Explore the vast range of titles available.

Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.

Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Atrial fibrillation (AF) has traditionally been considered an electrical heart disease. However, genetic studies have revealed that the structural architecture of the heart also play a significant role. We evaluated the functional and structural consequences of harboring a titin-truncating variant (TTNtv) in AF patients, using cardiac magnetic resonance (CMR). Seventeen early-onset AF cases carrying a TTNtv, were matched 1:1 with non-AF controls and a replication cohort of early-onset AF cases without TTNtv, and underwent CMR. Cardiac volumes and left atrial late gadolinium enhancement (LA LGE), as a fibrosis proxy, were measured by a blinded operator. Results: AF cases with TTNtv had significantly reduced left ventricular ejection fraction (LVEF) compared with controls (57 ± 4 vs 64 ± 5%, P < 0.001). We obtained similar findings in early-onset AF patients without TTNtv compared with controls (61 ± 4 vs 64 ± 5%, P = 0.02). We furthermore found a statistically significant increase in LA LGE when comparing early-onset AF TTNtv cases with controls. Using state-of-the-art CMR, we found that early-onset AF patients, irrespective of TTNtv carrier status, had reduced LVEF, indicating that early-onset AF might not be as benign as previously thought.

Background Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors. Methods We focused on the UK Biobank participants that were included in the UK Biobank Pharma Proteomics Project. After applying exclusions, 50,057 participants were included, aged 40–69 years at recruitment. We employed the Explainable Boosting Machine (EBM), an interpretable machine learning model, to predict the 10 year risk of primary coronary artery disease, ischemic stroke or myocardial infarction. The model had access to 2978 features (2923 proteins and 55 risk factors). Model performance was evaluated using 10-fold cross-validation. Results The EBM model using proteomics outperforms equation-based risk scores such as PREVENT, with a receiver operating characteristic curve (AUROC) of 0.767 and an area under the precision-recall curve (AUPRC) of 0.241; adding clinical features improves these figures to 0.785 and 0.284, respectively. Our models demonstrate consistent performance across sexes and ethnicities and provide insights into individualized disease risk predictions and underlying disease biology. Conclusions In conclusion, we present a more accurate and explanatory framework for proteomics data analysis, supporting future approaches that prioritize individualized disease risk prediction, and identification of target genes for drug development. Climente-González, Oh et al. introduce an interpretable machine learning model that integrates plasma proteomics with clinical risk factors and employs an explainable boosting machine algorithm for risk prediction. Authors show that their model outperforms existing models in predicting risk for cardiovascular disease. Plain language summary Cardiovascular diseases (CVDs) are a major cause of long-term disability and death. However, current prediction models are limited in their approach. We aimed to predict individual risk of CVD using blood protein markers, or biomarkers, that can serve as indicators of CVDs using an artificial intelligence model. Our findings show that this model was accurate and performed better than traditional risk prediction methods. The model provided personalized insights into disease risk and helped identify genes that could be targeted for prevention or development of new treatments. This research could improve early detection and prevention of CVD, leading to better health outcomes for many people in the future.

We aimed to investigate the pathogenicity of cardiac ion channel variants previously associated with SIDS. We reviewed SIDS-associated variants previously reported in databases and the literature in three large population-based cohorts; The ExAC database, the Inter99 study, and the UK Biobank (UKBB). Variants were classified according to the American College of Medical Genetics and Genomics (ACMG) guidelines. Of the 92 SIDS-associated variants, 59 (64%) were present in ExAC, 18 (20%) in Inter99, and 24 (26%) in UKBB. Using the Inter99 cohort, we found no difference in J-point amplitude and QTc-interval between carriers and non-carriers for 14/18 variants. There was no difference in the risk of syncope (P = 0.32), malignant ventricular arrhythmia (P = 0.96), and all-cause mortality (P = 0.59) between carriers and non-carriers. The ACMG guidelines reclassified 75% of all variants as variant-of-uncertain significance, likely benign, and benign. We identified ~2/3 of variants previously associated with SIDS and found no significant associations with electrocardiographic traits, syncope, malignant ventricular arrhythmia, or all-cause mortality. These data indicate that many of these variants are not highly penetrant, monogenic causes of SIDS and underline the importance of frequent reappraisal of genetic variants to avoid future misdiagnosis.

We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5 . We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels). Genome-wide association analyses identify 93 risk loci for venous thromboembolism (VTE). A polygenic score derived from these results identifies individuals at increased VTE risk equivalent to monogenic forms of the disease.

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and cardiac transplantation. We report a genome-wide association study and multi-trait analysis of DCM (14,256 cases) and three left ventricular traits (36,203 UK Biobank participants). We identified 80 genomic risk loci and prioritized 62 putative effector genes, including several with rare variant DCM associations ( MAP3K7 , NEDD4L and SSPN ). Using single-nucleus transcriptomics, we identify cellular states, biological pathways, and intracellular communications that drive pathogenesis. We demonstrate that polygenic scores predict DCM in the general population and modify penetrance in carriers of rare DCM variants. Our findings may inform the design of genetic testing strategies that incorporate polygenic background. They also provide insights into the molecular etiology of DCM that may facilitate the development of targeted therapeutics. Genome-wide association analyses comprising 14,256 cases and 1,199,156 controls and incorporating correlated cardiac magnetic resonance imaging traits provide insights into the molecular etiology of dilated cardiomyopathy.

We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis. A multi-ancestry genome-wide association study of liver cirrhosis and its associated endophenotypes identifies and validates 14 risk variants. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis.

ObjectivesElectrocardiographic bundle branch block (BBB) is common but the prognostic implications in primary care are unclear. We sought to investigate the relationship between electrocardiographic BBB subtypes and the risk of cardiovascular (CV) outcomes in a primary care population free of major CV disease.MethodsRetrospective cohort study of primary care patients referred for electrocardiogram (ECG) recording between 2001 and 2011. Cox regression models were used to estimate hazard ratios (HR) as well as absolute risks of CV outcomes based on various BBB subtypes.ResultsWe included 202 268 individuals with a median follow-up period of 7.8 years (Inter-quartile range [IQR] 4.9–10.6). Left bundle branch block (LBBB) was associated with heart failure (HF) in both men (HR 3.96, 95% CI 3.30 to 4.76) and women (HR 2.51, 95% CI 2.15 to 2.94) and with CV death in men (HR 1.80, 95% CI 1.38 to 2.35). Right bundle branch block (RBBB) was associated with pacemaker implantation in both men (HR 3.26, 95% CI 2.74 to 3.89) and women (HR 3.69, 95% CI 2.91 to 4.67), HF in both sexes and weakly associated with CV death in men. Regarding LBBB, we found an increasing hazard of HF with increasing QRS-interval duration (HR 1.25, 95% CI 1.11 to 1.42 per 10 ms increase in men and HR 1.23, 95% CI 1.08 to 1.40 per 10 ms increase in women). Absolute 10-year risk predictions across age-specific and sex-specific subgroups revealed clinically relevant differences between having various BBB subtypes.ConclusionsOpportunistic findings of BBB subtypes in primary care patients without major CV disease should be considered warnings of future HF and pacemaker implantation.

Obesity is associated with adverse effects on health and quality of life. Improved understanding of its underlying pathophysiology is essential for developing counteractive measures. To search for sequence variants with large effects on BMI, we perform a multi-ancestry meta-analysis of 13 genome-wide association studies on BMI, including data derived from 1,534,555 individuals of European ancestry, 339,657 of Asian ancestry, and 130,968 of African ancestry. We identify an intergenic 262,760 base pair deletion at the MC4R locus that associates with 4.11 kg/m 2 higher BMI per allele, likely through downregulation of MC4R . Moreover, a rare FRS3 missense variant, p.Glu115Lys, only found in individuals from Finland, associates with 1.09 kg/m 2 lower BMI per allele. We also detect three other low-frequency FRS3 missense variants that associate with BMI with smaller effects and are enriched in different ancestries. We characterize FRS3 as a BMI-associated gene, encoding an adaptor protein known to act downstream of BDNF and TrkB, which regulate appetite, food intake, and energy expenditure through unknown signaling pathways. The work presented here contributes to the biological foundation of obesity by providing a convincing downstream component of the BDNF-TrkB pathway, which could potentially be targeted for obesity treatment. Understanding the underlying pathophysiology of obesity can help prevent this condition. Here, the authors perform a GWAS of BMI in diverse ancestries, finding four missense variants in FRS3 that affect BMI.