For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study ...up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
For any given body mass index (BMI), individuals vary substantially in fat distribution, and this variation may have important implications for cardiometabolic risk. Here, we study disease ...associations with BMI-independent variation in visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) fat depots in 40,032 individuals of the UK Biobank with body MRI. We apply deep learning models based on two-dimensional body MRI projections to enable near-perfect estimation of fat depot volumes (R
in heldout dataset = 0.978-0.991 for VAT, ASAT, and GFAT). Next, we derive BMI-adjusted metrics for each fat depot (e.g. VAT adjusted for BMI, VATadjBMI) to quantify local adiposity burden. VATadjBMI is associated with increased risk of type 2 diabetes and coronary artery disease, ASATadjBMI is largely neutral, and GFATadjBMI is associated with reduced risk. These results - describing three metabolically distinct fat depots at scale - clarify the cardiometabolic impact of BMI-independent differences in body fat distribution.
Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to ...recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10
) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed ...a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.
Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been ...practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual's body shape outline-or "silhouette" -that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R
: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR
= 0.05-0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)-and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio (R
: 0.17-0.26), a silhouette-based model enables significant improvement (R
: 0.50-0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.
Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart ...failure. This study aimed to test the association of CHIP with new-onset arrhythmias.
UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP variant allele fraction (VAF) ≥ 2%, large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis T1 measured using cardiac magnetic resonance (CMR) were also tested.
This study included 410 702 participants CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%). Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 95% confidence interval (CI) 1.04-1.18; P = .001 and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR.
CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.
Summary
In several fields such as statistics, machine learning, and bioinformatics, categorical variables are frequently represented as one‐hot encoded vectors. For example, given eight distinct ...values, we map each value to a byte where only a single bit has been set. We are motivated to quickly compute statistics over such encodings. Given a stream of k‐bit words, we seek to compute k distinct sums corresponding to bit values at indexes 0, 1, 2, …, k − 1. If the k‐bit words are one‐hot encoded then the sums correspond to a frequency histogram. This multiple‐sum problem is a generalization of the population‐count problem where we seek the sum of all bit values. Accordingly, we refer to the multiple‐sum problem as a positional population‐count. Using SIMD (Single Instruction, Multiple Data) instructions from recent Intel processors, we describe algorithms for computing the 16‐bit position population count using less than half of a CPU cycle per 16‐bit word. Our best approach uses up to 400 times fewer instructions and is up to 50 times faster than baseline code using only regular (non‐SIMD) instructions, for sufficiently large inputs.
MYCN amplification and MYC signaling are associated with high-risk neuroblastoma with poor prognosis. Treating these tumors remains challenging, although therapeutic approaches stimulating ...differentiation have generated considerable interest. We have previously shown that the MYCN-regulated miR-17∼92 cluster inhibits neuroblastoma differentiation by repressing estrogen receptor alpha. Here, we demonstrate that this microRNA (miRNA) cluster selectively targets several members of the nuclear hormone receptor (NHR) superfamily, and we present a unique NHR signature associated with the survival of neuroblastoma patients. We found that suppressing glucocorticoid receptor (GR) expression in MYCN-driven patient and mouse tumors was associated with an undifferentiated phenotype and decreased survival. Importantly, MYCN inhibition and subsequent reactivation of GR signaling promotes neural differentiation and reduces tumor burden. Our findings reveal a key role for the miR-17∼92-regulated NHRs in neuroblastoma biology, thereby providing a potential differentiation approach for treating neuroblastoma patients.
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•The MYCN-regulated miR-17∼92 cluster targets nuclear hormone receptors•An NHR signature is associated with neural differentiation and patient survival•The induction of MYCN or miR-17∼92 attenuates glucocorticoid receptor signaling•Combined MYCN inhibition and GR activation drive differentiation and ease tumor burden
Ribeiro et al. show that expression of several nuclear hormone receptors is inhibited by the MYCN-regulated miR-17∼92 cluster in MYCN-amplified neuroblastoma. Furthermore, they demonstrate that activation of glucocorticoid signaling results in neural differentiation and reduced tumor burden. Together, these results suggest restoration of nuclear hormone signaling as a putative future therapy for neuroblastoma.
Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate ...variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4HEN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4HEN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4HEN-COX quintiles, ranging from 0.25% to 7.8%. ML4HEN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754–0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.
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•Elastic net regression is a useful selection tool with a large candidate variable space•This principled approach to predictor selection can improve CAD risk prediction•Performance improvement can be maintained in a simple Cox model using the 51 predictors
Current cardiovascular risk stratification tools are based on a relatively small number of risk factors modeled with Cox proportional hazards models and are known to imperfectly estimate risk. The increasing prevalence of “multimodal” data sources—such as survey data, biomarker concentrations, anthropometric measures, and clinical diagnoses—offers a potential route for improvement, but simple Cox models are not well suited to these complex and often highly correlated inputs.
Here, we develop a framework to select a subset of candidate predictors for a coronary artery disease (CAD) risk prediction tool from a multimodal space of 13,782 features using elastic net regularized Cox regression. Our approach selected 51 of 13,782 candidate predictors, and the resulting model demonstrated improved prediction of incident CAD compared with clinically used algorithms among a held out set of participants.
Current cardiovascular risk stratification tools are based on a relatively small number of risk factors modeled with Cox proportional hazards models and are known to imperfectly estimate risk. Here, we develop a framework to select a subset of candidate predictors for a coronary artery disease (CAD) risk prediction tool from a multimodal space of 13,782 features using machine learning. This approach is readily generalizable to a broad range of large, complex datasets and disease endpoints.
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