Longitudinal Assessment of Self- and Informant-Subjective Cognitive Complaints in a Sample of Healthy Late-Middle Aged Adults Enriched with a Family History of Alzheimer's Disease.

OBJECTIVES: The purpose of this study was to investigate the longitudinal trajectory of self- and informant-subjective cognitive complaints (SCC), and to determine if SCC predict longitudinal changes in objective measures (OM) of cognitive function. METHODS: The study included healthy and cognitively normal late middle-aged adults enriched with a family history of AD who were evaluated at up to three visits over a 4-year period. At each visit (Visit 1-3), self- and informant-SCC and OM were evaluated. Linear mixed models were used to determine if the longitudinal rate of change of self- and informant-SCC were associated with demographic variables, depressive symptoms, family history (FH), and apolipoprotein epsilon 4 (APOE4) status. The same modeling approach was used to examine the effect of Visit 1 SCC on longitudinal cognitive change after controlling for the same variables. RESULTS: At Visit 1, more self-SCC were associated with fewer years of education and more depressive symptoms. SCC were also associated with poorer performance on cognitive measures, such that more self-SCC at Visit 1 were associated with poorer performance on memory and executive functioning measures at Visit 1, while more informant-SCC were associated with faster rate of longitudinal decline on a measure of episodic learning and memory. FH and APOE4 status were not associated with SCC. DISCUSSION: Self- and informant-SCC showed an association with OM, albeit over different time frames in our late middle-aged sample. Additional longitudinal follow-up will likely assist in further clarifying these relationships as our sample ages and more pronounced cognitive changes eventually emerge. (JINS, 2017, 23, 617-626).

Advancing Alzheimer's disease diagnosis, treatment, and care: recommendations from the Ware Invitational Summit.

To address the pending public health crisis due to Alzheimer's disease (AD) and related neurodegenerative disorders, the Marian S. Ware Alzheimer Program at the University of Pennsylvania held a meeting entitled "State of the Science Conference on the Advancement of Alzheimer's Diagnosis, Treatment and Care," on June 21-22, 2012. The meeting comprised four workgroups focusing on Biomarkers; Clinical Care and Health Services Research; Drug Development; and Health Economics, Policy, and Ethics. The workgroups shared, discussed, and compiled an integrated set of priorities, recommendations, and action plans, which are presented in this article.

Machine learning amplifies the effect of parental family history of Alzheimer's disease on list learning strategy.

Identification of preclinical Alzheimer's disease (AD) is an essential first step in developing interventions to prevent or delay disease onset. In this study, we examine the hypothesis that deeper analyses of traditional cognitive tests may be useful in identifying subtle but potentially important learning and memory differences in asymptomatic populations that differ in risk for developing Alzheimer's disease. Subjects included 879 asymptomatic higher-risk persons (middle-aged children of parents with AD) and 355 asymptotic lower-risk persons (middle-aged children of parents without AD). All were administered the Rey Auditory Verbal Learning Test at baseline. Using machine learning approaches, we constructed a new measure that exploited finer differences in memory strategy than previous work focused on serial position and subjective organization. The new measure, based on stochastic gradient descent, provides a greater degree of statistical separation (p = 1.44 × 10-5) than previously observed for asymptomatic family history and non-family history groups, while controlling for apolipoprotein epsilon 4, age, gender, and education level. The results of our machine learning approach support analyzing memory strategy in detail to probe potential disease onset. Such distinct differences may be exploited in asymptomatic middle-aged persons as a potential risk factor for AD.

Early identification and treatment of Alzheimer's disease: social and fiscal outcomes.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that places substantial burdens on those who provide support for family members with declining cognitive and functional abilities. Many AD patients eventually require formal long-term care services because of the absence, exhaustion, or inability of family members to provide care. The costs of long-term care, and especially nursing home care, often deplete private financial resources, placing a substantial burden on state Medicaid programs. Current evidence suggests that pharmacological treatments and caregiver interventions can delay entry into nursing homes and potentially reduce Medicaid costs. However, these cost savings are not being realized because many patients with AD are either not diagnosed or diagnosed at late stages of the disease, and have no access to Medicare-funded caregiver support programs. METHODS AND RESULTS: A Monte Carlo cost-benefit analysis, based on estimates of parameters available in the medical literature, suggests that the early identification and treatment of AD have the potential to result in large, positive net social benefits as well as positive net savings for states and the federal government. CONCLUSIONS: These findings indicate that the early diagnosis and treatment of AD are not only socially desirable in terms of increasing economic efficiency, but also fiscally attractive from both state and federal perspectives. These findings also suggest that failure to fund effective caregiver interventions may be fiscally unsound.