Trani, JF

Background Hearing impairment (HI) has been identified as a potentially modifiable risk factor for dementia, yet its relationship with Alzheimer's disease (AD) neuropathology and the temporal directionality remain unclear, particularly in late life. Objective To examine cross-sectional and longitudinal associations between HI, clinical cognitive status, and AD biomarkers in community-dwelling older adults. Methods We included 474 DRIVES Project participants (mean age 73.5 ± 5.2 years). Hearing was assessed using the NIH Toolbox Words-in-Noise (WIN) test. Clinical cognitive status was assessed using Clinical Dementia Rating (CDR). AD biomarkers included cerebrospinal fluid (CSF) Aβ 42 /Aβ 40 , t-tau/Aβ 42 , and p-tau/Aβ 42 ratios and amyloid PET imaging. Multivariable linear, logistic, Cox proportional hazards, and Fine–Gray competing-risk models were used. Results HI was more prevalent among participants with mild cognitive impairment (MCI; defined as CDR = 0.5) than cognitively normal individuals (50% versus 24%, p < 0.001), and MCI status was independently associated with worse baseline WIN thresholds (β = 1.55 dB SNR; 95% CI 0.72–2.37). HI was not cross-sectionally associated with CSF amyloid or tau ratios or amyloid PET positivity. Longitudinally, baseline MCI was associated with a higher risk of incident HI (Cox HR = 2.63, 95% CI 1.48–4.67; Fine–Gray sHR = 2.79, 95% CI 1.06–7.40), whereas baseline HI was not associated with subsequent CDR progression. Conclusions In older adults, HI was associated with MCI but not core AD biomarkers or future CDR progression, suggesting that late-life HI may reflect cognitive vulnerability or broader brain aging rather than independently driving AD pathology.

Background Black adults experience disproportionately higher rates of Alzheimer's disease and related dementias (ADRD), yet remain underrepresented in blood-based biomarker research. Understanding how plasma biomarkers relate to cognitive performance is essential for equitable detection and monitoring of ADRD. Objective We examined cross-sectional and longitudinal associations of both core and non-core plasma biomarkers and cognition in a community-based cohort of Black adults. Methods Participants from the ARCHES study completed baseline plasma biomarker assessments and neuropsychological testing, including a Preclinical Alzheimer Cognitive Composite (PACC) score and the Montreal Cognitive Assessment (MoCA). Plasma biomarkers reflecting amyloid, tau, neurodegeneration, and astrocytic activation were quantified using immunoprecipitation–mass spectrometry and ultrasensitive immunoassay platforms. Linear regression was used to evaluate cross-sectional associations between biomarkers and cognition. Linear mixed-effects models examined whether baseline biomarker levels were associated with cognitive change over one year, adjusting for age, sex, and education. Results The sample included 334 participants with a mean baseline age of 64.6 years (SD = 10.1; range, 45–92.9). Cross-sectionally, higher brain-derived phosphorylated tau 181 was associated with poorer MoCA score (p = 0.04), and neurofilament light chain (NfL) level was also associated with lower PACC score (p = 0.04). Longitudinally, higher baseline NfL and glial fibrillary acidic protein (GFAP) were associated with faster cognitive decline (p < 0.001 and p = 0.018). Conclusions Non-core NfL and GFAP biomarkers are associated with both cross-sectional and longitudinal cognitive performance. These findings highlight the importance of inclusive biomarker research and suggest non-core biomarkers may be particularly informative for characterizing cognitive aging and decline in this population.

Abstract INTRODUCTION Naturalistic driving provides real‐world behavioral indicators of early cognitive and functional changes. This study integrated naturalistic driving GPS trajectories collected from in‐vehicle sensors with points of interest (POIs) to quantify daily environmental engagement among older adults who were cognitively normal at enrollment. METHODS Data from 438 participants enrolled in the Driving Real‐world in‐Vehicle Evaluation System Project were used to generate daily POI share vectors and model latent engagement patterns using a logistic‐normal hidden Markov model (HMM). Thirteen latent states described distinct modes of environmental interaction. From each participant's inferred state sequence, we derived mobility features – state occupancy, dwell time, transition entropy, and self‐transition probability – and examined their differences across clinical status groups and associations with Preclinical Alzheimer's Cognitive Composite (PACC) performance. RESULTS Transition entropy and several state‐specific occupancy and dwelling metrics differed across clinical groups, but none of the mobility features were significantly associated with PACC scores. DISCUSSION Mobility‐derived behavioral features differentiate clinical status groups and may reflect early functional changes preceding cognitive decline.