Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new... Show moreCoronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC. Show less
Background and ObjectivesIt is important to identify at what age brain atrophy rates in genetic frontotemporal dementia (FTD) start to accelerate and deviate from normal aging effects to find the... Show moreBackground and ObjectivesIt is important to identify at what age brain atrophy rates in genetic frontotemporal dementia (FTD) start to accelerate and deviate from normal aging effects to find the optimal starting point for treatment. We investigated longitudinal brain atrophy rates in the presymptomatic stage of genetic FTD using normative brain volumetry software.MethodsPresymptomatic GRN, MAPT, and C9orf72 pathogenic variant carriers underwent longitudinal volumetric T1-weighted magnetic resonance imaging of the brain as part of a prospective cohort study. Images were automatically analyzed with Quantib (R) ND, which consisted of volume measurements (CSF and sum of gray and white matter) of lobes, cerebellum, and hippocampus. All volumes were compared with reference centile curves based on a large population-derived sample of nondemented individuals (n = 4,951). Mixed-effects models were fitted to analyze atrophy rates of the different gene groups as a function of age.ResultsThirty-four GRN, 8 MAPT, and 14 C9orf72 pathogenic variant carriers were included (mean age = 52.1, standard deviation = 7.2; 66% female). The mean follow-up duration of the study was 64 +/- 33 months (median = 52; range 13-108). GRN pathogenic variant carriers showed a faster decline than the reference centile curves for all brain areas, though relative volumes remained between the 5th and 75th percentiles between the ages of 45 and 70 years. In MAPT pathogenic variant carriers, frontal lobe volume was already at the 5th percentile at age 45 years and showed a further decline between the ages 50 and 60 years. Temporal lobe volume started in the 50th percentile at age 45 years but showed fastest decline over time compared with other brain structures. Frontal, temporal, parietal, and cerebellar volume already started below the 5th percentile compared with the reference centile curves at age 45 years for C9orf72 pathogenic variant carriers, but there was minimal decline over time until the age of 60 years.DiscussionWe provide evidence for longitudinal brain atrophy in the presymptomatic stage of genetic FTD. The affected brain areas and the age after which atrophy rates start to accelerate and diverge from normal aging slopes differed between gene groups. These results highlight the value of normative volumetry software for disease tracking and staging biomarkers in genetic FTD. These techniques could help in identifying the optimal time window for starting treatment and monitoring treatment response. Show less
ObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this... Show moreObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way.MethodsWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes.ResultsLanguage functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA.ConclusionDegeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage. Show less
Panman, J.L.; Venkatraghavan, V.; Ende, E.L. van der; Steketee, R.M.E.; Jiskoot, L.C.; Poos, J.M.; ... ; Klein, S. 2021
ObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this... Show moreObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way.MethodsWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes.ResultsLanguage functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA.ConclusionDegeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage. Show less
Aims We examined the associations of pericardial adipose tissue with cardiac structures and cardiovascular risk factors in children.Methods and results We performed a cross-sectional analysis in a... Show moreAims We examined the associations of pericardial adipose tissue with cardiac structures and cardiovascular risk factors in children.Methods and results We performed a cross-sectional analysis in a population-based cohort study among 2892 children aged 10 years (2404 normal weight and 488 overweight/obese). Pericardial adipose tissue mass was estimated by magnetic resonance imaging (MRI) and indexed on height 3 . Left ventricular mass (LVM) and left ventricular mass-to-volume ratio (LMVR) were estimated by cardiac MRI. Cardiovascular risk factors included android adipose tissue percentage obtained by Dual-energy X-ray absorptiometry, blood pressure and glucose, insulin, cholesterol, and triglycerides concentrations. Adverse outcomes were defined as values above the 75 percentile. Median pericardial adipose tissue index was 3.6 (95% range 1.6-7.1) among normal weight and 4.7 (95% range 2.0-8.9) among overweight children. A one standard deviation (1 SD) higher pericardial adipose tissue index was associated with higher LMVR [0.06 standard deviation scores, 95% confidence interval (CI) 0.02-0.09], increased odds of high android adipose tissue [odd ratio (OR) 2.08, 95% CI 1.89-2.29], high insulin concentrations (OR 1.17, 95% CI 1.06-1.30), an atherogenic lipid profile (OR 1.22, 95% CI 1.11-1.33), and clustering of cardiovascular risk factors (OR 1.56, 95% CI 1.36-1.79). Pericardial adipose tissue index was not associated with LVM, blood pressure, and glucose concentrations. The associations showed largely the same directions but tended to be weaker among normal weight than among overweight children.Conclusion Pericardial adipose tissue is associated with cardiac adaptations and cardiovascular risk factors already in childhood in both normal weight and overweight children. Show less
ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3... Show moreObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE epsilon 2 and epsilon 4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR](any BMB) [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 x 10(-10)). APOE epsilon 4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 x 10(-6)) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE epsilon 2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE epsilon 4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers. Show less
Sargurupremraj, M.; Suzuki, H.; Jian, X.Q.; Sarnowski, C.; Evans, T.E.; Bis, J.C.; ... ; Int Headache Genomics Consortium I 2020
White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide... Show moreWhite matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p=2.5x10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials. White matter hyperintensities (WMH) are a common brain-imaging feature of cerebral small vessel disease. Here, the authors carry out a GWAS and followup analyses for WMH-volume, implicating several variants with potential for risk stratification and drug targeting. Show less
Ethnic differences in cardiovascular risk factors and disease are well-known and may originate in early-life. We examined the ethnic differences in cardiac structure and function in children using... Show moreEthnic differences in cardiovascular risk factors and disease are well-known and may originate in early-life. We examined the ethnic differences in cardiac structure and function in children using cardiac magnetic resonance imaging in a European migrant population, and whether any difference was explained by early life factors. We used a prospective population-based cohort study among 2317 children in Rotterdam, the Netherlands. We compared children from Dutch (73%), Cape Verdean (3.5%), Dutch Antillean (3.3%), Moroccan (6.1%), Surinamese-Creoles (3.9%), Surinamese-Hindustani (3.4%), and Turkish (6.4%) background. Main outcomes were cMRI-measured cardiac structures and function. Cardiac outcomes were standardized on body surface area. Cape Verdean, Surinamese-Hindustani, and Turkish children had smaller right ventricular end-diastolic volume and left ventricular end-diastolic volume relative to their body size than Dutch children (p < 0.05). These results were not fully explained by fetal and childhood factors. Right ventricular ejection fraction and left ventricular ejection fraction did not differ between ethnicities after adjustment for fetal and childhood factors. Conclusion: Right ventricular end-diastolic volume and left ventricular end-diastolic volume differ between ethnic subgroups in childhood, without affecting ejection fraction. Follow-up studies are needed to investigate whether these differences lead to ethnic differences in cardiac disease in adulthood.What is Known:center dot Ethnic differences in cardiovascular risk factors and disease are well-known and may originate in early-life.center dot The prevalence of cardiovascular disease differs between ethnic groups. What is New:center dot We examined ethnic differences in left and right cardiac structure and function in children using cMRI.center dot Right and left cardiac dimensions differ between ethnic groups in childhood and are only partly explained by fetal and childhood factors. Show less
Introduction: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH... Show moreIntroduction: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH).Methods: We studied associations of 143 plasma-based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta-analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures.Results: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH.Discussion: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials. Show less
Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first... Show moreBackground and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A).In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes:CALCRL(2q32.1),KLHL24(3q27.1),VCAN(5q27.1), andPOLR2F(22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only. Show less
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala,... Show moreSubcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease. Show less
ObjectiveTo determine the long-term association of hemoglobin levels and anemia with risk of dementia, and explore underlying substrates on brain MRI in the general population.MethodsSerum... Show moreObjectiveTo determine the long-term association of hemoglobin levels and anemia with risk of dementia, and explore underlying substrates on brain MRI in the general population.MethodsSerum hemoglobin was measured in 12,305 participants without dementia of the population-based Rotterdam Study (mean age 64.6 years, 57.7% women). We determined risk of dementia and Alzheimer disease (AD) (until 2016) in relation to hemoglobin and anemia. Among 5,267 participants without dementia with brain MRI, we assessed hemoglobin in relation to vascular brain disease, structural connectivity, and global cerebral perfusion.ResultsDuring a mean follow-up of 12.1 years, 1,520 individuals developed dementia, 1,194 of whom had AD. We observed a U-shaped association between hemoglobin levels and dementia (p = 0.005), such that both low and high hemoglobin levels were associated with increased dementia risk (hazard ratio [95% confidence interval (CI)], lowest vs middle quintile 1.29 [1.09-1.52]; highest vs middle quintile 1.20 [1.00-1.44]). Overall prevalence of anemia was 6.1%, and anemia was associated with a 34% increased risk of dementia (95% CI 11%-62%) and 41% (15%-74%) for AD. Among individuals without dementia with brain MRI, similar U-shaped associations were seen of hemoglobin with white matter hyperintensity volume (p = 0.03), and structural connectivity (for mean diffusivity, p < 0.0001), but not with presence of cortical and lacunar infarcts. Cerebral microbleeds were more common with anemia. Hemoglobin levels inversely correlated to cerebral perfusion (p < 0.0001).ConclusionLow and high levels of hemoglobin are associated with an increased risk of dementia, including AD, which may relate to differences in white matter integrity and cerebral perfusion. Show less
Lee, S.J. van der; Knol, M.J.; Chauhan, G.; Satizabal, C.L.; Smith, A.V.; Hofer, E.; ... ; DeCarli, C. 2019
Structural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are... Show moreStructural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain structure and function are also affected by ‘normal’ brain ageing. More information is needed on how functional connectivity relates to aging, particularly in the absence of overt neurodegenerative disease. We investigated the association of age with resting-state functional connectivity in 2878 non-demented persons between 50 and 95 years of age (54.1% women) from the population-based Rotterdam Study. We obtained nine well-known resting state networks using data-driven methodology. Within the anterior default mode network, ventral attention network, and sensorimotor network, functional connectivity was significantly lower with older age. In contrast, functional connectivity was higher with older age within the visual network. Between resting state networks, we found patterns of both increases and decreases in connectivity in approximate equal proportions. Our results reinforce the notion that the aging brain undergoes a reorganization process, and serves as a solid basis for exploring functional connectivity as a preclinical marker of neurodegenerative disease. Show less
