In Response to PA-13-168 (Secondary Analyses of Existing Data Sets and Stored Biospecimens To Address Clinical Aging Research Questions (R01)). Despite increasing sophistication in the application of biomarkers to the study of mild forms of cognitive impairment (MCI), sophistication in profiling cognition has not been commensurate. Criteria for MCI diagnosis in many large-scale studies rely on a single cognitive test score, screening measures, rating scales and clinical judgment, resulting in coarse characterizations of the types or severity of MCI being studied despite the availability of rich neuropsychological data from such studies. We propose to apply our novel actuarial neuropsychological and statistical methods to more accurately diagnose MCI and predict its progression. Applying these methods to large-scale existing open source (ADCS donepezil trial, ADNI, NACC/UDS) and institutional (FHS, MCSA, WHICAP) datasets will uncover stronger relationships between biomarkers, cognition, pathology, and progression rates, and will result in stronger treatment effects in clinical trials aimed at MCI. Our methods will improve effect sizes that inform power analyses for clinical trials and reduce the number of patients needed for such trials. Finally, our methods will be implemented to improve the NIA-AA operational definition of 'subtle cognitive decline' in Preclinical AD. These improvements will have important impacts on prospective design of future biomarker and clinical trial studies.
Specific aims :
Aim 1. Actuarial neuropsychological criteria for MCI diagnosis will better specify cognitive phenotypes as well as identify possible diagnostic errors from conventional criteria; removal of the resultant false positive (i.e., cognitively normal via neuropsychological criteria) cases and addition of false negative (i.e., `missed') cases will strengthen biomarker and trial findings from several large-scale studies.
Aim 2. Empirically derived MCI diagnostic criteria will result in more efficient tril and study designs (i.e., studies that need fewer subjects) compared to conventional MCI criteria.
Aim 3. An operational definition of subtle cognitive decline based on extensions of the above neuropsychological MCI criteria will improve characterization of NIA-AA criteria for Preclinical AD.
Aim 4. In exploratory analyses, we will use novel computational tools to harmonize and combine 1) cognitive and 2) multi-marker profiles predictive of progression/pathology across multiple datasets. Demonstrations of improvement in diagnostic precision in MCI and Preclinical AD will have an important impact on prospective design of future studies of genetics, biomarkers, treatments and ultimately prevention. If successful, we will be able to more clearly model effects of biomarkers changes and neurodegeneration, together with factors such as age and comorbidities, on specific profiles and trajectories of cognitive decline.

Public Health Relevance

We are entering a new era of research and clinical activity that will increasingly focus on the role of biomarkers in dementia detection, diagnosis, and clinical outcome. We seek to improve Mild Cognitive Impairment (MCI) diagnosis, characterization of its subtypes, and appraise operational definitions of the subtle cognitive decline of Preclinical AD. Demonstrations of improvement in diagnostic precision in MCI and Preclinical AD will have important impacts on prospective design of future studies of genetics, biomarkers, treatments and ultimately prevention.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG049810-04
Application #
9640217
Study Section
Adult Psychopathology and Disorders of Aging Study Section (APDA)
Program Officer
Silverberg, Nina B
Project Start
2016-03-15
Project End
2021-02-28
Budget Start
2019-03-01
Budget End
2020-02-29
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California, San Diego
Department
Psychiatry
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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Thomas, Kelsey R; Eppig, Joel; Edmonds, Emily C et al. (2018) Word-list intrusion errors predict progression to mild cognitive impairment. Neuropsychology 32:235-245
Emrani, Sheina; Libon, David J; Lamar, Melissa et al. (2018) Assessing Working Memory in Mild Cognitive Impairment with Serial Order Recall. J Alzheimers Dis 61:917-928
Stricker, Nikki H; Lundt, Emily S; Edwards, Kelly K et al. (2018) Comparison of PC and iPad administrations of the Cogstate Brief Battery in the Mayo Clinic Study of Aging: assessing cross-modality equivalence of computerized neuropsychological tests. Clin Neuropsychol :1-25

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