Considerations for Use of Blood-Based Biomarkers in Epidemiologic Dementia Research

Methods to crosswalk between cognitive test scores using data from the Alzheimer's Disease Neuroimaging Cohort

INTRODUCTION

Studies use multiple different instruments to measure dementia-related outcomes, making head-to-head comparisons of interventions difficult.

METHODS

To address this gap, we developed two methods to crosswalk estimated treatment effects on cognitive outcomes that are flexible, broadly applicable, and do not rely on strong distributional assumptions.

RESULTS

We present two methods to crosswalk effect estimates using one measure to estimates using another measure, illustrated with global cognitive measures from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, we develop crosswalks for the following measures and associated change scores over time: the clinical dementia rating scale sum of box (CDR-SB), Montreal Cognitive Assessment (MoCA), and Mini-Mental State Examination (MMSE) scores. Finally, a setting in which crosswalking is not appropriate is illustrated with plasma phosphorylated tau (p-tau) concentration and global cognitive measures.

DISCUSSION

Given the inconsistent collection and reporting of dementia and cognitive outcomes across studies, these crosswalking methods offer a valuable approach to harmonizing and comparing results reported on different scales.

HIGHLIGHTS

Developed methods to crosswalk from one cognitive outcome to another in studies of dementia interventions. Methods illustrated using combinations of global cognitive tests: the CDR-SB, MoCA, and MMSE. Illustrates scenarios where crosswalking may not be appropriate for certain combinations of measures. Crosswalking methods support comparison of interventions with accurate error propagation. Facilitates inclusion of more studies in meta-analyses by increasing data comparability.

Alzheimer disease-related biomarkers and cancer-related cognitive decline: the Thinking and Living with Cancer study

PURPOSE

We evaluated whether plasma Alzheimer disease (AD)-related biomarkers were associated with cancer-related cognitive decline among older breast cancer survivors.

METHODS

We included survivors aged 60-90 years with primary stage 0-III breast cancers (n = 236) and frequency-matched noncancer control paricipant (n = 154) who passed a cognitive screen and had banked plasma specimens. Participants were assessed at baseline (presystemic therapy) and annually for up to 60 months. Cognition was measured using tests of attention, processing speed, and executive function and learning and memory; perceived cognition was measured by the Functional Assessment of Cancer Therapy-Cognitive Function v3 Perceived Cognitive Impairments. Baseline plasma neurofilament light, glial fibrillary acidic protein, β-amyloid 42 and 40 and phosphorylated tau 181 were assayed using single molecule arrays. Mixed models tested associations between cognition and baseline AD biomarkers, time, group (survivor vs control participant), and their 2- and 3-way interactions, controlling for age, race, Wide Range 4 Achievement Test Word Reading score, comorbidity, and body mass index; 2-sided P values of .05 were considered statistically significant.

RESULTS

There were no group differences in baseline AD-related biomarkers except survivors had higher baseline neurofilament light levels than control participants (P = .013). Survivors had lower adjusted longitudinal attention, processing speed, and executive function than control participants starting from baseline and continuing over time (P ≤ .002). However, baseline AD-related biomarker levels were not independently associated with adjusted cognition over time, except control participants had lower attention, processing speed, and executive function scores with higher glial fibrillary acidic protein levels (P = .008).

CONCLUSION

The results do not support a relationship between baseline AD-related biomarkers and cancer-related cognitive decline. Further investigation is warranted to confirm the findings, test effects of longitudinal changes in AD-related biomarkers, and examine other mechanisms and factors affecting cognition presystemic therapy.

Contribution of Global Amyloid-PET Imaging for Predicting Future Cognition in the MEMENTO Cohort

BACKGROUND AND OBJECTIVES

Global amyloid-PET is associated with cognition and cognitive decline, but most research on this association does not account for past cognitive information. We assessed the prognostic benefit of amyloid-PET measures for future cognition when prior cognitive assessments are available, evaluating the added value of amyloid measures beyond information on multiple past cognitive assessments.

METHODS

The French MEMENTO cohort (a cohort of outpatients from French research memory centers to improve knowledge on Alzheimer disease and related disorders) includes older outpatients with incipient cognitive changes, but no dementia diagnosis at inclusion. Global amyloid burden was assessed using positron emission tomography (amyloid-PET) for a subset of participants; semiannual cognitive testing was subsequently performed. We predicted mini-mental state examination (MMSE) scores using demographic characteristics (age, sex, marital status, and education) alone or in combination with information on prior cognitive measures. The added value of amyloid burden as a predictor in these models was evaluated with percent reduction of the mean squared error (MSE). All models were conducted separately for evaluating the added value of dichotomous amyloid positivity status compared with a continuous amyloid-standardized uptake-value ratio.

RESULTS

Our analytic sample comprised 510 individuals who underwent amyloid-PET scans with at least 4 MMSE assessments. The mean age at the PET scan was 71.6 (standard deviation 7.4) years; 60.7% were female. The median follow-up was 4.6 years (interquartile range: 0.9 years). Adding amyloid burden when adjusting for only demographic characteristics reduced the MSE of predictions by 5.08% (95% CI 0.97%-10.86%) and 12.64% (95% CI 3.35%-25.28%) for binary and continuous amyloid, respectively. If the model included 1 past MMSE measure, the MSE improvement was 3.51% (95% CI 1.01%-7.28%) when adding binary amyloid and 8.83% (95% CI 2.63%-16.37%) when adding continuous amyloid. Improvements in model fit were smaller with the addition of amyloid burden when more than 1 past cognitive assessment was included. For all models incorporating past cognitive assessments, differences in predictions amounted to a fraction of 1 MMSE point on average.

DISCUSSION

In a clinical setting, global amyloid burden did not appreciably improve cognitive predictions when past cognitive assessments were available.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov Identifier: NCT02164643.