Launer, Lenore J.

AbstractBackgroundBrain glucose hypometabolism is among the earliest pathogenic changes in Alzheimer’s disease (AD). This metabolic dysfunction points to the personal bioenergetic capacity, defined as the ability to maintain energy homeostasis under all circumstances including deregulated glucose uptake, as a potential source of resilience to the disease. Fasting blood acylcarnitine profiles are a central readout for this capacity in the absence of dietary glucose and capture the activity and efficiency of glucose‐independent routes of mitochondrial energy metabolism.MethodWe used fasting serum acylcarnitine profiles of 1,531 participants (465 with normal cognition, 762 with mild cognitive impairment, and 304 with clinical AD) from the AD Neuroimaging Initiative to perform unsupervised subgroup identification using hierarchical clustering. Identified subgroups were investigated for differences in A/T/N biomarker profiles and cognitive status. The contributions of genetic and potentially modifiable factors defining the subgroups were quantified using analysis of explained variance. The influence of the strongest determining factors on longitudinal cognitive trajectories was estimated using linear mixed‐effects models and gene‐by‐environment interaction analysis.ResultWe found several bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. The strongest genetic contribution to this bioenergetic endophenotype seems to be specifically linked to succinylcarnitine metabolism and significantly modulates the rate of future cognitive decline. In contrast, potentially modifiable sustainment of beta‐oxidation efficiency seems to decelerate bioenergetic aging, thus creating a bioenergetic reserve that delays progression of cognitive decline. Using gene‐by‐environment interaction analysis, we demonstrate that this molecular framework identifies a subgroup of individuals that is likely to benefit significantly from personalized therapeutic, dietary or lifestyle interventions tailored to increase resilience against bioenergetic disturbances in AD.ConclusionOur study reports on a set of genetic and metabolic markers that define bioenergetically distinct subgroups with significant differences on the AD biomarker and cognitive level. Longitudinal data suggest that targeting the modifiable fraction of this endophenotype might be a promising strategy to slow down disease progression in individuals with specific allelic configurations.

AbstractIntroductionWe evaluated whether better cardiovascular health at midlife and improvement of cardiovascular health within midlife were associated with dementia risk.MethodsTwo longitudinal population‐based studies were used: Atherosclerosis Risk in Communities (ARIC) (n = 11,460/visits at ages 54 and 60), and Age, Gene/Environment Susceptibility (AGES)‐Reykjavik (n = 3907/visit at age 51). A cardiovascular health score (range 0–12/0–14, depending on diet availability) including six/seven items was calculated at each visit, with weight assigned to each item as poor (0), intermediate (1), or ideal (2). Cardiovascular health was defined as low (score 0–4/0–5), intermediate (5–7/6–9), or high (8–12/10–14). Incident dementia was ascertained through linkage to health records and with neuropsychological examinations.ResultsMidlife high compared to low cardiovascular health (hazard ratios [HRs]: for ARIC: 0.60 [95% confidence interval: 0.52, 0.69]); for AGES‐Reykjavik: 0.83 [0.66, 0.99] and improvement of cardiovascular health score within midlife (HR per one‐point increase: ARIC: 0.94 [0.92, 0.96]) were associated with lower dementia risk.DiscussionBetter cardiovascular health at midlife and improvement of cardiovascular health within midlife are associated with lower dementia risk.Highlights Cardiovascular health and dementia were studied in two large cohort studies. Better cardiovascular health at midlife relates to lower dementia risk. Improvement of cardiovascular health within midlife relates to lower dementia risk. Promotion of cardiovascular health at midlife can help to reduce dementia risk.

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.

AbstractBackgroundHypertension is a major risk factor for cardiovascular and cerebrovascular disease including stroke, small vessel disease, and dementia. The Systolic blood PRessure INTervention (SPRINT) randomized trial prospectively evaluated the effects of intensive systolic blood pressure (SBP) control (target SBP<120mmHg) versus standard control (SBP<140mmHg) on cardiovascular outcomes in individuals without baseline diabetes or stroke, and included cerebral blood flow (CBF) measurements. Previous studies with much smaller sample sizes and shorter duration suggested stable CBF in response to intensive treatment (SBP<125mmHg) [1], but long term effects are unknown. We evaluated the long‐term effects of intensive blood pressure treatment on CBF.MethodWhole brain CBF was measured at 3T using pseudocontinuous arterial spin labeled (ASL) perfusion MRI at baseline and after 3.9±0.3 years. Total white matter lesion (WML) load was measured from Fluid Attenuated Inversion Recovery T2 MRI. Data from 324 subjects with adequate ASL scan quality acquired from 6 sites were included.ResultDemographics and scanner specific CBF values are provided in Table 1. The intensive treatment group was older (p=0.01), but was otherwise similar to the standard group at baseline. There was a significant effect on the longitudinal CBF change (p=0.006) of the intensive treatment (5.7% increase) compared to the standard treatment (4.0% decrease, Fig 1). Secondary analysis showed a significant increase in age‐adjusted fractional CBF for the intensive treatment group (p<0.001) with no change (p=0.24) in the standard treatment group. Change in CBF was not associated with change in WML.ConclusionSustained intensive antihypertensive treatment does not reduce whole brain CBF, in fact it showed an increase in whole brain CBF relative to standard therapy. While improved CBF might contribute to the reduced WML progression observed in the intensive‐therapy group versus standard therapy in SPRINT[2], we did not observe a direct correlation between CBF changes and WML changes. References: (1) Croall et al. JAMA‐Neurol,2018; (2) Nasrallah et al., JAMA‐Neurol, 2019.

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS