A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium

Dual-task gait has prognostic value for cognitive decline in Parkinson's disease

INTRODUCTION

Cognitive decline frequently occurs in individuals with Parkinson's disease (PD), but the clinical methods to predict the onset of cognitive changes are limited. Given preliminary evidence of the link between gait and cognition, the purpose of this study was to determine if dual task (DT) gait was related to declines in cognition over two years in PD.

METHODS

A retrospective two-year longitudinal study of 48 individuals with PD using data from the Parkinson's Progression Markers Initiative of the Michael J. Fox Foundation. The following data were extracted at baseline: spatiotemporal gait (during single and DT), demographics (age, sex), PD disease duration (time since diagnosis), motor function (Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS)), and cognition (Montreal Cognitive Assessment (MoCA)), with MoCA scores also extracted after two years.

RESULTS

A binomial logistic regression was conducted, with all covariates (above) in block 1 and DT effect (DTE) of gait characteristics in block 2 entered in a stepwise fashion. The final model was statistically significant (χ2(6) = 23.20, p < 0.001) and correctly classified 78.7 % of participants by cognitive status after two years. Only DTE of arm swing asymmetry (ASA) (p = 0.030) was included in block 2 such that a 1 % decline in DTE resulted in 1.6 % increased odds of cognitive decline.

CONCLUSIONS

Individuals with greater change in arm swing asymmetry from single to DT gait may be more likely to experience a decline in cognition within two years. These results suggested that reduced automaticity or poor utilization of attentional resources may be indicative of subtle changes in cognition and indicate that DT paradigms may hold promise as a marker of future cognitive decline.

Longitudinal normative standards for cognitive tests and composites using harmonized data from two Wisconsin AD-risk-enriched cohorts

INTRODUCTION

Published norms are typically cross-sectional and often are not sensitive to preclinical cognitive changes due to dementia. We developed and validated demographically adjusted cross-sectional and longitudinal normative standards using harmonized outcomes from two Alzheimer's disease (AD) risk-enriched cohorts.

METHODS

Data from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were combined. Quantile regression was used to develop unconditional (cross-sectional) and conditional (longitudinal) normative standards for 18 outcomes using data from cognitively unimpaired participants (N = 1390; mean follow-up = 9.25 years). Validity analyses (N = 2456) examined relationships between percentile scores (centiles), consensus-based cognitive statuses, and AD biomarker levels.

RESULTS

Unconditional and conditional centiles were lower in those with consensus-based impairment or biomarker positivity. Similarly, quantitative biomarker levels were higher in those whose centiles suggested decline.

DISCUSSION

This study presents normative standards for cognitive measures sensitive to pre-clinical changes. Future directions will investigate potential clinical applications of longitudinal normative standards.

HIGHLIGHTS

Quantile regression was used to construct longitudinal norms for cognitive tests. Poorer percentile scores were related to concurrent diagnosis and Alzheimer's disease biomarkers. A ShinyApp was built to display test scores and norms and flag low performance.

Sensory and motor deficits as contributors to early cognitive impairment

INTRODUCTION

Age-related sensory and motor impairment are associated with risk of dementia. No study has examined the joint associations of multiple sensory and motor measures on prevalence of early cognitive impairment (ECI).

METHODS

Six hundred fifty participants in the Baltimore Longitudinal Study of Aging completed sensory and motor function tests. The association between sensory and motor function and ECI was examined using structural equation modeling with three latent factors corresponding to multisensory, fine motor, and gross motor function.

RESULTS

The multisensory, fine, and gross motor factors were all correlated (r = 0.74 to 0.81). The odds of ECI were lower for each additional unit improvement in the multisensory (32%), fine motor (30%), and gross motor factors (12%).

DISCUSSION

The relationship between sensory and motor impairment and emerging cognitive impairment may guide future intervention studies aimed at preventing and/or treating ECI.

HIGHLIGHTS

Sensorimotor function and early cognitive impairment (ECI) prevalence were assessed via structural equation modeling. The degree of fine and gross motor function is associated with indicators of ECI. The degree of multisensory impairment is also associated with indicators of ECI.

Predictors and outcomes of fluctuations in the clinical dementia rating scale

INTRODUCTION

Reversion, or change in cognitive status from impaired to normal, is common in aging and dementia studies, but it remains unclear what factors predict reversion.

METHODS

We investigated whether reverters, defined as those who revert from a Clinical Dementia Rating® (CDR®) scale score of 0.5 to CDR 0) differed on cognition and biomarkers from unimpaired participants (always CDR 0) and impaired participants (converted to CDR > 0 and had no reversion events). Models evaluated relationships between biomarker status, apolipoprotein E (APOE) ε4 status, and cognition. Additional models described predictors of reversion and predictors of eventual progression to CDR > 0.

RESULTS

CDR reversion was associated with younger age, better cognition, and negative amyloid biomarker status. Reverters that eventually progressed to CDR > 0 had more visits, were older, and were more likely to have an APOE ε4 allele.

DISCUSSION

CDR reversion occupies a transitional phase in disease progression between cognitive normality and overt dementia. Reverters may be ideal candidates for secondary prevention Alzheimer's disease (AD) trials.

HIGHLIGHTS

Reverters had more longitudinal cognitive decline than those who remained cognitively normal. Predictors of reversion: younger age, better cognition, and negative amyloid biomarker status. Reverting from CDR 0.5 to 0 is a risk factor for future conversion to CDR > 0. CDR reversion may be a transitional phase in Alzheimer's Disease progression. CDR reverters may be ideal for Alzheimer's disease secondary prevention trials.

Application and validation of an algorithmic classification of early impairment in cognitive performance

OBJECTIVE

Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging.

METHOD

Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia.

RESULTS

Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years.

CONCLUSIONS

Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.

A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores

BACKGROUND

More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes.

METHODS

We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software.

RESULTS

Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD.

CONCLUSION

Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.

Alzheimer's disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals

BACKGROUND

Both Alzheimer's disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition.

METHODS

Analyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function.

RESULTS

In mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition.

CONCLUSIONS

These results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics.

Application and validation of an algorithmic classification of early impairment in cognitive performance

Objective

Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging.

Method

Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non- memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia.

Results

Among 1,851 participants (mean age=65.2±11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR=3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years.

Conclusions

Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.

Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease

BACKGROUND

A preclinical stage of Alzheimer's disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset.

NEW METHOD

We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI.

RESULTS

A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms.

COMPARISON WITH EXISTING METHODS

Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models.

CONCLUSIONS

The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.

Dual Task Performance Is Associated with Amyloidosis in Cognitively Healthy Adults

BACKGROUND

Preclinical Alzheimer's disease (AD) provides an opportunity for the study and implementation of interventions and strategies aimed at delaying, mitigating, and preventing AD. While this preclinical state is an ideal target, it is difficult to identify efficiently and cost-effectively. Recent findings have suggested that cognitive-motor dual task paradigms may provide additional inference.

OBJECTIVES

Investigate the relationship between dual task performance and amyloidosis, suggestive of preclinical Alzheimer's disease and whether dual task performance provides additional information beyond a cognitive composite, to help in the identification of amyloidosis.

DESIGN

Cross-sectional.

SETTING

Outpatient specialty brain health clinical research institution in the United States.

PARTICIPANTS

52 cognitively healthy adults.

MEASUREMENTS

The data included demographics, amyloid standardized uptake value ratio obtained via florbetapir-PET, neuropsychological testing, apolipoprotien E genotype, and dual task performance measures. Data were analyzed via hierarchal multiple linear regression or logistic regression, controlling for age, education, and apolipoprotien E genotype. Receiver operating characteristic curves were plotted, and sensitivity and specificity calculated via 2x2 contingency tables.

RESULTS

There was a moderate relationship (rs>.30) between motor and cognitive dual task effects and amyloid standardized uptake value ratio (ps<.042). A strong relationship (r=.58) was found between combined dual task effect, a measure of automaticity derived from dual task performance, and amyloid standardized uptake value ratio (p<.001). Additionally, combined dual task effect showed promise in its unique contributions to amyloid standardized uptake value ratio, accounting for 7.8% of amyloid standardized uptake value ratio variance beyond cognitive composite scores (p=.018). Additionally, when incorporated into the cognitive composite, combined dual task effect resulted in improved diagnostic accuracy for determining elevated amyloid standardized uptake value ratio, and increased the sensitivity and specificity of the cognitive composite.

CONCLUSSION

Dual task performance using the combined dual task effect, a measure of automaticity, was a moderate predictor of cerebral amyloidosis, which suggests that it has utility in the screening and diagnosis of individuals for preclinical AD. Additionally, when combined with the cognitive composite, the combined dual task effect improves diagnostic accuracy. Further research is warranted.

Sharper in the morning: Cognitive time of day effects revealed with high-frequency smartphone testing

Decades of research has established a shift from an "eveningness" preference to a "morningness" preference with increasing age. Accordingly, older adults typically have better cognition in morning hours compared to evening hours. We present the first known attempt to capture circadian fluctuations in cognition in individuals at risk for Alzheimer disease (AD) using a remotely administered smartphone assessment that samples cognition rapidly and repeatedly over several days. Older adults (N = 169, aged 61-94 years; 93% cognitively normal) completed four brief smartphone-based testing sessions per day for 7 consecutive days at quasi-random time intervals, assessing associate memory, processing speed, and visual working memory. Scores completed during early hours were averaged for comparison with averaged scores completed during later hours. Mixed effects models evaluated time of day effects on cognition. Additional models included clinical status and cerebrospinal fluid (CSF) biomarkers for beta amyloid (Aβ42) and phosphorylated tau181 (pTau). Models with terms for age, gender, education, APOE ε4 status, and clinical status revealed significantly worse performance on associate memory in evening hours compared to morning hours. Contemporaneously reported mood and fatigue levels did not moderate relationships. Using CSF data to classify individuals with and without significant AD pathology, there were no group differences in performance in morning hours, but subtle impairment emerged in associate memory in evening hours in those with CSF-confirmed AD pathology. These findings indicate that memory is worse in evening hours in older adults, that this pattern is consistent across several days, and is independent of measures of mood and fatigue. Further, they provide preliminary evidence of a "cognitive sundowning" in the very earliest stages of AD. Time of day may be an important consideration for assessments in observational studies and clinical trials in AD populations.

Comparison of the predictive accuracy of multiple definitions of cognitive impairment for incident dementia: a 20-year follow-up of the Whitehall II cohort study

BACKGROUND

Studies generally use cognitive assessment done at one timepoint to define cognitive impairment in order to examine conversion to dementia. Our objective was to examine the predictive accuracy and conversion rate of seven alternate definitions of cognitive impairment for dementia.

METHODS

In this prospective study, we included participants from the Whitehall II cohort study who were assessed for cognitive impairment in 2007-09 and were followed up for clinically diagnosed dementia. Algorithms based on poor cognitive performance (defined using age-specific and sex-specific thresholds, and subsequently thresholds by education or occupation levels) and objective cognitive decline (using data from cognitive assessments in 1997-99, 2002-04, and 2007-09) were used to generate seven alternate definitions of cognitive impairment. We compared predictive accuracy using Royston's R 2, the Akaike information criterion (AIC), sensitivity, specificity, and Harrell's C-statistic.

FINDINGS

5687 participants, with a mean age of 65·7 years (SD 5·9) in 2007-09, were included and followed up for a median of 10·5 years (IQR 10·1-10·9). Over follow-up, 270 (4·7%) participants were clinically diagnosed with dementia. Cognitive impairment defined using both cognitive performance and decline had higher hazard ratios (from 5·08 [95% CI 3·82-6·76] to 5·48 [4·13-7·26]) for dementia than did definitions based on cognitive performance alone (from 3·25 [2·52-4·17] to 3·39 [2·64-4·36]) and cognitive decline alone (3·01 [2·37-3·82]). However, all definitions had poor predictive performance (C-statistic ranged from 0·591 [0·565-0·616] to 0·631 [0·601-0·660]), primarily due to low sensitivity (21·6-48·4%). A predictive model containing age, sex, and education without measures of cognitive impairment had better predictive performance (C-statistic 0·783 [0·758-0·809], sensitivity 74·2%, specificity 72·2%) than all seven definitions of cognitive impairment (all p<0·0001).

INTERPRETATION

These findings suggest that cognitive impairment in early old age might not be useful for dementia prediction, even when it is defined using longitudinal data on cognitive decline and thresholds of poor cognitive performance additionally defined by education or occupation.

FUNDING

National Institutes of Health, UK Medical Research Council.

Online Clinical Consensus Diagnosis of Dementia: Development and Validation

OBJECTIVES

To introduce cost-effective expert clinical diagnoses of dementia into population-based research using an online platform and to demonstrate their validity against in-person clinical assessment and diagnosis.

DESIGN

The online platform provides standardized data necessary for clinicians to rate participants on the Clinical Dementia Rating (CDR^® ). Using this platform, clinicians diagnosed 60 patients at a range of CDR levels at two clinical sites. The online consensus diagnosis was compared with in-person clinical consensus diagnosis.

SETTING

All India Institute of Medical Sciences (AIIMS), Delhi, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.

PARTICIPANTS

Thirty patients each at AIIMS and NIMHANS with equal numbers of patients previously independently rated in person by experts as CDR is 0 (cognitively normal), CDR is 0.5 (mild cognitive impairment), and CDR is 1 or greater (dementia).

MEASUREMENTS

Multiple clinicians independently rate each participant on each CDR domain using standardized data and expert clinical judgment. The overall summary CDR is calculated by algorithm. When there are discrepancies among clinician ratings, clinicians discuss the case through a virtual consensus conference and arrive at a consensus overall rating.

RESULTS

Online clinical consensus diagnosis based on standardized interview data provides consistent clinical diagnosis with in-person clinical assessment and consensus diagnosis (κ coefficient = 0.76).

CONCLUSION

A web-based clinical consensus platform built on the Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India interview data is a cost-effective way to obtain reliable expert clinical judgments. A similar approach can be used for other epidemiological studies of dementia. J Am Geriatr Soc 68:S54-S59, 2020.

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

BACKGROUND

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents.

OBJECTIVE

This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status.

METHODS

Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status.

RESULTS

Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%.

CONCLUSIONS

The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.

A Birth Cohort Analysis of Amnestic Mild Cognitive Impairment Incidence in the Einstein Aging Study (EAS) Cohort

BACKGROUND

The transition from normal cognition to Alzheimer's disease is considered a continuum, with amnestic mild cognitive impairment (aMCI) an intermediate clinical cognitive state. Although prior work suggests that dementia incidence rates may be declining, there is little information regarding temporal trends in aMCI incidence.

OBJECTIVE

To determine whether age specific rates of aMCI have changed over sequential birth cohorts among individuals included in the population-based Einstein Aging Study (EAS) cohort. A secondary objective was to examine trends in aMCI rates among Blacks and Whites and by sex.

METHODS

Age specific incidence of aMCI was examined by birth year among 1,233 individuals age 70 years and above enrolled in the population-based EAS cohort between November 1, 1993 and February 22, 2016 and who had at least one annual follow-up assessment (5,321 person years of follow-up). Poisson regression was used to determine whether there has been a change in age specific aMCI rates over sequential years of birth.

RESULTS

No significant change in aMCI rates was identified in the overall cohort, among Blacks or Whites, or among males or females born between 1899 and 1946.

CONCLUSIONS

Despite a trend for decreased dementia incidence in the EAS cohort, rates of incident aMCI have not changed. These apparently conflicting results may indicate a delay or decrease in the rates of transition from aMCI to dementia within the cohort. However, further studies are needed to confirm whether rates of aMCI have changed in other populations, and how aMCI rates are related to secular trends in dementia risk factors.

Diagnostic Stability of Mild Cognitive Impairment, and Predictors of Reversion to Normal Cognitive Functioning

OBJECTIVE

Studies that investigate predictive factors for spontaneous recovery (reversion) from mild cognitive impairment (MCI) are only beginning to emerge, and the long-term course of MCI is not properly understood. We aimed to investigate stability of the MCI diagnosis, predictors for reversion, as well as the trajectory of MCI over the course of 12 years.

MATERIALS AND METHODS

Data were drawn from the Swedish population study: Good Aging in Skåne with MCI defined according to the expanded Mayo Clinic criteria. A total of 331 participants, aged 60-95 years with MCI, were used to investigate 6-year MCI stability and reversion, and 410 participants were used to inspect 12-year MCI trajectory. Predictors for reversion included demographical factors, psychological status, and factors tied to the cognitive testing session and the operationalization of the MCI criteria.

RESULTS

Over half (58%, 95% CI 52.7-63.3) of the participants reverted back to normal cognitive functioning at 6-year follow-up. Of those with stable MCI, 56.5% (95% CI 48.2-64.8) changed subtype. A total of 23.9% (95% CI 13.7-34.1) of the 6-year follow-up reverters re-transitioned back to MCI at 12-year follow-up. ORs for reversion were significantly higher in participants with lower age (60-year-olds: OR 2.19, 95% CI 1.08-4.43, 70-year-olds: OR 3.11, 95% CI 1.27-7.62), better global cognitive functioning (OR 1.15, 95% CI 1.03-1.29), good concentration (OR 2.53, 95% CI 1.06-6.05), and single-domain subtype (OR 2.68, 95% CI 1.51-4.75).

CONCLUSION

Our findings provide further support that MCI reversion to normal cognitive functioning as well as re-transitioning to MCI is fairly common, suggesting that the MCI trajectory does not necessarily lead straight to dementia. Additionally, assessment of factors associated with reversion can aid clinicians to make accurate MCI progression prognosis.

A harmonized longitudinal biomarkers and cognition database for assessing the natural history of preclinical Alzheimer's disease from young adulthood and for designing prevention trials

INTRODUCTION

Large longitudinal biomarkers database focusing on middle age is needed for Alzheimer's disease (AD) prevention.

METHODS

Data for cerebrospinal fluid analytes, molecular imaging of cerebral fibrillar β-amyloid with positron emission tomography, magnetic resonance imaging-based brain structures, and clinical/cognitive outcomes were harmonized across eight AD biomarker studies. Statistical power was estimated.

RESULTS

The harmonized database included 7779 participants with clinical/cognitive data: 3542 were 18∼65 years at the baseline, 5865 had longitudinal cognitive data for a median of 4.7 years, 2473 participated in the cerebrospinal fluid studies (906 had longitudinal data), 2496 participated in the magnetic resonance imaging studies (1283 had longitudinal data), and 1498 participated in the positron emission tomography amyloid studies (849 had longitudinal data). The database provides adequate power for detecting early biomarker changes, and demonstrates the feasibility of AD prevention trials on middle-aged individuals.

DISCUSSION

The harmonized database is an optimum resource to design AD prevention trials decades before symptomatic onset.

Plasma Markers of Inflammation Linked to Clinical Progression and Decline During Preclinical AD

Objective

To examine the prospective association between blood biomarkers of immune functioning (i.e., innate immune activation, adaptive immunity, and inflammation) and subsequent cognitive decline and clinical progression to mild cognitive impairment (MCI) in cognitively normal individuals.

Methods

The BIOCARD study is an observational cohort study of N = 191 initially cognitively healthy participants (mean age 65.2 years). Blood plasma samples were assayed for markers of chronic inflammation (TNFR1, IL-6), adaptive immunity (CD25), and innate immune activation (CD14 and CD163). Participants were followed annually for ongoing clinical assessment and cognitive testing for up to 7.3 years. Primary study outcomes were progression to MCI and cognitive change over time, as measured by a global factor score encompassing multiple cognitive domains.

Results

Higher levels of plasma TNFR1 were associated with greater risk of progression from normal cognition to MCI (HR: 3.27; 95% confidence interval, CI: 1.27, 8.40). Elevated levels of TNFR1 were also associated with steeper rate of cognitive decline on follow-up but not with baseline cognitive performance. Baseline IL-6 levels and markers of innate and adaptive immune activation showed no relationship with MCI risk or cognitive decline.

Conclusion

Inflammation, mediated by TNF signaling, may play a selective role in the early phase of AD. Accordingly, plasma TNFR1 may facilitate improved prediction of disease progression for individuals in the preclinical stage of AD.

Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer's Disease

Objective: Several models have been proposed for the evolution of Alzheimer's disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD. Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset. Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10-15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset). Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.

ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes

OBJECTIVE

To examine the long-term cognitive trajectories of individuals with normal cognition at baseline and distinct amyloid/tau/neurodegeneration (ATN) profiles.

METHODS

Pooling data across 4 cohort studies, 814 cognitively normal participants (mean baseline age = 59.6 years) were classified into 8 ATN groups using baseline CSF levels of β-amyloid 1-42 as a measure of amyloid (A), phosphorylated tau 181 as a measure of tau (T), and total tau as a measure of neurodegeneration (N). Cognitive performance was measured using a previously validated global factor score and with the Mini-Mental State Examination. We compared the cognitive trajectories across groups using growth curve models (mean follow-up time = 7 years).

RESULTS

Using different model formulations and cut points for determining biomarker abnormality, only the group with abnormal levels of amyloid, tau, and neurodegeneration (A+T+N+) showed consistently greater cognitive decline than the group with normal levels of all biomarkers (A-T-N-). Replicating prior findings using the 2011 National Institute on Aging-Alzheimer's Association/suspected non-Alzheimer disease pathophysiology schema, only individuals with abnormal levels of both amyloid and phosphorylated tau 181 or total tau (stage 2) showed greater cognitive decline than those with normal biomarker levels (stage 0).

CONCLUSION

The results are consistent with the hypothesis that both elevated brain amyloid and neurofibrillary tangles are necessary to observe accelerated neurodegeneration, which in turn leads to cognitive decline.

Sex, amyloid, and APOE ε4 and risk of cognitive decline in preclinical Alzheimer's disease: Findings from three well-characterized cohorts

INTRODUCTION

Our objective was to investigate the effect of sex on cognitive decline within the context of amyloid β (Aβ) burden and apolipoprotein E genotype.

METHODS

We analyzed sex-specific effects on Aβ-positron emission tomography, apolipoprotein, and rates of change on the Preclinical Alzheimer Cognitive Composite-5 across three cohorts, such as the Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker and Lifestyle, and Harvard Aging Brain Study (n = 755; clinical dementia rating = 0; age (standard deviation) = 73.6 (6.5); female = 55%). Mixed-effects models of cognitive change by sex, Aβ-positron emission tomography, and apolipoprotein ε4 were examined with quadratic time effects over a median of 4 years of follow-up.

RESULTS

Apolipoprotein ε4 prevalence and Aβ burden did not differ by sex. Sex did not directly influence cognitive decline. Females with higher Aβ exhibited faster decline than males. Post hoc contrasts suggested that females who were Aβ and apolipoprotein ε4 positive declined faster than their male counterparts.

DISCUSSION

Although Aβ did not differ by sex, cognitive decline was greater in females with higher Aβ. Our findings suggest that sex may play a modifying role on risk of Alzheimer's disease-related cognitive decline.

The Wisconsin Registry for Alzheimer's Prevention: A review of findings and current directions

The Wisconsin Registry for Alzheimer's Prevention is a longitudinal observational cohort study enriched with persons with a parental history (PH) of probable Alzheimer's disease (AD) dementia. Since late 2001, Wisconsin Registry for Alzheimer's Prevention has enrolled 1561 people at a mean baseline age of 54 years. Participants return for a second visit 4 years after baseline, and subsequent visits occur every 2 years. Eighty-one percent (1270) of participants remain active in the study at a current mean age of 64 and 9 years of follow-up. Serially assessed cognition, self-reported medical and lifestyle histories (e.g., diet, physical and cognitive activity, sleep, and mood), laboratory tests, genetics, and linked studies comprising molecular imaging, structural imaging, and cerebrospinal fluid data have yielded many important findings. In this cohort, PH of probable AD is associated with 46% apolipoprotein E (APOE) ε4 positivity, more than twice the rate of 22% among persons without PH. Subclinical or worse cognitive decline relative to internal normative data has been observed in 17.6% of the cohort. Twenty-eight percent exhibit amyloid and/or tau positivity. Biomarker elevations, but not APOE or PH status, are associated with cognitive decline. Salutary health and lifestyle factors are associated with better cognition and brain structure and lower AD pathophysiologic burden. Of paramount importance is establishing the amyloid and tau AD endophenotypes to which cognitive outcomes can be linked. Such data will provide new knowledge on the early temporal course of AD pathophysiology and inform the design of secondary prevention clinical trials.

Personality Change in the Preclinical Phase of Alzheimer Disease

IMPORTANCE

Changes in behavior and personality are 1 criterion for the diagnosis of dementia. It is unclear, however, whether such changes begin before the clinical onset of the disease.

OBJECTIVE

To determine whether increases in neuroticism, declines in conscientiousness, and changes in other personality traits occur before the onset of mild cognitive impairment or dementia.

DESIGN, SETTING, AND PARTICIPANTS

A cohort of 2046 community-dwelling older adults who volunteered to participate in the Baltimore Longitudinal Study of Aging were included. The study examined personality and clinical assessments obtained between 1980 and July 13, 2016, from participants with no cognitive impairment at first assessment who were followed up for as long as 36 years (mean [SD], 12.05 [9.54] years). The self-report personality scales were not considered during consensus diagnostic conferences.

MAIN OUTCOMES AND MEASURES

Change in self-rated personality traits assessed in the preclinical phase of Alzheimer disease and other dementias with the Revised NEO Personality Inventory, a 240-item questionnaire that assesses 30 facets, 6 for each of the 5 major dimensions: neuroticism, extraversion, openness, agreeableness, and conscientiousness.

RESULTS

Of the 2046 participants, 931 [45.5%] were women; mean (SD) age at first assessment was 62.56 (14.63) years. During 24 569 person-years, mild cognitive impairment was diagnosed in 104 (5.1%) individuals, and all-cause dementia was diagnosed in 255 (12.5%) participants, including 194 (9.5%) with Alzheimer disease. Multilevel modeling that accounted for age, sex, race, and educational level found significant differences on the intercept of several traits: individuals who developed dementia scored higher on neuroticism (β = 2.83; 95% CI, 1.44 to 4.22; P < .001) and lower on conscientiousness (β = -3.34; 95% CI, -4.93 to -1.75; P < .001) and extraversion (β = -1.74; 95% CI, -3.23 to -0.25; P = .02). Change in personality (ie, slope), however, was not significantly different between the nonimpaired and the Alzheimer disease groups (eg, neuroticism: β = 0.00; 95% CI, -0.08 to 0.08; P = .91; conscientiousness: β = -0.06; 95% CI, -0.16 to 0.04; P = .24). Slopes for individuals who developed mild cognitive impairment (eg, neuroticism: β = 0.00; 95% CI, -0.12 to 0.12; P = .98; conscientiousness: β = -0.09; 95% CI, -0.23 to 0.05; P = .18) and all-cause dementia (eg, neuroticism: β = 0.02; 95% CI, -0.06 to 0.10; P = .49; conscientiousness: β = -0.08; 95% CI, -0.16 to 0.00; P = .07) were also similar to those for nonimpaired participants.

CONCLUSIONS AND RELEVANCE

No evidence for preclinical change in personality before the onset of mild cognitive impairment or dementia was identified. These findings provide evidence against the reverse causality hypothesis and strengthen evidence for personality traits as a risk factor for dementia.