discriminant potential and validity of the Digit Symbol Substitution Test (DSST) of the WAIS-R in the Greek elderly population meeting criteria for subjective cognitive decline (SCD), mild cognitive impairment (aMCI; amnestic subtype), or Alzheimer’s disease dementia (ADD). Method: Four hundred eighty-eight community-dwelling older adults, visitors of the Day Center of Alzheimer Hellas, participated in the study. Two hundred forty-three of them met the criteria for ADD, one hundred eighty-two for aMCI and sixty-three for SCD. Results: Path analysis indicated that the DSST score is affected by age group, educational level, and diagnostic category, but is not affected by gender. The ROC curve analysis showed that the DSST sum score could perfectly differentiate SCD from ADD patients, whereas test’s discriminant potential between aMCI and dementia ADD’s subtype was satisfactory. However, DSST was unable to separate the SCD from the aMCI group. Conclusion: It appears that the DSST is unable to separate the SCD from aMCI population. Therefore, the test in question may be insensitive to incipient cognitive decline. On the contrary, the discriminant potential of the DSST as regards SCD and ADD is excellent, while discrimination between aMCI and ADD is good.

Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures

A combination of plasma phospho-tau (P-tau) and other accessible biomarkers might provide accurate prediction about the risk of developing Alzheimer's disease (AD) dementia. We examined this in participants with subjective cognitive decline and mild cognitive impairment from the BioFINDER (n = 340) and Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 543) studies. Plasma P-tau, plasma Aβ42/Aβ40, plasma neurofilament light, APOE genotype, brief cognitive tests and an AD-specific magnetic resonance imaging measure were examined using progression to AD as outcome. Within 4 years, plasma P-tau217 predicted AD accurately (area under the curve (AUC) = 0.83) in BioFINDER. Combining plasma P-tau217, memory, executive function and APOE produced higher accuracy (AUC = 0.91, P < 0.001). In ADNI, this model had similar AUC (0.90) using plasma P-tau181 instead of P-tau217. The model was implemented online for prediction of the individual probability of progressing to AD. Within 2 and 6 years, similar models had AUCs of 0.90-0.91 in both cohorts. Using cerebrospinal fluid P-tau, Aβ42/Aβ40 and neurofilament light instead of plasma biomarkers did not improve the accuracy significantly. The clinical predictions by memory clinic physicians had significantly lower accuracy (4-year AUC = 0.71). In summary, plasma P-tau, in combination with brief cognitive tests and APOE genotyping, might greatly improve the diagnostic prediction of AD and facilitate recruitment for AD trials.

Health utility in preclinical and prodromal Alzheimer's disease for establishing the value of new disease-modifying treatments-EQ-5D data from the Swedish BioFINDER study

Quality of life and health utility are important outcomes for patients with Alzheimer's disease (AD) and central for demonstrating the value of new treatments. Estimates in biomarker-confirmed AD populations are missing, potentially delaying payer approval of treatment. We examined whether health utility, assessed with the EuroQoL-5 3-level version (EQ-5D-3L), differed between individuals with a positive or negative amyloid beta (Aβ) biomarker in patients with mild cognitive impairment (MCI) and cognitively unimpaired (CU) participants from the Swedish BioFINDER study (n = 578). Participants with prodromal AD (Aβ-positive MCI) reported better health utility (n = 79, mean = 0.81, 95% confidence interval [CI] 0.77-0.85) than Aβ-negative MCI (mean = 0.71, 95% CI 0.64-0.78), but worse than controls (Aβ-negative CU, mean = 0.87, 95% CI 0.86-0.89). Health utility in preclinical AD (Aβ-positive CU; mean = 0.86, 95% CI 0.83-0.89) was similar to controls. This relatively good health utility in prodromal AD suggests a larger value of delaying progression to dementia than previously anticipated and a great value of delaying clinical progression in preclinical AD.

Consistency and applicability of different brief screen instrument of cognitive function in elderly population

BACKGROUND

Screening for cognitive impairment (CI) is often hampered by lack of consensus as to which screening instrument to use. The aim is to assess the consistence and applicability of different CI screening tools.

METHOD

In a cross-sectional study from October 2017 to September 2018 in 7 communities in Shanghai, China, elder (≧60) residential volunteers with no history of major cardiovascular diseases, cancers and other comorbidities known to affect cognitive functions were recruited. The participants underwent tests with 7 cognitive function screening instruments. Multivariate linear regressions were performed to test correlations between demographic characteristics, including gender, age, education, and marital status, with cognitive test scores. Mini-Mental State Examination (MMSE) score adjusted according to the correlation coefficients was used to detect CI with a cutoff of 24. Other cognitive function scores were compared between participants with and without CI. In addition, Pearson's correlation test was used to detect association between different test scores.

RESULTS

172 participants with relatively low education levels were included. Age and education showed significant association with cognitive test scores. Using adjusted MMSE, 39.6% of participants were identified with CI, while the percentage was 87.2% when adjusted Montreal Cognitive Assessment (MoCA) with cutoff of 26 was used. Analysis of "abnormal" test scores showed that MMSE had the highest percentage of valid data (98.8%). MoCA and Isaacs test of Verbal Fluency (VF) score had correlation with most the other scores, while MMSE only significantly associated with VF and MoCA.

CONCLUSIONS

MMSE may still present the most applicable tools for quick screen of cognitive functions, especially when environmental conditions may interfere with participants' attention.

Different diagnostic strategies for different settings

Diagnostic strategies should always take into consideration the setting in which they are administered. The predictive value, positive or negative of a clinical sign or diagnostic test has a different weight in family medicine than in the hospital setting. Many tests have not been formally evaluated in primary care; unfortunately, very often low prevalence settings are used to conduct screening in these populations, and often result in unrealistically high prevalence estimates for chronic disease and these results are then used to conclude that General Practitioners are not good at detecting diseases. In primary care, the prevalence and incidence of disease differs from what appears in the hospital setting, and severe disease occurs less frequently in general practice than in hospital because there is no preventive selection. This requires a specific probability-based decision-making process, based by the knowledge of patients and the community. In primary care, the diagnostic strategy should begin with complaints and symptoms and address uncertainty and complexity, using step-by-step strategies, including watchful waiting, presumptive symptomatic treatment, and focusing on low-tech strategies.

Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations

We developed models for individualized risk prediction of cognitive decline in mild cognitive impairment (MCI) using plasma biomarkers of β-amyloid (Aβ), tau and neurodegeneration. A total of 573 patients with MCI from the Swedish BioFINDER study and the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included in the study. The primary outcomes were longitudinal cognition and conversion to Alzheimer's disease (AD) dementia. A model combining tau phosphorylated at threonine 181 (P-tau181) and neurofilament light (NfL), but not Aβ₄₂/Aβ₄₀, had the best prognosis performance of all models (area under the curve = 0.88 for 4-year conversion to AD in BioFINDER, validated in ADNI), was stronger than a basic model of age, sex, education and baseline cognition, and performed similarly to cerebrospinal fluid biomarkers. A publicly available online tool for individualized prognosis in MCI based on our combined plasma biomarker models is introduced. Combination of plasma biomarkers may be of high value to identify individuals with MCI who will progress to AD dementia in clinical trials and in clinical practice.

Association of Enlarged Perivascular Spaces and Measures of Small Vessel and Alzheimer Disease

OBJECTIVE

To investigate the relationship between enlarged perivascular spaces (EPVS) and measures of Alzheimer disease (AD), small vessel disease (SVD), cognition, vascular risk factors, and neuroinflammation, we tested associations between EPVS and different relevant neuroimaging, biochemical, and cognitive variables in 778 study participants.

METHODS

Four hundred ninety-nine cognitively unimpaired (CU) individuals, 240 patients with mild cognitive impairment, and 39 patients with AD from the Swedish Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably (BioFINDER) study were included. EPVS with diameter >1 mm in centrum semiovale (CSO), basal ganglia (BG), and hippocampus (HP); hippocampal volume; white matter lesions (WML); and other SVD markers were determined from MRI. CSF levels of β-amyloid₄₂ (Aβ₄₂), phosphorylated tau, total tau, and neuroinflammatory markers; amyloid accumulation determined with [¹⁸F]-flutemetamol PET; and vascular risk factors and results from cognitive tests were determined and collected.

RESULTS

EPVS in CSO, BG, and HP were associated with WML volume and Fazekas score in individuals without dementia. No associations were found between EPVS and CSF Aβ₄₂, total tau and phosphorylated tau, neuroinflammatory markers, vascular risk factors, and cognitive tests. EPVS in HP were associated with hippocampal atrophy. In a matched group of individuals with AD and CU, EPVS in HP were associated with AD diagnosis.

CONCLUSIONS

EPVS are related to SVD, also in early disease stages, but the lack of correlation with cognition suggests that their importance is limited. Our data do not support a role for EPVS in early AD pathogenesis.

Acute phase markers in CSF reveal inflammatory changes in Alzheimer's disease that intersect with pathology, APOE ε4, sex and age

It is unknown how neuroinflammation may feature in the etiology of Alzheimer's disease (AD). We profiled acute phase response (APR) proteins (α1-antitrypsin, α1-antichymotrypsin, ceruloplasmin, complement C3, ferritin, α-fibrinogen, β-fibrinogen, γ-fibrinogen, haptoglobin, hemopexin) in CSF of 1291 subjects along the clinical and biomarker spectrum of AD to investigate the association between inflammatory changes, disease outcomes, and demographic variables. Subjects were stratified by Aβ₄₂/t-tau as well as the following clinical diagnoses: cognitively normal (CN); subjective cognitive decline (SCD); mild cognitive impairment (MCI); and AD dementia. In separate multiple regressions (adjusting for diagnosis, age, sex, APOE-ε4) of each APR protein and a composite of all APR proteins, CSF Aβ₄₂/t-tau status was associated with elevated ferritin, but not any other APR protein in CN and SCD subjects. Rather, the APR was elevated along with symptomatic progression (CN < SCD < MCI < AD), and this was elevation was mediated by CSF p-tau181. APOE ε4 status did not affect levels of any APR proteins in CSF, while these were elevated in males and with increased age. The performance of the APR in predicting clinical diagnosis was influenced by APOE ε4 status, sex, and age. These data provide new insight into inflammatory changes in AD and how this intersects with pathology changes and patient demographics.