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Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis. Assessing brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies, a generative model identifies five dominant patterns of brain atrophy, with specific associations with biomedical, lifestyle and genetic factors.

People with kidney disease are disproportionately affected by atherosclerosis for unclear reasons. Soluble urokinase plasminogen activator receptor (suPAR) is an immune-derived mediator of kidney disease, levels of which are strongly associated with cardiovascular outcomes. We assessed suPAR's pathogenic involvement in atherosclerosis using epidemiologic, genetic, and experimental approaches. We found serum suPAR levels to be predictive of coronary artery calcification and cardiovascular events in 5,406 participants without known coronary disease. In a genome-wide association meta-analysis including over 25,000 individuals, we identified a missense variant in the plasminogen activator, urokinase receptor (PLAUR) gene (rs4760), confirmed experimentally to lead to higher suPAR levels. Mendelian randomization analysis in the UK Biobank using rs4760 indicated a causal association between genetically predicted suPAR levels and atherosclerotic phenotypes. In an experimental model of atherosclerosis, proprotein convertase subtilisin/kexin-9 (Pcsk9) transfection in mice overexpressing suPAR (suPARTg) led to substantially increased atherosclerotic plaques with necrotic cores and macrophage infiltration compared with those in WT mice, despite similar cholesterol levels. Prior to induction of atherosclerosis, aortas of suPARTg mice excreted higher levels of CCL2 and had higher monocyte counts compared with WT aortas. Aortic and circulating suPARTg monocytes exhibited a proinflammatory profile and enhanced chemotaxis. These findings characterize suPAR as a pathogenic factor for atherosclerosis acting at least partially through modulation of monocyte function.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry ( N  = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease. On the electrocardiogram, the PR interval reflects conduction from the atria to ventricles and also serves as risk indicator of cardiovascular morbidity and mortality. Here, the authors perform genome-wide meta-analyses for PR interval in multiple ancestries and identify 141 previously unreported genetic loci.

Evidence suggests an association between autonomical nervous system (ANS) function and atrial fibrillation (AF) development. We sought to examine the association of baseline resting heart rate (RHR) and short-term heart rate variability (HRV) as surrogates of (ANS) with incident AF in individuals without previous cardiovascular disease. A total of 6,261 participants of the Multi-Ethnic Study of Atherosclerosis who were free of AF and diagnosed cardiovascular disease were enrolled. Three standard 10-second, 12-lead electrocardiograms (ECG) were used to measure RHR, the standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences in RR intervals (RMSSD). Cox proportional hazards models adjusted for demographics, atrioventricular nodal agents, and known cardiovascular risk factors were used to examine the association of baseline RHR, and log transformed SDNN and RMSDD with incident AF. Over a mean follow-up of 11.3 ± 3.7 years, 754 (12%) participants developed AF. Spline curve analysis revealed a nonlinear association between RHR, HRV, and incident AF. In fully adjusted models higher (but not lower) baseline RHR (RHR >76 beats/min) was associated with incident AF (hazard ratio 1.48 95% confidence interval 1.18 to 1.86). Additionally, lower values of RMSDD and SDNN and higher values of RMSDD were independently associated with incident AF. In conclusion, cardiac ANS dysregulation indicated as higher RHR and lower HRV is associated with incident AF independent of known cardiovascular risk factors.

Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell–cell communication at 139 loci. Furthermore, we assayed chromatin accessibility using assay for transposase-accessible chromatin with sequencing and histone H3 lysine 4 trimethylation in stem cell-derived atrial cardiomyocytes. We observed a marked increase in chromatin accessibility for our sentinel variants and prioritized genes in atrial cardiomyocytes. Finally, a polygenic risk score (PRS) based on our updated effect estimates improved AF risk prediction compared to the CHARGE-AF clinical risk score and a previously reported PRS for AF. The doubling of known risk loci will facilitate a greater understanding of the pathways underlying AF. Genome-wide association meta-analyses identify more than 350 loci associated with atrial fibrillation. A polygenic score derived from these results improves atrial fibrillation risk prediction compared to published clinical and polygenic scores.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization. The QT interval is a heritable electrocardiographic measure associated with arrhythmia risk when prolonged. Here, the authors used a series of genetic analyses to identify genetic loci, pathways, therapeutic targets, and relationships with cardiovascular disease.

Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster , we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function. A meta-analysis of genome-wide association studies in more than 66,000 individuals identifies 68 new genomic loci that reliably associate with platelet count and volume, and reveals new gene functions. Gene scan for platelet production Blood platelets are involved in maintaining haemostasis, and platelet count and volume are controlled within tight limits. This meta-analysis of genome-wide association studies in more than 65,000 individuals identifies 53 new genomic loci that reliably associate with platelet count and volume. The loci preferentially map to gene-coding regions, and many of them fall within a single protein–protein interaction network. Functional experiments in zebrafish and fruitflies show that 11 of the newly identified genes encode novel regulators of blood cell formation.

Objective Atrial fibrillation (AF) is a common source of medical costs, morbidity and mortality. NT pro-brain natriuretic peptide (BNP) is a remarkably strong predictor of AF in older whites; we aimed to assess if this biomarker was as predictive in other racial groups. Design We used covariate-adjusted Cox model regressions to estimate the HRs of developing AF as a function of NT proBNP, and tested for interactions of NT proBNP with age, gender and race/ethnicity. Setting The Multi-Ethnic Study of Atherosclerosis (MESA). Patients 5518 subjects were followed over a median of 7.6 years. During this time, 267 developed AF. Results NT proBNP was statistically significantly associated with incident AF; for ln NT proBNP, the adjusted HR was 2.2 (95% CI 1.9 to 2.5). Assessed by quintiles, the relationship between NT proBNP was strong and graded; the unadjusted HR for the highest quintile of NT proBNP was 23.7 (95% CI 11.1 to 50.6) and adjusted was 11.4 (95% CI 5.1 to 25.3). NT proBNP was an excellent predictor of incident AF in the younger and older age groups, in men and women and in the different race/ethnicity groups: the HR for ln NT proBNP as a predictor of incident AF ranged from 2.0 to 3.9 in each subgroup. Conclusions NT proBNP is a robust predictor of incident AF; its prognostic value is more significant in younger patients and women compared with older patients and men. NT proBNP was also as strongly predictive in black patients, Hispanics and Asian/Chinese as in white patients despite a lower incidence of arrhythmia.

Sex hormones may influence sex differences in cardiovascular disease (CVD). N-terminal pro-B-type natriuretic peptide (NT-proBNP), a predictor of CVD, is higher in women than men, which may relate to sex hormones. To evaluate whether total testosterone (T), bioavailable T, free T, estradiol, dehydroepiandrosterone (DHEA), and SHBG are associated with NT-proBNP. Cohort study. Cross-sectional sample included 2371 postmenopausal women and 2688 men free of CVD, of which 2041 women and 2348 men were included longitudinally. NT-proBNP at baseline (2000 to 2002) and one or more repeat NT-proBNPs (through 2012). Analyses adjusted for CVD risk factors. Women had higher NT-proBNP than men (median 79.9 vs 38.5 pg/mL). Cross-sectionally, higher bioavailable T, free T, DHEA, and lower SHBG levels were independently associated with lower NT-proBNP among both women and men (all P < 0.05). Higher total T in women and estradiol in men were also associated with lower NT-proBNP (both P < 0.05). Longitudinally, in women, higher total T, bioavailable T, free T, DHEA, and lower estradiol and SHBG were associated with greater 10-year increase in NT-proBNP (all P < 0.05). In men, higher free T and estradiol were associated with greater NT-proBNP increase (both P < 0.05). A more androgenic sex hormone pattern was inversely associated with NT-proBNP cross-sectionally and may contribute to sex differences in NT-proBNP. Longitudinally, a more androgenic sex hormone pattern was associated with greater increase in NT-proBNP in women, which may reflect a mechanism for CVD risk after menopause.