Ganesh M. Babulal

Depression and Alzheimer's disease (AD) frequently overlap clinically, and depressive symptoms may signal major depressive disorder, a risk factor for AD, or an early manifestation of neurodegeneration. This scoping review mapped biomarker evidence differentiating depression and AD within and beyond the amyloid/tau/neurodegeneration (A/T/N) framework. Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews guidelines, MEDLINE, Embase, and Scopus were searched for studies published between 2010 and 2025 examining biomarkers in depression and AD. Eligible studies included clinical diagnoses of AD and/or depression and reported biomarker measures. Forty‐eight studies met inclusion criteria. Amyloid biomarkers were most consistently associated with clinically diagnosed AD, whereas findings for other biomarkers were more heterogeneous. Neurodegeneration markers, including hippocampal atrophy, were commonly associated with AD, while vascular abnormalities and altered functional connectivity were more frequently linked to depressive symptoms. Overall, AD and depression demonstrated partially distinct but overlapping biomarker profiles, highlighting the need for longitudinal studies using scalable biomarkers to clarify causal and temporal relationships.

Objectives: Early population-based screening for Alzheimer’s disease remains limited by the scalability and accessibility of conventional biomarker testing. We tested whether passive naturalistic driving changes predict progression from cognitively normal status to sustained mild cognitive impairment. Methods: Season-adjusted monthly driving data from 298 older adults, including 25 progressors, were summarized into within-person trajectory features capturing drift and instability in cornering and destination diversity relative to a six-month baseline. A GEE logistic regression was evaluated under leave-one-out cross-validation with a paired-consecutive flagging rule. Landmark survival analyses at 12 and 24 months assessed prognostic value. Five held-out clinical subgroups (n = 82 evaluable), plasma pTau217, MoCA trajectories, and depression scores provided independent post hoc validation. Results: The model detected 19/25 progressors (AUC=0.752, 95%CI: 0.627–0.863; 76% sensitivity; 75% specificity) with an 18-month median lead time before sustained CDR conversion. Adding static baseline driving features reduced discrimination (AUC=0.702), supporting that within-person driving change, not absolute level, carries a predictive signal. Driving markers stratified risk at 12 months (HR = 6.02, 95%CI: 2.35–15.41) and 24 months (HR = 13.38, 95%CI: 3.03–59.04). Signals converged or were directionally consistent with higher pTau217 among detected progressors and clinically ambiguous single-endpoint cases, whereas flagged stable participants showed MoCA decline without pTau217 enrichment. Depressive symptoms did not explain the driving signal (Spearman rho=0.05, p=0.53). The sustained conversion rate (12.3/1,000 person-years) matched epidemiological expectations. Discussion: Scalable driving-trajectory features can detect preclinical functional decline with clinically meaningful lead time, without demographic, genetic, or cognitive data, and may serve as an objective neurobehavioral screening tool.

Background: African American adults experience a disproportionate burden of dementia, shaped by structural and clinical factors that remain unclear. Objective: To quantify associations between multimorbidity, polypharmacy, and cognition in the context of neighborhood disadvantage. Methods: This study is a cross-sectional analysis of 348 African American adults aged 45–92 years (mean 64.3 years; 73.6% female) from the Aging Research Characterizing Health Exposome via Social drivers (ARCHES) cohort. Comorbidity burden was assessed using a modified Charlson Comorbidity Index (CCI), polypharmacy as ≥5 medications, and neighborhood disadvantage using the Area Deprivation Index (ADI). Linear regression estimated associations with a Preclinical Alzheimer’s Cognitive Composite (PACC). The polypharmacy model included modified CCI as a covariate, and effect modification by ADI was assessed using an interaction term between polypharmacy and ADI. Results: Higher modified CCI was associated with lower cognitive performance (B = −0.071; 95% CI: −0.130, −0.012). Polypharmacy was not independently associated with cognition after modified CCI adjustment (B = −0.081; β = −0.057; p = 0.257). No significant ADI interaction was found (p = 0.064).

Background: Systemic inflammation has been implicated in cognitive aging and neurodegeneration; however, inflammatory biomarkers are expressed in coordinated patterns rather than as isolated markers. Objective: To identify latent inflammatory biomarker groupings and evaluate their associations with cognitive performance among midlife and older adults. Methods: This cross-sectional study included 334 participants from the Aging Research Characterizing Health Exposome via Social Drivers (ARCHES) study. Cognitive performance was assessed using the Preclinical Alzheimer Cognitive Composite (PACC). Plasma inflammatory biomarkers were quantified using the NuLISA™ multiplex immunoassay platform. Exploratory factor analysis (EFA) (minimum residual extraction, oblimin rotation) identified latent inflammatory factors, retaining biomarkers with loadings ≥0.40. Factor scores were evaluated in multivariable linear regression models adjusted for age, sex, education, and genotype status; sensitivity analyses adjusted for socioeconomic context, medication use, BMI, lifestyle factors, and clinical diagnoses. Age-stratified and nonlinear spline analyses were conducted. Results: 27 of 43 biomarkers formed an eight-factor structure explaining 43% of variance. Two factors were significantly associated with cognitive performance after FDR correction. Factor 4 (CCL4, CXCL1, S100A12) and Factor 5 (IL2, IL5, IL13, IL10, CSF2) were inversely associated with PACC scores. These associations remained consistent across sensitivity analyses. Age-stratified analyses showed that several inflammatory factors were associated with cognitive performance among participants aged 45–<65 years, whereas no factors remained significant among participants aged ≥65 years after FDR correction. Nonlinear modeling indicated a non-linear age-cognitive performance relationship. Conclusions: EFA identified clusters of correlated inflammatory biomarkers associated with cognitive performance in this cohort of midlife and older adults.

Subjective cognitive decline (SCD) refers to cognitive concerns that may occur with or without objective impairment on standardized testing. Studies suggest that SCD may be an early clinical marker of Alzheimer's disease and related dementias, and that it can predict future objective cognitive decline and progression to dementia. However, the research that supports these conclusions is primarily based on samples with homogeneous socioeconomic and cultural backgrounds. The limited studies in samples with more diverse representations of racial, ethnic, gender, and sexual orientation characteristics show varying SCD prevalence, correlates, and outcomes. Here, we review this literature, focusing on how to apply the findings to advance our understanding of SCD. We further evaluate how our findings inform a roadmap for future research. Overall, we conclude that broader investigations across more diverse populations enhance our understanding of the factors that may differentially contribute to SCD and shape its utility as a clinical metric.

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.

Driving is a complex task that can be compromised by mood disorders and neurodegenerative conditions. Major depressive disorder (MDD) and Alzheimer’s disease (AD) increase in prevalence with older age. Preclinical AD, identified through protein biomarkers, is strongly associated with an increased risk of developing AD. This study tracked the daily driving behaviors of older adults over an average period of 54 months and found participants with MDD and Positron Emission Tomography (PET) biomarker tau had a faster increase in risky driving behaviors, such as hard braking, and a slower decline in randomness of driving patterns, compared to healthy controls. Notably, participants with MDD were more likely to exhibit PET tau positivity than PET amyloid positivity, suggesting a potential interaction between MDD and tau pathology. These findings highlight the importance of monitoring older drivers for emerging functional vulnerability associated with mood disorders and neurodegeneration at an early stage.

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.

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.