Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction.

OBJECTIVES: To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank. METHODS: PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods. RESULTS: We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models. CONCLUSIONS: The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF. CLINICAL RELEVANCE STATEMENT: This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF. KEY POINTS: •PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

Genome-Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants.

BACKGROUND Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity-adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome-wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance-derived measures of left ventricular structure and function. We discovered 12 genome-wide significant variants, with 2 independent sentinel variants (rs6428792, P=4.20×10-9 and rs11992444, P=1.30×10-12) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T-box transcription factor 15 (TBX15), tryptophanyl tRNA synthetase 2, mitochondrial (WARS2) and early B-cell factor-2 (EBF2) through functional annotation. Bayesian colocalization additionally suggested a role of RP4-712E4.1. Genetically predicted differences in adiposity-adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution.

Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants.

AIMS: We evaluated independent associations of cardiovascular magnetic resonance (CMR)-measured pericardial adipose tissue (PAT) with cardiovascular structure and function and considered underlying mechanism in 42 598 UK Biobank participants. METHODS AND RESULTS: We extracted PAT and selected CMR metrics using automated pipelines. We estimated associations of PAT with each CMR metric using linear regression adjusting for age, sex, ethnicity, deprivation, smoking, exercise, processed food intake, body mass index, diabetes, hypertension, height cholesterol, waist-to-hip ratio, impedance fat measures, and magnetic resonance imaging abdominal visceral adiposity measures. Higher PAT was independently associated with unhealthy left ventricular (LV) structure (greater wall thickness, higher LV mass, more concentric pattern of LV hypertrophy), poorer LV function (lower LV global function index, lower LV stroke volume), lower left atrial ejection fraction, and lower aortic distensibility. We used multiple mediation analysis to examine the potential mediating effect of cardiometabolic diseases and blood biomarkers (lipid profile, glycaemic control, inflammation) in the PAT-CMR relationships. Higher PAT was associated with cardiometabolic disease (hypertension, diabetes, high cholesterol), adverse serum lipids, poorer glycaemic control, and greater systemic inflammation. We identified potential mediation pathways via hypertension, adverse lipids, and inflammation markers, which overall only partially explained the PAT-CMR relationships. CONCLUSION: We demonstrate association of PAT with unhealthy cardiovascular structure and function, independent of baseline comorbidities, vascular risk factors, inflammatory markers, and multiple non-invasive and imaging measures of obesity. Our findings support an independent role of PAT in adversely impacting cardiovascular health and highlight CMR-measured PAT as a potential novel imaging biomarker of cardiovascular risk.

Automated Quality-Controlled Cardiovascular Magnetic Resonance Pericardial Fat Quantification Using a Convolutional Neural Network in the UK Biobank.

Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts. Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB). Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set (n = 87), the whole UKB Imaging cohort (n = 45,519), and an external dataset (n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB (n = 42,928). Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium-good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p-value 5.3 ×10-18). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index. Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

Detection of pup odors by non-canonical adult vomeronasal neurons expressing an odorant receptor gene is influenced by sex and parenting status.

BACKGROUND: Olfaction is a fundamental sense through which most animals perceive the external world. The olfactory system detects odors via specialized sensory organs such as the main olfactory epithelium and the vomeronasal organ. Sensory neurons in these organs use G-protein coupled receptors to detect chemosensory stimuli. The odorant receptor (OR) family is expressed in sensory neurons of the main olfactory epithelium, while the adult vomeronasal organ is thought to express other types of receptors. RESULTS: Here, we describe Olfr692, a member of the OR gene family identified by next-generation RNA sequencing, which is highly upregulated and non-canonically expressed in the vomeronasal organ. We show that neurons expressing this gene are activated by odors emanating from pups. Surprisingly, activity in Olfr692-positive cells is sexually dimorphic, being very low in females. Our results also show that juvenile odors activate a large number of Olfr692 vomeronasal neurons in virgin males, which is correlated with the display of infanticide behavior. . In contrast, activity substantially decreases in parenting males (fathers), where infanticidal aggressive behavior is not frequently observed. CONCLUSIONS: Our results describe, for the first time, a sensory neural population with a specific molecular identity involved in the detection of pup odors. Moreover, it is one of the first reports of a group of sensory neurons the activity of which is sexually dimorphic and depends on social status. Our data suggest that the Olfr692 population is involved in mediating pup-oriented behaviors in mice.

Geniculo-Cortical Projection Diversity Revealed within the Mouse Visual Thalamus.

The mouse dorsal lateral geniculate nucleus (dLGN) is an intermediary between retina and primary visual cortex (V1). Recent investigations are beginning to reveal regional complexity in mouse dLGN. Using local injections of retrograde tracers into V1 of adult and neonatal mice, we examined the developing organisation of geniculate projection columns: the population of dLGN-V1 projection neurons that converge in cortex. Serial sectioning of the dLGN enabled the distribution of labelled projection neurons to be reconstructed and collated within a common standardised space. This enabled us to determine: the organisation of cells within the dLGN-V1 projection columns; their internal organisation (topology); and their order relative to V1 (topography). Here, we report parameters of projection columns that are highly variable in young animals and refined in the adult, exhibiting profiles consistent with shell and core zones of the dLGN. Additionally, such profiles are disrupted in adult animals with reduced correlated spontaneous activity during development. Assessing the variability between groups with partial least squares regression suggests that 4-6 cryptic lamina may exist along the length of the projection column. Our findings further spotlight the diversity of the mouse dLGN--an increasingly important model system for understanding the pre-cortical organisation and processing of visual information. Furthermore, our approach of using standardised spaces and pooling information across many animals will enhance future functional studies of the dLGN.

A gene expression ratio-based diagnostic test for bladder cancer.

PURPOSE: Bladder cancer is relatively common but early detection techniques such as cystoscopy and cytology are somewhat limited. We developed a broadly applicable, platform-independent and clinically relevant method based on simple ratios of gene expression to diagnose human cancers. In this study, we sought to determine whether this technique could be applied to the diagnosis of bladder cancer. EXPERIMENTAL DESIGN: We developed a model for the diagnosis of bladder cancer using expression profiling data from 80 normal and tumor bladder tissues to identify statistically significant discriminating genes with reciprocal average expression levels in each tissue type. The expression levels of select genes were used to calculate individual gene pair expression ratios in order to assign diagnosis. The optimal model was examined in two additional published microarray data sets and using quantitative RT-PCR in a cohort of 13 frozen benign bladder urothelium samples and 13 bladder cancer samples from our institution. RESULTS: A five-ratio test utilizing six genes proved to be 100% accurate (26 of 26 samples) for distinguishing benign from malignant bladder tissue samples (P < 10(-6)). CONCLUSIONS: : We have provided a proof of principle study for the use of gene expression ratios in the diagnosis of bladder cancer. This technique may ultimately prove to be a useful adjunct to cytopathology in screening urine specimens for bladder cancer.