Objective To test the hypothesis that neighborhood-level disadvantage is associated with longitudinal measures of neurodegeneration and cognitive decline in an unimpaired cohort. Methods Longitudinal MRI and cognitive testing data were collected from 601 cognitively unimpaired participants in the Wisconsin Registry for Alzheimer9s Prevention Study and the Wisconsin Alzheimer9s Disease Research Center clinical cohort. Area Deprivation Index was geospatially determined based on participant residence geocode and ranked relative to state of residence. Linear regression models were fitted to test associations between neighborhood-level disadvantage and longitudinal change in cortical thickness and cognitive test performance. Mediation tests were used to assess whether neurodegeneration and cognitive decline were associated with neighborhood-level disadvantage along the same theoretical causal path. Results In our middle- to older-aged study population (mean baseline age 59 years), living in the 20% most disadvantaged neighborhoods (n = 19) relative to state of residence was associated with cortical thinning in Alzheimer signature regions (p = 0.002) and decline in the Preclinical Alzheimer9s Disease Cognitive Composite (p = 0.04), particularly the Trail-Making Test, part B (p < 0.001), but not Rey Auditory Verbal Learning Test (p = 0.77) or Story Memory Delayed Recall (p = 0.49) subtests. Associations were attenuated but remained significant after controlling for racial and demographic differences between neighborhood-level disadvantage groups. Cortical thinning partially mediated the association between neighborhood-level disadvantage and cognitive decline. Conclusions In this longitudinal study of cognitively unimpaired adults, living in the most highly disadvantaged neighborhoods was associated with accelerated degeneration in Alzheimer signature regions and cognitive decline. This study provides further evidence for neighborhood-level disadvantage as a risk factor for preclinical neurodegeneration and cognitive decline in certain populations. Limitations of the present study, including a small number of participants from highly disadvantaged neighborhoods and a circumscribed geographic setting, should be explored in larger and more diverse study cohorts.

Fragile X syndrome (FXS) is the most common inherited cause of autism and intellectual disability. The majority of FXS cases are caused by transcriptional repression of the FMR1 gene due to epigenetic changes that are not recapitulated in current animal disease models. FXS patient induced pluripotent stem cell (iPSC)-derived gene edited reporter cell lines enable novel strategies to discover reactivators of FMR1 expression in human cells on a much larger scale than previously possible. Here, we describe the workflow using FXS iPSC-derived neural cell lines to conduct a massive, unbiased screen for small molecule activators of the FMR1 gene. The proof-of-principle methodology demonstrates the utility of human stem-cell-based methodology for the untargeted discovery of reactivators of the human FMR1 gene that can be applied to other diseases.

Background: Frailty—feelings of weakness, fatigue, and lack of physiological resiliency—places older adults at increased risk for Alzheimer’s Disease (AD) and related dementias. A possible mechanism underlying these associations may be the effects of frailty on brain aging prior to old age.Method: A prospective observational study of approximately 502 community‐based men of predominantly European ancestry without dementia at baseline, from the Vietnam Era Twin Study of Aging (VETSA). Frailty index and MRI imaging were collected at three waves at average ages 56, 62, and 68. We created frailty index (FI) scores using 37 items from previously validated cumulative deficit model indices. Imaging measures included brain‐age (BA; Cole et al. 2018), as well as a validated thickness/hippocampal volume AD‐signature (CTS), and a novel gray matter mean diffusivity AD‐signature (MDS), both weighted averages of MRI‐based morphometry or diffusivity in eight AD‐related brain regions. Different longitudinal models were tested using structural equation modeling in Mplus. Models were adjusted for age, race/ethnicity, education, and smoking.Result: FI was highly correlated over time (range rs = 0.69 to 0.77), as were the brain signatures (range rs = 0.52 to 0.85). The best fitting model was the simplex/auto‐regressive model with correlated random intercepts. Figures 1‐3 show significant associations in the covariate‐adjusted models. FI is significantly associated with brain‐age and the cortical thickness/hippocampal volume AD‐signature random intercepts but not the mean diffusivity signature. Associations attenuate when smoking is added to the model. Moreover, when the cortical thickness/hippocampal volume and mean diffusivity AD‐signatures are adjusted for brain‐age then the associations further attenuate and are no longer significant. No cross‐lag associations were significant.Conclusion: The results are striking given the relatively young age of the sample. Across middle age, frailty was associated with worse brain age and a smaller cortical thickness/hippocampal volume AD‐signature. These associations do not appear to be causal, rather biological aging processes running in parallel. Combined with recent evidence showing associations between frailty and AD‐related biomarkers, the results suggest frailty is not just a geriatric disease and support the need for early intervention in biological aging.

Background: Cumulative deficit frailty (CDF) is a syndrome characterized by the accumulation of negative physical, functional and psychosocial insults. Accumulation of CDF and changes in brain structure both occur during the transition to older age and are associated with the development of Alzheimer’s disease (AD) and related dementias. However, how these phenomena are temporally related to each other during the aging process is not well understood. Methods: We examined bidirectional relationships between CDF and brain structure using structural MRI-based brain-predicted age difference and an AD brain signature. Longitudinal MRI and questionnaire-based data were collected from men in the Vietnam Era Twin Study of Aging at three study waves (average ages 56, 62, and 68). Results: Best-fitting longitudinal random intercepts cross-lagged panel models support the strong overall association between CDF and brain structural integrity with significant covariance between random intercepts for CDF and brain-predicted age difference (r = 0.36, p < .001) and between CDF and AD brain signature (r = −0.16, p = .005) after controlling for chronological age, smoking, race/ethnicity, and education. Significant bidirectional negative cross-lagged associations suggested attenuation over time of the association between CDF and nonspecific brain aging (βs = −0.33 to −0.23, p ≤ .012) but not between CDF and AD brain signature. Conclusions: CDF was associated with age-related brain structure via strong time-invariant common variance as well as weaker across-time factors. By contrast, the association between CDF and AD-related brain structure was via moderate common variance and was not temporally dynamic. Together, this suggests that CDF is differentially associated with age-related and AD-related brain changes and may be a clinically relevant risk factor for pathological brain aging.