Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Genomics; Perivascular spaceGenòmica; Espai perivascularGenómica; Espacio perivascularPerivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.Austrian Stroke Prevention Study (ASPS)/Austrian Stroke Prevention Family Study (ASPS-Fam) (E.H., P.G.G., H.S. and R.S.): We thank the staff and the participants for their valuable contributions. We thank B. Reinhart for her long-term administrative commitment, E. Hofer for the technical assistance in creating the DNA bank, J. Semmler and A. Harb for DNA sequencing and DNA analyses by TaqMan assays, and I. Poelzl for supervising the quality management processes after ISO9001 in the biobanking and DNA analyses. The Medical University of Graz and the Steiermärkische Krankenanstaltengesellschaft support the databank of the ASPS/ASPS-Fam. The research reported in this article was funded by the Austrian Science Fund (FWF) (grant nos. PI904, P20545-P05 and P13180) and supported by the Austrian National Bank Anniversary Fund (grant no. P15435) and the Austrian Ministry of Science under the aegis of the EU Joint Programme–Neurodegenerative Disease Research (JPND): www.jpnd.eu. Epidemiology of Dementia in Singapore (EDIS) (S.H., C.Chen, C.-Y.C., T.Y.W. and W.Z.): The EDIS study is supported by the National Medical Research Council (NMRC), Singapore (NMRC/CG/NUHS/2010 (grant no. R-184-006-184-511), NMRC/CSA/038/2013) and a Ministry of Education Tier 1 grant (no. A-0006106-00-00). Framingham Heart Study (FHS) (J.R.R., A.B., J.J.H., S.L., P.P., C.L.S., Q.Y. and S.Seshadri): This work was supported by the National Heart, Lung and Blood Institute’s FHS Contract (no. N01-HC-25195, no. HHSN268201500001I and no. 75N92019D00031). This study was also supported by grants from the National Institute of Aging (R01 grant nos. AG031287, AG054076, AG049607, AG059421, AG059725; U01 grant nos. AG049505, AG052409) and the National Institute of Neurological Disorders and Stroke (R01 grant no. NS017950). Funding for SHARe Affymetrix genotyping was provided by NHLBI Contract no. N02-HL64278. The computational work reported in this paper was performed on the Shared Computing Cluster which is administered by Boston University’s Research Computing Services. We also thank all the FHS study participants. Internet-based Students’ Health Research Enterprise (i-Share) study (C.B., J.Z., M.M., Q.LG., S. Schilling, Y.-C.Z., A.Tsuchida, M.-G.D., B.M., S.D. and C.T.): The i-Share study is conducted by the Universities of Bordeaux and Versailles Saint-Quentin-en-Yvelines (France). The i-Share study has received funding by the French National Agency (Agence Nationale de la Recherche, ANR), via the Investment for the Future program (grant nos. ANR-10-COHO-05 and ANR-18-RHUS-0002) and from the University of Bordeaux Initiative of Exellence (IdEX). This project has also received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program under grant agreement no. 640643 and from the Fondation pour la Recherche Médicale (grant no. DIC202161236446). Q.L.G. was supported by the Digital Public Health Graduate Program (DPH), a PhD program supported by the French Investment for the Future Program (grant no. 17-EURE-0019). Investigating Silent Strokes in Hypertensives: a Magnetic Resonance Imaging Study (ISSYS) (P.D., C.C. and I.F.-C.): This research was funded by the Instituto de Salud Carlos III (grant nos. PI10/0705, PI14/01535, PI17/02222), cofinanced by the European Regional Development Fund. Lothian Birth Cohort 1936 (LBC1936) (M.L., M.E.B., I.J.D., Z.M., S.M.M., M.C.V.H. and J.M.W.): We thank the LBC1936 cohort members and research staff involved in data collection, processing and preparation. The LBC1936 is supported by Age UK (Disconnected Mind program grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (grant no. MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (www.bric.ed.ac.uk), a center in the SINAPSE Collaboration (www.sinapse.ac.uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC), the UK Medical Research Council (MRC), the Row Fogo Charitable Trust (M.C.V.H.) and the UK Dementia Research Institute, which receives its funding from the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK (J.M.W.), is gratefully acknowledged. Genotyping was supported by a grant from the BBSRC (no. BB/F019394/1). The Nagahama Study (T.K., S.M., M.O., K.S., Y.T., K.Y., A.Tsuchida, P.B., B.M., M.J., M.-G.D. and F.M.): We are grateful to the Nagahama City Office and nonprofit organization Zeroji Club for their help in conducting the study. This project is supported by operational funds of Kyoto University and the Top Global University Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. We also received a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science, research grants from the Japan Agency for Medical Research and Development for the Practical Research Project for Rare/Intractable Diseases, and the Comprehensive Research on Aging and Health Science for Dementia R&D. We thank C. Galmiche for rating PVS in the validation dataset for the artificial intelligence-based method. The Northern Manhattan Study (NOMAS) (N.D.D., T.J. and R.L.S.): We gratefully acknowledge and thank the NOMAS participants. Funding was awarded through grants from the National Institute of Neurological Disorders and Stroke (R01 grant no. NS 29993) and the Evelyn F. McKnight Brain Institute. Rotterdam Study (M.J.K., F.D., M.W.V., M.A.I. and H.H.H.A.): The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands. The GWAS datasets are supported by the Netherlands Organisation for Scientific Research (NWO) Investments (no. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (grant no. 014-93-015; RIDE2), the Netherlands Genomics Initiative/NWO, the Netherlands Consortium for Healthy Aging, project no. 050-060-810. We thank P. Arp, M. Jhamai, M. Verkerk, L. Herrera, M. Peters and C. Medina-Gomez for their help in creating the GWAS database; and K. Estrada, Y. Aulchenko and C. Medina-Gomez for the creation and analysis of imputed data. H.H.H.A. is supported by ZonMW grant no. 916.19.151. Study of Health in Pomerania (SHIP) (S.F., R.B., A.T., K.W., H.J.G. and U.V.): SHIP is part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant no. 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. This study was further supported by the EU-JPND Funding for BRIDGET (grant no. FKZ:01ED1615). H.J.G. has received travel grants and speakers’ honoraria from Fresenius Medical Care, Servier, Neuraxpharm and Janssen Cilag, as well as research funding from Fresenius Medical Care. Sydney Memory and Ageing Study (MAS) & Older Australian Twins Study (OATS) (R.M.T., N.J.A., H.B., J.J., M.P., A.T., J.N.T., P.S.S., W.W., K.A.M. and M.J.W.): Sydney MAS: The Sydney MAS has been funded by three National Health & Medical Research Council (NHMRC) Program Grants (grant nos. ID350833, ID568969 and APP1093083). Collection of WGS data was supported by the NHMRC National Institute for Dementia Research Grants no. APP1115575 and no. APP1115462. MRI scans were processed with the support of NHMRC Project Grants (grant nos. 510175 and 1025243) and an Australian Research Council (ARC) Discovery Project Grant (no. DP0774213) and the John Holden Family Foundation. We also thank the MRI Facility at NeuRA. We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team: https://cheba.unsw.edu.au/research-projects/sydney-memory-and-ageing-study. OATS: The OATS study has been funded by an NHMRC and ARC Strategic Award Grant of the Ageing Well, Ageing Productively Program (grant no. 401162); NHMRC Project (seed) Grants (grant nos. 1024224 and 1025243); NHMRC Project Grants (grant nos. 1045325 and 1085606); and NHMRC Program Grants (grant nos. 568969 and 1093083). Collection of WGS data was supported by the NHMRC National Institute for Dementia Research Grants no. APP1115575 and no. APP1115462. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (no. 1079102) from the National Health and Medical Research Council. We thank the participants for their time and generosity in contributing to this research. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/project/older-australian-twins-study) to this study. Three-City Dijon Study (3C-Dijon) (S.D., M.-G.D., S. Schilling, C.T., B.M. and A.M.): This project is supported by a grant overseen by the French National Research Agency (ANR) as part of the ‘Investment for the Future Program’ no. ANR-18-RHUS-0002. It is also supported by a JPND project through the following funding organizations under the aegis of JPND: www.jpnd.eu: Australia, National Health and Medical Research Council; Austria, Federal Ministry of Science, Research and Economy; Canada, Canadian Institutes of Health Research; France, French National Research Agency; Germany, Federal Ministry of Education and Research; the Netherlands, the Netherlands Organisation for Health Research and Development; United Kingdom, Medical Research Council. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement nos. 643417, 640643, 667375 and 754517. The project also received funding from the French National Research Agency (ANR) through the VASCOGENE and SHIVA projects, and from the Initiative of Excellence of the University of Bordeaux (C-SMART project). Computations were performed on the Bordeaux Bioinformatics Center (CBiB) computer resources, University of Bordeaux. Funding support for additional computer resources at the CREDIM (Centre de Recherche et Développement en Informatique Médicale, University of Bordeaux) has been provided to S.D. by the Fondation Claude Pompidou. The Three-City (3C) Study: The 3C Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France and Ministry of Research–INSERM program ‘Cohortes et collections de données biologiques.’ C.T. and S.D. have received investigator-initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq. M.-G.D. received a grant from the ‘Fondation Bettencourt Schueller’. We thank P. Amouyel, U1167 Institut Pasteur de Lille - University of Lille - Inserm, for supporting funding of genome-wide genotyping of the 3C Study. This work was supported by the National Foundation for Alzheimer’s disease and related disorders, the Institut Pasteur de Lille, the labex DISTALZ and the Centre National de Génotypage. We thank A. Boland (CNG) for her technical help in preparing the DNA samples for analyses. UK Biobank (UKB) (M.J.K., F.D., M.W.V., M.A.I., H.H.H.A., A.M. and T.E.): This research has been conducted using the UK Resource under application no. 23509. McGill Genome Center (M.B., P.M., G.B. and M.Lathrop): Work done at the Canadian Center for Computational Genomics was supported by Genome Canada. Data analyses were enabled by computing and storage resources provided by Compute Canada and Calcul Québec. G.B. is supported by the Fonds de Recherche Santé Québec and the Canada Research Chair program. We thank all the participating cohorts for contributing to this study. We thank H. Jacqmin-Gadda, Bordeaux Population Health research center, University of Bordeaux/Inserm U1219 for statistical advice. We thank J. Thomas-Crusells, Bordeaux Population Health Research Center, University of Bordeaux/Inserm U1219, for editorial assistance. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript

Hum Brain Mapp

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseLaboratoire pour les applications en imagerie biomédicaleTranslational Research and Advanced Imaging LaboratoryInitiative d'excellence de l'Université de Bordeau

Hum Brain Mapp

Given the anatomical and functional similarities between the retina and the brain, the retina could be a "window" for viewing brain structures. We investigated the association between retinal nerve fiber layers (peripapillary retinal nerve fiber layer, ppRNFL; macular ganglion cell-inner plexiform layer, GC-IPL; and macular ganglion cell complex, GCC), and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students (mean age: 23.3 years, 71.3% women) from the i-Share study. We used multivariate linear models to study the cross-sectional association of each retinal nerve layer thickness assessed by spectral-domain optical coherence tomography (SD-OCT) with structural (volumes and cortical thickness), and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC-IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm(3) and 0.95 for PVSs larger than 15 mm(3). We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.Stopping cognitive decline and dementia by fighting covert cerebral small vessel diseas

Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process

Prevalence, Severity, and Clinical Management of Brain Incidental Findings in Healthy Young Adults: MRi-Share Cross-Sectional Study

Background and Objectives: Young adults represent an increasingly large proportion of healthy volunteers in brain imaging research, but descriptions of incidental findings (IFs) in this age group are scarce. We aimed to assess the prevalence and severity of IFs on brain MRIs of healthy young research participants aged 18-35 years, and to describe the protocol implemented to handle them. Methods: The study population comprised 1,867 participants aged 22.1 ± 2.3 years (72% women) from MRi-Share, the cross-sectional brain MRI substudy of the i-Share student cohort. IFs were flagged during the MRI quality control. We estimated the proportion of participants with IFs [any, requiring medical referral, potentially serious (PSIFs) as defined in the UK biobank]: overall, by type and severity of the final diagnosis, as well as the number of IFs. Results: 78/1,867 participants had at least one IF [4.2%, 95% Confidence Interval (CI) 3.4-5.2%]. IFs requiring medical referral (n = 38) were observed in 36/1,867 participants (1.9%, 1.4-2.7%), and represented 47.5% of the 80 IFs initially flagged. Referred IFs were retrospectively classified as PSIFs in 25/1,867 participants (1.3%, 0.9-2.0%), accounting for 68.4% of anomalies referred (26/38). The most common final diagnosis was cysts or ventricular abnormalities in all participants (9/1,867; 0.5%, 0.2-0.9%) and in those with referred IFs (9/36; 25.0%, 13.6-41.3%), while it was multiple sclerosis or radiologically isolated syndrome in participants with PSIFs (5/19; 26.3%, 11.5-49.1%) who represented 0.1% (0.0-0.4%) and 0.2% (0.03-0.5%) of all participants, respectively. Final diagnoses were considered serious in 11/1,867 participants (0.6%, 0.3-1.1%). Among participants with referred IFs, 13.9% (5/36) required active intervention, while 50.0% (18/36) were put on clinical surveillance. Conclusions: In a large brain imaging study of young healthy adults participating in research we observed a non-negligible frequency of IFs. The etiological pattern differed from what has been described in older adults.Programme d'investissements - Idex Bordeaux - LAPHIAStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseInvestissement d'aveni

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseStudy on Environmental and GenomeWide predictors of early structural brain Alterations in Young student

Cerebral small vessel disease genomics and its implications across the lifespan

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 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 = 2.5×10-7) 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.Peer reviewe

Cerebral small vessel disease genomics and its implications across the lifespan

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 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 = 2.5×10-7) 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.</p