Faculty Bibliography

BACKGROUND:This study examined the relationships between resilience and sleep disturbance in a diverse sample of older women with a history of hypertension and whether this relationship is moderated by individuals' race/ethnicity. METHODS:Sample includes 700 females from a community-based study in Brooklyn, New York with a mean age of 60.7 years (SD=6.52). Of the participants, 28.1% were born in the U.S.; 71% were African-descent, 17.4% were European and 11.6% were Hispanics descents. Data were gathered on demographics and sleep disturbance using the Comprehensive Assessment and Referral Evaluation (CARE) and the Stress Index Scale (SIS). Resilience Factors were assessed with both the Index of Self-Regulation of Emotion (ISE) and religious health beliefs. Chi-Square, Anova, Student t-tests, and multilinear regression analysis were conducted to explore associations between resilience factors and sleep disturbance. Associations between resilience factors and sleep disturbance were examined using stratified multilinear regression analysis in three models by race/ethnicity. Regression models was conducted examining the interaction between resilience factors and stress RESULTS: Resilience factor, ISE emerged as the strongest independent predictor of sleep disturbance [B(SE) = -0.368(0.008); p < .001] for African descents. ISE was not a significant predictor of sleep disturbance among Hispanic participants [B(SE) = -0.218(0.022);p = .052], however interaction effect analysis revealed that stress level moderates significantly the relationship between ISE, and their sleep disturbance [B(SE) = 0.243(0.001);p = .036]. CONCLUSIONS:Results of our study suggest that resilience factors might be a more important protective factor for sleep disturbance among diverse older females.

Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.

Aims/hypothesis/UNASSIGNED:Midlife obesity is a risk factor for dementia. We investigated the impact of obesity on brain structure, metabolism, and cerebrospinal fluid (CSF) core Alzheimer's disease (AD) biomarkers in healthy elderly. Methods/UNASSIGNED:We selected controls from ADNI2 with CSF AD biomarkers and/or fluorodeoxyglucose positron emission tomography (FDG-PET) and 3T-MRI. We measured cortical thickness, FDG uptake, and CSF amyloid beta (Aβ)1-42, p-tau, and t-tau levels. We performed regression analyses between these biomarkers and body mass index (BMI). Results/UNASSIGNED:). Higher BMI was related to less cortical thickness and higher metabolism in brain areas typically not involved in AD (family-wise error [FWE] <0.05), but not to AD CSF biomarkers. It is notable that the impact of obesity on brain metabolism and structure was also found in amyloid negative individuals. Conclusions/interpretation/UNASSIGNED:In the cognitively unimpaired elderly, obesity has differential effects on brain metabolism and structure independent of an underlying AD pathophysiology.

Mounting evidence shows a disproportionate COVID-19 burden among Blacks. Early findings indicate pre-existing metabolic burden (eg, obesity, hypertension and diabetes) as key drivers of COVID-19 severity. Since Blacks exhibit higher prevalence of metabolic burden, we examined the influence of metabolic syndrome on disparate COVID-19 burden. We analyzed data from a NIH-funded study to characterize metabolic burden among Blacks in New York (Metabolic Syndrome Outcome Study). Patients (n=1035) were recruited from outpatient clinics, where clinical and self-report data were obtained. The vast majority of the sample was overweight/obese (90%); diagnosed with hypertension (93%); dyslipidemia (72%); diabetes (61%); and nearly half of them were at risk for sleep apnea (48%). Older Blacks (age≥65 years) were characterized by higher levels of metabolic burden and co-morbidities (eg, heart disease, cancer). In multivariate-adjusted regression analyses, age was a significant (p≤.001) independent predictor of hypertension (OR=1.06; 95% CI: 1.04-1.09), diabetes (OR=1.03; 95% CI: 1.02-1.04), and dyslipidemia (OR=0.98; 95% CI: 0.97-0.99), but not obesity. Our study demonstrates an overwhelmingly high prevalence of the metabolic risk factors related to COVID-19 among Blacks in New York, highlighting disparate metabolic burden among Blacks as a possible mechanism conferring the greater burden of COVID-19 infection and mortality represented in published data.

Background and Objectives/UNASSIGNED:Sleep difficulties are common among older adults and are associated with cognitive decline. We used data from a large, nationally representative longitudinal survey of adults aged older than 50 in the United States to examine the relationship between specific sleep difficulties and cognitive function over time. Research Design and Methods/UNASSIGNED:= 16 201). Sleep difficulty measures included difficulty initiating sleep, nocturnal awakenings, early morning awakenings, and waking up feeling rested from rarely/never (1) to most nights (3). The modified Telephone Interview for Cognitive Status was used to measure cognitive function. Generalized linear mixed models were used with time-varying covariates to examine the relationship between sleep difficulties and cognitive function over time. Results/UNASSIGNED:< .05). Discussion and Implications/UNASSIGNED:Our findings highlight an association between early morning awakenings and worse cognitive function, but also an association between waking up feeling rested and better cognitive function over time.