Search

INTRODUCTION: Depressive symptoms are associated with higher risk of dementia, but how they impact cognition in diverse populations is unclear. METHODS: Asian, Black, Latino, or White participants (n = 2227) in the Kaiser Healthy Aging and Diverse Life Experiences (age 65+) and the Study of Healthy Aging in African Americans (age 50+) underwent up to three waves of cognitive assessments over 4 years. Multilevel models stratified by race/ethnicity were used to examine whether depressive symptoms were associated with cognition or cognitive decline and whether associations differed by race/ethnicity. RESULTS: Higher depressive symptoms were associated with lower baseline verbal episodic memory scores (−0.06, 95% CI: −0.12, −0.01; −0.15, 95% CI: −0.25, −0.04), and faster decline annually in semantic memory (−0.04, 95% CI: −0.07, −0.01; −0.10, 95% CI: −0.15, −0.05) for Black and Latino participants. Depressive symptoms were associated with lower baseline but not decline in executive function. DISCUSSION: Depressive symptoms were associated with worse cognitive outcomes, with some evidence of heterogeneity across racial/ethnic groups. HIGHLIGHTS: We examined whether baseline depressive symptoms were differentially associated with domain‐specific cognition or cognitive decline by race/ethnicity.Depressive symptoms were associated with worse cognitive scores for all racial/ethnic groups across different domains examined.Higher depressive symptoms were associated with faster cognitive decline for semantic memory for Black and Latino participants.The results suggest a particularly harmful association between depressive symptoms and cognition in certain racial/ethnic groups. [ABSTRACT FROM AUTHOR]

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain‐wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman‐rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103–0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043–0.068), while the proportional approach showed a decrease of −0.025 (95% CI: −0.035 to −0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cancer survivors are less likely than comparably aged individuals without a cancer history to develop Alzheimer's disease and related dementias (ADRD). METHODS: In the UK Biobank, we investigated associations between cancer history and five structural magnetic resonance imaging (MRI) markers for ADRD risk, using linear mixed‐effects models to assess differences in mean values and quantile regression to examine whether associations varied across the distribution of MRI markers. RESULTS: Cancer history was associated with smaller mean hippocampal volume (b = ‐19 mm3, 95% CI = ‐36, ‐1) and lower mean cortical thickness in the Alzheimer's disease signature region (b = ‐0.004 mm, 95% CI = ‐0.007, ‐0.000). Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history. DISCUSSION: Some brain MRI markers associated with ADRD risk were elevated in adults with a history of cancer. The magnitude of the adverse associations varied across quantiles of neuroimaging markers, and the pattern suggests possible harmful associations for individuals already at high ADRD risk. Highlights: We found no evidence of an inverse association between cancer history and ADRD‐related neurodegeneration.Cancer history was associated with smaller mean hippocampal volume and lower mean cortical thickness in the Alzheimer's disease signature region.Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history. [ABSTRACT FROM AUTHOR]

Background: Rapid developments in Alzheimer's disease (AD) biomarker research suggest predictive testing may soon become a feasible option available to the public. Future reduction of racial and ethnic disparities in AD requires understanding of potential differences in drivers of interest in AD diagnostic testing. Experiences of discrimination may influence attitude toward AD testing, particularly among racially and ethnically minoritized populations. This study examines whether everyday or lifetime discrimination shapes interest in AD predictive testing. Method: Data were collected from 1,472 participants in the 2010 and 2012 waves of the Health and Retirement Study who answered a module on interest in receiving a free test that could tell whether they would develop AD in the future, along with measures of Everyday Discrimination and Lifetime Discrimination. Logistic models were controlled for deciles of propensity scores (estimated using demographic and socioeconomic variables) and odds ratios were re‐expressed as risk differences (RD). The RD is the difference in the probability a person expressed interest in receiving a future AD predictive test based on past experiences of discrimination. Result: The mean age of the sample was 67 (SD = 10.2), 65.9% were white, and 80% endorsed interest in AD predictive testing. Most (64.1%) participants indicated experiencing everyday discrimination in at least one domain, and 34.8% experienced major lifetime discrimination in at least one domain. Those interested in predictive testing were younger (66 vs 70 years) and more likely to be Black (20% vs 15%) or Latinx (14% vs 8%) than participants uninterested in testing. The probability of wanting an AD test was not associated with discrimination experiences for Black (RD everyday discrimination = 0.104, p = 0.285; RD lifetime discrimination = ‐0.038; p = 0.626) or Latinx (RD everyday discrimination = ‐0.003, p = 0.974; RD lifetime discrimination = ‐0.053; p = 0.692) participants. Conclusion: Despite historical and contemporary experiences of discrimination, Black and Latinx individuals express interest in AD testing. This finding implicates structural and systemic barriers, such as systemic racism and institutional gaps in access to clinical research, as likely explanations for underrepresentation of racially and ethnically minoritized persons in research on AD testing. [ABSTRACT FROM AUTHOR]

Background: Gentrification, characterized by in‐migration of high socioeconomic (SES) residents into formerly disinvested neighborhoods, may impair late‐life cognitive function of existing residents. We hypothesized that disinvestment of neighborhood economic resources and reinvestment via gentrification both increase risk of cognitive decline among marginalized older adults. Method: We analyzed 17,613 Health and Retirement Study participants (aged 51‐101), 67% Non‐Hispanic White) with data on our outcomes of interest: biennial scores on immediate and delayed word recall batteries (2010‐2016). With 2000 Census data, we operationalized census tract disinvestment based on disproportionately high proportions of minority residents, low SES, and renters for the corresponding county. We defined gentrification among disinvested tracts as greater than county‐median increases in White and college‐educated residents, household income, and housing costs between the 2000 Census and 2008‐2012 American Community Survey. We fit linear mixed models to evaluate associations of gentrification with cognitive level and change. We also evaluated racial‐ethnic differences in cognitive function by residence in tracts that were 1) not disinvested in 2000, 2) remained disinvested, or 3) gentrified by 2012. All models were adjusted by individual‐level age, gender, race‐ethnicity, educational attainment, marital status, home ownership, health insurance coverage, and memory score practice effects. Result: 56% of participants lived tracts that were disinvested in 2000; by 2012, 34% of disinvested tracts gentrified. Baseline memory performance was worse in invested (e.g., mean immediate = 4.9) than in initially disinvested tracts that either gentrified (mean = 5.3) or remained disinvested (mean = 5.4) (p<0.02 for test of mean differences). Neighborhood disinvestment from 2000‐2012 compared to investment in 2000 was associated with better 6‐year immediate recall trajectories among White participants (β; 95% CI: 0.18; 0.01, 0.39) but not among their Hispanic/Latino counterparts (‐0.14; ‐0.29, ‐0.01; p‐value for interaction compared to White participants = 0.04). We observed similar patterns for delayed recall (p‐value for interaction comparing Hispanic/Latino to White participants = 0.05). Neighborhood disinvestment or gentrification was not associated with recall trajectories among Black participants. Conclusion: Differential findings by race in this nationally representative sample indicate that disinvestment and gentrification do not equally impact older adult cognitive health. [ABSTRACT FROM AUTHOR]

Background: Understanding determinants of cognitive decline relies on several modeling assumptions. Alternative longitudinal modeling strategies–such as accounting for aging using time‐on‐study (estimated based on within‐person comparisons), baseline age (estimated based on between‐person comparisons), or current age (estimated based on both within‐ and between‐person comparisons) –may produce qualitatively different results. Using simulations, we evaluated robustness of effect estimates for predictors of cognitive decline with current age (vs. time‐on‐study) as the timescale, in the context of practice and/or cohort effects. Method: Our simulation strategy approximates the data structures of several cohort studies of cognitive aging (figure 1). For scenarios with only practice effects and with both practice and cohort effects, we performed 1,000 simulations of a cohort of 2,000 individuals with annual cognitive assessments over 3 years. Longitudinal models were fit using three timescale specifications (with and without adjustment for practice effects for each): (1) time‐on‐study adjusted for baseline age; (2) current age without adjustment for baseline age; and (3) current age adjusted for baseline age. We evaluated the bias in estimated effect associated with each specification by comparing to known simulation parameters. Result: When practice effects were present and not accounted for, time‐on‐study and current age underestimated rate of cognitive decline (figure 2) but bias was smallest for current age as the timescale. When cohort effects were present, current age as the timescale led to biased estimates when baseline age was not adjusted for. With a correctly specified model, the variance in the estimate of the coefficient for current age was 36.3% smaller than the variance in the estimate of the coefficient for time‐on‐study with baseline adjustment. All estimates for the effect of education on cognitive decline were unbiased regardless of timescale, but 95% CIs were narrowest for current age as the timescale. Conclusion: In this simulation, current age as the timescale that combines within‐ and between‐person estimates led to more precise estimates than time‐on‐study with baseline age adjustment. Failure to account for practice effects can bias estimates of longitudinal within‐person age effects. [ABSTRACT FROM AUTHOR]