Dynamic sampling from ex vivo adipose tissue using droplet-based microfluidics supports separate mechanisms for glycerol and fatty acid secretion.

Pathologies in adipose (fat) tissue function are linked with human diseases such as diabetes, obesity, metabolic syndrome, and cancer. Dynamic, rapid release of metabolites has been observed in adipocyte cells and tissue, yet higher temporal resolution is needed to adequately study this process. In this work, a microfluidic device with precise and regular valve-automated droplet sampling, termed a microfluidic analog-to-digital converter (µADC), was used to sample secretions from ∼0.75 mm diameter adipose explants from mice, and on-chip salt water electrodes were used to merge sampled droplets with reagent droplets from two different fluorometric coupled enzyme assays. By integrating sampling and assays on-chip, either glycerol or non-esterified fatty acids (NEFA), or both, were quantified optically within merged 12 nanoliter droplets using a fluorescence microscope with as high as 20 second temporal resolution. Limits of detection were 6 µM for glycerol (70 fmol) and 0.9 µM for NEFA (10 fmol). Multiple ex vivo adipose tissue explants were analyzed with this system, all showing clear increases in lipolytic function after switching from feeding to fasting conditions. Enabled by high temporal resolution, lipolytic oscillations of both glycerol and NEFA were observed for the first time in the range of 0.2 to 1.6 min-1. Continuous wavelet transform (CWT) spectrograms and burst analyses (0.1 to 4.0 pmol bursts) revealed complex dynamics, with multiplexed assays (duplex for glycerol and NEFA) from the same explants showing mostly discordant bursts. These data support separate mechanisms of NEFA and glycerol release, although the connection to intracellular metabolic oscillations remains unknown. Overall, this device allowed automated and highly precise temporal sampling of tissue explants at high resolution and programmable downstream merging with multiple assay reagents, revealing unique biological information. Such device features should be applicable to various other tissue or spheroid types and to other assay formats.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Tissue-Based Predictors of Impaired Right Ventricular Strain in Coronary Artery Disease: A Multicenter Stress Perfusion Study.

BACKGROUND: Right ventricular (RV) dysfunction is known to impact prognosis, but its determinants in coronary artery disease are poorly understood. Stress cardiac magnetic resonance (CMR) has been used to assess ischemia and infarction in relation to the left ventricle (LV); the impact of myocardial tissue properties on RV function is unknown. METHODS: Vasodilator stress CMR was performed in patients with known coronary artery disease at 7 sites between May 2005 and October 2018. Myocardial infarction was identified on late gadolinium enhancement-CMR, and infarct transmurality was graded on a per-segment basis. Ischemia was assessed on stress CMR based on first-pass perfusion and localized by using segment partitions corresponding to cine and late gadolinium enhancement analyses. RV function was evaluated by CMR-feature tracking for primary analysis with a global longitudinal strain threshold of 20% used to define impaired RV strain (RVIS); secondary functional analysis via RV ejection fraction was also performed. RESULTS: A total of 2604 patients were studied, among whom RVIS was present in 461 patients (18%). The presence and magnitude of RVIS were strongly associated with LV dysfunction, irrespective of whether measured by LV ejection fraction or wall motion score (P<0.001 for all). Regarding tissue substrate, regions of ischemic and dysfunctional myocardium (ie, hibernating myocardium) and infarct size were each independently associated with RVIS (both P<0.001). During follow-up (median, 4.62 [interquartile range, 2.15-7.67] years), 555 deaths (21%) occurred. Kaplan-Meier analysis for patients stratified by presence and magnitude of RV dysfunction by global longitudinal strain and RV ejection fraction each demonstrated strong prognostic utility for all-cause mortality (P<0.001). RVIS conferred increased mortality risk (hazard ratio, 1.35 [95% CI, 1.11-1.66]; P=0.003) even after controlling for LV function, infarction, and ischemia. CONCLUSIONS: RVIS in patients with known coronary artery disease is associated with potentially reversible LV processes, including LV functional impairment due to ischemic and predominantly viable myocardium, which confers increased mortality risk independent of LV function and tissue substrate.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSION: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.

Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5840 patients (mean age, 55 years ± 19 [SD]; 3527 male patients) referred for clinical cardiac MRI from 2003 to 2021 at nine centers; images were acquired using 1.5- and 3-T scanners from three vendors. Data from three centers were used for training and testing (4:1 ratio). The remaining data were used for external testing. Cines with downsampled frame rates were restored using linear, bicubic, and model-based interpolation. The root mean square error between interpolated and original cine images was modeled using ordinary least squares regression. In a prospective study of 49 participants referred for clinical cardiac MRI (mean age, 56 years ± 13; 25 male participants) and 12 healthy participants (mean age, 51 years ± 16; eight male participants), the model was applied to cines acquired at 25 frames per second (fps), thereby doubling the frame rate, and these interpolated cines were compared with actual 50-fps cines. The preference of two readers based on perceived temporal smoothness and image quality was evaluated using a noninferiority margin of 10%. Results The model generated artifact-free interpolated images. Ordinary least squares regression analysis accounting for vendor and field strength showed lower error (P < .001) with model-based interpolation compared with linear and bicubic interpolation in internal and external test sets. The highest proportion of reader choices was "no preference" (84 of 122) between actual and interpolated 50-fps cines. The 90% CI for the difference between reader proportions favoring collected (15 of 122) and interpolated (23 of 122) high-frame-rate cines was -0.01 to 0.14, indicating noninferiority. Conclusion A transformer-based deep learning model increased cardiac cine frame rates while preserving both spatial resolution and scan time, resulting in images with quality comparable to that of images obtained at actual high frame rates. Keywords: Functional MRI, Heart, Cardiac, Deep Learning, High Frame Rate Supplemental material is available for this article. © RSNA, 2024.

Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T1 Mapping Across Vendors and Field Strengths.

BACKGROUND: In cardiac T1 mapping, a series of T1 -weighted (T1 w) images are collected and numerically fitted to a two or three-parameter model of the signal recovery to estimate voxel-wise T1 values. To reduce the scan time, one can collect fewer T1 w images, albeit at the cost of precision or/and accuracy. Recently, the feasibility of using a neural network instead of conventional two- or three-parameter fit modeling has been demonstrated. However, prior studies used data from a single vendor and field strength; therefore, the generalizability of the models has not been established. PURPOSE: To develop and evaluate an accelerated cardiac T1 mapping approach based on MyoMapNet, a convolution neural network T1 estimator that can be used across different vendors and field strengths by incorporating the relevant scanner information as additional inputs to the model. STUDY TYPE: Retrospective, multicenter. POPULATION: A total of 1423 patients with known or suspected cardiac disease (808 male, 57 ± 16 years), from three centers, two vendors (Siemens, Philips), and two field strengths (1.5 T, 3 T). The data were randomly split into 60% training, 20% validation, and 20% testing. FIELD STRENGTH/SEQUENCE: A 1.5 T and 3 T, Modified Look-Locker inversion recovery (MOLLI) for native and postcontrast T1 . ASSESSMENT: Scanner-independent MyoMapNet (SI-MyoMapNet) was developed by altering the deep learning (DL) architecture of MyoMapNet to incorporate scanner vendor and field strength as inputs. Epicardial and endocardial contours and blood pool (by manually drawing a large region of interest in the blood pool) of the left ventricle were manually delineated by three readers, with 2, 8, and 9 years of experience, and SI-MyoMapNet myocardial and blood pool T1 values (calculated from four T1 w images) were compared with conventional MOLLI T1 values (calculated from 8 to 11 T1 w images). STATISTICAL TESTS: Equivalency test with 95% confidence interval (CI), linear regression slope, Pearson correlation coefficient (r), Bland-Altman analysis. RESULTS: The proposed SI-MyoMapNet successfully created T1 maps. Native and postcontrast T1 values measured from SI-MyoMapNet were strongly correlated with MOLLI, despite using only four T1 w images, at both field-strengths and vendors (all r > 0.86). For native T1 , SI-MyoMapNet and MOLLI were in good agreement for myocardial and blood T1 values in institution 1 (myocardium: 5 msec, 95% CI [3, 8]; blood: -10 msec, 95%CI [-16, -4]), in institution 2 (myocardium: 6 msec, 95% CI [0, 11]; blood: 0 msec, [-18, 17]), and in institution 3 (myocardium: 7 msec, 95% CI [-8, 22]; blood: 8 msec, [-14, 30]). Similar results were observed for postcontrast T1 . DATA CONCLUSION: Inclusion of field strength and vendor as additional inputs to the DL architecture allows generalizability of MyoMapNet across different vendors or field strength. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.

Aims: Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results: Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusion: We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.

Native T1 Mapping for the Diagnosis of Myocardial Fibrosis in Patients With Chronic Myocardial Infarction.

BACKGROUND: Myocardial fibrosis is a fundamental process in cardiac injury. Cardiac magnetic resonance native T1 mapping has been proposed for diagnosing myocardial fibrosis without the need for gadolinium contrast. However, recent studies suggest that T1 measurements can be erroneous in the presence of intramyocardial fat. OBJECTIVES: The purpose of this study was to investigate whether the presence of fatty metaplasia affects the accuracy of native T1 maps for the diagnosis of myocardial replacement fibrosis in patients with chronic myocardial infarction (MI). METHODS: Consecutive patients (n = 312) with documented chronic MI (>6 months old) and controls without MI (n = 50) were prospectively enrolled. Presence and size of regions with elevated native T1 and infarction were quantitatively determined (mean + 5SD) on modified look-locker inversion-recovery and delayed-enhancement images, respectively, at 3.0-T. The presence of fatty metaplasia was determined using an out-of-phase steady-state free-precession cine technique and further verified with standard fat-water Dixon methods. RESULTS: Native T1 mapping detected chronic MI with markedly higher sensitivity in patients with fatty metaplasia than those without fatty metaplasia (85.6% vs 13.3%) with similar specificity (100% vs 98.9%). In patients with fatty metaplasia, the size of regions with elevated T1 significantly underestimated infarct size and there was a better correlation with fatty metaplasia size than infarct size (r = 0.76 vs r = 0.49). In patients without fatty metaplasia, most of the modest elevation in T1 appeared to be secondary to subchronic infarcts that were 6 to 12 months old; the T1 of infarcts >12 months old was not different from noninfarcted myocardium. CONCLUSIONS: Native T1 mapping is poor at detecting replacement fibrosis but may indirectly detect chronic MI if there is associated fatty metaplasia. Native T1 mapping for the diagnosis and characterization of myocardial fibrosis is unreliable.

Myocardial Contractile Mechanics in Ischemic Mitral Regurgitation: Multicenter Data Using Stress Perfusion Cardiovascular Magnetic Resonance.

BACKGROUND: Left ventricular (LV) ischemia has been variably associated with functional mitral regurgitation (FMR). Determinants of FMR in patients with ischemia are poorly understood. OBJECTIVES: This study sought to test whether contractile mechanics in ischemic myocardium underlying the mitral valve have an impact on likelihood of FMR. METHODS: Vasodilator stress perfusion cardiac magnetic resonance was performed in patients with coronary artery disease (CAD) at multiple centers. FMR severity was confirmed quantitatively via core lab analysis. To test relationship of contractile mechanics with ischemic FMR, regional wall motion and strain were assessed in patients with inducible ischemia and minimal (≤5% LV myocardium, nontransmural) infarction. RESULTS: A total of 2,647 patients with CAD were studied; 34% had FMR (7% moderate or greater). FMR severity increased with presence (P < 0.001) and extent (P = 0.01) of subpapillary ischemia: patients with moderate or greater FMR had more subpapillary ischemia (odds ratio [OR]: 1.13 per 10% LV; 95% CI: 1.05-1.21; P = 0.001) independent of ischemia in remote regions (P = NS); moderate or greater FMR prevalence increased stepwise with extent of ischemia and infarction in subpapillary myocardium (P < 0.001); stronger associations between FMR and infarction paralleled greater wall motion scores in infarct-affected territories. Among patients with inducible ischemia and minimal infarction (n = 532), wall motion and radial strain analysis showed impaired subpapillary contractile mechanics to associate with moderate or greater FMR (P < 0.05) independent of remote regions (P = NS). Conversely, subpapillary ischemia without contractile dysfunction did not augment FMR likelihood. Mitral and interpapillary dimensions increased with subpapillary radial strain impairment; each remodeling parameter associated with impaired subpapillary strain (P < 0.05) independent of remote strain (P = NS). Subpapillary radial strain (OR: 1.13 per 5% [95% CI: 1.02-1.25]; P = 0.02) and mitral tenting area (OR: 1.05 per 10 mm2 [95% CI: 1.00-1.10]; P = 0.04) were associated with moderate or greater FMR controlling for global remodeling represented by LV end-systolic volume (P = NS): when substituting sphericity for LV volume, moderate or greater FMR remained independently associated with subpapillary radial strain impairment (OR: 1.22 per 5% [95% CI: 1.02-1.47]; P = 0.03). CONCLUSIONS: Among patients with CAD and ischemia, FMR severity and adverse mitral apparatus remodeling increase in proportion to contractile dysfunction underlying the mitral valve.

Assessment of myocardial lipomatous metaplasia using an optimized out-of-phase cine steady-state free-precession sequence: Validation and clinical implementation.

Myocardial lipomatous metaplasia, which can serve as substrate for ventricular arrhythmias, is usually composed of regions in which there is an admixture of fat and nonfat tissue. Although dedicated sequences for the detection of fat are available, it would be time-consuming and burdensome to routinely use these techniques to image the entire heart of all patients as part of a typical cardiac MRI exam. Conventional steady-state free-precession (SSFP) cine imaging is insensitive to detecting myocardial regions with partial fatty infiltration. We developed an optimization process for SSFP imaging to set fat signal consistently "out-of-phase" with water throughout the heart, so that intramyocardial regions with partial volume fat would be detected as paradoxically dark regions. The optimized SSFP sequence was evaluated using a fat phantom, through simulations, and in 50 consecutive patients undergoing clinical cardiac MRI. Findings were validated using standard Dixon gradient-recalled-echo (GRE) imaging as the reference. Phantom studies of test tubes with diverse fat concentrations demonstrated good agreement between measured signal intensity and simulated values calculated using Bloch equations. In patients, a line of signal cancellation at the interface between myocardium and epicardial fat was noted in all cases, confirming that SSFP images were consistently out-of-phase throughout the entire heart. Intramyocardial dark regions identified on out-of-phase SSFP images were entirely dark throughout in 33 patients (66%) and displayed an India-ink pattern in 17 (34%). In all cases, dark intramyocardial regions were also seen in the same locations on out-of-phase GRE and were absent on in-phase GRE, confirming that these regions represent areas with partial fat. In conclusion, if appropriately optimized, SSFP cine imaging allows for consistent detection of myocardial fatty metaplasia in patients undergoing routine clinical cardiac MRI without the need for additional image acquisitions using dedicated fat-specific sequences.

Prognostic Value of Feature-Tracking Right Ventricular Longitudinal Strain in Severe Functional Tricuspid Regurgitation: A Multicenter Study.

OBJECTIVES: This study sought to evaluate the prognostic value of cardiac magnetic resonance (CMR) feature-tracking-derived right ventricular (RV) free wall longitudinal strain (RVFWLS) in a large multicenter population of patients with severe functional tricuspid regurgitation. BACKGROUND: Tricuspid regurgitation imposes a volume overload on the RV that can lead to progressive RV dilation and dysfunction. Overt RV dysfunction is associated with poor prognosis and increased operative risk. Abnormalities of myocardial strain may provide the earliest evidence of ventricular dysfunction. CMR feature-tracking techniques now allow assessment of strain from routine cine images, without specialized pulse sequences. Whether abnormalities of RV strain measured using CMR feature tracking have prognostic value in patients with tricuspid regurgitation is unknown. METHODS: Consecutive patients with severe functional tricuspid regurgitation undergoing CMR at 4 U.S. medical centers were included in this study. Feature-tracking RVFWLS was calculated from 4-chamber cine views. The primary endpoint was all-cause death. Cox proportional hazards regression modeling was used to examine the independent association between RVFWLS and death. The incremental prognostic value of RVFWLS was assessed in nested models. RESULTS: Of the 544 patients in this study, 128 died during a median follow-up of 6 years. By Kaplan-Meier analysis, patients with RVFWLS ≥median (-16%) had significantly reduced event-free survival compared with those with RVFWLS 

Ischemia-Mediated Dysfunction in Subpapillary Myocardium as a Marker of Functional Mitral Regurgitation.

OBJECTIVES: The goal of this study was to test whether ischemia-mediated contractile dysfunction underlying the mitral valve affects functional mitral regurgitation (FMR) and the prognostic impact of FMR. BACKGROUND: FMR results from left ventricular (LV) remodeling, which can stem from myocardial tissue alterations. Stress cardiac magnetic resonance can assess ischemia and infarction in the left ventricle and papillary muscles; relative impact on FMR is uncertain. METHODS: Vasodilator stress cardiac magnetic resonance was performed in patients with known or suspected coronary artery disease at 7 sites. Images were centrally analyzed for MR etiology/severity, mitral apparatus remodeling, and papillary ischemia. RESULTS: A total of 8,631 patients (mean age 60.0 ± 14.1 years; 55% male) were studied. FMR was present in 27%, among whom 16% (n = 372) had advanced (moderate or severe) FMR. Patients with ischemia localized to subpapillary regions were more likely to have advanced FMR (p = 0.003); those with ischemia localized to other areas were not (p = 0.17). Ischemic/dysfunctional subpapillary myocardium (odds ratio: 1.24/10% subpapillary myocardium; confidence interval: 1.17 to 1.31; p < 0.001) was associated with advanced FMR controlling for infarction. Among a subgroup with (n = 372) and without (n = 744) advanced FMR matched (1:2) on infarct size/distribution, patients with advanced FMR had increased adverse mitral apparatus remodeling, paralleled by greater ischemic/dysfunctional subpapillary myocardium (p < 0.001). Although posteromedial papillary ischemia was more common with advanced FMR (p = 0.006), subpapillary ischemia with dysfunction remained associated (p < 0.001), adjusting for posteromedial papillary ischemia (p = 0.074). During follow-up (median 5.1 years), 1,473 deaths occurred in the overall cohort; advanced FMR conferred increased mortality risk (hazard ratio: 1.52; 95% confidence interval: 1.25 to 1.86; p < 0.001) controlling for left ventricular ejection fraction, infarction, and ischemia. CONCLUSIONS: Ischemic and dysfunctional subpapillary myocardium provides a substrate for FMR, which predicts mortality independent of key mechanistic substrates.

Role of HCA2 in Regulating Intestinal Homeostasis and Suppressing Colon Carcinogenesis.

Hydroxycarboxylic acid receptor 2 (HCA2) is vital for sensing intermediates of metabolism, including ß-hydroxybutyrate and butyrate. It also regulates profound anti-inflammatory effects in various tissues, indicating that HCA2 may serve as an essential therapeutic target for mediating inflammation-associated diseases. Butyrate and niacin, endogenous and exogenous ligands of HCA2, have been reported to play an essential role in maintaining intestinal homeostasis. HCA2, predominantly expressed in diverse immune cells, is also present in intestinal epithelial cells (IECs), where it regulates the intricate communication network between diet, microbiota, and immune cells. This review summarizes the physiological role of HCA2 in intestinal homeostasis and its pathological role in intestinal inflammation and cancer.

Epicardial Surface Area of Infarction: A Stable Surrogate of Microvascular Obstruction in Acute Myocardial Infarction.

BACKGROUND: Microvascular obstruction (MO) is a pathophysiologic complication of acute myocardial infarction that portends poor prognosis; however, it is transient and disappears with infarct healing. Much remains unknown regarding its pathophysiology and whether there are predictors of MO that could function as stable surrogates. We tested for clinical and cardiovascular magnetic resonance predictors of MO to gain insight into its pathophysiology and to find a stable surrogate. METHODS: Three hundred two consecutive patients from 2 centers underwent cardiovascular magnetic resonance within 2 weeks of first acute myocardial infarction. Three measures of infarct morphology: infarct size, transmurality, and a new index-the epicardial surface area (EpiSA) of full-thickness infarction-were quantified on delayed-enhancement cardiovascular magnetic resonance. RESULTS: Considering all clinical characteristics, only measures of infarct morphology were independent predictors of MO. EpiSA was the strongest predictor of MO and provided incremental predictive value beyond that of infarct size and transmurality (P<0.0001). In patients with 3-month follow-up cardiovascular magnetic resonance (n=81), EpiSA extent remained stable while MO disappeared, and EpiSA was a predictor of adverse ventricular remodeling. After 20 months of follow-up, 11 died and 1 had heart transplantation. Patients with an EpiSA larger than the median value (≥6%) had worse outcome than those with less than the median value (adverse events: 6.4% versus 1.9%, P=0.045). CONCLUSIONS: The EpiSA of infarction is a novel index of infarct morphology which accurately predicts MO during the first 2 weeks of MI, but unlike MO, does not disappear with infarct healing. This index has potential as a stable surrogate of the presence of acute MO and may be useful as a predictor of adverse remodeling and outcome which is less dependent on the time window of patient assessment.

Relationship of LVEF and Myocardial Scar to Long-Term Mortality Risk and Mode of Death in Patients With Nonischemic Cardiomyopathy.

BACKGROUND: Nonischemic cardiomyopathy is a leading cause of reduced left ventricular ejection fraction (LVEF) and is associated with high mortality risk from progressive heart failure and arrhythmias. Myocardial scar on cardiovascular magnetic resonance imaging is increasingly recognized as a risk marker for adverse outcomes; however, left ventricular dysfunction remains the basis for determining a patient's eligibility for primary prophylaxis with implantable cardioverter-defibrillator. We investigated the relationship of LVEF and scar with long-term mortality and mode of death in a large cohort of patients with nonischemic cardiomyopathy. METHODS: This study is a prospective, longitudinal outcomes registry of 1020 consecutive patients with nonischemic cardiomyopathy who underwent clinical cardiovascular magnetic resonance imaging for the assessment of LVEF and scar at 3 centers. RESULTS: During a median follow-up of 5.2 (interquartile range, 3.8, 6.6) years, 277 (27%) patients died. On survival analysis, LVEF ≤35% and scar were strongly associated with all-cause (log-rank test P=0.002 and P<0.001, respectively) and cardiac death (P=0.001 and P<0.001, respectively). Whereas scar was strongly related to sudden cardiac death (SCD; P=0.001), there was no significant association between LVEF ≤35% and SCD risk (P=0.57). On multivariable analysis including established clinical factors, LVEF and scar are independent risk markers of all-cause and cardiac death. The addition of LVEF provided incremental prognostic value but insignificant discrimination improvement by C-statistic for all-cause and cardiac death, but no incremental prognostic value for SCD. Conversely, scar extent demonstrated significant incremental prognostic value and discrimination improvement for all 3 end points. On net reclassification analysis, the addition of LVEF resulted in no significant improvement for all-cause death (11.0%; 95% CI, -6.2% to 25.9%), cardiac death (9.8%; 95% CI, -5.7% to 29.3%), or SCD (7.5%; 95% CI, -41.2% to 42.9%). Conversely, the addition of scar extent resulted in significant reclassification improvement of 25.5% (95% CI, 11.7% to 41.0%) for all-cause death, 27.0% (95% CI, 11.6% to 45.2%) for cardiac death, and 40.6% (95% CI, 10.5% to 71.8%) for SCD. CONCLUSIONS: Myocardial scar and LVEF are both risk markers for all-cause and cardiac death in patients with nonischemic cardiomyopathy. However, whereas myocardial scar has strong and incremental prognostic value for SCD risk stratification, LVEF has no incremental prognostic value over clinical measures. Scar assessment should be incorporated into patient selection criteria for primary prevention implantable cardioverter-defibrillator placement.

Whole-slide image analysis outperforms micrograph acquisition for adipocyte size quantification.

The distinction between biological processes of adipose tissue expansion is crucial to understanding metabolic derangements, but a robust method for quantifying adipocyte size has yet to be standardized. Here, we compared three methods for histological analysis in situ: one conventional approach using individual micrographs acquired by digital camera, and two with whole-slide image analysis pipelines involving proprietary (Visiopharm) and open-source software (QuPath with a novel ImageJ plugin). We found that micrograph analysis identified 10-40 times fewer adipocytes than whole-slide methods, and this small sample size resulted in high variances that could lead to statistical errors. The agreement of the micrograph method to measure adipocyte area with each of the two whole-slide methods was substantially less (R2 of 0.6644 and 0.7125) than between the two whole-slide methods (R2 of 0.9402). These inconsistencies were more pronounced in samples from high-fat diet fed mice. While the use of proprietary software resulted in the highest adipocyte count, the lower cost, ease of use, and minimal variances of the open-source software provided a distinct advantage for measuring the number and size of adipocytes. In conclusion, we recommend whole-slide image analysis methods to consistently measure adipocyte area and avoid unintentional errors due to small sample sizes.

Society for Cardiovascular Magnetic Resonance (SCMR) recommended CMR protocols for scanning patients with active or convalescent phase COVID-19 infection.

The aim of this document is to provide specific recommendations on the use of cardiovascular magnetic resonance (CMR) protocols in the era of the COVID-19 pandemic. In patients without COVID-19, standard CMR protocols should be used based on clinical indication as usual. Protocols used in patients who have known / suspected active COVID-19 or post COVID-19 should be performed based on the specific clinical question with an emphasis on cardiac function and myocardial tissue characterization. Short and dedicated protocols are recommended.

The Absence of Adiponectin Alters Niacin's Effects on Adipose Tissue Inflammation in Mice.

Obesity is an immunometabolic disease associated with chronic inflammation and the dysregulation of pro- and anti-inflammatory cytokines. One hallmark of obesity is reduced concentrations of the anti-inflammatory adipokine, adiponectin. Pharmacologic doses of niacin produce multiple metabolic benefits, including attenuating high-fat diet (HFD)-induced adipose tissue inflammation and increasing adiponectin concentrations. To determine if adiponectin mediates the anti-inflammatory effects of niacin, male C57BL/6J (WT) and adiponectin null (Adipoq-/-) mice were maintained on a low-fat diet (LFD) or HFD for 6 weeks, before being administered either vehicle or niacin (360 mg/kg/day) for 5 weeks. HFD-fed mice had increased expression of genes associated with macrophage recruitment (Ccl2) and number (Cd68), and increased crown-like structure (CLS) number in adipose tissue. While niacin attenuated Ccl2 expression, there were no effects on Cd68 or CLS number. The absence of adiponectin did not hinder the ability of niacin to reduce Ccl2 expression. HFD feeding increased gene expression of inflammatory markers in the adipose tissue of WT and Adipoq-/- mice. While niacin tended to decrease the expression of inflammatory markers in WT mice, niacin increased their expression in HFD-fed Adipoq-/- mice. Therefore, our results indicate that the absence of adiponectin alters the effects of niacin on markers of adipose tissue inflammation in HFD-fed mice, suggesting that the effects of niacin on tissue cytokines may involve adiponectin.