ABSTRACT
Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging. In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics. We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near TTN, TNS1, LSM3, PALLD, DSP, PLEC, ANKRD1 and MYO18B ) and an additional 16 loci (near MECOM, NPR3, KLHL3, HDGFL1, CDKN1A, ELN, SLC25A37, PI15, AP3M1, HMGA2, ADPRHL1, PGAP3, WNT9B, UHRF1 and DOK5 ). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality. These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.
ABSTRACT
Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with an increased risk of cardiovascular (CV) disease in the general population. Currently, it is unclear whether this association is observed in large clinical trial cohorts with a high burden of existing CV disease or whether CV therapies can mitigate CHIP-associated CV risk. To address these questions, we studied 63,700 patients from five randomized trials that tested established therapies for CV disease, including treatments targeting the proteins PCSK9, SGLT2, P2Y12 and FXa. During a median follow-up of 2.5 years, 7,453 patients had at least one CV event (CV death, myocardial infarction (MI), ischemic stroke or coronary revascularization). The adjusted hazard ratio (aHR) for CV events for CHIP+ patients was 1.07 (95% CI: 0.99-1.16, P = 0.08), with consistent risk estimates across each component of CV risk. Significant heterogeneity in the risk of MI was observed, such that CHIP+ patients had a 30% increased risk of first MI (aHR = 1.31 (1.05-1.64), P = 0.02) but no increased risk of recurrent MI (aHR = 0.94 (0.79-1.13), Pint = 0.008), as compared to CHIP- patients. Moreover, no significant heterogeneity in treatment effect between individuals with and without CHIP was observed for any of the therapies studied in the five trials. These results indicate that in clinical trial populations, CHIP is associated with incident but not recurrent coronary events and that the presence of CHIP does not appear to identify patients who will derive greater benefit from commonly used CV therapies.
ABSTRACT
BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.
ABSTRACT
Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.
Subject(s)
Fibrosis , Genome-Wide Association Study , Magnetic Resonance Imaging , Humans , Male , Female , Middle Aged , Machine Learning , Aged , Pancreas/pathology , Pancreas/diagnostic imaging , Organ Specificity/genetics , Kidney/pathology , Liver/pathology , Liver/metabolism , Myocardium/pathology , Myocardium/metabolism , AdultABSTRACT
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.
Subject(s)
Atrial Fibrillation , Deep Learning , Genome-Wide Association Study , Heart Atria , Humans , Atrial Fibrillation/physiopathology , Atrial Fibrillation/genetics , Atrial Fibrillation/diagnostic imaging , Heart Atria/diagnostic imaging , Heart Atria/physiopathology , Heart Atria/pathology , Male , Female , Middle Aged , Aged , Magnetic Resonance Imaging , Mendelian Randomization Analysis , Risk Factors , Atrial Function, Left/physiology , Stroke Volume , Stroke , United Kingdom/epidemiology , Genetic Loci , Genetic Predisposition to DiseaseABSTRACT
Importance: Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects. Objective: To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort. Design, Setting, and Participants: A deep learning model was trained to quantify EPAT area from 4-chamber magnetic resonance images using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. Prospective associations were additionally controlled for abdominal visceral adipose tissue (VAT) volumes. A genome-wide association study was performed, and a polygenic score (PGS) for EPAT was examined in independent FinnGen cohort study participants. Data analyses were conducted from March 2022 to December 2023. Exposures: The primary exposures were magnetic resonance imaging-derived continuous measurements of epicardial and pericardial adipose tissue area and visceral adipose tissue volume. Main Outcomes and Measures: Prevalent and incident CAD, AF, heart failure (HF), stroke, and type 2 diabetes (T2D). Results: After exclusions, this study included 44â¯475 participants (mean [SD] age, 64.1 [7.7] years; 22â¯972 female [51.7%]) from the UK Biobank. Cross-sectional and prospective cardiovascular disease associations were evaluated for a mean (SD) of 3.2 (1.5) years of follow-up. Prospective associations were additionally controlled for abdominal VAT volumes for 38â¯527 participants. A PGS for EPAT was examined in 453â¯733 independent FinnGen cohort study participants. EPAT was positively associated with male sex (ß = +0.78 SD in EPAT; P < 3 × 10-324), age (Pearson r = 0.15; P = 9.3 × 10-229), body mass index (Pearson r = 0.47; P < 3 × 10-324), and VAT (Pearson r = 0.72; P < 3 × 10-324). EPAT was more elevated in prevalent HF (ß = +0.46 SD units) and T2D (ß = +0.56) than in CAD (ß = +0.23) or AF (ß = +0.18). EPAT was associated with incident HF (hazard ratio [HR], 1.29 per +1 SD in EPAT; 95% CI, 1.17-1.43), T2D (HR, 1.63; 95% CI, 1.51-1.76), and CAD (HR, 1.19; 95% CI, 1.11-1.28). However, the associations were no longer significant when controlling for VAT. Seven genetic loci were identified for EPAT, implicating transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2, and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The EPAT PGS was associated with T2D (odds ratio [OR], 1.06; 95% CI, 1.05-1.07; P =3.6 × 10-44), HF (OR, 1.05; 95% CI, 1.04-1.06; P =4.8 × 10-15), CAD (OR, 1.04; 95% CI, 1.03-1.05; P =1.4 × 10-17), AF (OR, 1.04; 95% CI, 1.03-1.06; P =7.6 × 10-12), and stroke in FinnGen (OR, 1.02; 95% CI, 1.01-1.03; P =3.5 × 10-3) per 1 SD in PGS. Conclusions and Relevance: Results of this cohort study suggest that epicardial and pericardial adiposity was associated with incident cardiovascular diseases, but this may largely reflect a metabolically unhealthy adiposity phenotype similar to abdominal visceral adiposity.
Subject(s)
Adiposity , Cardiovascular Diseases , Pericardium , Humans , Pericardium/diagnostic imaging , Female , Male , Middle Aged , Adiposity/genetics , Cardiovascular Diseases/genetics , Cardiovascular Diseases/epidemiology , Cross-Sectional Studies , Aged , Adipose Tissue/diagnostic imaging , Prospective Studies , Genome-Wide Association Study , Magnetic Resonance Imaging , Intra-Abdominal Fat/diagnostic imagingABSTRACT
PURPOSE: Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth rates. Our objective was to evaluate sex-specific temporal changes in radiologist-reported aortic size as well as sex differences in aortic reporting. METHOD: In this cohort study, we queried radiology reports of chest computed tomography or magnetic resonance imaging at an academic medical center from 1994 to 2022, excluding type A dissection. Aortic diameter was extracted using a custom text-processing algorithm. Growth rates were estimated using mixed-effects modeling with fixed terms for sex, age, and imaging modality, and patient-level random intercepts. Sex, age, and modality were evaluated as predictors of aortic reporting by logistic regression. RESULTS: This study included 89,863 scans among 46,622 patients (median [interquartile range] age, 64 [52-73]; 22,437 women [48%]). Aortic diameter was recorded in 14% (12,722/89,863 reports). Temporal trends were analyzed in 7194 scans among 1998 patients (age, 68 [60-75]; 677 women [34%]) with ≥2 scans. Aortic growth rate was significantly higher in women (0.22 mm/year [95% confidence interval 0.17-0.28] vs. 0.09 mm/year [0.06-0.13], respectively). Aortic reporting was significantly less common in women (odds ratio, 0.54; 95% CI, 0.52-0.56; p < 0.001). CONCLUSIONS: While aortic growth rates were small overall, women had over twice the growth rate of men. Aortic dimensions were much less frequently reported in women than men. Sex-specific standardized assessment of aortic measurements may be needed to address sex differences in aneurysm outcomes.
Subject(s)
Aneurysm , Aortic Aneurysm, Thoracic , Humans , Male , Female , Middle Aged , Aged , Cohort Studies , Sex Characteristics , Tomography, X-Ray Computed/methods , Magnetic Resonance Imaging , Aortic Aneurysm, Thoracic/diagnostic imaging , Risk FactorsABSTRACT
Discrete categorization of Mendelian disease genes into dominant and recessive models often oversimplifies their underlying genetic architecture. Cardiomyopathies (CMs) are genetic diseases with complex etiologies for which an increasing number of recessive associations have recently been proposed. Here, we comprehensively analyze all published evidence pertaining to biallelic variation associated with CM phenotypes to identify high-confidence recessive genes and explore the spectrum of monoallelic and biallelic variant effects in established recessive and dominant disease genes. We classify 18 genes with robust recessive association with CMs, largely characterized by dilated phenotypes, early disease onset and severe outcomes. Several of these genes have monoallelic association with disease outcomes and cardiac traits in the UK Biobank, including LMOD2 and ALPK3 with dilated and hypertrophic CM, respectively. Our data provide insights into the complex spectrum of dominance and recessiveness in genetic heart disease and demonstrate how such approaches enable the discovery of unexplored genetic associations.