Background: Depression is also common with older age. Alzheimer’s disease (AD) studies suggest that both cerebrospinal fluid and positron emission tomography (PET) amyloid biomarkers are associated with more depressive symptoms in cognitively normal older adults. The recent availability of tau radiotracers offers the ability to examine in vivo tauopathy. It is unclear if the tau biomarker is associated with depression diagnosis. Objective: We examined if tau and amyloid imaging were associated with a depression diagnosis among cognitively normal adults (Clinical Dementia Rating = 0) and whether antidepressants modified this relationship. Methods: Among 301 participants, logistic regression models evaluated whether in vivo PET tau was associated with depression, while another model tested the interaction between PET tau and antidepressant use. A second set of models substituted PET amyloid for PET tau. A diagnosis of depression (yes/no) was made during an annual clinical assessment by a clinician. Antidepressant use (yes/no) was determined by comparing medications the participants used to a list of 30 commonly used antidepressants. All models adjusted for age, sex, education, race, and apolipoprotein ɛ4. Similar models explored the association between the biomarkers and depressive symptoms. Results: Participants with elevated tau were twice as likely to be depressed. Antidepressant use modified this relationship where participants with elevated tau who were taking antidepressants had greater odds of being depressed. Relatedly, elevated amyloid was not associated with depression. Conclusions: Our results demonstrate that tau, not amyloid, was associated with a depression diagnosis. Additionally, antidepressant use interacts with tau to increase the odds of depression among cognitively normal adults.

Structural and social determinants of health (SSDoH) are environmental conditions in which individuals are born, live, learn, work, play, worship, and age that affect health, functioning, and quality-of-life outcomes across the life course. Growing evidence suggests that SSDoH can help to explain heterogeneity in outcomes in Alzheimer’s disease and Alzheimer’s disease and related dementias (AD/ADRD) research and clinical practice. The National Institute on Aging has prioritized collecting SSDoH data to elucidate disease mechanisms and aid discovery of disease-modifying treatments. However, a major nexus of AD/ADRD research, the national network of Alzheimer’s Disease Research Centers (ADRCs), collects few SSDoH data. We describe a framework for feasibly gathering and modeling SSDoH data across ADRCs. We lay out key constructs, their measures, and empirical evidence for their importance in elucidating disease and prevention mechanisms. Toward a goal of translation, the framework proposes a modular structure with a core set of measures and options for adjunctive modules. We describe considerations for measuring SSDoH in existing geographically and culturally diverse research cohorts. We also outline a rationale for universal implementation of a set of SSDoH measures and juxtapose the approach with alternatives aimed at collecting SSDoH data.

Has increased such that a 65-year-old is expected to live 19 or more years and an 85-year-old can expect to live, on average, 6 to 7 years longer. Individuals of certain ethnoracial groups (Black, Hispanic/Latino, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander) may be at a higher risk of incident ADRD compared to non-Hispanic/Latino White people. These differences in a higher risk of ADRD across ethnoracial groups persist despite no statistically significant differences in the rate of cognitive decline over time. The intersectionality of social determinants of health, experiences with discrimination and oppression, and access to care are related to the issue of justice and the risk for and expression of ADRD. The theoretical frameworks of various health disparities provide organized approaches to tracking the progression of health disparities for diverse patients. SUMMARY ADRD health disparities are complex. Neurologists and their care teams must consider the main reasons for clinical ADRD evaluations of members of ethnoracial groups and the factors that may impact patient adherence and compliance with diagnostic and management recommendations....

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking DeviceTM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven. Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition. Measurements: Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations. Temporal components included number of trips taken during different times of the day. Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods: Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS. Results: Primary locations were confirmed to be 100% accurate when compared to known addresses. Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns. Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night. Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.

A clinical consequence of symptomatic Alzheimer’s disease (AD) is impaired driving performance. However, decline in driving performance may begin in the preclinical stage of AD. We used a naturalistic driving methodology to examine differences in driving behavior over one year in a small sample of cognitively normal older adults with (n = 10) and without (n = 10) preclinical AD. As expected with a small sample size, there were no statistically significant differences between the two groups, but older adults with preclinical AD drove less often, were less likely to drive at night, and had fewer aggressive behaviors such as hard braking, speeding, and sudden acceleration. The sample size required to power a larger study to determine differences was calculated.