Genome-wide Association Research Articles

Utility of polygenic risk score for prediction of dilated cardiomyopathy and modulation of severity of LV dysfunction among cases

Abstract Background Dilated cardiomyopathy (DCM) is a predominant cause of heart failure and a leading indication for cardiac transplantation. While rare genetic variants are established as causative factors in familial forms of the disease, recent genome-wide association studies (GWAS) have also underscored the significant contribution of common genetic variation to DCM. Polygenic scores (PGS) constructed from GWAS have demonstrated promise in risk stratification for a range of common diseases and may similarly have value for risk prediction in DCM. However, data regarding the clinical utility of PGS in DCM remains sparse. Purpose We aimed to evaluate the utility of PGS for prediction of DCM and its association with left ventricular ejection fraction (LVEF) and family history. We further aimed to assess the contribution of PGS in DCM patients with established causative rare variants (genotype-positive) and those without (genotype-negative). Methods We used a previously-constructed PGS, which was developed from a GWAS meta-analysis (8387 DCM cases and 939161 controls) and multi-trait analysis (MTAG) with cardiac MRI traits. In the present analysis, we calculated PGS for a newly assembled cohort of 978 DCM cases from a major university medical centre and 7207 controls from a national register. To evaluate the clinical utility of PGS, we built logistic regression models and assessed the association between PGS and DCM status. Subsequently, we performed subgroup analyses, including: i) individuals of European ancestry, ii) non-European ancestry, iii) males, iv) females, v) genotype-positive participants(n=193), and vi) genotype-negative participants (n=294). We then used linear and logistic regression models, respectively, to assess the associations of PGS with LVEF and documented family history of DCM. Results PGS demonstrated a significant enrichment among DCM cases compared to controls (OR per SD 1.93, P = 9.47E-68). This association persisted with consistent effect estimates - ranging from OR 1.5 to 2.2 - across all examined subgroups and demonstrated sufficient statistical significance (all P<1.58E-06) (Figure 1). Genotype-negative cases had a significantly higher PGS compared to genotype-positive individuals (P = 0.0015), although PGS was significantly enriched in both groups as compared to controls. Furthermore, among DCM cases, higher PGS was significantly associated with lower LVEF at first presentation (P=0.03, beta=-0.5% per SD) (Figure 2). There was no significant association found between PGS and family history. Conclusions PGS is strongly associated with risk of DCM, and may modulate the severity of LV dysfunction among DCM cases. Polygenic burden contributes to DCM risk in both genotype-positive and negative cases, although the contribution is stronger in patients without known causative rare variants. Overall, our results suggest a potential clinical applicability for PGS in DCM risk stratification.Figure 1.Association of PGS with DCMFigure 2.Association of PGS with LVEF

A Genome-Wide Association Study (GWAS) of Takotsubo syndrome

Abstract Background Takotsubo syndrome (TS) is an acute cardiac condition of unknown etiology characterized by transient regional systolic dysfunction of the left and/or right ventricle. Several studies have suggested the importance of genetics; however, studies are limited and have presented conflicting results. Purpose To identify potential genetic risk variants associated with TS. Methods A case-control GWAS in three steps; I) A discovery study with Swedish takotsubo cases (n=260) and age- and sex-matched controls without heart disease from the UK Biobank (n=3034), II) A replication study of Swedish takotsubo cases (n=250) and sex- and age-matched Swedish controls without myocardial infarction from the Swedish coronary angiography and angioplasty register (n=229), III) A Meta-analysis of study I-II with extended controls from the UK-biobank (n=5161). Genotyping was performed using the Axiom® UK Biobank Genotyping Array including 820 000 single nucleotide polymorphisms (SNPs). After applying filters and QC control around 480 000 SNPs remained with a Bonferroni corrected p-value 1*10^-7, minor allele frequency (MAF 1.5-2%), Hardy-Weinberg (HWE) 10^-6. QC, filtering and analysis were performed in PLINK 1.9 and R v 4.0. Results In the discovery (I) 1490 SNP:s fulfilled a significance level of <10^-4, of which 36 SNP:s replicated (II) with a significance level of <10^-2, 11 reached a significance level of 10^-7 in the meta-analysis (III). One of the 36 SNP:s, rs1614887, replicated (II) with a significance level of 10^-3. This SNP is an intron variant in the butyrophilin subfamily 2 member A2 gene, a glycoprotein receptor involved in lipid, fatty-acid and sterol metabolism highly expressed in the heart. Of the 11 SNP:s with a significance level of 10^-7 in the meta-analysis (III) several risk variants were associated with psoriasis. Conclusions This is the largest GWAS of takotsubo syndrome performed so far, identifying one candidate gene and several potential risk variants. Further research of genetic risk variants in TS is warranted.

Role of common variants and new genes associated with arrhythmogenic cardiomyopathy

Abstract Introduction Arrhythmogenic Cardiomyopathy (ACM) is an inherited cardiac disease, predominantly affecting males, characterized by fibro-fatty myocardial replacement mainly in the right ventricle. It leads to 5% sudden cardiac death in affected young adults (<25 yo) without an implantable cardioverter defibrillator. Early diagnosis is crucial for appropriate clinical management to reduce mortality and prevent disease progression. Rare variants in desmosomal genes (PKP2,DSC2,DSG2,DSP and JUP) are found in 50% of cases. However, challenges persist since we observe a low penetrance in relatives (28-58%) and a high variability in expressivity and severity annihilating any preventive strategy. Objective We aim to decipher the genetic architecture of ACM by investigating the role of common variants in disease susceptibility/severity and to idendify new genes associated to ACM. Method A Genome Wide Association Study (GWAS) was performed on 1079 ACM index patients and 7305 controls, followed by conditional analysis. New candidate genes were knocked out (KO) in Zebrafish and/or hiPSC using CRISPR/Cas9. Impedance and external field potential measurements were performed on hiPSC derived cardiomyocytes (CM-hiPSC). Results The GWAS identified 5 significant loci (Figure 1, P<5.10-8), including 2 desmosomal genes (PKP2 a n d DSG2) and 3 new candidates (KLF12, CTNNA3, and ARL17B/WNT3). Conditional analysis revealed 3 additional loci with suggestive p-values (P<1.10-6), highlighting 3 new susceptible loci. Among them, we uncovered key non-coding regulatory regions near desmosomal genes (DSC2 and DSG2). Initial findings in PKP2-KO zebrafish present hearts with structural defects, while CM-hiPS KLF12-KO(-/-) exhibited altered interbeat interval and relaxation time compared to isogenic controls. Conclusion Our findings reveal ACM's complex genetic architecture, implicating common variants in disease susceptibility. A polygenic risk score will be implemented to improve patient risk stratification. Three novel loci/genes for ACM as well as specific regulatory regions of desmosomal genes have been highlighted. Their functional studies in isogenic hiPSC and Zebrafish models will offer new insights in ACM underlying molecular mechanism and potential therapeutic targets.

Genome wide association studies on three-dimensional aortic geometric indices measured with magnetic resonance imaging

Abstract Introduction To date, three-dimensional properties of the aorta have not been comprehensively quantified in a population level dataset, nor has there been a genome-wide association study (GWAS) to identify significant single nucleotide polymorphisms (SNPs) associated with geometric properties of the aorta. Methods We segmented the thoracic aorta of 48,045 subjects in the UK Biobank using a U-Net convolutional neural network. We then computed geometric measurements (centerline length, arch height, arch width, arch height-width ratio, curvature, and mean, minimum and maximum diameter) across six subsegments of the aorta. Centerline length was allometrically indexed to body height. Using generalized linear models, we performed GWAS on each geometric phenotype adjusted for the following covariates: (1) age, (2) sex, and (3) ten genetic principal components. SNPs with a minor allele frequency < 0.05 were excluded. We performed LD-based clumping with an r2 threshold of 0.001 to identify significant loci and used LDSC to compute the genetic heritability of each geometric phenotype. Results After performing LD clumping, we identified 457 significant(P<5e-8) SNPs across 36 aortic geometric parameters. GWAS-based heritability analyses showed a mean heritability of 13.8 (9.9) % across all aortic geometric parameters, with all intercepts being close to 1, suggesting minimal genomic inflation. Maximum aorta diameter exhibited the highest observed heritability of 34.5 (3.0) %. Allometric centerline length showed a heritability of 13.6 (1.5) %. Similarly, arch height and width showed heritability of 14.6 (1.5) %, and 13.64 (1.5) % respectively. Aortic curvature showed limited heritability at 8.54 (1.2) %, with arch curvature being further reduced to 7.6 (1.20) %. Conclusion To our knowledge, this was the first study that investigated the genetic architecture of aortic centerline length, aortic curvature, and aortic tortuosity. These results suggest that aortic remodeling associated with aging and pathological conditions has important genetic contributions. SNPs associated with aortic geometry may identify therapeutic targets to prevent aortic remodeling, which should be the focus of future work.significant_snpsheritability

Molecular landscape of the overlap between Alzheimer’s disease and somatic insulin-related diseases

Alzheimer’s disease (AD) is a multifactorial disease with both genetic and environmental factors contributing to its etiology. Previous evidence has implicated disturbed insulin signaling as a key mechanism that plays a role in both neurodegenerative diseases such as AD and comorbid somatic diseases such as diabetes mellitus type 2 (DM2). In this study, we analysed available genome-wide association studies (GWASs) of AD and somatic insulin-related diseases and conditions (SID), i.e., DM2, metabolic syndrome and obesity, to identify genes associated with both AD and SID that could increase our insights into their molecular underpinnings. We then performed functional enrichment analyses of these genes. Subsequently, using (additional) GWAS data, we conducted shared genetic etiology analyses between AD and SID, on the one hand, and blood and cerebrospinal fluid (CSF) metabolite levels on the other hand. Further, integrating all these analysis results with elaborate literature searches, we built a molecular landscape of the overlap between AD and SID. From the landscape, multiple functional themes emerged, including insulin signaling, estrogen signaling, synaptic transmission, lipid metabolism and tau signaling. We also found shared genetic etiologies between AD/SID and the blood/CSF levels of multiple metabolites, pointing towards “energy metabolism” as a key metabolic pathway that is affected in both AD and SID. Lastly, the landscape provided leads for putative novel drug targets for AD (including MARK4, TMEM219, FKBP5, NDUFS3 and IL34) that could be further developed into new AD treatments.

Prediction of mortality, future arrhythmia and cardiovascular disease: an artificial intelligence-enhanced electrocardiography platform

Abstract Background Artificial intelligence-enhanced electrocardiography (AI-ECG) can be used to identify existing disease, but could additionally be used to predict occurrence of future disease and death. Purpose We developed an AI-ECG platform that predicts mortality and a wide spectrum of future arrhythmia and cardiovascular disease. Methods The AI-ECG risk estimation platform (AIRE) was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves (Fig 1A). AIRE was fine-tuned to additionally predict the endpoints described below. Results AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776) and can differentiate prognosis in high- and low-risk subjects, even when only cardiologist-defined normal ECGs are considered (Fig 2B). Additionally, in Cox regression analyses, AIRE was superior to conventional risk factors (Fig 1B). AIRE accurately predicted future ventricular arrhythmia (c-index 0.760 (0.756-0.763)), future complete heart block (CHB) (0.809 (0.805-0.814), future atrial fibrillation (0.753 (0.751-0.756), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.789)) . AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF <50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p < 0.0001 ). For CHB prediction, AIRE was able to further risk stratify subjects with bifascicular block and syncope into low (1.9% 5 year CHB), intermediate (8.8%), and high risk (20.7%) groups, and could guide treatment decisions. We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK, including volunteers, primary care subjects and patients with Chagas disease, and found AIRE accurately predicted all-cause mortality in all four external cohorts (Fig 2). Non-mortality endpoints were successful externally validated in the UKB. In explainability analyses, we found features of QRS morphology, low QRS voltage, poor precordial R wave progression, inverted and biphasic T waves and ST segment changes were the most significant morphological features associated with high predicted mortality (Fig 1D). Through phenome- and genome-wide association studies (GWAS), we identified candidate biological pathways including changes in cardiac structure (LV mass, LV trabeculation and left atrial size). GWAS identified variants adjacent to VGLL2, KCNQ1, CCDC91 and TBX3 which have been described in association with QT interval, biological aging and metabolic syndrome (Fig 1E). Conclusion AIRE could be used worldwide across a range of clinical contexts for risk estimation of not only mortality but a wide spectrum of future arrhythmia and cardiovascular diseases.Figure 1Figure 2

Proteogenomic analysis in population biobanks identifies new therapeutic targets across heart failure subtypes

Abstract Background Heart failure (HF) represents a multifactorial clinical syndrome with differential response to existing therapies across its subtypes. Proteogenomic analysis of genetic variants associated with changes in circulating protein level and risk of heart failure subtypes provides an opportunity for a systematic identification of proteins with subtype-specific causal effects as novel therapeutic targets. Purpose To identify proteins as potential therapeutic targets for specific heart failure subtypes by leveraging proteomics and genomics data in population biobanks. Methods We evaluated the causal effects of 2,773 proteins on 5 HF-related outcomes, including overall HF, non-ischaemic HF (ni-HF), ni-HF with reduced / preserved ejection fraction (ni-HFrEF / ni-HFpEF), and dilated cardiomyopathy (DCM). For each protein-phenotype pair, we performed a two-sample Mendelian randomisation (MR) analysis using protein-specific genetic association estimates derived from protein quantitative trait locus (pQTL) studies in 114,145 individuals from deCODE, UK Biobank, Fenland study, and SCALLOP consortium and genome-wide association studies (GWAS) of HF outcomes in 145,795 individuals by HERMES Consortium. We further prioritised candidate protein targets based on replication of results across datasets, model parameters, assays, and colocalization of causal genetic variants. Finally, we profiled the causal effects of identified proteins on 32 related cardiac traits and risk factors to explore potential mechanisms of actions and on-target side effects. Results We identified 493 proteins associated with at least one HF outcome at a false discovery rate (FDR) < 1% in the primary MR analysis. We further prioritised 44 proteins with concordant effect direction across datasets, model parameters, and assays or colocalization in at least one dataset. All 44 identified proteins showed cross association with at least one of 35 tested traits other than HF or cardiomyopathies at FDR <1%, including coronary artery disease (18 proteins), body mass index (11 proteins), type 2 diabetes (6 proteins), systolic / diastolic blood pressure (13 proteins), and chronic kidney disease (9 proteins). Conclusion We characterised the causal associations of 2,773 circulating proteins across HF-related outcomes and prioritised 44 proteins by integrating proteomics and genomics data in population biobanks. These findings extend our understanding of the molecular mechanism underlying different forms of HF and may inform the development of new therapeutic strategies.Study design

Genetics of Some Wool Traits in Sheep: A Review

Greasy fleece yield, staple length and fibre diameter are complex quantitative traits influenced by various genetic and environmental factors. Among non-genetic factors year of birth, gender of lamb, centre of rearing, parity and type of birth are known to influence wool traits in different sheep genetic resources. To develop effective breeding objectives and accurately evaluate genetic potential, more precise analysis of genetic parameters and correlations between growth traits is essential. However, inconsistent results were observed across breeds and studies, highlighting the need for breed-specific optimization strategies. Greasy fleece yield, fibre diameter and staple length are critical determinants of wool quality and quantity. Wool traits are moderately (20–50%) to highly heritable (≥50%), so improving quality and quantity of a wool clip can happen even after one generation. The significant variations observed among breeds underscore the need for breed-specific optimization strategies. By integrating genetic and environmental factors, sheep breeders and producers can develop effective breeding programs, improve wool production, and enhance profitability. Wool traits are moderately to highly heritable, however, very low and very high estimates have also been reported in different sheep genetic resources. The heritability estimates correspond to the genetic variation in a trait. Gene mapping has identified chromosomal regions influencing fibre quality and production, enabling more efficient selection of superior wool production. Recent studies have employed various techniques to investigate the impact of fibre-related genes, including the candidate gene approach, transcriptome analysis and genome-wide association studies. It is concluded that wool traits being quantitative in nature are governed by different genes and are influenced by genetic and different environmental factors.

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Abstract Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.

Identification of genetic variants associated with exercise-induced atrial fibrillation in high-performing racehorses

Abstract Background Atrial fibrillation (AF) is the most common cardiac arrhythmia in athletes. Despite a lower burden of classical cardiovascular risk factors, the prevalence of AF in athletes is comparable to that in the general population of similar age (~13%, mean age 65.5), suggesting that exercise-induced AF may have different underlying mechanisms. The prevalence of AF in thoroughbred (TB) and standardbred (STB) racehorses is also relatively high (~5%), suggesting exercise as an independent risk factor. Due to selective breeding and complete control of pedigrees, racehorses are well-suited for genetic studies. Common and rare genetic variation explain a substantial part of AF variance, but no study has identified genetic variants associated with exercise-induced AF in any species. Objective In this project, we aimed to use the racehorse as a model to identify genetic variants associated with exercise-induced AF, and in a future study, possibly replicate the results in human athletes. Here we present preliminary results from the genome-wide association study (GWAS) of AF in racehorses. Methods We collected blood or hair samples from 181 STB and TB racehorses in which 71 had paroxysmal or persistent AF verified by ECG. We extracted DNA and performed whole-genome genotyping using a commercial equine genotyping platform (670k markers). We phased and imputed un-genotyped markers and increased the sample size of the control group, using publicly available equine data from similar breeds from the European Nucleotide Archive. We conducted a GWAS of 7.85 million markers in 71 cases and 296 controls using PLINK2 with covariates adjusted for population stratification, batch and sex. Results We identified one significant locus downstream of the protocadherin gene PCDH18 associated with AF in racehorses. The same locus has been associated with the ECG-trait P-wave terminal force in humans. Changes in P-wave terminal force is associated with increased atrial size, delayed interatrial conduction, left ventricular fibrosis and left atrial function, and considered a good predictor of AF occurrence. Several non-significant but interesting signals were also identified including the genes SLC6A15 (amino acid transport), MYBPH (myosin binding), MYF6 and MYOG (myoblast differentiation). Conclusion In these preliminary results, we have identified a new genetic locus associated with AF in racehorses. In addition, we have identified several candidate genes for further studies to elucidate the genetic background of exercise-induced AF. We aim to replicate our findings in a population of human athletes. Our results may aid the development of risk prediction tools for exercise-induced AF and new drugs for tailored treatment of AF in general.

Causal association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Abstract Aim Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. We aimed to investigate the causality between mineral metabolism and CAVS. Methods A systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis was applied to investigate the causal effect. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n=374,277 and 653,867). Results In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery=3.081 (1.649-5.760), Pdiscovery=4.21×10-4; ORreplication=2.280 (1.461-3.558), Preplication=2.82×10-4] without evidence of reverse causation (Psteiger=7.21×10-98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS causative genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P>0.05/5=0.01). Conclusions Elevated serum FGF23 level is a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Causal Associations Between the Gut Microbiota and Hypertension-Related Traits Through Mendelian Randomization: A Cross-Sectional Cohort Study.

Previous studies have suggested a link between the gut microbiome and hypertension-related traits like blood pressure. However, these reports are often limited by weak causal evidence. This study investigates the potential causal association between gut microbiota and hypertension-related traits using Mendelian randomization with summary data from genome-wide association studies. The inverse-variance weighted method revealed that the Clostridium innocuum group (Odds ratio [OR]: 1.0047, 95% confidence interval [CI]: 1.0004-1.0090, p=0.0336), Eubacterium fissicatena group (OR: 1.0047, 95% CI: 1.0005-1.0088, p=0.0266), Lachnospiraceae FCS020 group (OR: 1.0063, 95% CI: 1.0004-1.0122, p=0.0361), and Olsenella (OR: 1.0044, 95% CI: 1.0001-1.0088, p=0.0430) were associated with an increased risk of hypertension. Conversely, Flavonifractor (OR: 0.9901, 95% CI: 0.9821-0.9982, p=0.0166), Parabacteroides (OR: 0.9874, 95% CI: 0.9776-0.9972, p=0.0121), and Senegalimassilia (OR: 0.9907, 95% CI: 0.9842-0.9974, p=0.0063) were associated with a decreased risk of hypertension. External validation with the Guangdong Gut Microbiome Project confirmed a negative correlation between Parabacteroides and hypertension, potentially through metabolic pathways. These findings provide further evidence supporting the hypothesis that microbes and their metabolites play a role in blood pressure regulation.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Abstract Background Obesity, a key risk factor for cardiometabolic diseases, is poorly evaluated by body mass index (BMI), which doesn't account for visceral fat and fat distribution. Given that obesity-related cardiac changes can manifest in surface ECG changes, we hypothesised that an AI-ECG model could predict BMI, and the discrepancy between AI-predicted and actual BMI (delta-BMI) could indicate cardiometabolic health. We also sought to understand the biological mechanisms contributing to the AI-ECG-derived delta-BMI. Methods The AI-ECG model was developed using a data split of 60/10/30% in a USA secondary care dataset of 512,950 ECGs and externally validated in the UK Biobank (N = 42,386), employing a residual neural network architecture. Model performance was evaluated using Mean Absolute Error (MAE), Pearson correlation coefficient (r), and coefficient of determination (R2). The study aimed at identifying incident cardiometabolic diseases, using Cox regression analyses adjusted for BMI, age, and sex. To assess the underlying biological associations of delta-BMI, phenome-wide, genome-wide, metabolome-wide, and proteome-wide association studies were performed. Results In the test set, the model achieved an MAE of 3.95 (3.93–3.97), r of 0.65 (0.65-0.66), and R2 of 0.43 (0.42-0.43). In the UKB, the model achieved an MAE of 2.94 (2.91–2.96), r of 0.62 (0.62–0.63) and R2 of 0.39 (0.38-0.40). The top tertile of delta-BMI showed an increased risk of future cardiometabolic disease (test set: HR 1.15, p<0.001; UKB: HR 1.58, p<0.001) and diabetes mellitus (test set: HR 1.25, p<0.001; UKB: HR 2.28, p<0.001) after adjusting for covariates including measured BMI. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits. These results highlight delta-BMI's potential as a non-invasive, ECG-based biomarker for cardiometabolic risk stratification. Conclusion Our study demonstrates the effectiveness of an AI-enhanced ECG model in accurately predicting BMI, validated across diverse populations. The introduction of delta-BMI as a predictor of cardiometabolic disease offers valuable additional prognostic insights beyond traditional BMI assessments. Our exploration of biological pathways, spanning phenotypic, genotypic, metabolomic, and proteomic associations, contributes to a comprehensive understanding of the association between delta-BMI and cardiometabolic disease. The clinical implications could extend to enhanced screening strategies, personalised risk assessment, and motivation for lifestyle interventions.

A genome-wide association study identifies genetic determinants of hemoglobin glycation index with implications across sex and ethnicity

IntroductionWe investigated the genetic determinants of variation in the hemoglobin glycation index (HGI), an emerging biomarker for the risk of diabetes complications.MethodsWe conducted a genome-wide association study (GWAS) for HGI in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 7,913) using linear regression and additive genotype encoding on variants with minor allele frequency greater than 3%. We conducted replication analyses of top findings in the Atherosclerosis Risk in Communities (ARIC) study with inverse variance-weighted meta-analysis. We followed up with stratified GWAS analyses by sex and self-reported race.ResultsIn ACCORD, we identified single nucleotide polymorphisms (SNPs) associated with HGI, including a peak with the strongest association at the intergenic SNP rs73407935 (7q11.22) (P = 5.8e−10) with a local replication in ARIC. In black individuals, the variant rs10739419 on chromosome 9 in the Whirlin (WHRN) gene formally replicated (meta-P = 2.2e−9). The SNP-based heritability of HGI was 0.39 (P< 1e−10). HGI had significant sex-specific associations with SNPs in or near GALNT11 in women and HECW2 in men. Finally, in Hispanic participants, we observed genome-wide significant associations with variants near USF1 and NXNL2/SPIN1.DiscussionMany HGI-associated SNPs were distinct from those associated with fasting plasma glucose or HbA1c, lending further support for HGI as a distinct biomarker of diabetes complications. The results of this first evaluation of the genetic etiology of HGI indicate that it is highly heritable and point to heterogeneity by sex and race.

Type 2 diabetes mediating the increased cardiovascular risk in patients with rheumatoid arthritis: a bidirectional mendelian randomisation approach

Abstract Background Patients with rheumatoid arthritis (RA) are at increased risk of developing coronary artery disease (CAD) as is evident from epidemiological studies. This however is not a causative association, as we have shown by two-sample mendelian randomisation (MR) in previous work. Mediators linking RA to cardiovascular phenotypes have yet to be identified. Purpose The present study seeks to determine whether type 2 diabetes (DM II) is such a mediator conferring the increased cardiovascular risk of patients with RA. Methods The associations between the phenotypes RA, CAD and DM II were tested for causality with two-sample MR using genetic instruments from genome-wide association studies (GWAS) in largely non-overlapping cohorts. For RA, genetic instruments were obtained from 14,361 cases and 43,923 controls of European ancestry in the dataset by Okada, comprised of 18 studies (1). For the trait DM II, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium was used (2). This GWAS covered 120,000 SNPs from the Metabochip array in mixed European and Pakistani Population (34,840 cases and 114,981 controls). Genetic instruments for CAD were obtained from the CARDIoGRAMplusC4D consortium with 60,801 cases and 123,504 controls from 48 studies (3). Several MR regression methods such as MR Egger regression and inverse-variance weighted meta-analysis and sensitivity tests for pleiotropy and heterogeneity were performed for each exposure-outcome pair. Results Genetic predisposition to RA was not significantly associated with higher risk of CAD (OR = 1,02 [0,99-1,05]), see figure. In contrast, DM II was significantly, albeit weakly related as outcome to genetically determined manifestation of RA as exposure (OR = 1,04 [1,01-1,08]) and, conversely, genetic instruments of DM II conferred an increased risk of developing CAD ((OR = 1,11 [1,05-1,17]). Conclusions Despite the high prevalence of CAD among RA patients in observational studies, cardiovascular outcomes are not casually linked with RA. Instead, DM II could serve as a mediator, based on its significant associations with both RA and CAD in two-sample MR.

The orchestration of coding and non-coding genes at the CDH13 locus in coronary artery disease

Abstract Background The CDH13 locus was associated with coronary artery disease (CAD) by genome-wide association studies (GWAS). However, the causal gene(s) and underlying disease mechanisms at this locus remain undetermined. Purpose We intended to pinpoint and functionally validate the causal coding and non-coding genes at the CDH13 locus and delineate the underlying mechanisms for CAD. Methods and results We conducted the transcriptome-wide association analysis (TWAS) using nine types of CAD-relevant tissues from Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and Genotyped Tissue Expression (GTEx) projects and identified CDH13 (T-cadherin) and four long non-coding RNA (lncRNA) genes as candidate causal genes at The CDH13 locus. One of the antisense(AS) lncRNAs, CDH13-AS2, was predicted to bind on the 3’ untranslated region (UTR) of CDH13 using LncRRIsearch and RNAup servers. The binding interaction was validated by dCas13-medicated RNA immunoprecipitation. Weighted correlation network analysis (WGCNA) using transcriptomic data from CAD-relevant tissues of ~600 human subjects suggested a positive expression correlation between CDH13 and CDH13-AS2 in artery tissues. CRISPR/Cas9-based knockout (KO) of CDH13 or CDH13-AS2 in human umbilical vein endothelial cells (HUVECs) indicated synergistically atherogenic phenotypes, including reduced proliferation and migration, and increased apoptosis and monocyte adhesion. The data matched with the downregulation of CDH13 in artery samples of atherosclerosis patients. The cellular phenotypes were also in line with our in vivo data of Cdh13 and Apoe double-KO (Cdh13-/-/Apoe-/-) mice, which showed increased aortic lesion areas when fed on a high-fat diet compared to Apoe-/- mice. Furthermore, prediction using TargetScan, miRWalk, and scanMiRApp databases identified the potential binding of several EC-expression microRNAs (mir) on 3’UTR of CDH13. Several disease-associated variants were mapped to the predicted binding sites of a few microRNAs, including mir-let7, mir-30, and mir-125. The dual luciferase-based RNA interference (RNAi) assay confirmed the binding of mir-let7 on 3’UTR of CDH13. Transcriptional activation of CDH13-AS2 by the enzymatically inactivated Cas9 (dCas9) system diminished the binding of mir-let7 and increased CDH13 expression. Conclusions At the CDH13 locus, CDH13 and CDH13-AS2 are two causal genes, whose downregulation likely contributes to CAD. CDH13-AS2 bound and stabilized CDH13 partially by preventing mir-let7 mediated degradation of CDH13 mRNA, suggesting therapeutic potentials.

Novel insights of phenylalanine in Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) and cardiovascular complications

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is linked to cardiovascular risk, necessitating an exploration of potential metabolic contributors, such as amino acids. While amino acids are implicated in metabolic disorders, their specific relationship with MASLD and subsequent cardiovascular complications is not well-defined. We aimed to delineate the association between circulating amino acids and MASLD, identifying amino acids that could potentially link MASLD to cardiovascular issues. Methods Utilizing UK Biobank data, we tested the association of ten amino acids with MASLD in a cohort of 72,626 MASLD cases and 128,102 controls. Afterwards, we performed Mendelian Randomization (MR) using genome-wide association study (GWAS) data from 8,434 MASLD cases and 770,180 controls, and GWAS of amino acids including 115,052 participants from UK Biobank. Multiple MR methods, including MR-Egger, Cochran's Q-test, and MR-PRESSO, were applied to assess pleiotropy and heterogeneity. Survival analysis in MASLD patients assessed the link between significant amino acid and incident severe cardiovascular outcomes, including stroke, heart failure, myocardial infarction, and mortality, using three different covariate combinations: Model 1 included age, sex, and fasting time; Model 2 included in addition smoking, alcohol use, education, physical activity, and Model 3 included body mass index (BMI) in addition to model 2. Results Nine out of the ten amino acids were statistically significantly associated with MASLD across all three models (p<6.25×10^-3). Among them valine, leucine, isoleucine, total branched-chain amino acids, tyrosine, alanine, and phenylalanine were positively associated with MASLD, while glutamine and glycine were negatively associated with MASLD. The genetic predisposition of MASLD was significantly associated with increased phenylalanine (beta=0.05, p=4.0×10^-4) and tyrosine (beta=0.15, p=2.0×10^-3), suggesting that alterations in these amino acids may result from the progression of MASLD. We also detected heterogeneity (Cochran’s Q-test: p=8.4×10^-9) and pleiotropy (MR-Egger: p=0.02) for tyrosine, but not for phenylalanine. Furthermore, phenylalanine levels in MASLD patients were significantly (p<6.25×10^-3) associated with the incidence of heart failure, stroke, and mortality, but not with myocardial infarction across all three models. We did not identify a significant association between baseline tyrosine levels and any cardiovascular complications or mortality across the three models (Figure 1). Conclusion Our findings indicate a strong association between certain amino acids and MASLD, with phenylalanine, in particular, emerging as a potential mediator of cardiovascular risk in MASLD patients. The absence of heterogeneity and pleiotropy for phenylalanine strengthens its candidacy as a biomarker for future cardiovascular complications in MASLD.

Using deep learning to detect atherosclerotic plaques on carotid ultrasound images in the UK Biobank

Abstract Background Atherosclerosis is the main underlying cause of cardiovascular disease (CVD). Existing CVD risk assessment tools do not consider the burden of subclinical atherosclerosis. The presence of carotid plaques on carotid ultrasound is a well-known marker of subclinical atherosclerosis. The accumulation of population-scale data on the presence of atherosclerotic plaques, along with deep phenotyping, can allow not only to address the effectiveness of carotid ultrasound in routine clinical practice, but to shed light on the biology of atherosclerosis development. Purpose To develop an effective deep learning model for plaque detection in carotid ultrasound images in the UK Biobank. Methods We used 680 carotid ultrasound images with manually annotated plaques to train a deep learning model employing the YOLOv8 architecture. Different augmentation techniques were used to increase the generalizability of the model. The developed model was applied to automatically detect plaques in raw ultrasound images from 19,507 UK Biobank participants. Logistic and Cox regression were used to explore the associations of plaque presence and number as predicted by the model with conventional CVD risk factors and the risk of future CVD events over follow-up. To explore the genetic architecture of subclinical atherosclerosis, we conducted a genome-wide association study (GWAS) on plaque presence, followed by meta-analysis with data from the CHARGE Consortium. Results Our plaque detection model achieved high classification metrics of accuracy, sensitivity, and specificity (89.3%, 89.5%, and 89.2%, respectively) and detected atherosclerotic plaques in 44% of UK Biobank participants. As expected, plaques were more common among men than women and their prevalence increased linearly with age. Both plaque presence and number of plaques were correlated with conventional CVD risk factors including diabetes, hypertension, and hyperlipidemia, and showed strong associations with future risk of incident CVD events (Hazard Ratio for plaque presence: 1.48 [95%CI: 1.21-1.82], for 2 plaques or more: 1.65, [95% CI: 1.28-2.13]). Incorporating plaque-derived phenotypes minimally altered the C-index of the time-to-event model. GWAS meta-analysis of carotid plaque presence revealed 5 previously known loci, as well as a significant locus including the LPA gene that had not previously been associated with carotid plaque. Conclusion We have developed and implemented an efficient plaque detection model to data from the UK Biobank, which holds significant promise for studying atherosclerosis at a population-wide scale through integration with multiomics data and electronic health records.

Causal association between visceral fat and coronary artery disease: a large-scale mendelian randomization analysis

Abstract Background While increased body mass index (BMI) is an established risk factor, the residual risk associated with visceral fat, remains a concern. We aimed to perform a Mendelian randomization to explore the causal relationship between visceral fat and the risk of coronary artery disease (CAD), aiming to address gaps left by observational studies that may be influenced by confounding factors and reverse causation. Methods Leveraging large-scale genome-wide association study (GWAS) databases, we identified single-nucleotide polymorphisms (SNPs) associated with visceral fat. These genetic instruments were analyzed to infer causal relationships with CAD outcomes, drawing data from the CARDIoGRAMplusC4D consortium for CAD, and additional databases for related cardiovascular conditions. We performed a Two-sample Mendelian Randomization (MR) to test the genetic association between the visceral obesity and Coronary Artery Disease (CAD). Results The analysis encompassed data from a combined cohort exceeding 500,000 participants, revealing a significant causal relationship between increased levels of visceral fat and the risk of CAD. The observed odds ratio (OR) for CAD per standard deviation (SD) increase in visceral fat was [OR, 1.48 ] ([95% CI, 1.33-1.64]; P<0.001), indicating a robust association which remains independent from BMI on mediation analysis. Notably, the study's methodology ensured minimal risk of pleiotropy, reinforcing the validity of the findings. Conclusions This Mendelian randomization study provides compelling evidence of a genetic causal link between visceral fat and coronary artery disease. The findings suggest that beyond BMI, visceral fat represents a significant, independent risk factor for CAD, underscoring the importance of considering comprehensive lipid management in cardiovascular risk reduction strategies. Further research is warranted to explore potential mechanisms and therapeutic targets associated with visceral fat in the context of cardiovascular disease.

Causal relationships between cortical brain structural alterations and migraine subtypes: a bidirectional Mendelian randomization study of 2,347 neuroimaging phenotypes

BackgroundPrevious studies have shown that migraines are associated with brain structural changes. However, the causal relationships between these changes and migraine, as well as its subtypes, migraine with aura (MA) and migraine without aura (MO), remain largely unclear.MethodsWe utilized genome-wide association study (GWAS) summary statistics from European cohorts for 2,347 cortical structural magnetic resonance imaging (MRI) phenotypes, derived from both T1-weighted and diffusion tensor imaging scans (n = 36,663), with migraine and its subtypes (n = 147,970–375,752). Cortical phenotypes included both macrostructural (e.g., cortical thickness, surface area) and microstructural (e.g., fractional anisotropy, mean diffusivity) features. Genetic correlations were first assessed to identify significant associations, followed by bidirectional Mendelian randomization (MR) analyses to determine causal relationships between these brain phenotypes and migraine, as well as its subtypes (MA and MO). Sensitivity analyses were applied to ensure the robustness of the results.ResultsGenetic correlation analysis identified 510 significant associations between cortical structural phenotypes and migraine across 401 distinct traits. Forward MR analysis revealed nine significant causal effects of cortical structural changes on migraine risk. Specifically, increased cortical thickness and local gyrification index in specific cortical regions were associated with a decreased risk of overall migraine, MA, and MO, while intracellular volume fraction and orientation diffusion index in specific regions increased the risk of MA and MO. Reverse MR analysis demonstrated that MA causally increased mean diffusivity in the insular and frontal opercular cortex. Sensitivity analyses confirmed the robustness of these findings, with no evidence of horizontal pleiotropy or heterogeneity.ConclusionThis study identifies causal relationships between cortical neuroimaging phenotypes and migraine, highlighting potential biomarkers for migraine diagnosis, treatment, and prevention.