Beiser, Alexa

Abstract Background Our objective is to examine the generalizability of the relationships between four MRI markers of CVD: DTI‐derived free water (FW), fractional anisotropy (FA), perivascular space (PVS) and white matter hyperintensities (WMH) volume, with vascular risk factors (VRF) and cognition in the baseline POINTER Imaging and Framingham Heart Study (FHS) cohorts. Method We studied 907 U.S. POINTER and 797 FHS Offspring participants with available baseline demographic, clinical and imaging data (Table 1). Continuous FW, FA, WMH (log‐transformed) and PVS measurements were regressed against total cranial volume (TCV). Cognitive outcomes included a Global Cognitive Composite and executive function composite score for POINTER and FHS. For each MRI marker, we used linear and logistic regressions to assess the effect of age, sex, hypertension, diabetes and smoking status, adjusting for ethnicity. We then assessed, for each cognitive outcome, the individual effect of MRI markers using separate linear regressions adjusted for demographic and clinical factors, as well as TCV‐adjusted gray matter volume to control for atrophy. Result POINTER and FHS differed across most demographic and biomarker characteristics (Table 1). Despite these differences, there were similar associations between demographic and VRF and the 4 MRI vascular markers in POINTER and FHS (Figure 1): increased age was associated with worse outcomes for the 4 markers, hypertension was associated with increased FW, PVS and lower FA, diabetes was associated with increased FW only, smoking status did not impact any of the four markers. Only exception was that female had lower FW and increased WMH in POINTER and increased PVS in FHS. Associations between CVD measures and cognition were also similar across cohorts: reduced cognitive performance was significantly associated with both decreased FA and increased FW (Figure 2), but not with WMH nor PVS. Conclusion Our study explores four imaging markers representing diverse mechanisms by which CVD could impact brain structure and function in the POINTER and FHS studies. Despite differences in cohort characteristics, we found mostly consistent CVD relationships with demographic, VRF, and cognitive measures between cohorts which supports the generalizability of the relation of these markers with VRF and cognition identified in the POINTER cohort.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

Background: Previous studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent. Objective: To study the cross-sectional associations of pulmonary function with structural brain variables. Methods: Data from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses. Results: FEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume. Conclusion: In cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.

Abstract Cerebral small vessel disease is a leading cause of stroke and a major contributor to cognitive decline and dementia, but our understanding of specific genes underlying the cause of sporadic cerebral small vessel disease is limited. We report a genome-wide association study and a whole-exome association study on a composite extreme phenotype of cerebral small vessel disease derived from its most common MRI features: white matter hyperintensities and lacunes. Seventeen population-based cohorts of older persons with MRI measurements and genome-wide genotyping (n = 41 326), whole-exome sequencing (n = 15 965), or exome chip (n = 5249) data contributed 13 776 and 7079 extreme small vessel disease samples for the genome-wide association study and whole-exome association study, respectively. The genome-wide association study identified significant association of common variants in 11 loci with extreme small vessel disease, of which the chr12q24.11 locus was not previously reported to be associated with any MRI marker of cerebral small vessel disease. The whole-exome association study identified significant associations of extreme small vessel disease with common variants in the 5′ UTR region of EFEMP1 (chr2p16.1) and one probably damaging common missense variant in TRIM47 (chr17q25.1). Mendelian randomization supports the causal association of extensive small vessel disease severity with increased risk of stroke and Alzheimer’s disease. Combined evidence from summary-based Mendelian randomization studies and profiling of human loss-of-function allele carriers showed an inverse relation between TRIM47 expression in the brain and blood vessels and extensive small vessel disease severity. We observed significant enrichment of Trim47 in isolated brain vessel preparations compared to total brain fraction in mice, in line with the literature showing Trim47 enrichment in brain endothelial cells at single cell level. Functional evaluation of TRIM47 by small interfering RNAs-mediated knockdown in human brain endothelial cells showed increased endothelial permeability, an important hallmark of cerebral small vessel disease pathology. Overall, our comprehensive gene-mapping study and preliminary functional evaluation suggests a putative role of TRIM47 in the pathophysiology of cerebral small vessel disease, making it an important candidate for extensive in vivo explorations and future translational work.

Abstract Introduction There is increasing interest in plasma amyloid beta (Aβ) as an endophenotype of Alzheimer's disease (AD). Identifying the genetic determinants of plasma Aβ levels may elucidate important biological processes that determine plasma Aβ measures. Methods We included 12,369 non‐demented participants from eight population‐based studies. Imputed genetic data and measured plasma Aβ1‐40, Aβ1‐42 levels and Aβ1‐42/Aβ1‐40 ratio were used to perform genome‐wide association studies, and gene‐based and pathway analyses. Significant variants and genes were followed up for their association with brain positron emission tomography Aβ deposition and AD risk. Results Single‐variant analysis identified associations with apolipoprotein E ( APOE ) for Aβ1‐42 and Aβ1‐42/Aβ1‐40 ratio, and BACE1 for Aβ1‐40. Gene‐based analysis of Aβ1‐40 additionally identified associations for APP , PSEN2 , CCK , and ZNF397 . There was suggestive evidence for interaction between a BACE1 variant and APOE ε4 on brain Aβ deposition. Discussion Identification of variants near/in known major Aβ‐processing genes strengthens the relevance of plasma‐Aβ levels as an endophenotype of AD.

AbstractBackgroundPlasma amyloid‐β (Aβ) peptide levels have been examined as a low‐cost accessible marker for risk of incident Alzheimer's disease (AD) and dementia, but results have varied between studies. We reassessed these associations in one of the largest, prospective, community‐based studies to date.MethodsA total of 2189 dementia‐free, Framingham Study participants aged >60 years (mean age, 72 ± 8 years; 56% women) had plasma Aβ1‐42 and Aβ1‐40 measured and were followed prospectively (mean, 7.6 ± 3.0 years) for dementia/AD.ResultsIncreased plasma Aβ1‐42 levels were associated with lower risk of dementia (Aβ1‐42: hazard ratio [HR] = 0.80 [0.71‒0.90], P < .001; Aβ1‐42‐to‐Aβ1‐40 ratio: HR = 0.86 [0.76‒0.98], P = .027) and AD (Aβ1‐42: HR = 0.79 [0.69‒0.90], P < .001; Aβ1‐42‐to‐Aβ1‐40 ratio: HR = 0.83 [0.72‒0.96], P = .012).ConclusionOur results suggest that lower plasma Aβ levels are associated with risk of incident AD and dementia. They encourage further evaluation of plasma Aβ levels as a biomarker for risk of developing clinical AD and dementia.

This article focuses on the effects of operational differences in case ascertainment on estimates of prevalence and incidence of cognitive impairment and/or dementia of the Alzheimer type. Experience and insights are discussed by investigators from the Framingham Heart Study, the East Boston Senior Health Project, the Chicago Health and Aging Project, the Mayo Clinic Study of Aging, the Baltimore Longitudinal Study of Aging, and the Aging, Demographics, and Memory Study. There is a general consensus that the single most important factor determining prevalence estimates of Alzheimer's disease (AD) is the severity of cognitive impairment used as a threshold to define cases. Studies that require a level of cognitive impairment in which persons are unable to provide self‐care will have much lower estimates than the studies aimed at identifying persons in the earliest stages of AD. There are limited autopsy data from the aforementioned epidemiological studies to address accuracy in the diagnosis of etiological subtype, namely the specification of AD alone or in combination with other types of pathology. However, other community‐based cohort studies show that many persons with mild cognitive impairment and also some persons without dementia or mild cognitive impairment meet pathological criteria for AD, thereby suggesting that the number of persons who would benefit from an effective secondary prevention intervention is probably higher than the published prevalence estimates. Improved accuracy in the clinical diagnosis of AD is anticipated with the addition of molecular and structural biomarkers in the next generation of epidemiological studies.