Explore the vast range of titles available.

The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and \"asymptomatic Alzheimer's disease\" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

Evidence suggests that long-term exposure to air pollution may increase the risk of dementia and related cognitive outcomes. A major source of air pollution is automotive traffic, which is modifiable by technological and regulatory interventions. We examined associations of four traffic-related air pollutants with rates of cognitive decline in a cohort of older adults. We analyzed data from the Chicago Health and Aging Project (CHAP), a longitudinal (1993-2012) community-based cohort study of older adults that included repeated assessments of participants' cognitive performance. Leveraging previously developed air pollution models, we predicted participant-level exposures to the tailpipe pollutants oxides of nitrogen ( ) and nitrogen dioxide ( ), plus the nontailpipe pollutants copper and zinc found in coarse particulate matter [PM with aerodynamic diameter to ( ) and , respectively], over the 3 y prior to each participant's baseline assessment. Using generalized estimating equations, we estimated covariate-adjusted associations of each pollutant with rates of cognitive decline. We probed the robustness of our results via several sensitivity analyses, including alterations to the length of the exposure assessment window and exploring the influence of pre- and post-baseline selection bias. Using data from 6,061 participants, estimated associations of these pollutant exposures with cognitive decline were largely inconsistent with large adverse effects. For example, a standard deviation ( ) increment in corresponded to a slightly slower rate of cognitive decline [e.g., mean difference in change in global score, 0.010 standard unit/5 y, 95% confidence interval (CI): , 0.036]. The results of most of our sensitivity analyses were in generally similar to those of our main analyses, but our prebaseline selection bias results suggest that our analytic results may have been influenced by differential survivorship into our study sample. In this large prospective cohort study, we did not observe compelling evidence that long-term TRAP exposure is associated with cognitive decline. https://doi.org/10.1289/EHP14585.

INTRODUCTION Loneliness has a rising public health impact, but research involving neuropathology and representative cohorts has been limited. METHODS Inverse odds of selection weights were generalized from the autopsy sample of Rush Alzheimer's Disease Center cohorts (N = 680; 89 ± 9 years old; 25% dementia) to the US‐representative Health and Retirement Study (N = 8469; 76 ± 7 years old; 5% dementia) to extend external validity. Regressions tested cross‐sectional associations between loneliness and (1) Alzheimer's disease (AD) and cerebrovascular pathology; (2) five cognitive domains; and (3) relationships between pathology and cognition, adjusting for depression. RESULTS In weighted models, greater loneliness was associated with microinfarcts, lower episodic and working memory in the absence of AD pathology, lower working memory in the absence of infarcts, a stronger association of infarcts with lower episodic memory, and a stronger association of microinfarcts with lower working and semantic memory. DISCUSSION Loneliness may relate to AD through multiple pathways involving cerebrovascular pathology and cognitive reserve. Highlights Loneliness was associated with worse cognition in five domains. Loneliness was associated with the presence of microinfarcts. Loneliness moderated cognition–neuropathology associations. Transportability methods can provide insight into selection bias.

Background Several large, representative studies of older adults, like the US Health and Retirement Study (HRS), have collected longitudinal cognitive data on their participants; however, the cognitive batteries used by the HRS have changed over time, including the introduction of the Harmonized Cognitive Assessment Protocol (HCAP) battery for a subset of HRS participants. When cognitive batteries change over time, changes in measurement properties might result in misleading findings about cognitive trajectories. The aim of this study was to use simulation methods to assess the magnitude of bias in estimating cognitive decline that is induced by test batteries that changed over time. Method We simulated true cognition using non‐linear models of cognitive change derived from four harmonized longitudinal cognitive aging studies (UC Davis Alzheimer’ Disease Research Center longitudinal cohort, Kaiser Healthy Aging and Diverse Life Experiences study, Kaiser Study of Healthy Aging in African Americans, Kaiser Life After 90 Study). Empirical HRS‐HCAP item parameters were used as true item parameters, and item response theory methods were used to simulate measured test results for different batteries of cognitive tests in HRS and HRS‐HCAP. We estimated “blended” cognitive trajectories, artificially introducing mid‐course changes of the simulated test used to measure cognition. To illustrate the impact of these changes, we then used linear mixed‐effects models to estimate 11‐year cognitive trajectories, overall and by quartile of true decline. Result Using instruments with the highest measurement precision led to estimated cognitive trajectories that best matched the truth. At the same time, estimated trajectories using less informative test versions were very closely related (Figure 1), such that blended trajectories did not deviate from the truth substantially (Figure 2). Conclusion Our results support the use of high‐quality instruments, like the HCAP battery, for optimally studying cognitive trajectories. However, differences between estimated HCAP and HRS‐TICS trajectories were small, suggesting that, in practice, changes in test batteries over time may not meaningfully affect estimates of cognitive decline.

Several large, representative studies of older adults, like the US Health and Retirement Study (HRS), have collected longitudinal cognitive data on their participants; however, the cognitive batteries used by the HRS have changed over time, including the introduction of the Harmonized Cognitive Assessment Protocol (HCAP) battery for a subset of HRS participants. When cognitive batteries change over time, changes in measurement properties might result in misleading findings about cognitive trajectories. The aim of this study was to use simulation methods to assess the magnitude of bias in estimating cognitive decline that is induced by test batteries that changed over time. We simulated true cognition using non-linear models of cognitive change derived from four harmonized longitudinal cognitive aging studies (UC Davis Alzheimer' Disease Research Center longitudinal cohort, Kaiser Healthy Aging and Diverse Life Experiences study, Kaiser Study of Healthy Aging in African Americans, Kaiser Life After 90 Study). Empirical HRS-HCAP item parameters were used as true item parameters, and item response theory methods were used to simulate measured test results for different batteries of cognitive tests in HRS and HRS-HCAP. We estimated \"blended\" cognitive trajectories, artificially introducing mid-course changes of the simulated test used to measure cognition. To illustrate the impact of these changes, we then used linear mixed-effects models to estimate 11-year cognitive trajectories, overall and by quartile of true decline. Using instruments with the highest measurement precision led to estimated cognitive trajectories that best matched the truth. At the same time, estimated trajectories using less informative test versions were very closely related (Figure 1), such that blended trajectories did not deviate from the truth substantially (Figure 2). Our results support the use of high-quality instruments, like the HCAP battery, for optimally studying cognitive trajectories. However, differences between estimated HCAP and HRS-TICS trajectories were small, suggesting that, in practice, changes in test batteries over time may not meaningfully affect estimates of cognitive decline.

Abstract Studies investigating the effectiveness of intervention programs on cognitive ability in older adults are equivocal; however, these studies generally focus on traditional measures of cognition, and therefore may miss some improvements by not utilizing alternate measures. We evaluate the potential for intraindividual variability in cognitive speed (IIV), a demonstrated sensitive indicator of cognitive functioning, to be used as an index of cognitive plasticity from an intervention. The current study evaluated whether older adults in a school volunteering program showed a reduction in IIV, compared to a low-activity control group over two years of exposure. Non-demented aging older adults (n = 336) participated in the Baltimore Experience Corps Trial, an evaluation of a volunteering program conducted at elementary schools designed to increase older adults’ physical, cognitive, and social engagement. Participants completed a cognitive battery that included a computerized Stroop task at baseline and after 12 and 24 months. Participants who complied at the 80th percentile or above showed a significant reduction in IIV at 24 months, with an additional trend of improved IIV with increased compliance to the treatment protocol, both at 12 months, and at 24 months. Men specifically also showed significant dose-dependent improvements after 12 months. The Experience Corps program resulted in an improvement in cognitive performance as measured by IIV. Analyzing previously collected data with non-traditional measures of cognition, such as IIV, may be a potentially fruitful and cost-effective method for understanding how interventions impact cognition in aging populations.

Causal mediation analysis is a useful tool for epidemiological research, but it has been criticized for relying on a \"cross-world\" independence assumption that is empirically difficult to verify and problematic to justify based on background knowledge. In the present article we aim to assist the applied researcher in understanding this assumption. Synthesizing what is known about the cross-world independence assumption, we discuss the relationship between assumptions for causal mediation analyses, causal models, and non-parametric identification of natural direct and indirect effects. In particular we give a practical example of an applied setting where the cross-world independence assumption is violated even without any post-treatment confounding. Further, we review possible alternatives to the cross-world independence assumption, including the use of computation of bounds that avoid the assumption altogether. Finally, we carry out a numerical study in which the cross-world independence assumption is violated to assess the ensuing bias in estimating natural direct and indirect effects. We conclude with recommendations for carrying out causal mediation analyses.

In this guide, we present how to perform constraint-based causal discovery using three popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We focus on how these packages can be used with observational data and in the presence of mixed data (i.e., data where some variables are continuous, while others are categorical), a known time ordering between variables, and missing data. Throughout, we point out the relative strengths and limitations of each package, as well as give practical recommendations. We hope this guide helps anyone who is interested in performing constraint-based causal discovery on their data.

Previously the owner of that house would trade on up the chain. But with so many people buying a repossessed house there is no movement upwards. Property priced up to Pounds 175,000 is selling but anything above Pounds 250,000 is slower.

Previously the owner of that house would trade on up the chain. But with so many people buying a repossessed house there is no movement upwards. Property priced up to Pounds 175,000 is selling but anything above Pounds 250,000 is slower.