Maruff, Paul

Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E (APOE) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.

AbstractCognitive change affecting patients after anaesthesia and surgery has been recognised for more than 100 yr. Research into cognitive change after anaesthesia and surgery accelerated in the 1980s when multiple studies utilised detailed neuropsychological testing for assessment of cognitive change after cardiac surgery. This body of work consistently documented decline in cognitive function in elderly patients after anaesthesia and surgery, and cognitive changes have been identified up to 7.5 yr afterwards. Importantly, other studies have identified that the incidence of cognitive change is similar after non‐cardiac surgery. Other than the inclusion of non‐surgical control groups to calculate postoperative cognitive dysfunction, research into these cognitive changes in the perioperative period has been undertaken in isolation from cognitive studies in the general population. The aim of this work is to develop similar terminology to that used in cognitive classifications of the general population for use in investigations of cognitive changes after anaesthesia and surgery. A multispecialty working group followed a modified Delphi procedure with no prespecified number of rounds comprised of three face‐to‐face meetings followed by online editing of draft versions.Two major classification guidelines [Diagnostic and Statistical Manual for Mental Disorders, fifth edition (DSM‐5) and National Institute for Aging and the Alzheimer Association (NIA‐AA)] are used outside of anaesthesia and surgery, and may be useful for inclusion of biomarkers in research. For clinical purposes, it is recommended to use the DSM‐5 nomenclature. The working group recommends that ‘perioperative neurocognitive disorders’ be used as an overarching term for cognitive impairment identified in the preoperative or postoperative period. This includes cognitive decline diagnosed before operation (described as neurocognitive disorder); any form of acute event (postoperative delirium) and cognitive decline diagnosed up to 30 days after the procedure (delayed neurocognitive recovery) and up to 12 months (postoperative neurocognitive disorder).

ObjectivePrevious studies suggest that the brain‐derived neurotrophic factor (BDNF) Val66Met (rs6265) polymorphism may influence symptom onset in Alzheimer's disease (AD). Our recent cross‐sectional findings suggest that Met66 may influence clinical expression in dominantly inherited AD (DIAD) through its effects on tau. However, it remains unclear whether carriage of Met66 in DIAD results in faster increases in cerebrospinal fluid (CSF) tau and ptau181, and whether these increases are associated with accelerated brain volume loss and memory decline.MethodsA total of 211 subjects (101 mutation noncarriers, 110 mutation carriers), who were cognitively normal, as defined by a Clinical Dementia Rating global score of 0, completed assessments of cognitive function, neuroimaging, and CSF sampling over 3.5 years as part of the Dominantly Inherited Alzheimer's Network.ResultsIn mutation carriers, Met66 carriers showed faster memory decline (4×), hippocampal volume loss (16×), and CSF tau and ptau181increases (6×) than Val66 homozygotes.BDNFdid not influence rates of cortical β‐amyloid accumulation or change in CSF Aβ42levels in mutation carriers. In mutation noncarriers,BDNFgenotype had no effect on change in cognition, brain volume, cortical β‐amyloid accumulation, or change in any CSF measures of tau, ptau181, and CSF Aβ42.InterpretationAs in sporadic AD, the deleterious effects of β‐amyloid on cognitive function, brain volume loss, and CSF tau in DIAD mutation carriers are less in Val66 homozygotes. TheBDNFVal66Met polymorphism should be considered as a potential moderator of clinical trial outcomes in current treatment and prevention trials in DIAD and sporadic AD. Ann Neurol 2018;84:424–435

AbstractThe Alzheimer's Association's Research Roundtable met in November 2017 to explore the new National Institute on Aging and the Alzheimer's Association Research Framework for Alzheimer's disease. The meeting allowed experts in the field from academia, industry, and government to provide perspectives on the new National Institute on Aging and the Alzheimer's Association Research Framework. This review will summarize the “A, T, N System” (Amyloid, Tau, and Neurodegeneration) using biomarkers and how this may be applied to clinical research and drug development. In addition, challenges and barriers to the potential adoption of this new framework will be discussed. Finally, future directions for research will be proposed.

Introduction: This study set out to clarify the differential acute cognitive impact of lorazepam based on varying genetic risk for Alzheimer disease. Methods: Fifty-seven cognitively unimpaired individuals aged 51 to 88, genotyped according to apolipoprotein E (APOE) and translocase of outer mitochondrial membrane (40 homolog) poly-T lengths, completed cognitive testing before, 2.5 and 5 hours after receiving a 1 mg dose of lorazepam. Results: Post-lorazepam, there were significant (P<0.05) declines from baseline in memory, psychomotor processing speed, and executive function. At 2.5 hours, the magnitude of this lorazepam-induced cognitive change was significantly greater in the APOE3/4 group than in the APOE3/3 group for tests of working memory and visuospatial memory/executive function. At 5 hours postchallenge, verbal memory and working memory deficits persisted in the APOE3/4 group compared with the APOE3/3 group. At 5 hours after lorazepam challenge, as compared with the very long/very long group, the short/short group performed slightly worse on a test of working memory (P<0.05), but no other differences were observed among translocase of the outer mitochondrial membrane 40 homolog poly-T variant groups. Discussion: The lorazepam challenge may be unmasking presymptomatic cognitive dysfunction associated with APOE4 carriage.

AbstractWith increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long‐term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.

The fourth Leon Thal Symposium (LTS2010) was convened in Toulouse, France, on November 3, 2010. This symposium reviewed design parameters that are necessary to develop comprehensive national databases on healthy aging. Such datasets offer the potential to serve as the foundation for a systems‐approach to solve the dual public health problems of: (1) early detection of people who are at elevated risk for Alzheimer's disease, and (2) the development of interventions to delay onset of, or prevent, late‐life dementia. The symposium considered three interrelated components of a National Database for Longitudinal Studies on Healthy Aging as follows: (a) a registry of healthy aging adults; (b) refined computer‐based assessments for data gathering, including assessments of behavioral/memory changes associated with aging that are appropriate for broad use in nonexpert settings; and (c) high performance computing/supercomputer‐based approaches for health data modeling and mining.